
Agent Memory Is a Cache Coherence Problem
Lossless curated notes vs lossy auto-compression with vector recall: two AI-memory designs that fail differently. One fails like a cache — classical systems already mapped it.
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Lossless curated notes vs lossy auto-compression with vector recall: two AI-memory designs that fail differently. One fails like a cache — classical systems already mapped it.
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Why Claude Code uses grep instead of RAG: a cost-curve argument, audited against the source — with the Explore vs Fork A/B test nobody mentions.
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Go channels are synchronization primitives, not queues. They deliver backpressure only when the producer is bounded — and that's where the real OOM hides.
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Same model, same test cases, 20% better results. 7 out of 8 fixes were pure code, zero LLM cost. Here's exactly what I changed and why it worked.
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Don't bind to a single AI. Run three in competition, make the final call yourself, and let results judge everyone. The operating model for staying valuable in the AI age.
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Most discussions of Node vs Go concurrency stop at 'async vs threaded.' The real split is deeper — where does context switching happen, and what is the unit of scheduling?
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Building an AI agent that works is easy. Building one that doesn't break is 90% of the work. Here's what that 90% actually looks like — from leaked source code and production A/B data.
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How to use OpenAI's Codex plugin inside Claude Code — turning Claude Opus and GPT-5.4 into a dual-brain coding system. Setup, commands, rescue workflows, and when each brain wins.
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Claude Code's memory system looks simple on purpose. This piece breaks down the tradeoffs behind Markdown memories, Sonnet side-queries, and the decision to avoid vector databases.
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How Claude Code's context compression pipeline survives 1M-token sessions: 5 levels of progressive compression, dual-path algorithm, source walk-through.
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