While this is certainly a welcome step, I hope there is more work done to fix the underlying problem of easily creating correct BibTeX entries for the cited papers. Citations for any given paper can come from a wide range of journals with various publishers, conferences, and preprints. The same paper can be available from multiple sources with varying details, e.g. arXiv and the conference website. Tools like Zotero have certainly made it significantly easier to extract citations from webpages of publication, but I still find issues with the extracted BibTeX details. While author names and titles are often extracted correctly, I still have to manually ensure that details like publication venue, year, volume number, page number, URL, etc. are extracted correctly and also shown correctly in LaTeX format. Different publications can use different citation styles. This can unfortunately lead to taking shortcuts with AI-generated citation data due to the lack of an easy and unified approach to extract consistent citation data. I am not sure whether hallucinated citations are being generated in the main manuscript or in a separate BibTeX file, so I may be a bit off in my understanding.
HN user
mks_shuffle
Thanks for the reply. I’ve mostly heard that images generated by AI are considered non-copyrightable (prompts are written by a human). Would the situation be different for code compared to images, since both are created with generative AI tools? Or does it depend on whether the generated artifact is created by an individual versus within a company? Thanks.
How much Codex and Claude Code are different from each other? I have been using Codex for few weeks doing experiments related to data analysis and training models with some architecture modifications. I wouldn't say I have used it extensively, but so far my experience has been good. Only annoying part has been not able to use GPU in the Codex without using `--sandbox danger-full-access` flag. Today, I started using Claude Code, and ran similar experiments as Codex. I find the interface is quite similar to Codex. However, I hit the limit quite quickly in Claude Code. I will be exploring its features further. I would appreciate if anyone can share their experience of using both tools.
What could be the potential impact on smartphone and tablet prices in the coming months or years? I am assuming that laptop prices will start increasing next year.
I think the parent comment is referring to "Attention is All You Need", famous transformer paper.
These are recommendations provided on huggingface page under usage guidelines QwQ-32b: https://huggingface.co/Qwen/QwQ-32B DeepSeek-R1: https://huggingface.co/deepseek-ai/DeepSeek-R1
Does anyone have insights on the best approaches to compare reasoning models? It is often recommended to use a higher temperature for more creative answers and lower temperature values for more logical and deterministic outputs. However, I am not sure how applicable this advice is for reasoning models. For example, Deepseek-R1 and QwQ-32b recommend a temperature around 0.6, rather than lower values like 0.1–0.3. The Qwen3 blog provides performance comparisons between multiple reasoning models, and I am interested in knowing what configurations they used. However, the paper is not available yet. If anyone has links to papers focused on this topic, please share them here. Also, please feel free to correct me if I’m mistaken about anything. Thanks!
Can you please elaborate a bit more about your issues with PyTorch on M4 mac. I read PyTorch has some support for Mac GPU with MPS backend, but not sure how extensive it is. I am looking for a new machine, and use of PyTorch and LLM inference are one of the main uses. Sorry for being a bit off-topic from the thread. Thanks.
Another option with Python is
uploadserver: https://github.com/Densaugeo/uploadserver
python built-in: python -m http.server <port> (does not support upload)
You can try Groq API for faster inference. They use custom hardware to speed up the inference. Supported open models can be found here: https://console.groq.com/docs/models (includes llama-70b)
For ML/DL papers you can check https://paperswithcode.com/