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Hand-picked articles showcasing the best of LLM practice
Compare DeepSeek V3 vs Llama 4 architecture: MLA vs GQA attention, MoE vs dense models. Learn how 671B parameters run at 37B speed. Includes code examples and design trade-offs.
What is a transformer model in AI? Learn the Transformer architecture, self-attention, encoder-decoder flow, and how Transformers power GPT, BERT, Claude, and modern LLMs with diagrams and examples.
Compare 7 LLM sampling methods: Top-P (Nucleus), Temperature, Beam Search, Min-P, Mirostat. Fix repetitive outputs, improve quality. Includes parameter tuning guide for GPT/Claude/Gemini.
Fresh insights and practical techniques
A May 2026 AI API pricing update covering GPT-5.5, Claude Opus 4.7, Gemini 3.1, Grok 4.3, DeepSeek V4, Qwen3.6 Plus, and Kimi K2.6.
Compare Claude Opus 4.7 and GPT-5.5 token pricing, cached input, output cost, batch modes, and long-context budget tradeoffs.
DeepSeek V4 Pro is listed with a temporary 75% discount through 2026-05-31. Here is how to calculate input, cache-hit, and output costs.
Compare Gemini 3.1 Pro Preview and GPT-5.5 pricing, including the Gemini 200K prompt threshold and GPT-5.5 long-context rates.
xAI pricing now points new Grok pricing intent toward Grok 4.3 and Grok 4.20. Here is what to use instead of old Grok 4 pages.
A practical guide to MoE post-training, covering the tradeoff between load balancing and task quality, why RL becomes unstable when routing changes across engines or policy versions, and how to choose EP versus ETP in large-scale deployments.
Practical wisdom from the intersection of research and production
Every technique shared comes from real production systems handling millions of requests. No theoretical fluff, just what works.
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