Pi

PixCloak – Local image compression and redaction (no upload, OSS)

Hacker News

PixCloak – Local image compression and redaction (no upload, OSS)

I built a browser‑only image toolkit: Target size (KB) compression (binary search) Resize longest/exact; JPEG/WebP/PNG Batch + ZIP; rename rules Redaction with solid/pixelate; EXIF/GPS removed It’s open‑source (AGPL‑3.0) and works offline (PWA). Live: https://pixcloak.com/compress Redaction: https://pixcloak.com/redact Repo: https://github.com/LeeJinMing/PixCloak Would love feedback on quality vs speed, large files, and presets.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
59%59% 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
57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos
28%28% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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