Understand large language models, fine-tuning, prompt engineering, and model selection.
MiniMax M2.7 hits 78% SWE-Bench Verified with 10B active params and 3x Opus throughput. Here's when it wins, when Opus still does, and how it was trained.
SGLang delivers 29% higher throughput than vLLM on H100s and 3.1x faster DeepSeek V3 inference. Here's the architecture breakdown and decision framework for picking the right engine.
Xiaomi's MiMo-V2-Pro secretly topped OpenRouter as 'Hunter Alpha' before anyone knew it existed. 1T params, 42B active, near-Opus performance at $1/$3 per M tokens.
Karpathy's 630-line script runs ~12 ML experiments/hour autonomously—89 overnight, 15 kept, zero crashes. Here's how the loop works and when it breaks down.
DeepSeek and Qwen now hold 15% of the global AI market—up from 1% a year ago. Here's what V4 and 3.5 actually deliver, what they cost, and when they beat proprietary models.
We tested all three Feb 2026 frontier models on real code. Opus leads SWE-bench, Codex owns terminal workflows, Gemini costs 60% less—here's which to pick.
We fine-tuned Llama 3, Mistral, and Qwen with as few as 200 examples using LoRA. Here's exactly how many examples each model family needs by task type—with a dataset sizing table.
vLLM delivers 16x more throughput than Ollama under concurrent load. Here's exactly when each tool wins—and when switching saves your team months.
LLM model routing sends simple requests to cheap models and escalates complex ones to premium—cutting API costs 40-70% without losing response quality.
