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Continuing the work on the ADHD personal assistant – Part 4

Hacker News

Continuing the work on the ADHD personal assistant – Part 4

I’m building a local-first AI assistant to help ADHD users (like me) structure their days without losing autonomy or drowning in dopamine loops. Why I’m building it: ADHD brains tend to thrive with structure, but hate rigidity. Most productivity tools assume your brain already works "normally." Mine doesn’t. So I’m trying to build something different: → Works locally → Keeps your data private → Structures your day using AI summaries → Doesn’t nag — just helps you see what matters Core features (WIP): Daily Debriefs: What happened, what’s next Communication Triage: Who to reply to, based on context Meeting & Task Prep: Pulling key info before your brain crashes Progress so far: Working integrations with Gmail, Calendar, and Slack Auth0 handling secure logins Backend structure for conversations, tasks, and people Early debrief summaries starting to work (still refining prompts) But here’s the twist: I started procrastinating on the product… by working on my personal brand and how to build in public So I: → Defined my brand pillars via ChatGPT + podcast transcript → Set up a Substack + mirrored blog → Started sharing regularly, writing first, videos later → Trying to make my creative chaos more useful for others too If you’re building weird tools for brains like yours, or trying to get out of your own way, would love feedback or to jam.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, user, context · Missing: mac, agents, macos
97%97% 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: started · Missing: supports, reddit linkedin, podcasting
78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, video, users · Missing: mobile apps, ios, entrepreneurs
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: adhd, ide, io · Missing: https docs, excited, just released
47%47% 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: users · 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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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