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Introducing self-improvement loops. The concept is simple: what if agents could improve Skills by reviewing past conversations? Here's the three step loop: - Score conversations from criteria you define - Isolate failures - Generate skill improvements

Warp

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128,782 views • 2 months ago •via X (Twitter)

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Today, we're announcing content suggestions in Chatbase. Your agent can now self-improve by analyzing unresolved issues from past conversations, reviewing training data to surface conflicting information, then suggest improvements to fix any issues. Agents always fail in production unless they can learn from what went wrong. Missed answers, unresolved threads, and inconsistent knowledge. This is the missing layer that makes agents production-ready: continuous self-improvement.

Today, we're announcing content suggestions in Chatbase. Your agent can now self-improve by analyzing unresolved issues from past conversations, reviewing training data to surface conflicting information, then suggest improvements to fix any issues. Agents always fail in production unless they can learn from what went wrong. Missed answers, unresolved threads, and inconsistent knowledge. This is the missing layer that makes agents production-ready: continuous self-improvement.

Yasser

13,607 views • 9 months ago

Claude code + Skills >>>> Claude code We created a skill that, - Makes Claude Code read your past conversations - Understands what code issues you struggled at - Generates a detailed report on Slack Works with Rube Here's the link to the Skill:

Claude code + Skills >>>> Claude code We created a skill that, - Makes Claude Code read your past conversations - Understands what code issues you struggled at - Generates a detailed report on Slack Works with Rube Here's the link to the Skill:

Karan Vaidya

39,199 views • 11 months ago

Introducing ScienceBuddy — a free workspace for scientific agents that improve through researcher collaboration. Use GPT-6 in ScienceBuddy at no cost. GPU-accelerated, and fused with the JEV framework. 🧵 Two loops: 🔹 Inner loop — refines the agent harness 🔹 Outer loop — trains the model with rubric-guided RL Together: Recursive-in-Recursive Self-Improvement. ScienceBuddy explores how scientific agents can improve through sustained collaboration with researchers. 🔬Try it free: #ScienceBuddy #PhAILabs #AI4Science

Introducing ScienceBuddy — a free workspace for scientific agents that improve through researcher collaboration. Use GPT-6 in ScienceBuddy at no cost. GPU-accelerated, and fused with the JEV framework. 🧵 Two loops: 🔹 Inner loop — refines the agent harness 🔹 Outer loop — trains the model with rubric-guided RL Together: Recursive-in-Recursive Self-Improvement. ScienceBuddy explores how scientific agents can improve through sustained collaboration with researchers. 🔬Try it free: #ScienceBuddy #PhAILabs #AI4Science

PhAI Labs

1,257,328 views • 1 month ago

Anthropic just released 5 Claude workshops on the new skill every AI builder needs: Loop Engineering: 00:00 - Stop babysitting your agents 37:07 - Build a proactive Claude Code loop 1:11:23 - Build a production-ready Managed Agent 1:38:46 - Teach agents to learn from your team 2:07:15 - Hill-climb your agent with eval loops It is a 2 hour and 25 minute blueprint for Loop Engineering: proactive workflows, managed agents, team feedback, evals, and self-improvement. Watch it today, and read the article below to improve your skills with Fable 5.

Anthropic just released 5 Claude workshops on the new skill every AI builder needs: Loop Engineering: 00:00 - Stop babysitting your agents 37:07 - Build a proactive Claude Code loop 1:11:23 - Build a production-ready Managed Agent 1:38:46 - Teach agents to learn from your team 2:07:15 - Hill-climb your agent with eval loops It is a 2 hour and 25 minute blueprint for Loop Engineering: proactive workflows, managed agents, team feedback, evals, and self-improvement. Watch it today, and read the article below to improve your skills with Fable 5.

Alex

47,626 views • 3 months ago

Texas HC Steve Sarkisian - Self Reflection & Growth - Learning from past failures - Encouraging self-awareness in coaches and players - Growth mindset is key to improvement - Year-end evaluations for everyone in the organization - "You are what you do, not what you say you'll do."

Texas HC Steve Sarkisian - Self Reflection & Growth - Learning from past failures - Encouraging self-awareness in coaches and players - Growth mindset is key to improvement - Year-end evaluations for everyone in the organization - "You are what you do, not what you say you'll do."

James Light

11,650 views • 8 months ago

Claude Code creator: "Loops are as big a step as move from source code to agents. Loops - step from agents to the next thing. 30% of my code is fully written by loops right now." in a 40-minute fireside chat, Boris Cherny reveals his actual working setup. Loops + dynamic workflows + routines, and more. Watch the full episode, then read the article on loop engineering below.

Claude Code creator: "Loops are as big a step as move from source code to agents. Loops - step from agents to the next thing. 30% of my code is fully written by loops right now." in a 40-minute fireside chat, Boris Cherny reveals his actual working setup. Loops + dynamic workflows + routines, and more. Watch the full episode, then read the article on loop engineering below.

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379,813 views • 4 months ago

Practice makes perfect. A truly agentic robot shouldn’t just know what to do. It should also figure out how to get better. What if a robot could watch a demonstration, figure out what it needs to practice, and learn how to get better through practice? Introducing RPG: Guided Self-Improvement for Embodied Agents, a real-to-sim-to-real pipeline that turns real-world demonstrations into targeted practice. RPG reconstructs practice tasks from demonstrations, uses demo videos to guide skill improvement, and continually updates a shared skill system from failures. On real robots, RPG achieves 30/30 successes across 3 tasks. In simulation, it reaches 95.0% success across 22 manipulation tasks after 15 rounds of self-improvement. Demo → Reconstruct → Guided Practice → Self-Improve → Go Real

Practice makes perfect. A truly agentic robot shouldn’t just know what to do. It should also figure out how to get better. What if a robot could watch a demonstration, figure out what it needs to practice, and learn how to get better through practice? Introducing RPG: Guided Self-Improvement for Embodied Agents, a real-to-sim-to-real pipeline that turns real-world demonstrations into targeted practice. RPG reconstructs practice tasks from demonstrations, uses demo videos to guide skill improvement, and continually updates a shared skill system from failures. On real robots, RPG achieves 30/30 successes across 3 tasks. In simulation, it reaches 95.0% success across 22 manipulation tasks after 15 rounds of self-improvement. Demo → Reconstruct → Guided Practice → Self-Improve → Go Real

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166,327 views • 21 days ago

What if you could go from concept to a vision AI app without writing every line of code? ⚡ NVIDIA DeepStream, combined with powerful coding agents like Claude Code and reusable Skills, can generate complete vision AI pipelines from simple natural language prompts, reducing development cycles from weeks to hours. Read ➡️

What if you could go from concept to a vision AI app without writing every line of code? ⚡ NVIDIA DeepStream, combined with powerful coding agents like Claude Code and reusable Skills, can generate complete vision AI pipelines from simple natural language prompts, reducing development cycles from weeks to hours. Read ➡️

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SkillWeaver Web Agents can Self-Improve by Discovering and Honing Skills

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14,620 views • 1 year ago

Introducing Duet Autopilot, the first verified self-improving AI agent for CX. It automates agent improvement by turning conversation signals into validated improvements ready for human review, helping agents get better with every conversation. 🧵

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Decagon

27,607 views • 4 months ago

Most people don't get the most out of Claude Skills. That's why their outputs fall short. Here's the process to make your Skills 10X better: - Step 1: Understand the problem - Step 2: Explore where Claude fails without guidance - Step 3: Research the domain deeply - Step 4: Synthesize principles from research - Step 5: Draft the initial skill - Step 6: Self-critique against quality criteria - Step 7: Iterate with feedback - Step 8: Test on real scenarios - Step 9: Finalize into optimal structure The key shift: Make Claude think like an expert. Not follow steps. Produce output, not intermediate documents. Sound like a practitioner, not documentation. Skills aren't static. You improve them over time.

Most people don't get the most out of Claude Skills. That's why their outputs fall short. Here's the process to make your Skills 10X better: - Step 1: Understand the problem - Step 2: Explore where Claude fails without guidance - Step 3: Research the domain deeply - Step 4: Synthesize principles from research - Step 5: Draft the initial skill - Step 6: Self-critique against quality criteria - Step 7: Iterate with feedback - Step 8: Test on real scenarios - Step 9: Finalize into optimal structure The key shift: Make Claude think like an expert. Not follow steps. Produce output, not intermediate documents. Sound like a practitioner, not documentation. Skills aren't static. You improve them over time.

The Startup Ideas Podcast (SIP) 🧃

11,966 views • 9 months ago

NVIDIA CEO, Jensen Huang: "We already have self-improving agents. Set up the right loop, and every run makes it smarter without you." This is the shift that's going to define the rest of 2026. 53 minutes of pure insight from one of the richest men on earth. Watch it, then read the full guide on building self-improving loops below.

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39,771 views • 3 months ago

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

27,810 views • 3 months ago

Andrew Ng: "100% of my tasks now run through AI agents, the hype actually passed my expectations, loops are the next step" "In 3-6 months, everyone will be using self-improving loops, prompting alone is over" In a 30-minute talk, Andrew Ng breaks down how to build self-improving agentic systems from scratch Agent loops Feedback Memory Self-improvement Worth more than most $500 agentic courses

Andrew Ng: "100% of my tasks now run through AI agents, the hype actually passed my expectations, loops are the next step" "In 3-6 months, everyone will be using self-improving loops, prompting alone is over" In a 30-minute talk, Andrew Ng breaks down how to build self-improving agentic systems from scratch Agent loops Feedback Memory Self-improvement Worth more than most $500 agentic courses

rari🈀

67,545 views • 4 months ago

Do agents need skills? Here's 7,950 skills & workflows from GitHub mapped by what they actually do (out of 175,632 that I indexed). I ran evals on every skill: does it make the model's output better, or worse? Here's some findings👇

Do agents need skills? Here's 7,950 skills & workflows from GitHub mapped by what they actually do (out of 175,632 that I indexed). I ran evals on every skill: does it make the model's output better, or worse? Here's some findings👇

stepan

13,850 views • 4 months ago

Skills without self-improvement loops are... basically tech debt. This is the process we use on the Warp repo to keep agent Skills useful the more they're run, based on human feedback 👇

Skills without self-improvement loops are... basically tech debt. This is the process we use on the Warp repo to keep agent Skills useful the more they're run, based on human feedback 👇

Ben Holmes

42,968 views • 3 months ago

Simple ideas to fix Bangalore ✅ Learning from experts ✅ A weekend full of conversations ✅ Let's Hack Bangalore is done and dusted! Here's what went down at the hackathon:

Simple ideas to fix Bangalore ✅ Learning from experts ✅ A weekend full of conversations ✅ Let's Hack Bangalore is done and dusted! Here's what went down at the hackathon:

Obvious

16,141 views • 3 years ago

The team has been busy lately. This is what shipped on Replit this week: - Free Mode - Conversations - Routines - Steer conversations - Import Skills from GitHub - Control models across Workspaces - Black-box pen tests

The team has been busy lately. This is what shipped on Replit this week: - Free Mode - Conversations - Routines - Steer conversations - Import Skills from GitHub - Control models across Workspaces - Black-box pen tests

Raouf Chebri

31,493 views • 2 months ago

Every skill file I write ends with a self-improvement loop. It sits at the bottom of the skill.md and runs before the skill finishes: - Did any step fail or need a workaround? - Did I correct or reject anything meaningful? - Did you discover something a future run might need? Most runs it comes back with one or two suggested edits. I say not needed, or I say run it, and it rewrites its own file. It never edits without asking first, so every change still goes through you. Put the loop at the bottom of the skills you already have and let them self-improve while you use them.

Every skill file I write ends with a self-improvement loop. It sits at the bottom of the skill.md and runs before the skill finishes: - Did any step fail or need a workaround? - Did I correct or reject anything meaningful? - Did you discover something a future run might need? Most runs it comes back with one or two suggested edits. I say not needed, or I say run it, and it rewrites its own file. It never edits without asking first, so every change still goes through you. Put the loop at the bottom of the skills you already have and let them self-improve while you use them.

The Startup Ideas Podcast (SIP) 🧃

11,799 views • 2 months ago

It started by asking “what if” humans & ai could work together Now, Salesforce is about to pass 1M AI agent conversations on our — working side-by-side with our 1M human agent conversations in Salesforce Service Cloud. AI Agents + Humans = Customer Success. ❤️🤖🤝 #Dreamforce #AI

It started by asking “what if” humans & ai could work together Now, Salesforce is about to pass 1M AI agent conversations on our — working side-by-side with our 1M human agent conversations in Salesforce Service Cloud. AI Agents + Humans = Customer Success. ❤️🤖🤝 #Dreamforce #AI

Marc Benioff

68,694 views • 1 year ago