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SupaRes – AI-powered image enhancement platform

Hacker News

SupaRes – AI-powered image enhancement platform

Hello HN, A few days back we launched SupaRes which is an image restoration/enhancement platform. What I think is unique about that product is that it offers not only general single-image super-resolution but also a few other enhancements such as automatic white-balance, denoising, tone improvement, or low-light boost. We have some customers in our regular business [1] who requested something more than "just" image optimization and processing. We've put together a few custom models for them and, after a while, decided to rewrite the whole thing and launch it as a separate product which is now SupaRes. Most of the neural nets are PyTorch reimplementation of TF models while the others are trained from scratch on synthetic data to boost performance in certain verticals such as real estate or food. On the backend, we use Node for general apps and microservices which are managed by k8s. Rendering engine is obviously written in Python with Sanic being our framework of choice. We're running on bare-metal colocated in Frankfurt/DE and have two machines with 2 x A40 (48GB) each at our disposal. We are members of NVIDIA Inception program and that helped us secure GPUs at preferential prices. To avoid CUDA OOM every GPU has its dedicated Redis-based task queue where we manage inference requests. SupaRes is really just a beginning for us in the image restoration space but I'm very happy with what we've built so far. I personally think it looks nice on the frontend and, most importantly, does a really good job enhancing degraded visuals. Dropping this here as maybe some of you will find the service useful. [1]: https://optidash.ai

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
92%92% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: personal, apps, para · Missing: mobile apps, ios, entrepreneurs
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: mac, model, apps · Missing: agents, macos, agent
49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
38%38% 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
18%18% 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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