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AI agents often fail because their instructions, or skills, are manually modified with no guarantee of improvement. Learn how SkillOpt turns skill editing into a training process, making agent behavior more reliable without changing model weights:

Microsoft Research

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20,451 次观看 • 1 个月前 •via X (Twitter)

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Coding agents that actually get better the more your team uses them. Introducing Hivemind: continual learning for AI coding agents. Hivemind turns the traces from every agent your team runs (Claude Code, Codex, Cursor, Hermes, OpenClaw, Pi) into reusable skills, then pushes those skills across all of them. All on your cloud storage. Now with SkillOpt built in, your skills get trained: +19.1 points of accuracy in Claude Code, +24.8 in Codex, best or tied on all 52 setups tested. Open source, one line install.

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Support for Agent Plugins is live in Cursor (and I made a plugin for searching X!) Agent Plugins combine Skills and MCP servers into a single package Think about them like an Ikea furniture kit: your table comes with instructions, parts, and tools. You use the instructions to understand how things work (which tools on which parts), then use the tools to assemble your furniture Agent Plugins are bundles of tools & resources that allow agents to accomplish a goal: - MCP servers: fixed context and tools - Skills: dynamic instructions, loaded progressively Plugins bundle tools and instructions so your agent is able to use tokens efficiently Many of us already use MCP servers and skills together - Agent Plugins formalize this pattern for more powerful workflows I'm excited to see where the standard takes us!

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How will companies build agents that work alongside their employees? I discuss this with Guillermo Rauch, the CEO of Vercel, who’s building V... an internal agent used by almost 1,000 people across Vercel. We talk about: - The Openclaw Movement - How to get started building agents for your team? - Do we build a team of many agents or one GOD agent? - Hard parts of creating agents - Agents that delegate to other agents with different permissions and computer access - Eve The framework for building agents This is Agent Native #4 00:00 Intro 01:21 The State of Agent Adoption 07:26 "V" - Vercel's Internal Company Agent 14:32 Lessons from OpenClaw: Soul & Computer use 22:20 One God Agent OR a Team of Agents 27:11 Build Your Own Agent: Skills, Tools & Permissions 33:52 Proactive Agents, Triggers & Self-Improvement 40:30 Who Will Build Agents & Getting Started 46:22 Open Models - Cheaper, Faster, Better 53:05 Closing Thoughts: Owning Your Own Intelligence

Riley Brown

75,858 次观看 • 16 天前

New Course: Post-training of LLMs Learn to post-train and customize an LLM in this short course, taught by Banghua Zhu, Assistant Professor at the University of Washington University of Washington, and co-founder of @NexusflowX. Training an LLM to follow instructions or answer questions has two key stages: pre-training and post-training. In pre-training, it learns to predict the next word or token from large amounts of unlabeled text. In post-training, it learns useful behaviors such as following instructions, tool use, and reasoning. Post-training transforms a general-purpose token predictor—trained on trillions of unlabeled text tokens—into an assistant that follows instructions and performs specific tasks. Because it is much cheaper than pre-training, it is practical for many more teams to incorporate post-training methods into their workflows than pre-training. In this course, you’ll learn three common post-training methods—Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning (RL)—and how to use each one effectively. With SFT, you train the model on pairs of input and ideal output responses. With DPO, you provide both a preferred (chosen) and a less preferred (rejected) response and train the model to favor the preferred output. With RL, the model generates an output, receives a reward score based on human or automated feedback, and updates the model to improve performance. You’ll learn the basic concepts, common use cases, and principles for curating high-quality data for effective training. Through hands-on labs, you’ll download a pre-trained model from Hugging Face and post-train it using SFT, DPO, and RL to see how each technique shapes model behavior. In detail, you’ll: - Understand what post-training is, when to use it, and how it differs from pre-training. - Build an SFT pipeline to turn a base model into an instruct model. - Explore how DPO reshapes behavior by minimizing contrastive loss—penalizing poor responses and reinforcing preferred ones. - Implement a DPO pipeline to change the identity of a chat assistant. - Learn online RL methods such as Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO), and how to design reward functions. - Train a model with GRPO to improve its math capabilities using a verifiable reward. Post-training is one of the most rapidly developing areas of LLM training. Whether you’re building a high-accuracy context-specific assistant, fine-tuning a model's tone, or improving task-specific accuracy, this course will give you experience with the most important techniques shaping how LLMs are post-trained today. Please sign up here:

New Course: Post-training of LLMs Learn to post-train and customize an LLM in this short course, taught by Banghua Zhu, Assistant Professor at the University of Washington University of Washington, and co-founder of @NexusflowX. Training an LLM to follow instructions or answer questions has two key stages: pre-training and post-training. In pre-training, it learns to predict the next word or token from large amounts of unlabeled text. In post-training, it learns useful behaviors such as following instructions, tool use, and reasoning. Post-training transforms a general-purpose token predictor—trained on trillions of unlabeled text tokens—into an assistant that follows instructions and performs specific tasks. Because it is much cheaper than pre-training, it is practical for many more teams to incorporate post-training methods into their workflows than pre-training. In this course, you’ll learn three common post-training methods—Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning (RL)—and how to use each one effectively. With SFT, you train the model on pairs of input and ideal output responses. With DPO, you provide both a preferred (chosen) and a less preferred (rejected) response and train the model to favor the preferred output. With RL, the model generates an output, receives a reward score based on human or automated feedback, and updates the model to improve performance. You’ll learn the basic concepts, common use cases, and principles for curating high-quality data for effective training. Through hands-on labs, you’ll download a pre-trained model from Hugging Face and post-train it using SFT, DPO, and RL to see how each technique shapes model behavior. In detail, you’ll: - Understand what post-training is, when to use it, and how it differs from pre-training. - Build an SFT pipeline to turn a base model into an instruct model. - Explore how DPO reshapes behavior by minimizing contrastive loss—penalizing poor responses and reinforcing preferred ones. - Implement a DPO pipeline to change the identity of a chat assistant. - Learn online RL methods such as Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO), and how to design reward functions. - Train a model with GRPO to improve its math capabilities using a verifiable reward. Post-training is one of the most rapidly developing areas of LLM training. Whether you’re building a high-accuracy context-specific assistant, fine-tuning a model's tone, or improving task-specific accuracy, this course will give you experience with the most important techniques shaping how LLMs are post-trained today. Please sign up here:

Andrew Ng

125,146 次观看 • 1 年前

Google just dropped a free 1-hour course On building complete agent systems from scratch: 07:02 - Build your first AI agent 13:34 - Give every agent a specialized skill 23:08 - Engineer the right context 32:56 - Connect agents into graphs 54:11 - Run 1,000 agents in autonomous loops One agent saves you an hour A thousand agents replace the team you cannot afford to hire Most people are still learning how to prompt one chatbot Google is already teaching how to build entire agent systems Agent → Skills → Context → Graphs → Loops Single agents are the old workflow Agent swarms are the new one This free 1-hour course is worth more than most paid agent engineering programs Bookmark it before everyone starts building the same thing Then build your first agent with the guide below

Google just dropped a free 1-hour course On building complete agent systems from scratch: 07:02 - Build your first AI agent 13:34 - Give every agent a specialized skill 23:08 - Engineer the right context 32:56 - Connect agents into graphs 54:11 - Run 1,000 agents in autonomous loops One agent saves you an hour A thousand agents replace the team you cannot afford to hire Most people are still learning how to prompt one chatbot Google is already teaching how to build entire agent systems Agent → Skills → Context → Graphs → Loops Single agents are the old workflow Agent swarms are the new one This free 1-hour course is worth more than most paid agent engineering programs Bookmark it before everyone starts building the same thing Then build your first agent with the guide below

rari

28,241 次观看 • 20 天前

1/3 Money, money, money, moneyyyyy 💸💸💸💸 Today we’re making it possible for you to earn actual money from your Pika AI Self agent. Because we think your agent should work FOR you in every sense of the phrase. Every time someone talks with them, or uses one of their skills, you earn tokens redeemable for cash. Say goodbye to those deadbeat agents.

1/3 Money, money, money, moneyyyyy 💸💸💸💸 Today we’re making it possible for you to earn actual money from your Pika AI Self agent. Because we think your agent should work FOR you in every sense of the phrase. Every time someone talks with them, or uses one of their skills, you earn tokens redeemable for cash. Say goodbye to those deadbeat agents.

Pika

343,240 次观看 • 4 个月前

OpenAI just launched "agent skills", their version of what Claude calls skills. It finally clicked to me how these agents, skills and MCPs will work together. Basically, a skill is just the recipe for how something should be done, subagents are extra hands you spin up when the task is big, and MCPs are how the agent actually touches real systems like your repo or dashboards. Imagine a solo graphic designer setting up a “launch assets” skill: the skill defines brand rules, layouts, and export formats, sub-agents generate variations for social, web, and email in parallel, and MCPs pull from the actual Figma library and push finished assets into the client’s folder. Cool to see this kind of structure emerge in both OpenAI and Claude.... it feels like an early step toward a common way of building workflows that are reliable, composable, and actually useful in real work. I also gave away a trend, a business idea, an app ive been using for 14 years and more. (i answer every comment there) Happy building, my friends. Btw, what should I cover next? Or tag who should come on the show. Have a creative day.

GREG ISENBERG

70,525 次观看 • 8 个月前