Minima.js – a web-standards back end framework with req context via ALS

Hacker News

Hi HN — I’m building Minima.js, a backend framework that tries to feel “native” to the platform by leaning into Web standards and keeping request/response handling close to the primitives (e.g., Request/Response style APIs), instead of inventing a big framework-specific universe. Intro: https://minimajs.com/intro.md The five core ideas I’m most excited about: 1. Runtime-Native Support - Built from scratch for Bun and Node.js with zero abstractions 2. Web Standard First - Uses native Request, Response, File, Blob, and Uint8Array throughout — no Node.js-specific buffers or proprietary abstractions 3. File-Based Modules - Your folder structure defines your API structure 4. Context Functions - Access request data anywhere without prop drilling 5. Everything is a Plugin - Hooks, middleware, auth—all follow the same pattern Docs: https://minimajs.com LLM-friendly docs index: https://minimajs.com/llms.txt Would love feedback from people who’ve shipped production Node/Fastify/Express apps: does “web-standards + ALS-first context” resonate, and what would you want to see before trying it?

Visit product View on Hacker NewsFeb 27, 2026

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

82%82% 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 · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% 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: apps · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

17%17% 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.

Correct prediction on native model

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