A Node.js CLI that uses Ollama and LM Studio models (Llava, Gemma, Llama etc.) to intelligently rename files by their contents
Rename your files by their contents with AI
A Node.js CLI that uses Ollama and LM Studio models (Llava, Gemma, Llama etc.) to intelligently rename files by their contents
Thatβs awesome! AI Renamer can greatly boost the efficiency of renaming files, and itβs open-source! Thanks to Maker, this really saves a lot of time.
Lovely, I was searching something like this. Very useful. I am seeing error for certain pdf while renaming, like below π΄ No text content: (2) Digital Camera World - Dec 2013.pdf π΄ No text content: (5) Fit Pregnancy_ Mom & Baby - Mom & Baby.pdf π΄ No text content: (7) Popular Science - Apr 2013.pdf Also I see that meaningfull existing document in folder are renamed to meaningless π’ Renamed: 2018_Income Tax Return Acknowledgement.pdf to xx-0843-qug743-e4-pf-net-banking.pdf π’ Renamed: 2018_I
Surprised me. I'm gonna use Ai-Renamer to rename all of the screenshots.
Congratulations on your release! It's truly a great idea! I take a lot of screenshots and photos daily, and it would be fantastic if there were a feature to rename them or recognize the main content to add metadata for easier searching. However, I want to point out a security concern. Renaming files requires high-level permissions, so it's crucial to ensure the code is free of vulnerabilities. Additionally, I suggest generating and retaining a mapping table for users to keep track of the origina
Congrats on the launch! I had been thinking about something similar for a little while. Does this specifically use video-llava to rename videos or are frames sampled for use with regular llava?
Check the similar products section on this page, or browse the category pages linked in the tags above. Each category page shows all products for a given year, sorted by engagement.
A measure of community engagement at launch. Higher means more people noticed and interacted with the product. It's a traction signal, not a quality rating.
Discussion threads divided by interest score. Above 0.30 is strong. Below 0.15 suggests the product got clicks but not conversation.
Categories come from the product's launch tags. Most products appear in 2-3 categories. The primary category is listed first.