Mistral raises $830mn to build Nvidia-powered AI centres in Europe - Financial Times
<a href="https://news.google.com/rss/articles/CBMihAFBVV95cUxObm9rUy1DTFBBS3FlMm5OSDA4YXFMbnNEVENhY0JRLVJvUFZRU18zc0xGdHRxQk9KUWFUN3hYam1DWEFycFJPamlaYi13d2dlckdGQlJReVVtOW1FWi1jeGJfN1hxTmdjZjhqNmZHUzJmSmJMT2ZhZ1lnRHdBTHNnb09NdVc?oc=5" target="_blank">Mistral raises $830mn to build Nvidia-powered AI centres in Europe</a> <font color="#6f6f6f">Financial Times</font>
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mistraleurope![[P] Dante-2B: I'm training a 2.1B bilingual fully open Italian/English LLM from scratch on 2×H200. Phase 1 done — here's what I've built.](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-satellite-bUaXYHZsoMZjyA4XgfFqkD.webp)
[P] Dante-2B: I'm training a 2.1B bilingual fully open Italian/English LLM from scratch on 2×H200. Phase 1 done — here's what I've built.
The problem If you work with Italian text and local models, you know the pain. Every open-source LLM out there treats Italian as an afterthought — English-first tokenizer, English-first data, maybe some Italian sprinkled in during fine-tuning. The result: bloated token counts, poor morphology handling, and models that "speak Italian" the way a tourist orders coffee in Rome. I decided to fix this from the ground up. What is Dante-2B A 2.1B parameter, decoder-only, dense transformer. Trained from scratch — no fine-tune of Llama, no adapter on Mistral. Random init to coherent Italian in 16 days on 2× H200 GPUs. Architecture: LLaMA-style with GQA (20 query heads, 4 KV heads — 5:1 ratio) SwiGLU FFN, RMSNorm, RoPE d_model=2560, 28 layers, d_head=128 (optimized for Flash Attention on H200) Weight
Open Source AI Has an Intelligence Problem (That Isn't the Model)
Your Llama-3 instance is running in a hospital. It is processing thousands of clinical queries a day. It is making useful inferences. When it gets something wrong, a clinician corrects it. When it gets something right, a physician notes the reasoning. None of that goes anywhere. Across the city, another Llama-3 instance is running at a different hospital — same base model, different deployment, zero connection. The oncologist there is seeing the exact same failure modes. The same corrections are being made. The same patterns are emerging. Those two instances will never find out about each other. Multiply this by the 50,000+ Llama-3 deployments worldwide. By every Mistral instance running at law firms, research labs, and government agencies. By every fine-tuned Falcon model that has accumul
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Show HN: Gemma Gem – AI model embedded in a browser – no API keys, no cloud
Gemma Gem is a Chrome extension that loads Google's Gemma 4 (2B) through WebGPU in an offscreen document and gives it tools to interact with any webpage: read content, take screenshots, click elements, type text, scroll, and run JavaScript. You get a small chat overlay on every page. Ask it about the page and it (usually) figures out which tools to call. It has a thinking mode that shows chain-of-thought reasoning as it works. It's a 2B model in a browser. It works for simple page questions and running JavaScript, but multi-step tool chains are unreliable and it sometimes ignores its tools entirely. The agent loop has zero external dependencies and can be extracted as a standalone library if anyone wants to experiment with it. Comments URL: https://news.ycombinator.com/item?id=47655367 Poi
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[R] Reference model free behavioral discovery of AudiBench model organisms via Probe-Mediated Adaptive Auditing
Anthropic's AuditBench - 56 Llama 3.3 70B models with planted hidden behaviors - their best agent detects the behaviros 10-13% of the time (42% with a super-agent aggregating many parallel runs). a central finding is the "tool-to-agent gap" - white-box interpretability tools that work in standalone evaluation fail to help the agent in practice. most auditing work uses the base model as a reference to compare against. i wanted to know if you can detect these modifications blind - no reference model, no training data, just the target model itself. maybe you can? and the method is embarrassingly simple. LoRA fine-tuning tends to modify later layers more than earlier ones. so i train a Ridge regression from early-layer activations (~L12) to late-layer activations (~L60) and look at the residua
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