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The architecture that changed everything. Our LLM coverage spans transformer mechanics, attention mechanisms, RLHF and alignment, quantization techniques, fine-tuning strategies, and frontier model benchmarking across GPT, Claude, Gemini, Mistral, Llama, and every major system in between.
We publish original benchmark results, architecture deep-dives, and interviews with the researchers who build these systems — not summaries of press releases.
GPT-4o vs Claude 3.7 vs Gemini 2.0: The Most Comprehensive Benchmark Comparison Yet
200+ tasks across reasoning, coding, math, writing, and tool use. Which frontier model wins, and at what cost?
LoRA vs Full Fine-Tuning vs PEFT: When to Use Each Method and Why It Matters
A practical guide for ML engineers on the cost, quality, and latency tradeoffs of each fine-tuning approach across model sizes.
Understanding KV Cache: The Memory Optimization That Makes Long-Context Models Possible
From first principles: what key-value caching is, why it's necessary, and how different implementations trade memory for speed.