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The Code Was in Git. The AI Conversations TO Implement it,Was Gone

Mohammad Ali Abdul Wahed 2026年07月04日 11:38 2 次阅读 来源:Dev.to

I reopened an old project and found a working authentication implementation. What I could not find was the reason it looked that way. The commits showed the final code, but not: Why one approach had been chosen Which fixes had already failed What the coding agent warned me about Which tasks had been postponed The answers were scattered across a ChatGPT thread, a Codex session, and a terminal that no longer existed. There was another layer to it. I don't stick to one agent. I move between Codex, Claude Code, Cursor, and plain ChatGPT threads — sometimes because one tool genuinely fits the task better, more often because I simply run out of credits on one and switch to another mid-task. Every time that happened, the new agent started from zero. It had no idea what the previous one had already tried, decided, or ruled out. I either re-explained everything from memory, or let the new agent guess and re-discover things the old one already knew. This is not only a documentation problem. It is a structural problem in AI-assisted development. We use several tools to produce one project, but every tool keeps a separate, temporary memory. That experience became ContextVault. First: what is ContextVault? ContextVault is an open-source, local-first memory layer for AI work. It preserves useful context from browser LLM conversations, terminals, and coding-agent sessions, then makes that context searchable and reusable in later sessions. Think of the distinction this way: Git: what changed in the code? ContextVault: why did we change it, what failed, and what should happen next? The trigger for building it was specifically the agent-switching problem: whenever one agent ran out of credits or hit a limit, I needed the next one to pick up exactly where the last one left off, instead of restarting the investigation. ContextVault has three user-facing surfaces: Browser Capture — a Chrome extension that stores supported LLM conversations locally and exports Markdown or ZIP. Vault Term

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