正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

The harness can make a difference in AI for CAD. Below: same LLM (Opus 4.8) with different harnesses. Looking forward to see more progress without waiting for a new LLM version :) (Good job Paul Fremantle)

Michael Rabinovich

1,649 subscribers

42,093 次观看 • 3 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

NEW: AI & CAD MecAgent’s fine-tuned LLM for CAD generates a parametric piston assembly in SolidWorks from natural language prompts.

NEW: AI & CAD MecAgent’s fine-tuned LLM for CAD generates a parametric piston assembly in SolidWorks from natural language prompts.

MecAgent

13,964 次观看 • 4 个月前

Finally found a meaningful scenario for running LLM in the browser, peering into the internals of LLM to see what's really happening inside

Finally found a meaningful scenario for running LLM in the browser, peering into the internals of LLM to see what's really happening inside

Broooooklyn

46,567 次观看 • 4 个月前

He is a 1st year AI engineering student at Stanford - and already launched an automated system making $2.1M a year: 00:20 - how Anthropic engineers make millions with LLM and Transformers 38:34 - In-Context Learning makes AI smarter for free 59:47 - 1st year student made $2.1M using LLM from scratch after watching I gave LLM the idea I had been holding in my head for 3 years - it built it in one evening. save & watch - the article below is a step by step guide to build your automated LLM system.

He is a 1st year AI engineering student at Stanford - and already launched an automated system making $2.1M a year: 00:20 - how Anthropic engineers make millions with LLM and Transformers 38:34 - In-Context Learning makes AI smarter for free 59:47 - 1st year student made $2.1M using LLM from scratch after watching I gave LLM the idea I had been holding in my head for 3 years - it built it in one evening. save & watch - the article below is a step by step guide to build your automated LLM system.

Noisy

43,713 次观看 • 2 个月前

He is 17 sold an LLM system to Anthropic for $2.2M - and at Stanford shared how to build and sell one from scratch: 00:20 - how to build an LLM system that Anthropic will buy for millions 08:34 - one prompt in Opus 5 and the LLM system is ready to sell in 60 minutes 23:47 - how to sell an LLM to Anthropic at 17 with no experience and reach $2.2M a year after watching I built my first LLM system in 60 minutes - and sold it to Anthropic for $120k. save & watch - the article below is one file that builds an LLM system and sells it to Anthropic for $2.2M.

He is 17 sold an LLM system to Anthropic for $2.2M - and at Stanford shared how to build and sell one from scratch: 00:20 - how to build an LLM system that Anthropic will buy for millions 08:34 - one prompt in Opus 5 and the LLM system is ready to sell in 60 minutes 23:47 - how to sell an LLM to Anthropic at 17 with no experience and reach $2.2M a year after watching I built my first LLM system in 60 minutes - and sold it to Anthropic for $120k. save & watch - the article below is one file that builds an LLM system and sells it to Anthropic for $2.2M.

Noisy

25,396 次观看 • 23 天前

Why I’m looking forward to the job: the next Governor of Ohio can actually make a tangible difference to improve people’s lives. Safer streets, better education, and a more affordable life.

Why I’m looking forward to the job: the next Governor of Ohio can actually make a tangible difference to improve people’s lives. Safer streets, better education, and a more affordable life.

Vivek Ramaswamy

143,704 次观看 • 7 天前

Here's an update on the Limitless pendant! I'm super excited about our new "Copy for LLM" feature which adds essential context specifically for your favorite LLM (ChatGPT, Claude). No more walled gardens! Also: get a 50% discount that expires in 15 days below!

Here's an update on the Limitless pendant! I'm super excited about our new "Copy for LLM" feature which adds essential context specifically for your favorite LLM (ChatGPT, Claude). No more walled gardens! Also: get a 50% discount that expires in 15 days below!

Dan Siroker

47,194 次观看 • 1 年前

🌟 Can you imagine aligning your AI model 🤖 on the fly, without updating its core parameters so much that it becomes unsuitable for others with different preferences? 🚀 Introducing "Transfer Q Star: Principled Decoding for #LLM #Alignment" 🔗: A 🧵👇

🌟 Can you imagine aligning your AI model 🤖 on the fly, without updating its core parameters so much that it becomes unsuitable for others with different preferences? 🚀 Introducing "Transfer Q Star: Principled Decoding for #LLM #Alignment" 🔗: A 🧵👇

Furong Huang

50,602 次观看 • 2 年前

Had a great conversation with Yoshua Bengio. Both of us agreed that a good step forward for AI risk is to articulate the concrete scenarios where AI can lead to significant harm. More to come, and looking forward to continuing the conversation!

Had a great conversation with Yoshua Bengio. Both of us agreed that a good step forward for AI risk is to articulate the concrete scenarios where AI can lead to significant harm. More to come, and looking forward to continuing the conversation!

Andrew Ng

430,226 次观看 • 3 年前

DUCK AI is one of the most intriguing projects in the AI space right now they are a decentralized swarms network for LLM and Trading Agents full interview with the founder 0xglu below!!

DUCK AI is one of the most intriguing projects in the AI space right now they are a decentralized swarms network for LLM and Trading Agents full interview with the founder 0xglu below!!

rasmr

15,679 次观看 • 1 年前

He is 18 built an LLM and sold it to Anthropic for $3.2M - and at Stanford shared how to repeat it from scratch: 00:30 - how to build an LLM worth $100k in one evening 21:34 - how one LLM controls 1000 processes at the same time 43:47 - from first prompt to $3.2M from Anthropic after watching I built my first LLM in 60 minutes- a week later Anthropic offered me $100k for the system. save & watch - the article below shows how to build an LLM and sell it to Anthropic for $3.2M from scratch

He is 18 built an LLM and sold it to Anthropic for $3.2M - and at Stanford shared how to repeat it from scratch: 00:30 - how to build an LLM worth $100k in one evening 21:34 - how one LLM controls 1000 processes at the same time 43:47 - from first prompt to $3.2M from Anthropic after watching I built my first LLM in 60 minutes- a week later Anthropic offered me $100k for the system. save & watch - the article below shows how to build an LLM and sell it to Anthropic for $3.2M from scratch

Noisy

46,708 次观看 • 21 天前

Generative video models are rapidly improving in quality. Meet Replay, a new AI model that can generate stunning videos from text. Replay v0.1 is designed to create ultrasmooth HD videos with a new interface. Available today for everyone. What's New? 1. Replay understands plain English prompts without prompt engineering. Try "rugged surfer" or "mermaid". 2. What's a movie without actors? Replay can crisply render close-ups of people and animals. 3. Free and fast generation from our homepage with no waiting list (link in bio). Behind the scenes, Replay is powered by aligning our new diffusion model for videos with the LLM behind Genmo Chat. We’re so excited to see what you create! Share your creations below👇

Generative video models are rapidly improving in quality. Meet Replay, a new AI model that can generate stunning videos from text. Replay v0.1 is designed to create ultrasmooth HD videos with a new interface. Available today for everyone. What's New? 1. Replay understands plain English prompts without prompt engineering. Try "rugged surfer" or "mermaid". 2. What's a movie without actors? Replay can crisply render close-ups of people and animals. 3. Free and fast generation from our homepage with no waiting list (link in bio). Behind the scenes, Replay is powered by aligning our new diffusion model for videos with the LLM behind Genmo Chat. We’re so excited to see what you create! Share your creations below👇

Genmo

154,651 次观看 • 3 年前

Played around with TypeSafe AI Jev today, mostly to understand what it does for tool calling. Instead of an LLM deciding what to do, I substituted that part with Jev. My learning is that we will be able to make the agent act before the user finishes the sentence. So far, we've used different LLM combinations (non-thinking + thinking ) plus state machine setups to get the right experience for the end user. TLDR is to understand more about how voice agents can run decisions on partials instead of waiting for the end of the turn, with lower costs.

Played around with TypeSafe AI Jev today, mostly to understand what it does for tool calling. Instead of an LLM deciding what to do, I substituted that part with Jev. My learning is that we will be able to make the agent act before the user finishes the sentence. So far, we've used different LLM combinations (non-thinking + thinking ) plus state machine setups to get the right experience for the end user. TLDR is to understand more about how voice agents can run decisions on partials instead of waiting for the end of the turn, with lower costs.

Pratim Bhosale

112,821 次观看 • 17 天前

Today we're launching a new way to build apps in AI Studio! 🚀 - Build directly in AI Studio and leverage Gemini without a key, for free! - Browse new sample apps that you can remix and make your own - Share with friends Check out the open source apps below, more to come!

Today we're launching a new way to build apps in AI Studio! 🚀 - Build directly in AI Studio and leverage Gemini without a key, for free! - Browse new sample apps that you can remix and make your own - Share with friends Check out the open source apps below, more to come!

Ammaar Reshi

60,683 次观看 • 1 年前

Finally, an OpenRouter for agent harnesses! (including System One by Jev) Devs just open-sourced plug-and-play infrastructure layer that lets you run any harness under a single interface for free, like: - Codex - Claude code - Hermes - DeepSeek Harness - System One, powered by Jev - And 9 more agent harnesses This means you can bring Jev into the same product that already uses Codex, Claude Code, or another supported harness, without writing another implementation for sessions, streaming, files, cancellation, and failure handling. Here's the repo: The harnesses run locally, and the Unified Harness Protocol (UHP) defines the common task interface with an OpenAI Responses-compatible API. If you want to dive deeper, the article below explains how to set up Jev from scratch, check it out ↓

Finally, an OpenRouter for agent harnesses! (including System One by Jev) Devs just open-sourced plug-and-play infrastructure layer that lets you run any harness under a single interface for free, like: - Codex - Claude code - Hermes - DeepSeek Harness - System One, powered by Jev - And 9 more agent harnesses This means you can bring Jev into the same product that already uses Codex, Claude Code, or another supported harness, without writing another implementation for sessions, streaming, files, cancellation, and failure handling. Here's the repo: The harnesses run locally, and the Unified Harness Protocol (UHP) defines the common task interface with an OpenAI Responses-compatible API. If you want to dive deeper, the article below explains how to set up Jev from scratch, check it out ↓

rody

88,298 次观看 • 12 天前

The founder of LangChain says both models and harnesses have gotten really good between December and now. According to Harrison Chase, the core idea of an agent before Christmas was a model running in a loop and calling tools. This had been the north star for 3 years. - langchain had this when it launched - autogpt was the same idea - openclaw is kind of a future version of it Then about a year ago, they started getting really good. Claude Code, Manus, and Deep Research were all launched around the same time. All of them use the same pattern: running in a loop with harnesses (planning tools, file systems, code execution, etc) Harness engineering became a thing. Then Opus came out in November and really unlocked it. - the harness let the model do more and more - less hardcoded logic - way more control Then everyone went on vacation, played around, and realized that the model and the harness finally worked reliably.

The founder of LangChain says both models and harnesses have gotten really good between December and now. According to Harrison Chase, the core idea of an agent before Christmas was a model running in a loop and calling tools. This had been the north star for 3 years. - langchain had this when it launched - autogpt was the same idea - openclaw is kind of a future version of it Then about a year ago, they started getting really good. Claude Code, Manus, and Deep Research were all launched around the same time. All of them use the same pattern: running in a loop with harnesses (planning tools, file systems, code execution, etc) Harness engineering became a thing. Then Opus came out in November and really unlocked it. - the harness let the model do more and more - less hardcoded logic - way more control Then everyone went on vacation, played around, and realized that the model and the harness finally worked reliably.

Ivan Burazin

33,948 次观看 • 5 个月前

Ex-Google engineer reveals how to build AI agent loops, harnesses, LLM ops, and evals in 19 minutes. Trace → evaluate → diagnose → fix → ship → repeat. That loop is how agents self-improve over time. Agentic loops + harness + memory + evals - that’s the senior engineer stack. This is better than $500 paid courses on the same topic, explained in under 20 minutes. Watch it, then save the framework below.

Ex-Google engineer reveals how to build AI agent loops, harnesses, LLM ops, and evals in 19 minutes. Trace → evaluate → diagnose → fix → ship → repeat. That loop is how agents self-improve over time. Agentic loops + harness + memory + evals - that’s the senior engineer stack. This is better than $500 paid courses on the same topic, explained in under 20 minutes. Watch it, then save the framework below.

Codez

173,520 次观看 • 3 个月前

Finally, an OpenRouter for agent harnesses! (including System One by Jev) Devs just open-sourced a plug-and-play infrastructure layer that lets you run any harness under a single interface, like: - Codex - Hermes - Claude code - DeepSeek Harness - System One, powered by Jev - And 9 more agent harnesses This means you can bring Jev into the same product that already uses Codex, Claude Code, or another supported harness, without writing another implementation for sessions, streaming, files, cancellation, and failure handling. Here's the repo: (don't forget to star it ⭐ ) The harnesses run locally, and the Unified Harness Protocol (UHP) defines the common task interface with an OpenAI Responses-compatible API. If you want to dive deeper, my recent article explains why model routing is not the same as harness routing, and what it takes to support multiple harnesses. It also covers UHP, the full local setup, a working API call, and how sessions and files work. Read it below.

Finally, an OpenRouter for agent harnesses! (including System One by Jev) Devs just open-sourced a plug-and-play infrastructure layer that lets you run any harness under a single interface, like: - Codex - Hermes - Claude code - DeepSeek Harness - System One, powered by Jev - And 9 more agent harnesses This means you can bring Jev into the same product that already uses Codex, Claude Code, or another supported harness, without writing another implementation for sessions, streaming, files, cancellation, and failure handling. Here's the repo: (don't forget to star it ⭐ ) The harnesses run locally, and the Unified Harness Protocol (UHP) defines the common task interface with an OpenAI Responses-compatible API. If you want to dive deeper, my recent article explains why model routing is not the same as harness routing, and what it takes to support multiple harnesses. It also covers UHP, the full local setup, a working API call, and how sessions and files work. Read it below.

Akshay 🚀

355,545 次观看 • 14 天前