I built a tool to rename images based on their content

Hacker News

Hey everyone, A few weeks ago, while working on a personal project I needed to rename images based on their content, but I couldn’t find a tool that matched my needs. So, I decided to create a small program to do just that. I figured this might also be useful for others, so I put the first version of the service online. This is still a beta version, so if you have any feedback, suggestions, or if you encounter any bugs, I would really appreciate hearing from you so I can improve the tool. If you’re interested, here’s the link. Thanks for your attention and any feedback you might have!

Visit product View on Hacker NewsJan 23, 2025

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AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

58%58% predicted probability of success on AppSumo, 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 · Missing: mac, agents, macos

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, 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

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% 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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