0:00.000–0:02.304
New Qethos 27B is insane.
0:02.304–0:07.900
A tiny team called Empero AI just released a model that reads a
0:07.900–0:10.565
million words at once, looks at pictures, and
0:10.565–0:13.580
runs on your own computer for free. Let's break down
0:13.580–0:17.077
why this one actually matters. Most AI news is noise,
0:17.077–0:19.620
this one isn't. Empero AI just dropped
0:19.620–0:26.100
Qethos 27B and it's the big brother to their smaller Qethos 9B model that a lot of builders
0:26.100–0:29.894
were already using. This new version is almost three times bigger and
0:29.894–0:31.660
it kept every single feature the
0:31.660–0:34.053
small one had. Nothing got cut to make it fit.
0:34.053–0:36.640
Let's talk numbers first because they're wild.
0:37.060–0:40.532
This model can hold over 1 million words in its memory at one time,
0:40.532–0:42.140
not one page, not one chapter,
0:42.480–0:45.569
a whole shelf of books all at once, all in its head while
0:45.569–0:48.040
it works. Here's why that matters if you run
0:48.040–0:50.588
a business. Say you've got hundreds of pages of notes,
0:50.588–0:52.740
old emails, and customer questions piled up.
0:52.740–0:57.134
A normal AI tool forgets most of that the second the conversation gets long.
0:57.134–0:58.180
This one doesn't.
0:58.240–1:01.557
You could hand it every single conversation your customers ever had with you and
1:01.557–1:02.300
it would remember
1:02.300–1:04.591
all of it while helping you write the next one. Hey,
1:04.591–1:06.500
if we haven't met already, I'm the digital
1:06.500–1:10.134
avatar of Julian Goldie, CEO of SEO Agency Goldie Agency.
1:10.134–1:12.860
Whilst he's helping clients get more leads
1:12.860–1:16.916
and customers, I'm here to help you get the latest AI updates.
1:16.916–1:19.620
A year ago, most AI tools maxed out around
1:19.620–1:22.437
a few thousand words before they started forgetting things.
1:22.437–1:24.260
That's like trying to write a book while
1:24.260–1:26.406
only remembering the last paragraph you wrote.
1:26.406–1:28.660
Now we've got a model that remembers the whole book
1:28.660–1:31.159
cover to cover while it writes the next chapter.
1:31.159–1:33.220
That's the jump we just watched happen.
1:33.780–1:35.705
Let's get into why it can do that because
1:35.705–1:37.980
the reason is actually simple once you hear it.
1:38.260–1:40.574
Old AI models worked like a messy desk.
1:40.574–1:43.900
Every time you added a new paper, the pile got bigger and
1:43.900–1:45.191
harder to search through.
1:45.191–1:48.420
This new model works more like a filing cabinet with a smart index.
1:48.420–1:51.247
It doesn't need to reread the whole pile every time.
1:51.247–1:53.220
It just knows exactly where to look.
1:53.220–1:57.380
That's what lets it hold a million words without slowing down or forgetting the start.
1:57.380–2:00.424
Now here's the part I actually think is the biggest deal.
2:00.424–2:02.820
This model can see. You can hand it a picture,
2:02.820–2:05.434
a screenshot, a chart, even handwriting and
2:05.434–2:08.260
it understands what's in it. So if you had a messy
2:08.260–2:13.540
screenshot of feedback from a customer or a photo of a whiteboard from a planning session,
2:13.540–2:15.362
you could just show it to the model instead
2:15.362–2:16.820
of typing everything out by hand.
2:17.380–2:22.980
Quick example, someone building content for a business could screenshot their top 10 performing
2:22.980–2:25.086
posts, hand that image to the model and
2:25.086–2:28.180
ask it to spot the pattern in what's working. That used to
2:28.180–2:30.609
take a person an hour of scrolling and guessing.
2:30.609–2:33.220
Let's pause here for a second because this next part
2:33.220–2:36.656
matters a lot if you're serious about actually using this stuff instead
2:36.656–2:38.260
of just watching videos about it.
2:38.260–2:41.822
A model like Quethos 27B is powerful, but it's also
2:41.822–2:45.300
brand new and figuring out the right setup on your
2:45.300–2:47.229
own can eat up hours you don't have.
2:47.229–2:50.820
That's exactly why we built the AI profit boardroom around tools
2:50.820–2:55.244
like this one. The moment something like Quethos-27B drops,
2:55.244–2:58.020
we put together a real playbook for it,
2:58.020–3:02.219
not theory, an actual setup you can copy for handling customer messages,
3:02.219–3:03.940
sorting notes and speeding up
3:03.940–3:08.820
your content. Every week there's a live coaching call where you can bring your exact business and
3:08.820–3:11.348
ask how to plug a model like this into it. You're also
3:11.348–3:13.700
dropped into a community full of people already
3:13.700–3:16.353
testing these releases in their own businesses, so
3:16.353–3:18.820
you're never figuring it out completely alone.
3:18.820–3:22.020
Links in the comments and description if you want the full setup.
3:22.020–3:23.294
Alright, back to the model.
3:23.294–3:26.340
There's a feature under the hood called multi-token prediction,
3:26.340–3:29.814
and I'll explain it simply. Most AI models write one word,
3:29.814–3:32.420
then stop, think, then write the next word.
3:32.420–3:36.921
One at a time. This model can predict several words ahead in one move.
3:36.921–3:38.500
Think of someone typing
3:38.500–3:41.833
with auto-complete that's actually right most of the time instead
3:41.833–3:43.380
of typing every letter by hand.
3:43.380–3:46.491
That's why it can respond faster without losing quality.
3:46.491–3:48.500
Now let's talk about who can actually
3:48.500–3:52.057
use this because this is the part a lot of AI news skips.
3:52.057–3:55.140
Impero AI released this under a license called
3:55.140–3:59.197
Apache 2.0. In plain terms, that means anyone can use it,
3:59.197–4:01.700
build with it and even use it to run a
4:01.700–4:04.488
business with no weird fine print stopping them.
4:04.488–4:07.140
A lot of AI models come with rules that block you
4:07.140–4:09.866
from using them commercially. This one doesn't.
4:09.866–4:12.260
That's a big deal for small business owners
4:12.260–4:18.740
specifically. It means you're not stuck paying to access someone else's AI tool every single month.
4:18.740–4:22.201
You can download this one, run it, and it's yours to use.
4:22.201–4:25.060
On top of that, this model comes with far fewer
4:25.060–4:27.736
built-in guardrails than most big company models.
4:27.736–4:30.100
That means it will actually answer straight,
4:30.100–4:33.846
direct business questions without dodging around them or refusing to help.
4:33.846–4:34.900
If you've ever asked a
4:34.900–4:38.250
big AI tool for something and gotten a wishy-washy non-answer,
4:38.250–4:40.020
this is built to avoid that. Let's
4:40.020–4:43.866
talk about why a small team like Impero AI could even build something like this.
4:43.866–4:44.740
They didn't start
4:44.740–4:47.821
from nothing. Their smaller model, Quethos 9b,
4:47.821–4:50.980
was trained using huge amounts of reasoning data
4:50.980–4:54.019
pulled from some of the most advanced AI systems out there.
4:54.019–4:56.500
Then they scaled that same training method up to
4:56.500–4:57.348
a bigger model.
4:57.348–5:00.740
That's the short version of why this jumped in quality so fast.
5:01.300–5:03.237
Here's a real use case. Kept simple.
5:03.237–5:06.660
If you run a small business page and you're drowning in comments
5:06.660–5:12.260
and messages, you could feed this model a batch of your most common customer questions and ask it to
5:12.260–5:14.480
draft short, clear answers for each one.
5:14.480–5:17.940
That's a task that used to eat up a chunk of someone's afternoon.
5:17.940–5:20.750
Here's another one. Say you're planning content for the month.
5:20.750–5:22.340
You could hand the model your last 20
5:22.340–5:23.709
posts as one big file and
5:23.709–5:25.077
ask it which topics your
5:25.077–5:28.020
audience actually responded to because it holds so
5:28.020–5:32.040
much memory at once, it can spot patterns across all of it in one shot instead
5:32.040–5:33.380
of you scrolling through
5:33.380–5:36.084
analytics for an hour. Let's zoom out for a second.
5:36.084–5:38.660
What we're watching isn't one company winning.
5:38.660–5:42.315
It's the gap closing between what a giant tech company can build and
5:42.315–5:43.620
what a small team can put
5:43.620–5:47.171
out for free. A year or two ago, a model with this much memory and
5:47.171–5:49.220
vision built in would have come from
5:49.220–5:51.317
a massive company with a massive budget.
5:51.317–5:54.340
Now it's coming from a small independent team that matters
5:54.340–5:57.607
for you. Even if you never touch the technical side of this,
5:57.607–5:59.940
it means the tools that help you save time,
5:59.940–6:01.559
answer customers faster and
6:01.559–6:05.540
organize your business to getting cheaper to access and easier to run,
6:05.540–6:07.426
not harder. Here's the honest part though.
6:07.426–6:09.940
This is a fresh release. It's what's called a pre-release
6:09.940–6:14.900
checkpoint, meaning there's another version coming that gets even more fine tuned after this one.
6:14.900–6:20.500
So early testing will keep shaping how well it performs on real day to day tasks. That's normal
6:20.500–6:21.430
for any new model.
6:21.430–6:22.360
It doesn't take aw
6:22.360–6:25.460
ay from how strong the starting point already is. Where this
6:25.460–6:27.421
goes next is worth watching closely.
6:27.421–6:30.900
The team behind it has already said a follow-up version is coming,
6:30.900–6:34.069
trained even further using feedback and testing.
6:34.069–6:37.540
If the pattern holds, that next version will be sharper
6:37.540–6:39.407
and more consistent than this one,
6:39.407–6:43.140
the same way this one is already sharper than their smaller model was.
6:43.140–6:45.654
So here's what I'd actually do with this if I were you.
6:45.654–6:47.140
If you're not technical at all,
6:47.140–6:49.730
don't worry about downloading anything yet.
6:49.730–6:52.660
Just know a model like this exists and what it can do,
6:52.660–6:56.820
because it will show up inside more tools you already use over the next few months.
6:56.820–6:58.608
If you do run a business and want to try it,
6:58.608–7:00.660
the simplest place to start is feeding it a real
7:00.660–7:02.115
problem you have this week,
7:02.115–7:03.633
like sorting through custom
7:03.633–7:06.100
er messages or summarizing notes and see how it
7:06.100–7:09.683
handles it. And if you want someone to hand you the exact setup instead
7:09.683–7:10.980
of figuring it out alone,
7:10.980–7:14.500
that's exactly what we build inside the iProfit boardroom every single week.
7:15.060–7:18.171
That's the real story behind Qethos 27b, not hype,
7:18.171–7:21.060
a small team closing the gap on what used to only
7:21.060–7:25.191
belong to giant companies and handing it to anyone willing to use it.
7:25.191–7:27.220
Before you go, two things. First,
7:27.220–7:31.689
if a model like Qethos 27b feels like something you want running in your business,
7:31.689–7:32.740
but you're not sure
7:32.740–7:37.400
where to even start, that's the whole reason the AI profit boardroom exists.
7:37.400–7:39.220
We turn releases like this one
7:40.980–7:44.261
same week they drop. So you're not stuck guessing how to set it up.
7:44.261–7:45.780
You get weekly coaching calls
7:45.780–7:47.991
where you can ask about your exact business,
7:47.991–7:50.740
a full library of ready to use prompts and a community of
7:50.740–7:56.500
business owners already putting tools like Qethos 27b to work in their own day to day. Links in the
7:56.500–7:59.601
comments and description if you want the full walkthrough.
7:59.601–8:02.020
And second, if you just want the free version of
8:02.020–8:04.436
that, grab a spot in the AI success lab.
8:04.436–8:08.740
It's completely free. It comes with the notes from this exact video,
8:08.740–8:12.540
plus over a hundred other AI use cases you can start using right away.
8:12.540–8:14.340
There's a community of more than
8:14.340–8:20.260
87,000 people in there already applying this stuff to their own businesses every day. Links for both
8:20.260–8:23.140
are in the comments and description. See you in the next one.

0:00.000–0:02.304
新的 Qethos 27B 簡直瘋狂。
0:02.304–0:07.900
一個名為 Empero AI 的小團隊剛剛發布了一款能一次讀取
0:07.900–0:10.565
一百萬字、查看圖片，並且
0:10.565–0:13.580
免費在您的電腦上運行的模型。讓我們來拆解
0:13.580–0:17.077
為什麼這個模型真正重要。大多數 AI 新聞都是噪音，
0:17.077–0:19.620
但這個不是。Empero AI 剛剛發布了
0:19.620–0:26.100
Qethos 27B，它是他們較小的 Qethos 9B 模型的「大哥」，而許多開發者
0:26.100–0:29.894
已經在使用那個較小的模型。這個新版本幾乎大了三倍，
0:29.894–0:31.660
並且保留了小版本的所有功能。
0:31.660–0:34.053
為了適應大小而刪減任何功能。
0:34.053–0:36.640
讓我們先來看看數字，因為它們非常驚人。
0:37.060–0:40.532
這個模型一次可以在其記憶體中容納超過一百萬個字，
0:40.532–0:42.140
不是一頁，不是一章，
0:42.480–0:45.569
而是整排書籍同時全部放在它的頭腦中，同時
0:45.569–0:48.040
它正在運作。這就是為什麼這很重要，如果你經營
0:48.040–0:50.588
一家企業。假設你有數百頁的筆記、
0:50.588–0:52.740
舊電子郵件和客戶問題堆積在一起。
0:52.740–0:57.134
一般的 AI 工具在對話變長的那一刻就會忘記大部分內容。
0:57.134–0:58.180
這個模型不會。
0:58.240–1:01.557
你可以將你的客戶與你進行的每一次對話都交給它，
1:01.557–1:02.300
它會記住
1:02.300–1:04.591
所有內容，同時幫助你撰寫下一段對話。嘿，
1:04.591–1:06.500
如果我們還沒有見過面，我是 Julian Goldie 的數位
1:06.500–1:10.134
分身，他是 SEO Agency Goldie Agency 的首席執行官。
1:10.134–1:12.860
當他幫助客戶獲得更多潛在客戶和顧客時，
1:12.860–1:16.916
我在這裡幫助你獲取最新的 AI 更新。
1:16.916–1:19.620
一年前，大多數 AI 工具在開始遺忘內容之前，
1:19.620–1:22.437
最多只能處理幾千字。
1:22.437–1:24.260
這就像試圖寫一本書，卻
1:24.260–1:26.406
只記得你寫的最後一段。
1:26.406–1:28.660
現在我們擁有一款能記住整本書
1:28.660–1:31.159
從封面到封底的模型，同時它正在撰寫下一章。
1:31.159–1:33.220
這就是我們剛剛目睹的躍進。
1:33.780–1:35.705
讓我們深入探討它為什麼能做到這一點，因為
1:35.705–1:37.980
一旦你聽過原因，其實很簡單。
1:38.260–1:40.574
舊的 AI 模型運作方式就像一張凌亂的書桌。
1:40.574–1:43.900
每當你新增一張紙，堆疊就會變大，並且
1:43.900–1:45.191
更難搜尋。
1:45.191–1:48.420
這個新模型的運作方式更像是一個帶有智慧索引的檔案櫃。
1:48.420–1:51.247
它不需要每次重新讀取整堆文件。
1:51.247–1:53.220
它只是清楚知道該往哪裡查找。
1:53.220–1:57.380
這就是為什麼它能儲存一百萬個字詞，卻不會變慢或遺漏開頭內容的原因。
1:57.380–2:00.424
現在，這是我認為真正最具影響力的部分。
2:00.424–2:02.820
這個模型具備視覺能力。你可以提供它一張圖片，
2:02.820–2:05.434
截圖、圖表，甚至是手寫字跡，
2:05.434–2:08.260
它都能理解其中的內容。所以，如果你有一張凌亂的
2:08.260–2:13.540
來自客戶的反饋截圖，或是規劃會議中白板的照片，
2:13.540–2:15.362
你只需將它展示給模型，
2:15.362–2:16.820
而不必手動輸入所有內容。
2:17.380–2:22.980
快速範例：為企業製作內容的人員可以截取其表現最好的前 10 篇
2:22.980–2:25.086
帖子，將該圖片提供給模型，並
2:25.086–2:28.180
要求它找出運作良好的模式。過去這
2:28.180–2:30.609
需要人員花一個小時進行滾動瀏覽和猜測。
2:30.609–2:33.220
讓我們在這裡暫停一下，因為接下來的部分
2:33.220–2:36.656
如果你認真考慮實際使用這些工具，而非只是觀看相關影片，
2:36.656–2:38.260
這將非常重要。
2:38.260–2:41.822
像 Quethos 27B 這樣的模型雖然強大，但也
2:41.822–2:45.300
非常新穎，自行摸索正確的設定可能會
2:45.300–2:47.229
耗盡你本就不多的時間。
2:47.229–2:50.820
這正是我們建構 AI 利潤董事會議室，圍繞這類工具
2:50.820–2:55.244
的原因。當像 Quethos-27B 這樣的模型發布時，
2:55.244–2:58.020
我們會為其制定實際的操作手冊，
2:58.020–3:02.219
而非理論，而是你可以直接複製用於處理客戶訊息、
3:02.219–3:03.940
整理筆記並加速
3:03.940–3:08.820
內容製作的實際設定。每週都有現場輔導會議，你可以帶著自己的具體業務
3:08.820–3:11.348
詢問如何將這類模型整合其中。你還會
3:11.348–3:13.700
被加入一個社群，裡面充滿了已經
3:13.700–3:16.353
在自己的業務中測試這些發布版本的人，因此
3:16.353–3:18.820
你不會完全獨自摸索。
3:18.820–3:22.020
如果希望獲得完整設定，請參考評論區和描述中的連結。
3:22.020–3:23.294
好了，回到模型本身。
3:23.294–3:26.340
在底層有一個稱為多詞元預測的功能，
3:26.340–3:29.814
我會簡單解釋。大多數 AI 模型會寫一個字詞，
3:29.814–3:32.420
然後停止、思考，再寫下一個字詞。
3:32.420–3:36.921
一次只寫一個。這個模型可以在一步中預測好幾個字詞。
3:36.921–3:38.500
想像有人打字
3:38.500–3:41.833
使用大部分時間都準確的自動補全功能，而不是
3:41.833–3:43.380
手動輸入每個字母。
3:43.380–3:46.491
這就是為什麼它能在不降低品質的情況下回應更快。
3:46.491–3:48.500
現在讓我們談談誰實際上
3:48.500–3:52.057
使用它，因為這是很多 AI 新聞會跳過的環節。
3:52.057–3:55.140
Impero AI 以一個名為
3:55.140–3:59.197
Apache 2.0 的授權條款發布了它。簡單來說，這意味著任何人都可以使用它，
3:59.197–4:01.700
基於它進行開發，甚至用它來經營
4:01.700–4:04.488
業務，而沒有什麼奇怪的細則限制。
4:04.488–4:07.140
許多 AI 模型都帶有規則，禁止你
4:07.140–4:09.866
進行商業使用。這個模型則沒有。
4:09.866–4:12.260
這對小型企業主來說
4:12.260–4:18.740
尤為重要。這意味著你不必每月都付費才能使用別人的 AI 工具。
4:18.740–4:22.201
你可以下載這個模型，運行它，然後自由使用。
4:22.201–4:25.060
此外，這個模型比大多數大公司的模型
4:25.060–4:27.736
內建的防護機制要少得多。
4:27.736–4:30.100
這意味著它會直接回答
4:30.100–4:33.846
直接的商業問題，不會閃爍其詞或拒絕協助。
4:33.846–4:34.900
如果你曾經向
4:34.900–4:38.250
大型 AI 工具請求某事，卻得到一個含糊不清的無效回答，
4:38.250–4:40.020
這個模型就是為了避免這種情況而設計的。讓我們
4:40.020–4:43.866
談談為什麼像 Impero AI 這樣的小型團隊甚至能構建出這樣的東西。
4:43.866–4:44.740
他們並非
4:44.740–4:47.821
從零開始。他們較小的模型 Quethos 9b，
4:47.821–4:50.980
是使用來自一些最先進 AI 系統的龐大推理數據進行訓練的。
4:50.980–4:54.019
然後，他們將同樣的訓練方法擴展到
4:54.019–4:56.500
更大的模型上。
4:56.500–4:57.348
這就是為什麼它的品質會如此快速提升的簡短說明。
4:57.348–5:00.740
這裡有一個實際的使用案例，保持簡單。
5:01.300–5:03.237
如果你經營一個小型企業頁面，並且被評論
5:03.237–5:06.660
和訊息淹沒，你可以將你最常見的客户問題批次輸入這個模型，並要求它
5:06.660–5:12.260
為每個問題起草簡短、清晰的回答。
5:12.260–5:14.480
這是一項過去會佔用某人整個下午的任務。
5:14.480–5:17.940
這裡還有另一個例子。假設你正在規劃當月的內容。
5:17.940–5:20.750
你可以將你最後 20 篇
5:20.750–5:22.340
帖子作為一個大文件交給模型，並
5:22.340–5:23.709
要求它分析你的
5:23.709–5:25.077
受眾實際上對哪些主題有反應，因為它同時擁有如此
5:25.077–5:28.020
龐大的記憶容量，它可以一次性在所有數據中發現模式，而不是
5:28.020–5:32.040
讓你花一個小時去瀏覽
5:32.040–5:33.380
分析數據。讓我們暫時拉遠視角。
5:33.380–5:36.084
我們所看到的並非一家公司獨贏。
5:36.084–5:38.660
而是大型科技公司能構建的東西與
5:38.660–5:42.315
這並非一家科技巨頭獨贏的局面，而是巨頭能建構的技術與
5:42.315–5:43.620
大型科技公司能建構的，以及
5:43.620–5:47.171
小型團隊能免費提供的。一年或兩年前，具備如此龐大記憶體和
5:47.171–5:49.220
內建視覺能力的模型，會來自
5:49.220–5:51.317
一家擁有龐大預算的大型公司。
5:51.317–5:54.340
現在它卻來自一個對你來說很重要的小型獨立團隊。
5:54.340–5:57.607
即使你從未接觸技術層面，
5:57.607–5:59.940
這也意味著能幫助你節省時間的工具、
5:59.940–6:01.559
更快回應客戶的工具，以及
6:01.559–6:05.540
讓你的業務運作變得更便宜且更易於管理的工具，
6:05.540–6:07.426
而不是變得更困難。不過，這裡有個實話。
6:07.426–6:09.940
這是最新發布的版本。它被稱為預發布
6:09.940–6:14.900
檢查點，意味著在此版本之後，還會有另一個經過更細緻調整的版本即將推出。
6:14.900–6:20.500
因此，早期測試將持續影響它在日常實際任務中的表現。這對於任何新模型來說都是正常的。
6:20.500–6:21.430
這並不減弱它作為起點的強大實力。
6:21.430–6:22.360
接下來它會走向何方，值得密切關注。
6:22.360–6:25.460
背後的團隊已經表示，後續版本即將推出，
6:25.460–6:27.421
將利用反饋和測試進行進一步訓練。
6:27.421–6:30.900
如果這種模式持續下去，下一個版本將比這個版本更精確、
6:30.900–6:34.069
也更一致，就像這個版本比他們的小型模型更精確一樣。
6:34.069–6:37.540
所以，如果我是你，我會這樣使用這個模型。
6:37.540–6:39.407
如果你完全不懂技術，
6:39.407–6:43.140
現在還不用擔心下載任何東西。
6:43.140–6:45.654
只要知道有這樣的模型存在，以及它能做什麼，
6:45.654–6:47.140
因為在接下來的幾個月裡，它會出現在你已經使用的更多工具中。
6:47.140–6:49.730
如果你經營業務並想嘗試它，
6:49.730–6:52.660
最簡單的開始方式是將你這週面臨的真實
6:52.660–6:56.820
問題交給它，
6:56.820–6:58.608
例如整理客戶訊息或摘要筆記，看看它如何處理。
6:58.608–7:00.660
如果你希望有人直接給你確切的設定，而不是獨自摸索，
7:00.660–7:02.115
那正是我們每週在 iProfit 董事會議室裡建構的內容。
7:02.115–7:03.633
這就是 Qethos 27b 背後的真實故事，不是炒作，
7:03.633–7:06.100
而是一個小型團隊縮小了過去僅屬於大型公司的差距，
7:06.100–7:09.683
並將它交給任何願意使用的人。
7:09.683–7:10.980
在你離開之前，有兩件事。首先，
7:10.980–7:14.500
如果像 Qethos 27b 這樣的模型感覺像是你想在業務中運行的東西，
7:15.060–7:18.171
這才是 Qethos 27b 背後的真實故事，並非炒作，
7:18.171–7:21.060
而是小團隊正在縮小差距，將過去僅屬於
7:21.060–7:25.191
大型公司的技術，交給任何願意使用的人。
7:25.191–7:27.220
在離開之前，有兩件事。首先，
7:27.220–7:31.689
如果你認為像 Qethos 27b 這樣的模型適合在你的業務中運行，
7:31.689–7:32.740
但你不太確定
7:32.740–7:37.400
從何開始，這就是 AI 利潤董事會存在的主要原因。
7:37.400–7:39.220
我們將這類發布
7:40.980–7:44.261
在發布的同一週就處理好。所以你不必苦惱如何設定。
7:44.261–7:45.780
你會獲得每週的教練諮詢
7:45.780–7:47.991
你可以針對你的具體業務進行提問，
7:47.991–7:50.740
還有完整的即用型提示詞庫，以及一個由
7:50.740–7:56.500
已經在日常工作中使用 Qethos 27b 等工具的企業主組成的社群。連結位於
7:56.500–7:59.601
評論區和描述區，如果你想要完整的操作指南。
7:59.601–8:02.020
第二，如果你只想使用免費版本，
8:02.020–8:04.436
請搶佔 AI 成功實驗室的名額。
8:04.436–8:08.740
這完全免費。它包含本影片的重點筆記，
8:08.740–8:12.540
以及超過一百個你可以立即開始使用的 AI 應用案例。
8:12.540–8:14.340
社群裡已有超過
8:14.340–8:20.260
87,000 人每天將這些方法應用於他們自己的業務。兩個連結都在
8:20.260–8:23.140
評論區和描述區。我們下一部影片見。

0:00.000–0:02.304
New Qethos 27B is insane.
新的 Qethos 27B 簡直瘋狂。
0:02.304–0:07.900
A tiny team called Empero AI just released a model that reads a
一個名為 Empero AI 的小團隊剛剛發布了一款能一次讀取
0:07.900–0:10.565
million words at once, looks at pictures, and
一百萬字、查看圖片，並且
0:10.565–0:13.580
runs on your own computer for free. Let's break down
免費在您的電腦上運行的模型。讓我們來拆解
0:13.580–0:17.077
why this one actually matters. Most AI news is noise,
為什麼這個模型真正重要。大多數 AI 新聞都是噪音，
0:17.077–0:19.620
this one isn't. Empero AI just dropped
但這個不是。Empero AI 剛剛發布了
0:19.620–0:26.100
Qethos 27B and it's the big brother to their smaller Qethos 9B model that a lot of builders
Qethos 27B，它是他們較小的 Qethos 9B 模型的「大哥」，而許多開發者
0:26.100–0:29.894
were already using. This new version is almost three times bigger and
已經在使用那個較小的模型。這個新版本幾乎大了三倍，
0:29.894–0:31.660
it kept every single feature the
並且保留了小版本的所有功能。
0:31.660–0:34.053
small one had. Nothing got cut to make it fit.
為了適應大小而刪減任何功能。
0:34.053–0:36.640
Let's talk numbers first because they're wild.
讓我們先來看看數字，因為它們非常驚人。
0:37.060–0:40.532
This model can hold over 1 million words in its memory at one time,
這個模型一次可以在其記憶體中容納超過一百萬個字，
0:40.532–0:42.140
not one page, not one chapter,
不是一頁，不是一章，
0:42.480–0:45.569
a whole shelf of books all at once, all in its head while
而是整排書籍同時全部放在它的頭腦中，同時
0:45.569–0:48.040
it works. Here's why that matters if you run
它正在運作。這就是為什麼這很重要，如果你經營
0:48.040–0:50.588
a business. Say you've got hundreds of pages of notes,
一家企業。假設你有數百頁的筆記、
0:50.588–0:52.740
old emails, and customer questions piled up.
舊電子郵件和客戶問題堆積在一起。
0:52.740–0:57.134
A normal AI tool forgets most of that the second the conversation gets long.
一般的 AI 工具在對話變長的那一刻就會忘記大部分內容。
0:57.134–0:58.180
This one doesn't.
這個模型不會。
0:58.240–1:01.557
You could hand it every single conversation your customers ever had with you and
你可以將你的客戶與你進行的每一次對話都交給它，
1:01.557–1:02.300
it would remember
它會記住
1:02.300–1:04.591
all of it while helping you write the next one. Hey,
所有內容，同時幫助你撰寫下一段對話。嘿，
1:04.591–1:06.500
if we haven't met already, I'm the digital
如果我們還沒有見過面，我是 Julian Goldie 的數位
1:06.500–1:10.134
avatar of Julian Goldie, CEO of SEO Agency Goldie Agency.
分身，他是 SEO Agency Goldie Agency 的首席執行官。
1:10.134–1:12.860
Whilst he's helping clients get more leads
當他幫助客戶獲得更多潛在客戶和顧客時，
1:12.860–1:16.916
and customers, I'm here to help you get the latest AI updates.
我在這裡幫助你獲取最新的 AI 更新。
1:16.916–1:19.620
A year ago, most AI tools maxed out around
一年前，大多數 AI 工具在開始遺忘內容之前，
1:19.620–1:22.437
a few thousand words before they started forgetting things.
最多只能處理幾千字。
1:22.437–1:24.260
That's like trying to write a book while
這就像試圖寫一本書，卻
1:24.260–1:26.406
only remembering the last paragraph you wrote.
只記得你寫的最後一段。
1:26.406–1:28.660
Now we've got a model that remembers the whole book
現在我們擁有一款能記住整本書
1:28.660–1:31.159
cover to cover while it writes the next chapter.
從封面到封底的模型，同時它正在撰寫下一章。
1:31.159–1:33.220
That's the jump we just watched happen.
這就是我們剛剛目睹的躍進。
1:33.780–1:35.705
Let's get into why it can do that because
讓我們深入探討它為什麼能做到這一點，因為
1:35.705–1:37.980
the reason is actually simple once you hear it.
一旦你聽過原因，其實很簡單。
1:38.260–1:40.574
Old AI models worked like a messy desk.
舊的 AI 模型運作方式就像一張凌亂的書桌。
1:40.574–1:43.900
Every time you added a new paper, the pile got bigger and
每當你新增一張紙，堆疊就會變大，並且
1:43.900–1:45.191
harder to search through.
更難搜尋。
1:45.191–1:48.420
This new model works more like a filing cabinet with a smart index.
這個新模型的運作方式更像是一個帶有智慧索引的檔案櫃。
1:48.420–1:51.247
It doesn't need to reread the whole pile every time.
它不需要每次重新讀取整堆文件。
1:51.247–1:53.220
It just knows exactly where to look.
它只是清楚知道該往哪裡查找。
1:53.220–1:57.380
That's what lets it hold a million words without slowing down or forgetting the start.
這就是為什麼它能儲存一百萬個字詞，卻不會變慢或遺漏開頭內容的原因。
1:57.380–2:00.424
Now here's the part I actually think is the biggest deal.
現在，這是我認為真正最具影響力的部分。
2:00.424–2:02.820
This model can see. You can hand it a picture,
這個模型具備視覺能力。你可以提供它一張圖片，
2:02.820–2:05.434
a screenshot, a chart, even handwriting and
截圖、圖表，甚至是手寫字跡，
2:05.434–2:08.260
it understands what's in it. So if you had a messy
它都能理解其中的內容。所以，如果你有一張凌亂的
2:08.260–2:13.540
screenshot of feedback from a customer or a photo of a whiteboard from a planning session,
來自客戶的反饋截圖，或是規劃會議中白板的照片，
2:13.540–2:15.362
you could just show it to the model instead
你只需將它展示給模型，
2:15.362–2:16.820
of typing everything out by hand.
而不必手動輸入所有內容。
2:17.380–2:22.980
Quick example, someone building content for a business could screenshot their top 10 performing
快速範例：為企業製作內容的人員可以截取其表現最好的前 10 篇
2:22.980–2:25.086
posts, hand that image to the model and
帖子，將該圖片提供給模型，並
2:25.086–2:28.180
ask it to spot the pattern in what's working. That used to
要求它找出運作良好的模式。過去這
2:28.180–2:30.609
take a person an hour of scrolling and guessing.
需要人員花一個小時進行滾動瀏覽和猜測。
2:30.609–2:33.220
Let's pause here for a second because this next part
讓我們在這裡暫停一下，因為接下來的部分
2:33.220–2:36.656
matters a lot if you're serious about actually using this stuff instead
如果你認真考慮實際使用這些工具，而非只是觀看相關影片，
2:36.656–2:38.260
of just watching videos about it.
這將非常重要。
2:38.260–2:41.822
A model like Quethos 27B is powerful, but it's also
像 Quethos 27B 這樣的模型雖然強大，但也
2:41.822–2:45.300
brand new and figuring out the right setup on your
非常新穎，自行摸索正確的設定可能會
2:45.300–2:47.229
own can eat up hours you don't have.
耗盡你本就不多的時間。
2:47.229–2:50.820
That's exactly why we built the AI profit boardroom around tools
這正是我們建構 AI 利潤董事會議室，圍繞這類工具
2:50.820–2:55.244
like this one. The moment something like Quethos-27B drops,
的原因。當像 Quethos-27B 這樣的模型發布時，
2:55.244–2:58.020
we put together a real playbook for it,
我們會為其制定實際的操作手冊，
2:58.020–3:02.219
not theory, an actual setup you can copy for handling customer messages,
而非理論，而是你可以直接複製用於處理客戶訊息、
3:02.219–3:03.940
sorting notes and speeding up
整理筆記並加速
3:03.940–3:08.820
your content. Every week there's a live coaching call where you can bring your exact business and
內容製作的實際設定。每週都有現場輔導會議，你可以帶著自己的具體業務
3:08.820–3:11.348
ask how to plug a model like this into it. You're also
詢問如何將這類模型整合其中。你還會
3:11.348–3:13.700
dropped into a community full of people already
被加入一個社群，裡面充滿了已經
3:13.700–3:16.353
testing these releases in their own businesses, so
在自己的業務中測試這些發布版本的人，因此
3:16.353–3:18.820
you're never figuring it out completely alone.
你不會完全獨自摸索。
3:18.820–3:22.020
Links in the comments and description if you want the full setup.
如果希望獲得完整設定，請參考評論區和描述中的連結。
3:22.020–3:23.294
Alright, back to the model.
好了，回到模型本身。
3:23.294–3:26.340
There's a feature under the hood called multi-token prediction,
在底層有一個稱為多詞元預測的功能，
3:26.340–3:29.814
and I'll explain it simply. Most AI models write one word,
我會簡單解釋。大多數 AI 模型會寫一個字詞，
3:29.814–3:32.420
then stop, think, then write the next word.
然後停止、思考，再寫下一個字詞。
3:32.420–3:36.921
One at a time. This model can predict several words ahead in one move.
一次只寫一個。這個模型可以在一步中預測好幾個字詞。
3:36.921–3:38.500
Think of someone typing
想像有人打字
3:38.500–3:41.833
with auto-complete that's actually right most of the time instead
使用大部分時間都準確的自動補全功能，而不是
3:41.833–3:43.380
of typing every letter by hand.
手動輸入每個字母。
3:43.380–3:46.491
That's why it can respond faster without losing quality.
這就是為什麼它能在不降低品質的情況下回應更快。
3:46.491–3:48.500
Now let's talk about who can actually
現在讓我們談談誰實際上
3:48.500–3:52.057
use this because this is the part a lot of AI news skips.
使用它，因為這是很多 AI 新聞會跳過的環節。
3:52.057–3:55.140
Impero AI released this under a license called
Impero AI 以一個名為
3:55.140–3:59.197
Apache 2.0. In plain terms, that means anyone can use it,
Apache 2.0 的授權條款發布了它。簡單來說，這意味著任何人都可以使用它，
3:59.197–4:01.700
build with it and even use it to run a
基於它進行開發，甚至用它來經營
4:01.700–4:04.488
business with no weird fine print stopping them.
業務，而沒有什麼奇怪的細則限制。
4:04.488–4:07.140
A lot of AI models come with rules that block you
許多 AI 模型都帶有規則，禁止你
4:07.140–4:09.866
from using them commercially. This one doesn't.
進行商業使用。這個模型則沒有。
4:09.866–4:12.260
That's a big deal for small business owners
這對小型企業主來說
4:12.260–4:18.740
specifically. It means you're not stuck paying to access someone else's AI tool every single month.
尤為重要。這意味著你不必每月都付費才能使用別人的 AI 工具。
4:18.740–4:22.201
You can download this one, run it, and it's yours to use.
你可以下載這個模型，運行它，然後自由使用。
4:22.201–4:25.060
On top of that, this model comes with far fewer
此外，這個模型比大多數大公司的模型
4:25.060–4:27.736
built-in guardrails than most big company models.
內建的防護機制要少得多。
4:27.736–4:30.100
That means it will actually answer straight,
這意味著它會直接回答
4:30.100–4:33.846
direct business questions without dodging around them or refusing to help.
直接的商業問題，不會閃爍其詞或拒絕協助。
4:33.846–4:34.900
If you've ever asked a
如果你曾經向
4:34.900–4:38.250
big AI tool for something and gotten a wishy-washy non-answer,
大型 AI 工具請求某事，卻得到一個含糊不清的無效回答，
4:38.250–4:40.020
this is built to avoid that. Let's
這個模型就是為了避免這種情況而設計的。讓我們
4:40.020–4:43.866
talk about why a small team like Impero AI could even build something like this.
談談為什麼像 Impero AI 這樣的小型團隊甚至能構建出這樣的東西。
4:43.866–4:44.740
They didn't start
他們並非
4:44.740–4:47.821
from nothing. Their smaller model, Quethos 9b,
從零開始。他們較小的模型 Quethos 9b，
4:47.821–4:50.980
was trained using huge amounts of reasoning data
是使用來自一些最先進 AI 系統的龐大推理數據進行訓練的。
4:50.980–4:54.019
pulled from some of the most advanced AI systems out there.
然後，他們將同樣的訓練方法擴展到
4:54.019–4:56.500
Then they scaled that same training method up to
更大的模型上。
4:56.500–4:57.348
a bigger model.
這就是為什麼它的品質會如此快速提升的簡短說明。
4:57.348–5:00.740
That's the short version of why this jumped in quality so fast.
這裡有一個實際的使用案例，保持簡單。
5:01.300–5:03.237
Here's a real use case. Kept simple.
如果你經營一個小型企業頁面，並且被評論
5:03.237–5:06.660
If you run a small business page and you're drowning in comments
和訊息淹沒，你可以將你最常見的客户問題批次輸入這個模型，並要求它
5:06.660–5:12.260
and messages, you could feed this model a batch of your most common customer questions and ask it to
為每個問題起草簡短、清晰的回答。
5:12.260–5:14.480
draft short, clear answers for each one.
這是一項過去會佔用某人整個下午的任務。
5:14.480–5:17.940
That's a task that used to eat up a chunk of someone's afternoon.
這裡還有另一個例子。假設你正在規劃當月的內容。
5:17.940–5:20.750
Here's another one. Say you're planning content for the month.
你可以將你最後 20 篇
5:20.750–5:22.340
You could hand the model your last 20
帖子作為一個大文件交給模型，並
5:22.340–5:23.709
posts as one big file and
要求它分析你的
5:23.709–5:25.077
ask it which topics your
受眾實際上對哪些主題有反應，因為它同時擁有如此
5:25.077–5:28.020
audience actually responded to because it holds so
龐大的記憶容量，它可以一次性在所有數據中發現模式，而不是
5:28.020–5:32.040
much memory at once, it can spot patterns across all of it in one shot instead
讓你花一個小時去瀏覽
5:32.040–5:33.380
of you scrolling through
分析數據。讓我們暫時拉遠視角。
5:33.380–5:36.084
analytics for an hour. Let's zoom out for a second.
我們所看到的並非一家公司獨贏。
5:36.084–5:38.660
What we're watching isn't one company winning.
而是大型科技公司能構建的東西與
5:38.660–5:42.315
It's the gap closing between what a giant tech company can build and
這並非一家科技巨頭獨贏的局面，而是巨頭能建構的技術與
5:42.315–5:43.620
what a small team can put
大型科技公司能建構的，以及
5:43.620–5:47.171
out for free. A year or two ago, a model with this much memory and
小型團隊能免費提供的。一年或兩年前，具備如此龐大記憶體和
5:47.171–5:49.220
vision built in would have come from
內建視覺能力的模型，會來自
5:49.220–5:51.317
a massive company with a massive budget.
一家擁有龐大預算的大型公司。
5:51.317–5:54.340
Now it's coming from a small independent team that matters
現在它卻來自一個對你來說很重要的小型獨立團隊。
5:54.340–5:57.607
for you. Even if you never touch the technical side of this,
即使你從未接觸技術層面，
5:57.607–5:59.940
it means the tools that help you save time,
這也意味著能幫助你節省時間的工具、
5:59.940–6:01.559
answer customers faster and
更快回應客戶的工具，以及
6:01.559–6:05.540
organize your business to getting cheaper to access and easier to run,
讓你的業務運作變得更便宜且更易於管理的工具，
6:05.540–6:07.426
not harder. Here's the honest part though.
而不是變得更困難。不過，這裡有個實話。
6:07.426–6:09.940
This is a fresh release. It's what's called a pre-release
這是最新發布的版本。它被稱為預發布
6:09.940–6:14.900
checkpoint, meaning there's another version coming that gets even more fine tuned after this one.
檢查點，意味著在此版本之後，還會有另一個經過更細緻調整的版本即將推出。
6:14.900–6:20.500
So early testing will keep shaping how well it performs on real day to day tasks. That's normal
因此，早期測試將持續影響它在日常實際任務中的表現。這對於任何新模型來說都是正常的。
6:20.500–6:21.430
for any new model.
這並不減弱它作為起點的強大實力。
6:21.430–6:22.360
It doesn't take aw
接下來它會走向何方，值得密切關注。
6:22.360–6:25.460
ay from how strong the starting point already is. Where this
背後的團隊已經表示，後續版本即將推出，
6:25.460–6:27.421
goes next is worth watching closely.
將利用反饋和測試進行進一步訓練。
6:27.421–6:30.900
The team behind it has already said a follow-up version is coming,
如果這種模式持續下去，下一個版本將比這個版本更精確、
6:30.900–6:34.069
trained even further using feedback and testing.
也更一致，就像這個版本比他們的小型模型更精確一樣。
6:34.069–6:37.540
If the pattern holds, that next version will be sharper
所以，如果我是你，我會這樣使用這個模型。
6:37.540–6:39.407
and more consistent than this one,
如果你完全不懂技術，
6:39.407–6:43.140
the same way this one is already sharper than their smaller model was.
現在還不用擔心下載任何東西。
6:43.140–6:45.654
So here's what I'd actually do with this if I were you.
只要知道有這樣的模型存在，以及它能做什麼，
6:45.654–6:47.140
If you're not technical at all,
因為在接下來的幾個月裡，它會出現在你已經使用的更多工具中。
6:47.140–6:49.730
don't worry about downloading anything yet.
如果你經營業務並想嘗試它，
6:49.730–6:52.660
Just know a model like this exists and what it can do,
最簡單的開始方式是將你這週面臨的真實
6:52.660–6:56.820
because it will show up inside more tools you already use over the next few months.
問題交給它，
6:56.820–6:58.608
If you do run a business and want to try it,
例如整理客戶訊息或摘要筆記，看看它如何處理。
6:58.608–7:00.660
the simplest place to start is feeding it a real
如果你希望有人直接給你確切的設定，而不是獨自摸索，
7:00.660–7:02.115
problem you have this week,
那正是我們每週在 iProfit 董事會議室裡建構的內容。
7:02.115–7:03.633
like sorting through custom
這就是 Qethos 27b 背後的真實故事，不是炒作，
7:03.633–7:06.100
er messages or summarizing notes and see how it
而是一個小型團隊縮小了過去僅屬於大型公司的差距，
7:06.100–7:09.683
handles it. And if you want someone to hand you the exact setup instead
並將它交給任何願意使用的人。
7:09.683–7:10.980
of figuring it out alone,
在你離開之前，有兩件事。首先，
7:10.980–7:14.500
that's exactly what we build inside the iProfit boardroom every single week.
如果像 Qethos 27b 這樣的模型感覺像是你想在業務中運行的東西，
7:15.060–7:18.171
That's the real story behind Qethos 27b, not hype,
這才是 Qethos 27b 背後的真實故事，並非炒作，
7:18.171–7:21.060
a small team closing the gap on what used to only
而是小團隊正在縮小差距，將過去僅屬於
7:21.060–7:25.191
belong to giant companies and handing it to anyone willing to use it.
大型公司的技術，交給任何願意使用的人。
7:25.191–7:27.220
Before you go, two things. First,
在離開之前，有兩件事。首先，
7:27.220–7:31.689
if a model like Qethos 27b feels like something you want running in your business,
如果你認為像 Qethos 27b 這樣的模型適合在你的業務中運行，
7:31.689–7:32.740
but you're not sure
但你不太確定
7:32.740–7:37.400
where to even start, that's the whole reason the AI profit boardroom exists.
從何開始，這就是 AI 利潤董事會存在的主要原因。
7:37.400–7:39.220
We turn releases like this one
我們將這類發布
7:40.980–7:44.261
same week they drop. So you're not stuck guessing how to set it up.
在發布的同一週就處理好。所以你不必苦惱如何設定。
7:44.261–7:45.780
You get weekly coaching calls
你會獲得每週的教練諮詢
7:45.780–7:47.991
where you can ask about your exact business,
你可以針對你的具體業務進行提問，
7:47.991–7:50.740
a full library of ready to use prompts and a community of
還有完整的即用型提示詞庫，以及一個由
7:50.740–7:56.500
business owners already putting tools like Qethos 27b to work in their own day to day. Links in the
已經在日常工作中使用 Qethos 27b 等工具的企業主組成的社群。連結位於
7:56.500–7:59.601
comments and description if you want the full walkthrough.
評論區和描述區，如果你想要完整的操作指南。
7:59.601–8:02.020
And second, if you just want the free version of
第二，如果你只想使用免費版本，
8:02.020–8:04.436
that, grab a spot in the AI success lab.
請搶佔 AI 成功實驗室的名額。
8:04.436–8:08.740
It's completely free. It comes with the notes from this exact video,
這完全免費。它包含本影片的重點筆記，
8:08.740–8:12.540
plus over a hundred other AI use cases you can start using right away.
以及超過一百個你可以立即開始使用的 AI 應用案例。
8:12.540–8:14.340
There's a community of more than
社群裡已有超過
8:14.340–8:20.260
87,000 people in there already applying this stuff to their own businesses every day. Links for both
87,000 人每天將這些方法應用於他們自己的業務。兩個連結都在
8:20.260–8:23.140
are in the comments and description. See you in the next one.
評論區和描述區。我們下一部影片見。

影片筆記：NEW Qwythos-27B-v1 is INSANE!

一句話總結

由小型團隊 Empero AI 發布的新模型 Qethos 27B（Qethos 9B 的擴充版），具備超過 100 萬字的超大上下文窗口、多模態視覺能力以及多令牌預測技術，採用 Apache 2.0 授權允許免費商業使用，並強調可在用戶本地電腦運行。

核心重點

模型發布者與背景：由小型團隊 Empero AI 發布，是較小版本 Qethos 9B 的「大哥」，透過擴充訓練方法（使用先進 AI 系統的推理數據）開發而成。
超大上下文窗口：能一次性處理超過 100 萬字的內容（相當於一整排書籍），解決傳統 AI 在長對話中遺忘資訊的問題，具備類似檔案櫃的智能索引機制。
多模態視覺能力：具備「看」的能力，能理解圖片、截圖、圖表、白板照片甚至手寫字跡，無需手動輸入即可分析模式。
技術創新：採用「多令牌預測」（multi-token prediction）技術，一次預測多個字，提升回應速度且不損失品質。
商業友好授權：採用 Apache 2.0 授權，允許免費商業使用，無奇怪限制條款。
本地運行與低防護：強調可免費在用戶自己的電腦上運行，無需每月付費給大型科技公司；內建防護機制（guardrails）較少，回答更直接明確。
推廣與應用：由 SEO Agency Goldie Agency CEO Julian Goldie 的數位分身介紹，推廣其付費社群「AI profit boardroom」及免費社群「AI success lab」。

詳細大綱

I. 模型發布與核心規格

發布者：小型團隊 Empero AI。
產品名稱：Qethos 27B（Qethos 9B 的擴充版）。
主要特性：
體積約為小版本的三倍。
保留所有小版本的功能，未進行刪減。
可在用戶本地電腦免費運行。

II. 核心功能解析

海量記憶體（上下文窗口）：
容量：超過 100 萬字。
應用場景：一次性讀取數百頁筆記、舊郵件、客戶問題；記住所有歷史對話並協助撰寫新內容。
對比：傳統 AI 在對話變長時會遺忘資訊，此模型不會。
視覺識別能力（Vision）：
功能：能看懂圖片、截圖、圖表、白板照片及手寫字跡。
應用場景：上傳雜亂的客戶反饋截圖或規劃會議白板照片，讓模型分析模式，無需手動輸入。
範例：上傳前 10 名表現最好的帖子截圖，讓模型找出成功模式。
技術原理：
智能索引機制：類似檔案櫃而非凌亂的桌面，不需重複閱讀整個堆疊，直接定位資訊。
多令牌預測（Multi-token prediction）：
傳統 AI：一次寫一個字，停頓思考。
Qethos 27B：一次預測多個字（類似精準的自動補全），提升回應速度且不損失品質。

III. 授權與商業應用優勢

授權條款：Apache 2.0。
意義：任何人可使用、建構，甚至用於商業運作，無奇怪的限制條款。
對比：許多大型 AI 模型有商業使用限制。
本地運行優勢：
無需每月付費給第三方 AI 工具。
下載後歸用戶所有，自行運行。
低防護機制（Fewer Guardrails）：
相比大型公司模型，內建限制較少。
結果：能直接、明確地回答商業問題，避免含糊或拒絕回答。

IV. 開發背景與技術來源

訓練數據：
基於較小模型 Qethos 9B 的訓練方法。
使用來自最先進 AI 系統的大量「推理數據」（reasoning data）。
擴展方式：將相同的訓練方法擴展至更大模型，從而快速提升品質。

V. 實際使用案例

客服自動化：
輸入常見客戶問題批次。
要求模型草擬簡潔、清晰的回答。
內容策略分析：
上傳過去 20 篇帖子的單一檔案。
分析觀眾實際回應的主題，識別模式，節省分析時間。

VI. 市場意義與未來展望

市場趨勢：
小型團隊正在縮小與大型科技公司之間的技術差距。
具備高記憶體與視覺功能的工具正變得更易於獲取且成本更低。
版本狀態：
目前為「預發布檢查點」（pre-release checkpoint）。
未來將推出經過更多微調（fine-tuned）的後續版本，預計更銳利、一致。
早期測試影響：實際日常任務表現仍會隨測試持續調整。

VII. 行動建議與推廣

對非技術用戶：
暫不需下載，但需了解此類模型的存在。
預期未來數月內將內建於更多現有工具中。
對商業用戶：
從實際問題開始嘗試（如整理客戶訊息、摘要筆記）。
若需完整設定指南，可加入「AI profit boardroom」。
推廣資源：
AI profit boardroom：付費社群，提供每週教練電話、提示詞庫、社區支持。
AI success lab：免費社群，提供影片筆記及 100+ AI 應用案例，擁有超過 87,000 名成員。

工具 / 模型 / 名詞整理

Qethos 27B：影片主要介紹的 AI 模型，由 Empero AI 發布。
Qethos 9B：Empero AI 發布的較小版本模型，Qethos 27B 是其擴充版。
Empero AI：開發 Qethos 系列模型的小型團隊。
Apache 2.0：模型使用的授權協議，允許免費商業使用。
SEO Agency Goldie Agency：講者所屬的機構。
Julian Goldie：SEO Agency Goldie Agency 的 CEO。
AI profit boardroom：講者推廣的付費社群/工具套件。
AI success lab：講者推廣的免費社群。
多令牌預測（Multi-token prediction）：提升回應速度的技術，一次預測多個字。
預發布檢查點（pre-release checkpoint）：模型目前的狀態描述。

操作流程整理

下載與運行：用戶下載模型後，在本地電腦上免費運行，無需每月付費給第三方。
輸入資料：

針對客服：輸入常見客戶問題批次。
針對內容策略：上傳過去帖子的單一檔案或截圖（如前 10 名表現最好的帖子）。

模型處理：

利用超大上下文窗口（100 萬字）一次性讀取大量資訊。
利用視覺能力分析圖片、截圖或手寫字跡。
利用多令牌預測技術快速生成回應。

獲取結果：

獲得簡潔、清晰的客服回答草稿。
獲得觀眾回應主題的分析與成功模式識別。

後續行動：

非技術用戶可等待未來內建於現有工具。
商業用戶可加入「AI profit boardroom」獲取設定指南與教練支持。

值得注意的限制或風險

版本狀態：目前為「預發布檢查點」（pre-release checkpoint），實際日常任務表現仍會隨測試持續調整，未來可能有更多微調版本。
本地運行硬體需求：雖強調免費本地運行，但未提及具體硬體規格要求，用戶需自行確認電腦是否能負荷。
防護機制較少：內建防護機制（guardrails）較少，雖能直接回答商業問題，但也可能導致回答缺乏某些大型模型常見的安全限制或過濾。
小型團隊開發：由小型團隊 Empero AI 開發，相較於大型科技公司，其長期支援與生態系整合能力可能較弱。

逐字稿辨識疑點

Qethos / Quethos：逐字稿中多次出現「Qethos 27B」、「Qethos 9B」以及「Quethos-27B」等拼寫。考慮到常見 AI 模型命名習慣，此名稱疑似為聽寫錯誤或特定品牌拼寫，需查證實際模型名稱是否為其他拼寫（例如 Qwen, Llama 等，但不得自行更動）。
Empero AI：逐字稿中出現此團隊名稱，需查證是否為真實存在的開發團隊名稱，或為聽寫錯誤（例如 Emperor AI 或其他變體）。
iProfit boardroom：講者在部分段落稱呼推廣產品為「iProfit boardroom」，與前文「AI profit boardroom」拼寫不一致，需查證正確產品名稱。
1 million words：模型記憶體容量標示為「100 萬字」，需查證此技術規格是否準確，或是否指「tokens」而非「words」。
pre-release checkpoint：模型狀態描述為「預發布檢查點」，需確認此為官方定義的技術術語或講者自行描述。
Qwythos-27B-v1：影片標題中的名稱與筆記中的「Qethos 27B」拼寫差異巨大，需確認是否為同一模型的不同標記或辨識錯誤。

生字列表

生字	讀音	類型	中文
noise	/nɔɪz/	noun	噪音；無效資訊
builder	/ˈbɪldər/	noun	開發者；建構者
wild	/waɪld/	adjective	驚人的；不可思議的
piled up	/paɪld ʌp/	phrasal verb	堆積；累積
avatar	/ˈævətɑːr/	noun	分身；化身
leads	/liːdz/	noun	潛在客戶
maxed out	/mækst aʊt/	phrasal verb	達到極限；用盡
cover to cover	/ˈkʌvər tu ˈkʌvər/	adverbial phrase	從頭到尾；完整
filing cabinet	/ˈfaɪlɪŋ ˈkæbɪnɪt/	noun	檔案櫃
big deal	/bɪɡ diːl/	noun phrase	大事；重要事物
whiteboard	/ˈwaɪtˌbɔːrd/	noun	白板
under the hood	/ˈʌndər ðə hʊd/	idiom	底層；內部機制
wishy-washy	/ˈwɪʃiˌwɒʃi/	adjective	含糊不清的；軟弱的
checkpoint	/ˈtʃekpɔɪnt/	noun	檢查點；階段性版本

生字解說

noise /nɔɪz/

noun · B2

意思：噪音；無效資訊

解說：在此指大量無意義或干擾性的資訊，與真正重要的內容形成對比。

影片原句
Most AI news is noise, this one isn't.
大多數 AI 新聞都是噪音，但這個不是。

延伸例句
Ignore the noise and focus on the data.
忽略那些無效資訊，專注於數據。

builder /ˈbɪldər/

noun · C1

意思：開發者；建構者

解說：在科技領域，指開發軟體、模型或系統的人員。

影片原句
Qethos 27B and it's the big brother to their smaller Qethos 9B model that a lot of builders were already using.
Qethos 27B，它是他們較小的 Qethos 9B 模型的「大哥」，而許多開發者已經在使用那個較小的模型。

延伸例句
The builder released a new update for the app.
開發者為該應用程式發布了新更新。

wild /waɪld/

adjective · B2

意思：驚人的；不可思議的

解說：口語用法，形容事物程度之深超出預期，令人驚訝。

影片原句
Let's talk numbers first because they're wild.
讓我們先來看看數字，因為它們非常驚人。

延伸例句
The prices at that restaurant are wild.
那家餐廳的價格驚人得離譜。

piled up /paɪld ʌp/

phrasal verb · B2

意思：堆積；累積

解說：形容物品或任務大量聚集，通常暗示難以處理。

影片原句
old emails, and customer questions piled up.
舊電子郵件和客戶問題堆積在一起。

延伸例句
Unpaid bills piled up on the desk.
未付帳單堆積在書桌上。

avatar /ˈævətɑːr/

noun · C1

意思：分身；化身

解說：指代表某人的數位形象或虛擬代理人。

影片原句
I'm the digital avatar of Julian Goldie, CEO of SEO Agency Goldie Agency.
我是 Julian Goldie 的數位分身，他是 SEO Agency Goldie Agency 的首席執行官。

延伸例句
He created an avatar to represent him in the virtual world.
他創建了一個分身來代表他在虛擬世界中的形象。

leads /liːdz/

noun · B2

意思：潛在客戶

解說：商業術語，指可能對產品或服務感興趣的聯絡對象。

影片原句
Whilst he's helping clients get more leads and customers, I'm here to help you get the latest AI updates.
當他幫助客戶獲得更多潛在客戶和顧客時，我在這裡幫助你獲取最新的 AI 更新。

延伸例句
The sales team is working hard to generate new leads.
銷售團隊正努力開發新的潛在客戶。

maxed out /mækst aʊt/

phrasal verb · B2

意思：達到極限；用盡

解說：指達到容量、能力或數值的最高點。

影片原句
A year ago, most AI tools maxed out around a few thousand words before they started forgetting things.
一年前，大多數 AI 工具在開始遺忘內容之前，最多只能處理幾千字。

延伸例句
My credit card is maxed out.
我的信用卡已刷到最高額度。

cover to cover /ˈkʌvər tu ˈkʌvər/

adverbial phrase · B2

意思：從頭到尾；完整

解說：形容閱讀或處理內容時，從開始到結束全部涵蓋。

影片原句
Now we've got a model that remembers the whole book cover to cover while it writes the next chapter.
現在我們擁有一款能記住整本書從封面到封底的模型，同時它正在撰寫下一章。

延伸例句
I read the manual cover to cover to understand the settings.
我從頭到尾閱讀手冊以了解設定。

filing cabinet /ˈfaɪlɪŋ ˈkæbɪnɪt/

noun · B1

意思：檔案櫃

解說：比喻具有組織性和檢索功能的儲存系統。

影片原句
This new model works more like a filing cabinet with a smart index.
這個新模型的運作方式更像是一個帶有智慧索引的檔案櫃。

延伸例句
Organize your documents in a filing cabinet.
將你的文件整理在檔案櫃中。

big deal /bɪɡ diːl/

noun phrase · B1

意思：大事；重要事物

解說：指具有重大影響或意義的事情。

影片原句
Now here's the part I actually think is the biggest deal.
現在，這是我認為真正最具影響力的部分。

延伸例句
It's not a big deal if you're late.
如果你遲到，這不是什麼大事。

whiteboard /ˈwaɪtˌbɔːrd/

noun · B1

意思：白板

解說：用於書寫或繪圖的光滑表面，常見於會議室。

影片原句
a screenshot of feedback from a customer or a photo of a whiteboard from a planning session,
來自客戶的反饋截圖，或是規劃會議中白板的照片，

延伸例句
Write the ideas on the whiteboard.
將想法寫在白板上。

under the hood /ˈʌndər ðə hʊd/

idiom · C1

意思：底層；內部機制

解說：比喻技術或系統的內部運作原理。

影片原句
There's a feature under the hood called multi-token prediction,
在底層有一個稱為多詞元預測的功能，

延伸例句
Let's look under the hood to see how the engine works.
讓我們看看引擎內部的運作方式。

wishy-washy /ˈwɪʃiˌwɒʃi/

adjective · C1

意思：含糊不清的；軟弱的

解說：形容回答或立場不堅定、缺乏明確性。

影片原句
If you've ever asked a big AI tool for something and gotten a wishy-washy non-answer, this is built to avoid that.
如果你曾經向大型 AI 工具請求某事，卻得到一個含糊不清的無效回答，這個模型就是為了避免這種情況而設計的。

延伸例句
His answer was wishy-washy and didn't help at all.
他的回答含糊不清，完全沒有幫助。

checkpoint /ˈtʃekpɔɪnt/

noun · B2

意思：檢查點；階段性版本

解說：在開發過程中，指某個特定時間點的模型狀態或版本。

影片原句
It's what's called a pre-release checkpoint, meaning there's another version coming that gets even more fine tuned after this one.
它被稱為預發布檢查點，意味著在此版本之後，還會有另一個經過更細緻調整的版本即將推出。

延伸例句
Save your progress at every checkpoint.
在每個檢查點保存你的進度。

句型解說（含實例）

not one..., not one..., [but] a whole...

意思：不是一……，不是一……，而是整……

接續：not + noun phrase, not + noun phrase, (but) + noun phrase

解說：用於強調後者與前兩者的巨大差異，突出後者的規模或完整性。

影片原句
This model can hold over 1 million words in its memory at one time, not one page, not one chapter, a whole shelf of books all at once, all in its head while it works.
這個模型一次可以在其記憶體中容納超過一百萬個字，不是一頁，不是一章，而是整排書籍同時全部放在它的頭腦中，同時它正在運作。

實例

It's not a small gift, not a medium gift, a whole fortune.
那不是一份小禮物，不是一份中等禮物，而是一整筆財富。
Don't just read one sentence, not one paragraph, read the whole book.
不要只讀一句話，不要只讀一段，讀完整本書。

Say you've got...

意思：假設你有……

接續：Say + subject + verb + object

解說：用於引入一個假設性的場景或情況，以便進行說明或建議。

影片原句
Say you've got hundreds of pages of notes, old emails, and customer questions piled up.
假設你有數百頁的筆記、舊電子郵件和客戶問題堆積在一起。

實例

Say you need to hire a new employee.
假設你需要僱用一名新員工。
Say you want to travel to Japan next year.
假設你想明年去日本旅遊。

That's like trying to...

意思：這就像試圖……

接續：That's like + gerund phrase

解說：用於通過比喻來解釋某個概念或情況的荒謬性或困難度。

影片原句
That's like trying to write a book while only remembering the last paragraph you wrote.
這就像試圖寫一本書，卻只記得你寫的最後一段。

實例

That's like trying to swim against a strong current.
這就像試圖逆著強烈的水流游泳。
That's like trying to catch a bus with no doors.
這就像試圖搭乘一扇門都沒有的巴士。

It doesn't need to..., It just...

意思：它不需要……，它只是……

接續：It doesn't need to + verb, It just + verb

解說：用於對比舊方法與新方法的效率，強調新方法的簡潔性。

影片原句
It doesn't need to reread the whole pile every time. It just knows exactly where to look.
它不需要每次重新讀取整堆文件。它只是清楚知道該往哪裡查找。

實例

You don't need to memorize everything. You just need to know where to find it.
你不需要記住所有事情。你只需要知道在哪裡找到它。
It doesn't need to calculate every step. It just follows the rule.
它不需要計算每一步。它只是遵循規則。

Think of someone...

意思：想像有人……

接續：Think of + noun/pronoun + gerund/participle

解說：用於引導聽者進行想像，以幫助理解抽象概念。

影片原句
Think of someone typing with auto-complete that's actually right most of the time instead of typing every letter by hand.
想像有人打字使用大部分時間都準確的自動補全功能，而不是手動輸入每個字母。

實例

Think of a world without internet.
想像一個沒有網路的世界。
Think of yourself standing on the stage.
想像你自己站在舞台上。

In plain terms, that means...

意思：簡單來說，這意味著……

接續：In plain terms, that means + clause

解說：用於將複雜或專業的術語轉化為通俗易懂的解釋。

影片原句
Impero AI released this under a license called Apache 2.0. In plain terms, that means anyone can use it, build with it and even use it to run a business with no weird fine print stopping them.
Impero AI 以一個名為 Apache 2.0 的授權條款發布了它。簡單來說，這意味著任何人都可以使用它，基於它進行開發，甚至用它來經營業務，而沒有什麼奇怪的細則限制。

實例

In plain terms, that means you have to pay extra.
簡單來說，這意味著你需要額外付費。
In plain terms, that means the project is cancelled.
簡單來說，這意味著專案已取消。

That's exactly why...

意思：這正是……的原因

接續：That's exactly why + clause

解說：用於強調某個決定或行動的具體原因。

影片原句
That's exactly why we built the AI profit boardroom around tools like this one.
這正是我們建構 AI 利潤董事會議室，圍繞這類工具的原因。

實例

That's exactly why I decided to quit my job.
這正是我決定辭職的原因。
That's exactly why we need to save money.
這正是我們需要儲蓄的原因。