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Eunomia, Open Source Data Governance for LLM-Based Applications

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Eunomia, Open Source Data Governance for LLM-Based Applications

Like many of you, we've spent the last two years dealing with requests to sprinkle LLM-powered features everywhere. One recurring problem we faced in almost every project was related to data governance. In most cases, it was extremely hard to implement the needed granular control over the retrieved data. We developed custom solutions each time, but when we realized most of the solutions could be reused in subsequent projects, we started thinking about creating a modular framework. Today we're releasing that framework! We've already built some modules using open-source solutions such as Microsoft Presidio (1) or custom fine-tuned models like our own Financial BERT (2), but the goal is to let you play with it and add the modules you need for your own use case. Leave a star on GitHub if you find this problem interesting, join our community if you want to stay in the loop, and let us know what you think about Eunomia! We'd love to hear your feedback! (1) https://github.com/microsoft/presidio (2) https://huggingface.co/whataboutyou-ai/financial_bert

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, using · Missing: mac, agents, macos
82%82% 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.
Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: recurring · Missing: arr, mrr, revenue
12%12% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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