Tinyfeed – CLI to generate static webpage from a collection of feeds

Hacker News

This project started because I wanted to consume my RSS/Atoms feeds the same way I consume news on HN: a lightweight webpage with a minimalist user interface where I can comfortably browse interesting links. I didn't like existing solutions for feeds reader. Many of them seemed overly complex, most of them require a database and don't event support sqlite. For a simple feed reader, it felt overkill. What I wanted was an easy setup that didn't rely on external databases or configurations. My solution was to build a small CLI tool that print an HTML page from a list of feed URLs. I can just set it up in Cron, point NGINX to the generate HTML file and that's it, I have a lovely webpage with my feeds. This was my first real project in Go and I'm still learning so I'm eager to receive feedback on the code. Checkout the demo here: https://feed.lovergne.dev/

Visit product View on Hacker NewsSep 22, 2023

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

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

nativeThis product was originally launched on this platform.

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

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive

62%62% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: started · Missing: supports, reddit linkedin, podcasting

40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

13%13% 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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