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
SkyTwin
Personal AI. Your machine. Your rules.
SkyTwin: personal AI you can inspect, question, and run locally. It models preferences, explains itself, and gates actions behind a policy layer. Apache-2.0. Local by default—no silent remote fallback. Early preview, not a finished agent. Start with the screenshot walkthrough, then the fictional-data sample (no inbox, no key, can’t touch real accounts). Site has guides plus architecture/MCP docs. Feedback wanted on review-and-correct, what it should remember, and where it must ask first.
I’m building SkyTwin around a question: what would personal AI look like if you could inspect what it learned about you, question its decisions, and choose where the reasoning happens?
It models preferences and decision patterns, shows explanations, and has a policy layer between a suggestion and an action. The source is Apache-2.0. Local reasoning is the default, with no silent remote fallback.
This is an early source preview, not a finished autonomous assistant. The best place to start is the screenshot-led walkthrough, then the fictional-data sample if you want to run it locally. That sample needs no inbox or model key and its approval/correction simulation cannot affect real accounts. Google/Microsoft account connections and a supported signed installer release are not available yet.
The site has both plain-language guides and detailed architecture/MCP documentation. I’d especially value feedback on whether the review-and-correct interaction feels useful, what context you would want a twin to remember, and where you would insist it stop and ask.
I've been building this for many months before Meta's muse came out, but I think it actually does most of what it does, but is open source, and has a lot of qualities that actually make it even better
About SkyTwin on Product Hunt
“Personal AI. Your machine. Your rules.”
SkyTwin was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #160 on the daily leaderboard. SkyTwin: personal AI you can inspect, question, and run locally. It models preferences, explains itself, and gates actions behind a policy layer. Apache-2.0. Local by default—no silent remote fallback. Early preview, not a finished agent. Start with the screenshot walkthrough, then the fictional-data sample (no inbox, no key, can’t touch real accounts). Site has guides plus architecture/MCP docs. Feedback wanted on review-and-correct, what it should remember, and where it must ask first.
On the analytics side, SkyTwin competes within Artificial Intelligence, GitHub and OpenAI Day — topics that collectively have 520.5k followers on Product Hunt. The dashboard above tracks how SkyTwin performed against the three products that launched closest to it on the same day.
Who hunted SkyTwin ?
SkyTwin was hunted by Jay Zalowitz. 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 SkyTwin including community comment highlights and product details, visit the product overview.
Jay Zalowitz
I’m building SkyTwin around a question: what would personal AI look like if you could inspect what it learned about you, question its decisions, and choose where the reasoning happens?
It models preferences and decision patterns, shows explanations, and has a policy layer between a suggestion and an action. The source is Apache-2.0. Local reasoning is the default, with no silent remote fallback.
This is an early source preview, not a finished autonomous assistant. The best place to start is the screenshot-led walkthrough, then the fictional-data sample if you want to run it locally. That sample needs no inbox or model key and its approval/correction simulation cannot affect real accounts. Google/Microsoft account connections and a supported signed installer release are not available yet.
The site has both plain-language guides and detailed architecture/MCP documentation. I’d especially value feedback on whether the review-and-correct interaction feels useful, what context you would want a twin to remember, and where you would insist it stop and ask.
I've been building this for many months before Meta's muse came out, but I think it actually does most of what it does, but is open source, and has a lot of qualities that actually make it even better