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Stanford CS professor just dropped the ultimate framework for building self-learning ai agents. 65-minutes. free. by stanford ai faculty. "reward hacking - your agent is gonna learn to do exactly what it is that you tell him to do" here's what they cover: •solving the credit assignment problem and... show more

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14,835 views • 1 month ago •via X (Twitter)

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Amazon AI engineer just explained how to build agentic loops from scratch in under 10-minutes. this is one of the clearest explanations of AI agentic loops you’ll find anywhere. Loop + model + agent + hooks + MCP = an autonomous, self-improving system. People are paying $500 for courses that teach less than this. Watch it, then read the step-by-step guide below on building loops for your own agents.

Amazon AI engineer just explained how to build agentic loops from scratch in under 10-minutes. this is one of the clearest explanations of AI agentic loops you’ll find anywhere. Loop + model + agent + hooks + MCP = an autonomous, self-improving system. People are paying $500 for courses that teach less than this. Watch it, then read the step-by-step guide below on building loops for your own agents.

Codez

26,868 views • 22 days ago

Anthropic's Claude team just showed how to give your AI agents real memory in under 18 minutes. Free. From the people who built it. You've been building agents that forget everything between sessions. This fixes that tonight. One person + 10 agents with memory + dreaming = a team that runs 24/7, learns from every task, and improves itself overnight. Worth more than any $500 AI agent course. Bookmark it. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Anthropic's Claude team just showed how to give your AI agents real memory in under 18 minutes. Free. From the people who built it. You've been building agents that forget everything between sessions. This fixes that tonight. One person + 10 agents with memory + dreaming = a team that runs 24/7, learns from every task, and improves itself overnight. Worth more than any $500 AI agent course. Bookmark it. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

11,128 views • 2 months ago

Anthropic engineer just dropped a full walkthrough on building AI agents from scratch this single video is worth more than any $600 AI agents course here's what she covers: > what Agent, Environment, and Session actually mean > how to stream live events from your agent > wiring up custom tools from scratch > the server-side loop that makes agents actually work > roadmap to subagents, vaults, and memory 37 minutes that will answer every question you have about building agents the people who understand what Claude can actually do are building things everyone else thinks requires a whole team most of them aren't smarter, they just know features that most people have never heard of full breakdown on every Claude feature worth knowing in the article below

Anthropic engineer just dropped a full walkthrough on building AI agents from scratch this single video is worth more than any $600 AI agents course here's what she covers: > what Agent, Environment, and Session actually mean > how to stream live events from your agent > wiring up custom tools from scratch > the server-side loop that makes agents actually work > roadmap to subagents, vaults, and memory 37 minutes that will answer every question you have about building agents the people who understand what Claude can actually do are building things everyone else thinks requires a whole team most of them aren't smarter, they just know features that most people have never heard of full breakdown on every Claude feature worth knowing in the article below

Anatoli Kopadze

38,627 views • 1 month ago

AI Phd Professor just showed how to build Hidden Markov Models for time-series data. 61-minutes. free. By AI Researcher for Quants. "for stock market prediction is the same idea you want to predict what prices are going to be in the future" here's what they cover: •why the independent data assumption fails •calculating transition and emission distributions •predicting future states step-by-step •extracting hidden sequences with the Viterbi algorithm Bookmark it & watch today. Then read the article below.

AI Phd Professor just showed how to build Hidden Markov Models for time-series data. 61-minutes. free. By AI Researcher for Quants. "for stock market prediction is the same idea you want to predict what prices are going to be in the future" here's what they cover: •why the independent data assumption fails •calculating transition and emission distributions •predicting future states step-by-step •extracting hidden sequences with the Viterbi algorithm Bookmark it & watch today. Then read the article below.

venus

43,465 views • 1 month ago

Stanford professor just gave away the entire foundation of how AI Agents & automation actually works. 1-hour lecture. Tool calling. Multi-step workflows. Planning. Reflection. SAVE this to watch this before you open Netflix tonight. More valuable than 6 months of copying Make and n8n tutorials, for building Ai Agents Most people learn by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward instead of just entertaining you for 30 seconds. ↓ Why your automations keep breaking. You copied a Make tutorial. Built the exact workflow. Worked for a week. Then the API changed. The trigger failed. An edge case broke everything. You had no idea how to fix it. Because you never understood why it worked. You were copying keystrokes. The people shipping real automation were understanding architecture. ↓ What Stanford actually teaches. Tool calling: how an agent decides which tool to use by scoring each option against the current task state, not just matching keywords. ReAct loop: the agent reasons, acts, observes, then reasons again. Break this cycle and your workflow fails silently. Planning vs execution: why agents that plan all steps upfront break on dynamic inputs, and why iterative planners survive production. Memory architecture: short-term context for the current task, long-term vector memory for patterns. Most automations fail because they confuse the two. Reflection: how agents catch their own errors by evaluating outputs against original intent before moving to the next step. Tool composition: why chaining 10 tools blindly creates cascading failures, and how to structure dependencies so one broken node doesn't kill the whole workflow. This is the foundation behind every automation that actually works. Not prompting tricks. Not "10 best AI tools" reels. Actual architecture. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward. ↓ Your weekend plan. Tonight: watch the Stanford lecture. 1 hour. Saturday to Sunday: build 3 projects applying what you learned. Next 2 weekends: 6 more projects. 9 projects. 2 weeks. APIs, webhooks, LLM integration, real workflows. No theory. Just build. ↓ Stanford Agentic AI lecture: free on YouTube. Watch it this weekend or buy another $500 "AI automation course" in 2027 that teaches less than this one free lecture. Bookmark. Watch tonight. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward.

Stanford professor just gave away the entire foundation of how AI Agents & automation actually works. 1-hour lecture. Tool calling. Multi-step workflows. Planning. Reflection. SAVE this to watch this before you open Netflix tonight. More valuable than 6 months of copying Make and n8n tutorials, for building Ai Agents Most people learn by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward instead of just entertaining you for 30 seconds. ↓ Why your automations keep breaking. You copied a Make tutorial. Built the exact workflow. Worked for a week. Then the API changed. The trigger failed. An edge case broke everything. You had no idea how to fix it. Because you never understood why it worked. You were copying keystrokes. The people shipping real automation were understanding architecture. ↓ What Stanford actually teaches. Tool calling: how an agent decides which tool to use by scoring each option against the current task state, not just matching keywords. ReAct loop: the agent reasons, acts, observes, then reasons again. Break this cycle and your workflow fails silently. Planning vs execution: why agents that plan all steps upfront break on dynamic inputs, and why iterative planners survive production. Memory architecture: short-term context for the current task, long-term vector memory for patterns. Most automations fail because they confuse the two. Reflection: how agents catch their own errors by evaluating outputs against original intent before moving to the next step. Tool composition: why chaining 10 tools blindly creates cascading failures, and how to structure dependencies so one broken node doesn't kill the whole workflow. This is the foundation behind every automation that actually works. Not prompting tricks. Not "10 best AI tools" reels. Actual architecture. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward. ↓ Your weekend plan. Tonight: watch the Stanford lecture. 1 hour. Saturday to Sunday: build 3 projects applying what you learned. Next 2 weekends: 6 more projects. 9 projects. 2 weeks. APIs, webhooks, LLM integration, real workflows. No theory. Just build. ↓ Stanford Agentic AI lecture: free on YouTube. Watch it this weekend or buy another $500 "AI automation course" in 2027 that teaches less than this one free lecture. Bookmark. Watch tonight. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward.

Himanshu Kumar

28,120 views • 3 months ago

STANFORD JUST PUT ITS ENTIRE ARTIFICIAL INTELLIGENCE CURRICULUM ON YOUTUBE FOR FREE. CS221. The same course that produced engineers now running AI labs, building frontier models, and getting paid $500,000 a year at the companies everyone is trying to work for. Most people have never heard of it. The ones who have are not telling you about it. Here is what the course actually covers: Search algorithms. The mathematical foundation behind every AI that finds optimal solutions in complex environments. Constraint satisfaction. How AI reasons through problems with thousands of interdependent variables simultaneously. Markov decision processes. The probabilistic framework behind every AI agent that makes sequential decisions under uncertainty. Machine learning from first principles. Not how to use sklearn. How the math actually works underneath it. Neural networks. Built from the ground up before jumping to applications. Logic and knowledge representation. How AI systems reason about the world formally. Natural language processing. The foundation of everything happening in LLMs right now. Robotics and computer vision. How AI perceives and acts in physical environments. Every concept that powers every AI product you use daily is in this curriculum. Not a surface level overview. The actual mathematics. The actual algorithms. The actual reasoning. This is what separates engineers who build AI from operators who use it. Stanford charged $60,000 a year for students to sit in this classroom. They put the whole thing on YouTube. Bookmark this before you open any other AI resource today. Follow CyrilXBT for more elite resources that build real depth the moment they drop.

STANFORD JUST PUT ITS ENTIRE ARTIFICIAL INTELLIGENCE CURRICULUM ON YOUTUBE FOR FREE. CS221. The same course that produced engineers now running AI labs, building frontier models, and getting paid $500,000 a year at the companies everyone is trying to work for. Most people have never heard of it. The ones who have are not telling you about it. Here is what the course actually covers: Search algorithms. The mathematical foundation behind every AI that finds optimal solutions in complex environments. Constraint satisfaction. How AI reasons through problems with thousands of interdependent variables simultaneously. Markov decision processes. The probabilistic framework behind every AI agent that makes sequential decisions under uncertainty. Machine learning from first principles. Not how to use sklearn. How the math actually works underneath it. Neural networks. Built from the ground up before jumping to applications. Logic and knowledge representation. How AI systems reason about the world formally. Natural language processing. The foundation of everything happening in LLMs right now. Robotics and computer vision. How AI perceives and acts in physical environments. Every concept that powers every AI product you use daily is in this curriculum. Not a surface level overview. The actual mathematics. The actual algorithms. The actual reasoning. This is what separates engineers who build AI from operators who use it. Stanford charged $60,000 a year for students to sit in this classroom. They put the whole thing on YouTube. Bookmark this before you open any other AI resource today. Follow CyrilXBT for more elite resources that build real depth the moment they drop.

CyrilXBT

54,956 views • 2 months ago

Anthropic Head of Product: "Fable 5 is the best model we've ever shipped for agentic work. Our engineers now ship 8x more code than they did in past years." in 42 minutes, the Anthropic team shows how to build self-improving agent systems with Fable 5: managed agents, dreaming, dynamic workflows worth more than a $500 agent building course live from Anthropic's latest stage in Japan, just hours ago i turned everything they showed on stage into a step-by-step guide on building your own agent you can find the full guide below

Anthropic Head of Product: "Fable 5 is the best model we've ever shipped for agentic work. Our engineers now ship 8x more code than they did in past years." in 42 minutes, the Anthropic team shows how to build self-improving agent systems with Fable 5: managed agents, dreaming, dynamic workflows worth more than a $500 agent building course live from Anthropic's latest stage in Japan, just hours ago i turned everything they showed on stage into a step-by-step guide on building your own agent you can find the full guide below

Anatoli Kopadze

79,808 views • 1 month ago

Anthropic AI engineer just showed how to give AI agents real memory in 4 steps - and it changes everything in 28 minutes he shows exactly how agents can remember across sessions, completely free worth more than any $500 AI engineering course here's what he covers: • why agents forget everything between sessions • memory stores - agents read, write across sessions • dreaming - agents that improve their own memory • 95% cache hit rate, so it stays cheap most people are still copy-pasting context into every new chat - while the people who figured this out are building agents that get smarter every single night watch full video then read article below

Anthropic AI engineer just showed how to give AI agents real memory in 4 steps - and it changes everything in 28 minutes he shows exactly how agents can remember across sessions, completely free worth more than any $500 AI engineering course here's what he covers: • why agents forget everything between sessions • memory stores - agents read, write across sessions • dreaming - agents that improve their own memory • 95% cache hit rate, so it stays cheap most people are still copy-pasting context into every new chat - while the people who figured this out are building agents that get smarter every single night watch full video then read article below

Movez

709,117 views • 2 months ago

📣 Agent Forge is LIVE! What if creating your own team of AI agents took minutes — not months? Say hello to Agent Forge — the platform that puts AI agents at your fingertips. Here’s what you can do: 🔹 Automate your operations 🔹 Create an entire team of AI Agents 🔹 Use real-time data fetching Agents 🔹 Build powerful AI agent workflows fast 🔹 Design intelligent workflows using our drag & drop mechanism Whether you're building a startup, launching a side hustle, or just exploring AI for trading & research — Agent Forge is your new playground. ✨ Get $65 in free credits as a first-time user — no strings attached. 👉 Try it now:

📣 Agent Forge is LIVE! What if creating your own team of AI agents took minutes — not months? Say hello to Agent Forge — the platform that puts AI agents at your fingertips. Here’s what you can do: 🔹 Automate your operations 🔹 Create an entire team of AI Agents 🔹 Use real-time data fetching Agents 🔹 Build powerful AI agent workflows fast 🔹 Design intelligent workflows using our drag & drop mechanism Whether you're building a startup, launching a side hustle, or just exploring AI for trading & research — Agent Forge is your new playground. ✨ Get $65 in free credits as a first-time user — no strings attached. 👉 Try it now:

AITECH CLOUD NETWORK

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Andrew Ng: “100% of my tasks are now handled by AI agents - hype has exceeded my expectations. Next step is 'loops'. in 3-6 months, everyone will be using self-improving loops. No more prompting.” In just 50-minutes, Andrew Ng reveals what the future of agentic AI will look like and how to prepare for it. Worth more than a $500 agentic course.

Andrew Ng: “100% of my tasks are now handled by AI agents - hype has exceeded my expectations. Next step is 'loops'. in 3-6 months, everyone will be using self-improving loops. No more prompting.” In just 50-minutes, Andrew Ng reveals what the future of agentic AI will look like and how to prepare for it. Worth more than a $500 agentic course.

Movez

57,565 views • 6 days ago

Claude Skills might be the biggest upgrade to AI agents so far! Some say it's even bigger than MCP. I've been testing skills for the past 3-4 days, and they're solving a problem most people don't talk about: agents just keep forgetting everything. In this video, I'll share everything I've learned so far. It covers: > The core idea (skills as SOPs for agents) > Anatomy of a skill > Skills vs. MCP vs. Projects vs. Subagents > Building your own skill > Hands-on example Skills are the early signs of continual learning, and they can change how we work with agents forever! Here's everything you need to know:

Claude Skills might be the biggest upgrade to AI agents so far! Some say it's even bigger than MCP. I've been testing skills for the past 3-4 days, and they're solving a problem most people don't talk about: agents just keep forgetting everything. In this video, I'll share everything I've learned so far. It covers: > The core idea (skills as SOPs for agents) > Anatomy of a skill > Skills vs. MCP vs. Projects vs. Subagents > Building your own skill > Hands-on example Skills are the early signs of continual learning, and they can change how we work with agents forever! Here's everything you need to know:

Akshay 🚀

286,149 views • 8 months ago

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Data Science Agents): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, May 21st, I'm sharing one of my best AI Projects: AI Customer Segmentation Agent with Python Register here (570+ registered):

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Data Science Agents): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, May 21st, I'm sharing one of my best AI Projects: AI Customer Segmentation Agent with Python Register here (570+ registered):

Matt Dancho (Business Science)

35,452 views • 1 year ago

Met someone whose agents ship more code in a week than my whole team does in a month. I asked him what actually made him this good - which loop framework, which prompt pack. He laughed and sent me a Stanford lecture. Andrew Ng - CS230, Intro to Deep Learning. Free, an hour long. "Everyone memorizes loop tricks," he said. "Almost nobody understands the thing the loop is wrapped around." I watched it last night. Halfway through, I realized I'd spent a year duct-taping agents together without ever understanding what was happening underneath any of it. Bookmark it. Watch it today - then the loop guide below finally clicks.

Met someone whose agents ship more code in a week than my whole team does in a month. I asked him what actually made him this good - which loop framework, which prompt pack. He laughed and sent me a Stanford lecture. Andrew Ng - CS230, Intro to Deep Learning. Free, an hour long. "Everyone memorizes loop tricks," he said. "Almost nobody understands the thing the loop is wrapped around." I watched it last night. Halfway through, I realized I'd spent a year duct-taping agents together without ever understanding what was happening underneath any of it. Bookmark it. Watch it today - then the loop guide below finally clicks.

dunik

35,917 views • 23 days ago

A Google Cloud engineer just showed how to build a full app with Claude from scratch he spent 26 minutes showing exactly what one person with Claude can do, completely free worth more than any $500 vibe-coding course here's what he covers: > raw idea to deployed app in a single session > using Claude as the entire engineering team > the exact workflow they use at Google > no big team, no prior experience needed the people who figure out what Claude can actually do are building things everyone else thinks requires a team that's exactly why I put together a step-by-step guide on building your first AI agent from scratch the full walkthrough is in the article below

A Google Cloud engineer just showed how to build a full app with Claude from scratch he spent 26 minutes showing exactly what one person with Claude can do, completely free worth more than any $500 vibe-coding course here's what he covers: > raw idea to deployed app in a single session > using Claude as the entire engineering team > the exact workflow they use at Google > no big team, no prior experience needed the people who figure out what Claude can actually do are building things everyone else thinks requires a team that's exactly why I put together a step-by-step guide on building your first AI agent from scratch the full walkthrough is in the article below

Anatoli Kopadze

43,005 views • 1 month ago

This 1-hour Stanford lecture covers more about the tech inside Claude than most $1,000 AI bootcamps it answers the exact questions they'll ask you in an AI product or engineering interview: > why can Claude see images but only reply with text? > why does making the model "smarter" at reading make it better at generating output? > what is the "MoT" architecture that lets Claude learn vision without losing logic? > why is physical world interaction still the hardest problem to solve? the person who understands this framework doesn't just use Claude - they explain how it's built save this before your next interview

This 1-hour Stanford lecture covers more about the tech inside Claude than most $1,000 AI bootcamps it answers the exact questions they'll ask you in an AI product or engineering interview: > why can Claude see images but only reply with text? > why does making the model "smarter" at reading make it better at generating output? > what is the "MoT" architecture that lets Claude learn vision without losing logic? > why is physical world interaction still the hardest problem to solve? the person who understands this framework doesn't just use Claude - they explain how it's built save this before your next interview

Mr. Buzzoni

77,740 views • 1 month ago

Anthropic Engineer Andrej Karpathy: "The biggest mistake in AI right now - people are forcing agents to work instead of mastering the model first We made that mistake in 2016 at OpenAI - It cost us 5 years " what Karpathy actually means: step 1 → stop forcing your agent to do everything, understand the model underneath first step 2 → demos are easy - products take a decade. self-driving proved it - if you skip the foundation, everything breaks step 3 → the agent is not the product. the foundation is. build that - and agents emerge on their own "you building agents right now - you're at the forefront. not OpenAI. not DeepMind. you " watch - bookmark, then read article below ↓

Anthropic Engineer Andrej Karpathy: "The biggest mistake in AI right now - people are forcing agents to work instead of mastering the model first We made that mistake in 2016 at OpenAI - It cost us 5 years " what Karpathy actually means: step 1 → stop forcing your agent to do everything, understand the model underneath first step 2 → demos are easy - products take a decade. self-driving proved it - if you skip the foundation, everything breaks step 3 → the agent is not the product. the foundation is. build that - and agents emerge on their own "you building agents right now - you're at the forefront. not OpenAI. not DeepMind. you " watch - bookmark, then read article below ↓

codila

1,900,924 views • 19 days ago

full stack ai agents --- what if you could get root access into the robot brain _while it's working_ and make changes? What would it look like? here's 9 minutes where I pitch building UIs for your robot butlers. very easy if your agent is already a server (which, of course, your durable object gives you for free) feel free to argue whether you should use SSR or SPAs or local first or the framework du jour or whatever. link to post in reply, live demo and code later. that's video 3 of the series, done and dusted.

full stack ai agents --- what if you could get root access into the robot brain _while it's working_ and make changes? What would it look like? here's 9 minutes where I pitch building UIs for your robot butlers. very easy if your agent is already a server (which, of course, your durable object gives you for free) feel free to argue whether you should use SSR or SPAs or local first or the framework du jour or whatever. link to post in reply, live demo and code later. that's video 3 of the series, done and dusted.

sunil pai

34,770 views • 1 year ago

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

GREG ISENBERG

375,365 views • 4 months ago

AI didn't make code cheap. it made bad code lethal. Matt Pocock: "Software fundamentals matter more than ever" AI in a good codebase ships gold. AI in a bad codebase ships garbage. framework mastery doesn't compound. fundamentals do. if you're starting with AI agents, build these 5 patterns before anything else: > Tool Use Design Pattern. tool calling is the core of every useful agent. > Agentic RAG. grounded answers over hallucination. maps to real products. > Planning Design Pattern. agents that break a goal into steps before acting. > Multi-Agent Design Pattern. orchestrator-subagent is the only shape that ships in production. > Building Trustworthy AI Agents. reliability, safety, knowing when not to use one. this article covers what to learn, build, and skip in AI agents in 2026. bookmark and read this today. repo with 500 more in comments

AI didn't make code cheap. it made bad code lethal. Matt Pocock: "Software fundamentals matter more than ever" AI in a good codebase ships gold. AI in a bad codebase ships garbage. framework mastery doesn't compound. fundamentals do. if you're starting with AI agents, build these 5 patterns before anything else: > Tool Use Design Pattern. tool calling is the core of every useful agent. > Agentic RAG. grounded answers over hallucination. maps to real products. > Planning Design Pattern. agents that break a goal into steps before acting. > Multi-Agent Design Pattern. orchestrator-subagent is the only shape that ships in production. > Building Trustworthy AI Agents. reliability, safety, knowing when not to use one. this article covers what to learn, build, and skip in AI agents in 2026. bookmark and read this today. repo with 500 more in comments

Rohit

28,332 views • 2 months ago

SITUATION EXPLAINED: 70% of frontier model queries could run locally for free. clem 🤗, co-founder and CEO of Hugging Face: "There was an interesting study from Stanford published last year showing that 70% of the queries that people ask to ChatGPT could be accurately answered locally on your laptop. For free." "Most of the AI workloads that people do today with frontier models could be done by models that are cheaper, faster, more customizable, more controllable. And they don't do it because frankly it's a pain to pick the right model." "You're subsidized so you don't have to care because you have your subscription, so you route everything to Einstein. 'Hey Einstein, what's the weather today?'" "In normal life, he would be like, 'I'm not answering your silly question.' But because it's AI and subsidized by the AI labs, all the questions are getting routed to Einstein versus in an ideal world you have different models that are more specialized and better at answering your questions in different domains." "It's possible that routing is gonna redistribute a lot of the value capture from frontier models to a more long tail of models. It's like AI maturing... we are in the first phase where it's very simple, everyone using just one giant model. Now we're moving to the second phase."

SITUATION EXPLAINED: 70% of frontier model queries could run locally for free. clem 🤗, co-founder and CEO of Hugging Face: "There was an interesting study from Stanford published last year showing that 70% of the queries that people ask to ChatGPT could be accurately answered locally on your laptop. For free." "Most of the AI workloads that people do today with frontier models could be done by models that are cheaper, faster, more customizable, more controllable. And they don't do it because frankly it's a pain to pick the right model." "You're subsidized so you don't have to care because you have your subscription, so you route everything to Einstein. 'Hey Einstein, what's the weather today?'" "In normal life, he would be like, 'I'm not answering your silly question.' But because it's AI and subsidized by the AI labs, all the questions are getting routed to Einstein versus in an ideal world you have different models that are more specialized and better at answering your questions in different domains." "It's possible that routing is gonna redistribute a lot of the value capture from frontier models to a more long tail of models. It's like AI maturing... we are in the first phase where it's very simple, everyone using just one giant model. Now we're moving to the second phase."

MTS

49,884 views • 27 days ago