In July 2025, a developer's Claude Code instance hit a recursion loop and burned through 1.67 billion tokens in 5 hours, generating an estimated $16,000 to $50,000 in API charges before anyone noticed. The agent did not crash. It did not throw an error. It just kept calling tools, getting confused, calling more tools, and silently accumulating cost. Old software crashes. LLM agents spend. This is
Modern applications are fragmented by default. A typical stack today might use: SQL for transactions MongoDB for flexible documents Redis for realtime state Pinecone or Weaviate for vectors Firebase for sync Separate tools for analytics, permissions, and operations That works — until the complexity starts to hurt. You end up with duplicated data, inconsistent permissions, fragile pipelines, multip
You're in another app and there's a timer counting down at the top of your phone. You lock the screen and the same timer is sitting there. You swipe down to the Notification Center and it's there too, still ticking. It looks like a notification, but a notification can't tick. That's a Live Activity. It looks like three different surfaces (Dynamic Island, lock-screen banner, Notification Center ent
Most API documentation is written for humans. MCP tool descriptions are different. They are read by the model that decides what to call next. That means tool names, descriptions, schemas, and error messages are not just documentation garnish. They are part of the safety boundary. A risky MCP tool often looks like this: name: query input: free-form string description: “Run SQL against the database
I finished an English series on the way I think ordinary people can start using AI for real work. The point is not to become an AI expert first. The point is to have one place where you can say what you want, give the tool access to the right folder, and check the result. Anything important still needs a human pause: publishing, deleting, paying, or authorizing. My preferred starting point is simp
Elasticsearch Cluster Health 101: Understanding, Monitoring, and Maintaining Your Cluster Author: Prithvi S, Staff Software Engineer at Cloudera and Open‑source Enthusiast You ship your Elasticsearch cluster to production. Traffic spikes. Suddenly your dashboard flashes YELLOW. What does that mean? Are you about to lose data? Can you keep the service running? This guide teaches you how to read y
In Q1 2026, our 120-person engineering org increased base salaries by 30% across all IC levels, and by year-end, voluntary dev churn dropped from 24% to 12% — a 50% reduction that saved us $4.2M in annual recruiting and onboarding costs. How OpenAI delivers low-latency voice AI at scale (218 points) I am worried about Bun (372 points) Talking to strangers at the gym (1080 points) Pulitzer
The Model Context Protocol (MCP) has become the default standard for connecting AI agents to external tools and APIs. Governed by the Linux Foundation since early 2025 and adopted by OpenAI, Anthropic, Microsoft, and Vercel, MCP is the USB-C port of the AI ecosystem — one protocol that lets any LLM application talk to any tool server. But there's a gap between reading the spec and building somethi