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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.
Complete guide to Transformer architecture: self-attention mechanisms, encoder-decoder design, and how Transformers power GPT, BERT, and modern LLMs. With code examples and visual diagrams.
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
Clawdbot is an open-source AI agent with memory, proactive notifications, and task automation. Learn how to set it up for $5/month and why developers call it early AGI.
After 6 months of LLM RL training failures and breakthroughs, I share battle-tested solutions for training collapse, GRPO instability, exploration bottlenecks, and why Thinking models need special handling. Practical fixes you can apply today.
Master Google Nano Banana Pro to create publication-ready academic diagrams. Learn our proven 2-step workflow combining LLMs with AI image generation for CVPR/NeurIPS-quality scientific illustrations.
Master Nano Banana Pro (Google's AI image editor) with 50+ expert prompts. Learn multi-turn refinement, character consistency, product shots, and scene replacement techniques.
Discover 12 battle-tested lessons from months of production RL training. Learn why stability trumps everything, how agentic RL differs from reasoning RL, and practical strategies to avoid reward hacking in LLM training pipelines.
Discover why file systems are making a comeback in AI agent architecture. Learn how AGFS's 'everything is a file' philosophy enables seamless multi-agent collaboration and simplifies LLM tool integration with Unix-style commands.
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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