teaching less-technical folks how to use agent
AGENTS
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Building Better AI Agents with Traces and LangChain
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Join us in NY for a meetup on building better agents with @palashshah
, Applied AI Engineer @LangChain
. Palash will walk through the agent improvement loop and how teams use traces as the foundation for continuous improvement. We’ll cover how to:
– Capture traces of real agent -
Beginner’s Guide to Building AI Agents
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A Beginner’s Guide to Building AI Agents AI agents are becoming more accessible — this guide walks you through how to start building and using them effectively. Read more https://
bernardmarr.com/a-beginners-gu
ide-to-building-ai-agents/
… #AI #Automation #TechGuide #BernardMarr -
User Scoped Memory Management for Deployed AI Agents
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User scoped memory is one of those things that doesn’t matter if you’re building a toy agent for yourself, but when you release at scale you gotta get it right Deepagents deploy helps you do that, easily
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Ouro: Self-Improving AI Through Iterative Learning Loops
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Ouro: Building Self-Improving AI Through Iterative Learning Loops
— Satya Mallick (@LearnOpenCV) 15 avril 2026
In this episode of Artificial Intelligence: Papers and Concepts, we explore Ouro, a new approach to AI that focuses on self-improvement through iterative feedback and learning loops. Instead of relying solely on… pic.twitter.com/V34i1Tk4prOuro: Building Self-Improving AI Through Iterative Learning Loops In this episode of Artificial Intelligence: Papers and Concepts, we explore Ouro, a new approach to AI that focuses on self-improvement through iterative feedback and learning loops. Instead of relying solely on
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Stanford-Princeton LabWorld AI Enables Autonomous Self-Learning Laboratory
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LabWorld just gave AI an infinite self-learning lab powered by reinforcement learning.
— AI Highlight (@AIHighlight) 15 avril 2026
No hand-coded protocols. No manual biology. The system runs experiments, learns from results, and evolves autonomously.
Stanford + Princeton built this. It's live now. https://t.co/p8tvHQzGsPLabWorld just gave AI an infinite self-learning lab powered by reinforcement learning. No hand-coded protocols. No manual biology. The system runs experiments, learns from results, and evolves autonomously. Stanford + Princeton built this. It's live now.
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From Tools to AI Personalities: The New Startup Paradigm
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old startup thinking: make a tool to solve a problem new startup thinking: make a personality that can solve problems
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Connected AI Systems: Integrated Workflows Across Platforms
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The shift isn’t “more AI tools” It’s connected AI across systems What stood out to me with this approach: → Pulls data from email, CRM, Slack, docs, code
→ Works across multiple LLMs, not locked into one
→ Executes workflows, not just answers questions Example: Ask in -
OpenClaw-RL: Train Personalized AI Agents Through Conversation
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Train your OpenClaw agent by just talking to it! OpenClaw-RL is a reinforcement learning framework that turns everyday conversations into training signals for personalized AI agents. Most RL systems for LLMs assume batch-mode training with pre-collected datasets. You label data
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AGI Ascension: Jobs Market Accelerating at Machine Speed
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AGI Ascension, unfolding via AGI Jobs at machine speed. $AGIALPHA