ofou

Demoniac move by Spotify

The model released is called Latam-GPT, the first large-scale open-source language model developed specifically for Latin America and the Caribbean. Its size is 70 billion parameters (70B). It is a continuous pre-training (CPT) adaptation of Meta’s Llama 3.1 70B base model, trained on a regionally-curated corpus of ~300 billion tokens that emphasize Latin American languages, cultures, and contexts.

Press release: https://cenia.cl/2026/02/10/latam-gpt-la-primera-ia-regional...

Presentation (in Spanish): https://www.youtube.com/watch?v=FdLzAiQizhA

HF: https://huggingface.co/latam-gpt

Github: https://github.com/latam-gpt

btw, you can change the hashing algorithm in git easily

This is great news for the community. We should measure a bit more closely how tool usage behaves in the wild. The gains you can get from different providers are not marginal.

Sometimes it's not just raw intelligence, but engineering as well

UTF-8 should be a universal tokenizer

the internet's own boy :( I'll be always deeply touched by him

Shadow libraries maintainers deserve a Nobel prize for their contributions to humanity. Satoshi would be proud.

This is stupid because a UTF8 is a tokenizer that covers all Unicode with a vocab of only 256 (yes, without a K). This is the only way of scaling the bitter lesson with tokenizers. Also, with architectures that span +1M context windows, it’s no longer an argument/issue the reduced context windows.

I'd submit a PR with this idea to improve coverage of agents

Is this Garcon [1], or a new tool?

[1] https://transformer-circuits.pub/2021/garcon/index.html

No luck so far with: When does the BB(6) halt?

considering the amount of bots in HN, not really that much

mmm... like a whole Harry Potter series

My most sincere love to all shadow libraries out there, you're doing god's work.

You gotta love these guys, they're really pushing the open source frontier for all of us, thanks for sharing

https://www.emergentmind.com also offers Deep Research on ArXiv papers (experimental)

This is one of the reasons I've been advocating to use UTF-8 as a tokenizer for a long time. The actual problem IMHO are tokenizers themselves, which obscure the encoding/decoding process in order to gain some compression during training to fit more data in for the same budget, and arguably gaining some better understanding from the beginning. Again just a lack of computing power.

If you use UTF-8 directly as tokenizer, this problem becomes evident once you fit it into the context window. Plus, you can run multiple tests for this type of injection; no emoji should take more than up to 40 bytes (10 code points * 4 bytes per code point in the worst case). This is an attack on tokenizers, not on UTF-8.

Plus, Unicode publishes the full list of sequences valid containing the ZWJ character in emoji-zwj-sequences.txt

Who would have known that BitTorrent, shadow libraries, and seeders will help to train the best AI models out there, that adds a whole new meaning to a "seed".

It’s called DeepSeek. The founder just confirmed a few days ago that he got the data from Anna's to train on, I think for their latest vision model.

This is a wonderful submission to Anna's archive [1]. I really love people pushing the boundaries of shadow source initiatives that benefit all of us, especially providing great code and design. Can't emphasize enough the net plus of open source, BitTorrent, and shadow libraries that have had in the world. You can also make the case that LLMs wouldn't have been possible without shadow libraries; it's just no way of getting enough data to learn.

Just thank you.

https://software.annas-archive.li/AnnaArchivist/annas-archiv...

I find quite interesting they're releasing three compute levels (low, medium, high), I guess now there's some way to cap the thinking tokens when using their API.

Pricing for o3-mini [1] is $1.10 / $4.40 per 1M tokens.

[1] https://platform.openai.com/docs/pricing#:~:text=o3%2Dmini

competition is beneficial for all of us, this is great

Where can I get the actual tokenizer data?

Nevermind, it's here

https://api-docs.deepseek.com/quick_start/token_usage

By the way, I think this is a fantastic reading list for creating AI products, and especially for staying updated on the latest in the AI space. However, it feels a bit scattered and might be hard for beginners to follow, IMO.

I read your book, The Coding Career Handbook, we need something similar for AI Engineering! I really enjoyed it. Thank you for creating and sharing such high-quality multimodal content :)

Dive into Deep Learning is implemented using various libraries such as PyTorch, NumPy/MXNet, JAX, and TensorFlow.

Here’s an example: https://d2l.ai/chapter_natural-language-processing-pretraini...

I believe that most of the papers presented here focus on acquiring knowledge rather than deep understanding. If you’re completely unfamiliar with the subject, I recommend starting with textbooks rather than papers. The latest Bishop’s "Deep Learning: Foundations and Concepts (2024)" [1] is an excellent resource that covers the "basics" of deep learning and is quite updated. Another good option is Chip Huyen’s "AI Engineering (2024)" [2]. Another excellent choice will be "Dive into Deep Learning" [3], Understanding Deep Learning [4], or just read anything from fast.ai and watch Karpathy's lectures on YouTube.

[1] https://www.bishopbook.com [2]: https://www.oreilly.com/library/view/ai-engineering/97810981... [3]: https://d2l.ai [4]: https://udlbook.github.io/udlbook/

Learn to pronounce German sounds accurately, and then read the entire Harry Potter series out loud. By the time you're halfway through, you'll be well on your way to fluency. Many focus too much on understanding meaning, but what's crucial is also training your mouth and larynx muscles to form the sounds naturally. Understanding comes pretty much from context alone. Use a dictionary sparingly.

From the City of London wiki:

In December 2012, following criticism that it was insufficiently transparent about its finances, the City of London Corporation revealed that its "City's Cash" account – an endowment fund built up over the past 800 years that it says is used "for the benefit of London as a whole"[51] – holds more than £1.3bn. As of March 2016, it had net assets of £2.3bn.[52] The fund collects money made from the corporation's property and investment earnings.[53]

this is a list of books mentioned

https://www.goodreads.com/list/show/121496.SWEBOK_Consolidat...

Whether you're doing research or just keeping up with AI news, EM is doing great work. We've gathered millions of data points for each paper. Going forward, we plan to improve the capabilities and expand the sources behind every paper's knowledge graph. This is just the start.

The goal is to build the first AI research assistant that combines paper knowledge with insights from researchers and communicators. Always up to date.