{
  "text": "Hi everyone and a very warm welcome to this course on Codex.\nReally excited to have you here today and hope to learn a lot of things together.\nWe will learn how to use Codex for enterprise development.\nWe will see how it works as a coding assistant and its many functions in different situations.\nCodex is a tool made by OpenAI.\nYou might know their famous tool called ChatGPT.\nOpenAI is the main company behind ChatGPT and they handle all the AI generation and work.\nFirst, I will show you the OpenAI website.\nOn their website, they say their research will lead to artificial general intelligence,\nwhich is a system that can solve human problems.\nIn the products section, you can see tools like ChatGPT, Codex, Atlas and Prism.\nChatGPT is a very large tool that includes images, deep research and GPTs, codecs, multiple applications and libraries.\nIf we talk about apps, we have multiple apps connection.\nYou can connect it with popular apps like Adobe, Airtable, Booking.com, Canva, Spotify and more.\nNow let us look at OpenAI Codex.\nCodex is an AI software engineering agent.\nIt helps developers write, understand, change, test and ship code easily.\nIt is not just a simple chatbot that only answers questions.\nIt works directly with your project files and helps with the whole repository structure\nand can make the changes, run commands and assists with development cycles.\nIt gives developers real engineering results.\nCodex is also inside ChatGPT, so you can use it directly.\nThere is a specific app for Codex, and you can download it for Windows.\nWe will see how Codex makes real engineering work faster, from planning and building the features, to fixing and releases and cross-checking.\nThere is also a command line interface, or CLI, on GitHub for Codex.\nCLI means Command Line Interface. It is a text-based interface used to talk to software and computers.\nInstead of clicking buttons with a mouse on visual icons, you type text commands and the\ntext-based user interface used to interact with the software, operating system and on\nyour keyboard instead of using a mouse to click on visual items and press enter to make\nthem work.\nWe will learn how to use this CLI.\nCodex helps what a developer wants to do and turns it into engineering output.\nYou can describe a bug or a new feature, test environment, documentation, task, in natural\nlanguage.\nCodex can analyse the database identifying files, proposed changes and generate limitation\nsteps.\nThis makes it useful not only for the writer but also for navigating unfamiliar systemd.\nHere we have codex discussion.\nIn this course we will talk about daily development, prompting, making agents and using codex to\nfor a long time. We will cover all of these important topics together.\nSo, moving on, the next critical discussion is primarily related to exactly\nhow we are going to securely install codecs on our machine. First, well before\nactually installing it anywhere else, we will practically use the standard\ncommand-line interface to get things started. But right before that specific\nstep, we can smoothly download the dedicated version specifically meant for Windows operating\nsystems. Now, I already have the necessary Codex installer fully ready to go here. I\nsimply open up the main system downloads folder right now. Then, I firmly run the executable\ninstaller directly as a system administrator. The core software immediately starts to install\nitself smoothly. Simultaneously, the Microsoft Store seamlessly begins its essential download\nprocess. Finally, when the entire download completely finishes, Codex will successfully\nrun. Interestingly, we can certainly also manage to install it safely across multiple\ndifferent computer systems. As we smoothly scroll further down the main page, you will\nclearly see three distinct setup options presented. The very first one is the\nstandard standalone app. If we really want to use a code editor we can install it\ndirectly within our preferred IDE. Fortunately we already have Visual Studio\ncode fully set up. Right inside VS Code a highly helpful AI is fully available. It\nis completely automated and we can easily use various models right here. To\nTo successfully add this to our daily workflow, we simply need to install it in VS Code.\nWe securely click Allow in VS Code and Codex will seamlessly open.\nEssentially, it is OpenAI's powerful coding agent that constantly works with you.\nIt is fully included in ChatGPT+, Pro, Business, Education and Enterprise plans.\nFirst and foremost we absolutely must carefully check our specific ChatGPT subscription plan.\nI will officially open up the ChatGPT interface right now.\nAs you can clearly see, we currently have the Plus version fully activated.\nLooking closely in the dedicated upgrade plan section, you clearly see our active billing\nplan.\nIt is indeed the standard Plus plan.\nThis essentially means all our ongoing coding work is completely safe and secure.\nCodex is fully available to use today.\nThe strictly required language model is situated right here.\nMoving back into VS Code, we will effectively start the complete software installation.\nIt securely installs just like a standard web extension.\nFirst, we smoothly pair it directly with Codex.\nWe basically add Codex as a dedicated side panel within VS Code to seamlessly chat, intuitively\nedit and carefully check all our changes.\nThen, we can confidently use Codex directly in the cloud, sending massive jobs straight\nto it.\nMoving gracefully to the very next step, we simply need to safely sign in using our primary\nChatGPT account credentials.\nOnce fully authenticated, our main application will automatically install directly into the\nworkspace.\nNow, the third available setup option is to actually install this directly inside our\nsystem terminal.\nFor this specific method, just look closely at the Keep going in the terminal prompt section.\nWe carefully copy this exact command string to our clipboard.\nFollowing that, we explicitly open up the system command prompt, making sure to run it strictly as an administrator.\nThe Windows PowerShell interface naturally opens up.\nWithin PowerShell itself, we formally install the OpenAI Codex tool utilizing the NPM Package Manager.\nNaturally, it will definitely take a little bit of time to fully install properly.\nThe underlying system will sequentially gather necessary data in a much better way.\nCodex is actively installing and we have our app ready.\nNow we go to settings.\nWe purposefully open the main application settings directly from the main dashboard.\nsettings menu there are actually many different customisable options\navailable. I specifically navigate over to the appearance tab in order to make\nsome visual interface changes. First, I completely change the overall display\ntheme to a comfortable dark mode. Next, I noticeably increase the base system UI\nsize for better visibility. We then carefully choose our preferred font\nstyle and slightly adjust the visual contrast. Initially, I confidently write\nin 32 for the primary font size, however upon seeing it, 32 is admittedly just a bit too\nbig for my screen. So I quickly change it down to exactly 24. Honestly, 24 is a genuinely\ncomfortable size for long coding sessions. For the dedicated code font, we firmly use\nsize 20. Moving on, we strategically turn off the reduce motion option. We ensure we\nuse standard pointer cursors and gently lower the screen contrast. Everything is working\nwell. Honestly, the overall reading experience is just so much better right now. Before we\nmade those crucial visual tweaks, the default text font was incredibly small and honestly\nhard to read. We absolutely had to step in and make the font significantly bigger for\nbasic clarity. I truly believe that 24 and 20 are excellent, highly optimized sizes for\nour daily coding tasks.\nNow if we quickly navigate over to the General Configuration Settings menu, we can clearly\nsee multiple distinct operational modes available to choose from. I promise that I will carefully\nexplain all of these specific working modes in much greater detail a little bit later on.\nRight now, all our required system packages are successfully installed.\nThe powerful OpenAI codex itself is completely installed.\nNow, we just essentially need to forcefully run it one more time.\nI smoothly tab back over to the chat GPT window.\nI explicitly type out and clearly ask.\nI have basically run this command in the terminal.\nWhat do I do next to start it?\nWe will now officially start the require process right here within our local system terminal.\nThe entire background installation procedure is completely finished.\nIt successfully managed to add two vital software packages in just about 36 seconds total.\nThe software carefully checks the current NPM installation status alongside the available AI models.\nFirst and foremost, we must verify the active codex version.\nI simply type NPM codex directly into the prompt.\nThe resulting codex CLI version currently shows up as 0.139.0.\nNext, we actively run codex so we can finally start to code.\nto code. We type codex right into the system environment. A brand new security message\nsuddenly appears on the screen. It clearly asks, do you currently trust the exact contents\nof this specific directory? Working directly with untrusted contents definitely carries\ninherent security risks or potential prompt injections. Trusting that directory directly\nallows for reading local configuration settings.\nI confidently click yes and smoothly continue.\nAfter successfully getting that required yes confirmation,\nwe now finally have our direct working option ready to go.\nWe have the full power of the OpenAI codex at our fingertips.\nOur specifically selected AI model is currently GPT 5.5.\nOur active working directory is listed safely as System32.\nWe can comfortably make direct code changes right here in this space.\nIf I quickly send over a very simple introductory message,\nit impressively replies back almost automatically.\nI simply write out,\ncan you explicitly help me in generating some basic HTML code?\nIt quickly gives a remarkably short, concise response.\nIt plainly says, yes, absolutely.\nJust tell me exactly what you want the final HTML page to do, or what you want it to look like.\nPaste any existing code snippets you might already have.\nIf you happen to start entirely from scratch, I can easily create a simple HTML framework for you.\nOur coding journey begins.\nI press Ctrl C to pause.\nIt shows the response.\nThe interface clearly displays our complete token usage statistics right on the screen.\nThe grand total comes out to exactly 7,939 tokens used.\nThe initial input strictly accounted for 7,881 tokens.\nMeanwhile, the generated output was a remarkably short 58 tokens.\nTo seamlessly continue this exact same chat session, we carefully copy the provided resume\ncommand.\nI smoothly paste that specific command right back in here.\nInstantly, the previous conversation thread beautifully continues right where we left\noff.\nIf I suddenly decide that I want to completely change the underlying AI model, we currently\nhave three distinct options available.\nNext is GPT 5.5, which is essentially the frontier model specifically built for complex\ncoding tasks.\nNext is GPT 5.4, the strongest overall model strictly meant for everyday standard coding.\nFinally, we have the incredibly lightweight GPT 5.4 mini.\nWe also have very handy slash commands completely ready to use.\nwe will clearly show exactly how to effectively use them moving forward.\nLet's use slash commands.\nWhen I proactively type out a forward slash, Codex immediately shows a complete, highly\ndetailed working menu of commands.\nFirst and foremost, we can fully configure how Codex strictly operates.\nWe have the primary model selection option available.\nWe also have the specific fast option explicitly listed.\nWe have the IDE integration, to easily include currently open files and broader context.\nWe actively have detail permissions to precisely choose exactly what codecs can or cannot do\nsafely.\nWe have dedicated key maps specifically designed for setting custom keyboard shortcuts.\nWe have Vim mode strictly intended for our main code composer.\nWe logically have the Sandbox Add to Directory feature ready.\nFinally, we have the Experimental Feature section.\nI intentionally select the very first configuration option to explore the available models.\nI ultimately choose the GPT 5.4 Mini variant.\nNow we selectively choose the preferred reasoning level.\nWe specifically select low reasoning instead of the standard medium.\nWe can carefully view current IDE sessions and files.\nNext, we review permissions.\nThere are several highly specific model permissions carefully listed here.\nThe Read Only setting strictly means that Codex can only safely read active files located\nwithin the current workspace environment.\nIt absolutely requires your direct manual approval before it can ever attempt to edit\nfiles or directly access the live internet.\nThe Ask for Approval setting actually serves as the standard default mode, where Codex\ncan seamlessly run, read or even edit files inside the current workspace and fully read\nthe active command prompt. Still, explicit manual approval is completely required to\npermanently edit files or access the internet. Approved for me intelligently only asks for\nexplicit permission when it encounters potentially unsafe system actions. Full access simply\nmeans Codex can freely edit files far outside the designated workspace and easily access\nthe internet without ever asking for prior approval. Exercise extreme caution and ask\nvital questions when using this. I explicitly choose the Approve for me setting. Our updated\npermissions are cleanly applied. I press Enter and transition to the Keymap configuration.\nIn Keymap we have our transcript, external editor, clear terminal and more options.\nWe practically have multiple distinct configuration options readily available right here, alongside\nsome incredibly useful direct operational options.\nWhat we can actually do here is utilise the basic left and right keyboard arrow keys to\nsmoothly navigate and group items.\nWe firmly use the Enter key whenever we strictly need to modify or edit an existing shortcut.\nWe smartly use the star symbol strictly for creating completely custom shortcuts, the\ndash symbol to quickly unbind an active key, and the Escape key to easily close out the\nmenu entirely.\nI simply press Escape right now to smoothly go back to the previous screen.\nRight after successfully configuring our system permissions, we logically have our custom key\nmaps fully sorted out.\nNext up, we carefully review our active system approvals.\nYou can transparently see absolutely all the recent system changes we manually made right\nhere.\nWe actually changed our primary AI model twice today.\nWe closely reviewed the IDE context settings.\nWe officially updated our security permissions to Approve for me.\nFinally, I press Control-C.\nExecuting that specific command officially closes out the active chat session entirely.\nIt is crucial to note that this specific underlying configuration genuinely remains the exact same\nacross every single linked application environment.\nThankfully, the powerful Codex tool is readily available right here too.\nWe can practically do so many different amazing things with it.\nIf we smartly decide to switch over to the exclusive pre-release software version, absolutely\nall the newest experimental features become instantly available.\nI am going to intentionally open up a brand new dedicated project folder officially named\nMetabrains for our work.\nWe carefully select this exact folder directory and smoothly open up our fresh chat interface.\nI explicitly confirm that I fully trust the verified authors operating in this specific\nenvironment.\nI smoothly close this secondary pop-up window.\nI promptly open up a brand new code file appropriately named index.html.\nYou can distinctly see the familiar codex logo right inside this document.\nWe can now finally start to actively write out our core underlying code snippets.\nThe generated Codex AI response will seamlessly appear right here on the interface.\nWe will be able to clearly see our active IDE connection, the linked source repository,\nthe comprehensive onboarding bug log and of course, our preferred dark mode visual theme.\nWe will consistently get highly accurate direct responses moving forward.\nNow we need to take a very close look at our overall system usage metrics.\nI purposefully navigate back over to the main settings page.\nThe exact same user account is actively being used uniformly everywhere.\nWe can easily modify our deeper codec software settings right here.\nYou clearly see broad configurations, custom personalisation, advanced MCP servers, webhook\nintegrations, system usage and active billing.\nWe absolutely need to check these panels timely and consistently to perfectly know exactly\nhow we are efficiently carrying all these things forward.\nWe can chat and utilise commands.\nWe can switch models and connect apps.\nInside the primary settings menu, we prominently have the visual appearance and core system\nconfiguration tabs right at the top.\nWe absolutely also have detail personalization settings, custom keyboard shortcuts, active\nusage limits and standard billing information.\nThe general designated application usage limit strictly caps out at exactly 5 hours.\nwe happily have a massive 99% of that time left available.\nThe broader weekly limit currently shows 100% completely left over.\nWe have successfully expanded our overall codex usage capabilities\nalongside the powerful GPT 5.5 deep thinking mode.\nWe can effortlessly purchase an additional credit balance whenever needed.\nSupported system integrations proudly include\nseamless browser connections,\nbroad computer use permissions and robust MCP servers.\nThe core coding section beautifully includes active webhooks, secure remote connections,\ncomplete Git source control, safe isolated environments and managed work trees.\nOur foundational setup phase is now entirely complete.\nThe highly anticipated direct usage strictly begins right now.\nWe will now efficiently set up highly secure cloud-based development environments right here in this specific module.\nYou probably remember that we previously ran our Codex agent directly here on the local machine.\nAbsolutely all of our core operational functions were safely contained directly inside it.\nNow, however, we will purposefully run our advanced cloud-based development environment\nsecurely integrated directly within our main codec setup.\nFirst things first, I will quickly navigate back over to the main settings panel\nand intentionally make the system font slightly smaller for better viewing.\nWe gracefully go over to the visual appearance tab\nand actively change the primary tech size down from 24 to precisely 20.\nNow, the overall tech size is just so much better and more manageable.\nI will clearly explain to you generally what these advanced cloud-based development environments actually are.\nWe will deeply understand this entire concept right here today.\nYou absolutely should know right up front that this is essentially a pre-configured, entirely remotely hosted software workspace.\nIt actively contains your preferred IDE alongside all your necessary background tools.\nModern developers can effortlessly code, rigorously test and smoothly deploy complex software straight from the web browser.\nThe acronym CDE specifically stands for Cloud Development Environment.\nIt effectively solves that highly frustrating, age-old problem\nwhen a specific piece of code miraculously only works on one specific person's local computer.\nBrand new software engineers can easily start actively coding in mere minutes.\nThis is our dedicated cloud system.\nNow, let's take a moment to talk about Codex itself.\nCodex fundamentally has some massive, undeniably big advantages here.\nCodex genuinely works absolute best when it can safely access the entire full code repository.\nIt impressively also has powerful built-in deployment tools.\nA cloud environment safely lets Codex seamlessly talk directly to remote code repositories.\nIt can reliably run terminal commands, execute unit tests and thoroughly check projects.\nIt safely makes automated code changes.\nThe biggest overall benefit is undeniable, absolute consistency.\nIn significantly older traditional local development environments,\nvarious developers constantly use completely different underlying operating systems\nand vastly different local machine settings.\nsettings. This inherently creates the infamous, frustrating, well, it perfectly works on my\nmachine problem. Modern, streamlined cloud environments completely remove these highly\nannoying problems entirely. Absolutely everything that we actively do right here, for example,\nthe previously pinned chat log specifically about making a functional calculator application,\nall our deep technical discussions are safely stored permanently in this robust cloud system.\nWe can effortlessly and rapidly review our collective past work right here at any given time.\nOther incredibly massive benefits strictly include nearly instant team onboarding,\nhighly consistent underlying tool setups, absolute hardware independence and reliable\nlong-term session persistence.\nNow, moving forward, we will thoughtfully choose a specific cloud development platform to use.\nI will happily show you some excellent real-world examples shortly.\nFirst and foremost, we will take a very close look at GitHub Codespaces today.\nIt is a remarkably secure, highly robust cloud development environment used by many.\nIf we genuinely do not want to use this specific one, we can alternatively utilise Gitpod.\nWe actually use the Gitpod platform quite a lot in our daily workflows.\nIt is a fantastic, strictly on-demand, highly scalable development environment.\nFor massively big enterprise companies, we consistently have the AWS Cloud9 infrastructure safely available.\nfor our larger, more complex coding tasks.\nIt is basically a fully complete cloud-based integration development environment from Amazon.\nIt is truly a complete, fully featured IDE right in your browser.\nWe can effortlessly write, smoothly run and quickly fix our complex code directly inside it.\nThese are a few older, very solid examples.\nNow, I will explicitly search the web to see if Codex itself is officially classified as a cloud-based development environment.\nIt confidently says, yes.\nCodex, created by OpenAI, is officially a cloud-based AI software engineering agent.\nIt remarkably handles incredibly complex programming tasks, consistently fixes highly stubborn software bugs, and smoothly proposes absolutely all the necessary fundamental work changes for your review.\nThis essentially means that the powerful tool we are actively utilising right now acts almost exactly like a completely standalone, fully robust cloud-based development environment in itself.\nI strongly suggest that we consistently use this incredible tool to intentionally make\na highly secure, deeply isolated remote workspace.\nThere are honestly quite a lot of highly technical back-end things actively running here.\nI will quickly hop back and change the UI font size once again, because it simply does\nnot look visually right on this display.\nI will purposefully make it just a little bit bigger, strictly for the sake of our video\ncourse clarity. Now we simply go straight back into our main codex application. Everything\nvisually looks exceptionally good now. We have our advanced reasoning engine and other\nvital backend things running. We will take a complete comprehensive look at this next.\nThis perfectly shows how we put things into a complete cloud-based setup.\nNow, moving smoothly forward, our very next crucial task is to securely connect our remote\nGitHub repositories directly.\nWe absolutely need to establish a solid link to our GitHub account directly within the\nmain codec system.\nInside our primary GitHub platform, a tremendous amount of distinct configurable options are\nalways readily available.\nYou can clearly see the active status regarding the connection with GitHub right here on the\ninterface.\nIf I cautiously go over to the main settings panel, specifically looking closely in the\nConnections option area, there is currently absolutely no active connection established.\nThe standard SSH and secure Git credential options similarly show that there are simply\nno active connections present.\nFurthermore, down in the Remote Servers and Local Browser tab, there is similarly no connection.\nIn the Computer Use section, we can alternatively use the Google Chrome browser extension.\nSo, our primary GitHub repository profile is clearly not effectively connected to the system just yet.\nI will intentionally just type out the specific query.\nIs GitHub firmly connected to Codex right now?\nto quickly see if we actually get a reassuring green verification tick.\nThis built-in diagnostic tool will thoroughly check the back-end systems\nand explicitly tell us if our GitHub account is successfully connected or not.\nSimultaneously, I will efficiently search for the precise phrasing\nGitHub and official codex integration securely online.\nThis highly powerful system connection can miraculously be added directly without any significant friction.\nIt prominently includes highly advanced automated pull request code reviews,\na dedicated co-pilot AI coding agent and deeply integrated GitHub workflow actions.\nWe will comprehensively discuss absolutely all of these incredible back-end setup options in extreme detail.\nThe diagnostic system now clearly shows that the official GitHub integration plugin\nis indeed fully available directly within the primary codex plugin section.\nBut, unfortunately, this specific local workspace folder\nis actually not initialised as a valid Git repository right now.\nA brand new Git tracking folder is quickly being created in the background.\nNow, if we explicitly click the prominent plus button on the user interface,\nwe can clearly see the GitHub icon safely listed in the available plugins directory.\nBut we critically want to verify exactly which specific user account is currently connected directly to our active GitHub plugin extension.\nWe currently cannot seem to find our primary GitHub profile, so we will manually initiate a deep search for it.\nWhen we effectively run the search, the official GitHub authentication app finally appears.\nThe application safely requests broad permission to actively access repositories, track software issues and manage pull requests.\nBut we have simply not fully connected it just yet.\nSo we will proceed to log securely into our main GitHub account.\nThis is our dedicated primary account explicitly named metabrains-del.\nIt undeniably has many repositories and we have done a lot of work here.\nWe will now meticulously connect this specific GitHub account directly to our active Codex\nsystem workspace.\nWhen I confidently click the connect button, it explicitly asks for final permission to\nsecurely connect to GitHub servers.\nIt safely allows the underlying ChatGPT engine to carefully read our previous chats and stored\nmemories to consistently give significantly better, context-aware answers.\nIt clearly states that your deep personal privacy permissions are heavily respected,\nand you are always completely in control.\nIt does warn that external connectors may possibly introduce some slight security risk.\nI will boldly open up our Direct GitHub Developer Dashboard.\nYou can visually see that the secure GitHub app authorisation connection is actively starting\nin the background.\nAbsolutely all our ongoing critical development work is safely housed right here.\nWe will press firmly to continue onward to GitHub so we can comfortably go straight to\nour main profile account.\nIt will naturally take a little bit of processing time to fully connect.\nA basic connection will form.\nYou can see it is connected.\nI open plugins again.\nNow you will actively see that our dedicated GitHub plugin is finally working precisely\nas intended.\nIt smoothly works specifically because it is successfully connected to the backend.\nIf it is ever mysteriously not connected, we can easily check our credentials again to see if we can actively reconnect it.\nWe currently have our active user login, but unfortunately, adding the specific external link just failed.\nWhy exactly did it suddenly fail here?\nWe must remember that this is our specific ChatGPT Plus tier account.\nYou absolutely must remember this vital detail because it impacts our permissions.\nNow we will purposefully open up the completely alternative ChatGPT account instead.\nWe will securely bring our dedicated Michael DuPont user account right here into the active workspace to try again.\nOur premium ChatGPT plus subscription tier is fully available right here.\nNow we will carefully attempt to securely connect the integration system once again.\nIt will systematically connect our main chat GPT interface to the repository.\nIf we ever eventually want to completely remove it for security reasons, we can easily uninstall it and quickly reinstall it from scratch later again.\nWe obviously also have the standard option to immediately disconnect the authorization any time.\nWe will gracefully proceed straight directly over to the main GitHub authorisation portal.\nNow it will successfully build the required connection directly in our correct targeted application.\nIt strictly demands highly secure multi-factor user authentication.\nWe will comfortably utilise the standard Authenticator mobile app to handle this.\nThe mobile Authenticator app will swiftly finish processing our deep security verification.\nNow you can clearly see that our primary GitHub profile is finally directly connected perfectly.\nI will quickly pivot back over to our main codex interface.\nYou can clearly see that our secure software link is now fully established and solidly connected.\nIf we carefully look directly at our active system plugins list right now,\nabsolutely all our relevant GitHub integration tasks have delightfully started showing up directly in there.\nWe will smoothly navigate back over to our main chat terminal interface.\nYou can very clearly see our dedicated GitHub functionality is directly added and fully available right there.\nWe will quickly go back over to the plugins tab and our GitHub features will be permanently included in the active workflow loop.\nIt will effortlessly come directly into our daily software development work.\nUltimately, we have incredibly successfully added our robust Codex AI tool to perfectly\nsync with our remote GitHub code repositories.\nMoving logically to the very next crucial step, we will thoroughly talk about the highly\nspecific agents.md configuration file.\nThis incredibly important file is primarily used for organising massively big software\ndevelopment projects. We specifically utilise it whenever we happen to have\nmany complex automated tasks or a full highly detailed business use case to\ncarefully look at. I will intentionally open it up right here on the screen so\nyou can clearly see exactly where these vital agent configuration files\nnaturally come from and exactly where to easily find them. It will reliably give\nus the system response directly and accurately.\nThe dedicated agents.md file immensely helps us to deeply inspect and standardise our overall\nproject architecture.\nYou can clearly see it actively looking deep inside our raw project files.\nHere, it explicitly states that the active workspace is entirely empty right now.\nThere is absolutely no agents.md file found.\nIt checked the temporary scratch space and verified there is no file.\nTo successfully create one from scratch, we absolutely must make a brand new, correctly\nformatted Agents.md file.\nI will manually go ahead and create a brand new foundational file right here for you.\nThis incredibly efficient way, we can consistently see absolutely all the necessary high level\nproduction details firmly embedded safely within this specific file.\nFirst, the core Structured Agents file is quickly drafted out.\nRight after that initial phase, we can comfortably make far more highly nuanced text changes\nto this preliminary draft.\nIn this specific advanced technical setup, we usually really need to carefully check\nif our highly specific basic operational rules or distinct business use cases are actually\nworking properly.\nsuccessfully and directly created an excellent base foundational agent file\nright here for us. If we carefully review the generated file we can clearly see\nits intended core purpose. Besides the purpose there is a clear project\noverview. We also see a working agreement, a layout and editing rules. We can\nactively cross-check all of these fundamental operational rules precisely\nhere in the document. I will also make sure to explicitly tell you exactly where\nthis specialized configuration file is typically used. We consistently use the\nspecialized agents.md file to effectively establish strict working\nboundaries and core rules for literally any AI agent operating in the workspace.\nThere are basically no other bizarre tasks or unrelated work explicitly meant\nfor it. Sometimes, unfortunately, we literally have to go deep down into our underlying operating\nsystem just to manually set up the complex agents.md file perfectly. This tedious manual\nprocess admittedly takes quite a lot of valuable development time. We essentially have to carefully\nlook at so many different distinct things, like our strict project requirements, core team work\nethics and several other highly detailed technical options. We absolutely need to fully know how to\nproperly handle these nuanced things. But for massively big projects, we only deliberately\ninclude our basic setup requirements here. Moving right along, next we will deeply discuss exactly\nhow to effectively use this incredibly vital agents.md file in standard practice.\nDeeply understanding this specific core concept is an undeniably very important foundational part of the entire development process\nWe absolutely need to fully know exactly how to actively create the underlying file directly from absolute scratch\nNow we purposefully transition and move seamlessly right over to the very next important part\nWe will fundamentally learn exactly how to appropriately handle highly complex things and deeply understand them right after actively creating the foundational file.\nThis is fundamentally a highly special, deeply vital system instruction file.\nIt consistently provides invaluable deep guidance and much needed environmental context directly for our automated work.\nIt can absolutely also be used incredibly effectively, exactly like a traditional readme developer file.\nYou might vividly remember that standard software developers constantly use structured readme files.\nIn the exact same way, we can use this file.\nI have created a basic agents.md file, but incredibly, we can undoubtedly also meticulously make a highly reusable foundational agents.md configuration template right here in the workspace.\nWe can comfortably and securely include absolutely all of our strict foundational base development requirements firmly inside it.\nThis proactive step will flawlessly kickstart our broader automation work immediately.\nI will gladly proceed to show you an excellent, highly structured example right now.\nI will purposely open up Microsoft Word or a standard blank doc text document.\nI am deliberately using a blank doc right now, strictly so we can comfortably start\na fresh, entirely new, empty document.\nHere, you can very clearly see exactly what critical structural things are highly recommended\nand available.\nFirst, we logically have the high-level project overview section.\nThis clearly tells us exactly what core deliverables we strictly need in the project.\nNext, we definitely have the strict team coding standards that we must absolutely follow.\nAfter that, we discuss repository structures, testing requirements, security rules and large-scale\nsystems.\nWe will practically use a fully functional, highly complete, overarching working system\nhere.\nOur strictly prohibited, highly restricted system areas will absolutely also be meticulously\nexplicitly listed right here for immense safety.\nThen, our highly nuanced, deeply specific primary AI operational instructions will be\nthoroughly included next.\nRight after all the comprehensive AI baseline instructions, we will seamlessly have all\nour broader, full-feature architectural options carefully documented.\nThis is essentially exactly what we traditionally call a comprehensive, well-structured readme\nfile in standard development.\nIt practically has all our complete, strict baseline instructions deeply hard-coded and\nbuilt directly right into it.\nAfter this section is successfully completed, the very next essential technical thing dynamically\navailable is dealing with our sensitive back-end environment variables.\nWe will deeply see precisely how we can effectively, securely manage these highly critical system\nenvironment variables.\nNow, our next incredibly critical technical discussion is primarily about handling robust\nbackend environment variables. These are essentially highly dynamic, configurable key values operating\nin the background. We can actively store them either directly or indirectly deep within\nour underlying operating system infrastructure. We can comfortably and securely utilise them\nlater for absolutely all of our other automated complex coding tasks. Usually, we deeply involve\nour complete, highly systematic overall workflow process directly here to explicitly see exactly\nhow a full, robust software system is successfully constructed.\nThese are highly dynamic back-end key values that are stored strictly and directly inside\nthe native operating system, kept entirely safely outside of our vulnerable, raw source\ncode files. They heavily detect and dictate precisely how the running process fundamentally\nbehaves. This brilliant system effortlessly allows developers to incredibly safely store\nhighly sensitive security credentials. What exactly are sensitive credentials? It is highly\nvital to see this clearly. If we fail to understand this, our work suffers. Sometimes developers\nare understandably incredibly afraid that their highly sensitive personal or corporate\ninformation will eventually accidentally leak to the dangerous public internet.\nWhy exactly does this enormous fear exist?\nBasically, because our complete interconnected development environment inherently has some\ndeeply guarded secrets.\nThese highly confidential items definitely include things like private API keys, secure\nadmin account usernames and highly encrypted user passwords.\nWe absolutely have to carefully look closely at them and protect them.\nNow we officially pivot to selectively talk about the massive, overarching enterprise operational level.\nWhat exactly inherently happens operating at the massive enterprise scale level?\nLet us quickly intentionally open up a brand new, fresh, isolated chat window.\nAt the highest enterprise level, actively managing profound structural secrets properly\nis absolutely critical for safely protecting tremendously sensitive proprietary information.\nSecrets routinely may include remote cloud access keys,\nmassive database passwords, complex encryption keys, vital third-party API credentials,\nand various access authentication tokens.\nHandling environment variables securely with codecs is important.\nThese specific, crucial security items are undeniably very important to actively manage appropriately right here.\nUsually, wildly different distinct deployment environments inherently fundamentally require\ncompletely different sets of unique configuration values.\nWe can intelligently deploy and actively utilise them safely over there.\nLet me clearly tell you, whenever I actively try to perform a direct system search for standard environment variables,\nyou immediately see our primary system environment variables are visibly available precisely right here.\nYou will undeniably see that absolutely all of our current environment variables are entirely accessible and available literally right now.\nSometimes it happens to merely be the static number of active system processors.\nSometimes it is a highly confidential external API key.\nWe can smoothly dive deep directly right into the back-end system variables menu,\ncarefully enter the specific custom variable name\nand its corresponding strict value directly and permanently safely store it.\nSo, the next time we run a use case, all variables are automatically set perfectly.\nHow exactly is this properly handled inside a secure underlying database architecture?\nAs you can visibly observe precisely here, located deep inside the primary configuration\ninterface, we purposely actively include a dedicated corporate company database link.\nIf there happens to clearly be a specific network port required, we firmly include it.\nIf there explicitly is an overarching master application configuration requirement, we\nwe safely add the core app environment variable alongside the specific designated app port.\nSecure, highly confidential API credentials will absolutely come directly right here.\nFor one clear example, if we genuinely want to safely utilise an external OpenAI API authentication\nkey, we securely do it precisely here.\nWe can undoubtedly also clearly see secure encrypted payment gateway keys stored right\nhere.\nThen, there are massive authentication backend settings designed purely to store multiple\ndynamic secrets.\nThese environment variables brilliantly enable large organisations to safely manage app configurations.\nIt prevents leaking sensitive information.\nhandles keys safely too without hard coding. I will definitively also sincerely tell you\nthat strict environment variables frankly may not initially actively seem incredibly\nvery important right now when we merely successfully create basic simple software,\nabsolutely like a fundamental basic calculator or standard normal terminal tools.\nhowever the exact moment when we actively rigorously try to fundamentally change our\nmassive software architecture or alter major things safely on a significantly much larger\nincredibly massive corporate scale literally many distinct individual things wildly realistically\nstart changing aggressively side by side simultaneously we immediately rapidly actively\nsee literally multiple deep cascading fundamental code structural changes happening.\nNow, moving confidently right forward seamlessly, we will deeply, actively, rigorously, explicitly\ndiscuss all of these major systematic fundamental software changes and deeply integrated highly\ncomplex AI functions directly later.\nWe will explore this in the next sections perfectly.\nAuthentication and access configuration is a critical part of using OpenAI codecs in an enterprise development environment.\nAuthentication verifies the identity of the user or system attempting to use codecs,\nwhile access configuration defines what that user or system is allowed to do.\nTogether, these controls ensure that only authorised developers, teams and automation tools can interact with repositories, environments, APIs and sensitive project resources.\nIn enterprise software development, Codex usually works with cloud-based development environments, GitHub repositories, CI-cd pipelines and internal tools.\nBecause these systems may contain confidential source code, credentials, customer data and deployment configurations, access must be carefully managed.\nA weak authentication setup can expose the organisation to unauthorised code access, accidental changes or security breaches.\nTherefore, enterprises commonly rely on identity providers, single sign-on, role-based access\ncontrol and audit logging. The authentication process normally begins when a developer signs\nin using an approved identity provider. This may include enterprise login systems such as SSO,\nOAuth or multi-factor authentication. Once the user identity is confirmed, the\nsystem checks whether the user has permission to access Codex, the\ndevelopment workspace and the connected repositories. This ensures that Codex\nactivities are tied to a verified user and can be tracked for accountability.\nAccess configuration determines the scope of Codex's permissions. For example,\ncodecs may be allowed to read a repository, analyse code, create a branch, run tests or open a pull request.\nIn some cases, it may not be allowed to directly merge code or access production secrets.\nThese permissions should follow the principle of least privilege,\nmeaning codecs and users should receive only the access required to complete their assigned tasks.\nRepository access is especially important. Codecs must often inspect project files,\nunderstand dependencies and modify code. However, not every repository should be\naccessible to every user or AI workflow. Enterprises should configure repository\npermissions based on teams, projects and business sensitivity.\nFor example, a front-end developer may receive access to UI repositories\nbut not to payment infrastructure or identity management services.\nEnvironment variables and secrets also require strict control.\nCodecs may need to understand variable names, configuration patterns or runtime requirements,\nbut it should not expose or hard code secret values.\nSensitive credentials should be stored in secure secret managers and injected only when required.\nThis prevents accidental leakage of API keys, tokens, database passwords or cloud credentials\ninto source code.\nAuditability is another major requirement in enterprise access configuration.\nOrganisations should be able to review who accessed codecs, which repositories were used,\nwhat tasks were performed and what changes were generated.\nlogs help security teams investigate incidents, enforce governance policies, and demonstrate\ncompliance with internal or regulatory standards.\nA well-designed authentication and access configuration model allows enterprises to use codecs safely\nand efficiently.\nIt protects sensitive assets while still enabling developers to benefit from AI-assisted coding.\nBy combining strong identity verification, scoped permissions, secure secret handling\nand continuous monitoring, organisations can create a controlled environment where Codex\nimproves productivity without increasing operational risk.\nWe discuss Codex workflows now.\nWe want to see how Codex uses large workflows.\nWe look at complete software solutions and workability.\nSometimes we must see how the whole process grows over time.\nFor this, we change and improve code on a large scale.\nAfter these changes, we check the overall process understanding.\nI will tell you about Codex workflows.\nCodex helps developers in the software development lifecycle.\nIt does repeating tasks.\nIt makes the coding environment fast.\nIt helps us understand how to fix different problems.\nIt helps us understand different work processes.\nThe most important discussion is how we use codecs.\nWe start with a prompt.\nWe can add details in the prompt.\nThis helps software work well.\nWe use prompts to make software processes easy, fast.\nWe start with the prompt.\nWe can add details in the prompt.\nThe important thing in the prompt is a defined objective.\nWe must have a defined objective.\nWe bring this objective into our software and working process.\nFirst, we take the generation process.\nWe understand the process on a large scale.\nWe see how we generate everything.\nWe have one big question.\nThe question is how to define the objective.\nWe look at things to define it.\nWe will see how these things work together.\nThis is a very strong and important point.\nWe must know how to set up objectives for large generation.\nAfter we set the objective, we look at the next steps.\nThe next step is feature development.\nFeature development is a process in our pipeline.\nAfter development, the next thing is software defect identification.\nWe must make this defect identification process strong.\nAfter we find defects, we move to next steps.\nThe next steps are refactoring and optimization.\nFirst, we must define a task.\nWe must have a clear task definition.\nAfter we define the task, we provide background context.\nWe provide this context on a large scale.\nI will explain this process to you.\nI will use a flow diagram to show how things work.\nFirst, we have the task definition.\nNext, we have Task Context.\nWe must know how to add the context.\nAfter the context, we need a detailed prompt.\nThen we connect our main codecs.\nWe connect codecs with our own codebases.\nWe connect codecs with our codebases to show complete analysis.\nAfter analysis, we must have Solution Generation.\nSolution Generation must be available.\nAfter solution generation, the next step is review.\nI tell you we need these things in our prompt.\nWhen we have these things, our prompt starts.\nInside this prompt, our solution generation process starts.\nHere we have our solution generation.\nAfter we generate the solution, we review it.\nWe check everything in the review step.\nAfter the review, another process starts.\nWe call this new process testing. Testing is very important here. In testing we include\ndebugging. Both testing and debugging must happen together. We need both in our workflow.\nThey help us find and fix problems. These steps make the software better and ready for\nfinal stages. Both testing and debugging must be present.\nThe last step available here is refinement.\nWe call this refinement process iteration.\nWe do refinement to make software better.\nAfter the refinement step, we commit changes.\nWe save all the changes we made.\nThis is the complete process.\nAt the end, our workflow closes.\nIt closes at the deployment stage.\nWe go to deployment.\nWe deploy our project into our system.\nThese steps are helpful on a large scale.\nThey help us create a better score.\nThey help us create a better decision tree for work.\nThis is how the complete workflow operates from start to finish.\nWe follow these simple rules to make good software.\nEvery step is important for the final product.\nDeployment is the final goal of this journey.\nOur next talk is about codecs.\nWe will learn about our projects in codecs.\nProject navigation is a very important AI skill in modern software development.\nIn big business places, developers work with big repositories often.\nThese repositories have thousands of files.\nWe can see our multiple services directly.\nIt also sees big documentation, complex dependencies and multiple servers.\nUnderstanding these projects by hand needs much time and effort.\nhelps developers understand the project structures well. It finds correct files, traces dependencies,\nand locates our implementation details. I go to the project menu directly. I click\nStart from scratch. I name the new project Metabrains. I start one project directly here.\nI save it. Now we have a new project. There is no chat available yet in the project.\nYou see our Explorer and Object options.\nIf we turn on the Explorer, our file opens automatically.\nYou can see we have no document available yet.\nWith time, we will make many additions and changes.\nAutomatically, our response and style will change.\nI will start from the beginning.\nWe will look at all the changes we make from start to now.\nAlong with changes, we will also see the time.\nAlong with changes we will also see the time. We will see how much time one full cycle takes.\nWe can look at the complete process directly. Sometimes we must approve things directly\nto understand them, I will tell you. I will type what things should be in our project.\nI write create an agent.md file. We will create the agent.md file here. This makes our agent\nwork in a much better way. I type because this is a corporate sales manager and content\ncreator project. It will do the first step of our project here. First of all, we give\nthe context and our details. We are making the employee do these things here. We will\ninclude this directly for our approval. I tell you that a complete process discussion\nis very important here. In this, we have to involve all our details. We must involve our\nfull working process here. You can see it will take some time, obviously. It is working\nin our exact folder. If I refresh our folder here, you will see the agent.md file is created.\nWe can directly review it here. It created the agent.md file with a tailored operating\nbrief. This is for the corporate sales manager and content project. The agent.md file is\nmade here. You can see our exact source code and other things are available. We can open\nit directly in our VS code. We can open it in the default application. We can also open\nit in our terminal. These things are much better here. We have a complete system environment.\nAutomatically, we see all divisions and working ability here. Sometimes, this working ability\nor complete process causes problems. It causes problems on a big scale. We see many issues\nin our work. We usually create our complete empty MD file at the very start of the project\nsetup. So, on a big scale, if any issue happens later, we can understand it early. We can\ndo things early according to our system. This is a basic discussion now. We see how complete\ngeneration happens. We see how we take a complete process or understanding with us. After the\nagent.md file we will directly look at our project structure. I will go into the exact\nsame folder. I will include a new folder here. This is from a previously created section.\nYou can see our portfolios are available here. I will do all the portfolio discussion here.\nI will now analyse our repository. I write analyse this repository and provide a comprehensive\noverview of the project structure. It includes the main purpose of the application. Then we have\nhigh-level architecture. Then we have important directories. Then we have all options for\nimportant directories on a big scale. After major frameworks, we have entry points. After entry\npoints, we have configuration files. Then we have deployment-related files. Then we have testing\nstructure. Then it says present the information in a structured format,\nsuitable for onboarding a new developer. We can take our things directly in\ndeveloper onboarding too. We can involve them. This is a complete structured\nrepository layout that we use here. Sometimes we must see some changes in\nthis too. In this we also involve multiple repositories and options\ndirectly. When I enter this it will directly go to our folder. The index file\nis inside. You can see this is like our index file. When the index file opens a\ncomplete portfolio website is available. Now we will see how to enhance and\nunderstand this portfolio website on a big scale. This will happen in our codex\nhere. It understood the child item here. After that it showed our files. It has\nshown the key pattern too. We have one HTML document. We have one style sheet. We\nhave one script bundle. Then we have some static assets. We also have all other\nthings available to use directly. Now I will take our details. I will see how our\nprocess runs. I will see how all direct discussions and details run. With this we\nwill take our setup. Usually this needs a lot of time if we take these things in\nanother environment but here our things are much simpler now. We can include much\nbetter things and accounts here. I will look at our direct use case now. It read\nout our whole folder here. After that it told us there are no platform specific\ndeployment manifests checked into the repository. The deployment guidance is\ndocument only. After that we have a testing structure. Then all other things\nhave come. Now we have gone inside our project. We have seen how we will include\nour system. Now I will see what entry points are available there. I will write\nto find the entry points. If we want to look at something other than entry points\nwe will involve all user authentication features here.\nWe will see how we can look at them.\nAlong with this, we will get a direct response from the complete system.\nWe will see how we take things directly in our system first.\nI will type to locate and explain the implementation of the user authentication feature.\nI will identify all the components involved here.\nThen I will look at the authentication flow.\nThen I will look at the authentication middleware\nThen I will look at password handling\nThen I will look at the token generation process\nThen I will look at the session or token management\nThen I will provide a step-by-step explanation of how the application starts\nThen I will process the requests from login to successful authentication\nThese things bring all our details here\nAll our handling has come here now.\nGenerating code from requirements is one of the most practical uses of OpenAI codecs in everyday development.\nInstead of translating every single business requirement into working code by hand,\ndevelopers can provide codecs with a vast amount of information or context.\nWe can include clear instructions, behaviours and context.\nWe have multiple forms of languages available to use.\nLet me open Excalibur here.\nAs you can see, we have some business languages.\nFor example, we can allow users to export reports as CSV or involve role-based access control, also known as RBAC.\nCodex helps bridge the gap between these requirements and implementations\nby identifying relevant files, suggesting changes, creating new components,\nupdating APIs, and adding tests where needed.\nHowever, Codex works best when the requirements are very specific in nature.\nLet me show you how to include basics in Codex right from the start.\nSuppose you want a plan mode, and after that you want to pursue a goal.\nThen you might want to create a document.\nAfter document creation, you might want to include photos.\nYou have multiple plugins that you can use side by side.\nbrowser plugin you can use that one too. If you have specific content to use that\ncan be utilized as well. For instance we can go to documents or downloads, select\nSelect an available document and use it.\nAfter that we select our model.\nThis latency is quite important, therefore I will use a latest model here.\nWith that latest model, we can increase speed and manage latency well.\nAfter this, our prompt should contain very detailed options.\nFirst of all, a well-set prompt has some specific elements available within it.\nI will explain these elements to you.\nA good prompt includes all these necessary components.\nThe very first thing is context, followed by the role, details, and then the requirements.\nWhen discussing requirements, it is a crucial point because codex works better when the\nrequirements are specific.\nA vague prompt may produce incomplete or misaligned code.\nA strong prompt includes the feature goal, affected users, input and output behaviour,\nor modules to consider, coding standards, test expectations and what our codex\nshould avoid changing. Developers should treat the codex generated code as a\nfirst draft so we get a complete response right from the very start of it.\nNext, after the requirements, we need to establish our acceptance criteria. This\ndefines the standard our response must maintain when we receive it. At the end\nwe sometimes use a negative prompt. In a negative prompt we specify the things\nthat we do not need. In the rest of the prompt we list all the necessary things\nthat we want. However, in a negative prompt we mention all those exact things\nthat are not required for our output at all. For example, let me show you a sample\nprompt right here in this section. We can go into Codex and type out our prompt.\nWe will write that you are working in this repository as a senior software engineer with a lot of experience and solid technical background knowledge today.\nThis defines our role. The complete task is outlined in plain words.\nAfter the task, you can see our functional requirements are discussed, such as adding an Export CSV button near the Reports table.\nTowards the end, all the detailed steps we need to perform are available.\nThis is our acceptance criteria.\nThe export button appears on the report page.\nClicking the button downloads a valid CSV file,\nand the CSV contains the same rows displayed on the table.\nIt will take data straight from the browser, commit the repository in GitHub,\nand provide the response in document form.\nGenerating code from requirements with Codex improves overall development speed.\nWhen our prompts are better, the final results we get will also be much better,\nbetter, which we can then utilise today. Clear requirements, a defined scope and\nstrong acceptance criteria help codecs produce code that is easier to review,\ntest and merge in big enterprise teams. We are discussing how big enterprise\nteams can incorporate these modern features on a larger scale today. Our\nnext discussion will be related to editing and refactoring existing code\nWe have generated this exact code as an example project where we are utilising all these core elements in real time.\nNow, suppose our initial code is generated and ready.\nTo demonstrate this point well, I will show you a custom calculator code from our older previous generations.\nIn that existing code, we will perform and showcase our past iterations and refactoring processes step by step for all of you to see right here today.\ntoday. Now we will discuss editing or refactoring the code. Here you can see\nthat we asked to create a calculator code. It took a quick look and made a\nplan for us. After making the plan it implemented the calculator in the\ncalculator.py file. This is our calculator. Everything in it is\nfunctional from start to finish. If we want to run it we will see that the\nverification passed using the bundled Python runtime. Note that Python and py\nare not on our system path, so if we want to use it in our environment we need all\nthese requirements. I will open PowerShell here or I will just open the\ncommand prompt. In the command prompt we will give this command. When I press enter\nyou will see that we have a complete response included here. Now I will\ninclude it here. There is an issue in the first line of our code. So we will run it\nfrom here to here. I will copy this. After copying I will go back to the command\nprompt. Here is the command prompt. Now we will include it in the command prompt.\nWhen we include it you can see that because this file is very old our file\nis not available here. But we can still run it in our browser. Now we will look\nat our editing and refactoring here. Usually this is the most valuable\ncapability of our OpenAI codecs for enterprise software development. While\ngenerating new code is useful, developers spend a lot of time improving,\nmaintaining and modernizing existing applications. Codecs can analyze the\ncurrent code base. It can understand relationships between files, identify code smells and suggest\nimprovements for better readability. First I will explain the first thing. This is what\nwe call refactoring code. This is a process of restructuring the existing code. It improves\nreadability, maintainability and internal design without changing its external behaviour.\nWe primarily involve this in our code review.\nIf I go to Excalordraw, I can tell you that after refactoring, the next thing is restructuring\nor editing.\nThat is very simple.\nIt is simple because we can come into our code, include any line here, and include things\nby giving a local comment.\nOr, if we want to do overall refactoring, I can say to make this into an HTML file that\nI can run in VS Code.\nHere you can see that now it will directly take our prompt.\nThe code was previously available.\nNow it will connect our Python calculator into a browser-friendly HTML version.\nAlternatively I will open our VS Code.\nYou might remember that we already included codecs in Visual Studio Code before, so this\nwill also be available for us to use in a much easier way.\nAlong with this, in Visual Studio Code where everything else is, we will automatically\ninclude our calculator file.\nCodex is open.\nAll our generations and things are available in it.\nNow you will see that our codex file has arrived here.\nIn the codex file, all our things are directly included.\nNow you can see that if we want, we can directly open the calculator in the browser, or we\nWe can copy its link and go to another browser.\nIf we copy its link, we just have to go here.\nWhen we open it here, the calculator will open in our browser.\nThis is a very simple calculator.\nIf we want to open it in the internal browser of Codex, that is also possible.\nwill load and run our calculator in the Codex browser.\nHere you can see that if we want, we can directly use this file in our VS Code\nfrom the options.\nWe can include annotations. If we want,\nwe can save a screenshot here. After saving the screenshot here,\nwe can also use it in another browser.\nWe can come here and use it here too.\nSo, if we want to make any design changes, we can do that directly.\nWhen working with legacy systems, developers often face challenges such as inconsistent coding styles.\nHere, the coding we have is AI-based coding.\nHowever, if we talk about legacy systems, they are systems where we manually write all the code.\nWe make all the changes and alterations in that code ourselves.\nThere, sometimes there are outdated frameworks, poor documentation and tightly coupled components.\nCodex can help developers understand the purpose of existing code before making modifications.\nIt does this by analysing the related files and dependencies.\nIt can recommend safer refactoring strategies and identify areas where changes may have downstream\neffects. This significantly reduces the time required to understand unfamiliar code bases.\nDespite these advantages, developers should carefully review all refactoring changes before\nmerging them into the production branches. Even when functionality appears unchanged,\nmodifications can introduce unintended side effects. Now, I will work on our code here.\nI will go to our codex. In codex we will write a prompt that you are working as a\nsenior software engineer in this repository. The task is to refactor the\nexisting code without changing its functionality. We will improve the code\nreadability and maintainability and remove duplicate logic with simple\ncomplex methods. There is a lot of detail here. The general overview of this\ndetail is that we should make our code readability and maintainability better.\nAs soon as I improve this, we start getting our response.\nWe can directly include this response.\nFirst, it will look at the current state.\nThe main refactor target is that we will slightly change the HTML file.\nFirst of all, we will include the keyboard handling.\nAfter that, we will see multiple changes in this HTML file.\nIf we want to review it, our changes will automatically appear wherever changes are\nbeing made in our file. You will see that the refactor is done in one focused pass.\nI am validating both sides now. Python behaviour should still pass, and the HTML script should\nstill calculate and respond to the button and keyboard actions. Here our checks have\npassed. Now, if we reload our calculator, many changes will be included in our calculator.\nNow we have another very important thing.\nIt is not just doing a simple check.\nIt also performed a Python check.\nIf I show you the working, after the Python check it also included HTML behaviour checks.\nThe refactored HTML is built here.\nIt has only 123 lines.\nI can say that our workability is much easier now.\nIf we look at the code review, it changed many lines in the code.\nFor example, instead of display value 0, it has set the display value to the default value.\nOverall, it showed our functionality in depth.\nIt directly changed the error coming in the display into show error.\nHere too, it changed set display error to show error.\nIn simple words, it improved our code maintainability and nullified the error-causing paths on a\nlarge scale.\nIt manipulated the errors into the ShowError function.\nExactly here too, the SetDisplayError was changed to ShowError.\nThis means, in simple words, our code maintainability has improved.\nIt has removed the paths that cause errors on a large scale.\nwe will discuss context windows and scope. Context window is a new topic. People do not\ndiscuss it much. I will tell you what a context window is. A context window is the maximum\ntext an AI model can hold at one time. It works as the AI's memory. Everything inside\nthis window, like prompts, history or files, is seen by the model. Before explaining how\nit works, I will give you a simple example. For a simple example, I will go to ChatGPT.\nFirst, I write a message in ChatGPT. I say, hi, I want to work with AI and HTML.\nIf I continue this conversation, it will write the code. This is simple prompting.\nWe get our answer through it. But if we do something else, if we give a very long prompt\nagain and again. You will see a message. The message says it is too long. Let us see the\nsize of the context window in ChatGPT. I will search for the context window of ChatGPT.\nIt will show the context window size. Usually the model has a 128,000 token window. This\nThis means it takes 96,000 words or 250 to 300 pages of text.\nI will copy this text and paste it many times.\nI will do this until the prompt becomes too long.\nI will copy it and go to a word counter website.\nIt is a famous word counter.\nWhen I paste the words here, you will see the website become slow.\nThis is because there are 9,776 words in total.\nWe put this in our prompt.\nIt has 76,000 characters.\nNow suppose I paste the last text here.\nYou will see a message.\nIt says the long text is added as a file.\nThe text will show up here.\nI will write that if I put this file in the chat it cannot process it.\nThe message is too long.\nWe want to see the context window size of ChatGPT.\nIt will take time.\nYou will see that the context window depends on the model.\nOpenAI does not always show the exact limit, but if you see the message it means the text\nis too long for one input, the file is too big, or the user interface has strict limits.\nIf we use fewer words, we will get our answer.\nIn the same way, when we go to Codex we can see limits.\nIn the new chat we have limits.\nWait a minute, I will open it here.\nWe know that Codex has some limits.\nEven if we add many images, files and documents there are still limits.\nIn real business projects, apps have thousands of files and millions of lines of code.\nNo AI can read all the code at once.\nDevils must help codecs focus on important information.\nScope sets the limits of a task.\nIt includes files, modules and features.\nSetting the right scope stops bad changes.\nIt helps codecs focus on the right parts.\nNow I will tell you directly.\nSuppose we get an answer.\nHow will a full developer work?\nFor example, we will take an e-commerce store.\nIn our example we have an e-commerce store.\nA developer wants to add a discount box to a product page.\nCodex. He only shares the product model, service, controller, form and tests. This limits the\ncontext. Codex can focus on the updates. It will not make bad changes in other\nplaces. Where do we use this? When we make changes this is very helpful. We can\nmodify things easily. You will see two or three options here. We can open it in VS\ncode. We have a full MetaBrain's project here. It has all our files. We can run\nthe index file here. On the side we have our chat. Our codex is also running\ndirectly. Besides codex we have an agents option. We can use agents to do\nour work directly. Now we will verify our GitHub account. Do you remember how I did\nGitHub authentication? We will authorize VS code directly. Our workflow and\npersonal details will be added here. This works for public and private projects.\nWe can include this directly. If I show you the sign in again, authorisation\ncomes directly. We can confirm it. Suppose we want to verify using email.\nThe email will come automatically. We will confirm the email here. These emails\nrun through a base setup. We got the code here. Then we will verify it. Our VS code\nwill sign in and connect automatically. After signing in, MetaBrainz connects with our co-pilot.\nWe are running many windows. In one place we run the agent. In another place we use\nour IDE. In a third place, we use it as a chatbot. So, we must manage our context carefully.\nContext window and scope management are very important for AI software development. By\ngiving the right information and clear boundaries, developers help codecs. It creates accurate,\ngood and reliable code. We must look at these important things carefully.\nNow, our discussion is about reviewing our AI-generated code.\nIn the AI-generated code, we want to see how we previously used codes like\nCreateCalculator multiple times, as you can see.\nWe have created the calculator here, and you can see that all our changes are also available here.\nFor these AI-generated changes, we first need to understand two or three things.\nFirst, in this complete software we are using, whenever there are changes, the red values\nwill be all our previous values, and the green values will be our new possible changes that\nwe have used here.\nIf you look here, you can first see the set display error, but now in our new code, some\nthings have expanded.\nLet us suppose I directly include a prompt here saying, I want a scientific calculator.\nNow, a scientific calculator will be created here.\nUsually we have this one code and we can call it version 1.\nAutomatically, this version of ours will be applied here, and along with this version,\nif we have any other versions available here, they will be used.\nFirst of all, it will take some time to think about what things were discussed in the previous\ncontext.\nIt will review what discussions we were having previously.\nAfter all those discussions, our code will automatically start generating here.\nAlright, it is reconnecting with our work.\nOur connection with the model is being created to see how a complete connection will be generated\nand how our thinking will work on a larger scale.\nUsually, when we are looking at such a code, we have to do validation.\nWe can separate our unified difference from here.\nWe call this split and we call the other one unified.\nIn the split view, our complete previous codebase is on one side and our new code is included\non the other side.\nIn the previous one, you can see the display error and here you can see how much our error\nevaluation is available now.\nSimilarly, our if action is also available here.\nWe can see how much if action we had previously, and after that, how many evaluations are coming\nin our run action.\nAlright, so all these things are being directly included in our system.\nWe are looking at multiple changes here.\nYou can see that our connection is being created once again here.\nWe can change the model to a very simple one, make the speed fast, and also change the reasoning\nhere.\nNow, besides all these things, we have two options available here, Review and Undo.\nIn the Review option, we can directly review our code once in the first scenario.\nAnd Undo works exactly like our version control system.\nIt keeps multiple versions in our adopted system.\nHere you can see that our system is now running, and it is telling us that it is upgrading\nthe existing HTML.\nNow, let us suppose I take you to this HTML file.\nI will show all the differences here.\nThey are here now and we will also enable the rich preview.\nNow you can see that this is our newer file.\nEven after the newer file, when we make changes again, those changes will also be visible\nhere.\nYou will see plus 32 and minus 21.\nThis shows how many lines of code were removed and how many lines of code were added to our\ntotal system.\nWhen both these things work together, we automatically start getting our response and work results.\nIt shows how we are managing things.\nAlright, so this is our very detailed working process.\nHere you can see all the changes.\nFor a simple example, as we are reviewing now, the title was previously Calculator, but now\nNow our new title has become Scientific Calculator.\nAlright, if we look here now, all the design changes of our calculator are shown here with\nplus and minus signs.\nAlso, you can see that if we want to add something here, we can directly include a local comment.\nFor example, I can say here, I want round buttons.\nOkay, so now we want our round buttons.\nAs soon as I post this comment, you can see it is included here.\nNow this comment is added, alright?\nSimilarly, if we go here, I will say, I want more buttons.\nI am making very simple changes in front of you.\nI will add the comment.\nNow there are two comments here, alright?\nIn the same way, at the end of this file, I will say that we need simpler functionality\nhere.\nOkay, I will say, simple functionality please.\nAlright, this comment is added here.\nAfter that, we can go to the end or just go to the start.\nIn the start, I will change our name here.\nChange the name as MetaBrainzCal.\nOkay, MetaBrainzCal.\nAlright, now our comment is added.\nYou can see that three or four comments are here now.\nIn our calculator, we have a comment in row number 46 and a comment in row number 102.\nAfter that, there is a comment in row number 200 and then a comment in row number 6.\nNow what we have to do here is I can say make the possible changes asked. Okay what will happen here\nis that based on the changes we included in these comments the changes will happen at those exact\npoints. I will only show you this one change that we demanded at the end in the comments.\nWe will see how it changes the name of our scientific calculator here,\nand this is a very advanced feature. We are reviewing our complete AI generated changes\nto see how our reviews can be applied on a larger scale and how we can include them.\nIt will take some time to think. Obviously you can see that it is thinking here,\nand after that it says, I will apply the review comments directly.\nFirst of all, our name will be changed to MetaBrainz.\nAfter that, the calculator buttons will be made circular.\nA few practical scientific buttons will be included.\nHere you can see our calculation.\nMetaBrainzCal is written here.\nAfter that, we also included a comment here that we want our buttons to be rounded.\nSo, you can see that by including height, aspect ratio, border radius and all these things,\nit has provided us with some rounded buttons that we can directly see here.\nAlright, so all these things are greatly included in our working process here.\nAnd through this, we can include it in our system, and it will work here.\nNow we will get all our changes here.\nYou can see that whatever visual changes we have,\nit has included some lines in a file and showed us the changes here.\nWe can also open that here.\nOK, after that, for the next changes it makes, you can see our thinking process here.\nIn the thinking process, as it is including our constants and other values, these files\nwill automatically come here side by side.\nNow it has updated our file.\nWe have all the commands here.\nAs soon as we open this in our browser, all our discussed changes are here, like Metabrains\nCal.\nOur buttons have become rounded.\nAnd now, if we include anything here, that thing will automatically be directly included\nhere.\nOK, our error came here.\nWe are seeing everything working.\nAlright, if we suppose we need the value of pi here, we get our pi value.\nIf we need the value of log8, we can directly input it here.\nAlso, you can see that this delete option is working directly here too.\nIf we look at all the signs here, every sign that we have is working very easily.\nWe can directly enter all our values here.\nIf there is no responsiveness, we can go ahead and directly include responsiveness in our\ncode later.\nBut code review is the main thing that I have discussed with you.\nAfter this, our next discussion is related to our prompt hints.\nNext we discuss the main usage of a prompt library.\nWe will see how we can use it and where more options are included.\nFirst I come here.\nInstead of using everything, I will search the prompt library in Codex.\nHere you see Codex prompting.\nFirst I tell you this is a built-in collection of reusable workflows, templates and skills\nin OpenAI Codecs and Agentic CLI.\nWhen I go to prompting, you see we often use prompts.\nYou interact with the codecs by sending prompts that describe what you want.\nExample prompts are shown here.\nThread is a single session.\nIt has your prompt plus the model outputs and tool calls that follow.\nA thread can include multiple prompts.\nWe use a chain of process to take things forward.\nI will explain this exact process right here in our codex.\nIn codex, we go to our main user settings.\nInside these settings we have personalization.\nIn personalization, our own custom instructions are included.\nWe can also switch our AI personality from pragmatic to friendly.\nBoth of these personalization options are here.\nIn custom instructions, we can include all custom-made, personalised instructions that\nwe want in our tool.\nIt is quite simple to include our text responses.\nBut sometimes, we must see our clear deviation and how we are working on stuff, and how these\ncustom instructions work.\nWe have our memory, which we can induce right here in our experimental form.\nWe see how our things can be included and how we can take our many processes forward.\nOur standard options show how we will best include our own memory.\nFirst is the main Enable Memories.\nSecond is our Good Tool Assisted Memories.\nWe can also involve Tool Assisted Memories and involve all our process or workability\nin it.\nNext thing is our own custom prompt libraries.\nWhat is a good prompt library?\nI will tell you first that our prompt library is a great reusable prompt hub.\nIn this we can do our text discussion multiple times.\nWe can do multiple workings for our single tool.\nWe must see one more thing here.\nFrom the ChatGPT side, there is no prompt library available here yet.\nWe have clear information about basic prompting.\nBut if I talk about the Claude code, there is a distinct prompt library available.\nWe can go there and see the many prompts are collected from the various Anthropic guides.\nThis includes our common workflows and best practices, and how Anthropic teams take Claude\ncode forward.\nIf I want, we can also see the core understand prompts.\nWe see how we can use our own prompts if we need a new prompt for git, release, data,\nautomate or product.\nA set number of proper prompts are available for every single thing.\nWe can reuse them multiple times over.\nWe can utilise multiple variations or our many functions.\nThis very same thing is available here in our Claude,\nbut if we go to our codex, then until now we do not have these full prompt libraries available.\nWe can do one other good thing here. We can come right here and ask a prompt question.\nIf we go into ChatGPT and for our codecs we include our own best prompts,\nI can say I want a whole new prompt library for my codecs.\nIt will then give a good reusable clean codecs prompt library.\nInside it we have all our good useful prompts. We have our bug fixes, text test generator,\ncode review, refactoring, feature builder, API design, performance optimization and security audit prompt.\nAll these things will involve in that exact thing.\nFrom there we will take and use it well. These various things are included in our entire core\nsystem on a large scale. We must look at all these many things on a large scale to see how we can\nreuse them. We will copy it and come inside this main system to use all our many prompts.\nWe will not use these exact things but besides this we have all our 12 core prompts.\nAs we go forward our prompt library will increase even more. From 1 to 12 our many different options\nwill be used and we will include them. These things are used very much inside the entire system.\nWith this, we have a lot of great automation in our work on a large scale.\nWe can use that well.\nI will tell you, we also have cool automations here on the side.\nWe can run our day brief, week review or smart project monitor.\nBut besides that, if we look at our cool templates, we have very many.\nNow our next discussion relates to integration.\nWe will see how to perform AI-enhanced CI slash CD.\nFirst, I will explain the full definition of CI slash CD.\nCI stands for continuous integration.\nCD stands for continuous delivery and deployment.\nUsually, when enterprise-level workflow loops run, we have multiple projects.\nWe have standalone projects, collaborative projects, or autonomous projects connected to multiple companies, industries, and businesses.\nFor them, we must look at integration and deployment.\nThis is a DevOps methodology that automates building, testing, and releasing software.\nWe can discuss this as automation.\nThis pipeline allows development teams to ship code updates frequently, safely and reliably,\nwhile catching bugs early in the development cycle.\nUsually we do CI slash CD at an industrial or enterprise level where we need automation.\nFirst we have continuous integration.\nIt is a practice where developers regularly merge their code into a central repository\nlike GitHub or GitLab. Every time code is merged, an automated system builds the application\nand runs tests until the integration finishes. This ensures that the new code does not break\nthe existing code base. It allows teams to catch errors instantly. Then we have continuous delivery\nversus continuous deployment. How can we use CI slash CD? First, it gives a faster time to market.\nNew features and bug fixes reach users in hours instead of weeks or months.\nThere are fewer bugs, easier rollbacks and better developer productivity.\nCI slash CD pipelines can be used in GitHub Actions, GitLab CI, Jenkins and Circle CI.\nI will search for codecs on Google now.\nFor example, if I share here that I want this in Codex, then we can also do that, how it becomes possible.\nIntegrating the Codex command line into the pipeline enables OpenAI Codex to automatically evaluate build failures.\nIt analyses vulnerability scans and proposes minimal changes required to make the tests pass.\nIt executes code quality checks directly.\nIt also generates remediation patches or pull requests.\nUsually, when we talk about our process, we can perform CI slash CD in multiple forms\ninside our projects.\nHere in this interface and outside it, if I go directly to the terminal, I can run the\nCI slash CD pipeline.\nHow can I do this?\nFirst, I will search for Codex CLI.\nThe developer platform will open, which we can access directly.\nHere we must install it.\nI will come here and command the system to install Codex.\nWe will copy and paste the required command.\nYou will see that Codex is already set up here.\nBecause of this, we do not need to install it again.\nWe simply type our Codex command and it will start running.\nYou have seen this here too.\nWhenever we give a command, we have a terminal option available on the side.\nInside this, we can automatically initiate our working process.\nWe also have a browser option to run CI-cd pipelines.\nUsually, continuous integration and continuous deployment have become a foundation of modern\nsoftware engineering.\nTraditional CI-cd pipelines automate the building, testing and deployment processes.\nThis reduces manual effort and ensures consistent software delivery.\nWith the introduction of AI-powered development tools like OpenAI, Enhanced CI and CD introduces\na new layer of intelligence into the software delivery lifecycle.\nInstead of merely reporting failures, AI systems can investigate the root cause.\nThey propose correlations and create pull requests containing fixes.\nThis dramatically reduces the time developers spend troubleshooting failed builds.\nIt allows engineering teams to focus on high value work such as feature development and\narchitectural improvement.\nLet me show you a simple example.\nWe can take any system here.\nWhen we give a prompt, you can see our working process begins.\nWe get options to debug an issue or review a plan.\nI will select GitHub and ask it to review the newest repository.\nThe newest repository will appear.\nThen, I will command it to analyse the latest pull request.\nIt must generate unit tests for newly added functions,\nmodified business logic and edge cases\nto improve our error handling completely.\nAs soon as I include this,\nthe error handling scenarios will become much better.\nThe other responses will also appear here for us to analyse directly.\nUsually, the GitHub workflow is infused here.\nInside the GitHub workflow, we can directly use our other generations or processes.\nOur other regions and our understanding will also appear.\nSide by side, I must tell you another thing.\nWe usually face a concern about how to check our processes inside out on a larger scale.\nWe will see that in a much better form now.\nIn the CI slash CD pipeline, you can see the local workspace is empty.\nThe GitHub account is authenticated, but Git is not available.\nIf we set up these things, we can cross-check our CI slash CD pipelines.\nWe prepare a repository and use the CI slash CD pipeline perfectly.\nHere, I will give you the example of a pipeline.\nIf you prepare a pipeline of build, which have a request, it has many stage such as\nnames and tests will appear here.\nIf you connect codecs here, then our AI tasks can work side by side with it.\nOur next topic is putting codecs into our work process.\nThis means adding codecs to our build system.\nWhen we do this, the system helps us find problems.\nIf a build stops working or a task breaks, the tool sees the code quality issues.\nBefore, people had to look at every single problem by hand.\nNow the system looks at the failures for us.\nIt gives ideas to fix them and creates tests.\nIt can even make pull requests with good answers.\nIn a normal process, a person sends code to GitHub.\nThen our complete system starts running automatically.\nThings begin to work on their own.\nWe usually watch these actions in our deployment area.\nFor enterprises, this saves a lot of time.\ndo not have to fix the same small problems again.\nThe Codex Assistant helps keep the work active.\nIt understands the whole process of how things run.\nWe like having a place without mistakes.\nNow let us look at consistency.\nThis is a very important benefit.\nSometimes working directly with these pipelines can be hard.\nFirst, I will go to our plugins section. We can see the GitHub plugin here.\nNext, I will search for codecs in the bar.\nWe see many results, but I want to find it in the profile settings.\nI look at the list of options like packages, co-pilot, pages and security.\nHere I will search for the codecs.\nAnd here we have our codecs.\nWe can also search in GitHub or settings.\nThen I decide to grant access.\nTo do this, I open our repository.\nI click on the code button and copy the link.\nI will ask the chat, do you have access to this project?\nThe system will now check if it can reach the repository and provide us with it.\nAnd how we can initiate our working or a larger scale.\nSometimes we have to look at these details ourselves.\nAlso, we can take a complete process out here and our generation will be completed in that.\nThis is usually present at a lesser extent.\nBut now we will initiate a full-fledged process here.\nYou will see that it has told us here that yes, I can access the repository.\nThe access to our repository has also arrived here.\nThe second thing it said is that our default branch is main, visibility is public and effective\npermissions in the session are admin, maintain, push, pull and triage. I can\nsimply say here that we now have to include an agents.md file inside this.\nThis will directly include it and give it to us. But the thing is we cannot\ncreate things inside this. So right now we will only cross-check our agents.md\nfile here to see if it is directly available to us or not. These are our\nthings. I will now take our full detailed discussion. You will see that directly\nafter just one second our agents.md file has been included here. Inside it, as you\nsee, git isn't available. So here, after inspecting, it has directly arrived. I can\ngive access here to directly push the code to see how we can improve or\ninclude things on a large scale. Side by side another very important thing is\nrunning here. Usually in a base pipeline I could not run git push origin here\nbecause the directory is not a git repository. Git is not installed in our\ncurrent shell. Let us suppose we want to install git. How can we do that? First of\nall we will open our PowerShell here. We come inside this one. Here I say our\nPowerShell. This is our PowerShell window. Coming directly inside PowerShell, we will\nstate our Git version here. You are seeing that the Git version is available to us here.\nLet us suppose we want to install our Git. If we use this directly, you are seeing that\nnow we will say we want to install Git with our basic widget. Automatically our widget\nwill be installed here. On the basis of that widget, we will take our things inside running.\nUsually these things are not explained to us.\nWe are just initiating our production and working on a large scale.\nHere it will take some time to install.\nUntil it installs we will wait here.\nNow, due to some issues, our Git is not becoming active.\nI tried a lot to activate our Git.\nYou are seeing the prefix.\nBranch prefix is working. Everything is working.\nWe usually did not include our commit instructions, but still, it is possible that by opening these things, all these items come into working.\nBut still, as a matter of fact, the discussion here is that when integrating codecs, organisations start in non-production environments.\nThis allows codecs to analyse failures and generate suggestions.\nOnce the team gains confidence in the results, they can gradually introduce more advanced workflows.\nThese include automated pull request generations and our AI-assisted bug fixing.\nOverall, Codex acts as an intelligent engineering assistant inside our CI-slash-CD pipeline.\nIt will overall help us in building all our things.\nThese entire matters have been discussed here.\nAn autofix workflow begins the moment a developer pushes code to a repository or submits a pull request for a review.\nThe CI slash CD pipeline automatically starts a series of validation processes such as compiling the application, running automated tests, checking code formatting standards, verifying linting rules and executing security scans.\nIn traditional software development environments, any failure in these checks would require\na developer to manually investigate logs, identify the source of the issue, and implement\na fix.\nWith codex-powered autofix workflows, the process becomes significantly more efficient.\nInstead of merely reporting that something failed, the workflow can immediately begin\nanalysing the problem and preparing a potential solution.\nThis transforms CI-CD from a passive validation system into an active participant in software\nmaintenance and quality assurance.\nOnce a failure is detected, Codex examines all available information related to the problem.\nThis includes error messages, build logs, test outputs, recently modified files, project\ndocumentation, repository instructions and any development guidelines defined in agents.md.\nBy reviewing these sources together, Codex gains a much broader understanding of the\nissue than a simple rule-based automation tool.\nFor example, if a login-related test starts failing after a recent update, Codex can inspect\nthe authentication logic, compare the recent code changes, review the affected test cases\nand determine where the behaviour diverged from expectations.\nThis contextual understanding is one of the major advantages of AI-assisted development workflows.\nAfter gathering the necessary information, Codex performs root cause analysis.\nRather than attempting random modifications until a test passes,\nit tries to determine why the failure occurred in the first place.\nThe underlying issue could be a missing import statement,\na renamed function that was not updated everywhere,\na changed API response structure,\nan incorrect configuration value,\na dependency upgrade that introduced breaking changes,\nor a test case that no longer reflects\nthe intended application behaviour.\nBy identifying the actual source of the problem,\ncodecs can generate fixes that are more accurate,\nmaintainable, and less likely to introduce new defects\nelsewhere in the system.\nOnce the root cause has been identified, Codex generates a proposed solution.\nA well-designed autofix workflow emphasises minimal and focused changes.\nThe goal is not to rewrite large portions of the application, but to make the smallest\npossible modification that resolves the failure.\nThis approach reduces risk and makes the resulting changes easier for developers to review.\nFor example, if a build fails because of an incorrect import path, codecs should update\nonly the affected import statement rather than refactoring multiple unrelated files.\nalso provide explicit instructions to guide this process, such as requesting\nthat only necessary files be modified, preserving existing coding conventions,\nand updating tests only when required to reflect legitimate behaviour changes.\nFollowing fixed generation, validation becomes the next critical step.\nThe proposed solution is tested using the same pipeline checks that originally detected the problem.\nAutomated builds, unit tests, integration tests, linting tools and security scans are executed again\nto confirm that the issue has been resolved successfully.\nIf the validation process still detects failures, developers can provide additional context or refined instructions and allow codecs to perform another iteration.\nThis feedback loop helps improve solution quality while ensuring that every proposed change is verified before moving forward.\nEven when automated validation succeeds, human review remains an essential part of the workflow.\nDevelopers examine the AI-generated modifications to confirm that the solution is technically correct, aligns with business requirements, follows architectural standards and does not introduce unintended side effects.\nThis review process also provides accountability and governance, which are especially important in enterprise environments where compliance, security and maintainability requirements must be carefully enforced.\nOnce the fix has been approved, the changes can be merged into the main branch and included\nin future deployments.\nOver time, teams can analyse recurring issues and improve their development processes accordingly.\nRepository instructions, testing strategies, agents.md guidelines and CI-CD configurations\ncan be updated to help codecs handle similar situations more effectively in the future.\nAs a result, autofix workflows not only resolve immediate problems, but also contribute to\ncontinuous improvement across the software development lifecycle, enabling teams to deliver\nhigher quality software faster, while reducing the manual effort required to maintain complex\nsystems.\nNow, the discussion we have is related to configuring automated bug resolution.\nWe can say this on a large scale.\nIn our codecs, along with other things, a discussion was going on.\nThis is most valuable for enterprise use cases.\nFor OpenAI codecs, instead of developers manually investigating every test field, linting error,\nsecuring warnings or production defects, codecs can analyse the issue, identify the affected\nfiles, generate a proposed fix and create a pull request for human review.\nThis significantly reduces the time spent on repetitive maintenance tasks, while allowing\nfocus for engineers to concentrate on high-level architectural business problems.\nIn modern software teams, bugs are often discovered through CI-CD pipelines, monitoring systems,\nissue trackers and security scanners.\nCodecs can integrate into these workflows so that when an issue occurs, the AI automatically\nreceives the failure logs, repository context, coding standards, and our project instructions.\nAlright, the critical aspect we have for automated bug resolution is defining clear boundaries.\nEnterprise organisations should never allow AI to directly deploy files into production.\nInstead, codecs should generate code changes, execute tests, and create pull requests that\nthat require developer approval.\nThis maintains governance and accountability\nwhile still benefiting from automation.\nHuman reviewers can remain fully responsible\nfor validating the basic logic.\nNow, let me tell you a very important thing.\nOur agents.md file plays a crucial role in the process.\nWhat happens is they provide our project specifications\nand instructions that guide Codex's decision making\nduring bug resolution.\nThese instructions define the coding conventions, testing requirements, security constraints, dependency policies and our architectural patterns.\nThe more structured the agents.md file is, the more accurate and reliable the fixes become.\nOrganisations must look at another thing alongside agents.md – confidence thresholds.\nFor example, codecs may automatically fix linting errors and formatting issues, while\nsecurity vulnerabilities or database-related bugs require a mandatory senior engineer review.\nAn example of this is that first of all, we have to do this step by step.\nThe first part of our process is to identify our bug trigger resources and define where\nthe bug reports originate.\nUsually what happens is we see this in our CI slash CD builds, or we can see this in our\nunit tests we perform, or we can see this in our integration test failures.\nSometimes issues can arise in GitHub, or our security scans can cause an issue.\nAlright, after that we have to identify the AI and look at the AI's fix permissions.\nIf, let's suppose, there's a formatting issue, a linting issue, or a unit test failure,\nWe'll automatically look at all of them, and a detailed discussion will take place regarding\nproduction incidents and everything.\nAgents.md is our most important file which I mentioned here.\nWhat happens in Agents.md is we can see the automated bug resolution rules.\nWhenever we fix a bug, we should minimize the code changes, we should never modify the\ndatabase schemas, we should preserve the API contracts, add our tests for everyone, and\nrun all of the affected test suites.\nWe must explain the root cause and also create a pull request summary.\nYou can see all of this in agents.md, where we address the bugs, and multiple things are\nincluded.\nThen, if I, let's suppose, perform proper CI slash CD integration, meaning after identifying\nthe trigger sources, we've defined our AI.\nThen if I say we want to configure CI slash CD here, what I'll do first is take you to\nthe configuration.\nIn the configuration workflow, we first discuss the build failure, then we understand our\ncodex investigation, generate the fix, run the tests, create the pull request, develop\nour reviews and also then merge our workings.\nSo this will come here.\nThen the next and most important process, which will be discussed in our setup here,\nwill be our validation of fixes.\nHere we will require our unit tests, integration tests, security scans, code reviews and our\nbuild verifications.\nThese will directly be used to approve our process.\nAlright, now as an example, if I go into Codex and open a new chat, what will happen in our\nchat is I'll say directly that a GitHub action has failed. We'll include the\nrepository name directly. We have our failure logs here and the tasks are\nbeing discussed the most here. Alright, after that we have our unit test\nfailure which we will use. After that, whatever automated bug resolution we\nhave directly we will configure it. We will discuss a OneGo project today. We\nwill direct a full repository in our OneGo project. First we will create all\nproject code and details. After that we will check our iterations and possible\nimprovements. Then we will use GitHub and Codex.\nHere is our Codex. We open Codex.\nInside Codex, we generate a full project before using prompts.\nIn Codex, we must include the SetAgents file.\nBut we will first exit this project.\nThen we will build a new project here.\nI will close this one.\nWe close it and build a new project again.\nYou see projects are available here.\nI can organise them or start from scratch.\nWe will name our project OneGo.\nIn this project we will build a complete application with everything.\nFirst we will pin this project.\nAfter pinning, we open it in our Explorer.\nOur Explorer has no file inside.\nLater, all generations will come into this file.\nWe have our VS Code and Terminal.\nI told you before we can use codecs in our terminal.\nIn VS Code, our cursor is available.\nI will first tell you the plan.\nWe will open our plan mode.\nWe have no plugins yet.\nWe will not use the browser much.\nWe will use the 5.4 model in our GPT.\nWe will use the medium 5.4 model.\nWe will use fast speed here, not default speed.\nIt will be used here.\nWe will not connect GitHub for now.\nI will say in one go that I am going to make a project\nabout a health care application.\nCan you plan a project for me?\nProject planning will start here.\nFirst, it will settle output resources.\nI will not include them now.\nWe look at current workspace.\nYou see nothing is there in workspace.\nWho is the primary user for the first version of the healthcare app?\nWe have patients and doctors.\nI will say, this application is for patients.\nWe want to build an application for our patients.\nWhat kind of first release do you want to plan? MVP or balanced or ambitious?\nI will choose MVP. What is our delivery target? We want to build a web application here.\nI will not build a mobile application. I will build a web app here.\nThe current decision is clear. Patient facing, MVP sized and optimised for a web app.\nI still need to lock the feature set.\nNow we have our feature sets.\nWhich feature bundle should define the MVP?\nBooking and records?\nWe will use this simple option.\nHow should the plan treat healthcare compliance in the first version?\nWe will use HIPAA ready design, which is recommended.\nWe can also use full compliance track or lightweight prototype.\nHow much external integration should the first plan include?\nI will say minimal integration will be included.\nThese things are recommended here.\nThrough them our entire process comes.\nWhat will be the delivery timeline?\nWhat will be the delivery timeline for our project?\nI will say I want it in an urgent manner.\nThis is a fourth part where we can include what we want.\nWhat team should this plan assume?\nOur small team is here.\nInstead of a small team, we can choose Solo Builder.\nOur solo builder is here now.\nWhat kind of plan do you need?\nBuild plan or business build?\nWe will work with a simple build plan.\nNow it reduces the project scope and other things.\nYou see, multiple questions are asked here.\nFor a solo builder, what does urgent mean for the first usable release?\nWe will answer one week.\nWe will include one week here.\nOur plan will come here.\nBuild a patient-facing WebMVP for a solo builder\noptimised for one week urgent prototype\nwhile keeping the design HIPAA aware and future ready\nfor real healthcare data handling.\nI will read the plan once, then we will implement it.\nI am saying that we have the entire process.\nWe say our product definition is also happening here.\nAfter product definition, our recommended MVP features have also arrived here.\nFrontend and backend have come in our technical approach.\nI will tell it that I want just a working frontend and minimal backend.\nAlso, it should be just HTML and CSS with JS logics.\nWe want our product in a simpler way.\nAll these things will come here.\nThe stack has arrived.\nWhat should a minimal backend do?\nWe can use a tiny mock API.\nWe can use a basic form backend.\nWe will not use any real backend.\nHow should the frontend be structured?\nI can say a multi-page application will be required here.\nWhat level of frontend quality is needed?\nI will say we need a portfolio quality user interface.\nOur plan is created.\nYou can see we have JSON objects and local storage here.\nKey changes are to reframe the project as a frontend prototype, not a production ready\noption.\nNow we can implement this plan.\nDuring implementation, it will bring our coding.\nIt is checking the workspace shape.\nWe will look at static app files and shared styles and JavaScript layers here.\nWe will also check the open file to see what things are discussed in it.\nFirst it took basic index HTML.\nWe will open our file explorer.\nWe will open 1Go from Documents.\nFor the time being, we have no file or format available in one go.\nBut later our files will appear.\nIt has confirmed our HTML pages and state stylesheet and JS layer.\nIn plugins, our GitHub is not installed yet.\nI will go to our codec settings to install GitHub once.\nWe will go to our settings.\nYou can see our connections here.\nIn settings, you can see we have our connections.\nBesides connections, we have our MCP servers here which we can use.\nWe also use plugins here which we can connect.\nI will go to our plugins.\nYou can see our plugins are available.\nWhere all our other details are, we will include our direct option.\nIn configuration we have our plugins.\nWe can use user configuration.\nWe have sandbox settings and current version and everything is working.\nIn MCP servers our plugins have arrived.\nWe can also see our most used plugin here and how it is discussed.\nThen we have plugins inside our hooks.\nAnd we have our computer use plugin, so any app and our Google Chrome browser has also\narrived here.\nI will bring our plugins into our settings search.\nThese are our plugins.\nI will use GitHub here.\nWhen I connect GitHub, you can see all details have arrived here.\nBecause we are using the Michael DuPont account, we will bring the same Michael DuPont email\nhere.\nWe click on Add Plugin.\nAdding GitHub will appear.\nIt is approved by our admin.\nWe will go to Connect and continue to GitHub.\nWe will get our detail in our browser, which we will use.\nYou will see that GitHub is now connected.\nWe can try it out in the chat too.\nIn GitHub, we have our repositories and pull requests, and all things have arrived.\nOur GitHub access is now connected.\nWe have our repositories and issues, and pull requests inside it.\nWe have 97 actions available in the GitHub app related to Read and Write.\nWe can use it for Read and Write functions.\nIf we want to try it in the chat, we can do that too.\nAfter this, we go to our healthcare application.\nYou will see that a total of 9 files generated here.\nYou can see in these files we have our app.js.\nIf I go here and reload it, our file structure is still not very strong.\nBut you will see that all our assets are available here.\nIndex and records.\nAnd all these files are generated here overall.\nNow it also told us it found a portability issue in the static assets.\nA few non-ASCII characters slipped into the labels.\nIt will run them.\nThe command is normalised.\nI am loading the normal browser.\nNow we will connect things in our in-app browser.\nYou will see I did not give any prompt.\nI did not give any prompt on a large scale.\nI gave a basic prompt that I need a healthcare application.\nIt planned it and took multiple recommendations from me during planning.\nIn the end it generated all our assets in one go.\nAppointments and dashboard and doctors and index and record files are generated.\nNow browser automation has also arrived.\nWe have all files for implementation and we can run prototypes.\nHere is our documentation.\nNow if I open our file explorer and go to documents and one go, our folder is empty right now.\nBut if I look at our index.html here in the file explorer, you will see that we have a\nfile available.\nIf I open this file in our browser, you will see our project has arrived.\nIt is a healthcare app prototype.\nWe are accessing healthcare here with appointments and records and reminders.\nWhen we want to enter a dashboard we sign in here.\nOur doctors have arrived in it.\nAfter doctors our appointments have arrived.\nAnd our records have arrived.\nThis is our whole process as you can see.\nIf we want we can confirm any other appointment too.\nI will confirm an appointment.\nI will say I have a heart pain.\nI will just include a reason.\nWhen we include this part our second appointment will come.\nThis means our page is loading.\nIf we go to records we have our medications and allergies and recent visits available.\nIf we go to doctors, all doctors we have are listed here.\nThen comes our dashboard.\nInside this dashboard we have our current summary showing the plan and conditions and\nrecent visits.\nOur insurance card and hydration reminder and medication review are all available.\nMembers and unwanted things are available here. If we want we can build a new demo\naccount instead of demo credentials. I will close this one. I will give a prompt\nhere. I liked my first draft and I want to make this an official app for the\npatients. Add some vibrant health-based colors in the website. Also try to make\nit more real. Our prototype is very much liked. In the prototype our login and\ndoctors are very real. But this dashboard has some issues. When I bring it to the phone\nview it does not look special. It has many issues. This dashboard does not go higher\nfrom here. The second thing is our login panel goes too far back. If we go to our network\ninside the console, nothing special is loading. This means we have no back end. It created\na working front end for us. I have given a prompt to update our prototype. You see we\nhave our browser available in plugins. Due to this we can use it. It says I am updating\nthe prototype. It is updating the prototype towards a more launch-oriented Alaska patient\napp. First I am verifying the current front end structure. Then I will replace the mock\npositioning with real Alaska provider data. It will shift the design towards a premium\nApple adjacent aesthetic without copying Apple owned logos or icons, because we cannot\ncopy the icons. But if we want, we can copy aesthetics based on an ecosystem. And we are\ngoing to do the same thing here. This thing will be induced here. In styles you see we\nwe have many changes. It is performing enhancements in our file overall.\nI have included one thing in the OneGo project. I want to run the Apple ecosystem here.\nApple ecosystem means the user interface enhancements in our MacBook or iPhones. I want to adapt\nthem in our project. All those things are happening here. You see what it is doing first.\nI gave this information here.\nUse real doctor information from Alaska.\nI want to launch it there.\nIt is taking Alaska doctor profiles from the list.\nFirst, it read all our files.\nThen it understood index.js file.\nThen it understood all files in data.js.\nAfter that it is carrying on our research purpose.\nWhen these things are included, our final website version will come in a better form\nLet me tell you an important thing. In Codex, whatever things are happening now, you can see them all here.\nIf we have any output, we can see it too. If we have any source, we can see which source it is utilising here.\nLike here, from a regional hospital list, it is retrieving doctors.\nFirst, it makes concrete changes, replacing generic healthcare content with Alaska-specific facility and care context.\nSecond, it is rebuilding the visual system into a brighter, premium look that feels native\non iPhone and Mac without copying Apple trademarks or proprietary icon sets.\nAll these changes are being created right now here.\nIn the Styles section, you can see that we have many changes appearing.\nWe are experiencing many changes because it is performing complete enhancements in our\nfile overall.\nAll the styling updates are being processed to match the ecosystem we requested.\nOur project design is improving to look professional.\nThe software implementation has reached the required final stages.\nAll web files were edited.\nThe main index file is now running.\nThe visual brand appears much better now.\nDemo logging credentials exist in the database.\nEntering the main dashboard shows AuroraCare Alaska.\nNavigating to care teams displays available health and vascular care, internal medicine\nand real-time schedules.\nAlaska Regional Cardiology and Providence Primary Care Access Details are visible with exact\naddresses and contact access details.\nWe can book a medical appointment here.\nFor example, a normal check-up for a child can be scheduled at 10.15.\nA new medical appointment request is submitted for 1.30.\nThe system updates the patient schedule.\nThe active list shows scheduled visits.\nWe can cancel any booked visit from the records.\nPrevious health records display current patient medications and known allergies.\nRecent clinical visits and lab reviews are also listed.\nAll recommended system features are implemented in this application version.\nAll these recommended software features are now complete.\nNext, a brand new change will be adopted.\nA new user profile feature is needed.\nThe end user should create a new account, add personal details and access a personalized dashboard.\nLogging out of the current active session brings us to the main screen.\nClicking on Create a Demo Account opens a new form.\nA new name, Alex, is entered.\nThe email address, alex at gmail dot com, is provided.\nA secure password is created for this new account.\nThe brand new demo account is accessed.\nLooking at the health records for this new user,\nprevious medications and known allergies are still visible.\nThe recent clinical visit data is also showing\nold information. This problem happens because synthetic mock data is used.\nWhen a new user account is created, all personal details should start fresh.\nWe need to include actual user inputs instead of synthetic data.\nEven if previous scheduled appointments are cancelled\nthey remain in the application database for the new patient.\nThis behaviour applies to every new patient profile created.\nNew data is not available yet in the interface.\nTo solve this issue, new code logic must be introduced.\nA text prompt is written to update the application logic.\nThe missing profile fields need to be added\nand displayed on the main dashboard. The user account creation flow must be\ncomplete from\nend to end. The new user must feel personalized\nwithout falling back to generic demo data.\nThe core application logic is updating.\nFive project files are being edited to implement the changes.\nThese code items include data, HTML, dashboard, index and records files.\nMore software changes are expected to arrive soon.\nThe main project goal is defined now.\nA new created user account must feel custom and personalized.\nIt should not fall back to generic demo data.\nThe user profile flow is implemented in the source code.\nSyntax checks are running to verify the software changes.\nThe main index file now includes a complete sign-up form.\nLogging out again allows us to test the updated demo account creation.\nThe new registration form requires comprehensive user details.\nFull name, email, phone number, date of birth, blood type, address and medical insurance information must be provided.\nEmergency contact details are also required to complete the setup.\nThese user details will be stored across the application assets.\nThe new account registration form is filled with specific user data.\nThe first name, Alex, is entered along with the email address.\nA random phone number is provided.\nThe proper date of birth is set. The blood type is selected as A positive.\nThe user city is entered as Alaska. The geographic region is specified as USA, region, state, Alaska.\nAn insurance plan named Master Insure Pro is added.\nAn emergency contact number is also provided.\nIn the end, a secure password is created to finalise the account.\nThe new user account is created.\nNavigating to the medical records section shows the updated patient identity.\nAll personal details provided during sign-up are displayed here.\nThe scheduled appointments section is now empty and ready for new bookings.\nThe care team section reflects the correct operational details.\nThe personalised user dashboard is functional.\nMultiple future system changes can be integrated as the project evolves.\nAll main project details are configured.\nThe next crucial step is to upload the project code.\nA command request is made to push all files into a remote GitHub repository.\nThe operating system checks available GitHub capabilities within the active workspace.\nA local system Git installation is not found.\nThe active folder is not recognised as a valid Git repository.\nTwo concrete system blockers prevent the code push process.\nFirst, the Git software is not installed on the machine.\nSecond, there is no connected remote GitHub repository available.\nSuggested options are provided to resolve these software issues.\nA new public code repository must be created first.\nThe main GitHub website is open to create a new online repository.\nThe user login credentials are saved.\nA new repository named OneGoProjectHealthcare is created.\nThe access visibility is set to public.\nThe repository creation process completes.\nThe Internet repository link is now available for connection.\nThe required remote repository link is copied from GitHub.\nThis specific link is pasted into the system prompt to initiate the web connection.\nThe active system processes the target online repository URL.\nIt checks whether the main GitHub connector has the necessary account access.\nThe code push access is verified.\nThe current local project files are being read in order.\nThe development plan is to publish them into the main code branch.\nThis action is done through the built-in GitHub connector.\nThe raw text content of all project files is extracted.\nThe public code files are prepared for web publishing.\nA small project readme file is also generated for the repository.\nAt this moment, the online repository is empty.\nRefreshing the web GitHub page confirms no files are uploaded yet.\nThe background publishing application is running the pull request.\nOnce the upload process is approved, the new content will be available online.\nThe computer system waits for the background publishing process to execute.\nA new approval prompt appears on the screen.\nIt requests security permission to allow GitHub to run the file creation tool.\nThe required permission is granted by selecting the Allow option.\nThis choice ensures automatic file creation without repetitive manual user approvals.\nA subsequent system request asks to create code git blobs.\nThis security permission is also allowed to proceed.\nThese technical steps are necessary to transfer the local workspace files to the remote web environment.\nIf the source code is left in the local workspace, it cannot be shared or deployed.\nMoving it to the online repository ensures the system is integrated.\nTwo separate system blockers are identified again.\nthe local machine still lacks a local software Git installation.\nThe web GitHub integration can read files, but write operations are blocked.\nThis problem happens because the integration cannot access the necessary developer tools.\nTo resolve this exact issue, a local system installation is required.\nA new internet browser tab is opened.\ninstall git is executed. The official git software installation page is accessed.\nThe Windows desktop operating system option is selected. The standalone\nsoftware installer for Windows is downloaded. The software download process\nwill take some time to finish. The installation setup will be executed soon.\nThe main git installer file is saved to the computer desktop. The file download\ncontinues in the background. In the meantime the computer system provides a\ntext list of terminal commands. These commands are required to push the\nsource code by hand. Before executing them, remote repository permissions must\nbe verified. The main GitHub settings page is opened to ensure proper developer\naccess rights. The account security and variables section is examined. The account\nmoderation options are also visible. Navigating to the repository security\npermissions is necessary to allow new code uploads. The active pull request\nsettings are reviewed. The code merge commits and squash merging options are\nenabled by default. The version commit settings and archive features are\nchecked. The critical danger zone is avoided to prevent accidental project\ndeletion. Everything appears to be configured in a proper state. The git\nsoftware installer download is now complete. The application setup file will\nbe executed next to install the software on the computer. The downloaded git\nsoftware installer is executed from the computer desktop. The application setup\nwizard appears on the screen. The standard default installation options are\nselected by clicking the next button multiple times. The default components\nare chosen for the system installation. The default code editor is maintained.\nThe initial project branch name configuration is left as a default\nchoice. The system path environment is updated. The software installation process\nbegins extracting core files. The visual progress bar indicates the current\ninstallation status. The program installation is completed without errors.\nThe active current terminal window is closed. A fresh new terminal session is\nlaunched to apply the updated installation paths. The specific git\ninitialize command is executed again. The operating system initializes an empty\nlocal git repository. The local development project folder is now tracked by the git tool.\nThe next required command is copied from the text instructions.\nThe project branch is renamed to main using the active terminal.\nThe text command executes without any prompt errors.\nThe remote origin target command is copied next. This specific command links the local code\nrepository to the new created online GitHub repository. The web link is pasted\ninto the terminal and executed. The internet connection is established\nwithout issues. The next development step requires adding all project files to the\ncode staging area. The standard git add command is prepared. An error prompt\noccurs stating access permission is denied. The operating system cannot open\na distinct application data directory.\nThis file access issue must be resolved.\nThe access permission denied error halts the running process.\nThe operating system attempts to add developer files from the root user directory instead\nof the project folder.\nThis behaviour is a crucial mistake.\nThe code terminal is operating in the wrong system directory path.\nA new Terminal Application tab must be opened within the exact correct project folder.\nThe root user terminal is closed without delay.\nThe new command terminal now displays the correct document path for the software project.\nThe standard git initialize command is executed again in the right folder.\nAn empty code repository is initialized here without any issues.\nThe Branch Rename Text command is executed to set the main branch.\nThe Remote Origin command is pasted and executed to link the online GitHub repository.\nThe correct project folder is now connected.\nThe next workflow step is to add all files to the staging area.\nThe simple Git Add command is executed without errors this time.\nAll main project files are added to the code staging area.\nThe standard git commit command is executed with a descriptive message.\nThe operating system returns an author identity unknown error.\nThe code commit cannot proceed without proper user identification.\nThe global user configuration commands must be executed first.\nThe terminal command to set the user email is copied.\nThe account email address is updated to match the online GitHub account credentials.\nThe command is executed without any system errors.\nThe terminal command to set the username is copied next.\nThe developer username is retrieved from the Web GitHub profile page.\nThe target username is pasted into the terminal command line.\nThe system configuration is updated without issues.\nThe required author identity is now established.\nThe standard git commit command is executed once again.\nThis specific time, the code commit is a success.\nAll modified files are recorded in the local repository.\nThe source code is ready for the final push.\nThe main commit process includes all necessary application files, the design assets, HTML\nweb pages and application data files are secured.\nThe final terminal command to push the source code is executed.\nThe command terminal requests user authentication to access the remote GitHub repository.\nA new internet browser window opens for the user login process.\nThe Sign in with internet browser option is selected.\nThe existing active session credentials are used to authorise the upload transaction.\nThe access authorisation succeeds without delay.\nReturning to the command terminal confirms the code push process is active.\nThe data objects are compressed and written to the remote web server.\nThe main code branch is set up to track the remote origin point.\nThe entire application project is uploaded.\nThe online GitHub repository page is refreshed in the internet browser.\nAll local project files are now visible online.\nThe assets folder and HTML text documents are listed.\nThe code upload process is verified and a success.\nThe uploaded index document file is opened on GitHub to verify its code contents.\nThe online code matches the local computer version without flaws.\nThe source blame and file history options are functional.\nThe raw text code can be accessed any time.\nThe entire upload process is now concluded.\nThe initial project objective was to build a medical healthcare application project.\nA detailed development plan was formulated first.\nbasic minimum viable product. The database backend requirements were later removed to\nto focus on the user front-end interface.\nThe HTML and CSS design structures were developed with minimal JavaScript logic.\nVarious design iterations improved the visual page aesthetics and user experience.\nThe personalized user profile creation feature was integrated.\nIn the end, the local source code was pushed to a public code repository.\nThe application project is stored and accessible.\nFuture code modifications can be implemented and tracked through this version repository\nsystem without effort.\nThe detailed software implementation plan proved effective in practice.\nWe established a strong code foundation using standard modern web technologies.\nThe front-end user interface provides a seamless, smooth experience for clinical patients managing\ntheir healthcare needs.\nThe synthetic mock data limitations were overcome by implementing dynamic user input handling.\nThe medical appointment scheduling and user cancellation workflows operate without any\nsystem errors.\nEssential patient medical records remain accessible on the personalised user dashboard.\nBy utilising the Git version control system, the main source code remains protected against\naccidental data loss. The open public repository allows other software developers to review\nthe application project structure. This developer workflow demonstrates a practical approach\nto rapid web application development and code deployment. The initial system setup challenges\nregarding local machine software requirements were resolved with great speed. The comprehensive\ncommand terminal instructions ensured execution of necessary developer commands.\nThe final software outcome is a functional, well-documented and versioned front-end application.\nThe established project goals are fulfilled.\nThe discussion relates to possible iterations we can include here.\nIn our use cases, we create projects multiple times, requiring iterations.\nWe can include these iterations to see what is possible.\nIterations start from the beginning.\nWe include project details and formats.\nAnother option is available after project creation.\nMoving to the end, our index was complete at this step.\nHere, in frontend, backend, appointments and records, our option has arrived.\nChanges are never small.\nFor changes, we will include details multiple times.\nAnother important thing is working in an environment requiring code changes.\nIterations become version-based.\nThe first website version was quite different from the current version.\nWe must understand version histories to see which was more compatible.\nSometimes we include specific things from a specific version.\nGoing into our project, this was the final version.\nLooking at this project, considering the whole use case, the logo is incorrect.\nWe can change the logo.\nThe dashboard should be a sliding dashboard, not a consistent one.\nInstead of this, some analytics should be involved here.\nLooking here, the card distance and the empty space in the website should be minimal.\nWe should fill such spaces beforehand.\nIn our website text, search engine optimization is not involved.\nThe colors involved here are quite dull.\nWe can improve all these things just by giving it some prompts.\nOur iteration will start happening now.\nAlongside iteration, another important thing is making large scale improvements via prompts.\nSince this tool is based on our prompts, any new improvement will be based on prompts.\nthere is no other way to improve our stuff.\nThese things will be induced here with our ongoing time.\nAs our time increases, we must iterate these things\nand see how we are taking them forward.\nAlong with this, another important thing\nis that when making improvements based on prompts,\nerror chances increase.\nTherefore, we should review our code first.\nAfter reviewing the code, we should take things\ntowards our other generations and discussions.\nSometimes, within our code itself,\nthere are multiple errors we spend time correcting.\nWe are focusing on making the dashboard intuitive.\nThe layout needs adjustments so upcoming visits\nand active reminders are visible to the user.\nCare teams and appointment sections\nmust be integrated together.\nThis ensures the entire application functions\nwithout visual or technical interruptions.\nThis completes our entire discussion related to our iteration.\nAlong with the iteration, we covered possible improvements\nthat needed to be discussed.\nLooking at the bottom section, the reminders and profile\nloaded cards have gaps needing fixes.\nThe design should feel premium and responsive.\nThe patient readiness items must be tracked\non one premium dashboard.\nWhen looking at source options for medical centres,\nlike Providence Alaska Medical Centre or Alaska Native Medical\nCentre, information must be displayed.\nThe layout structure lacks proper alignment,\nand text blocks feel disconnected.\nWe will use specific commands to refine these interface elements.\nOnce we apply new styles, the final product\nwill look more professional.\nEverything will be committed to the repository, ensuring all changes are saved and tracked for future updates and continuous development.\nNow comes the last part.\nAt the end of this course, you saw one main thing.\nIt was our GitHub and Codex tools.\nWe tried to connect GitHub.\nThen we got many tech issues.\nI will say here I will use our plugin of GitHub now.\nI will start a discussion with the GitHub plugin tool.\nI will ask, can you check if GitHub plugin is working?\nThis will test our plugin once.\nWe will see if our plugin works in any user case.\nIf our plugin is not available, we can check it.\nYou can see we have verification now.\nYou can see that I have verified it.\nThis login account Robert is here.\nYou will remember our main account here.\nThat is connected here.\nIts user ID has also come.\nthat confirm that the plugin is installed, reachable and authenticated.\nNow I can say, what can I do with this?\nThis will start giving us a response.\nIt will tell us where we can use this plugin.\nThis tool is not available on a weak level,\nbut still we have to see how our tool can work here.\nYou will see here it says you can ask it to read multiple actions.\nIn this we also get more recommendations.\nI will suggest to you that we should use multiple plugins.\nLike right now, you are seeing that we can use our windows.\nWe have our Chrome and data analytics and product design.\nWe also have Apollo and Atio and Carter CRM and Clay and Circleback.\nWe have Granola and Otter and Canva.\nWe can use all these plugins.\nWe have many options.\nIf we bring any of these in here, we can use it out.\nFor every niche, we have separate options available for us.\nLike for our education and research, we also have options available right here.\nWe have our Dow Jones Factiva here.\nWe have our GovTribe and we have Life Science Research.\nFor finance, we have multiple softwares available right here.\nSo what Codex does is on a large scale it gives us multiple plugins to use.\nFrom these, the used plugin on our developer side is our GitHub plugin.\nBut if we want to use multiple plugins on our whole enterprise level, we can do that too.\nWith that, our price will increase.\nOur usage cost will also become a cost that is quite high.\nThe OpenAI ecosystem is a collection of tools, models and platforms\nthat help individuals and organisations use artificial intelligence in practical workflows.\nIn this diagram, OpenAI ecosystem is the central idea\nand the main branches show the important parts connected to it.\nChatGPT is one of the most familiar parts of the ecosystem.\nIt helps users write content, explain concepts, solve problems,\nsummarise information and support daily work.\nIn software development, ChatGPT can help developers understand errors,\nwrite documentation, plan features and improve prompts before using them with coding tools.\nThe OpenAI API allows developers to connect OpenAI models directly into their own applications.\nFor example, a company can build a chatbot, document assistant, coding assistant, customer support tool or automation system using the API.\nThis makes AI usable inside real business products and enterprise systems.\nCodex is focused on software development.\nIt helps developers generate code, refactor files, explain existing code, create tests, debug issues, and work with repositories.\nIn an Enterprise course, Codex is important because it shows how AI can support real development workflows instead of only answering questions.\nThe Models branch represents the AI models behind the ecosystem.\nThese models can work with text, code, images and other types of input depending on the\nproduct or configuration.\nBetter models usually mean better reasoning, better code understanding and more accurate\nresponses.\nFinally, enterprise tools are important for organisations.\nEnterprises need security, governance, permissions, auditing and control over how AI is used.\nThese tools help companies adopt AI safely while protecting data, managing access and\nfollowing internal policies.\nOverall, the diagram shows that the OpenAI ecosystem is not just one product.\nIt includes user-facing tools, developer APIs, coding assistance, advanced models and enterprise\ncontrols.\nTogether, these parts help teams build, automate, learn and develop software more efficiently.\nThis conclusion mind map summarises the key lessons learned throughout the OpenAI Codex Enterprise Development course.\nThe central idea is that AI-assisted development is becoming an important part of modern software engineering\nand organisations that learn to use these tools effectively can significantly improve productivity, software quality and development speed.\nThe first branch focuses on OpenAI Codex itself.\nThroughout the course, learners discovered how Codex helps developers generate code, analyse repositories, refactor applications, create tests and automate repetitive development tasks.\nRather than replacing developers, Codex acts as an intelligent development assistant that helps teams work more efficiently.\nThe next branch highlights the development workflow.\nOne of the most important lessons from the course is that AI can support every stage of software development.\nDevelopers can use Codex during planning, implementation, testing, debugging and deployment preparation.\nInstead of viewing AI as a tool used only for coding,\norganisations can integrate it into the complete software development lifecycle.\nThe Enterprise Adoption section emphasises that successful AI implementation\nrequires more than technical capability.\nOrganisations must establish governance frameworks,\nsecurity controls, compliance processes and collaboration standards.\nEnterprise teams need visibility into how AI is used and must ensure that generated code meets organisational policies and quality requirements.\nThe best practices branch represents the habits that produce the best results when working with codecs.\nEffective prompt engineering helps developers communicate requirements clearly.\nCode reviews ensure generated code is maintainable and secure.\nValidation and testing confirm that AI-generated features satisfy business requirements.\nThese practices help maximise the value of AI-assisted development.\nAnother important area is operational excellence.\nAs AI adoption grows, organisations must monitor usage, manage costs, optimise resources and measure performance.\nUnderstanding token consumption, tracking productivity improvements and evaluating return on investment help organisations scale AI responsibly and efficiently.\nThe continuous learning branch reminds learners that AI technologies evolve rapidly.\nDevelopers should stay informed about new codex capabilities, open AI platform updates, community best practices and industry trends.\nTeams that continuously learn and adapt will gain the greatest long-term benefits from AI-assisted development.\nThe Future of Development section looks ahead to emerging trends.\nAI assistants will become more capable, agent-based workflows will become more common, and human-AI collaboration will continue to evolve.\nDevelopers will increasingly focus on architecture, business logic and strategic decision making,\nwhile AI handles more implementation tasks.\nFinally, the most important takeaway is that AI augments developers rather than replacing them.\nHuman expertise remains essential for understanding business requirements,\nmaking architectural decisions, reviewing code quality, managing risk and ensuring software meets organisational goals.\nWhen used effectively, Codex enables developers to deliver better software faster while maintaining quality, security and reliability.\nIn summary, the course demonstrates that successful enterprise AI adoption requires a combination\nof technical skills, governance practices, continuous learning, and thoughtful collaboration\nbetween humans and AI systems.",
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      "text": "We will see how it works as a coding assistant and its many functions in different situations.",
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      "text": "Codex is a tool made by OpenAI.",
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      "text": "You might know their famous tool called ChatGPT.",
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      "text": "OpenAI is the main company behind ChatGPT and they handle all the AI generation and work.",
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      "text": "First, I will show you the OpenAI website.",
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      "text": "On their website, they say their research will lead to artificial general intelligence,",
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      "text": "which is a system that can solve human problems.",
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      "text": "In the products section, you can see tools like ChatGPT, Codex, Atlas and Prism.",
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      "text": "ChatGPT is a very large tool that includes images, deep research and GPTs, codecs, multiple applications and libraries.",
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      "text": "If we talk about apps, we have multiple apps connection.",
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      "text": "You can connect it with popular apps like Adobe, Airtable, Booking.com, Canva, Spotify and more.",
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      "text": "Now let us look at OpenAI Codex.",
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      "text": "Codex is an AI software engineering agent.",
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      "text": "It helps developers write, understand, change, test and ship code easily.",
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      "text": "It is not just a simple chatbot that only answers questions.",
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      "text": "It works directly with your project files and helps with the whole repository structure",
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      "text": "and can make the changes, run commands and assists with development cycles.",
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      "text": "It gives developers real engineering results.",
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      "text": "Codex is also inside ChatGPT, so you can use it directly.",
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      "text": "There is a specific app for Codex, and you can download it for Windows.",
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      "text": "We will see how Codex makes real engineering work faster, from planning and building the features, to fixing and releases and cross-checking.",
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      "text": "There is also a command line interface, or CLI, on GitHub for Codex.",
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      "text": "CLI means Command Line Interface. It is a text-based interface used to talk to software and computers.",
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      "text": "Instead of clicking buttons with a mouse on visual icons, you type text commands and the",
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      "text": "text-based user interface used to interact with the software, operating system and on",
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      "text": "your keyboard instead of using a mouse to click on visual items and press enter to make",
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      "text": "We will learn how to use this CLI.",
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      "text": "Codex helps what a developer wants to do and turns it into engineering output.",
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      "text": "You can describe a bug or a new feature, test environment, documentation, task, in natural",
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      "text": "Codex can analyse the database identifying files, proposed changes and generate limitation",
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      "text": "This makes it useful not only for the writer but also for navigating unfamiliar systemd.",
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      "text": "Here we have codex discussion.",
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      "text": "In this course we will talk about daily development, prompting, making agents and using codex to",
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      "text": "for a long time. We will cover all of these important topics together.",
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      "text": "So, moving on, the next critical discussion is primarily related to exactly",
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      "text": "how we are going to securely install codecs on our machine. First, well before",
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      "text": "actually installing it anywhere else, we will practically use the standard",
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      "text": "command-line interface to get things started. But right before that specific",
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      "text": "step, we can smoothly download the dedicated version specifically meant for Windows operating",
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      "text": "systems. Now, I already have the necessary Codex installer fully ready to go here. I",
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      "text": "simply open up the main system downloads folder right now. Then, I firmly run the executable",
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      "text": "installer directly as a system administrator. The core software immediately starts to install",
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      "text": "itself smoothly. Simultaneously, the Microsoft Store seamlessly begins its essential download",
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      "text": "run. Interestingly, we can certainly also manage to install it safely across multiple",
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      "text": "different computer systems. As we smoothly scroll further down the main page, you will",
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      "text": "clearly see three distinct setup options presented. The very first one is the",
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      "text": "standard standalone app. If we really want to use a code editor we can install it",
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      "text": "directly within our preferred IDE. Fortunately we already have Visual Studio",
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      "text": "We securely click Allow in VS Code and Codex will seamlessly open.",
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      "text": "First and foremost we absolutely must carefully check our specific ChatGPT subscription plan.",
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      "text": "This essentially means all our ongoing coding work is completely safe and secure.",
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      "text": "Codex is fully available to use today.",
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      "text": "The strictly required language model is situated right here.",
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      "text": "Moving back into VS Code, we will effectively start the complete software installation.",
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      "text": "It securely installs just like a standard web extension.",
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      "text": "First, we smoothly pair it directly with Codex.",
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      "text": "We basically add Codex as a dedicated side panel within VS Code to seamlessly chat, intuitively",
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      "text": "edit and carefully check all our changes.",
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      "text": "Then, we can confidently use Codex directly in the cloud, sending massive jobs straight",
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      "text": "to it.",
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      "text": "Moving gracefully to the very next step, we simply need to safely sign in using our primary",
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      "text": "ChatGPT account credentials.",
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      "text": "Once fully authenticated, our main application will automatically install directly into the",
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      "text": "Now, the third available setup option is to actually install this directly inside our",
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      "text": "system terminal.",
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      "text": "For this specific method, just look closely at the Keep going in the terminal prompt section.",
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      "text": "We carefully copy this exact command string to our clipboard.",
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      "text": "Following that, we explicitly open up the system command prompt, making sure to run it strictly as an administrator.",
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      "text": "The Windows PowerShell interface naturally opens up.",
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      "text": "Within PowerShell itself, we formally install the OpenAI Codex tool utilizing the NPM Package Manager.",
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      "text": "Naturally, it will definitely take a little bit of time to fully install properly.",
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      "text": "The underlying system will sequentially gather necessary data in a much better way.",
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      "text": "Codex is actively installing and we have our app ready.",
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      "text": "Now we go to settings.",
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      "text": "We purposefully open the main application settings directly from the main dashboard.",
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      "text": "settings menu there are actually many different customisable options",
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      "text": "available. I specifically navigate over to the appearance tab in order to make",
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      "text": "some visual interface changes. First, I completely change the overall display",
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      "text": "theme to a comfortable dark mode. Next, I noticeably increase the base system UI",
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      "text": "size for better visibility. We then carefully choose our preferred font",
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      "text": "style and slightly adjust the visual contrast. Initially, I confidently write",
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      "text": "in 32 for the primary font size, however upon seeing it, 32 is admittedly just a bit too",
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      "text": "big for my screen. So I quickly change it down to exactly 24. Honestly, 24 is a genuinely",
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      "text": "comfortable size for long coding sessions. For the dedicated code font, we firmly use",
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      "text": "size 20. Moving on, we strategically turn off the reduce motion option. We ensure we",
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      "text": "use standard pointer cursors and gently lower the screen contrast. Everything is working",
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      "text": "well. Honestly, the overall reading experience is just so much better right now. Before we",
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      "text": "made those crucial visual tweaks, the default text font was incredibly small and honestly",
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      "text": "hard to read. We absolutely had to step in and make the font significantly bigger for",
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      "text": "basic clarity. I truly believe that 24 and 20 are excellent, highly optimized sizes for",
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      "text": "our daily coding tasks.",
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      "text": "Now if we quickly navigate over to the General Configuration Settings menu, we can clearly",
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      "text": "see multiple distinct operational modes available to choose from. I promise that I will carefully",
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      "text": "explain all of these specific working modes in much greater detail a little bit later on.",
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      "text": "Right now, all our required system packages are successfully installed.",
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      "text": "The powerful OpenAI codex itself is completely installed.",
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      "text": "Now, we just essentially need to forcefully run it one more time.",
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      "text": "I smoothly tab back over to the chat GPT window.",
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      "text": "I explicitly type out and clearly ask.",
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      "text": "I have basically run this command in the terminal.",
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      "text": "What do I do next to start it?",
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      "text": "We will now officially start the require process right here within our local system terminal.",
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      "text": "The entire background installation procedure is completely finished.",
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      "text": "It successfully managed to add two vital software packages in just about 36 seconds total.",
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      "text": "The software carefully checks the current NPM installation status alongside the available AI models.",
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      "text": "First and foremost, we must verify the active codex version.",
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      "text": "I simply type NPM codex directly into the prompt.",
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      "text": "The resulting codex CLI version currently shows up as 0.139.0.",
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      "avg_logprob": -0.14204086259353993,
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      "text": "Next, we actively run codex so we can finally start to code.",
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      "text": "to code. We type codex right into the system environment. A brand new security message",
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      "start": 682.903,
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      "text": "suddenly appears on the screen. It clearly asks, do you currently trust the exact contents",
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      "no_speech_prob": 2.5628446847364428e-12,
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      "start": 689.663,
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      "text": "of this specific directory? Working directly with untrusted contents definitely carries",
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      "avg_logprob": -0.11274327172173394,
      "no_speech_prob": 2.5628446847364428e-12,
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      "text": "inherent security risks or potential prompt injections. Trusting that directory directly",
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      "text": "allows for reading local configuration settings.",
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      "text": "I confidently click yes and smoothly continue.",
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      "text": "we now finally have our direct working option ready to go.",
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      "text": "Our specifically selected AI model is currently GPT 5.5.",
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      "text": "Our active working directory is listed safely as System32.",
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      "text": "We can comfortably make direct code changes right here in this space.",
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      "text": "If I quickly send over a very simple introductory message,",
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      "text": "it impressively replies back almost automatically.",
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      "text": "can you explicitly help me in generating some basic HTML code?",
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      "text": "It quickly gives a remarkably short, concise response.",
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      "text": "It plainly says, yes, absolutely.",
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      "text": "Just tell me exactly what you want the final HTML page to do, or what you want it to look like.",
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      "text": "Paste any existing code snippets you might already have.",
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      "text": "If you happen to start entirely from scratch, I can easily create a simple HTML framework for you.",
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      "text": "Our coding journey begins.",
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      "text": "I press Ctrl C to pause.",
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      "text": "The interface clearly displays our complete token usage statistics right on the screen.",
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      "text": "The grand total comes out to exactly 7,939 tokens used.",
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      "text": "Meanwhile, the generated output was a remarkably short 58 tokens.",
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      "text": "To seamlessly continue this exact same chat session, we carefully copy the provided resume",
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      "text": "I smoothly paste that specific command right back in here.",
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      "text": "We also have very handy slash commands completely ready to use.",
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      "text": "First and foremost, we can fully configure how Codex strictly operates.",
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      "text": "We actively have detail permissions to precisely choose exactly what codecs can or cannot do",
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      "text": "We logically have the Sandbox Add to Directory feature ready.",
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      "text": "Finally, we have the Experimental Feature section.",
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      "text": "I intentionally select the very first configuration option to explore the available models.",
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      "text": "Now we selectively choose the preferred reasoning level.",
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      "text": "We can carefully view current IDE sessions and files.",
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      "text": "Next, we review permissions.",
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      "text": "There are several highly specific model permissions carefully listed here.",
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      "text": "The Read Only setting strictly means that Codex can only safely read active files located",
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      "text": "within the current workspace environment.",
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      "text": "It absolutely requires your direct manual approval before it can ever attempt to edit",
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      "text": "can seamlessly run, read or even edit files inside the current workspace and fully read",
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      "text": "the active command prompt. Still, explicit manual approval is completely required to",
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      "text": "permanently edit files or access the internet. Approved for me intelligently only asks for",
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      "text": "explicit permission when it encounters potentially unsafe system actions. Full access simply",
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      "text": "means Codex can freely edit files far outside the designated workspace and easily access",
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      "text": "the internet without ever asking for prior approval. Exercise extreme caution and ask",
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      "text": "vital questions when using this. I explicitly choose the Approve for me setting. Our updated",
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      "text": "permissions are cleanly applied. I press Enter and transition to the Keymap configuration.",
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      "text": "In Keymap we have our transcript, external editor, clear terminal and more options.",
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      "text": "We practically have multiple distinct configuration options readily available right here, alongside",
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      "text": "some incredibly useful direct operational options.",
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      "text": "What we can actually do here is utilise the basic left and right keyboard arrow keys to",
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      "text": "We firmly use the Enter key whenever we strictly need to modify or edit an existing shortcut.",
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      "text": "We smartly use the star symbol strictly for creating completely custom shortcuts, the",
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      "text": "dash symbol to quickly unbind an active key, and the Escape key to easily close out the",
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      "text": "I simply press Escape right now to smoothly go back to the previous screen.",
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      "text": "Right after successfully configuring our system permissions, we logically have our custom key",
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      "text": "Next up, we carefully review our active system approvals.",
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      "text": "You can transparently see absolutely all the recent system changes we manually made right",
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      "text": "We actually changed our primary AI model twice today.",
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      "text": "We closely reviewed the IDE context settings.",
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      "text": "We officially updated our security permissions to Approve for me.",
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      "text": "Finally, I press Control-C.",
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      "text": "Executing that specific command officially closes out the active chat session entirely.",
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      "text": "It is crucial to note that this specific underlying configuration genuinely remains the exact same",
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      "text": "across every single linked application environment.",
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      "text": "Thankfully, the powerful Codex tool is readily available right here too.",
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      "text": "We can practically do so many different amazing things with it.",
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      "text": "If we smartly decide to switch over to the exclusive pre-release software version, absolutely",
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      "text": "I am going to intentionally open up a brand new dedicated project folder officially named",
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      "text": "Metabrains for our work.",
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      "text": "We carefully select this exact folder directory and smoothly open up our fresh chat interface.",
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      "text": "I explicitly confirm that I fully trust the verified authors operating in this specific",
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      "text": "I smoothly close this secondary pop-up window.",
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      "text": "I promptly open up a brand new code file appropriately named index.html.",
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      "text": "You can distinctly see the familiar codex logo right inside this document.",
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      "text": "We can now finally start to actively write out our core underlying code snippets.",
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      "text": "We will be able to clearly see our active IDE connection, the linked source repository,",
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      "text": "the comprehensive onboarding bug log and of course, our preferred dark mode visual theme.",
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      "text": "We will consistently get highly accurate direct responses moving forward.",
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      "text": "Now we need to take a very close look at our overall system usage metrics.",
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      "text": "I purposefully navigate back over to the main settings page.",
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      "text": "we happily have a massive 99% of that time left available.",
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      "text": "The broader weekly limit currently shows 100% completely left over.",
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      "text": "We have successfully expanded our overall codex usage capabilities",
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      "text": "alongside the powerful GPT 5.5 deep thinking mode.",
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      "text": "We can effortlessly purchase an additional credit balance whenever needed.",
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      "text": "broad computer use permissions and robust MCP servers.",
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      "text": "complete Git source control, safe isolated environments and managed work trees.",
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      "text": "Our foundational setup phase is now entirely complete.",
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      "text": "We will now efficiently set up highly secure cloud-based development environments right here in this specific module.",
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      "text": "You probably remember that we previously ran our Codex agent directly here on the local machine.",
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      "text": "Absolutely all of our core operational functions were safely contained directly inside it.",
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      "text": "Now, however, we will purposefully run our advanced cloud-based development environment",
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      "text": "First things first, I will quickly navigate back over to the main settings panel",
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      "text": "and actively change the primary tech size down from 24 to precisely 20.",
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      "text": "You absolutely should know right up front that this is essentially a pre-configured, entirely remotely hosted software workspace.",
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      "text": "The acronym CDE specifically stands for Cloud Development Environment.",
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      "text": "when a specific piece of code miraculously only works on one specific person's local computer.",
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      "text": "Now, let's take a moment to talk about Codex itself.",
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      "text": "Codex fundamentally has some massive, undeniably big advantages here.",
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      "text": "Codex genuinely works absolute best when it can safely access the entire full code repository.",
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      "text": "settings. This inherently creates the infamous, frustrating, well, it perfectly works on my",
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      "text": "machine problem. Modern, streamlined cloud environments completely remove these highly",
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      "text": "annoying problems entirely. Absolutely everything that we actively do right here, for example,",
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      "text": "the previously pinned chat log specifically about making a functional calculator application,",
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      "text": "We can effortlessly and rapidly review our collective past work right here at any given time.",
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      "text": "Other incredibly massive benefits strictly include nearly instant team onboarding,",
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      "text": "highly consistent underlying tool setups, absolute hardware independence and reliable",
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      "text": "Now, moving forward, we will thoughtfully choose a specific cloud development platform to use.",
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      "text": "I will happily show you some excellent real-world examples shortly.",
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      "text": "First and foremost, we will take a very close look at GitHub Codespaces today.",
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      "text": "It is a remarkably secure, highly robust cloud development environment used by many.",
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      "text": "If we genuinely do not want to use this specific one, we can alternatively utilise Gitpod.",
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      "text": "It is a fantastic, strictly on-demand, highly scalable development environment.",
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      "text": "For massively big enterprise companies, we consistently have the AWS Cloud9 infrastructure safely available.",
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      "text": "for our larger, more complex coding tasks.",
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      "text": "It is basically a fully complete cloud-based integration development environment from Amazon.",
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      "text": "It is truly a complete, fully featured IDE right in your browser.",
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      "text": "We can effortlessly write, smoothly run and quickly fix our complex code directly inside it.",
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      "text": "These are a few older, very solid examples.",
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      "text": "Now, I will explicitly search the web to see if Codex itself is officially classified as a cloud-based development environment.",
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      "text": "Codex, created by OpenAI, is officially a cloud-based AI software engineering agent.",
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      "text": "course clarity. Now we simply go straight back into our main codex application. Everything",
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      "text": "Now, moving smoothly forward, our very next crucial task is to securely connect our remote",
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      "text": "to quickly see if we actually get a reassuring green verification tick.",
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      "text": "I will quickly pivot back over to our main codex interface.",
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      "text": "If we carefully look directly at our active system plugins list right now,",
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      "text": "We will quickly go back over to the plugins tab and our GitHub features will be permanently included in the active workflow loop.",
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      "text": "Moving logically to the very next crucial step, we will thoroughly talk about the highly",
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      "text": "discuss all of these major systematic fundamental software changes and deeply integrated highly",
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      "text": "complex AI functions directly later.",
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      "text": "We will explore this in the next sections perfectly.",
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      "text": "Authentication and access configuration is a critical part of using OpenAI codecs in an enterprise development environment.",
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      "text": "Authentication verifies the identity of the user or system attempting to use codecs,",
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      "text": "while access configuration defines what that user or system is allowed to do.",
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      "text": "Together, these controls ensure that only authorised developers, teams and automation tools can interact with repositories, environments, APIs and sensitive project resources.",
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      "text": "In enterprise software development, Codex usually works with cloud-based development environments, GitHub repositories, CI-cd pipelines and internal tools.",
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      "text": "Because these systems may contain confidential source code, credentials, customer data and deployment configurations, access must be carefully managed.",
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      "text": "A weak authentication setup can expose the organisation to unauthorised code access, accidental changes or security breaches.",
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      "text": "Therefore, enterprises commonly rely on identity providers, single sign-on, role-based access",
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      "text": "control and audit logging. The authentication process normally begins when a developer signs",
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      "text": "in using an approved identity provider. This may include enterprise login systems such as SSO,",
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      "text": "OAuth or multi-factor authentication. Once the user identity is confirmed, the",
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      "text": "system checks whether the user has permission to access Codex, the",
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      "text": "codecs may be allowed to read a repository, analyse code, create a branch, run tests or open a pull request.",
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      "text": "In some cases, it may not be allowed to directly merge code or access production secrets.",
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      "text": "These permissions should follow the principle of least privilege,",
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      "text": "meaning codecs and users should receive only the access required to complete their assigned tasks.",
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      "text": "accessible to every user or AI workflow. Enterprises should configure repository",
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      "text": "Environment variables and secrets also require strict control.",
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      "text": "This prevents accidental leakage of API keys, tokens, database passwords or cloud credentials",
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      "text": "Organisations should be able to review who accessed codecs, which repositories were used,",
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      "text": "It protects sensitive assets while still enabling developers to benefit from AI-assisted coding.",
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      "text": "and continuous monitoring, organisations can create a controlled environment where Codex",
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      "text": "We discuss Codex workflows now.",
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      "text": "We want to see how Codex uses large workflows.",
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      "text": "We look at complete software solutions and workability.",
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      "text": "Sometimes we must see how the whole process grows over time.",
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      "text": "For this, we change and improve code on a large scale.",
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      "text": "After these changes, we check the overall process understanding.",
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      "text": "They help us create a better decision tree for work.",
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      "text": "In big business places, developers work with big repositories often.",
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      "text": "These repositories have thousands of files.",
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      "text": "We can see our multiple services directly.",
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      "text": "It also sees big documentation, complex dependencies and multiple servers.",
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      "text": "Understanding these projects by hand needs much time and effort.",
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      "text": "helps developers understand the project structures well. It finds correct files, traces dependencies,",
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      "text": "and locates our implementation details. I go to the project menu directly. I click",
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      "text": "Start from scratch. I name the new project Metabrains. I start one project directly here.",
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      "text": "I save it. Now we have a new project. There is no chat available yet in the project.",
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      "text": "You see our Explorer and Object options.",
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      "text": "If we turn on the Explorer, our file opens automatically.",
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      "text": "You can see we have no document available yet.",
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      "text": "With time, we will make many additions and changes.",
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      "text": "Automatically, our response and style will change.",
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      "text": "I will start from the beginning.",
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      "text": "We will look at all the changes we make from start to now.",
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      "text": "Along with changes, we will also see the time.",
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      "text": "Along with changes we will also see the time. We will see how much time one full cycle takes.",
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      "text": "We can look at the complete process directly. Sometimes we must approve things directly",
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      "text": "to understand them, I will tell you. I will type what things should be in our project.",
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      "text": "I write create an agent.md file. We will create the agent.md file here. This makes our agent",
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      "text": "work in a much better way. I type because this is a corporate sales manager and content",
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      "text": "creator project. It will do the first step of our project here. First of all, we give",
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      "text": "the context and our details. We are making the employee do these things here. We will",
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      "text": "include this directly for our approval. I tell you that a complete process discussion",
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      "text": "is very important here. In this, we have to involve all our details. We must involve our",
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      "text": "full working process here. You can see it will take some time, obviously. It is working",
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      "text": "in our exact folder. If I refresh our folder here, you will see the agent.md file is created.",
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      "text": "We can directly review it here. It created the agent.md file with a tailored operating",
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      "text": "brief. This is for the corporate sales manager and content project. The agent.md file is",
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      "text": "made here. You can see our exact source code and other things are available. We can open",
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      "text": "it directly in our VS code. We can open it in the default application. We can also open",
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      "text": "it in our terminal. These things are much better here. We have a complete system environment.",
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      "text": "Automatically, we see all divisions and working ability here. Sometimes, this working ability",
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      "text": "or complete process causes problems. It causes problems on a big scale. We see many issues",
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      "text": "in our work. We usually create our complete empty MD file at the very start of the project",
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      "text": "setup. So, on a big scale, if any issue happens later, we can understand it early. We can",
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      "text": "do things early according to our system. This is a basic discussion now. We see how complete",
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      "text": "generation happens. We see how we take a complete process or understanding with us. After the",
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      "text": "agent.md file we will directly look at our project structure. I will go into the exact",
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      "text": "same folder. I will include a new folder here. This is from a previously created section.",
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      "text": "You can see our portfolios are available here. I will do all the portfolio discussion here.",
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      "text": "I will now analyse our repository. I write analyse this repository and provide a comprehensive",
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      "text": "overview of the project structure. It includes the main purpose of the application. Then we have",
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      "text": "high-level architecture. Then we have important directories. Then we have all options for",
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      "text": "important directories on a big scale. After major frameworks, we have entry points. After entry",
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      "text": "points, we have configuration files. Then we have deployment-related files. Then we have testing",
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      "text": "structure. Then it says present the information in a structured format,",
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      "text": "suitable for onboarding a new developer. We can take our things directly in",
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      "text": "developer onboarding too. We can involve them. This is a complete structured",
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      "text": "repository layout that we use here. Sometimes we must see some changes in",
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      "text": "this too. In this we also involve multiple repositories and options",
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      "text": "directly. When I enter this it will directly go to our folder. The index file",
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      "text": "is inside. You can see this is like our index file. When the index file opens a",
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      "text": "complete portfolio website is available. Now we will see how to enhance and",
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      "text": "understand this portfolio website on a big scale. This will happen in our codex",
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      "text": "here. It understood the child item here. After that it showed our files. It has",
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      "text": "shown the key pattern too. We have one HTML document. We have one style sheet. We",
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      "text": "have one script bundle. Then we have some static assets. We also have all other",
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      "text": "things available to use directly. Now I will take our details. I will see how our",
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      "text": "process runs. I will see how all direct discussions and details run. With this we",
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      "text": "will take our setup. Usually this needs a lot of time if we take these things in",
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      "text": "another environment but here our things are much simpler now. We can include much",
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      "text": "better things and accounts here. I will look at our direct use case now. It read",
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      "text": "out our whole folder here. After that it told us there are no platform specific",
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      "text": "deployment manifests checked into the repository. The deployment guidance is",
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      "text": "document only. After that we have a testing structure. Then all other things",
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      "text": "have come. Now we have gone inside our project. We have seen how we will include",
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      "start": 4168.867,
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      "text": "our system. Now I will see what entry points are available there. I will write",
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      "start": 4174.487,
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      "text": "to find the entry points. If we want to look at something other than entry points",
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      "text": "we will involve all user authentication features here.",
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      "text": "We will see how we can look at them.",
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      "start": 4186.047,
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      "text": "Along with this, we will get a direct response from the complete system.",
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      "text": "We will see how we take things directly in our system first.",
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      "text": "I will type to locate and explain the implementation of the user authentication feature.",
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      "text": "I will identify all the components involved here.",
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      "text": "Then I will look at the authentication flow.",
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      "text": "Then I will look at the authentication middleware",
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      "text": "Then I will look at password handling",
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      "text": "Then I will look at the token generation process",
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      "text": "Then I will look at the session or token management",
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      "text": "Then I will provide a step-by-step explanation of how the application starts",
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      "text": "Then I will process the requests from login to successful authentication",
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      "start": 4230.687,
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      "text": "These things bring all our details here",
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      "text": "All our handling has come here now.",
      "chunk": 8,
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      "text": "Generating code from requirements is one of the most practical uses of OpenAI codecs in everyday development.",
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      "start": 4244.347,
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      "text": "Instead of translating every single business requirement into working code by hand,",
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      "text": "developers can provide codecs with a vast amount of information or context.",
      "chunk": 8,
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      "start": 4254.787,
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      "text": "We can include clear instructions, behaviours and context.",
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      "start": 4259.207,
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      "text": "We have multiple forms of languages available to use.",
      "chunk": 8,
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      "start": 4262.027,
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      "text": "Let me open Excalibur here.",
      "chunk": 8,
      "language": "en",
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      "text": "As you can see, we have some business languages.",
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      "start": 4269.027,
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      "text": "For example, we can allow users to export reports as CSV or involve role-based access control, also known as RBAC.",
      "chunk": 8,
      "language": "en",
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      "text": "Codex helps bridge the gap between these requirements and implementations",
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      "start": 4283.027,
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      "text": "by identifying relevant files, suggesting changes, creating new components,",
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      "start": 4289.027,
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      "text": "updating APIs, and adding tests where needed.",
      "chunk": 8,
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      "text": "However, Codex works best when the requirements are very specific in nature.",
      "chunk": 8,
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      "avg_logprob": -0.11630572853507576,
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      "start": 4299.027,
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      "text": "Let me show you how to include basics in Codex right from the start.",
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      "start": 4303.027,
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      "text": "Suppose you want a plan mode, and after that you want to pursue a goal.",
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      "start": 4308.027,
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      "text": "Then you might want to create a document.",
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      "text": "After document creation, you might want to include photos.",
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      "text": "You have multiple plugins that you can use side by side.",
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      "start": 4320.701,
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      "text": "browser plugin you can use that one too. If you have specific content to use that",
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      "start": 4326.281,
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      "text": "can be utilized as well. For instance we can go to documents or downloads, select",
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      "text": "Select an available document and use it.",
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      "start": 4335.641,
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      "text": "After that we select our model.",
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      "start": 4338.421,
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      "text": "This latency is quite important, therefore I will use a latest model here.",
      "chunk": 9,
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      "start": 4344.241,
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      "text": "With that latest model, we can increase speed and manage latency well.",
      "chunk": 9,
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      "text": "After this, our prompt should contain very detailed options.",
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      "start": 4353.921,
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      "text": "First of all, a well-set prompt has some specific elements available within it.",
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      "start": 4358.801,
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      "text": "I will explain these elements to you.",
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      "text": "A good prompt includes all these necessary components.",
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      "text": "The very first thing is context, followed by the role, details, and then the requirements.",
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      "start": 4372.541,
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      "text": "When discussing requirements, it is a crucial point because codex works better when the",
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      "text": "requirements are specific.",
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      "text": "A vague prompt may produce incomplete or misaligned code.",
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      "start": 4384.261,
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      "text": "A strong prompt includes the feature goal, affected users, input and output behaviour,",
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      "start": 4390.761,
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      "text": "or modules to consider, coding standards, test expectations and what our codex",
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      "start": 4396.521,
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      "text": "should avoid changing. Developers should treat the codex generated code as a",
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      "start": 4401.321,
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      "text": "first draft so we get a complete response right from the very start of it.",
      "chunk": 9,
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      "avg_logprob": -0.12104634805159135,
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      "start": 4406.081,
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      "text": "Next, after the requirements, we need to establish our acceptance criteria. This",
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      "start": 4412.061,
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      "text": "defines the standard our response must maintain when we receive it. At the end",
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      "text": "we sometimes use a negative prompt. In a negative prompt we specify the things",
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    {
      "start": 4422.561,
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      "text": "that we do not need. In the rest of the prompt we list all the necessary things",
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      "start": 4427.561,
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      "text": "that we want. However, in a negative prompt we mention all those exact things",
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      "text": "that are not required for our output at all. For example, let me show you a sample",
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      "text": "prompt right here in this section. We can go into Codex and type out our prompt.",
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      "text": "We will write that you are working in this repository as a senior software engineer with a lot of experience and solid technical background knowledge today.",
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      "text": "This defines our role. The complete task is outlined in plain words.",
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      "text": "After the task, you can see our functional requirements are discussed, such as adding an Export CSV button near the Reports table.",
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      "text": "Towards the end, all the detailed steps we need to perform are available.",
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      "text": "This is our acceptance criteria.",
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      "text": "The export button appears on the report page.",
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      "text": "Clicking the button downloads a valid CSV file,",
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      "text": "and the CSV contains the same rows displayed on the table.",
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      "text": "It will take data straight from the browser, commit the repository in GitHub,",
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      "text": "and provide the response in document form.",
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      "text": "Generating code from requirements with Codex improves overall development speed.",
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      "text": "When our prompts are better, the final results we get will also be much better,",
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      "text": "better, which we can then utilise today. Clear requirements, a defined scope and",
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      "text": "strong acceptance criteria help codecs produce code that is easier to review,",
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      "text": "test and merge in big enterprise teams. We are discussing how big enterprise",
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      "text": "teams can incorporate these modern features on a larger scale today. Our",
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      "text": "next discussion will be related to editing and refactoring existing code",
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      "text": "We have generated this exact code as an example project where we are utilising all these core elements in real time.",
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      "text": "Now, suppose our initial code is generated and ready.",
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      "text": "To demonstrate this point well, I will show you a custom calculator code from our older previous generations.",
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      "text": "In that existing code, we will perform and showcase our past iterations and refactoring processes step by step for all of you to see right here today.",
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      "text": "today. Now we will discuss editing or refactoring the code. Here you can see",
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      "text": "that we asked to create a calculator code. It took a quick look and made a",
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      "text": "plan for us. After making the plan it implemented the calculator in the",
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      "text": "calculator.py file. This is our calculator. Everything in it is",
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      "text": "functional from start to finish. If we want to run it we will see that the",
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      "text": "verification passed using the bundled Python runtime. Note that Python and py",
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      "text": "are not on our system path, so if we want to use it in our environment we need all",
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      "text": "these requirements. I will open PowerShell here or I will just open the",
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      "text": "command prompt. In the command prompt we will give this command. When I press enter",
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      "text": "you will see that we have a complete response included here. Now I will",
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      "text": "include it here. There is an issue in the first line of our code. So we will run it",
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      "text": "from here to here. I will copy this. After copying I will go back to the command",
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      "text": "prompt. Here is the command prompt. Now we will include it in the command prompt.",
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      "text": "When we include it you can see that because this file is very old our file",
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      "text": "is not available here. But we can still run it in our browser. Now we will look",
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      "text": "at our editing and refactoring here. Usually this is the most valuable",
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      "text": "capability of our OpenAI codecs for enterprise software development. While",
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      "text": "generating new code is useful, developers spend a lot of time improving,",
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      "text": "maintaining and modernizing existing applications. Codecs can analyze the",
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      "text": "current code base. It can understand relationships between files, identify code smells and suggest",
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      "text": "improvements for better readability. First I will explain the first thing. This is what",
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      "text": "we call refactoring code. This is a process of restructuring the existing code. It improves",
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      "text": "readability, maintainability and internal design without changing its external behaviour.",
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      "text": "We primarily involve this in our code review.",
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      "text": "If I go to Excalordraw, I can tell you that after refactoring, the next thing is restructuring",
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      "text": "That is very simple.",
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      "text": "It is simple because we can come into our code, include any line here, and include things",
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      "text": "Or, if we want to do overall refactoring, I can say to make this into an HTML file that",
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      "text": "I can run in VS Code.",
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      "text": "Here you can see that now it will directly take our prompt.",
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      "text": "The code was previously available.",
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      "text": "Now it will connect our Python calculator into a browser-friendly HTML version.",
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      "text": "Alternatively I will open our VS Code.",
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      "text": "You might remember that we already included codecs in Visual Studio Code before, so this",
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      "text": "will also be available for us to use in a much easier way.",
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      "text": "Along with this, in Visual Studio Code where everything else is, we will automatically",
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      "text": "include our calculator file.",
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      "text": "Codex is open.",
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      "text": "senior software engineer in this repository. The task is to refactor the",
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      "text": "It has removed the paths that cause errors on a large scale.",
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      "text": "we will discuss context windows and scope. Context window is a new topic. People do not",
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      "text": "discuss it much. I will tell you what a context window is. A context window is the maximum",
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      "text": "text an AI model can hold at one time. It works as the AI's memory. Everything inside",
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      "text": "this window, like prompts, history or files, is seen by the model. Before explaining how",
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      "text": "it works, I will give you a simple example. For a simple example, I will go to ChatGPT.",
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      "text": "First, I write a message in ChatGPT. I say, hi, I want to work with AI and HTML.",
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      "text": "If I continue this conversation, it will write the code. This is simple prompting.",
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      "text": "We get our answer through it. But if we do something else, if we give a very long prompt",
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      "start": 5118.084,
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      "text": "again and again. You will see a message. The message says it is too long. Let us see the",
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      "text": "size of the context window in ChatGPT. I will search for the context window of ChatGPT.",
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      "text": "It will show the context window size. Usually the model has a 128,000 token window. This",
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      "text": "This means it takes 96,000 words or 250 to 300 pages of text.",
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      "text": "I will copy this text and paste it many times.",
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      "text": "I will do this until the prompt becomes too long.",
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      "text": "I will copy it and go to a word counter website.",
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      "text": "It is a famous word counter.",
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      "text": "When I paste the words here, you will see the website become slow.",
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      "text": "This is because there are 9,776 words in total.",
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      "text": "We put this in our prompt.",
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      "text": "It has 76,000 characters.",
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      "text": "Now suppose I paste the last text here.",
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      "text": "You will see a message.",
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      "text": "It says the long text is added as a file.",
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      "text": "The text will show up here.",
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      "text": "I will write that if I put this file in the chat it cannot process it.",
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      "text": "We want to see the context window size of ChatGPT.",
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      "text": "It will take time.",
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      "text": "You will see that the context window depends on the model.",
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      "text": "OpenAI does not always show the exact limit, but if you see the message it means the text",
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      "text": "is too long for one input, the file is too big, or the user interface has strict limits.",
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      "text": "If we use fewer words, we will get our answer.",
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      "text": "In the same way, when we go to Codex we can see limits.",
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      "text": "In the new chat we have limits.",
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      "start": 5225.784,
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      "text": "Wait a minute, I will open it here.",
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      "text": "We know that Codex has some limits.",
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      "text": "Even if we add many images, files and documents there are still limits.",
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      "text": "In real business projects, apps have thousands of files and millions of lines of code.",
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      "text": "No AI can read all the code at once.",
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      "text": "Devils must help codecs focus on important information.",
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      "text": "Scope sets the limits of a task.",
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      "text": "It includes files, modules and features.",
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      "text": "Setting the right scope stops bad changes.",
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      "text": "It helps codecs focus on the right parts.",
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      "start": 5263.164,
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      "text": "Now I will tell you directly.",
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      "start": 5265.404,
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      "text": "Suppose we get an answer.",
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      "start": 5267.484,
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      "text": "How will a full developer work?",
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      "text": "For example, we will take an e-commerce store.",
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      "text": "In our example we have an e-commerce store.",
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      "text": "A developer wants to add a discount box to a product page.",
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      "start": 5282.073,
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      "text": "Codex. He only shares the product model, service, controller, form and tests. This limits the",
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      "text": "context. Codex can focus on the updates. It will not make bad changes in other",
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      "start": 5295.553,
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      "text": "places. Where do we use this? When we make changes this is very helpful. We can",
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      "text": "modify things easily. You will see two or three options here. We can open it in VS",
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      "text": "code. We have a full MetaBrain's project here. It has all our files. We can run",
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      "start": 5315.873,
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      "text": "the index file here. On the side we have our chat. Our codex is also running",
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      "start": 5322.053,
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      "text": "directly. Besides codex we have an agents option. We can use agents to do",
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      "start": 5328.413,
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      "text": "our work directly. Now we will verify our GitHub account. Do you remember how I did",
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      "text": "GitHub authentication? We will authorize VS code directly. Our workflow and",
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      "text": "personal details will be added here. This works for public and private projects.",
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      "text": "We can include this directly. If I show you the sign in again, authorisation",
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      "text": "comes directly. We can confirm it. Suppose we want to verify using email.",
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      "text": "The email will come automatically. We will confirm the email here. These emails",
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      "text": "run through a base setup. We got the code here. Then we will verify it. Our VS code",
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      "text": "will sign in and connect automatically. After signing in, MetaBrainz connects with our co-pilot.",
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      "text": "We are running many windows. In one place we run the agent. In another place we use",
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      "text": "our IDE. In a third place, we use it as a chatbot. So, we must manage our context carefully.",
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      "text": "Context window and scope management are very important for AI software development. By",
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      "text": "giving the right information and clear boundaries, developers help codecs. It creates accurate,",
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      "text": "good and reliable code. We must look at these important things carefully.",
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      "text": "Now, our discussion is about reviewing our AI-generated code.",
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      "text": "In the AI-generated code, we want to see how we previously used codes like",
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      "text": "CreateCalculator multiple times, as you can see.",
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      "text": "We have created the calculator here, and you can see that all our changes are also available here.",
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      "text": "For these AI-generated changes, we first need to understand two or three things.",
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      "text": "First, in this complete software we are using, whenever there are changes, the red values",
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      "text": "will be all our previous values, and the green values will be our new possible changes that",
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      "text": "If you look here, you can first see the set display error, but now in our new code, some",
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      "text": "Let us suppose I directly include a prompt here saying, I want a scientific calculator.",
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      "text": "Now, a scientific calculator will be created here.",
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      "text": "Usually we have this one code and we can call it version 1.",
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      "text": "Automatically, this version of ours will be applied here, and along with this version,",
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      "text": "First of all, it will take some time to think about what things were discussed in the previous",
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      "text": "After all those discussions, our code will automatically start generating here.",
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      "text": "Alright, it is reconnecting with our work.",
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      "text": "and how our thinking will work on a larger scale.",
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      "text": "Usually, when we are looking at such a code, we have to do validation.",
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      "text": "We can separate our unified difference from here.",
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      "text": "We call this split and we call the other one unified.",
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      "text": "In the previous one, you can see the display error and here you can see how much our error",
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      "text": "Similarly, our if action is also available here.",
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      "text": "Alright, so all these things are being directly included in our system.",
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      "text": "We are looking at multiple changes here.",
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      "text": "You can see that our connection is being created once again here.",
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      "text": "We can change the model to a very simple one, make the speed fast, and also change the reasoning",
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      "text": "Now, besides all these things, we have two options available here, Review and Undo.",
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      "text": "In the Review option, we can directly review our code once in the first scenario.",
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      "text": "And Undo works exactly like our version control system.",
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      "text": "Here you can see that our system is now running, and it is telling us that it is upgrading",
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      "text": "Now, let us suppose I take you to this HTML file.",
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      "text": "I will show all the differences here.",
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      "text": "They are here now and we will also enable the rich preview.",
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      "text": "Now you can see that this is our newer file.",
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      "text": "Even after the newer file, when we make changes again, those changes will also be visible",
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      "text": "You will see plus 32 and minus 21.",
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      "text": "This shows how many lines of code were removed and how many lines of code were added to our",
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      "text": "When both these things work together, we automatically start getting our response and work results.",
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      "text": "It shows how we are managing things.",
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      "text": "Alright, so this is our very detailed working process.",
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      "text": "For a simple example, as we are reviewing now, the title was previously Calculator, but now",
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      "text": "Now our new title has become Scientific Calculator.",
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      "text": "Alright, if we look here now, all the design changes of our calculator are shown here with",
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      "text": "plus and minus signs.",
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      "text": "Also, you can see that if we want to add something here, we can directly include a local comment.",
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      "text": "For example, I can say here, I want round buttons.",
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      "text": "Okay, so now we want our round buttons.",
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      "text": "As soon as I post this comment, you can see it is included here.",
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      "text": "Now this comment is added, alright?",
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      "text": "Similarly, if we go here, I will say, I want more buttons.",
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      "text": "I am making very simple changes in front of you.",
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      "text": "I will add the comment.",
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      "text": "Now there are two comments here, alright?",
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      "text": "In the same way, at the end of this file, I will say that we need simpler functionality",
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      "text": "here.",
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      "text": "Okay, I will say, simple functionality please.",
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      "text": "Alright, this comment is added here.",
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      "text": "After that, we can go to the end or just go to the start.",
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      "text": "In the start, I will change our name here.",
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      "text": "Change the name as MetaBrainzCal.",
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      "text": "Okay, MetaBrainzCal.",
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      "text": "Alright, now our comment is added.",
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      "text": "You can see that three or four comments are here now.",
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      "text": "In our calculator, we have a comment in row number 46 and a comment in row number 102.",
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      "text": "After that, there is a comment in row number 200 and then a comment in row number 6.",
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      "text": "Now what we have to do here is I can say make the possible changes asked. Okay what will happen here",
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      "text": "is that based on the changes we included in these comments the changes will happen at those exact",
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      "text": "points. I will only show you this one change that we demanded at the end in the comments.",
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      "text": "We will see how it changes the name of our scientific calculator here,",
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      "text": "and this is a very advanced feature. We are reviewing our complete AI generated changes",
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      "text": "to see how our reviews can be applied on a larger scale and how we can include them.",
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      "text": "It will take some time to think. Obviously you can see that it is thinking here,",
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      "text": "and after that it says, I will apply the review comments directly.",
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      "text": "First of all, our name will be changed to MetaBrainz.",
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      "text": "After that, the calculator buttons will be made circular.",
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      "text": "A few practical scientific buttons will be included.",
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      "text": "Here you can see our calculation.",
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      "text": "MetaBrainzCal is written here.",
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      "text": "After that, we also included a comment here that we want our buttons to be rounded.",
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      "text": "So, you can see that by including height, aspect ratio, border radius and all these things,",
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      "text": "it has provided us with some rounded buttons that we can directly see here.",
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      "text": "Alright, so all these things are greatly included in our working process here.",
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      "text": "And through this, we can include it in our system, and it will work here.",
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      "text": "Now we will get all our changes here.",
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      "text": "You can see that whatever visual changes we have,",
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      "text": "it has included some lines in a file and showed us the changes here.",
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      "text": "We can also open that here.",
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      "text": "OK, after that, for the next changes it makes, you can see our thinking process here.",
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      "text": "In the thinking process, as it is including our constants and other values, these files",
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      "text": "will automatically come here side by side.",
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      "text": "Now it has updated our file.",
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      "text": "We have all the commands here.",
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      "text": "As soon as we open this in our browser, all our discussed changes are here, like Metabrains",
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      "text": "Our buttons have become rounded.",
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      "text": "And now, if we include anything here, that thing will automatically be directly included",
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      "text": "OK, our error came here.",
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      "text": "We are seeing everything working.",
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      "text": "Alright, if we suppose we need the value of pi here, we get our pi value.",
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      "text": "If we need the value of log8, we can directly input it here.",
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      "text": "Also, you can see that this delete option is working directly here too.",
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      "text": "If we look at all the signs here, every sign that we have is working very easily.",
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      "text": "We can directly enter all our values here.",
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      "text": "If there is no responsiveness, we can go ahead and directly include responsiveness in our",
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      "text": "code later.",
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      "text": "But code review is the main thing that I have discussed with you.",
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      "text": "After this, our next discussion is related to our prompt hints.",
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      "text": "Next we discuss the main usage of a prompt library.",
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      "text": "We will see how we can use it and where more options are included.",
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      "text": "First I come here.",
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      "text": "Instead of using everything, I will search the prompt library in Codex.",
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      "text": "Here you see Codex prompting.",
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      "text": "First I tell you this is a built-in collection of reusable workflows, templates and skills",
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      "text": "When I go to prompting, you see we often use prompts.",
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      "text": "You interact with the codecs by sending prompts that describe what you want.",
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      "text": "It has your prompt plus the model outputs and tool calls that follow.",
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      "text": "I will explain this exact process right here in our codex.",
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      "text": "In codex, we go to our main user settings.",
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      "text": "Inside these settings we have personalization.",
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      "text": "But sometimes, we must see our clear deviation and how we are working on stuff, and how these",
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      "text": "We have our memory, which we can induce right here in our experimental form.",
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      "text": "We see how our things can be included and how we can take our many processes forward.",
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      "text": "Our standard options show how we will best include our own memory.",
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      "text": "First is the main Enable Memories.",
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      "text": "Second is our Good Tool Assisted Memories.",
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      "text": "We can also involve Tool Assisted Memories and involve all our process or workability",
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      "text": "Next thing is our own custom prompt libraries.",
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      "text": "What is a good prompt library?",
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      "text": "I will tell you first that our prompt library is a great reusable prompt hub.",
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      "text": "In this we can do our text discussion multiple times.",
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      "text": "We must see one more thing here.",
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      "text": "From the ChatGPT side, there is no prompt library available here yet.",
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      "text": "We have clear information about basic prompting.",
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      "text": "But if I talk about the Claude code, there is a distinct prompt library available.",
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      "text": "We can go there and see the many prompts are collected from the various Anthropic guides.",
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      "text": "This includes our common workflows and best practices, and how Anthropic teams take Claude",
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      "text": "If I want, we can also see the core understand prompts.",
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      "text": "We see how we can use our own prompts if we need a new prompt for git, release, data,",
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      "text": "A set number of proper prompts are available for every single thing.",
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      "text": "We can reuse them multiple times over.",
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      "text": "We can utilise multiple variations or our many functions.",
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      "text": "This very same thing is available here in our Claude,",
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      "text": "but if we go to our codex, then until now we do not have these full prompt libraries available.",
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      "text": "We can do one other good thing here. We can come right here and ask a prompt question.",
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      "text": "I can say I want a whole new prompt library for my codecs.",
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      "text": "It will then give a good reusable clean codecs prompt library.",
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      "text": "Inside it we have all our good useful prompts. We have our bug fixes, text test generator,",
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      "text": "code review, refactoring, feature builder, API design, performance optimization and security audit prompt.",
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      "text": "All these things will involve in that exact thing.",
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      "text": "From there we will take and use it well. These various things are included in our entire core",
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      "text": "system on a large scale. We must look at all these many things on a large scale to see how we can",
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      "text": "reuse them. We will copy it and come inside this main system to use all our many prompts.",
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      "text": "We will not use these exact things but besides this we have all our 12 core prompts.",
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      "text": "As we go forward our prompt library will increase even more. From 1 to 12 our many different options",
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      "text": "will be used and we will include them. These things are used very much inside the entire system.",
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      "text": "With this, we have a lot of great automation in our work on a large scale.",
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      "text": "We can use that well.",
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      "text": "I will tell you, we also have cool automations here on the side.",
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      "text": "We can run our day brief, week review or smart project monitor.",
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      "text": "But besides that, if we look at our cool templates, we have very many.",
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      "text": "Now our next discussion relates to integration.",
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      "text": "We will see how to perform AI-enhanced CI slash CD.",
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      "text": "First, I will explain the full definition of CI slash CD.",
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      "text": "CI stands for continuous integration.",
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      "text": "CD stands for continuous delivery and deployment.",
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      "text": "Usually, when enterprise-level workflow loops run, we have multiple projects.",
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      "text": "We have standalone projects, collaborative projects, or autonomous projects connected to multiple companies, industries, and businesses.",
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      "text": "For them, we must look at integration and deployment.",
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      "text": "This is a DevOps methodology that automates building, testing, and releasing software.",
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      "text": "We can discuss this as automation.",
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      "text": "This pipeline allows development teams to ship code updates frequently, safely and reliably,",
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      "text": "while catching bugs early in the development cycle.",
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      "text": "Usually we do CI slash CD at an industrial or enterprise level where we need automation.",
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      "text": "First we have continuous integration.",
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      "text": "It is a practice where developers regularly merge their code into a central repository",
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      "text": "like GitHub or GitLab. Every time code is merged, an automated system builds the application",
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      "text": "and runs tests until the integration finishes. This ensures that the new code does not break",
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      "text": "the existing code base. It allows teams to catch errors instantly. Then we have continuous delivery",
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      "text": "versus continuous deployment. How can we use CI slash CD? First, it gives a faster time to market.",
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      "text": "New features and bug fixes reach users in hours instead of weeks or months.",
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      "text": "There are fewer bugs, easier rollbacks and better developer productivity.",
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      "text": "CI slash CD pipelines can be used in GitHub Actions, GitLab CI, Jenkins and Circle CI.",
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      "text": "I will search for codecs on Google now.",
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      "text": "For example, if I share here that I want this in Codex, then we can also do that, how it becomes possible.",
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      "text": "Integrating the Codex command line into the pipeline enables OpenAI Codex to automatically evaluate build failures.",
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      "text": "It analyses vulnerability scans and proposes minimal changes required to make the tests pass.",
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      "text": "It executes code quality checks directly.",
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      "text": "It also generates remediation patches or pull requests.",
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      "text": "Usually, when we talk about our process, we can perform CI slash CD in multiple forms",
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      "text": "inside our projects.",
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      "text": "Here in this interface and outside it, if I go directly to the terminal, I can run the",
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      "text": "How can I do this?",
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      "text": "First, I will search for Codex CLI.",
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      "text": "The developer platform will open, which we can access directly.",
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      "text": "Here we must install it.",
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      "text": "I will come here and command the system to install Codex.",
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      "text": "We will copy and paste the required command.",
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      "text": "You will see that Codex is already set up here.",
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      "text": "Because of this, we do not need to install it again.",
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      "text": "We simply type our Codex command and it will start running.",
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      "text": "You have seen this here too.",
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      "text": "Whenever we give a command, we have a terminal option available on the side.",
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      "text": "Inside this, we can automatically initiate our working process.",
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      "text": "We also have a browser option to run CI-cd pipelines.",
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      "text": "Usually, continuous integration and continuous deployment have become a foundation of modern",
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      "text": "Traditional CI-cd pipelines automate the building, testing and deployment processes.",
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      "text": "This reduces manual effort and ensures consistent software delivery.",
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      "text": "With the introduction of AI-powered development tools like OpenAI, Enhanced CI and CD introduces",
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      "text": "Instead of merely reporting failures, AI systems can investigate the root cause.",
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      "text": "They propose correlations and create pull requests containing fixes.",
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      "text": "This dramatically reduces the time developers spend troubleshooting failed builds.",
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      "text": "architectural improvement.",
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      "text": "Let me show you a simple example.",
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      "text": "We can take any system here.",
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      "text": "When we give a prompt, you can see our working process begins.",
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      "text": "We get options to debug an issue or review a plan.",
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      "text": "I will select GitHub and ask it to review the newest repository.",
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      "text": "The newest repository will appear.",
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      "text": "Then, I will command it to analyse the latest pull request.",
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      "text": "It must generate unit tests for newly added functions,",
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      "text": "modified business logic and edge cases",
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      "text": "to improve our error handling completely.",
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      "text": "As soon as I include this,",
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      "text": "the error handling scenarios will become much better.",
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      "text": "The other responses will also appear here for us to analyse directly.",
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      "text": "Usually, the GitHub workflow is infused here.",
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      "text": "Inside the GitHub workflow, we can directly use our other generations or processes.",
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      "text": "Our other regions and our understanding will also appear.",
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      "text": "Side by side, I must tell you another thing.",
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      "text": "We usually face a concern about how to check our processes inside out on a larger scale.",
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      "text": "We will see that in a much better form now.",
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      "text": "In the CI slash CD pipeline, you can see the local workspace is empty.",
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      "text": "The GitHub account is authenticated, but Git is not available.",
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      "text": "If we set up these things, we can cross-check our CI slash CD pipelines.",
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      "text": "We prepare a repository and use the CI slash CD pipeline perfectly.",
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      "text": "Here, I will give you the example of a pipeline.",
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      "text": "If you prepare a pipeline of build, which have a request, it has many stage such as",
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      "text": "names and tests will appear here.",
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      "text": "If you connect codecs here, then our AI tasks can work side by side with it.",
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      "text": "Our next topic is putting codecs into our work process.",
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      "text": "This means adding codecs to our build system.",
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      "text": "When we do this, the system helps us find problems.",
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      "text": "If a build stops working or a task breaks, the tool sees the code quality issues.",
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      "text": "Before, people had to look at every single problem by hand.",
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      "text": "Now the system looks at the failures for us.",
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      "text": "It gives ideas to fix them and creates tests.",
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      "text": "It can even make pull requests with good answers.",
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      "text": "In a normal process, a person sends code to GitHub.",
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      "text": "Then our complete system starts running automatically.",
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      "text": "Things begin to work on their own.",
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      "text": "We usually watch these actions in our deployment area.",
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      "text": "For enterprises, this saves a lot of time.",
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      "text": "do not have to fix the same small problems again.",
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      "text": "The Codex Assistant helps keep the work active.",
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      "text": "It understands the whole process of how things run.",
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      "text": "We like having a place without mistakes.",
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      "text": "Now let us look at consistency.",
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      "text": "This is a very important benefit.",
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      "text": "Sometimes working directly with these pipelines can be hard.",
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      "text": "First, I will go to our plugins section. We can see the GitHub plugin here.",
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      "text": "Next, I will search for codecs in the bar.",
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      "text": "We see many results, but I want to find it in the profile settings.",
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      "text": "I look at the list of options like packages, co-pilot, pages and security.",
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      "text": "Here I will search for the codecs.",
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      "text": "And here we have our codecs.",
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      "text": "We can also search in GitHub or settings.",
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      "text": "Then I decide to grant access.",
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      "text": "To do this, I open our repository.",
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      "text": "I click on the code button and copy the link.",
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      "text": "I will ask the chat, do you have access to this project?",
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      "text": "The system will now check if it can reach the repository and provide us with it.",
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      "text": "And how we can initiate our working or a larger scale.",
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      "text": "Sometimes we have to look at these details ourselves.",
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      "text": "Also, we can take a complete process out here and our generation will be completed in that.",
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      "text": "This is usually present at a lesser extent.",
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      "text": "But now we will initiate a full-fledged process here.",
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      "text": "You will see that it has told us here that yes, I can access the repository.",
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      "text": "The access to our repository has also arrived here.",
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      "text": "The second thing it said is that our default branch is main, visibility is public and effective",
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      "text": "permissions in the session are admin, maintain, push, pull and triage. I can",
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      "text": "simply say here that we now have to include an agents.md file inside this.",
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      "text": "This will directly include it and give it to us. But the thing is we cannot",
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      "text": "create things inside this. So right now we will only cross-check our agents.md",
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      "text": "file here to see if it is directly available to us or not. These are our",
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      "text": "things. I will now take our full detailed discussion. You will see that directly",
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      "text": "see, git isn't available. So here, after inspecting, it has directly arrived. I can",
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      "text": "include things on a large scale. Side by side another very important thing is",
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      "text": "running here. Usually in a base pipeline I could not run git push origin here",
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      "text": "because the directory is not a git repository. Git is not installed in our",
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      "text": "current shell. Let us suppose we want to install git. How can we do that? First of",
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      "text": "all we will open our PowerShell here. We come inside this one. Here I say our",
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      "text": "state our Git version here. You are seeing that the Git version is available to us here.",
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      "text": "now we will say we want to install Git with our basic widget. Automatically our widget",
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      "text": "will be installed here. On the basis of that widget, we will take our things inside running.",
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      "text": "We usually did not include our commit instructions, but still, it is possible that by opening these things, all these items come into working.",
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      "text": "But still, as a matter of fact, the discussion here is that when integrating codecs, organisations start in non-production environments.",
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      "text": "Overall, Codex acts as an intelligent engineering assistant inside our CI-slash-CD pipeline.",
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      "text": "Now, let me tell you a very important thing.",
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      "text": "An example of this is that first of all, we have to do this step by step.",
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      "text": "Usually what happens is we see this in our CI slash CD builds, or we can see this in our",
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      "text": "Then the next and most important process, which will be discussed in our setup here,",
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      "text": "These will directly be used to approve our process.",
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      "text": "Alright, now as an example, if I go into Codex and open a new chat, what will happen in our",
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      "text": "chat is I'll say directly that a GitHub action has failed. We'll include the",
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      "text": "repository name directly. We have our failure logs here and the tasks are",
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      "text": "being discussed the most here. Alright, after that we have our unit test",
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      "text": "have directly we will configure it. We will discuss a OneGo project today. We",
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      "text": "will direct a full repository in our OneGo project. First we will create all",
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      "text": "improvements. Then we will use GitHub and Codex.",
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      "text": "Here is our Codex. We open Codex.",
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      "text": "Inside Codex, we generate a full project before using prompts.",
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      "text": "In Codex, we must include the SetAgents file.",
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      "text": "But we will first exit this project.",
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      "text": "I will close this one.",
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      "text": "You see projects are available here.",
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      "text": "I can organise them or start from scratch.",
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      "text": "We will name our project OneGo.",
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      "text": "In this project we will build a complete application with everything.",
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      "text": "First we will pin this project.",
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      "text": "After pinning, we open it in our Explorer.",
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      "text": "Later, all generations will come into this file.",
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      "text": "We have our VS Code and Terminal.",
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      "text": "I told you before we can use codecs in our terminal.",
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      "text": "I will first tell you the plan.",
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      "start": 7747.287,
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      "text": "We will open our plan mode.",
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      "text": "We have no plugins yet.",
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      "start": 7752.207,
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      "text": "We will not use the browser much.",
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      "language": "en",
      "avg_logprob": -0.09572968116173378,
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      "start": 7754.887,
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      "text": "We will use the 5.4 model in our GPT.",
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      "language": "en",
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      "start": 7758.847,
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      "text": "We will use the medium 5.4 model.",
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      "start": 7762.367,
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      "text": "We will use fast speed here, not default speed.",
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      "start": 7766.267,
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      "text": "It will be used here.",
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      "start": 7768.647,
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      "text": "We will not connect GitHub for now.",
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      "language": "en",
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      "start": 7771.427,
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      "text": "I will say in one go that I am going to make a project",
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      "compression_ratio": 1.6751269035532994
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    {
      "start": 7774.927,
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      "text": "about a health care application.",
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      "start": 7777.667,
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      "text": "Can you plan a project for me?",
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      "compression_ratio": 1.6751269035532994
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    {
      "start": 7780.627,
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      "text": "Project planning will start here.",
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      "avg_logprob": -0.3233940124511719,
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    {
      "start": 7783.207,
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      "text": "First, it will settle output resources.",
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      "start": 7786.467,
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      "text": "I will not include them now.",
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      "start": 7788.807,
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      "text": "We look at current workspace.",
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      "start": 7791.767,
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      "text": "You see nothing is there in workspace.",
      "chunk": 16,
      "language": "en",
      "avg_logprob": -0.3233940124511719,
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    {
      "start": 7795.207,
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      "text": "Who is the primary user for the first version of the healthcare app?",
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      "language": "en",
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      "no_speech_prob": 2.1839795597000666e-12,
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      "start": 7799.367,
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      "text": "We have patients and doctors.",
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      "start": 7802.167,
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      "text": "I will say, this application is for patients.",
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      "start": 7806.367,
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      "text": "We want to build an application for our patients.",
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      "language": "en",
      "avg_logprob": -0.3233940124511719,
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    {
      "start": 7809.027,
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      "text": "What kind of first release do you want to plan? MVP or balanced or ambitious?",
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      "start": 7816.367,
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      "text": "I will choose MVP. What is our delivery target? We want to build a web application here.",
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      "language": "en",
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    {
      "start": 7824.167,
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      "text": "I will not build a mobile application. I will build a web app here.",
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      "language": "en",
      "avg_logprob": -0.23494439008759288,
      "no_speech_prob": 1.3042013215919246e-12,
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      "start": 7829.247,
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      "text": "The current decision is clear. Patient facing, MVP sized and optimised for a web app.",
      "chunk": 16,
      "language": "en",
      "avg_logprob": -0.23494439008759288,
      "no_speech_prob": 1.3042013215919246e-12,
      "compression_ratio": 1.6080402010050252
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    {
      "start": 7835.547,
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      "text": "I still need to lock the feature set.",
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      "language": "en",
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      "no_speech_prob": 1.3829159171974137e-12,
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      "start": 7840.067,
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      "text": "Now we have our feature sets.",
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      "language": "en",
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      "start": 7842.147,
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      "text": "Which feature bundle should define the MVP?",
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      "language": "en",
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    {
      "start": 7845.527,
      "end": 7846.567,
      "text": "Booking and records?",
      "chunk": 16,
      "language": "en",
      "avg_logprob": -0.2661600324842665,
      "no_speech_prob": 1.3829159171974137e-12,
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    {
      "start": 7847.327,
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      "text": "We will use this simple option.",
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      "start": 7850.787,
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      "text": "How should the plan treat healthcare compliance in the first version?",
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      "language": "en",
      "avg_logprob": -0.2661600324842665,
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      "start": 7854.867,
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      "text": "We will use HIPAA ready design, which is recommended.",
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      "language": "en",
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    {
      "start": 7859.167,
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      "text": "We can also use full compliance track or lightweight prototype.",
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      "language": "en",
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    {
      "start": 7863.027,
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      "text": "How much external integration should the first plan include?",
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      "start": 7868.727,
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      "text": "I will say minimal integration will be included.",
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    {
      "start": 7872.147,
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      "text": "These things are recommended here.",
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    {
      "start": 7874.347,
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      "text": "Through them our entire process comes.",
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    {
      "start": 7878.007,
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      "text": "What will be the delivery timeline?",
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    {
      "start": 7880.327,
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      "text": "What will be the delivery timeline for our project?",
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      "language": "en",
      "avg_logprob": -0.2243210928780692,
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      "start": 7883.967,
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      "text": "I will say I want it in an urgent manner.",
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    {
      "start": 7887.867,
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      "text": "This is a fourth part where we can include what we want.",
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      "language": "en",
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    {
      "start": 7890.987,
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      "text": "What team should this plan assume?",
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    {
      "start": 7895.007,
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      "text": "Our small team is here.",
      "chunk": 16,
      "language": "en",
      "avg_logprob": -0.27168928447522617,
      "no_speech_prob": 1.8903953923066208e-12,
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    {
      "start": 7897.127,
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      "text": "Instead of a small team, we can choose Solo Builder.",
      "chunk": 16,
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      "avg_logprob": -0.27168928447522617,
      "no_speech_prob": 1.8903953923066208e-12,
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    {
      "start": 7901.007,
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      "text": "Our solo builder is here now.",
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      "avg_logprob": -0.27168928447522617,
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    {
      "start": 7904.327,
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      "text": "What kind of plan do you need?",
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    {
      "start": 7906.107,
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      "text": "Build plan or business build?",
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      "start": 7908.727,
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      "text": "We will work with a simple build plan.",
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    {
      "start": 7912.107,
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      "text": "Now it reduces the project scope and other things.",
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    {
      "start": 7915.607,
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      "text": "You see, multiple questions are asked here.",
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    {
      "start": 7918.107,
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      "text": "For a solo builder, what does urgent mean for the first usable release?",
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      "start": 7923.847,
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      "text": "We will answer one week.",
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      "start": 7926.327,
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      "text": "We will include one week here.",
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    {
      "start": 7929.007,
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      "text": "Our plan will come here.",
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    {
      "start": 7931.547,
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      "text": "Build a patient-facing WebMVP for a solo builder",
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      "start": 7935.327,
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      "text": "optimised for one week urgent prototype",
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    {
      "start": 7938.327,
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      "text": "while keeping the design HIPAA aware and future ready",
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      "start": 7942.107,
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      "text": "for real healthcare data handling.",
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      "language": "en",
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      "start": 7944.047,
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      "text": "I will read the plan once, then we will implement it.",
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      "start": 7949.267,
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      "text": "I am saying that we have the entire process.",
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      "start": 7952.647,
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      "text": "We say our product definition is also happening here.",
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      "start": 7956.547,
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      "text": "After product definition, our recommended MVP features have also arrived here.",
      "chunk": 16,
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      "no_speech_prob": 1.5247901682599596e-12,
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      "start": 7962.687,
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      "text": "Frontend and backend have come in our technical approach.",
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      "start": 7966.307,
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      "text": "I will tell it that I want just a working frontend and minimal backend.",
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      "start": 7970.707,
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      "text": "Also, it should be just HTML and CSS with JS logics.",
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      "text": "We want our product in a simpler way.",
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      "text": "All these things will come here.",
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      "text": "The stack has arrived.",
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      "text": "What should a minimal backend do?",
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      "text": "We can use a tiny mock API.",
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      "text": "We can use a basic form backend.",
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      "text": "We will not use any real backend.",
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      "text": "How should the frontend be structured?",
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      "text": "I can say a multi-page application will be required here.",
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      "text": "What level of frontend quality is needed?",
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      "text": "I will say we need a portfolio quality user interface.",
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      "text": "Our plan is created.",
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      "text": "You can see we have JSON objects and local storage here.",
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      "text": "Key changes are to reframe the project as a frontend prototype, not a production ready",
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      "text": "option.",
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      "text": "Now we can implement this plan.",
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      "text": "During implementation, it will bring our coding.",
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      "text": "It is checking the workspace shape.",
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      "text": "We will look at static app files and shared styles and JavaScript layers here.",
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      "text": "We will also check the open file to see what things are discussed in it.",
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      "text": "First it took basic index HTML.",
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      "text": "We will open our file explorer.",
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      "text": "We will open 1Go from Documents.",
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      "text": "But later our files will appear.",
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      "text": "It has confirmed our HTML pages and state stylesheet and JS layer.",
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      "text": "In plugins, our GitHub is not installed yet.",
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      "text": "I will go to our codec settings to install GitHub once.",
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      "text": "In settings, you can see we have our connections.",
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      "text": "Besides connections, we have our MCP servers here which we can use.",
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      "text": "Where all our other details are, we will include our direct option.",
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      "text": "In configuration we have our plugins.",
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      "text": "We have sandbox settings and current version and everything is working.",
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      "text": "We can also see our most used plugin here and how it is discussed.",
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      "text": "Then we have plugins inside our hooks.",
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      "text": "And we have our computer use plugin, so any app and our Google Chrome browser has also",
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      "text": "When I connect GitHub, you can see all details have arrived here.",
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      "text": "Because we are using the Michael DuPont account, we will bring the same Michael DuPont email",
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      "text": "We click on Add Plugin.",
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      "text": "It is approved by our admin.",
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      "text": "You will see that GitHub is now connected.",
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      "text": "We can try it out in the chat too.",
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      "text": "In GitHub, we have our repositories and pull requests, and all things have arrived.",
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      "text": "We have 97 actions available in the GitHub app related to Read and Write.",
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      "text": "After this, we go to our healthcare application.",
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      "text": "You will see that a total of 9 files generated here.",
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      "text": "You can see in these files we have our app.js.",
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      "text": "If I go here and reload it, our file structure is still not very strong.",
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      "text": "But you will see that all our assets are available here.",
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      "text": "And all these files are generated here overall.",
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      "text": "Now it also told us it found a portability issue in the static assets.",
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      "text": "A few non-ASCII characters slipped into the labels.",
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      "text": "It will run them.",
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      "text": "The command is normalised.",
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      "text": "Now we will connect things in our in-app browser.",
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      "text": "You will see I did not give any prompt.",
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      "text": "I gave a basic prompt that I need a healthcare application.",
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      "text": "It planned it and took multiple recommendations from me during planning.",
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      "text": "In the end it generated all our assets in one go.",
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      "text": "Appointments and dashboard and doctors and index and record files are generated.",
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      "text": "Now browser automation has also arrived.",
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      "text": "We have all files for implementation and we can run prototypes.",
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      "text": "Here is our documentation.",
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      "text": "Now if I open our file explorer and go to documents and one go, our folder is empty right now.",
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      "text": "But if I look at our index.html here in the file explorer, you will see that we have a",
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      "text": "If I open this file in our browser, you will see our project has arrived.",
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      "text": "We are accessing healthcare here with appointments and records and reminders.",
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      "text": "When we want to enter a dashboard we sign in here.",
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      "text": "After doctors our appointments have arrived.",
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      "text": "And our records have arrived.",
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      "text": "This is our whole process as you can see.",
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      "text": "If we want we can confirm any other appointment too.",
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      "text": "I will say I have a heart pain.",
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      "text": "I will just include a reason.",
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      "text": "When we include this part our second appointment will come.",
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      "text": "This means our page is loading.",
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      "text": "If we go to records we have our medications and allergies and recent visits available.",
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      "text": "If we go to doctors, all doctors we have are listed here.",
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      "text": "Then comes our dashboard.",
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      "text": "Inside this dashboard we have our current summary showing the plan and conditions and",
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      "text": "Our insurance card and hydration reminder and medication review are all available.",
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      "text": "Members and unwanted things are available here. If we want we can build a new demo",
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      "text": "account instead of demo credentials. I will close this one. I will give a prompt",
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      "text": "here. I liked my first draft and I want to make this an official app for the",
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      "text": "patients. Add some vibrant health-based colors in the website. Also try to make",
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      "text": "it more real. Our prototype is very much liked. In the prototype our login and",
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      "text": "doctors are very real. But this dashboard has some issues. When I bring it to the phone",
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      "text": "view it does not look special. It has many issues. This dashboard does not go higher",
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      "text": "from here. The second thing is our login panel goes too far back. If we go to our network",
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      "text": "inside the console, nothing special is loading. This means we have no back end. It created",
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      "text": "a working front end for us. I have given a prompt to update our prototype. You see we",
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      "text": "have our browser available in plugins. Due to this we can use it. It says I am updating",
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      "text": "the prototype. It is updating the prototype towards a more launch-oriented Alaska patient",
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      "text": "app. First I am verifying the current front end structure. Then I will replace the mock",
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      "text": "positioning with real Alaska provider data. It will shift the design towards a premium",
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      "text": "Apple adjacent aesthetic without copying Apple owned logos or icons, because we cannot",
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      "text": "copy the icons. But if we want, we can copy aesthetics based on an ecosystem. And we are",
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      "start": 8462.348,
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      "text": "going to do the same thing here. This thing will be induced here. In styles you see we",
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      "text": "we have many changes. It is performing enhancements in our file overall.",
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      "text": "I have included one thing in the OneGo project. I want to run the Apple ecosystem here.",
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      "text": "Apple ecosystem means the user interface enhancements in our MacBook or iPhones. I want to adapt",
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      "start": 8487.708,
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      "text": "them in our project. All those things are happening here. You see what it is doing first.",
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      "text": "I gave this information here.",
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      "text": "Use real doctor information from Alaska.",
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      "text": "I want to launch it there.",
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      "text": "First, it read all our files.",
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      "text": "Then it understood index.js file.",
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      "text": "After that it is carrying on our research purpose.",
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      "text": "When these things are included, our final website version will come in a better form",
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      "text": "Let me tell you an important thing. In Codex, whatever things are happening now, you can see them all here.",
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      "text": "If we have any output, we can see it too. If we have any source, we can see which source it is utilising here.",
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      "text": "Like here, from a regional hospital list, it is retrieving doctors.",
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      "text": "First, it makes concrete changes, replacing generic healthcare content with Alaska-specific facility and care context.",
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      "text": "In the Styles section, you can see that we have many changes appearing.",
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      "text": "All the styling updates are being processed to match the ecosystem we requested.",
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