This product was not featured by Product Hunt yet.
It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).

Product upvotes vs the next 3

Waiting for data. Loading

Product comments vs the next 3

Waiting for data. Loading

Product upvote speed vs the next 3

Waiting for data. Loading

Product upvotes and comments

Waiting for data. Loading

Product vs the next 3

Loading

Lock

The decision protocol for product teams and AI agents

Your AI coding agent just shipped a fix that contradicts a decision your team made six months ago in Slack. Nobody noticed. Lock captures product decisions where they happen, in Slack and in the terminal, then flags when a new one contradicts an old one. The GitHub Action catches conflicts in PRs, including the ones your agent opens. Auto-classified, tracked through supersession, searchable by meaning. The decision layer for teams shipping with agents.

Top comment

Hi Product Hunt,

I'm Philippe. I work in product. The reason I built Lock is specific to this moment.

Two years ago, "why did we build it this way?" was a question you could ask the engineer who shipped the feature. Today that engineer is half-pairing with Claude Code or Cursor, and half the decisions were made in a ten-minute agent session that nobody else saw. The PR lands. The transcript closes. The decision is gone.

Volume of decisions: up. Visibility of decisions: down.

The problem I was trying to solve: capture product decisions wherever they're made, before they disappear. ADRs, Notion pages, Linear descriptions all share the same failure mode. The decision happens in one place. The documentation lives somewhere else. Nobody writes the second step. In the agentic era this gap is bigger, not smaller, because more decisions get made and fewer of them are made by people who own the documentation.

How Lock works: one storage, multiple capture surfaces.

- Slack bot: `@lock ` in any channel, or extract from a thread
- CLI: `lock ""` from the terminal, where humans review agent output
- GitHub Action: comments on PRs with relevant past decisions, including the PRs opened by agents

Under the hood: PostgreSQL with pgvector, auto-classification by type (product, technical, business, design, process), conflict detection against past decisions, semantic search, supersession chains. The conflict detection is the part that gets more important the faster your team ships. A contradicting decision should be caught at PR time, not three months later when someone asks why.

Where it's at: early. Functional, but small. MIT licensed, self-hosted, telemetry off by default. Two stars on GitHub today, which I'd rather you raise because you tried it than because you upvoted.

What I'd love feedback on:
1. For teams using Claude Code or Cursor: where do your agent-era product decisions live right now? PRs? Closed transcripts? Vibes?
2. What would have to be true about Lock for it to be the first place your team writes a decision down, not the last?

Site: https://www.uselock.ai/

Happy to answer anything.

Philippe

About Lock on Product Hunt

The decision protocol for product teams and AI agents

Lock was submitted on Product Hunt and earned 8 upvotes and 2 comments, placing #53 on the daily leaderboard. Your AI coding agent just shipped a fix that contradicts a decision your team made six months ago in Slack. Nobody noticed. Lock captures product decisions where they happen, in Slack and in the terminal, then flags when a new one contradicts an old one. The GitHub Action catches conflicts in PRs, including the ones your agent opens. Auto-classified, tracked through supersession, searchable by meaning. The decision layer for teams shipping with agents.

On the analytics side, Lock competes within Productivity, Open Source, Developer Tools and GitHub — topics that collectively have 1.3M followers on Product Hunt. The dashboard above tracks how Lock performed against the three products that launched closest to it on the same day.

Who hunted Lock?

Lock was hunted by Philippe HEBRARD. 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 Lock including community comment highlights and product details, visit the product overview.