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Thread to Self – a private message thread with yourself

Hacker News

Thread to Self – a private message thread with yourself

Hello, Thread to Self was created because I wanted a way to capture thoughts (random or otherwise) in a single stream. I didn't want to have to deal with folders or tags or any other type of organization took that would make it become an admin heavy burden. This is a naive IOS and local first, so no accounts, no metrics and no mining. It's you and your thoughts. I did use AI to build this but it was not a 1 day endeavor. To get her took months of building and refining. I use it daily (in fact multiple times a day) but want to see if other people would actually want to use it. Where are the pain points that make the app to simplistic. Thanks, Carl

Visit productView on Hacker NewsSep 8, 2026
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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: single · Missing: mac, agents, macos
79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: created, ios · Missing: supports, reddit linkedin, podcasting
73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, month, way · Missing: mobile apps, personal, entrepreneurs
60%60% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
38%38% 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 · Missing: plus, platform, intuitive
26%26% 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
24%24% 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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