bguberfain

This was the first time I saw a major mainstream media outlet prominently feature a statistical predictive model live during the vote count (and it was spot on): https://www.estadao.com.br/politica/eleicoes/em-tempo-real-p...

This was the first time I saw a major mainstream media outlet prominently feature a statistical predictive model live during the vote count (and it was spot on): https://www.estadao.com.br/politica/eleicoes/em-tempo-real-p...

Isn't it just learning to map specific words, from the "knife world", to the correct class? If so, a simple dictionary would fit. What I think is a better way to validate is to split train/validation by words used presented in NER classes (like, it should be able to find new brands never seen before). It is a interesting problem.

Plot twist: the GLM optimization agent figured out that it can hack and use NVIDIA GPUs on a US Cloud provider and make the inference 10x faster.

If you are planing to watch the eclipse, a good resource is to look at http://xjubier.free.fr/en/site_pages/SolarEclipsesGoogleEart... In 2028 it will go straight to Sydney, Australia

Nowadays we use LLMs mostly for doing agentic-based work. LLMs new Pareto frontier only make the headlines if they push the boundaries on benchmarks that are deterministic tasks. So models are encouraged to focus on these deterministic tasks that are, in nature, structured texts. I think that this makes models more “plastic” or “polished”, as opposed to natural and pleasant to read. User-based benchmarks, like LLM Arena, are for me the best we can do in order to rank models in this way, but come with its own drawback (subjective evaluation, prone to spam or techniques to promote a giving model).

It is good to se big companies like Microsoft launching LLMs. They have large amount of compute power and good scientists to create useful models.

Any plans to port to sglang or vLLM?

It seems to be something related the moving average calculation. So it is just a glitch on the chart.

This guy seems to be talking seriously.

Not to demerit the recording, but I felt more nostalgic for the last sentence of the article "Sometimes, the internet is good" than for the musics itself.

We all know it... but I think they were very bold in this warning about using your private messages to train public models. _Your messages with AIs will be used to improve AI at Meta. Don't share information, including sensitive topics, about others or yourself that you don't want the AI to retain and use_

"A watchdog kernel thread monitors RAM and NVMe pressure and signals userspace before things get dangerous." - which kind of danger this type of solution can have?

We can finally search for playlists with a giving song! A basic feature that Spotify is missing!

So they used a LLM with knowledge cut in mid 2023 to evaluate 2023? Seems like a classic leakage problem.

From paper: "testing set: January 1, 2023, to December 31, 2023"

From the Llama 2 doc: "(...) some tuning data is more recent, up to July 2023."

I think that there may be another solution for this, that is the LLM write a valid code that calls the MCP's as functions. See it like a Python script, where each MCP is mapped to a function. A simple example:

  def process(param1, param2):
     my_data = mcp_get_data(param1)
     sorted_data = mcp_sort(my_data, by=param2)
     return sorted_data

Not available in my country :(

Unfortunately, it uses Miniconda, which does not allow usage in companies with more than 200 employees. I think it conflicts with AGPL license. I created a PR to fix that.

Can you provide more information about this “bigger teacher” model?

Until GPT-4.5, GPT-4 32K was certainly the most heavy model available at OpenAI. I can imagine the dilemma between to keep it running or stop it to free GPU for training new models. This time, OpenAI was clear whether to continue serving it in the API long-term.

Any chance you could release the dataset to the public? I imagine NewsCatcher and Polymarket might not agree..

It remembers me Theano [0].

[0] https://en.wikipedia.org/wiki/Theano_(software)

I agree. One image of what it is doing would improve the comprehension of the algorithm.

The file I mentioned is just the begining... there is a folder full of .dll files, renamed to .pyd. I understand that this is the proprietary part, that limits usage for 30 minutes, but I think it is too closed for a MIT license.

Thanks for sharing this! But I have some doubts about hidden installation procedures. It imports all functions from one_click (from one_click import *), which points to a compiled file. It then runs functions like install_webui and install_extra_packages. At least suspicious.