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Imagor v1 – fast image processing server in Go and libvips

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Imagor v1 – fast image processing server in Go and libvips

Imagor is a fast, Docker-ready image processing server written in Go, with libvips. The v1 milestone has got more advancements to its internals. At the moment, all existing popular Go + libvips image applications (imgproxy, imaginary, bimg etc.) bridge libvips through buffer. While these are all good with normal web images, the latency and memory overhead can be noticeable when working through large, raw images, as they are all loading the whole image buffer in memory, sequentially. Imagor v1 now bridges libvips through streams (i.e. Go io.Reader/Seeker/Closer). This greatly increases network throughput especially with network sources like HTTPs, S3, GCS, by giving the ability to overlap processing pipelines. With streaming in place, same goes for image Exif metadata. Imagor can try to retrieve data just enough to extract the header, without reading and processing the whole image in memory. Though of course cgo + stream means a lot of moving parts that can go wrong. Imagor has increased test coverages since then. It has been running in production for months, serving over a million of images everyday. Feel free to create a pull request or report an issue if you found bugs, suggestions or enhancements.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
82%82% 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: dock · Missing: mac, agents, macos
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, pipe · Missing: https docs, excited, just released
71%71% 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 · Missing: mobile apps, ios, personal
44%44% 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
40%40% 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
14%14% 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
1%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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