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Soot, a tool for visualizing and searching sets of images

Hacker News

Soot, a tool for visualizing and searching sets of images

Hey everyone, I’m excited to share a project I’ve been working on: a tool for visualizing and searching massive sets of images. To demonstrate its capabilities, I decided to upload the 5000 best Magic: The Gathering cards into it. Here’s what you can do with it: Advanced Search: Find images based on any criteria you desire Image tagging and metadata: Cluster your images based on attributes Interactive Filters: Narrow down your search with various filters and categories Generative Content: Based on your content, generate similar images with Dall-E 3 Check it out and let me know what you think! Your feedback would be greatly appreciated.

Visit productView on Hacker NewsJul 24, 2024
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Actual performance

5points
3comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
63%63% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: visual · Missing: mac, agents, macos
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, 000 · Missing: https docs, just released, exist
51%51% 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 · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, active · Missing: mrr, revenue, profit
10%10% 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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