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Feed.style – better UX for RSS and Atom news feed links

Hacker News

Feed.style – better UX for RSS and Atom news feed links

I really went down the rabbit hole on this one: I was working on a side project that has an RSS feed and I want people to use it, so I made the link be first & prominent. But the user experience was abysmal! A wall of gibberish text. So… Feed.style! A quick and easy way for an RSS link to do something reasonable for newbies (and non-newbies!). Of course, being the attention hound that I am, I went through HN’s karma leaders to see what their feed UX was like, figuring I’d call out a few people to get some attention & upvotes. But wow, what a sorry bunch. Finding issues is like shooting fish in a barrel. Before I begin, a shout out: ColinWright, you are literally the only one with decent-looking feed. Way to go! Everybody else, well, let’s just say there is room for improvement. I made some before & (potential) after links in the example section on the Feed.style home page. So, on with naming & shaming: tptacek/fly.io (bad self link, no discovery link), jacquesm (bad self link), davewiner (no self link), danso (insecure self link), tosh (self link mismatch, though it does work), luu (bad self link)... And the internet elders? Worst of all. If there could be one way of dealing with news feeds worse than a wall of text, what would it be? Popping up the “Save as…” dialog. I’m looking at you: mnot, tbray, jgrahamc, aphyr, thomaspark… Seriously, it doesn’t matter if you use the standard content type if it makes the UX this unfriendly. And yes, I’m using Firefox: aren’t you? On a serious note: I am a big fan of RSS & think it has a ton of potential, but we have to do better or it will remain a geeks-only sideshow.

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% 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: user, new, using · Missing: mac, agents, macos
71%71% 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
68%68% 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 · Strong signals: friendly · Missing: plus, platform, intuitive
53%53% 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
43%43% 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
11%11% 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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