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coldstart
Persistent, self-maintaining memory for AI coding agents
coldstart gives a coding agent persistent, self-maintaining memory of your codebase — a notebook it writes itself, plus a fast static index to find the right file without wasted reads. No embeddings, no API key — it runs on the model you already pay for. It is a a single‑purpose tool for codebase memory & navigation by building a knowledge graph.
Why I built coldstart
The MCP version
After working with coding agents for over 8 months, I was extremely uncomfortable with the fact that an agent rediscovers the codebase every time we start a new session. It felt wasteful — the rediscovery effort in token cost, compute, and time, every single session, for a codebase that hadn't changed since the last one.
There was a second symptom that bothered me just as much: agents would sometimes think longer and give different answers to the same question, depending on the session. No persistent context meant no consistency either. I kept observing this pattern before it finally got to me, and I decided to take things into my own hands.
Existing memory solutions didn't fit what I actually needed — most of them wanted a separate API key, a separate model call, to build and maintain that memory. But the agent that just worked on the task already knows what it did and what it found. Paying to have a different model rediscover and re-summarize that felt backwards. That's the gap coldstart's first version filled: a deterministic index over the codebase — file paths, exported symbols, path segments, built once with Tree-sitter — plus a search mechanism, so the agent gets an accurate map without spinning up another model to build one.
The v2 version
The index solved "where is the code" — it didn't solve "what have we already figured out about this code." Agents don't search a codebase the way a human does, and that gap is why coldstart has two parts: the deterministic index and search from v1, and the notebook, which ended up mattering more.
Asking an AI to generate docs or notes about a codebase isn't the hard part — that's easy. The hard part is keeping those notes fresh and only handing an agent the ones actually relevant to the task in front of it, without that becoming manual overhead I have to babysit. That's the actual problem v2 solves: a mechanism that maintains freshness on the notebook and injects the relevant notes via hooks at the point the user submits their prompt — so the agent starts with exactly what's still true and relevant, not a stale dump of everything ever written.
About coldstart on Product Hunt
“Persistent, self-maintaining memory for AI coding agents”
coldstart was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #157 on the daily leaderboard. coldstart gives a coding agent persistent, self-maintaining memory of your codebase — a notebook it writes itself, plus a fast static index to find the right file without wasted reads. No embeddings, no API key — it runs on the model you already pay for. It is a a single‑purpose tool for codebase memory & navigation by building a knowledge graph.
On the analytics side, coldstart competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how coldstart performed against the three products that launched closest to it on the same day.
Who hunted coldstart?
coldstart was hunted by Akash Goenka. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of coldstart including community comment highlights and product details, visit the product overview.
Akash Goenka