Faultkey

@m4npreet006 @X faultkey.pages.dev

Hey @X I'm looking to #connect with builders & devs interested in: • Frontend • Backend • Full Stack • UI/UX • Freelancing • Startups • SaaS • iOS Development • AI Agents / Observability I'm a full-stack engineer Say hello, drop your project or domain 👇 and let's grow together! 🚀 #BuildInPublic #DevCommunity #SaaS #AI #IndieHackers

@Akkii_learner @X faultkey.pages.dev

Hey @X I want to #connect with people interested in: -Frontend -Backend -Full Stack -Data Science -UI/UX -Freelancing -Startup -SaaS Say 'HELLO' & let's grow together Mention your field 👇

yeah, perfect example honestly. That’s exactly the gap FaultKey’s trying to fill — not replacing the lawyers or underwriters, just giving them an evidence layer underneath that anyone can recompute and verify. The humans still do the judgment; the engine just makes the reasoning underneath actually hold up.

@Faultkey I agree! I saw a case in the news not too long ago of someone firing their legal team and using ChatGPT as their lawyer. They lost the case lol.

@PlugYourBuild Thanks man I kept seeing AI-generated legal work get sanctioned for hallucinated citations (Mata v. Avianca and the cases that followed), it felt obvious incident post-mortems were heading the same way — and ‘the LLM said it’ isn’t going to survive Rule 702 scrutiny.”

@Faultkey Oh man this is brilliant. Definitely something that is currently missing! How did you come up with this idea??

FaultKey is an evidence layer for AI incidents. When something an AI did causes harm, it produces a deterministic, cryptographically anchored fault decomposition — a reproducible vendor/deployer/user split that an insurer, a regulator, or a court can recompute themselves without trusting us. Open verifier, public anchor log. Different layer from agent ops, not a competitor.

@Faultkey What is faultkey??

Honestly, for ops you’re right — chained skills with checks are the right architecture and what most builders need. FaultKey isn’t trying to replace that. It’s the evidentiary layer underneath: a closed-form, deterministic fault decomposition that produces byte-identical 100-point splits across vendor/deployer/user that anyone can recompute offline. An LLM chain can describe fault. It can’t reproduce the same numeric split twice — sampling, model updates, prompt drift. That’s disqualifying for EU AI Act Article 73 reporting (15-day clock, regulator-acceptable evidence) and for insurance claims. Public Rekor anchor: rekor.sigstore.dev/api/v1/log/ent… · Open verifier: github.com/smq9sn5jck-cod…

@Faultkey It may be worth making your own. Claude can do it with a proper skill workflow. I chain skills together with checks and balances between their execution phases which indirectly solves this problem when something goes wrong.

Has anyone seen a good citation/attribution engine for AI agent chains? Like when 4 agents collaborate on a task and something breaks, a system that deterministically traces which step caused the fault. Struggling to find anything that isn’t just logging.

@PlugYourBuild reddit.com/r/antiai/s/ftq…

Hello! 👋 We want to connect with people interested in: - AI - SaaS - Course Development - Product Launches - Marketing - Web Design - Indie Hackers Let’s grow together! 👇

@vaaselene Has anyone seen a good citation/attribution engine for AI agent chains? Like when 4 agents collaborate on a task and something breaks, a system that deterministically traces which step caused the fault. Struggling to find anything that isn’t just logging.

founders 👋 what did you build this week? show it off and let's get some traffic!

@ionleu Has anyone seen a good citation/attribution engine for AI agent chains? Like when 4 agents collaborate on a task and something breaks, a system that deterministically traces which step caused the fault. Struggling to find anything that isn’t just logging.

drop ur startup link