AI Dev Stack

Anthropic shipped Tool Search Tool, Programmatic Tool Calling, and Tool Use Examples — three features that cut token overhead by 85%, eliminate inference passes on intermediate results, and teach correct API usage through examples instead of schemas.

Anthropic cracked the long-running agent problem with a two-phase approach: an initializer that scaffolds the environment, and a coding agent that works incrementally across context windows using git, feature checklists, and mandatory browser testing.

Anthropic's research team spent a year building evals for Claude Code and multi-agent systems. Here's what actually works: start with 20-50 real failures, combine deterministic and model-based graders, and read the transcripts. Teams that invest early ship faster.

Anthropic's performance engineering take-home has been redesigned three times because each new Claude model solved it. The latest version uses deliberately weird puzzles to stay ahead of AI — because realism is now a luxury they can't afford.

Anthropic researcher ran 16 parallel Claude instances for 2 weeks to build a 100,000-line C compiler that boots Linux. No human intervention. $20k in API costs. This is what autonomous agents can barely do today — and what they'll do routinely tomorrow.