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LLmHub.dev – A Unified API for Multi-LLM Automatic Routing

Hacker News

LLmHub.dev – A Unified API for Multi-LLM Automatic Routing

Hi HN, I’m Prateek, a solo founder who’s just launched LLmHub (llmhub.dev) – a simple, unified API that makes it easier to work with multiple large language models without the headache of managing separate keys or juggling between providers. The idea behind LLmHub came from my own frustration with vendor lock-in, unpredictable costs, and the complexity of switching between models when one isn’t performing as expected. With LLmHub, you get an API that automatically picks the best model for your query based on factors like cost and performance. It’s really aimed at making life easier for developers, AI researchers, and anyone building on LLMs. I’m keeping it really lean and open to feedback. If you email me at prateek@llmhub.dev and share your interest, I’ll give you $10 in credits to try it out. And if you end up building something with it or share your experience on social media, I’d love to send an extra $25 your way as a thank-you. I’m not here to “sell” you something fancy – just sharing something I built because I believe it can help others avoid the hassle of dealing with multiple APIs. Would love to hear your thoughts, questions, or even criticisms. Thanks for checking it out! — Prateek Feel free to reach out with any feedback or ideas!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, email · Missing: mac, agents, macos
95%95% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
57%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.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
40%40% 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
39%39% 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
22%22% 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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