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Turnal
A flight recorder for AI coding agents.
Turnal is a local-first flight recorder for AI coding agents. It captures what an agent changed, why it changed it, and the state of your workspace before and after every turn so you can inspect, understand, and roll back agent work with confidence.
AI agents are becoming capable of making large, multi-file changes across a codebase. But when something goes wrong, it can still be difficult to answer basic questions:
What exactly did the agent change? Why did it make that decision? What did the project look like before that turn? How can I undo one bad step without losing everything else?
Turnal records each agent interaction as an auditable checkpoint. It captures the agent’s activity alongside snapshots of the workspace before and after every turn.
Turnal is designed to be local-first. Your source code and activity history stay on your machine, and normal recording does not add commits to your existing Git history. The goal is not to slow agents down or require approval for every edit. It is to let agents move quickly while making their work observable, reversible, and accountable.
Turnal is publicly available starting today. I’d love to hear how you currently review, debug, or roll back work performed by coding agents, and what you think an agent accountability layer should capture next.
About Turnal on Product Hunt
“A flight recorder for AI coding agents.”
Turnal was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #112 on the daily leaderboard. Turnal is a local-first flight recorder for AI coding agents. It captures what an agent changed, why it changed it, and the state of your workspace before and after every turn so you can inspect, understand, and roll back agent work with confidence.
On the analytics side, Turnal competes within Developer Tools, Artificial Intelligence, GitHub and Tech — topics that collectively have 1.7M followers on Product Hunt. The dashboard above tracks how Turnal performed against the three products that launched closest to it on the same day.
Who hunted Turnal?
Turnal was hunted by Advait Johari. 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 Turnal including community comment highlights and product details, visit the product overview.
Advait Johari
Hey Product Hunt! I’m excited to launch Turnal, a local accountability layer for AI coding agents.
Website: https://turnal.johari-dev.com/
AI agents are becoming capable of making large, multi-file changes across a codebase. But when something goes wrong, it can still be difficult to answer basic questions:
What exactly did the agent change?
Why did it make that decision?
What did the project look like before that turn?
How can I undo one bad step without losing everything else?
Turnal records each agent interaction as an auditable checkpoint. It captures the agent’s activity alongside snapshots of the workspace before and after every turn.
Turnal is designed to be local-first. Your source code and activity history stay on your machine, and normal recording does not add commits to your existing Git history.
The goal is not to slow agents down or require approval for every edit. It is to let agents
move quickly while making their work observable, reversible, and accountable.
Turnal is publicly available starting today. I’d love to hear how you currently review, debug, or roll back work performed by coding agents, and what you think an agent accountability layer should capture next.