Self-explaining data graphs for apps and AI agents.
Interactive demos for .me — Smart Cities, Robots, and more.
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Maker
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I run very different businesses: a trucking company, farms, a radio station, a newspaper... Every new app asked the same questions:
who is this person?
what can they see?
what is it related to?
what changes when one piece of data changes?
Each time I was rebuilding the same infrastructure again, with auth, APIs, databases and servers, and it took up the space I needed to think about the business. So I kept going down, from the interface to state, to relationships, to identity, to permissions, until I found something basic enough to build everything else on. .me wasn't designed top-down. It came from solving everyday problems.
What feedback would help most? Try one of the demos and tell me where the syntax doesn't make sense to you, or where explain() doesn't answer the "why" you expected. I'd also love to hear about a real case of yours that you'd want to model, so I can see where it breaks.
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Maker
me is a small kernel: you write data as paths, define derived values with formulas, and the kernel keeps them up to date.
When one value changes, it doesn't recompute the whole graph, only the k values that actually depend on it. We measure that in public benchmarks you can rerun, including one on GTFS-Madrid-Bench transit data.
Every derived value keeps its derivation. me.explain(path) returns the formula, the inputs and their values, so when something is false, you can see exactly which input made it false.
Identity and permissions live in the same structure: a branch marked secret can't be read without the key.
Replies
I run very different businesses: a trucking company, farms, a radio station, a newspaper... Every new app asked the same questions:
who is this person?
what can they see?
what is it related to?
what changes when one piece of data changes?
Each time I was rebuilding the same infrastructure again, with auth, APIs, databases and servers, and it took up the space I needed to think about the business. So I kept going down, from the interface to state, to relationships, to identity, to permissions, until I found something basic enough to build everything else on. .me wasn't designed top-down. It came from solving everyday problems.
What feedback would help most?
Try one of the demos and tell me where the syntax doesn't make sense to you, or where explain() doesn't answer the "why" you expected. I'd also love to hear about a real case of yours that you'd want to model, so I can see where it breaks.
me is a small kernel: you write data as paths, define derived values with formulas, and the kernel keeps them up to date.
When one value changes, it doesn't recompute the whole graph, only the k values that actually depend on it. We measure that in public benchmarks you can rerun, including one on GTFS-Madrid-Bench transit data.
Every derived value keeps its derivation. me.explain(path) returns the formula, the inputs and their values, so when something is false, you can see exactly which input made it false.
Identity and permissions live in the same structure: a branch marked secret can't be read without the key.
Benchmarks and demos are at neurons-me.github.io/.me. I'd love to hear where it breaks for your use case.