GistReader – an RSS reader that uses AI to summarise your content

Hacker News

Hi everyone! Aron (@arondeparon) here, founder of Gistreader.com. I was an avid user of Google Reader back in the day, but for some reason, I silently rolled over when they decided to kill it and never looked at RSS again. Lately, I am getting more and more frustrated by the fact that most of the content that I consume is being pushed at me by some kind of algorithm. I wanted more control over this process, so decided to build GistReader. In its basic form, GistReader is an RSS reader that aims to make your reading experience as fast and pleasant as possible. In addition to that, it uses AI to automatically create summaries of your articles and uses text-to-speech to optionally read a condensed version of an article to you out loud. GistReader is my first-ever product launch, so as you might expect, I'm pretty nervous about that, especially when it comes to sharing it on HN :) Please, feel free to let me know what you think. It's all very early-days (lots of things I want to fix/improve/build) and the only way I can improve is by getting solid feedback from people that might actually use it. Thanks!

Visit product View on Hacker NewsApr 10, 2023

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

88%88% 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: google, user · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% 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: google, way · Missing: mobile apps, ios, personal

42%42% 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

31%31% 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

18%18% 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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