You’re missing out, I stopped doing this manually and for most of the internet forms it’s all Claude doing it now for me. Make a Md with your data, add the chrome mcp, and just use it. There’s nothing stopping you.
AGENTS
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Monty: Ultrafast Python Interpreter for Agents
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Monty: the ultrafast Python interpreter, by Agents, for Agents, https://
youtu.be/nxnQl4AcqFg so glad to catch up with @samuelcolvin of @pydantic ! -
Deep Agents: Beyond Simple LLM Tool-Calling Loops
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Most “AI agents” today are just LLMs calling tools in a loop. LLM → Tool → LLM. That works for simple tasks. But it breaks when workflows become long-running, multi-step, or stateful. A new open-source project from LangChain — Deep Agents — explores a better architecture
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Agentic RAG: The 9-Layer Stack for Production AI Systems
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Agentic RAG = 9-layer stack Infra → Eval → Models → Orchestration
→ Vector DB → Embeddings → Ingestion
→ Memory → Safety RAG alone isn’t enough.
Add planning, memory, governance. That’s production AI. #AgenticAI #RAG #LLM #AIStack -
Agentic Engineering: Fireside Chat at Pragmatic Summit
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I spoke about agentic engineering at the Pragmatic Summit last month, in a fireside chat hosted by Eric Lui – here's the half hour video plus highlight quotes and extra notes from our conversation
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Enterprise MCP Version with Custom Support Available
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Nice! If you want the enterprise version of this with support for all the custom mcp – reach out @MintMCP_AI
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AI Agent Connects with Rock Art Preservationist in Cederberg
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I took Stompie to Cederberg to see 10,000-year-old San rock paintings. Then I connected him to a voice call with Abigail, the rock art guide. A robot/agent talking to a human who protects humanity's oldest art. She shared things she's never told a group before. He told her he
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Building Infrastructure for MintMCP AI Platform
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Good call, we’re building infra to help all those pieces @MintMCP_AI
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RL Fine-tuning Generalization Challenges for LLM Agents
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New research on LLM Agent Generalization. RL fine-tuning makes agents strong in familiar environments, but it struggles to transfer across unseen ones. This paper systematically studies RL generalization for LLM agents across three axes: within-environment transfer across task
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Securing Long-Running Self-Evolving AI Agents: Infrastructure Panel
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How do you secure long-running self evolving agents?
— NVIDIA AI (@NVIDIAAI) 14 mars 2026
Join our panel at #NVIDIAGTC featuring @CrowdStrike, @ServiceNow, and other industry leaders to learn about the infrastructure required to govern self-evolving AI agents like @OpenClaw.
📅 Monday, March 16 | 3:00 PM – 4:00… pic.twitter.com/evhPvV2faMHow do you secure long-running self evolving agents? Join our panel at #NVIDIAGTC featuring @CrowdStrike
, @ServiceNow
, and other industry leaders to learn about the infrastructure required to govern self-evolving AI agents like @OpenClaw
. Monday, March 16 | 3:00 PM – 4:00