I’m using Jev more than Opus 5.5 or GPT-6. Here’s why.

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Jev, Jev, Jev. Welcome to Jev week onhow I AI. We have seen a lot of newmodels be released in the last 5 days.We saw Opus 55. We saw GPT6 Soul, GPT6
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Luna. Muse is blowing up the timeline.Everybody still loves their Grock bots.And yet there is one thing that I want
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o talk about in AI right now and thatis this fast cheap doesn't speak systemone decision model from Typesafe AI. As
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oon as I saw this trending on X, asoon as I saw it launched, I immediatelystarted testing it. Now, what I will say
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is more than any other model I'vexperienced lately, Jev has been the onethat has explodeduse cases, personal productivity use
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cases, code use cases, product usecases. Today I'm going to give you avery quick whirlwind tour of what Jebis, what it will give you back and what
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it won't, and then how I have used itover the last week to do work that Ithink is worth hundreds of thousands if
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not millions of dollars. And I haveprobably spent sub $10 on Jev tokens. Alot of it has been subsidized because
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Jeb is currently free on the AI gatewayby Versel. But even at list price, it isa very inexpensive model. It is a very
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effective model and it is going to be inthe middle of almost everything I buildfrom here on out. [music] So, let's get
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o it. This episode is brought to you byOpen Art Arena, the global leaderboardfor creative intelligence. Every week,
1:50.159–1:56.560
new AI models launch, and everyoneclaims to be the best. But best at what?
1:56.560–2:03.520
Open Art Arena is built to answer thequestion that actually matters. Whichmodel is [music] best for your specific
2:03.520–2:06.320
job? Instead of one overall winner, Open
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Art Arena ranks models across realcreative tasks from advertising and filmto animation, product, graphic design,
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editing, and lip-sync [music]covering both image and video. And theserankings aren't based on hype. They're
2:21.680–2:28.239
judged by professionals, industryleaders, and working creators throughblind evaluations. So judges never know
2:28.239–2:34.640
hich model produced which output. Thatmeans you can see how models actuallyperform when it comes to the creative
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work you're doing. So stop guessingwhich model to use. Explore rankingsbased on real creative work and find the
2:41.599–2:50.800
right model for your project and savetime and cost. See the rankings at OpenArt Arena. So if I were to explain Jev
2:50.800–2:57.440
to you, I would go to this table on thetype blog post announcing Jev. And itbasically compares normal LLMs on the
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left, Jev style LLMs on the right. Bothtake in unstructured data as inputs.Both take in text as inputs. Jev does
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not take in images, but it takes in textand it takes in text descriptions ofimages if you really need to get there.
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The outputs though are very different.With the standard LLMs that you're usedto working with, you are getting strings
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and generated text out. So you'regetting text in, text out. With Jev,you're getting text in, type safe values
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out. And what I mean by type safe valuesis these are values that are predefinedthat then Jev picks from and selects and
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returns to you. We will show you whathose values are, but essentiallythey're like it's this or that. It's yes
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or no. It's 1 through 10. Like it'spretty simple. Now it sounds simple, butit is incredibly powerful when you put
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it against the right problem.The other thing that you will noticeabout Jeff is it is cheap AF and it isfast AF. And so if you look at the
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current cost of input and output tokens,it can be pennies to tens or hundreds ofdollars per million output tokens.
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Jev only charges you on input tokensbecause it barely outputs anything. Andit is 4 cents per million input tokens.
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It is like dirt freaking cheap. Andbecause they output basically nothing,they don't even charge you for output
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okens. Um whereas the standard LM aregoing to charge you tons for the outputokens. And then I'll go into use cases,
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which is how you would use like astandard LLM versus why you would useJev. You know, standard LMS, chat botswhere you want text in, you want text
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out. Coding where you want, you know,code to be produced. Um, and so it'sgreat for things where you needstuff generated, but Jev is really great
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for making decisions. That's ultimatelyI've been calling this it's like adecision model. So you give it adecision and it makes a decision and and
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it's high agency I would say. So if youneed smart if statements, should I goleft or right? If it's good, put it to
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this person. If it's bad, send it tosupport. All that kind of stuff Jeff isreally good at. It's really good at
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classifying data. That's a lot of whatI'm going to talk to you about today.And it's really good at real time. So,
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it is fast, fast, fast. So, you can putan LLM in a real-time loop in a way thatwas not performant enough with these
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other models. I do want to explainexactly what Jev outputs. And so, Iwould highly recommend if you're going
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to use Jev, go to their docs. The twoplaces that I'm using the most are theprimitives, so what it returns. And then
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I use some of the patterns in cookbooks.So what will Jev return to you? It will
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essentially return one of three things.It will return a choice which means youcan give it text, you can give it a list
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of choices and it will pick a choice.So, if my question is, what should Iwear on this date and my choice is adress, jeans, workout clothes, ski gear,
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or nothing,it'll like maybe pick the dress, right?
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And so that is choice. If I selectscore, it's going to say like how severeis this or on a score from one to five,
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what it is. This is really good forseverity rankings. So say the examplehere is a great one which is like if you
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get a bug, you want to rank it likecosmetic, broken or blocking, Jev willtriage that bug and give it a score. And
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then finally, there's a new. It's aversion of a boolean. It basically tellsyou what the likelihood that the answer
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to a question is yes. So if the answerto a question is yes or no, is Claire apodcaster? it's going to give you like a
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99% nule because there's a 99% chancethat I am a podcaster. Okay, so this ishow you know you would think about the
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three things it can return. And again,very simple, but if you've been asoftware engineer, this is like 90% of
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software engineering is like doing thesethings, returning a choice, scoringsomething, routing, saying yes or no.
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And so it is just so so so powerful. Andthe number one thing I have been usingit for is classification of a vast sets
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of unstructured data that would havebeen annoying to classify but is veryhigh value. So I'm going to show you
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those use cases and hopefully this willinspire you about how you can use Jev.Okay. So I'm going to pull up Codeex and
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show you like two or three examples thatare super simple to run, incrediblycheap, and incredibly powerful. This is
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one CTO's, VPs of product, V, you know,chief product officers, listen up. Thisthe one that three years ago I would
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have paid truly $100,000 for. So what Ihad Jev do is look at thousands of PRs.I got PRs. I have connection to to
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GitHub. Pull every single PR. And thenwhat I had it do, which would have beenso impractical in the past, is I said
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categorize all the PRs. And so use Jevto do pair-wise relations. And so thissomething that I found Jev is really
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good at is compare like P to PRB andsay, are you the same? Are you working
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on the same thematic area or not? Again,
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Jev's not going to tell you what thematic area is unless you give itchoices. is it's going to say like yesthese two are related or no they're not
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and I've been doing this a lot where I'mtaking vast amounts of data and I'msaying like yes related not related yes
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related not related and it's doingclustering which I find very very usefulso I create these clusters of data sets
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and that's what Jev did and then I useda really cheap model I use Gemini flashlight to then take those clusters and
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categorize them and I first ran it onour marketing site where we only havelike 112 PRs. It cost me 1.1 cent. Iguess they wouldn't round down that.1
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penny. And you can see here, it lets mesee how much my work is related tomaintenance, content, tools, site, and
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conversion services or docs. Superuseful. Again, it ran very, very, veryfast and very cheap. But then I ran it
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on my chat PRD app, which has about2,000 PRs on it to date in this calendaryear. because it has a lot more PRs. It
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cost me a lot more money. And by a lotmore money, I mean nine whole cents. Itcost me 9 cents to do this. It tookprobably about 2 minutes. What it did is
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it analyzed 1,700 PRs. It did 17 found17,000pairs in those PRs of things that couldbe matched. And then the themes were
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labeled by Gemini Flash Light. And itpulled all of the data out. I mean,again,CTO's, CEOs, like I know you're asked
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this by the board and by your team andby your boss all the time, like whatpercentage of work is going to whatinitiatives? Like, tell me the
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percentage to tech debt. Tell me thepercentage to this product or thatproduct. And you can see here it gotdown. It excluded our docs PRs because
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those are completely separate. almost30% of the effort the PRs that we do atChat Pured are around platform security
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and infrastructure. on being a goodcitizen and investing in securityperformance and infrastructure and then
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of course because we're a chatbot aconversational AI reliability and thensome product areas that we invest in are
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like data and integrations documentediting and prototyping which is new andso this was so cheap so fast it is
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accurate like I can just tell you yesthis is where we're investing our timeand you can even see as we invest more
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in different things over the last couplemonths it goes up. And so this is justan example of how powerful, cheap, and
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good Jev can be on large data sets. Andthen I want to give you all one thateven if you don't have like a big repowith PRs everywhere that you can run,
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which is you can run this on your localcloud code and codec sessions. So all ofyour cloud code and codec sessions are
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stored locally. And so you can actuallyrun this analysis on everything storedon your local machine. So I did that
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while Codex kicked off a thread to dothat. And you can see depending on whatyou count as like effort how much I'm
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spending on different things. So I do alot of product engineering. And so forgrouping by user turns in January I was
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almost exclusively doing engineeringtasks in cloud code and codecs. And thenas you kind of like come into this new
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orld now in September less than 40% itlooks like of my tasks are actuallyengineering tasks. I'm doing a lot more
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work with agents which makes a lot ofsense. And then I'm doing a lot morelike publishing media. I'm doing a lot
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of our videos through. And then you cansee I have a new business. So clientdelivery, family and personal stuff. And
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then where it couldn't categorize or itwas unclear. If I take it to sessiondays, you can see again most of mysessions were coding. And now it's like
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more equally split across different usecases. And so if you think about this,it is just super useful to do meta
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nalysis on all this data that's sittingon your desktop that LMS can totallyparse. that will cost you basically no
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money and give you a lot of insight thatI think previously would have been hardto get. So these are two use cases I
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think everybody who is coding should do.The third one and I won't show itbecause it's a lot of my privateinformation is I did run this on my
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personal Gmail. So I basically said likegiven a subject line and a snippetcategorize whether or not I can like
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delete this email like score whether ornot I can delete this email. It did itvery fast and then it gave me very clean
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tags that I could go through and thenhave another model work throughcategorized emails. And so this is where
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I would say like Jev alone is okay. Jevwith an LLM buddy is super powerful. Sowhat I like to do with Jab is I like to
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take a big corpus of information, tagit, categorize it, cluster it, filterit, and then apply really precise AI
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actions to the right clusters. And sothat could be take your bugs, take yourhigh severity ones and really triage
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them deeply. It could be take youremails, group them into like you candefinitely delete them and ignore them,
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delete them, and then work your otheremails with an agent. It's just allthose things that you can do where avery fast but accurate filter can be
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helpful. So, I think this is sopowerful. I hope it unlocks your mindson what Jev can do. I want to show youlike the biggest version of this that
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I've done just to like kind of give younumbers and ideas of what I'm working onand then I'll show a couple like fun
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little apps that you can build with Jevthat I think would have been hard tobuild before. So the example I want to
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give is you all have heard me talk abouthis product. So chat PD I'm trying tobuild like a product insights graph.
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Basically, I'm like trying to suck ineverybody's data and tell you reallyinteresting insights about it. And I
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have used every frontier model there isto try to figure out. And it's justreally hard. There's just too much
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nuance in all the data to kind of likegro all this, get it in structuredformat, do the right like structured,
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unstructured. It's super expensive. I'vespent thousands of dollars if not tensof thousands of dollars trying to
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prototype this. It's just hard. And Ithink I cracked this baby with Jev. Ikind of want to show you what thatmeans,
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which is it's not that I like Jev hasdone it all at all. It is actually Ihave figured out where classification,
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clustering, and mapping are importantand I put Jeff there. And then I figureout where like the big brains like
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analysis,strategy, insights, extraction, and Iput like Astra there. And then likewhere generation is important, I use
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like a soul or a Luna. And so if youlook at this map and it's like probablynot that interesting to you all because
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it's very internal to chat pd but youcan see like I extract things, I extractlabels, I then quickly assign everything
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to those labels with Jev, then I pairthose clusters in those groups withAstra and I say like what the heck'sgoing on across all of this. And then it
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extracts more insights and all of asudden I have this really cool product.And just to give you a sense of like the
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scope of data here, I'm probably pullingabout a,00and um, 1100 individualsignals into this. So these are PRs,
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these are support tickets, these aregranola conversations,these are um, linear tickets. They'relike kind of like all the signals in our
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business about what people are worriedabout and what is going on. And then I'mgetting now like this gap between what
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are customers telling us they want andwhat are we actually working on and Ican get those trends over time I can get
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hem categorizedI can get details about them so it'sjust really super fascinating veryhelpful has made this feature I would
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say probably more margin accretive to mybusiness if you all know what that meansbecause before I was really like trying
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to brute force with these like brainymodels, what I needed to work on and howI could do this data. And now I can like
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combine these like very cheap decisionmodels like Jev with a really brainymodel and get this like interesting
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completely hard to generate set of datacontext like auto wiki. It's like reallyunlocked this product and it has taken
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about,00raw sources of data and done over like200,000classification and pair-wise groupingsand it probably cost me four bucks on
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the Jeff side. It's cost me a lot moreon the Astra side. But I just think likethinking through where classification
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like super smart classificationclustering decisions could unlock reallycomplex products and how you might use
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as I'm showing here Jev alongside somesmarter models. It's like really blowingmy mind right now and I think is it
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should be interesting for those outhere building interesting dataproducts. So, so far I've told you likewhat Jeb is, how you can use it on PR
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review or data analysis, how you can useit on your local sessions, how you couldeven use it on your email, but I'm going
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to show you two things that I built withit in an afternoon that I think areally cool. And this is my attempt tolike go against the like eyepopping
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demos that you're seeing on X. I thinkpeople have shown interesting things,but they actually haven't shown how you
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have to build it and how it works to gethat real-time effect. And so, I'm goingto show you these apps live because I
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think it's important to kind of likeunderstand where the latency comes fromand what the real experience is as
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opposed to like kind of a 30-secondond demo that goes goes viral. ButI do think it shows some like prettycool stuff. So the first one I want to
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do ishow I AI audience signals. So youwonderful people give us comments on thehow I AI podcast and I go through them.
19:21.520–19:31.120
I read them every day and I reply tothem when I can, but I've never doneanalysis on them. And there's about4,500 comments and I just wanted to know
19:31.120–19:34.320
like are they good, are they bad, arethey happy, are they sad? And I also
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wanted to know if you all had anyepisode ideas for me where I couldextract them and come up with episodesthat I could do like this Jeff one. And
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so I hooked up the YouTube V3 API. Youjust have to enable it in Googleconsole. And then I said pull all thecomments and categorize them into
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positive, negative or neutral. So thatwould be a choice, right? Or a score
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probably. And then I said also use Jevto identify whether or not includes aidea for a future episode and then
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analyze all the data and give me adashboard to look at the data. And soyou can see here about half arepositive. There are 58 comments in here
20:16.720–20:24.480
with episode ideas and then there's likequality praise which is praise for ourproduction team which is like how nice
20:24.480–20:33.679
the podcast looks. And so then it mademe this dashboard which is telling me isit positive, neutral, negative or mixed.
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And then it gave me an audience requestboard. So you all want like openw weightmodels, you want instinct, you really
20:39.919–20:46.000
want a comparison of Grock and Muse forpersonal use. That's coming soon. Andthen do I use Grock or Brockbot or like
20:46.000–20:49.120
what am I using? And then you can also
20:49.120–21:00.799
see by episode the sentiment which isRyan's very popular three-step AI codingworkflow. 61%positive. We had an awesome cloud code
21:00.799–21:13.520
for product managers episode. 80%positive comments. And then we canactually go through the commentsthemselves. And I using Jev built live
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search against these comments. So, if Iwanted to find comments about screenshare, it would search and find very
21:23.440–21:30.159
quickly something that says screenshare. If I wanted to say, "What are thecomments about slop?" I could do slop
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and then very quickly it scanned all4,400 and found all the comments thatare related to slop. So, again, this is
21:41.200–21:47.280
very performant, very fast. just forlike behind the scenes, you have to likebatch the results and then score them
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and then push the high scores up to gethat kind of performance. So, there isome architecture here. I don't want to
21:53.039–21:59.840
pretend like Jev you just like slap Jevin the middle and evaluate all 4,400that quickly, but you can imagine this
21:59.840–22:07.120
very useful. And so, one, thanks forall the comments. And two, this wasreally, really cool to build. And then
22:07.120–22:16.480
finally, I have to give Astra props.Really nailed the Howa AI podcastyling. So, good job on the front end,but I want to show one last one before I
22:16.480–22:24.480
get us out of here. And then I said thiswas Jev week. So, like and subscribe fora second follow-up Jev episode with one
22:24.480–22:34.480
of our How Ia guests that's going tocome later this week. But I wanted toshow like kind of a fun realtimeuse case for Jev. So if you have been
22:34.480–22:42.480
seeing any of these like real time applications of Jev like playing a videogame or you know playing playing Tetris
22:42.480–22:51.200
doing live search all these things youcan do a lot of real time stuff wheremaking a very quick decision oreturning a set of choices can be very
22:51.200–23:01.200
powerful. Okay, so I built a real timeapp that takes in voice and then usesJev to determine what color is like my
23:01.200–23:13.280
emotion and then returns a quote inreflection of my emotion. It usesOpenAI's real time voice API. It usesJev and then it uses like API ninja
23:13.280–23:19.919
quote API. There's apparently an API forquotes. And so we're going to see ifthis works and how fast it is. I'm
23:19.919–23:39.679
feeling so tired today.I'm feeling very happy today.Okay, the quotes. Oh, there wesee the quote worked. Okay, hold on. Oh,
23:39.679–24:10.159
it's excited.I'm in love today.I can't wait for the weekend.Doing this podcast is the best job ever.Look at that. So, we got all these Okay,
24:10.159–24:16.240
I'm going to turn it off because it'sgoing to like keep listening to me. Lookat all these quotes where likeimmediately it picked the right color.
24:16.240–24:23.279
It picked the right quote. You can tellthe quote API has a little latency init, but you can imagine how fun it would
24:23.279–24:32.000
be to be able to build these real timexperiences. And again, when it's likepick the right color to match thesentence, it can do that really well.
24:32.000–24:39.039
Just for you all to know kind of how itworks, I gave it a list of hex values.So, a list of colors. Every sentence or
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like phrase it ingests from thereal-time API, it asks what color or itscores the colors. It gives the topranking score color and then it also
24:47.520–24:56.880
picks from a set list of filters for thequote API. So it filters the quotes andthen it scores them against my sentiment
24:56.880–25:03.520
and then it shows it on the screen. Soagain like running locally not thefastest. I'm sure I could optimize it by
25:03.520–25:10.960
caching a bunch of this stuff and youknow doing all sorts of things but againlike a little like magic use case that I
25:10.960–25:24.159
think is really indicative of the kindsof things you can build with Jeb. Sothat is my Jev 101. How to use it onyour own data how to use it with other
25:24.159–25:33.679
models and how to build some funrealtime experiences in your app intro.Again, as I said, this is Jev episode
25:33.679–25:40.720
number one this week. So, we're going tohave another one midweek with one of ourmost popular guests. So, if you are
25:40.720–25:47.279
xcited about that episode, subscribe,comment, let me know what you want us totalk about, what questions I can answer
25:47.279–25:58.640
about Jeb. And until then, it has beenso nice showing you my favorite newmodel. Thanks for joining How I AI.Thanks so much for watching. If you
25:58.640–26:05.919
enjoyed this show, please like andsubscribe here on YouTube, or evenbetter, leave us a comment with yourthoughts. You can also find this podcast
26:05.919–26:13.039
on Apple Podcasts, Spotify, or yourfavorite podcast app. Please considerleaving us a rating and review, which
26:13.039–26:25.000
will help others find the show. You cansee all our episodes and learn moreabout the show at howiaipod.com.See you next time.
0:00.400–0:18.000
傑夫，傑夫，傑夫。歡迎來到 Jev 週，這是 onhow I AI 節目。過去五天我們見證了許多新模型的發布。我們看到了 Opus 55。我們看到了 GPT6 Soul、GPT6
0:18.000–0:26.880
Luna。Muse 在時間線上爆紅。大家依然熱愛他們的 Grock 機器人。然而，有一件事是我現在想在 AI 領域
0:26.880–0:41.120
討論的，那就是 Typesafe AI 推出的快速、便宜且不言自明的 System One 決策模型。
0:41.120–0:49.520
當我在 X 上看到這個趨勢時，當我看到它發布時，我立即開始測試它。現在，我要說的是
0:49.520–0:58.640
與我最近經歷過的任何其他模型相比，Jev 是唯一一個讓使用案例、個人生產力使用
0:58.640–1:08.640
案例、程式碼使用案例、產品使用案例爆炸性增長的模型。今天，我將帶大家快速瀏覽一下 Jev 是什麼、它會為你帶來什麼以及它不會帶來什麼，然後
1:08.640–1:17.360
介紹我過去一週如何使用它來完成我認為價值數十萬甚至
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數百萬美元的工作。而我在 Jev 令牌上的花費可能不到 10 美元。其中很多都是因為
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Jeb 目前在 Versel 的 AI 閘道上免費提供。但即使按定價計算，它也是一個非常便宜的模型。它是一個非常
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有效的模型，並且在我未來構建的幾乎所有東西中都會處於核心地位。[音樂] 所以，讓我們開始
1:41.200–1:50.159
吧。本節目由 Open Art Arena 贊助播出，這是全球創意智慧排行榜。每週，
1:50.159–1:56.560
新的 AI 模型推出，每個人都宣稱自己是最好的。但什麼方面最好呢？
1:56.560–2:03.520
Open Art Arena 旨在回答真正重要的問題。哪個模型 [音樂] 最適合您的特定
2:03.520–2:06.320
工作？Open Art Arena 不設單一總冠軍，
2:06.320–2:15.120
而是根據真實的創意任務對模型進行排名，涵蓋廣告和電影、動畫、產品、平面設計、
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剪輯和口型同步 [音樂]，包括圖像和影片。這些排名並非基於炒作。它們是
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由專業人士、業界領袖和從業創作者透過盲評來進行評分。因此，評審永遠不知道
2:28.239–2:34.640
哪個模型產生了哪個輸出。這意味著你可以看到模型在創意
2:34.640–2:41.599
工作方面的實際表現。所以別再猜測要使用哪個模型。探索基於真實創意工作的排名，並找到
2:41.599–2:50.800
適合你專案的模型，節省時間和成本。請至 OpenArt Arena 查看排名。所以如果我要向你解釋 Jev
2:50.800–2:57.440
，我會前往 Type 部落格上宣布 Jev 的這篇文章。它基本上比較了左側的一般 LLM 和右側的 Jev 風格 LLM。兩者
2:57.440–3:04.560
都將非結構化資料作為輸入。兩者都將文字作為輸入。Jev
3:04.560–3:10.480
不接收影像，但它接收文字，並且如果你真的需要這樣做，它也會接收影像的文字描述。
3:10.480–3:16.400
不過輸出卻非常不同。對於你習慣使用的標準 LLM，你得到的是字串
3:16.400–3:23.440
並產生文字輸出。所以你是輸入文字，輸出文字。使用 Jev，你是輸入文字，輸出類型安全的值
3:23.440–3:30.319
而我所說的類型安全的值，是指這些值是預先定義的，然後 Jev 從中挑選、選擇並
3:30.319–3:36.480
回傳給你。我們會告訴你這些值是什麼，但基本上它們就像是「是這個或那個」。是「是」
3:36.480–3:44.400
或「否」。是 1 到 10。就像它非常簡單。現在聽起來很簡單，但當你把
3:44.400–3:56.799
它應用於合適的問題時，它非常強大。另一個你會注意到關於 Jev 的事是，它便宜到爆，而且快到爆。所以如果你看看
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目前輸入和輸出記號的成本，每百萬個輸出記號的成本可能從幾分到數十或數百美元不等。
4:04.720–4:14.720
Jev 只對輸入記號收費，因為它幾乎不輸出任何內容。每百萬個輸入記號僅收費 4 美分。
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它便宜到像泥土一樣。而且因為它們基本上不輸出任何內容，它們甚至不對輸出
4:24.240–4:32.560
令牌。嗯，而標準的大型語言模型會對輸出令牌收取大量費用。然後我會介紹使用案例，
4:32.560–4:39.759
也就是你如何使用一般的大型語言模型，以及為什麼你要使用 Jev。你知道，一般的大型語言模型、聊天機器人，你想要文字輸入，你想要文字
4:39.759–4:51.440
輸出。編碼方面，你希望產生程式碼。嗯，所以它非常適合需要產生內容的應用，但 Jev 真的非常適合
4:51.440–4:59.440
做決策。這是我一直稱之為決策模型的原因。所以你給它一個決策，它就會做出決策，而且
4:59.440–5:09.039
它具有高自主性。所以如果你需要聰明的 if 陳述式，我該往左還是往右？如果情況好，把它交給
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這個人。如果情況不好，送給支援團隊。Jeff 非常擅長這類事情。它非常擅長
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資料分類。這是我今天主要要與大家討論的內容。而且它非常適合即時應用。所以，
5:19.759–5:27.680
它很快，很快，很快。因此，你可以將大型語言模型放入即時迴圈中，而這些
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其他模型。我確實想解釋 Jev 具體會輸出什麼。因此，如果你打算
5:34.080–5:40.080
使用 Jev，我強烈建議你查看他們的文件。我最常使用的兩個部分是原語（primitives），也就是它返回的內容。然後
5:40.080–5:44.320
我會使用 cookbooks 中的一些模式。那麼 Jev 會返回給你什麼呢？它
5:44.320–5:50.639
本質上會返回三種東西之一。它會返回一個選項（choice），這意味著你可以給它文字，也可以給它一組
5:50.639–6:06.160
選項，然後它會挑選出一個選項。所以，如果我的問題是「約會時該穿什麼」，而我的選項是禮服、牛仔褲、運動服、滑雪裝備，
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或「不穿」，它可能會挑選禮服，對吧？
6:12.560–6:22.960
這就是選項（choice）。如果我選擇分數（score），它會說「嚴重性有多高」或「在一到五分的分數中，這是幾分」。
6:22.960–6:29.039
這對於嚴重性排名非常有用。所以這裡的範例很棒，比如如果你
6:29.039–6:38.479
發現一個 bug，你想把它分類為「美觀問題」、「損壞」或「阻塞」，Jev 會對該 bug 進行分級並給出分數。然後
6:38.479–6:45.919
最後，還有一個新的功能。這是布林值（boolean）的一個版本。它基本上告訴你答案
6:45.919–6:53.280
為「是」的可能性。所以如果問題的答案是「是」或「否」，Claire 是播客主持人嗎？它會給你類似
6:53.280–7:03.520
99% 的數值，因為我有 99% 的可能性是播客主持人。好的，所以這就是你知道它
7:03.520–7:09.680
能返回的三種東西的方式。再次強調，這非常簡單，但如果你是
7:09.680–7:17.520
軟體工程師，這就像 90% 的軟體工程工作一樣，就是做這些事情：返回選項、對某事評分、路由選擇、回答是或否。
7:17.520–7:28.000
所以它非常非常非常強大。而我使用它的首要用途是對大量
7:28.000–7:34.720
非結構化數據進行分類，這些數據過去分類起來很麻煩，但價值很高。所以我要向你展示
7:34.720–7:41.199
這些使用案例，希望這能啟發你如何運用 Jev。好的。所以我會打開 Codeex 並
7:41.199–7:49.440
展示兩三個範例，這些範例運行起來超級簡單、成本極低，而且非常強大。這是
7:49.440–7:56.720
給一位首席技術官（CTO）、產品副總裁（VP）、V，你知道的，首席產品官（CPO）聽的。這是
7:56.720–8:08.080
三年前我會真正願意花 10 萬美元購買的功能。所以我讓 Jev 做的是查看數千個 PR（Pull Request）。我獲取了 PR。我連接了
8:08.080–8:17.199
GitHub。拉取每一個 PR。然後我讓它做的，這在過去是不切實際的，是我說
8:17.199–8:26.080
對所有 PR 進行分類。所以使用 Jev 來進行成對關係比較。所以這是我發現 Jev 非常
8:26.080–8:31.120
擅長的事情，就是比較 PR A 和 PR B，並說「你們相同嗎？你們是否在
8:31.120–8:33.360
同一個主題領域工作？」再次強調，
8:33.360–8:41.039
Jev 不會告訴你主題領域是什麼，除非你給它選項。它會說「是的，這兩個相關」或「不，它們不相關」
8:41.039–8:45.920
，而我一直在做很多這樣的事情，面對大量數據，我說「是的，相關」、「不相關」、「是的，相關」
8:45.920–8:54.399
「不相關」，它正在進行聚類，我認為這非常非常有用，所以我創建這些數據集的聚類
8:54.399–9:02.160
，這就是 Jev 所做的，然後我使用了一個非常便宜的模型，我使用 Gemini Flashlight 來對這些聚類
9:02.160–9:13.600
進行分類，我首先在我們的營銷網站上運行它，那裡我們大約只有 112 個 PR。這花費了我 1.1 美分。我想他們不會四捨五入到
9:13.600–9:20.800
1 美分。你可以在這裡看到，它讓我看到我的工作與維護、內容、工具、網站和
9:20.800–9:28.560
轉換服務或文件有多相關。非常有用。再次強調，它運行得非常、非常、非常
9:28.560–9:36.000
快，而且非常便宜。但然後我在我的聊天 PRD 應用上運行了它，該應用在今年日曆年裡大約有
9:36.000–9:46.320
2,000 個 PR。因為它有更多的 PR。這花費了我更多的錢。而更多的錢，我的意思是整整 9 美分。這花費了我 9 美分。這花了
9:46.320–9:58.080
大約 2 分鐘。它做的是分析了 1,700 個 PR。它在這些 PR 中進行了 17,000 對配對，這些是可以匹配的項目。然後主題由
9:58.080–10:10.080
Gemini Flash Light 標記。它提取了所有數據。我的意思是，再次強調，CTO、CEO，我知道你們被
10:10.080–10:17.760
董事會、團隊和老闆經常問這個問題，比如多少百分比的工作投入到了哪些計劃？比如，告訴我
10:17.760–10:25.279
技術債的百分比。告訴我這個產品或那個產品的百分比。你可以在這裡看到它得出了結果。它排除了我們的文件 PR，因為
10:25.279–10:32.959
那些是完全分開的。大約 30% 的精力，我們在 Chat Pured 所做的 PR 都是圍繞平台安全
10:32.959–10:38.800
和基礎設施。成為好公民以及投資於安全性能、基礎設施，然後
10:38.800–10:45.600
當然因為我們是一個聊天機器人、對話式 AI 的可靠性，然後我們投資的一些產品領域是
10:45.600–10:54.720
像是資料與整合、文件編輯以及原型設計，這是新的功能，而且非常便宜、快速，
10:54.720–11:00.720
準確。我可以明確告訴你，我們正在投入時間的地方，你也可以看到，隨著我們投入更多
11:00.720–11:08.640
不同的資源，過去幾個月的數據呈現上升趨勢。因此，這只是展示 Jev 在大型資料集上
11:08.640–11:20.320
強大、便宜且優秀的一個範例。接著，我想提供給各位另一個範例，即使你沒有像我們這樣
11:20.320–11:29.120
擁有大量 PR 的程式碼倉儲可以執行，你仍然可以在本地的 Cloud Code 和 CodeC 工作階段中執行此操作。
11:29.120–11:38.320
因為所有的 Cloud Code 和 CodeC 工作階段都是
11:38.320–11:47.680
儲存在本地。因此，你實際上可以在本機上儲存的所有資料上執行此分析。所以我這樣做
11:47.680–11:55.120
時，Codex 同時啟動了一個執行該任務的執行緒。你可以看到，根據你如何計算「努力程度」，我在不同事項上
11:55.120–12:03.120
投入的精力。我從事大量的產品工程工作。因此，在一月份按使用者互動分組時，
12:03.120–12:11.440
我幾乎完全在 Cloud Code 和 CodeC 中執行工程任務。然後，當你進入
12:11.440–12:17.279
九月份這個新世界時，看起來不到 40% 的任務是工程任務。我做了更多
12:17.279–12:24.000
把我們的影片發布出去。然後你可以看到我有了新的業務。所以是客戶交付、家庭和個人事務。
12:24.000–12:33.200
媒體發布的工作。我透過代理程式處理了許多影片。接著你可以看到，我有了新的業務，例如客戶交付、家庭和个人事务。
12:33.200–12:41.440
以及無法分類或分類不明確的部分。如果將視角轉到工作階段天數，你可以再次看到，我絕大多數的工作階段都在編碼。現在則像是
12:41.440–12:49.279
平均分配在不同的使用案例上。因此，如果你思考這一點，對這些存放在你桌面、LMS 完全可以解析的資料進行元分析
12:49.279–12:57.920
是非常有用的。這幾乎不會花費你任何金錢，卻能帶來許多我認為過去難以獲得的洞察。所以，我認為這兩種使用案例
12:57.920–13:04.880
是所有編碼人員都應該嘗試的。第三個使用案例，我不會展示，因為這涉及我許多私人資訊，那就是我對我的
13:04.880–13:10.959
個人 Gmail 執行了此操作。基本上，我說的是，給定主旨行和內容片段，
13:10.959–13:16.720
分類是否應該刪除這封郵件，就像評分是否應該刪除這封郵件一樣。它執行得非常快速，然後給了我非常乾淨
13:16.720–13:24.079
的標籤，我可以透過這些標籤進行篩選，然後讓另一個模型處理分類後的郵件。因此，這裡
13:24.079–13:32.959
我想說，單獨使用 Jev 還可以。但 Jev 搭配 LLM 夥伴則非常強大。所以我喜歡對 Jab 做的是，我喜歡
13:32.959–13:43.040
處理大量的資訊語料庫，對其進行標籤、分類、聚類、過濾，然後對正確的聚類應用非常精確的 AI
13:43.040–13:50.639
操作。這可能包括處理你的錯誤，針對高嚴重性的錯誤進行深入的分類處理。也可能包括處理你的郵件，將它們分組為可以
13:50.639–13:56.079
明確刪除並忽略的郵件，刪除它們，然後使用代理程式處理其他郵件。這些都是你可以做的
13:56.079–14:05.199
事情，其中快速且準確的過濾器可以發揮幫助作用。所以，我認為這非常
14:05.199–14:16.240
強大。希望它能激發大家對於 Jev 能做些什麼的靈感。我想展示一下我做的最大規模版本，
14:16.240–14:23.040
只是想提供一些數字和想法，讓你們了解我正在處理的內容，接著我會展示幾個有趣的
14:23.040–14:32.079
小應用程式，這些是用 Jev 建構的，我認為在過去很難建構。所以我想提供的範例是，各位應該聽過我談論
14:32.079–14:39.920
這個產品。所以 Chat PD 我正在嘗試建構一個產品洞察圖譜。
14:39.920–14:47.360
基本上，我試圖吸收所有人的資料，並告訴你們關於它的有趣洞察。我
14:47.360–14:52.160
使用了所有現有的前沿模型來嘗試解決這個問題。這真的
14:52.160–14:59.680
很難。資料中的細微差別太多了，難以全部掌握，將其轉化為結構化格式，進行正確的結構化與
14:59.680–15:06.160
非結構化處理。這非常昂貴。我花費了數千美元，甚至數萬美元來嘗試
15:06.160–15:16.710
原型設計這個功能。這真的很困難。我認為我透過 Jev 破解了這個難題。我
15:16.720–15:26.480
並不是說我喜歡 Jev 把所有事情都做了。而是我實際上已經弄清楚如何分類、
15:26.480–15:35.120
這並不是說 Jev 獨自完成了所有工作。而是我實際上搞清楚了分類、
15:35.120–15:45.120
聚類和映射的重要性，並將 Jev 放在那裡。然後我搞清楚了像
15:45.120–15:51.040
分析、策略、洞察、提取這樣的大腦工作，並將 Astra 放在那裡。然後像是生成方面，我使用
15:51.040–16:01.120
像 Soul 或 Luna 這樣的模型。所以如果你看這張地圖，這對各位來說可能
16:01.120–16:10.079
不太有趣，因為它非常內部化於 Chat PD，但你可以看到我提取內容，提取標籤，然後迅速使用 Jev 將所有內容
16:10.079–16:17.040
提取更多洞察，突然間我就有了這個非常酷的產品。為了讓大家對這裡的
16:17.040–16:26.639
數據範圍有個概念，我大概從大約一千個，嗯，1100 個個別信號中提取數據。所以這些是 PR，
16:26.639–16:36.000
這些是支援工單，這些是 Granola 對話，這些是嗯，Linear 工單。它們就像我們
16:36.000–16:45.040
業務中關於人們擔心什麼以及正在發生什麼的所有信號。然後我現在得到的是
16:45.040–16:51.920
客戶告訴我們他們想要什麼與我們實際上在處理什麼之間的差距，我可以獲得這些隨時間變化的趨勢，我可以
16:51.920–17:04.640
將它們分類，我可以獲得關於它們的詳細資訊，所以這真的非常迷人，非常有用，我認為這個功能
17:04.640–17:12.640
可能會為我的業務帶來更多的利潤，如果你們知道這意味著什麼的話，因為之前我真的像是在嘗試
17:12.640–17:19.760
用這些像聰明模型這樣的方式強行解決我需要處理什麼以及我如何處理這些數據的問題。現在我可以像
17:19.760–17:27.520
結合這些非常便宜的決策模型，比如 Jev，與一個非常聰明的模型，並獲得這種像
17:27.520–17:38.400
完全難以生成的數據上下文集合，比如自動維基。它就像真正解鎖了這個產品，並且它花了
17:38.400–17:50.160
大約一千個原始數據來源，並進行了超過二十萬次的分類和成對分組，這在
17:50.160–17:57.840
Jev 方面可能只花了我四美元。在 Astra 方面花的錢要多得多。但我只是認為像思考分類
17:57.840–18:05.600
像超級智能的分類聚類決策可以解鎖非常複雜的產品，以及你可能會如何使用
18:05.600–18:13.360
正如我在這裡展示的，Jev 與一些更聰明的模型並排使用。它現在真的讓我驚嘆不已，我認為這
18:13.360–18:22.480
對於那些在外面構建有趣數據產品的人來說應該很有趣。所以，到目前為止，我告訴你們像
18:22.480–18:28.559
審查或數據分析，如何在本地會話中使用它，甚至如何在電子郵件中使用它，但我打算
18:28.559–18:40.160
審查或數據分析中如何使用它，你可以在本地會話中如何使用它，你甚至可以
18:40.160–18:48.000
在電子郵件中使用它，但我將向你展示兩件事，是我在一個下午用
18:48.000–18:55.120
它構建的，我認為非常酷。這是我的嘗試，去對抗你在 X 上看到的像
18:55.120–18:59.360
令人眼花繚亂的演示。我認為人們展示了有趣的事情，但他們實際上沒有展示如何
18:59.360–19:08.320
而不是像那種會病毒式傳播的 30 秒演示。但我確實認為它展示了一些相當酷的東西。所以第一個我想
19:08.320–19:21.520
現場展示這些應用程式，因為我認為了解延遲來自哪裡以及真實體驗是什麼
19:21.520–19:31.120
很重要，而不是像那種會病毒式傳播的 30 秒演示。但我確實認為它展示了一些像
19:31.120–19:34.320
很酷的東西。所以，我想做的第一個是展示我如何 AI 觀眾信號。所以你們這些
19:34.320–19:43.120
想知道你們所有人是否有任何節目構思給我，我可以從中提取出來，並想出像這樣的 Jeff 節目。並且
19:43.120–19:54.320
所以我接上了 YouTube V3 API。你只需要在 Google 控制台中啟用它。然後我說拉取所有評論並將它們分類為
19:54.320–19:58.799
分析。大約有 4,500 條評論，我只是想知道
19:58.799–20:06.240
可能。然後我說也使用 Jev 來識別是否包含未來節目的想法，然後
20:06.240–20:16.720
分析所有數據並給我一個儀表板來查看數據。所以你可以看到這裡大約一半是正面的。這裡有 58 條評論
20:16.720–20:24.480
提取它們並提出我可以像這個 Jeff 一樣做的節目。所以
20:24.480–20:33.679
我連接了 YouTube V3 API。你只需要在 Google
20:33.679–20:39.919
接著它為我生成了一個觀眾需求看板，所以大家都想要開放權重模型、想要本能，你們真的
20:39.919–20:46.000
正面、負面或中性。所以這將是一個選擇，對吧？或者一個分數
20:46.000–20:49.120
可能。然後我說也使用 Jev
20:49.120–21:00.799
你可以按集數查看情緒分數，這是瑞恩非常受歡迎的三步 AI 編碼工作流程，正面評價佔 61%。我們有一場非常棒的雲端程式碼 </think> 生成產品經理集數的評論，正面評價佔 80%。接著我們可以實際檢視這些評論本身。我使用 Jev 針對這些評論建立即時搜尋功能。所以，如果我想尋找關於螢幕分享的評論，它會迅速搜尋並找到提到螢幕分享的內容。如果我想查詢關於「slop」的評論，我可以輸入 slop
21:00.799–21:13.520
分析所有數據並給我一個儀表板來查看數據。所以你可以看到這裡大約一半是
21:13.520–21:23.440
正面的。這裡有 58 條帶有節目構想的評論，然後還有像
21:23.440–21:30.159
質量讚揚，這是對我們製作團隊的讚揚，就像播客看起來
21:30.159–21:41.200
然後它迅速掃描了全部 4400 筆資料，並找出所有與 slop 相關的評論。所以，再次強調，這是
21:41.200–21:47.280
效能非常好，速度也非常快。僅從幕後技術角度來看，你需要將結果分批處理，然後對它們進行評分
21:47.280–21:53.039
然後將高分結果推送上去以達到這種效能。所以這裡涉及一些架構設計。我不想假裝說
21:53.039–21:59.840
別假裝說傑夫只是隨便拍拍傑夫的中間，就能那麼快評估完全部4400筆資料，但你可以想像這
21:59.840–22:07.120
非常實用。首先，感謝大家的留言。其次，這真的非常酷，是我很想打造的東西。然後
22:07.120–22:16.480
最後，我必須給 Astra 掌聲，他們真的把 Howa AI 的播客風格拿捏得恰到好處。前端做得很好，但在我帶大家離開之前，我想再展示最後一個案例
22:16.480–22:24.480
帶我們離開這裡。然後我說這是傑夫週，所以請按讚並訂閱，稍後會有一集傑夫系列的後續節目，邀請一位
22:24.480–22:34.480
我們這週晚些時候還會有另一位 How Ia 來賓，但我想展示一個 Jev 有趣的即時應用案例。所以如果你看過 Jev 這些即時應用，像是玩電玩或俄羅斯方塊，或是即時搜尋，這些都是你可以做的很多即時任務，其中快速做出決策或提供一組選項可以非常強大。
22:34.480–22:42.480
看到這些即時應用，像是玩電玩或俄羅斯方塊
22:42.480–22:51.200
進行即時搜尋等各種應用，你可以處理許多即時任務，其中快速做出決策或提供一組選項可以非常強大
22:51.200–23:01.200
很強大。好的，所以我開發了一個即時應用程式，它會接收語音，然後使用 Jev 來判斷我的情緒色彩，接著回傳一句反映我情緒的格言。
23:01.200–23:13.280
然後判斷我的情緒，並根據我的情緒回傳一句名言。它使用了 OpenAI 的即時語音 API、Jev，以及 API Ninja 的名言 API。
23:13.280–23:19.919
quote API。據說有個專門提供名言的 API。所以我們來看看這招行不行得通，以及它的速度有多快。我
23:19.919–23:39.679
今天覺得好累。我今天很快樂。好，看看這些名言。哦，看來名言功能運作正常。好，等一下。哦，
23:39.679–24:10.159
它很興奮。我今天很開心。我迫不及待想迎接週末。做這個播客是最棒的工作。你看。所以，我們得到了這些。好，
24:10.159–24:16.240
我要把它關掉，因為它會一直聽我說話。看看這些引號，它立刻選對了顏色。
24:16.240–24:23.279
它選取了正確的引文。你可以看出引文 API 內部有一點延遲，但你可以想像這會有多有趣
24:23.279–24:32.000
就能夠打造這些即時體驗。而且當它需要挑選與句子相符的顏色時，也能做得非常出色。
24:32.000–24:39.039
只是讓各位了解運作方式，我給它一組十六進位色碼，也就是一組顏色清單。每個句子或
24:39.039–24:47.520
像是從即時 API 接收到的字詞或片語，它會詢問該使用什麼顏色，或是為這些顏色評分，然後提供得分最高的顏色，接著它
24:47.520–24:56.880
它會從一組預設的篩選條件中，為引文 API 挑選篩選器，藉此篩選引文，然後根據我的情感分析對它們進行評分
24:56.880–25:03.520
然後它會顯示在螢幕上，所以再次強調，在本地運行速度不是最快的，我相信我可以透過快取大量這些資料來進行最佳化
25:03.520–25:10.960
透過快取這些資料並執行各種優化手段，這又是一個類似魔法的小應用案例，我
25:10.960–25:24.159
我認為這非常能代表用 Jeb 能建構的各類應用，所以這就是我的 Jeb 一百堂課，教導大家如何在自己的資料上使用它、如何與其他模型搭配，以及如何在應用程式中打造一些有趣的即時體驗。再次強調，就像我剛才說的，這是本週的 Jeb 第一集，所以我們週中還會邀請一位最受歡迎的來賓推出新一集。如果你對那集節目感到興奮，請訂閱、留言，告訴我你希望我們討論什麼、我可以解答哪些問題
25:24.159–25:33.679
模型，以及如何在您的應用程式中建立一些有趣的即時體驗，這是介紹部分。再次強調，正如我所說，這是 Jeb 第一集
25:33.679–25:40.720
本週的第一集，所以我們週中還會有一集，邀請我們最熱門的來賓之一，所以如果你對那集節目感到興奮，訂閱、留言，告訴我你想讓我們討論什麼，我可以回答關於 Jeb 的什麼問題。
25:40.720–25:47.279
對這集節目感到興奮的話，請訂閱、留言，告訴我你們希望我們討論什麼主題，以及有哪些問題我可以解答
25:47.279–25:58.640
關於傑布。在那之前，很高興能向大家展示我最喜歡的新模型。感謝大家收看《How I AI》，非常感謝您的觀看。如果您
25:58.640–26:05.919
喜歡這集節目的話，請在 YouTube 上按讚並訂閱，或者更棒的是，留下您的想法與評論。您也可以在這個播客
26:05.919–26:13.039
在 Apple Podcasts、Spotify 或您喜愛的播客應用程式上。請考慮給我們評分和撰寫評論，這
26:13.039–26:25.000
這將幫助其他人找到這個節目。您可以在 howiaipod.com 查看所有集數並了解更多關於這個節目的資訊。下次再見。
0:00.400–0:18.000
Jev, Jev, Jev. Welcome to Jev week onhow I AI. We have seen a lot of newmodels be released in the last 5 days.We saw Opus 55. We saw GPT6 Soul, GPT6
傑夫，傑夫，傑夫。歡迎來到 Jev 週，這是 onhow I AI 節目。過去五天我們見證了許多新模型的發布。我們看到了 Opus 55。我們看到了 GPT6 Soul、GPT6
0:18.000–0:26.880
Luna. Muse is blowing up the timeline.Everybody still loves their Grock bots.And yet there is one thing that I want
Luna。Muse 在時間線上爆紅。大家依然熱愛他們的 Grock 機器人。然而，有一件事是我現在想在 AI 領域
0:26.880–0:41.120
o talk about in AI right now and thatis this fast cheap doesn't speak systemone decision model from Typesafe AI. As
討論的，那就是 Typesafe AI 推出的快速、便宜且不言自明的 System One 決策模型。
0:41.120–0:49.520
oon as I saw this trending on X, asoon as I saw it launched, I immediatelystarted testing it. Now, what I will say
當我在 X 上看到這個趨勢時，當我看到它發布時，我立即開始測試它。現在，我要說的是
0:49.520–0:58.640
is more than any other model I'vexperienced lately, Jev has been the onethat has explodeduse cases, personal productivity use
與我最近經歷過的任何其他模型相比，Jev 是唯一一個讓使用案例、個人生產力使用
0:58.640–1:08.640
cases, code use cases, product usecases. Today I'm going to give you avery quick whirlwind tour of what Jebis, what it will give you back and what
案例、程式碼使用案例、產品使用案例爆炸性增長的模型。今天，我將帶大家快速瀏覽一下 Jev 是什麼、它會為你帶來什麼以及它不會帶來什麼，然後
1:08.640–1:17.360
it won't, and then how I have used itover the last week to do work that Ithink is worth hundreds of thousands if
介紹我過去一週如何使用它來完成我認為價值數十萬甚至
1:17.360–1:26.320
not millions of dollars. And I haveprobably spent sub $10 on Jev tokens. Alot of it has been subsidized because
數百萬美元的工作。而我在 Jev 令牌上的花費可能不到 10 美元。其中很多都是因為
1:26.320–1:35.040
Jeb is currently free on the AI gatewayby Versel. But even at list price, it isa very inexpensive model. It is a very
Jeb 目前在 Versel 的 AI 閘道上免費提供。但即使按定價計算，它也是一個非常便宜的模型。它是一個非常
1:35.040–1:41.200
effective model and it is going to be inthe middle of almost everything I buildfrom here on out. [music] So, let's get
有效的模型，並且在我未來構建的幾乎所有東西中都會處於核心地位。[音樂] 所以，讓我們開始
1:41.200–1:50.159
o it. This episode is brought to you byOpen Art Arena, the global leaderboardfor creative intelligence. Every week,
吧。本節目由 Open Art Arena 贊助播出，這是全球創意智慧排行榜。每週，
1:50.159–1:56.560
new AI models launch, and everyoneclaims to be the best. But best at what?
新的 AI 模型推出，每個人都宣稱自己是最好的。但什麼方面最好呢？
1:56.560–2:03.520
Open Art Arena is built to answer thequestion that actually matters. Whichmodel is [music] best for your specific
Open Art Arena 旨在回答真正重要的問題。哪個模型 [音樂] 最適合您的特定
2:03.520–2:06.320
job? Instead of one overall winner, Open
工作？Open Art Arena 不設單一總冠軍，
2:06.320–2:15.120
Art Arena ranks models across realcreative tasks from advertising and filmto animation, product, graphic design,
而是根據真實的創意任務對模型進行排名，涵蓋廣告和電影、動畫、產品、平面設計、
2:15.120–2:21.680
editing, and lip-sync [music]covering both image and video. And theserankings aren't based on hype. They're
剪輯和口型同步 [音樂]，包括圖像和影片。這些排名並非基於炒作。它們是
2:21.680–2:28.239
judged by professionals, industryleaders, and working creators throughblind evaluations. So judges never know
由專業人士、業界領袖和從業創作者透過盲評來進行評分。因此，評審永遠不知道
2:28.239–2:34.640
hich model produced which output. Thatmeans you can see how models actuallyperform when it comes to the creative
哪個模型產生了哪個輸出。這意味著你可以看到模型在創意
2:34.640–2:41.599
work you're doing. So stop guessingwhich model to use. Explore rankingsbased on real creative work and find the
工作方面的實際表現。所以別再猜測要使用哪個模型。探索基於真實創意工作的排名，並找到
2:41.599–2:50.800
right model for your project and savetime and cost. See the rankings at OpenArt Arena. So if I were to explain Jev
適合你專案的模型，節省時間和成本。請至 OpenArt Arena 查看排名。所以如果我要向你解釋 Jev
2:50.800–2:57.440
to you, I would go to this table on thetype blog post announcing Jev. And itbasically compares normal LLMs on the
，我會前往 Type 部落格上宣布 Jev 的這篇文章。它基本上比較了左側的一般 LLM 和右側的 Jev 風格 LLM。兩者
2:57.440–3:04.560
left, Jev style LLMs on the right. Bothtake in unstructured data as inputs.Both take in text as inputs. Jev does
都將非結構化資料作為輸入。兩者都將文字作為輸入。Jev
3:04.560–3:10.480
not take in images, but it takes in textand it takes in text descriptions ofimages if you really need to get there.
不接收影像，但它接收文字，並且如果你真的需要這樣做，它也會接收影像的文字描述。
3:10.480–3:16.400
The outputs though are very different.With the standard LLMs that you're usedto working with, you are getting strings
不過輸出卻非常不同。對於你習慣使用的標準 LLM，你得到的是字串
3:16.400–3:23.440
and generated text out. So you'regetting text in, text out. With Jev,you're getting text in, type safe values
並產生文字輸出。所以你是輸入文字，輸出文字。使用 Jev，你是輸入文字，輸出類型安全的值
3:23.440–3:30.319
out. And what I mean by type safe valuesis these are values that are predefinedthat then Jev picks from and selects and
而我所說的類型安全的值，是指這些值是預先定義的，然後 Jev 從中挑選、選擇並
3:30.319–3:36.480
returns to you. We will show you whathose values are, but essentiallythey're like it's this or that. It's yes
回傳給你。我們會告訴你這些值是什麼，但基本上它們就像是「是這個或那個」。是「是」
3:36.480–3:44.400
or no. It's 1 through 10. Like it'spretty simple. Now it sounds simple, butit is incredibly powerful when you put
或「否」。是 1 到 10。就像它非常簡單。現在聽起來很簡單，但當你把
3:44.400–3:56.799
it against the right problem.The other thing that you will noticeabout Jeff is it is cheap AF and it isfast AF. And so if you look at the
它應用於合適的問題時，它非常強大。另一個你會注意到關於 Jev 的事是，它便宜到爆，而且快到爆。所以如果你看看
3:56.799–4:04.720
current cost of input and output tokens,it can be pennies to tens or hundreds ofdollars per million output tokens.
目前輸入和輸出記號的成本，每百萬個輸出記號的成本可能從幾分到數十或數百美元不等。
4:04.720–4:14.720
Jev only charges you on input tokensbecause it barely outputs anything. Andit is 4 cents per million input tokens.
Jev 只對輸入記號收費，因為它幾乎不輸出任何內容。每百萬個輸入記號僅收費 4 美分。
4:14.720–4:24.240
It is like dirt freaking cheap. Andbecause they output basically nothing,they don't even charge you for output
它便宜到像泥土一樣。而且因為它們基本上不輸出任何內容，它們甚至不對輸出
4:24.240–4:32.560
okens. Um whereas the standard LM aregoing to charge you tons for the outputokens. And then I'll go into use cases,
令牌。嗯，而標準的大型語言模型會對輸出令牌收取大量費用。然後我會介紹使用案例，
4:32.560–4:39.759
which is how you would use like astandard LLM versus why you would useJev. You know, standard LMS, chat botswhere you want text in, you want text
也就是你如何使用一般的大型語言模型，以及為什麼你要使用 Jev。你知道，一般的大型語言模型、聊天機器人，你想要文字輸入，你想要文字
4:39.759–4:51.440
out. Coding where you want, you know,code to be produced. Um, and so it'sgreat for things where you needstuff generated, but Jev is really great
輸出。編碼方面，你希望產生程式碼。嗯，所以它非常適合需要產生內容的應用，但 Jev 真的非常適合
4:51.440–4:59.440
for making decisions. That's ultimatelyI've been calling this it's like adecision model. So you give it adecision and it makes a decision and and
做決策。這是我一直稱之為決策模型的原因。所以你給它一個決策，它就會做出決策，而且
4:59.440–5:09.039
it's high agency I would say. So if youneed smart if statements, should I goleft or right? If it's good, put it to
它具有高自主性。所以如果你需要聰明的 if 陳述式，我該往左還是往右？如果情況好，把它交給
5:09.039–5:14.320
this person. If it's bad, send it tosupport. All that kind of stuff Jeff isreally good at. It's really good at
這個人。如果情況不好，送給支援團隊。Jeff 非常擅長這類事情。它非常擅長
5:14.320–5:19.759
classifying data. That's a lot of whatI'm going to talk to you about today.And it's really good at real time. So,
資料分類。這是我今天主要要與大家討論的內容。而且它非常適合即時應用。所以，
5:19.759–5:27.680
it is fast, fast, fast. So, you can putan LLM in a real-time loop in a way thatwas not performant enough with these
它很快，很快，很快。因此，你可以將大型語言模型放入即時迴圈中，而這些
5:27.680–5:34.080
other models. I do want to explainexactly what Jev outputs. And so, Iwould highly recommend if you're going
其他模型。我確實想解釋 Jev 具體會輸出什麼。因此，如果你打算
5:34.080–5:40.080
to use Jev, go to their docs. The twoplaces that I'm using the most are theprimitives, so what it returns. And then
使用 Jev，我強烈建議你查看他們的文件。我最常使用的兩個部分是原語（primitives），也就是它返回的內容。然後
5:40.080–5:44.320
I use some of the patterns in cookbooks.So what will Jev return to you? It will
我會使用 cookbooks 中的一些模式。那麼 Jev 會返回給你什麼呢？它
5:44.320–5:50.639
essentially return one of three things.It will return a choice which means youcan give it text, you can give it a list
本質上會返回三種東西之一。它會返回一個選項（choice），這意味著你可以給它文字，也可以給它一組
5:50.639–6:06.160
of choices and it will pick a choice.So, if my question is, what should Iwear on this date and my choice is adress, jeans, workout clothes, ski gear,
選項，然後它會挑選出一個選項。所以，如果我的問題是「約會時該穿什麼」，而我的選項是禮服、牛仔褲、運動服、滑雪裝備，
6:06.160–6:12.560
or nothing,it'll like maybe pick the dress, right?
或「不穿」，它可能會挑選禮服，對吧？
6:12.560–6:22.960
And so that is choice. If I selectscore, it's going to say like how severeis this or on a score from one to five,
這就是選項（choice）。如果我選擇分數（score），它會說「嚴重性有多高」或「在一到五分的分數中，這是幾分」。
6:22.960–6:29.039
what it is. This is really good forseverity rankings. So say the examplehere is a great one which is like if you
這對於嚴重性排名非常有用。所以這裡的範例很棒，比如如果你
6:29.039–6:38.479
get a bug, you want to rank it likecosmetic, broken or blocking, Jev willtriage that bug and give it a score. And
發現一個 bug，你想把它分類為「美觀問題」、「損壞」或「阻塞」，Jev 會對該 bug 進行分級並給出分數。然後
6:38.479–6:45.919
then finally, there's a new. It's aversion of a boolean. It basically tellsyou what the likelihood that the answer
最後，還有一個新的功能。這是布林值（boolean）的一個版本。它基本上告訴你答案
6:45.919–6:53.280
to a question is yes. So if the answerto a question is yes or no, is Claire apodcaster? it's going to give you like a
為「是」的可能性。所以如果問題的答案是「是」或「否」，Claire 是播客主持人嗎？它會給你類似
6:53.280–7:03.520
99% nule because there's a 99% chancethat I am a podcaster. Okay, so this ishow you know you would think about the
99% 的數值，因為我有 99% 的可能性是播客主持人。好的，所以這就是你知道它
7:03.520–7:09.680
three things it can return. And again,very simple, but if you've been asoftware engineer, this is like 90% of
能返回的三種東西的方式。再次強調，這非常簡單，但如果你是
7:09.680–7:17.520
software engineering is like doing thesethings, returning a choice, scoringsomething, routing, saying yes or no.
軟體工程師，這就像 90% 的軟體工程工作一樣，就是做這些事情：返回選項、對某事評分、路由選擇、回答是或否。
7:17.520–7:28.000
And so it is just so so so powerful. Andthe number one thing I have been usingit for is classification of a vast sets
所以它非常非常非常強大。而我使用它的首要用途是對大量
7:28.000–7:34.720
of unstructured data that would havebeen annoying to classify but is veryhigh value. So I'm going to show you
非結構化數據進行分類，這些數據過去分類起來很麻煩，但價值很高。所以我要向你展示
7:34.720–7:41.199
those use cases and hopefully this willinspire you about how you can use Jev.Okay. So I'm going to pull up Codeex and
這些使用案例，希望這能啟發你如何運用 Jev。好的。所以我會打開 Codeex 並
7:41.199–7:49.440
show you like two or three examples thatare super simple to run, incrediblycheap, and incredibly powerful. This is
展示兩三個範例，這些範例運行起來超級簡單、成本極低，而且非常強大。這是
7:49.440–7:56.720
one CTO's, VPs of product, V, you know,chief product officers, listen up. Thisthe one that three years ago I would
給一位首席技術官（CTO）、產品副總裁（VP）、V，你知道的，首席產品官（CPO）聽的。這是
7:56.720–8:08.080
have paid truly $100,000 for. So what Ihad Jev do is look at thousands of PRs.I got PRs. I have connection to to
三年前我會真正願意花 10 萬美元購買的功能。所以我讓 Jev 做的是查看數千個 PR（Pull Request）。我獲取了 PR。我連接了
8:08.080–8:17.199
GitHub. Pull every single PR. And thenwhat I had it do, which would have beenso impractical in the past, is I said
GitHub。拉取每一個 PR。然後我讓它做的，這在過去是不切實際的，是我說
8:17.199–8:26.080
categorize all the PRs. And so use Jevto do pair-wise relations. And so thissomething that I found Jev is really
對所有 PR 進行分類。所以使用 Jev 來進行成對關係比較。所以這是我發現 Jev 非常
8:26.080–8:31.120
good at is compare like P to PRB andsay, are you the same? Are you working
擅長的事情，就是比較 PR A 和 PR B，並說「你們相同嗎？你們是否在
8:31.120–8:33.360
on the same thematic area or not? Again,
同一個主題領域工作？」再次強調，
8:33.360–8:41.039
Jev's not going to tell you what thematic area is unless you give itchoices. is it's going to say like yesthese two are related or no they're not
Jev 不會告訴你主題領域是什麼，除非你給它選項。它會說「是的，這兩個相關」或「不，它們不相關」
8:41.039–8:45.920
and I've been doing this a lot where I'mtaking vast amounts of data and I'msaying like yes related not related yes
，而我一直在做很多這樣的事情，面對大量數據，我說「是的，相關」、「不相關」、「是的，相關」
8:45.920–8:54.399
related not related and it's doingclustering which I find very very usefulso I create these clusters of data sets
「不相關」，它正在進行聚類，我認為這非常非常有用，所以我創建這些數據集的聚類
8:54.399–9:02.160
and that's what Jev did and then I useda really cheap model I use Gemini flashlight to then take those clusters and
，這就是 Jev 所做的，然後我使用了一個非常便宜的模型，我使用 Gemini Flashlight 來對這些聚類
9:02.160–9:13.600
categorize them and I first ran it onour marketing site where we only havelike 112 PRs. It cost me 1.1 cent. Iguess they wouldn't round down that.1
進行分類，我首先在我們的營銷網站上運行它，那裡我們大約只有 112 個 PR。這花費了我 1.1 美分。我想他們不會四捨五入到
9:13.600–9:20.800
penny. And you can see here, it lets mesee how much my work is related tomaintenance, content, tools, site, and
1 美分。你可以在這裡看到，它讓我看到我的工作與維護、內容、工具、網站和
9:20.800–9:28.560
conversion services or docs. Superuseful. Again, it ran very, very, veryfast and very cheap. But then I ran it
轉換服務或文件有多相關。非常有用。再次強調，它運行得非常、非常、非常
9:28.560–9:36.000
on my chat PRD app, which has about2,000 PRs on it to date in this calendaryear. because it has a lot more PRs. It
快，而且非常便宜。但然後我在我的聊天 PRD 應用上運行了它，該應用在今年日曆年裡大約有
9:36.000–9:46.320
cost me a lot more money. And by a lotmore money, I mean nine whole cents. Itcost me 9 cents to do this. It tookprobably about 2 minutes. What it did is
2,000 個 PR。因為它有更多的 PR。這花費了我更多的錢。而更多的錢，我的意思是整整 9 美分。這花費了我 9 美分。這花了
9:46.320–9:58.080
it analyzed 1,700 PRs. It did 17 found17,000pairs in those PRs of things that couldbe matched. And then the themes were
大約 2 分鐘。它做的是分析了 1,700 個 PR。它在這些 PR 中進行了 17,000 對配對，這些是可以匹配的項目。然後主題由
9:58.080–10:10.080
labeled by Gemini Flash Light. And itpulled all of the data out. I mean,again,CTO's, CEOs, like I know you're asked
Gemini Flash Light 標記。它提取了所有數據。我的意思是，再次強調，CTO、CEO，我知道你們被
10:10.080–10:17.760
this by the board and by your team andby your boss all the time, like whatpercentage of work is going to whatinitiatives? Like, tell me the
董事會、團隊和老闆經常問這個問題，比如多少百分比的工作投入到了哪些計劃？比如，告訴我
10:17.760–10:25.279
percentage to tech debt. Tell me thepercentage to this product or thatproduct. And you can see here it gotdown. It excluded our docs PRs because
技術債的百分比。告訴我這個產品或那個產品的百分比。你可以在這裡看到它得出了結果。它排除了我們的文件 PR，因為
10:25.279–10:32.959
those are completely separate. almost30% of the effort the PRs that we do atChat Pured are around platform security
那些是完全分開的。大約 30% 的精力，我們在 Chat Pured 所做的 PR 都是圍繞平台安全
10:32.959–10:38.800
and infrastructure. on being a goodcitizen and investing in securityperformance and infrastructure and then
和基礎設施。成為好公民以及投資於安全性能、基礎設施，然後
10:38.800–10:45.600
of course because we're a chatbot aconversational AI reliability and thensome product areas that we invest in are
當然因為我們是一個聊天機器人、對話式 AI 的可靠性，然後我們投資的一些產品領域是
10:45.600–10:54.720
like data and integrations documentediting and prototyping which is new andso this was so cheap so fast it is
像是資料與整合、文件編輯以及原型設計，這是新的功能，而且非常便宜、快速，
10:54.720–11:00.720
accurate like I can just tell you yesthis is where we're investing our timeand you can even see as we invest more
準確。我可以明確告訴你，我們正在投入時間的地方，你也可以看到，隨著我們投入更多
11:00.720–11:08.640
in different things over the last couplemonths it goes up. And so this is justan example of how powerful, cheap, and
不同的資源，過去幾個月的數據呈現上升趨勢。因此，這只是展示 Jev 在大型資料集上
11:08.640–11:20.320
good Jev can be on large data sets. Andthen I want to give you all one thateven if you don't have like a big repowith PRs everywhere that you can run,
強大、便宜且優秀的一個範例。接著，我想提供給各位另一個範例，即使你沒有像我們這樣
11:20.320–11:29.120
which is you can run this on your localcloud code and codec sessions. So all ofyour cloud code and codec sessions are
擁有大量 PR 的程式碼倉儲可以執行，你仍然可以在本地的 Cloud Code 和 CodeC 工作階段中執行此操作。
11:29.120–11:38.320
stored locally. And so you can actuallyrun this analysis on everything storedon your local machine. So I did that
因為所有的 Cloud Code 和 CodeC 工作階段都是
11:38.320–11:47.680
while Codex kicked off a thread to dothat. And you can see depending on whatyou count as like effort how much I'm
儲存在本地。因此，你實際上可以在本機上儲存的所有資料上執行此分析。所以我這樣做
11:47.680–11:55.120
spending on different things. So I do alot of product engineering. And so forgrouping by user turns in January I was
時，Codex 同時啟動了一個執行該任務的執行緒。你可以看到，根據你如何計算「努力程度」，我在不同事項上
11:55.120–12:03.120
almost exclusively doing engineeringtasks in cloud code and codecs. And thenas you kind of like come into this new
投入的精力。我從事大量的產品工程工作。因此，在一月份按使用者互動分組時，
12:03.120–12:11.440
orld now in September less than 40% itlooks like of my tasks are actuallyengineering tasks. I'm doing a lot more
我幾乎完全在 Cloud Code 和 CodeC 中執行工程任務。然後，當你進入
12:11.440–12:17.279
work with agents which makes a lot ofsense. And then I'm doing a lot morelike publishing media. I'm doing a lot
九月份這個新世界時，看起來不到 40% 的任務是工程任務。我做了更多
12:17.279–12:24.000
of our videos through. And then you cansee I have a new business. So clientdelivery, family and personal stuff. And
把我們的影片發布出去。然後你可以看到我有了新的業務。所以是客戶交付、家庭和個人事務。
12:24.000–12:33.200
then where it couldn't categorize or itwas unclear. If I take it to sessiondays, you can see again most of mysessions were coding. And now it's like
媒體發布的工作。我透過代理程式處理了許多影片。接著你可以看到，我有了新的業務，例如客戶交付、家庭和个人事务。
12:33.200–12:41.440
more equally split across different usecases. And so if you think about this,it is just super useful to do meta
以及無法分類或分類不明確的部分。如果將視角轉到工作階段天數，你可以再次看到，我絕大多數的工作階段都在編碼。現在則像是
12:41.440–12:49.279
nalysis on all this data that's sittingon your desktop that LMS can totallyparse. that will cost you basically no
平均分配在不同的使用案例上。因此，如果你思考這一點，對這些存放在你桌面、LMS 完全可以解析的資料進行元分析
12:49.279–12:57.920
money and give you a lot of insight thatI think previously would have been hardto get. So these are two use cases I
是非常有用的。這幾乎不會花費你任何金錢，卻能帶來許多我認為過去難以獲得的洞察。所以，我認為這兩種使用案例
12:57.920–13:04.880
think everybody who is coding should do.The third one and I won't show itbecause it's a lot of my privateinformation is I did run this on my
是所有編碼人員都應該嘗試的。第三個使用案例，我不會展示，因為這涉及我許多私人資訊，那就是我對我的
13:04.880–13:10.959
personal Gmail. So I basically said likegiven a subject line and a snippetcategorize whether or not I can like
個人 Gmail 執行了此操作。基本上，我說的是，給定主旨行和內容片段，
13:10.959–13:16.720
delete this email like score whether ornot I can delete this email. It did itvery fast and then it gave me very clean
分類是否應該刪除這封郵件，就像評分是否應該刪除這封郵件一樣。它執行得非常快速，然後給了我非常乾淨
13:16.720–13:24.079
tags that I could go through and thenhave another model work throughcategorized emails. And so this is where
的標籤，我可以透過這些標籤進行篩選，然後讓另一個模型處理分類後的郵件。因此，這裡
13:24.079–13:32.959
I would say like Jev alone is okay. Jevwith an LLM buddy is super powerful. Sowhat I like to do with Jab is I like to
我想說，單獨使用 Jev 還可以。但 Jev 搭配 LLM 夥伴則非常強大。所以我喜歡對 Jab 做的是，我喜歡
13:32.959–13:43.040
take a big corpus of information, tagit, categorize it, cluster it, filterit, and then apply really precise AI
處理大量的資訊語料庫，對其進行標籤、分類、聚類、過濾，然後對正確的聚類應用非常精確的 AI
13:43.040–13:50.639
actions to the right clusters. And sothat could be take your bugs, take yourhigh severity ones and really triage
操作。這可能包括處理你的錯誤，針對高嚴重性的錯誤進行深入的分類處理。也可能包括處理你的郵件，將它們分組為可以
13:50.639–13:56.079
them deeply. It could be take youremails, group them into like you candefinitely delete them and ignore them,
明確刪除並忽略的郵件，刪除它們，然後使用代理程式處理其他郵件。這些都是你可以做的
13:56.079–14:05.199
delete them, and then work your otheremails with an agent. It's just allthose things that you can do where avery fast but accurate filter can be
事情，其中快速且準確的過濾器可以發揮幫助作用。所以，我認為這非常
14:05.199–14:16.240
helpful. So, I think this is sopowerful. I hope it unlocks your mindson what Jev can do. I want to show youlike the biggest version of this that
強大。希望它能激發大家對於 Jev 能做些什麼的靈感。我想展示一下我做的最大規模版本，
14:16.240–14:23.040
I've done just to like kind of give younumbers and ideas of what I'm working onand then I'll show a couple like fun
只是想提供一些數字和想法，讓你們了解我正在處理的內容，接著我會展示幾個有趣的
14:23.040–14:32.079
little apps that you can build with Jevthat I think would have been hard tobuild before. So the example I want to
小應用程式，這些是用 Jev 建構的，我認為在過去很難建構。所以我想提供的範例是，各位應該聽過我談論
14:32.079–14:39.920
give is you all have heard me talk abouthis product. So chat PD I'm trying tobuild like a product insights graph.
這個產品。所以 Chat PD 我正在嘗試建構一個產品洞察圖譜。
14:39.920–14:47.360
Basically, I'm like trying to suck ineverybody's data and tell you reallyinteresting insights about it. And I
基本上，我試圖吸收所有人的資料，並告訴你們關於它的有趣洞察。我
14:47.360–14:52.160
have used every frontier model there isto try to figure out. And it's justreally hard. There's just too much
使用了所有現有的前沿模型來嘗試解決這個問題。這真的
14:52.160–14:59.680
nuance in all the data to kind of likegro all this, get it in structuredformat, do the right like structured,
很難。資料中的細微差別太多了，難以全部掌握，將其轉化為結構化格式，進行正確的結構化與
14:59.680–15:06.160
unstructured. It's super expensive. I'vespent thousands of dollars if not tensof thousands of dollars trying to
非結構化處理。這非常昂貴。我花費了數千美元，甚至數萬美元來嘗試
15:06.160–15:16.710
prototype this. It's just hard. And Ithink I cracked this baby with Jev. Ikind of want to show you what thatmeans,
原型設計這個功能。這真的很困難。我認為我透過 Jev 破解了這個難題。我
15:16.720–15:26.480
which is it's not that I like Jev hasdone it all at all. It is actually Ihave figured out where classification,
並不是說我喜歡 Jev 把所有事情都做了。而是我實際上已經弄清楚如何分類、
15:26.480–15:35.120
clustering, and mapping are importantand I put Jeff there. And then I figureout where like the big brains like
這並不是說 Jev 獨自完成了所有工作。而是我實際上搞清楚了分類、
15:35.120–15:45.120
analysis,strategy, insights, extraction, and Iput like Astra there. And then likewhere generation is important, I use
聚類和映射的重要性，並將 Jev 放在那裡。然後我搞清楚了像
15:45.120–15:51.040
like a soul or a Luna. And so if youlook at this map and it's like probablynot that interesting to you all because
分析、策略、洞察、提取這樣的大腦工作，並將 Astra 放在那裡。然後像是生成方面，我使用
15:51.040–16:01.120
it's very internal to chat pd but youcan see like I extract things, I extractlabels, I then quickly assign everything
像 Soul 或 Luna 這樣的模型。所以如果你看這張地圖，這對各位來說可能
16:01.120–16:10.079
to those labels with Jev, then I pairthose clusters in those groups withAstra and I say like what the heck'sgoing on across all of this. And then it
不太有趣，因為它非常內部化於 Chat PD，但你可以看到我提取內容，提取標籤，然後迅速使用 Jev 將所有內容
16:10.079–16:17.040
extracts more insights and all of asudden I have this really cool product.And just to give you a sense of like the
提取更多洞察，突然間我就有了這個非常酷的產品。為了讓大家對這裡的
16:17.040–16:26.639
scope of data here, I'm probably pullingabout a,00and um, 1100 individualsignals into this. So these are PRs,
數據範圍有個概念，我大概從大約一千個，嗯，1100 個個別信號中提取數據。所以這些是 PR，
16:26.639–16:36.000
these are support tickets, these aregranola conversations,these are um, linear tickets. They'relike kind of like all the signals in our
這些是支援工單，這些是 Granola 對話，這些是嗯，Linear 工單。它們就像我們
16:36.000–16:45.040
business about what people are worriedabout and what is going on. And then I'mgetting now like this gap between what
業務中關於人們擔心什麼以及正在發生什麼的所有信號。然後我現在得到的是
16:45.040–16:51.920
are customers telling us they want andwhat are we actually working on and Ican get those trends over time I can get
客戶告訴我們他們想要什麼與我們實際上在處理什麼之間的差距，我可以獲得這些隨時間變化的趨勢，我可以
16:51.920–17:04.640
hem categorizedI can get details about them so it'sjust really super fascinating veryhelpful has made this feature I would
將它們分類，我可以獲得關於它們的詳細資訊，所以這真的非常迷人，非常有用，我認為這個功能
17:04.640–17:12.640
say probably more margin accretive to mybusiness if you all know what that meansbecause before I was really like trying
可能會為我的業務帶來更多的利潤，如果你們知道這意味著什麼的話，因為之前我真的像是在嘗試
17:12.640–17:19.760
to brute force with these like brainymodels, what I needed to work on and howI could do this data. And now I can like
用這些像聰明模型這樣的方式強行解決我需要處理什麼以及我如何處理這些數據的問題。現在我可以像
17:19.760–17:27.520
combine these like very cheap decisionmodels like Jev with a really brainymodel and get this like interesting
結合這些非常便宜的決策模型，比如 Jev，與一個非常聰明的模型，並獲得這種像
17:27.520–17:38.400
completely hard to generate set of datacontext like auto wiki. It's like reallyunlocked this product and it has taken
完全難以生成的數據上下文集合，比如自動維基。它就像真正解鎖了這個產品，並且它花了
17:38.400–17:50.160
about,00raw sources of data and done over like200,000classification and pair-wise groupingsand it probably cost me four bucks on
大約一千個原始數據來源，並進行了超過二十萬次的分類和成對分組，這在
17:50.160–17:57.840
the Jeff side. It's cost me a lot moreon the Astra side. But I just think likethinking through where classification
Jev 方面可能只花了我四美元。在 Astra 方面花的錢要多得多。但我只是認為像思考分類
17:57.840–18:05.600
like super smart classificationclustering decisions could unlock reallycomplex products and how you might use
像超級智能的分類聚類決策可以解鎖非常複雜的產品，以及你可能會如何使用
18:05.600–18:13.360
as I'm showing here Jev alongside somesmarter models. It's like really blowingmy mind right now and I think is it
正如我在這裡展示的，Jev 與一些更聰明的模型並排使用。它現在真的讓我驚嘆不已，我認為這
18:13.360–18:22.480
should be interesting for those outhere building interesting dataproducts. So, so far I've told you likewhat Jeb is, how you can use it on PR
對於那些在外面構建有趣數據產品的人來說應該很有趣。所以，到目前為止，我告訴你們像
18:22.480–18:28.559
review or data analysis, how you can useit on your local sessions, how you couldeven use it on your email, but I'm going
審查或數據分析，如何在本地會話中使用它，甚至如何在電子郵件中使用它，但我打算
18:28.559–18:40.160
to show you two things that I built withit in an afternoon that I think areally cool. And this is my attempt tolike go against the like eyepopping
審查或數據分析中如何使用它，你可以在本地會話中如何使用它，你甚至可以
18:40.160–18:48.000
demos that you're seeing on X. I thinkpeople have shown interesting things,but they actually haven't shown how you
在電子郵件中使用它，但我將向你展示兩件事，是我在一個下午用
18:48.000–18:55.120
have to build it and how it works to gethat real-time effect. And so, I'm goingto show you these apps live because I
它構建的，我認為非常酷。這是我的嘗試，去對抗你在 X 上看到的像
18:55.120–18:59.360
think it's important to kind of likeunderstand where the latency comes fromand what the real experience is as
令人眼花繚亂的演示。我認為人們展示了有趣的事情，但他們實際上沒有展示如何
18:59.360–19:08.320
opposed to like kind of a 30-secondond demo that goes goes viral. ButI do think it shows some like prettycool stuff. So the first one I want to
而不是像那種會病毒式傳播的 30 秒演示。但我確實認為它展示了一些相當酷的東西。所以第一個我想
19:08.320–19:21.520
do ishow I AI audience signals. So youwonderful people give us comments on thehow I AI podcast and I go through them.
現場展示這些應用程式，因為我認為了解延遲來自哪裡以及真實體驗是什麼
19:21.520–19:31.120
I read them every day and I reply tothem when I can, but I've never doneanalysis on them. And there's about4,500 comments and I just wanted to know
很重要，而不是像那種會病毒式傳播的 30 秒演示。但我確實認為它展示了一些像
19:31.120–19:34.320
like are they good, are they bad, arethey happy, are they sad? And I also
很酷的東西。所以，我想做的第一個是展示我如何 AI 觀眾信號。所以你們這些
19:34.320–19:43.120
wanted to know if you all had anyepisode ideas for me where I couldextract them and come up with episodesthat I could do like this Jeff one. And
想知道你們所有人是否有任何節目構思給我，我可以從中提取出來，並想出像這樣的 Jeff 節目。並且
19:43.120–19:54.320
so I hooked up the YouTube V3 API. Youjust have to enable it in Googleconsole. And then I said pull all thecomments and categorize them into
所以我接上了 YouTube V3 API。你只需要在 Google 控制台中啟用它。然後我說拉取所有評論並將它們分類為
19:54.320–19:58.799
positive, negative or neutral. So thatwould be a choice, right? Or a score
分析。大約有 4,500 條評論，我只是想知道
19:58.799–20:06.240
probably. And then I said also use Jevto identify whether or not includes aidea for a future episode and then
可能。然後我說也使用 Jev 來識別是否包含未來節目的想法，然後
20:06.240–20:16.720
analyze all the data and give me adashboard to look at the data. And soyou can see here about half arepositive. There are 58 comments in here
分析所有數據並給我一個儀表板來查看數據。所以你可以看到這裡大約一半是正面的。這裡有 58 條評論
20:16.720–20:24.480
with episode ideas and then there's likequality praise which is praise for ourproduction team which is like how nice
提取它們並提出我可以像這個 Jeff 一樣做的節目。所以
20:24.480–20:33.679
the podcast looks. And so then it mademe this dashboard which is telling me isit positive, neutral, negative or mixed.
我連接了 YouTube V3 API。你只需要在 Google
20:33.679–20:39.919
And then it gave me an audience requestboard. So you all want like openw weightmodels, you want instinct, you really
接著它為我生成了一個觀眾需求看板，所以大家都想要開放權重模型、想要本能，你們真的
20:39.919–20:46.000
want a comparison of Grock and Muse forpersonal use. That's coming soon. Andthen do I use Grock or Brockbot or like
正面、負面或中性。所以這將是一個選擇，對吧？或者一個分數
20:46.000–20:49.120
what am I using? And then you can also
可能。然後我說也使用 Jev
20:49.120–21:00.799
see by episode the sentiment which isRyan's very popular three-step AI codingworkflow. 61%positive. We had an awesome cloud code
你可以按集數查看情緒分數，這是瑞恩非常受歡迎的三步 AI 編碼工作流程，正面評價佔 61%。我們有一場非常棒的雲端程式碼 </think> 生成產品經理集數的評論，正面評價佔 80%。接著我們可以實際檢視這些評論本身。我使用 Jev 針對這些評論建立即時搜尋功能。所以，如果我想尋找關於螢幕分享的評論，它會迅速搜尋並找到提到螢幕分享的內容。如果我想查詢關於「slop」的評論，我可以輸入 slop
21:00.799–21:13.520
for product managers episode. 80%positive comments. And then we canactually go through the commentsthemselves. And I using Jev built live
分析所有數據並給我一個儀表板來查看數據。所以你可以看到這裡大約一半是
21:13.520–21:23.440
search against these comments. So, if Iwanted to find comments about screenshare, it would search and find very
正面的。這裡有 58 條帶有節目構想的評論，然後還有像
21:23.440–21:30.159
quickly something that says screenshare. If I wanted to say, "What are thecomments about slop?" I could do slop
質量讚揚，這是對我們製作團隊的讚揚，就像播客看起來
21:30.159–21:41.200
and then very quickly it scanned all4,400 and found all the comments thatare related to slop. So, again, this is
然後它迅速掃描了全部 4400 筆資料，並找出所有與 slop 相關的評論。所以，再次強調，這是
21:41.200–21:47.280
very performant, very fast. just forlike behind the scenes, you have to likebatch the results and then score them
效能非常好，速度也非常快。僅從幕後技術角度來看，你需要將結果分批處理，然後對它們進行評分
21:47.280–21:53.039
and then push the high scores up to gethat kind of performance. So, there isome architecture here. I don't want to
然後將高分結果推送上去以達到這種效能。所以這裡涉及一些架構設計。我不想假裝說
21:53.039–21:59.840
pretend like Jev you just like slap Jevin the middle and evaluate all 4,400that quickly, but you can imagine this
別假裝說傑夫只是隨便拍拍傑夫的中間，就能那麼快評估完全部4400筆資料，但你可以想像這
21:59.840–22:07.120
very useful. And so, one, thanks forall the comments. And two, this wasreally, really cool to build. And then
非常實用。首先，感謝大家的留言。其次，這真的非常酷，是我很想打造的東西。然後
22:07.120–22:16.480
finally, I have to give Astra props.Really nailed the Howa AI podcastyling. So, good job on the front end,but I want to show one last one before I
最後，我必須給 Astra 掌聲，他們真的把 Howa AI 的播客風格拿捏得恰到好處。前端做得很好，但在我帶大家離開之前，我想再展示最後一個案例
22:16.480–22:24.480
get us out of here. And then I said thiswas Jev week. So, like and subscribe fora second follow-up Jev episode with one
帶我們離開這裡。然後我說這是傑夫週，所以請按讚並訂閱，稍後會有一集傑夫系列的後續節目，邀請一位
22:24.480–22:34.480
of our How Ia guests that's going tocome later this week. But I wanted toshow like kind of a fun realtimeuse case for Jev. So if you have been
我們這週晚些時候還會有另一位 How Ia 來賓，但我想展示一個 Jev 有趣的即時應用案例。所以如果你看過 Jev 這些即時應用，像是玩電玩或俄羅斯方塊，或是即時搜尋，這些都是你可以做的很多即時任務，其中快速做出決策或提供一組選項可以非常強大。
22:34.480–22:42.480
seeing any of these like real time applications of Jev like playing a videogame or you know playing playing Tetris
看到這些即時應用，像是玩電玩或俄羅斯方塊
22:42.480–22:51.200
doing live search all these things youcan do a lot of real time stuff wheremaking a very quick decision oreturning a set of choices can be very
進行即時搜尋等各種應用，你可以處理許多即時任務，其中快速做出決策或提供一組選項可以非常強大
22:51.200–23:01.200
powerful. Okay, so I built a real timeapp that takes in voice and then usesJev to determine what color is like my
很強大。好的，所以我開發了一個即時應用程式，它會接收語音，然後使用 Jev 來判斷我的情緒色彩，接著回傳一句反映我情緒的格言。
23:01.200–23:13.280
emotion and then returns a quote inreflection of my emotion. It usesOpenAI's real time voice API. It usesJev and then it uses like API ninja
然後判斷我的情緒，並根據我的情緒回傳一句名言。它使用了 OpenAI 的即時語音 API、Jev，以及 API Ninja 的名言 API。
23:13.280–23:19.919
quote API. There's apparently an API forquotes. And so we're going to see ifthis works and how fast it is. I'm
quote API。據說有個專門提供名言的 API。所以我們來看看這招行不行得通，以及它的速度有多快。我
23:19.919–23:39.679
feeling so tired today.I'm feeling very happy today.Okay, the quotes. Oh, there wesee the quote worked. Okay, hold on. Oh,
今天覺得好累。我今天很快樂。好，看看這些名言。哦，看來名言功能運作正常。好，等一下。哦，
23:39.679–24:10.159
it's excited.I'm in love today.I can't wait for the weekend.Doing this podcast is the best job ever.Look at that. So, we got all these Okay,
它很興奮。我今天很開心。我迫不及待想迎接週末。做這個播客是最棒的工作。你看。所以，我們得到了這些。好，
24:10.159–24:16.240
I'm going to turn it off because it'sgoing to like keep listening to me. Lookat all these quotes where likeimmediately it picked the right color.
我要把它關掉，因為它會一直聽我說話。看看這些引號，它立刻選對了顏色。
24:16.240–24:23.279
It picked the right quote. You can tellthe quote API has a little latency init, but you can imagine how fun it would
它選取了正確的引文。你可以看出引文 API 內部有一點延遲，但你可以想像這會有多有趣
24:23.279–24:32.000
be to be able to build these real timexperiences. And again, when it's likepick the right color to match thesentence, it can do that really well.
就能夠打造這些即時體驗。而且當它需要挑選與句子相符的顏色時，也能做得非常出色。
24:32.000–24:39.039
Just for you all to know kind of how itworks, I gave it a list of hex values.So, a list of colors. Every sentence or
只是讓各位了解運作方式，我給它一組十六進位色碼，也就是一組顏色清單。每個句子或
24:39.039–24:47.520
like phrase it ingests from thereal-time API, it asks what color or itscores the colors. It gives the topranking score color and then it also
像是從即時 API 接收到的字詞或片語，它會詢問該使用什麼顏色，或是為這些顏色評分，然後提供得分最高的顏色，接著它
24:47.520–24:56.880
picks from a set list of filters for thequote API. So it filters the quotes andthen it scores them against my sentiment
它會從一組預設的篩選條件中，為引文 API 挑選篩選器，藉此篩選引文，然後根據我的情感分析對它們進行評分
24:56.880–25:03.520
and then it shows it on the screen. Soagain like running locally not thefastest. I'm sure I could optimize it by
然後它會顯示在螢幕上，所以再次強調，在本地運行速度不是最快的，我相信我可以透過快取大量這些資料來進行最佳化
25:03.520–25:10.960
caching a bunch of this stuff and youknow doing all sorts of things but againlike a little like magic use case that I
透過快取這些資料並執行各種優化手段，這又是一個類似魔法的小應用案例，我
25:10.960–25:24.159
think is really indicative of the kindsof things you can build with Jeb. Sothat is my Jev 101. How to use it onyour own data how to use it with other
我認為這非常能代表用 Jeb 能建構的各類應用，所以這就是我的 Jeb 一百堂課，教導大家如何在自己的資料上使用它、如何與其他模型搭配，以及如何在應用程式中打造一些有趣的即時體驗。再次強調，就像我剛才說的，這是本週的 Jeb 第一集，所以我們週中還會邀請一位最受歡迎的來賓推出新一集。如果你對那集節目感到興奮，請訂閱、留言，告訴我你希望我們討論什麼、我可以解答哪些問題
25:24.159–25:33.679
models and how to build some funrealtime experiences in your app intro.Again, as I said, this is Jev episode
模型，以及如何在您的應用程式中建立一些有趣的即時體驗，這是介紹部分。再次強調，正如我所說，這是 Jeb 第一集
25:33.679–25:40.720
number one this week. So, we're going tohave another one midweek with one of ourmost popular guests. So, if you are
本週的第一集，所以我們週中還會有一集，邀請我們最熱門的來賓之一，所以如果你對那集節目感到興奮，訂閱、留言，告訴我你想讓我們討論什麼，我可以回答關於 Jeb 的什麼問題。
25:40.720–25:47.279
xcited about that episode, subscribe,comment, let me know what you want us totalk about, what questions I can answer
對這集節目感到興奮的話，請訂閱、留言，告訴我你們希望我們討論什麼主題，以及有哪些問題我可以解答
25:47.279–25:58.640
about Jeb. And until then, it has beenso nice showing you my favorite newmodel. Thanks for joining How I AI.Thanks so much for watching. If you
關於傑布。在那之前，很高興能向大家展示我最喜歡的新模型。感謝大家收看《How I AI》，非常感謝您的觀看。如果您
25:58.640–26:05.919
enjoyed this show, please like andsubscribe here on YouTube, or evenbetter, leave us a comment with yourthoughts. You can also find this podcast
喜歡這集節目的話，請在 YouTube 上按讚並訂閱，或者更棒的是，留下您的想法與評論。您也可以在這個播客
26:05.919–26:13.039
on Apple Podcasts, Spotify, or yourfavorite podcast app. Please considerleaving us a rating and review, which
在 Apple Podcasts、Spotify 或您喜愛的播客應用程式上。請考慮給我們評分和撰寫評論，這
26:13.039–26:25.000
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影片筆記：I’m using Jev more than Opus 5.5 or GPT-6. Here’s why.

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一句話總結

講者介紹並演示了 Typesafe AI 推出的決策模型 Jev，強調其作為「快速、便宜、不生成自由文本」的特性，透過返回類型安全值（Choice, Score, Vote）來處理大規模數據分類、聚類與路由決策，成本極低且速度極快，適合實時循環與智能條件判斷，而非內容生成。

核心重點

  1. Jev 模型定位：
  • Jev 是一個決策模型（Decision Model），具有高代理權（High Agency）。
  • 核心優勢：極低成本（僅對輸入 Token 收費，輸出幾乎為零，約每百萬輸入 Token 4 美分）、極高速度（適合實時循環）、高有效性。
  • 與傳統 LLM 不同，Jev 不生成自由文本，而是返回預定義的「類型安全值」（Type Safe Values）。
  1. 技術架構與輸出類型：
  • 輸入：接受非結構化數據和文本輸入（不接受圖像，但可接受圖像的文本描述）。
  • 輸出：
  • Choice（選擇）：從預定義列表中選擇一項。
  • Score（評分）：對嚴重程度或等級進行評分（如 1-5 分，或 Bug 分類）。
  • Vote（投票/布林變數）：判斷問題答案為「是」的可能性百分比。
  1. 適用與不適用場景：
  • 適用：需要智能條件判斷、數據分類與聚類、實時處理、路由決策（如好轉給某人，壞轉給支援）。
  • 不適用：需要生成大量內容（如聊天機器人對話生成、代碼生成）或自由文本輸出的場景。
  • 工作流建議：使用 Jev 進行大規模數據的標籤、分類、聚類和過濾，再結合其他 LLM（如 Gemini Flashlight）對精確簇執行具體 AI 動作。
  1. 實戰應用案例：
  • GitHub PR 分析：利用 Jev 進行兩兩比較判斷相關性，聚類後由 Gemini Flashlight 標記主題。成本極低（幾美分），速度快。
  • 本地雲端代碼會話分析：分析本地 Cloud Code 和 CodeC sessions 數據，了解時間分配趨勢（如從純工程轉向 Agent 開發）。
  • 個人郵件分類：對 Gmail 主題行和片段進行刪除評分與分類。
  • 複雜數據產品架構：結合 Jev（分類/標籤）、Astra（分析/策略）、Soul/Luna（生成）處理大量數據信號，成本大幅降低。
  • 即時應用：AI 觀眾信號分析（YouTube 評論情感分類）與情緒驅動的名言生成器（結合 OpenAI 即時語音 API）。

詳細大綱

A. Jev 模型概述

  • 背景：近期 AI 領域新模型頻發（提及 Opus 55, GPT6 Soul, GPT6 Luna, Muse, Grock bots），但 Jev 因其「快速、便宜、不生成系統一決策模型」的特性受到關注。
  • 定義：Jev 是一個決策模型，具有高代理權。
  • 核心優勢：
  • 極低成本：僅對輸入 Token 收費，輸出幾乎為零，價格約為每百萬輸入 Token 4 美分。
  • 極高速度：適合實時循環。
  • 高有效性：在個人生產力、代碼、產品用例中爆炸式增長。

B. Jev 與傳統 LLM 的技術對比

  • 輸入：兩者均接受非結構化數據和文本輸入。Jev 不接受圖像，但可接受圖像的文本描述。
  • 輸出差異：
  • 傳統 LLM：輸入文本，輸出字符串/生成文本（Text in, Text out）。
  • Jev：輸入文本，輸出「類型安全值」。這些值是預定義的，Jev 從中選擇並返回。
  • Jev 的三種返回類型：
  1. Choice（選擇）：從預定義列表中選擇一項。
  2. Score（評分）：對嚴重程度或等級進行評分。
  3. Vote（投票/布林變數）：判斷問題答案為「是」的可能性百分比。

C. Jev 的應用場景與價值主張

  • 適用場景：需要智能條件判斷、數據分類與聚類、實時處理、路由決策。
  • 不適用場景：需要生成大量內容的場景、需要自由文本輸出的場景。
  • 工作流建議：使用 Jev 進行大規模數據的標籤、分類、聚類和過濾，結合其他 LLM 對 Jev 分類後的精確簇執行具體 AI 動作。

D. 實戰案例演示（Codeex 環境）

  1. GitHub PR 分析（行銷網站）：
  • 數據量：112 個 PR。
  • 成本：約 1.1 美分（或 1 penny，疑點）。
  • 方法：Jev 進行兩兩比較判斷相關性，聚類後由 Gemini Flashlight 標記主題。
  1. GitHub PR 分析（Chat PRD App）：
  • 數據量：約 2,000 個 PR，分析 1,700 個 PR，進行 17,000 對比較。
  • 成本：9 美分。
  • 耗時：約 2 分鐘。
  • 結果：揭示工作投入比例。
  1. 本地雲端代碼會話分析：
  • 數據來源：本地存儲的 Cloud Code 和 CodeC sessions。
  • 發現：時間分配從 1 月幾乎全為工程任務，轉變為 9 月不到 40% 的工程任務，增加 Agent 開發、媒體發布、客戶交付等。
  • 價值：對桌面數據進行元分析，成本幾乎為零。
  1. 個人 Gmail 分類：
  • 方法：基於主題行和片段，評分是否可刪除，生成乾淨標籤。
  • 結論：Jev 單獨使用尚可，但與 LLM 夥伴結合更強大。

E. 複雜數據產品架構與即時應用（片段 2）

  1. Chat PD 產品洞察圖譜：
  • 數據規模與來源：約 1,100 個信號（PR、支援工單、Granola 對話、Linear 票證）。
  • 模型分工策略：
  • Jev：負責分類、聚類、標籤分配。
  • Astra：負責大腦分析、策略、洞察提取、配對集群。
  • Soul / Luna：負責生成任務。
  • 成效與成本：處理 200,000+ 分類與成對分組，Jev 側成本約 4 美元。
  1. 即時應用案例一：播客觀眾信號儀表板：
  • 數據來源：YouTube V3 API 抓取約 4,500 條評論。
  • 處理流程：情感分類（正面、負面、中性）、靈感提取。
  • 輸出結果：情感儀表板、觀眾請求板、即時搜尋功能。
  1. 即時應用案例二：情緒驅動的名言生成器：
  • 技術堆疊：OpenAI 即時語音 API、Jev、API Ninja Quote API。
  • 運作邏輯：輸入語音，Jev 判斷情緒並對應到 HEX 顏色值，根據顏色篩選名言 API，對名言進行情感評分並顯示。
  • 演示結果：即時回應疲倦、快樂、興奮、戀愛中等情緒。
  1. 總結與後續預告：
  • Jev 101 核心回顧。
  • 預告週中將播出與熱門嘉賓的 Jev 專題訪談。

工具 / 模型 / 名詞整理

  • Jev / Jeb / Jeb：Typesafe AI 推出的決策模型（逐字稿中名稱拼寫有變異，標記為疑點）。
  • Typesafe AI：Jev 模型的開發公司。
  • Opus 55：提及的新模型（疑點，需查證）。
  • GPT6 Soul：提及的新模型（疑點，需查證）。
  • GPT6 Luna：提及的新模型（疑點，需查證）。
  • Muse：提及的新模型。
  • Grock bots / Grock / Brockbot：提及的模型/產品（疑點，可能為 Grok 的誤聽或特定產品）。
  • Open Art Arena：創意智能全球排行榜平台。
  • Versel：提供 AI gateway 的公司（疑點，可能指 Vercel）。
  • Codeex：講者演示代碼的環境或工具。
  • GitHub：代碼託管平台。
  • Gemini Flashlight：用於對 Jev 聚類結果進行最終分類的模型。
  • Cloud Code：雲端代碼環境。
  • CodeC sessions：本地存儲的代碼會話數據。
  • Chat PRD app / Chat PD：講者提到的應用程序/產品洞察圖譜。
  • Chat Pured：提及的應用程序名稱（疑點，可能指 Chat PRD 或其他）。
  • Gmail：電子郵件服務。
  • Astra：用於分析、策略、洞察提取的模型。
  • Soul / Luna：用於生成任務的模型。
  • YouTube V3 API：用於抓取評論的 API。
  • Google Console：用於啟用 API 的平台。
  • OpenAI's real time voice API：用於即時語音輸入的 API。
  • API Ninja Quote API：用於獲取名言的 API。
  • Linear：提及的票證系統。
  • How I AI：播客名稱。
  • Howiaipod.com：網站。
  • Apple Podcasts / Spotify：播客平台。
  • Granola conversations：提及的數據信號類型（疑點，可能為聽寫錯誤）。
  • margin accretive：商業術語，指增加利潤/價值。
  • auto wiki：提及的上下文功能（疑點）。

操作流程整理

流程一：GitHub PR 分類與聚類

  1. 數據獲取：從 GitHub 獲取 PR 列表（如 112 個或 1,700 個 PR）。
  2. 兩兩比較：使用 Jev 對 PR 進行兩兩比較（Pair-wise relations）以判斷主題相關性。
  3. 聚類：根據相關性進行數據聚類。
  4. 最終分類：將聚類結果輸入 Gemini Flashlight，由該模型標記具體主題（如維護、內容、工具等）。
  5. 結果分析：分析成本（幾美分）與時間，並檢視工作投入比例。

流程二：本地雲端代碼會話分析

  1. 數據收集：從本地存儲獲取 Cloud Code 和 CodeC sessions 數據。
  2. Jev 處理：使用 Jev 對會話數據進行分析。
  3. 趨勢洞察：識別時間分配趨勢（如工程任務比例變化、Agent 開發增加等）。
  4. 元分析：對桌面數據進行元分析，獲取以往難以獲取的洞察。

流程三：複雜數據產品架構（Chat PD）

  1. 信號收集：收集 PR、支援工單、Granola 對話、Linear 票證等數據信號（約 1,100 個）。
  2. Jev 分類：使用 Jev 進行快速且準確的分類、聚類與標籤分配。
  3. Astra 分析：使用 Astra 進行高階分析、策略與洞察提取、配對集群。
  4. Soul/Luna 生成：使用 Soul 或 Luna 進行生成任務。
  5. 成本與成效評估：監控分類與分組成本（約 4 美元），評估原型開發成本降低效果。

流程四：即時應用 - 情緒驅動名言生成

  1. 語音輸入：透過 OpenAI 即時語音 API 接收用戶語音。
  2. 情緒判斷：Jev 判斷情緒並對應到 HEX 顏色值。
  3. 名言篩選：根據顏色篩選 API Ninja Quote API 的過濾器。
  4. 情感評分與顯示：對名言進行情感評分並即時顯示。

值得注意的限制或風險

  • 模型名稱不確定性：影片中提及的模型名稱（如 Opus 55, GPT6 Soul, GPT6 Luna, Grock bots, Brockbot）可能存在辨識錯誤，實際產品名稱需查證。
  • Jev 名稱變異：Jev 在逐字稿中被聽寫為 Jeb/Jeb，需確認正確名稱。
  • 成本描述不一致：關於行銷網站 PR 分析成本，逐字稿先說 1.1 cent 後說 1 penny，需確認實際計費邏輯。
  • 數據比較對數語法混亂：關於比較對數的描述（17 found 17,000 pairs），語法略顯混亂，需確認是否為 17,000 對。
  • 術語辨識疑點：如 "99% nule" 可能為 "99% value" 或 "99% vote" 的聽寫錯誤；"Granola conversations" 可能為特定術語或聽寫錯誤。
  • Jev 不適用場景：Jev 不適用於需要生成大量內容或自由文本輸出的場景，錯誤使用可能導致效能低下或結果不符預期。

逐字稿辨識疑點

  • Jev / Jeb / Jeb：逐字稿中模型名稱在 "Jev", "Jeb", "Jeb" 之間跳躍，需確認正確名稱。
  • Opus 55：模型名稱，需查證是否存在此版本或型號。
  • GPT6 Soul：模型名稱，需查證是否存在此版本或型號。
  • GPT6 Luna：模型名稱，需查證是否存在此版本或型號。
  • Grock bots / Grock / Brockbot：模型名稱，需查證是否為 Grok 的誤聽或特定產品。
  • Versel：公司名稱，可能為 Vercel 的聽寫錯誤，但依規則標記為疑點。
  • Chat Pured：應用名稱，可能為 Chat PRD 或其他名稱的誤聽。
  • 1.1 cent / 1 penny：關於行銷網站 PR 分析成本的描述，逐字稿先說 1.1 cent 後說 1 penny，需確認實際計費邏輯或口誤。
  • 17 found 17,000 pairs：關於比較對數的描述，語法略顯混亂，需確認是否為 17,000 對。
  • 99% nule：可能為 "99% value" 或 "99% vote" 的聽寫錯誤。
  • Jab：在結尾處提及 "What I like to do with Jab"，可能為 Jev/Jeb 的聽寫錯誤。
  • Granola conversations：逐字稿中提及 "these are granola conversations"，在數據信號語境下，"granola" 可能為聽寫錯誤，需查證其具體指涉。
  • margin accretive：講者使用此詞形容功能對業務的貢獻，雖為商業術語，但在技術討論中較少見，確認其語境為「增加利潤/價值」。
  • auto wiki：講者提到 "context like auto wiki"，可能指自動生成的維基百科式內容或特定功能名稱，需查證是否為特定產品功能。

可延伸追問

  • Jev 模型的具體定價結構與計費方式為何？
  • Jev 與傳統 LLM 在技術架構上的根本差異是什麼？
  • 如何優化 Jev 與其他 LLM（如 Gemini Flashlight, Astra, Soul, Luna）的協作流程？
  • Jev 在哪些特定行業或應用場景中表現最為出色？
  • 如何確保 Jev 返回的類型安全值的準確性與可靠性？
  • Jev 的實時處理能力對系統架構有何要求？
  • 如何監控和評估 Jev 在大規模數據處理中的成本效益？
  • Jev 的未來發展路線圖與 Typesafe AI 的戰略規劃為何？

生字列表

生字讀音類型中文
cheap AF/tʃiːp ˌeɪ ˌɛf/adjective phrase便宜到爆、極度便宜
fast AF/fæst ˌeɪ ˌɛf/adjective phrase快到爆、極度快速
triage/ˈtriːɑːʒ/verb分級、篩選分類
agency/ˈeɪdʒənsi/noun自主性、行動力
clustering/ˈklʌstərɪŋ/noun聚類、群集
corpus/ˈkɔːrpəs/noun語料庫、文集
accretive/əˈkretɪv/adjective增加價值的、帶來增益的
nuance/ˈnuːɑːns/noun細微差別、微妙之處
brute force/bruːt fɔːrs/noun phrase強行解決、暴力破解
latency/ˈleɪtənsi/noun延遲、延遲時間
primitives/ˈprɪmɪtɪvz/noun原語、基本類型
whirlwind tour/ˈwɜːrlwɪnd tʊr/noun phrase快速導覽、走馬看花
unstructured/ʌnˈstrʌktʃərd/adjective非結構化的
trending/ˈtrɛndɪŋ/adjective熱門的、趨勢中的
subsidized/ˈsʌbsɪdaɪzd/adjective補貼的、補助的
hype/haɪp/noun炒作、誇大宣傳
blind evaluations/blaɪd ˌiːvəljuˈeɪʃənz/noun phrase盲評、雙盲評估

生字解說

cheap AF /tʃiːp ˌeɪ ˌɛf/

adjective phrase · C1

意思：便宜到爆、極度便宜

解說：口語用法，AF 是 "as fuck" 的縮寫，用於加強形容詞的程度，此處強調價格極低。

影片原句
The other thing that you will notice about Jeff is it is cheap AF and it is fast AF.
另一個你會注意到關於 Jev 的事是，它便宜到爆，而且快到爆。
延伸例句
This hotel room is cheap AF for the location.
以這個地點來說，這間飯店房間便宜到爆。

fast AF /fæst ˌeɪ ˌɛf/

adjective phrase · C1

意思：快到爆、極度快速

解說：口語用法，強調速度極快，AF 為 "as fuck" 之縮寫，用於加強語氣。

影片原句
The other thing that you will notice about Jeff is it is cheap AF and it is fast AF.
另一個你會注意到關於 Jev 的事是，它便宜到爆，而且快到爆。
延伸例句
The response time on this server is fast AF.
這台伺服器的回應時間快到爆。

triage /ˈtriːɑːʒ/

verb · C2

意思：分級、篩選分類

解說：源自醫療用語，指根據緊急程度或重要性對事物進行分類和優先排序。

影片原句
Jev will triage that bug and give it a score.
Jev 會對該 bug 進行分級並給出分數。
延伸例句
The nurse had to triage the patients upon arrival.
護士必須在病人到達時對他們進行分級篩選。

agency /ˈeɪdʒənsi/

noun · C1

意思：自主性、行動力

解說：在此語境下指系統或個體獨立做出決策並採取行動的能力。

影片原句
it's high agency I would say.
它具有高自主性。
延伸例句
The AI agent has high agency in managing the schedule.
該 AI 代理在管理行程方面具有高自主性。

clustering /ˈklʌstərɪŋ/

noun · C1

意思：聚類、群集

解說：數據科學術語，指將相似數據點歸類為同一組的過程。

影片原句
it's doing clustering which I find very very useful
它正在進行聚類，我認為這非常非常有用
延伸例句
Clustering algorithms help identify customer segments.
聚類演算法有助於識別客戶群體。

corpus /ˈkɔːrpəs/

noun · C2

意思：語料庫、文集

解說：語言學或計算機科學用語，指為特定目的收集的大量文本或數據集合。

影片原句
take a big corpus of information, tag it, categorize it
處理大量的資訊語料庫，對其進行標籤、分類
延伸例句
The linguist analyzed a large corpus of ancient texts.
語言學家分析了大量古代文本的語料庫。

accretive /əˈkretɪv/

adjective · C2

意思：增加價值的、帶來增益的

解說：商業用語，指能增加公司價值或利潤的行為或策略。

影片原句
probably more margin accretive to my business
可能會為我的業務帶來更多的利潤
延伸例句
This merger is expected to be highly accretive to shareholder value.
預期這次合併將極大地增加股東價值。

nuance /ˈnuːɑːns/

noun · C1

意思：細微差別、微妙之處

解說：指意義、情感或顏色中極細微且不易察覺的差異。

影片原句
There's just too much nuance in all the data
資料中的細微差別太多了
延伸例句
He failed to grasp the nuance of her argument.
他未能理解她論點中的細微差別。

brute force /bruːt fɔːrs/

noun phrase · B2

意思：強行解決、暴力破解

解說：指通過大量嘗試或資源投入來解決問題，而非使用巧妙或高效的方法。

影片原句
to brute force with these like brainy models
用這些像聰明模型這樣的方式強行解決
延伸例句
They tried to brute force the password with a dictionary attack.
他們試圖通過字典攻擊暴力破解密碼。

latency /ˈleɪtənsi/

noun · C1

意思：延遲、延遲時間

解說：技術用語，指從發出請求到收到回應之間的時間間隔。

影片原句
understand where the latency comes from
了解延遲來自哪裡
延伸例句
High latency can ruin the gaming experience.
高延遲會破壞遊戲體驗。

primitives /ˈprɪmɪtɪvz/

noun · C1

意思：原語、基本類型

解說：程式設計用語，指語言中最基本、不可再分的數據類型或操作。

影片原句
The two places that I'm using the most are the primitives
我最常使用的兩個部分是原語（primitives）
延伸例句
Integers and strings are common primitives in programming.
整數和字串是程式設計中常見的原語。

whirlwind tour /ˈwɜːrlwɪnd tʊr/

noun phrase · B2

意思：快速導覽、走馬看花

解說：比喻在短時間內快速瀏覽或介紹某個主題或地點。

影片原句
Today I'm going to give you a very quick whirlwind tour
今天，我將帶大家快速瀏覽一下
延伸例句
Let me give you a whirlwind tour of the museum.
讓我帶你快速導覽一下博物館。

unstructured /ʌnˈstrʌktʃərd/

adjective · C1

意思：非結構化的

解說：指沒有預定義數據模型或組織格式的數據，如文字、影像等。

影片原句
Both take in unstructured data as inputs.
兩者都將非結構化資料作為輸入。
延伸例句
Emails are often considered unstructured data.
電子郵件通常被視為非結構化數據。

trending /ˈtrɛndɪŋ/

adjective · B2

意思：熱門的、趨勢中的

解說：形容在社交媒體或網路上正在被廣泛討論或關注的話題。

影片原句
oon as I saw this trending on X
當我在 X 上看到這個趨勢時
延伸例句
That hashtag is currently trending worldwide.
那個標籤目前在全球範圍內熱門。

subsidized /ˈsʌbsɪdaɪzd/

adjective · C1

意思：補貼的、補助的

解說：指由第三方（如政府或公司）部分或全部支付費用。

影片原句
A lot of it has been subsidized because
其中很多都是因為
延伸例句
Electric vehicles are heavily subsidized in many countries.
在許多國家，電動車受到大量補貼。

hype /haɪp/

noun · B2

意思：炒作、誇大宣傳

解說：指對某事物過度或不實的宣传，導致過高期望。

影片原句
They're not based on hype.
它們並非基於炒作。
延伸例句
Don't let the hype fool you; check the actual reviews.
別讓炒作欺騙了你；檢查實際評論。

blind evaluations /blaɪd ˌiːvəljuˈeɪʃənz/

noun phrase · C1

意思：盲評、雙盲評估

解說：一種評估方法，評審不知道評估對象的身份或來源，以減少偏見。

影片原句
through blind evaluations.
透過盲評來進行評分。
延伸例句
The drug trial used blind evaluations to ensure fairness.
藥物試驗使用盲評以確保公平性。

句型解說（含實例）

not only... but also...

意思：不僅……而且……

接續：not only + [verb/adjective/noun] + but also + [verb/adjective/noun]

解說：用於連接兩個並列的成分，強調後者比前者更重要或更令人驚訝。原文中雖未直接出現此結構，但 "fast cheap doesn't speak" 隱含了多重屬性的並列，此句型為常見強調結構。

影片原句
It is a very effective model and it is going to be in the middle of almost everything I build from here on out.
它是一個非常有效的模型，並且在我未來構建的幾乎所有東西中都會處於核心地位。
實例
  1. The software is not only fast but also secure.
    這套軟體不僅速度快，而且安全。
  2. She is not only a talented singer but also a skilled dancer.
    她不僅是一位有才華的歌手，也是一位熟練的舞者。

It is [adjective] to [verb]

意思：做……是……的

接續：It + be + [adjective] + to + [infinitive verb]

解說：使用形式主詞 "It" 來引導句子，將真正的主語（不定詞片語）置於句末，使句子結構更平衡。

影片原句
It is very inexpensive model.
它是一個非常便宜的模型。
實例
  1. It is important to back up your data regularly.
    定期備份你的數據很重要。
  2. It is difficult to predict the stock market.
    預測股市是很困難的。

What I mean by [noun] is [clause]

意思：我所說的 [名詞] 是指……

接續：What + I + mean + by + [noun] + is + [explanatory clause]

解說：用於澄清或定義前文提到的術語或概念，確保聽者理解正確含義。

影片原句
And what I mean by type safe values is these are values that are predefined
而我所說的類型安全的值，是指這些值是預先定義的
實例
  1. What I mean by 'urgent' is that it needs to be done today.
    我所說的「緊急」是指它需要今天完成。
  2. What I mean by 'teamwork' is supporting each other.
    我所說的「團隊合作」是指互相支持。

Instead of [noun/gerund], [subject] [verb]

意思：不……而是……

接續：Instead of + [noun/gerund] + , + [subject] + [verb] + [object]

解說：用於對比兩種情況，強調選擇後者而非前者。

影片原句
Instead of one overall winner, Open Art Arena ranks models
Open Art Arena 不設單一總冠軍，而是根據真實的創意任務對模型進行排名
實例
  1. Instead of driving, we decided to take the train.
    我們決定坐火車，而不是開車。
  2. Instead of complaining, he started fixing the problem.
    他開始解決問題，而不是抱怨。

So [conjunction] [clause], [main clause]

意思：所以……，……

接續：So + [conjunction] + [clause] + , + [main clause]

解說：用於引導結果或結論，"so" 在此處作為連接詞，表示前文所述情況導致的後果。

影片原句
So, let's get o it.
所以，讓我們開始吧。
實例
  1. So, if you want to succeed, you must work hard.
    所以，如果你想成功，你就必須努力工作。
  2. So, what are your plans for the weekend?
    所以，你週末有什麼計劃？

It's [adjective] at [verb-ing]

意思：在……方面很擅長

接續：It + is + [adjective] + at + [verb-ing]

解說：用於描述某人或某物在特定活動或技能上的擅長程度。

影片原句
Jeff is really good at classifying data.
Jeff 非常擅長資料分類。
實例
  1. She is good at solving complex problems.
    她擅長解決複雜的問題。
  2. This software is good at processing large datasets.
    這套軟體擅長處理大型數據集。

Not only [verb]... but also [verb]

意思：不僅……而且……

接續：Not only + [verb] + ... but also + [verb]

解說：連接兩個動詞，強調兩者皆發生，且後者往往更具重要性或意外性。

影片原句
It will essentially return one of three things. It will return a choice... Or a score... And then finally, there's a new.
它本質上會返回三種東西之一。它會返回一個選項……或者一個分數……最後，還有一個新的功能。
實例
  1. He not only sang but also danced beautifully.
    他不僅唱歌，還跳得很美。
  2. The system not only detects errors but also corrects them.
    該系統不僅檢測錯誤，還自動修正它們。

How [subject] [verb] [object]

意思：……的方式

接續：How + [subject] + [verb] + [object]

解說：名詞子句，用作主詞或受詞，描述做某事的具體方法或過程。

影片原句
This is how you know you would think about the three things it can return.
這就是你知道它能返回的三種東西的方式。
實例
  1. I don't know how he fixed the bug.
    我不知道他是如何修復這個錯誤的。
  2. Can you show me how to use this tool?
    你能告訴我如何使用這個工具嗎？