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I built a social RSS feed aggregator (with Go, Tailwind, and Htmx)

Hacker News

I built a social RSS feed aggregator (with Go, Tailwind, and Htmx)

Hey HN, I built a small/fun side project mostly over the holiday break (been busy, so haven't shared it much yet) that I like to think of as a small social RSS aggregator called onread, https://onread.io . The general idea is that you sign up and provide an RSS feed which is polled at some frequency and automatically posts new content to your followers feeds. Feeds are composed of posts sourced from those RSS feeds of people you follow in addition to comments 'shared to your feed' which will share your thoughts on a post in addition to the post to your followers. Overall I feel like it's a pretty basic web app, but I was able to capture the "core idea" despite having a ton of other features I wanted to build into it, like using tags to automatically group posts and create better discoverability/communities. Part of the motivation behind building onread was that I wanted to write more Go and experience building a full-stack HTML application with it since I have a Rails background (spoiler: it was a good experience!). There was a lot to like, but I also ended up using it as an opportunity to explore what a generic focused web package in Go might look like ( https://github.com/blakewilliams/fernet if you're curious). It went through several iterations but I'm relatively happy with the result since it allows composition of routing concerns like fetching records and handling authorization based on the routing tree via composing "controllers" (needs better naming, tbh). It's not likely production ready and I have a few more modules I need to extract into the contrib package or core, but it felt pretty productive once I got the core structure in place. The front-end is pretty standard Tailwind and interactions use HTMX, which had a few challenges but overall was great. Like I said it was mostly for fun and to hack on some Go, but definitely interested in hearing feedback y'all may have or chat about the tech.

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3points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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: new, using · Missing: mac, agents, macos
88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
52%52% 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
40%40% predicted probability of success on TrustMRR, 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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