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UniVersCell – Universal constraint language and decision framework

Hacker News

UniVersCell – Universal constraint language and decision framework

We built two systems over 13 phases: 1. UniVersCell: Express constraints universally (cpu < 80%, model_accuracy >= 0.92) - Works across K8s, ML, finance, IoT, gaming, blockchain - 15 primitives, 7 domains, all at 10/10 score 2. Framework: Immutable timeline of who decided what - Hash-chained like blockchain - Can't lie about authority - Complete audit trail Both on PyPI now. 300+ tests. Production-ready. Looking for 5 early adopter teams to try it and give feedback. pip install universcell pip install universcell-framework

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: model · Missing: mac, agents, macos
37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
34%34% 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
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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