Ti

Tidyread, an ADHD-Friendly RSS Reader, Powered by AI

Hacker News

Tidyread, an ADHD-Friendly RSS Reader, Powered by AI

Living with ADHD, I often get easily distracted. I find myself frustrated with certain algorithmic recommendations that seem to exploit my vulnerabilities. These algorithms trigger FOMO or cater to my impulses, leaving me feeling out of control and overwhelmed. I once thought RSS readers could solve my problem, but I still faced new issues that made me want to escape: 1. Many sources only provide titles or truncated summaries, requiring me to click through to see if it’s the content I want. Often, I realize halfway through that it’s not what I want, making me feel like I wasted my time. 2. When I don’t read for a while, a lot of information accumulates. Although I can clear it with one click, there’s a huge psychological burden before I start reading again. It’s like having 300 overdue tasks silently lying in my to-do list; I can’t clear them all at once and need to think about how to handle them. 3. As the number of subscribed sources increases, reading during fragmented times makes me feel overwhelmed by a torrent of information, exacerbating my anxiety. It’s like an endless social media feed. So, I created an ADHD-friendly reader, Tidyread, which can: 1. Filter, summarize, and translate interesting sources using AI, and deliver a daily digest, making reading a routine. 2. Pause at any granularity: the entire Tidyread, a specific topic, or a particular source. 3. Allow you to freely plan topics and customize summary prompts by theme, providing more tailored content. 4. Offer an efficient reading mode to keep your reading flow smooth. You can give it a try on tidyread.ai. Looking forward to your feedback!

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, tasks, using · Missing: mac, agents, macos
90%90% 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 · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, 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.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: friendly, efficient · Missing: plus, platform, intuitive
39%39% 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
12%12% 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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