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DecisionBox – Continuous Accuracy Improvement for LLM Apps

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DecisionBox – Continuous Accuracy Improvement for LLM Apps

Just released DecisionBox, an open-source SDK that helps developers make high-accuracy decisions in LLM apps, which continuously improve with more data. DecisionBox tackles the challenge of maintaining decision accuracy beyond the limitations of prompt engineering. It streamlines the data science process with an easy-to-use API and enables ongoing accuracy metrics and model improvements. Get started: https://github.com/fmops/decisionbox We’re excited to hear feedback and answer any questions! AMA.

Visit productView on Hacker NewsSep 27, 2024
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Actual performance

2points
1comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, open · Missing: mac, agents, macos
86%86% 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: excited, just released, io · Missing: https docs, exist, lua
75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: apps · Missing: mobile apps, ios, personal
66%66% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
66%66% 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
28%28% 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
13%13% 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.

Incorrect prediction on native model

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