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Snapsked – Turn screenshots & photos into action items on calendar

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Snapsked – Turn screenshots & photos into action items on calendar

Like many, I save a ton of screenshots and photos to remember things -- and then forget lol. Snapsked is a simple tool that takes images, extracts action items, and syncs to your calendar automatically. Some real-life examples: https://bit.ly/snapskeddemo It’s a small step towards an ambitious idea I’ve had for a while (see background in the comment below if interested): What if an AI could look what you’re looking at (your screens, handwritten notes etc.) while you’re working; understood relevant action items; and helped with things on demand? Just like having a peer look over your shoulder to help when needed. I know it feels like it should be easy to build something like this with vision models available today, but there is one key issue: Models like GPT-4V, even when they work reliably, get expensive soon and are not ideal for end-user use-cases. I spent the last few months working on addressing this key technical problem, and I’ve been able to build an inference flow that works reliably at ~1/10th of the cost of GPT-4V, for this specific use case; thus making it affordable for many today. I'm looking for early users and useful feedback. Sign up at snapsked.org and follow x.com/snapsked for updates. Also open for any questions here or on my personal X/Twitter (same id as my HN).

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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: model, user, models · Missing: mac, agents, macos
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
63%63% 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: personal, month, users · Missing: mobile apps, ios, entrepreneurs
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon, users · Missing: plus, platform, intuitive
32%32% 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
13%13% 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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