Node.js back end library is 1.0 now

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After more of a decade working on my hobby https://github.com/vseryakov/backendjs project and use it many times in many places i am ready to make it 1.0. It has never been ready all this time without documentation and legacy sloppy code, well, it started with nodejs 10, me being lazy or not focused enough to polish it...you know, life, production, startup stress, delivering deadlines, i will do it later... (un)Fortunately i had some spare time last year and used LLMs to document what i have, refactor it, and i acknowledge i would never produce good documentation, local/frontier LLMs helped a lot to document it, but the code is mine 100%, good or bad, my responsibility.

Visit product View on Hacker NewsAug 28, 2026

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1points

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

79%79% 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

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

55%55% 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 · Missing: mobile apps, ios, personal

37%37% 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

22%22% 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

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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