Resilient LLM

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Resilient LLM

ResilientLLM is a minimalist but robust LLM integration layer designed to ensure reliable, seamless interactions across multiple LLM providers by intelligently handling failures and rate limits out-of-the-box. Available as a node.js package `npm i resilient-llm`, support for other languages coming soon. With just few lines of code, you can avoid writing and maintaining the code to deal with challenges such as: 1. Unstable network conditions 2. Intermittent errors and inconsistent error handling 3. Unpredictable LLM API rate limit errors More about the motivation and features in the README. Keen to hear your thoughts. If you like this, do star the repo and share your feature requests.

Visit product View on Hacker NewsNov 24, 2025

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Actual performance

1points

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

61%61% 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive

40%40% 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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

25%25% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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