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Public story · 2026-02-27 · source-backed
(advanced, prompt-engineering) Each sub-agent explores using thousands of tokens but returns only 1-2K condensed summary. Lead agent gets high-signal, pre-filtered context. Anthropic's research system showed 90.2% performance improvement despite 15x more total tokens. Anthropic Engineering
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Given everything above about token burn, this is the most immediately useful thing you can do this week. Anthropic's engineering team documented compaction: when a context window nears its ceiling, you high-fidelity-summarize it so the agent continues with minimal performance...
The most useful engineering blog post I've read this year dropped today with zero fanfare. Anthropic's engineering team published the actual architecture they use for long-running autonomous coding: a two-agent harness where an initializer agent sets up the project environment...
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Everyone covered the ChatGPT Work launch. Almost nobody read the API changelog, which is where the story actually is. GPT-5.6 shipped two things that change how you build agents. First, Programmatic Tool Calling: instead of the model returning one tool call at a time to your l...
1. Flip your multi-model pipeline to review-then-generate. Instead of using a reasoning model to plan before code generation, let the specialist generate freely and use reasoning tokens for review. Paper shows 90.2% pass@1 vs 87.2% for the planning pattern. Source 2. Audit you...
The New Stack's coverage of Cursor 3 leads with a provocative framing: the IDE is now a fallback, not the default. That's deliberately inflammatory. It's also not wrong. Cursor 3 is a full redesign built around an "Agents Window" command hub. The headline feature is multi-agen...
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