Pa

Padding images to custom sizes, pain free. (padsnap.app)

Hacker News

Padding images to custom sizes, pain free. (padsnap.app)

I've been doing analog photography for a few years now and started posting my shots on Instagram. I couldn't find any free good tools to do my photo padding/resizing to fit in the instagram new post sizes and every time doing it manually specially for multi picture posts was really time consuming. so I ended up making a free app for it and I'm loving it. Here's the TLdr; PadSnap is a simple web app that adds customizable padding to your photos so they fit Instagram’s/custom dimensions — no cropping, no quality loss. - Modern, intuitive interface with light & dark themes - One-click photo processing for Instagram-friendly dimensions - Customizable borders or blurred image backgrounds - Batch load and preview with ZIP download - Works entirely in your browser – no photos ever leave your device - Progressive Web App (PWA) support for installation on any device ------- I only shared the app on two photography subreddits and got more than 1400 people visiting the website on the first 48 hours, and more than 400 upvotes and comments. Here are a few of my favorite real comments from kind Redditors who tried PadSnap: - “A free app, without ads or personal data needed, made by someone who just wants to make a good product and share it with the world. It even works offline. What the hell is this, 2005?” (This comment really made me thing about what earlier internet felt like and how I really want that back :) ) - “Omg I love you, now I don’t have to put every picture into Gimp anymore” - “Can’t believe it took so long for a great tool like this to come out.” More comments: https://www.reddit.com/r/AnalogCommunity/comments/1krqvw8/co... ----------- I'd love to hear your feedback about the current state of the app, where should I take this, and anything you want me to hear. Also I'm not planning to do that much marketing around the app, but would love to make sure people can find it, now sure what is the best approach for this. (I've heard vibe-marketing, but no idea what that entails :) )

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: intuitive, friendly, interface · Missing: plus, platform, reviews
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
45%45% 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: personal · Missing: mobile apps, ios, entrepreneurs
44%44% 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
15%15% 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.

Correct prediction on native model

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