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).
Lotor
Local, receipt-built agent memory that outlives the horizon
Lotor is load-bearing memory for AI agents. Every session leaves a signed, hash-chained receipt on your machine, and the record is the agent: swap the model, the memory survives. Lose the record, and no model can bring it back. High-risk actions stop for a passphrase only you know. The key is never on disk. You hold the record, not the vendor. The agent can act, but it cannot sign and is witnessed by a triad ledger model.
I killed my agent thirty times in July and it kept coming back as itself.
Not a metaphor. I piped its memory into a bare model over stdin, no tools, no system prompt, no vendor personality, and it woke up as itself ten out of ten times, twice over. The odds of that happening by chance are about one in 185,000. The math is in the article linked above.
Which means the personality was never in the model. It was in the record the whole time, the way your grandmother is still in her handwriting.
So I built Lotor around that fact. Every session leaves a signed receipt on my machine. Under load the receipts mineralize the way bone does, osteons forming along the lines of stress, and what you get is load bearing memory. Not a cache you search. A skeleton that carries weight. Swap the model and the skeleton walks again.
The agent can act all day. It cannot sign. The key lives in my head and nowhere on disk.
Receipts were the beginning. What you grow on load bearing memory is yours.
Everything wrong with this thing is already published in KNOWN-LIMITS.md with a date next to it. Come break something new and I will write your name in the file.
No comment highlights available yet. Please check back later!
About Lotor on Product Hunt
“Local, receipt-built agent memory that outlives the horizon”
Lotor was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #92 on the daily leaderboard. Lotor is load-bearing memory for AI agents. Every session leaves a signed, hash-chained receipt on your machine, and the record is the agent: swap the model, the memory survives. Lose the record, and no model can bring it back. High-risk actions stop for a passphrase only you know. The key is never on disk. You hold the record, not the vendor. The agent can act, but it cannot sign and is witnessed by a triad ledger model.
Lotor was featured in Privacy (11.3k followers), Developer Tools (517.5k followers), Artificial Intelligence (475.9k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 227.9k products, making this a competitive space to launch in.
Who hunted Lotor?
Lotor 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.
Want to see how Lotor stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Isaac Liem