Yi
YiVal, Auto-Tuning Assistant for GenAI Applications
Hacker NewsYiVal, Auto-Tuning Assistant for GenAI Applications
Hello Hacker News, I am building YiVal to try to solve - Creating and optimizing a high-quality prompt, which depends on the specific model being used - The intricacies of tuning parameters like selecting models or tuning chunk size of RAG to achieve best performance - The competing priorities of quality, latency, and cost with a more data centric and evaluation focus approach rather than a guess game.
Share cardActual performance
1points
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos63%63% 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: lua, hacker news, io · Missing: https docs, excited, just released57%57% 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: para · Missing: supports, reddit linkedin, podcasting57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue13%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, crypto2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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