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Img.pro – Image Optimization API built on R2 (zero egress)

Hacker News

Img.pro – Image Optimization API built on R2 (zero egress)

I’ve been playing with the idea that Cloudflare’s R2 (with zero egress fees) opens the door to a simpler way of handling image optimization. Instead of juggling S3, a CDN, and custom pipelines, what if you could just drop your originals in one place and always get back optimized, cached images at the edge? That’s what I’m experimenting with at img.pro. How it works: + Upload an original (JPEG, PNG, WebP, HEIC, AVIF, PSD, etc.) + The API generates optimized variants automatically (S/M/L sizes by default) + Originals are stored in R2 → cheap, no hidden bandwidth costs + Optimized images are delivered through Cloudflare’s edge (300+ PoPs) + Output formats are predictable (JPEG for opaque, WebP for transparency, GIF/SVG preserved) The idea is: if R2 really makes egress “free,” can an image API scale without the usual CDN/storage tradeoffs? Right now I’m focused on making the pipeline as simple as possible (no knobs, just sane defaults). Pricing is mostly placeholder ($10 = 1k images, $50 = 10k, etc.), but I’m more interested in whether the developer experience feels right. To get real feedback, I’m offering free access forever to the Launch plan (1,000 uploads/month) with the code: LAUNCHN I’d love to hear: + Does the R2-backed model make sense to you? + What’s missing for you to trust this in production? + Are the defaults (sizes/formats) useful, or would you expect more control up front? Live at https://img.pro .

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% 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, code, open · Missing: mac, agents, macos
66%66% 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, pipe, 000 · Missing: https docs, excited, just released
52%52% 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: month, way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
30%30% 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
19%19% 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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