SV
SVG Fitter – Rust+WASM Vectorizer
Hacker NewsSVG Fitter – Rust+WASM Vectorizer
I went crazy with a tool that helps me tracing raster images. Thought other might like it. It doesn't auto vectorize image, but rather allow for guided process. Final SVG still should be edited. Few fun features like genetic algorithm fit optimization, semi-manual tracing and color preservation. Perfect if you want to have lightweight SVG from huge PNG image. Note: If there's interest I might open-source it, just not sure if anyone would want to see it :)
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3points
Did not reach leaderboard
Launch Intel predictions
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting66%66% 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: open · Missing: mac, agents, macos53%53% 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 released51%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, personal40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive37%37% 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, revenue10%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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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