oh inmortal poseidon el del furioso tridente

🔴 ÚLTIMA HORA | Muere a los 89 años el expresidente de Uruguay José ‘Pepe’ Mujica, el revolucionario tranquilo elpais.com/america/2025-0…

Announcing the newest releases from Meta FAIR. We’re releasing new groundbreaking models, benchmarks, and datasets that will transform the way researchers approach molecular property prediction, language processing, and neuroscience. 1️⃣ Open Molecules 2025 (OMol25): A dataset

Como de normalizado tienen los trabajadores del hospital de Jerez que haya entre 5 y 6 personas esperando cama horas/días y los dejen de cualquier manera sin atender a la sintomatología, dando por bueno que se queden en un sillón toda la noche. Incluso algunos repitiendo noche.

Announcing Magistral, our first reasoning model designed to excel in domain-specific, transparent, and multilingual reasoning.

Today is my first day at Google Cloud. I'm using GPT-4o for coding. It's super powerful. It helped me find and delete some code that wasn't doing anything!

Transformer vs. Mixture of Experts in LLMs, clearly explained (with visuals):

Huge drop from Baidu From 0.3B to 424B Multiple checkpoints (Pretrained, Post-trained, and base models) kalomaze Competitive with Qwen3 and Deepseek V3 0324 Trained on Kunlun III chips using Paddle Paddle framework (Baidu's CUDA)

I can’t beat my mom.

Every tech company can and should train their own deepseek R1, Llama or GPT5, just like every tech company writes their own code (and AI is no more than software 2.0). This is why we're releasing the Ultra-Scale Playbook. 200 pages to master: - 5D parallelism (DP, TP, PP, EP,

Can a 1-bit or 3-bit quantized model outperform GPT-4.1 or Claude-Opus-4? Yes! Today, we're excited to show how LLMs like DeepSeek-V3.1 can be quantized to just 1-bit or 3-bit, and still beat SOTA models like Claude-Opus-4 (thinking) on Aider Polyglot. Details and blog below!

[paper release!] Did you know that you can - speed up any LLM by 4x - and reduce its memory footprint by 2x - and improve its results - without modifying the model at all How??? Here is how we do it 🧵

Efficient training of neural networks is difficult. Our second Connectionism post introduces Modular Manifolds, a theoretical step toward more stable and performant training by co-designing neural net optimizers with manifold constraints on weight matrices.

valendra.tech/es/blog/black-…

Kimi Linear Tech Report is dropped! 🚀 huggingface.co/moonshotai/Kim… Kimi Linear: A novel architecture that outperforms full attention with faster speeds and better performance—ready to serve as a drop-in replacement for full attention, featuring our open-sourced KDA kernels! Kimi

> created the Linux kernel at 21 > built Git because no existing tool was good enough > accidentally became the backbone of servers, Android, cloud, supercomputers > never chased fame, money, titles or hype > stayed private, consistent, and brutally honest for decades > still

valendra.tech/es/blog/cve-20…

Mathematician Terence Tao: Training and running LLMs isn't mathematically difficult; any math undergrad could understand the basics The mystery is that we have no theory to predict why models excel at certain tasks and fail at others "we can only make empirical experiments"

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