Cerelyze

Thanks for the hunt, Michael! Hi PH! 👋 I am Sarang, Founder of Cerelyze. In my previous life, I was a computer vision engineer building fitness trackers, shopping carts and checkout kiosks. Searching for research papers was the default way to look for novel solutions to complex problems. And yet as I used to search on sites like scholar, arxiv, paperswithcode - it was quite rare to find papers with a github repository that just runs out of the box. Most of the papers just outright did not have any code, and the tiny fraction that had code never ran out of the box. (Some of them required me to downgrade my CUDA version.. like Hello?? 🙄 ) All I wanted was a quick example run of what the paper is trying to do, not in text, but in code, so that I can run it through my own sample data to see if it's even worth investigating. Kinda like HuggingFace, but not just for running ML models, but for running the entire approach of the paper end to end. So I started with dockerizing existing codebases and building a catalog of these "guaranteed to run" images. I showed it to a few folks and got some interest. But I noticed something interesting. Most of the people who showed interest were NOT into computer vision. In fact, their fields of expertise were very different than the usual ML crowd that I used to digitally hang out with. These were people who were trying to reproduce software approaches from papers in fields like Drug Discovery, Biotech, Simulations, Material science, and even art! And the fraction of papers that come with code is even lower in those fields. After talking with them, it was clear that there's a big bottleneck between published research and research that finds its way into commercial products. So after a lot of caffeine, and a couple GPTs later -> Cerelyze was born. It's still in beta so things don't work all the time, but the underlying problem being worked on is to remove that bottleneck. Cerelyze currently is a browser-based environment where users can upload papers, and generate code for specific methods discussed in the paper. Users can also run the generated code directly in the browser. Under the hood is a combination of powerful parsers built for scientific content and LLMs that understand code. It also enables users to ask specific questions about the paper in natural language, making it easy to understand. So feel free to use it, give any feedback, join our discord, and stay tuned! Its completely free and can be accessed at: https://cerelyze.com

Hats off for your standout launch on ProductHunt!

looks interesting, congrats on the launch guys!

This is sick! I revisited some of the research papers I used to read/work on when I was in Academia, and looked at the output, from a brief glimpse, it looks decent/legit! Would you support papers outside of arXiv soon? Keen to see if it wrote better code than me for the papers I published. Also, does it support non-Python code output (thinking Matlab, Fortan etc)?

this is amazing , definitely going to test it out! In our startup we spend tons of time reading papers and being on the edge of LLM and ML, this would save us real money. i'll check it out and give you feedback as soon possible! :)

Hi @sarangzambare ! I'm really excited about the potential of Cerelyze. As someone who often finds themselves exploring research papers, the idea of a tool that can generate running code directly sounds amazing. A question - How accurate are the code snippets generated by your system? And how does it handle different coding languages? Thanks for bringing this to the community and looking forward to seeing the project evolve!

Congrats