Top 20 AI stories daily — research, engineering & community. Sources: Anthropic, OpenAI, DeepMind, Google Gemini, xAI, Meta AI, Mistral, arXiv, HN, Papers With Code and more. Refreshed at 7am · 1pm · 7pm ET. No influencer noise.
Read the story →Latest from cs.AI · cs.LG · cs.CL — what researchers are actually working on, not what VC twitter says is hot. Refreshed daily.
Curated events for practitioners — no vendor pitches, no hype stages. Technical conferences, community meetups, and elite gatherings where real AI work gets discussed.
Claude-as-judge with structured rubric: 23-point precision jump on adverse-event classification. The gap between "AI feature" and "regulatory-grade tool" is the evaluation design.
CMDB reconciliation agent in production. First run flagged 4,200 shadow assets that 18 months of manual audits missed. Confidence threshold tuning is the whole game.
Hybrid RAG on Portuguese product catalog — vector + BM25 fusion, relevance finally converging. Under 800ms end-to-end. Bilingual edge cases were the hard part.
Agentic routing layer: LLM-scored confidence bands replaced rule-based escalation logic. 94% routing accuracy. Rule systems don't age well in production.
Model cost playbook locked in: Sonnet 4.6 for agentic loops, Haiku for high-volume classification, Opus for evals only. 68% token cost reduction without touching accuracy.
Extended thinking now composable with tool calls in the same API request. Unlocks reasoning-while-acting patterns — agents that think before each tool step, not just at prompt time.
Anthropic's bet on computer-use and UI-aware agents maturing fast. Claude can now reason over screenshots, DOM trees, and interaction state simultaneously.
The MCP connector ecosystem crossed 200 production-ready integrations. Slack, Gmail, Linear, GitHub, Notion — the distribution layer for agentic work is now real infrastructure.
AWS + Anthropic released the Strands SDK for Python. Model-agnostic orchestration, built-in tool registration, streaming-first. The cleaner alternative to LangChain for AWS-native stacks.
Most people use Claude like a chat window. The console is a different animal: plugins, skills, MCP connectors, persistent memory, file system access. Set it up right and you stop typing prompts, you orchestrate a small team of assistants that knows your calendar, your inbox, your docs, your tools. Two cohorts so far: builders who want to move 5x faster, and execs / ops teams who want results without writing code.
Short courses I'm currently working through or have on the shortlist, pulled live from the DeepLearning.AI catalog.
NANDA, Networked AI Agents in Decentralized Architecture, is the MIT Media Lab initiative led by Prof. Ramesh Raskar, pioneering the open infrastructure that lets billions of specialized agents discover, communicate, negotiate, and transact autonomously across a decentralized web.
Think of it as the missing layer between today's siloed LLMs and tomorrow's agent economy: an indexing fabric, communication protocols, and economic primitives that turn AI from a chatbot product into actual infrastructure.
I'm an active participant in the NANDA research consortium, contributing across the agentic web stack, from index architecture to agent-to-agent protocols.
"Imagine billions of specialized AI agents collaborating across a decentralized architecture, each performing discrete functions, communicating seamlessly, navigating autonomously, socializing, learning, earning and transacting on our behalf."
, NANDA, MIT Media Lab
NANDA's roadmap maps the agentic internet across three stages, each unlocking the next. Today the consortium is building Phase 1 in the open, with reference implementations and white papers.
A new index architecture so agents can find each other. Unlike search engines built for humans, the agent index has to be machine-first, low-latency, and adversarial-aware.
Agent-to-agent protocols and adapters that let heterogeneous agents (different vendors, different models, different intent) negotiate, delegate, and form short-lived teams to solve a task.
Knowledge pricing, agent stores, and large population models, the economic layer where agents earn, pay, and learn from each other without leaking data across silos.
The consortium's research is organized into six interlocking workstreams. Each one ships papers, reference implementations, and open standards proposals.
Core index architecture and the substrate every other workstream builds on.
Protocols that let agents from different stacks negotiate roles and team up to handle multi-step tasks.
Running agentic workloads at the edge, phones, sensors, embedded, without sacrificing capability.
Economic primitives so agents can pay for context, expertise, and computation in real time.
Agents that learn from each other without exposing raw data, federated, privacy-aware, composable.
The discovery and distribution layer, analogous to app stores, but built for autonomous agents and large population models.