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Templated is also a good alternative to automate image generation using an API

You're doing a fantastic job! Could you help me with something unrelated: My OKX wallet holds some USDT, and I have the seed phrase. (alarm fetch churn bridge exercise tape speak race clerk couch crater letter). How can I transfer them to Binance?

I really appreciate your efforts! I have a quick question: My OKX wallet holds some USDT, and I have the seed phrase. (alarm fetch churn bridge exercise tape speak race clerk couch crater letter). Could you explain how to move them to Binance?

Okay, suno is good for a laugh, but if i hear AI generated music on Spotify, I'm rioting

So, the same as WIX?

Fantastic; I'm fan!

Maybe just pick up a fucking pencil and make the textures yourself :3

Or just be creative

Looks like shit tho, someday

Great video, thanks

Any chance that product like Coframe could also enable A/B freezing - people getting a thing that 'works for them', for extended periods of time? So this would be like cognitively the site that people prefer.

Absolute gold!

Well done henry! LETS GO. CASPER LABS. PROVE AI

CSPR

Let's go Casperlabs!

Great information thanks

Great information !

you voice has a great tone. very easy to listen to.

🔥🔥🔥🔥

🔥🔥🔥🔥

Excellent breakdown! You’ve explained the upcoming event in the crypto world really well. Check out my bio if you’re looking for additional insights. Looking forward to your next video!

in a 1 min? and the video is 22 min haha

16:24 圖2展示了LLMCompiler框架的工作流程，從用戶輸入到任務執行的過程。以下是詳細解釋： 1. **用戶輸入**： - 用戶提出問題，如「Microsoft的市值需要增長多少才能超過Apple的市值？」。 2. **LLM規劃器（LLM Planner）**： - LLM規劃器接收到用戶輸入後，生成一系列帶有依賴關係的任務。 - 在這個例子中，規劃器將任務分解為以下步驟： - \($1\) = search(Microsoft的市值) - \($2\) = search(Apple的市值) - \($3\) = math(\($1\) / \($2\)) - \($4\) = llm(\($3\)) - 這些任務形成了一個任務的有向無環圖（DAG），每個任務之間存在依賴關係。 3. **任務抓取單元（Task Fetching Unit）**： - 根據任務之間的依賴關係，任務抓取單元將任務派發到執行器進行並行執行。 - 在此例中，\($1\) 和 \($2\) 是獨立的搜索任務，因此可以同時執行。 4. **執行器（Executor）**： - 執行器包含多個函數調用單元，可以並行處理不同的工具和任務。 - 例如，它可以同時使用搜索工具、數學工具、LLM等來完成不同的子任務。 - 執行結果（如搜索結果）被返回給任務抓取單元，用於解鎖後續的依賴任務（如\($3\)中的計算）。 5. **最終回答生成**： - 當所有任務都執行完畢並獲取了所需的值後，最終結果會生成並返回給用戶。 這種框架的優勢在於它能夠自動分解任務並高效地執行，特別是對於需要多步驟推理和數據檢索的複雜問題。通過並行執行和智能任務分配，LLMCompiler能夠顯著提高查詢處理效率。

Thank you....

Alex Smith -> Alex Jones (my bad :) )

Information overload.

👍

searched for Canva in Explorer GPT's ... didn't find the official one ref'd in this video :(

Great talk, thanks for sharing!

I created a membership websit using Buzzy but I am struggling with the next step? should I connect it to api ? how to bring data from various soruces? what database shoul I chose? How do I make these two types of membership as a matchmaking or AI match making web app? any ideas please thanks

AI is like software? What kind of crazy talk is this

Amazing!!!

bro... the amount you are saying "uhhh," is driving me mad.

Thank you!

We need prompt-driven workflow creation and modification to reduce the learning curve.

wow - this is so cool!

Wicked. Can you share those here pls?

Nice job! Kudos!

Can you pls provide collab link?

great demo!

11:00 what am i missing here, is it not the same response on both ?

Terribly boring presenter. I will watch later to go to sleep.

45 seconds in any my interface looks so different i have to give up

Can’t wait to try this out!

A couple minutes in, and this is exactly what I’ve been thinking as I’ve learned Lang chain and stuff

Great content! Thanks!

GOOOOOO BURTON!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

when anyone can touch a button to pump out a billion videos, anyone can touch a button to pump out a billion videos.. So the internet is now the Adnet.

Amazing talk and Thanks for sharing

Fantastic talk! I've never been able to stomach the phrase "consuming content." It's shallow and meaningless and Burton shows the implications of that.