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Guiderail – How I (partly) solved my procrastination problem

Hacker News

Guiderail – How I (partly) solved my procrastination problem

A few years back I made a bet with a friend regarding a 3D printing project: If I didn't do it in time, he'd send a check of 1000 dollars I had written in advance, to any charity he wanted. At first, well, I... procrastinated. However, as I saw the deadline getting closer and my friend started taunting me about the charity to which he was going to send my check, it really lit a fire under me. I finished the project, though barely on time (we're talking minutes here). That experience gave me an idea: what if I could automate this motivation boost, but for more generalized goals, such as learning a language or spending less time on YouTube? Thus, I created https://guiderail.io . At the moment, it's in a very early stage and only interfaces with Duolingo, Github, Fitbit, RescueTime, and Todoist, but I have plans to add more services later. So, what do you think, HN? Am I out of my mind? Also that's my first venture into entrepreneurship so I welcome any advice you could have.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting
80%80% 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 · Missing: mac, agents, macos
52%52% 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, 000, 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: entrepreneurs · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
33%33% 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
14%14% 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.

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