Context Compression and Persistent Memory Design for Terminal AI Assistants

<p>Most AI agents have amnesia.</p> <p>You close the chat window. They forget everything. Every session starts from zero. Every context window fills up and dumps the earliest

<p>`---<br /> title: "Why I Built a Brain for My AI Agents — and What It Taught Me About Memory"<br /> published: true<br /> description: "Anamnesis v0.3.0: The cognitive

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<p>Most agent frameworks treat memory as an afterthought. They give your agent tools, prompts, and orchestration patterns — but when you restart the conversation, everything

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1wxfdt9/qwen38_27b_1_x_r9700_262k_context_half_a_million/"> <img alt="Qwen3.8 27B | 1 x R9700: 262K context,

<!-- SC_OFF --><div class="md"><p>We spent the last year building a code indexing system and tried pretty much every retrieval approach along the way. Sharing what actually worked

<p>The easiest way to make an LLM application look smart is to give the model more context.</p> <p>The harder problem is deciding what context deserves to be there.<br /> While

<p>You have 200,000 tokens to work with—but most people are still copy-pasting text like they're fighting a 4K limit that doesn't exist anymore.</p> <p>I watched a senior