Pi

Pinocchio: Harness for Verifiable Work

Hacker News

Pinocchio: Harness for Verifiable Work

Hey HN, we've been trying to solve LLM Hallucinations. So we built Pinocchio. Pinocchio is a research preview for AI outputs with provenance and verification built in. Give it a task and source material. For numerics, the values used in the computation are carried directly from the source rather than regenerated by the model across reasoning steps. Pinocchio records the computations and derivations applied to them, and a deterministic verifier can replay and check the resulting computation. The output retains a connection to: • the exact source inputs used • the operations performed on them • the intermediate and final results they produced • the checks those results passed Every output points back to its sources. You can trace it backwards through its derivation and sources. Really looking forward to what you guys think about this. P.S. we're limiting to one run at a time. Other than there are no other restrictions. It does take a while though, so I'd let it be and check back in a while.

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Product HuntOn track for Day 1 leaderboard · Strong signals: model · Missing: mac, agents, macos
87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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