A live feed of the top 10 AI stories across general, engineering, and enterprise. Curated by an AI agent grounded on reliable sources only (labs, tier-1 press, research). Refreshed at 7am · 1pm · 7pm ET. No influencer noise.
Read the story →Today's reminder: spot price is not a strategy. Spot diversification is.
If your post-mortem template doesn't have a 'systemic' section, it's a blame template.
Bedrock + Claude for tool-use, OpenAI for streaming-heavy chat, Anthropic API direct for evals. Right tool for the job.
Karpenter + spot is the single biggest lever for EKS FinOps. Nothing else is close.
32 years in IT and I'm still rebuilding my dotfiles every Sunday morning. Some things never change.
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.