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Today I Learned XYZ

Hacker News

Today I Learned XYZ

Today I Learned XYZ is a website where users can document the interesting things they learn, either from hobbies or jobs. Users can also read and vote on the submissions of other users, based on how interesting and useful the submission is. To stay updated on the findings of the most interesting users, RSS feeds are available. Why I built this I built Today I Learned XYZ to learn more about full stack web development, as well as infrastructure and software engineering in general. However, I wanted whatever software I made to be useful, with a more specific purpose than just a generic blogging platform. Before starting the project, I saw this post on Hacker News: What To Blog About [1]. I found particularly interesting the section about Today I Learned style blog posts, in which the author writes about new findings that interest them. The TIL format is mostly for the benefit of the author, as the subject can be something that has already been covered in other blogs. All that matters was that the author learned something new and wants to share about it, for the benefit of others. I thought this would be a refreshing change of pace from social networking sites where users are constantly trying to make the most popular content. You can view the source code for this project on GitHub [2] and I wrote instructions for self hosting. Any feedback is appreciated, either on the implementation or the premise of the site! [1] https://simonwillison.net/2022/Nov/6/what-to-blog-about/ [2] https://github.com/cuppajoe123/Today-I-Learned-App

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
76%76% 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.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io · Missing: https docs, excited, just released
60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, users · Missing: plus, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, new, code · Missing: mac, agents, macos
39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
34%34% 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
19%19% 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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