Models
- The AI Model Landscape: A Practical Guide for Engineering Teams
The model landscape has shifted again: Qwen 3 replaces Qwen 2.5 as the self-hosting recommendation, Llama 4 Scout and Maverick are now options for local inference, and the Mac Studio cluster story has changed the team-scale economics calculation.
- Gemini 3.1 Pro: #1 on the intelligence index, with caveats
Gemini 3.1 Pro launched February 19 with a 77.1% ARC-AGI-2 score (more than double its predecessor), #1 on the Artificial Analysis Intelligence Index, 1M token context, and $2/$12 per million pricing. The caveats: preview status and notably high verbosity. Where it fits in the frontier developer choice.
- MiniMax M2.7: Self-Evolving RL and the End of China's Open-Source Playbook
MiniMax M2.7 used earlier model versions to handle 30-50% of its own RL research pipeline -- log-reading, failure analysis, code modification across 100+ iteration loops. The model is also proprietary, marking a strategic shift from Chinese AI's open-source playbook. What the self-evolving loop actually means and why the strategy change matters.
- Mistral Forge: When the Generic API Hits Its Ceiling
Mistral Forge lets enterprises train frontier-grade AI models on their own proprietary knowledge -- with launch partners including ASML, the ESA, and Ericsson. The engineering argument: RAG gets you retrieval, not reasoning. When your domain knowledge isn't on the internet, you need a different approach.
- Mistral Small 4: One Model for Reasoning, Multimodal, and Coding
Mistral Small 4 unifies reasoning, multimodal, and coding agent capabilities into a single 119B MoE model under Apache 2.0. 6B active parameters at inference, 256K context, configurable reasoning effort. One deployment replaces three specialised models.
- The $1B Bet Against Transformers: LeCun's World Models Thesis
Yann LeCun raised $1.03 billion to prove the AI industry got it wrong. Here's the technical argument behind AMI Labs, what world models actually are, and what it means for engineers building today.
- Nvidia's $26 Billion Open-Weight Bet
Nvidia released Nemotron 3 Super -- a 120B-parameter hybrid reasoning model -- and Wired surfaced a $26 billion commitment to open-weight AI buried in a 2025 financial filing. The hardware monopoly is building the models too.
- NVIDIA Nemotron 3: What the Architecture Tells Us About Agentic AI Infrastructure
NVIDIA's Nemotron 3 family -- 31.6B parameters, 3.6B active, hybrid Mamba-Transformer MoE -- is engineered specifically for multi-agent systems. Here's what the architectural choices tell engineers about where agentic AI infrastructure is heading.