ContextLayer: Local-First Markdown Memory Layer for AI Coding Agents

I built a local-first project memory system for AI coding agents

I built a local-first project memory system for AI coding agents

The problem is clear, and I think the local-first angle is the strongest part. AI coding workflows are starting to fail less because the model is bad and more because the project state is messy: old decisions, stale notes, hidden context, and handoffs that live only in chat history. A few things I would sharpen: 1. Lead with the exact failure mode, not the architecture. Something like: "your agent keeps re-solving solved problems because project memory is not a first-class repo artifact." 2. Make the setup promise very concrete. For dev tools like this, people need to know the first 10 minutes: install, point at repo/docs, run one command, get one useful cited answer. 3. The killer feature is probably not just search. It is "resume this project without hallucinating old state." If the tool can show what changed since last session, what decisions are still valid, and what source each claim came from, that is much easier to trust. 4. I would add a freshness/conflict layer if it does not exist already: when two notes disagree, or a decision was superseded, the agent should surface the conflict instead of averaging the context. 5. For usefulness, I would include a small sample repo/demo with a deliberately messy history and show before/after: normal agent loses context vs agent with grounded project memory. Would I use it? Yes, if it stays boring and inspectable. I would not want another magic memory box. I would want a local project-memory layer that treats Markdown, decisions, tests, and handoff notes as evidence the agent has to cite.

The problem is clear, and I think the local-first angle is the strongest part. AI coding workflows are starting to fail less because the model is bad and more because the project state is messy: old decisions, stale notes, hidden context, and handoffs that live only in chat history. A few things I would sharpen: 1. Lead with the exact failure mode, not the architecture. Something like: "your agent keeps re-solving solved problems because project memory is not a first-class repo artifact." 2. Make the setup promise very concrete. For dev tools like this, people need to know the first 10 minutes: install, point at repo/docs, run one command, get one useful cited answer. 3. The killer feature is probably not just search. It is "resume this project without hallucinating old state." If the tool can show what changed since last session, what decisions are still valid, and what source each claim came from, that is much easier to trust. 4. I would add a freshness/conflict layer if it does not exist already: when two notes disagree, or a decision was superseded, the agent should surface the conflict instead of averaging the context. 5. For usefulness, I would include a small sample repo/demo with a deliberately messy history and show before/after: normal agent loses context vs agent with grounded project memory. Would I use it? Yes, if it stays boring and inspectable. I would not want another magic memory box. I would want a local project-memory layer that treats Markdown, decisions, tests, and handoff notes as evidence the agent has to cite.