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I made voice operated todo lists with Gemini Flash 2.0

Hacker News

I made voice operated todo lists with Gemini Flash 2.0

The goal of this app is to be a simple datastore for all the details a family might need to manage in a given week — meal plans, pickup rosters, date night ideas, shopping lists — everything in one place, organized into simple, purpose-built lists. It’s my take on what Apple Intelligence could be. Ordinarily, an app like this would be tedious to use, how do you find your todo on 50+ todo lists? This is where you can leverage multimodal AI, like Gemini Flash 2.0, users can query with their voice what they're after, the whole store can fit in context. All the data is cached in the browser thanks to https://tinybase.org/ and synced via Cloudflare's durable objects. The lists themselves are all generated by Claude Sonnet 3.5, and you can create new ones in the app to capture any routines or tasks you like. They’re similar to GPTs from OpenAI — you can give them custom instructions that affect how they interpret instructions or modify the lists. You can also pair lists, transforming one list’s data into another, like converting a recipe into a shopping list. You can try it out without creating an account, bring your own API keys, and all data is stored in localStorage on your device until you upgrade.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, apple, user · Missing: mac, agents, macos
93%93% 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: gemini · Missing: supports, reddit linkedin, podcasting
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal
57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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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