Simpler web dev with Htmx, SQLite and TypeScript

Hacker News

i'm building this minimalistic framework mostly for myself. it's really just a project template + a tiny library that adds file based routes to express and persistent task queues. i wanted something that: 1. is a single long-running process to deploy and manage (thanks sqlite!) 2. has type-safe database queries and type-safe server rendered React-like `.tsx` components 3. addresses 80% of common full-stack concerns like emails, task queues and routing 4. doesn't have a frontend build process curious to hear what you think!

Visit product View on Hacker NewsJun 19, 2024

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

81%81% 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

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

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

58%58% 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

35%35% 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

26%26% 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

15%15% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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