AI can implement a surprising amount of software. But when the input is a thin prompt, it still has to invent the product decisions: who can do what, how failures are handled, which recovery paths exist, where the security boundaries sit, what gets measured, and how the capability is rolled out.
That s the problem behind ProductAtlas.
Toggly
Hi Product Hunt — I’m Alexandru, the maker of ProductAtlas.
I started building it after seeing the same gap appear in AI-assisted product work: coding agents could produce an implementation quickly, but the request rarely contained the product decisions behind a mature capability.
“Add account recovery” does not say how to prevent account enumeration, invalidate old links, handle delayed delivery, revoke sessions, support externally managed identities, or measure abuse without logging secrets. “Add feature flags” does not say who can change targeting, what the safe fallback is, how rollout stops, or when stale flags are removed.
Teams learn those decisions through product work, incidents, and iteration. Too much of that knowledge remains scattered or has to be rediscovered.
ProductAtlas turns it into Blueprints: structured product context covering outcomes, requirements, flows, edge cases, acceptance criteria, security, telemetry, rollout, and production lessons.
It is for product managers, founders, developers, and technical teams using coding agents who want a stronger starting point than a blank prompt.
The working product lets people browse public Blueprints, inspect an exact Blueprint Release, and see its version, Publisher, license, and scoped evidence. Publishers can create and edit private Blueprint Drafts, validate them, and submit an exact revision for review. Public released knowledge can also be searched and retrieved by agents through anonymous read-only MCP.
The Draft/Release distinction matters to me. A Draft can change as the Publisher learns. A Release is immutable. Updating published guidance creates a new version, so a person or agent can identify the exact product context it used.
That immutability is not a claim that the content fits every product. ProductAtlas keeps Publisher-authored content untrusted and makes the context, license, and Release-specific evidence inspectable so users can decide what applies.
I would especially value feedback on three things:
1. Which product context actually improves the work you do with coding agents?
2. Where does the Blueprint structure feel useful, and where does it feel like ceremony?
3. Are the Draft, Release, version, license, and trust boundaries clear in the product?
You can try ProductAtlas here: https://productatlas.app
Example Blueprint Release: https://productatlas.app/blueprints/productatlas-labs/ai-investigations
Thank you for taking a look. I’ll be here throughout the day to answer questions, investigate problems, and learn from what does not make sense yet.