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
Lotor v3.0
Retrograde replay of RAE; selectively shareable proof
Lotor now records every step an agent takes on your machine, down to the reasoning behind it. You can replay a run, or start at the result and walk it back to see what actually worked and what was a dead end. Then you share it as a signed cartridge and only show what you choose. It’s for anyone using agents to reverse engineer software or build on someone else’s work who needs to show how it got done.
So I’ve been watching everybody reverse engineer stuff with agents the last couple weeks, and it’s impressive. But nobody can show you how they got there. You get the result, you don’t get the method. And if you can’t replay it, you can’t really say what was good about it and what was bad.
That’s what Lotor does now. It records every step the agent takes on your own machine, with the reasoning behind it, so you can replay the whole run. Or start at the end and walk it backwards, see what actually produced the result and what was a dead end. I call that the retrograde.
Then you share it. The run goes out as a cartridge, a signed QR tied to your chain, and you pick what goes in. People get the steps and the proof, not your whole history. Identities get resolved locally first, so you’re in control of your data the whole time.
It’s built for more than one agent, too. The one doing the work and the ones checking it are all on the record, so you’re not taking any single model’s word for it.
If you want to contribute, same rule. Bring the evidence that your change fixes something and doesn’t break something else.
About Lotor v3.0 on Product Hunt
“Retrograde replay of RAE; selectively shareable proof”
Lotor v3.0 was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #241 on the daily leaderboard. Lotor now records every step an agent takes on your machine, down to the reasoning behind it. You can replay a run, or start at the result and walk it back to see what actually worked and what was a dead end. Then you share it as a signed cartridge and only show what you choose. It’s for anyone using agents to reverse engineer software or build on someone else’s work who needs to show how it got done.
On the analytics side, Lotor v3.0 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 Lotor v3.0 performed against the three products that launched closest to it on the same day.
Who hunted Lotor v3.0?
Lotor v3.0 was hunted by Isaac Liem. 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 Lotor v3.0 including community comment highlights and product details, visit the product overview.
Isaac Liem
So I’ve been watching everybody reverse engineer stuff with agents the last couple weeks, and it’s impressive. But nobody can show you how they got there. You get the result, you don’t get the method. And if you can’t replay it, you can’t really say what was good about it and what was bad.
That’s what Lotor does now. It records every step the agent takes on your own machine, with the reasoning behind it, so you can replay the whole run. Or start at the end and walk it backwards, see what actually produced the result and what was a dead end. I call that the retrograde.
Then you share it. The run goes out as a cartridge, a signed QR tied to your chain, and you pick what goes in. People get the steps and the proof, not your whole history. Identities get resolved locally first, so you’re in control of your data the whole time.
It’s built for more than one agent, too. The one doing the work and the ones checking it are all on the record, so you’re not taking any single model’s word for it.
If you want to contribute, same rule. Bring the evidence that your change fixes something and doesn’t break something else.