Codebase hafızası üzerine,
ve AI'a gerçek bağlam vermek.

Every agent memory tool is built around retrieval. The bottleneck sits upstream: something has to decide what deserves to be remembered, and the human mostly does not. We counted on our own repo, and 4 of 40 decisions had been written down.

We measured the token cost of answering a question about a codebase from a structure-aware map instead of the source. About 5 to 9x fewer tokens per question across five real repos. Here's the method, the numbers, and what it does not prove.

A note bound to a function should die the moment that function does, not quietly lie to your next agent. Here's how SecondOS anchors memory to real symbols, flags it stale on drift, and revives it when the code comes back.

"Memory for AI agents" usually means remembering a conversation. If your agent writes code, that's only half of what it needs. Here's the difference, and why it matters.

An MCP memory server lets any MCP-compatible AI tool read a persistent memory of your codebase instead of re-scanning it every session. Here's what it is and how to set one up.

Pasting files into the chat doesn't scale and goes stale. Here are the real ways to give AI tools context about your codebase, with the tradeoffs and the token math.

Five 'codebase memory for AI' tools trended in a single week. Almost all of them solve retrieval — the easy half. Here's the hard half, and what it takes to stay correct as your code changes.

AI coding tools are brilliant and forgetful — they re-learn your project from scratch every session. Codebase memory fixes that. Here's what it means, how it works, and how to measure it.

Every AI coding session starts cold. Here's why your tools keep re-reading your project — and how a portable, structure-aware memory fixes it.

Hand-written architecture docs go stale the moment the code moves. A living knowledge base writes and maintains them for you — from structure, never your source.

Not every AI memory is the same. Structure-aware codebase memory understands your code's shape — symbols, calls, routes — not just what a chat said. Here's the distinction, and why it matters.

A step-by-step guide to connecting an MCP memory server to your editor, so your AI tools read a structure-aware map of your repo instead of re-scanning it.

Everyone in this category cites a cherry-picked accuracy number. We publish the methodology and the real-repo results instead — including the unflattering ones.

Grep finds text. A resolved call graph answers "who calls this?" and "what breaks if I change it?" — across modules, ranked by confidence, without guessing.

Eleven documents that write themselves from your codebase's structure and your team's memory — and what each one is actually for.

SecondOS uploads a map of your codebase, never the code itself. Here's exactly what that map contains, what it doesn't, and why the distinction matters.

A hand-written CONTEXT.md is a good instinct that loses to a moving codebase. Here's the token math, and what a self-maintaining alternative changes.

The bug isn't in the code you're reading — it's in the gap between the code and the schema, the env, or the manifest. SecondOS surfaces all three, read-only.