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ADHD STASH. A curated collection of ADHD friendly products and services

Hacker News

ADHD STASH. A curated collection of ADHD friendly products and services

Over the past few years I've struggled to find good/trusted resources for things that might be useful to those with ADHD. Common issues with other sites were: * Very limited options and/or info, or products that weren't actually useful. * Blog post or news articles like "top 10 things to help with your ADHD" often had limited info or didn't have enough context to be useful. * Unclear if some authors had ADHD or experience with ADHD. In some cases you could tell a random writer got assigned this post to write, but had no idea what ADHD is really like. * Walls of text or cluttered/overwhelming design, meaning that the content was too hard to read and/or the site made me anxious (after which I bounced). As a result, I created ADHD STASH, where I've tried to: * Limit content length and complexity, while sharing key info * Use design that feels friendly and fun * Give people the option to find products/services based on use case or problem areas * Only recommend products where I own them and have tried them for a significant period. Questions/Feedback Any constructive feedback is welcome, especially from people who have ADHD! In particular, it would be great to get real world feedback to help me validate/invalidate my hypothesis that the design/content is ADHD friendly and if the latter, what I could improve upon. P.S. Since I bought all these products to test out of my own pocket and that I use Apple ecosystem devices, it's only natural that some of the options listed are Apple centric. That being said, I'm saving up to get some more Google/Windows related devices to test! P.P.S. Some affiliate links for the US are a bit fiddly (aka, showing search results instead of products, even when something is in stock), but I'm trying to fix that atm.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
93%93% 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: apple, google, new · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: adhd, ide, io · Missing: https docs, excited, just released
53%53% 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: friendly · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
41%41% 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
10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real world · 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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