Kompressr – One Step CDN which optimizes all your files

Hacker News

In my previous organization, we went from a monolith to multiple FE & BE repos & then wanted Image Optimization, which took more than a year for us to get it right & even then it was not fully done for old images!! At first it looks like "we just need image optimization." Then the scope grows: - existing site assets need a CDN path - uploads need storage and permanent URLs I saw the pain & the thought of Kompressr was born. Kompressr is my attempt to make that pipeline boring & make things just work! There are two live paths: 1. Prepend a project CDN URL to assets you already host: https://cdn.kompressr.com/PROJECT_ID/https://example.com/hero.jpg 2. Upload a file directly and get back a CDN URL: curl -X POST https://uploads.kompressr.com/api/upload \ -H "Authorization: Bearer $KOMPRESSR_API_KEY" \ -F file=@output.png The returned URL is usable immediately. If Kompressr already has an optimized version, it serves that. If not, the original goes out first while optimization runs in the background. It handles images, video, PDFs, fonts, CSS, JS, HTML, SVG, JSON, and XML on paid plans. Free tier starts with image optimization. For AI apps, the Upload API can be the file layer: upload generated output, store it, optimize it, and hand your user a permanent URL. There is also an MCP server (`npx -y @kompressr/mcp-server`) if you want agents to upload files directly. Pricing is storage + bandwidth, not per-transformation. Autopilot is coming next for the no-code path, where you point your domain to Kompressr & all assets are optimized. Free tier: 250 optimizations, no credit card. Take the Free Audit to know how much you can actually save on a page: https://kompressr.com/audit AI apps guide: https://kompressr.com/agents Docs: https://kompressr.com/docs Pricing: https://kompressr.com/pricing

Visit product View on Hacker NewsApr 28, 2026

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

79%79% 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: agents, agent, mcp · Missing: mac, macos, cursor

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, video · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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