Pi

Pictiur: Image converter, optimizer and resizer, all in one

Hacker News

Pictiur: Image converter, optimizer and resizer, all in one

Disclaimer: This project was entirely vibe coded, this was done by a professional, do not try to reproduce this at home. I also fully understand that this may not match some people's values, so I'd like to point out that I built this project for my personal use first and foremost. If my approach still bothers you, well, that's ok. For personal reasons, I've had to do document scans every day for the past month, photos of my ID papers, and I realized something: the online offering for processing images is horrible. Generally these tools focus on one thing, conversion, optimization, or resizing, but rarely 2 tools in one, and even less 3. It could pass if, on top of that, the offering wasn't riddled with sites full of dumbass ads, with "DOWNLOAD" buttons that are a trap, and with data processing that's dubious at best. I've also been running a home server for a while now, on which I install lots of self-hosted projects (with Coolify <3), and I wondered whether I couldn't make my own tool, one that would work completely offline (PWA), and wouldn't transmit any data. I also wanted to see what Deepseek Flash was capable of, beyond the last questions I had asked it, namely how to set up my vegetable garden on 100m², and whether I should put my rainwater collector on a stand or not. So I dove into a benchmark on the best algorithms to use for my idea, how to architect the app, etc... In the end, after about an hour of back and forth, I had a complete implementation plan and a tech stack I already knew, in case I had to debug by hand and use my brain again. A few hours in the IDE and here is the final result: https://pictiur.pages.dev/ The site is 100% local, the whole process runs in the browser, it's a PWA so the app is installable and usable offline, and for now it supports the most well-known formats but I fully intend to add more. The repo is public in case you want to install it on your own server (and feel free to take the opportunity to trash talk the code quality and explain just how much of a disaster AI is, and how your nephew who's currently learning PHP would have structured this in a much more professional way). https://github.com/Etheonor/pictiur And feel free to tell me if you see any improvements to make. As I said at the beginning, it is built for my personal use, but if other ideas can be grafted onto it, I'm all ears.

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2points
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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
91%91% 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
66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, code, plain · Missing: mac, agents, macos
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month, way · Missing: mobile apps, ios, entrepreneurs
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% 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.

Incorrect prediction on native model

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