Deep dives into AI concepts, architectures, and how models actually work.
7 articles
Vibe-coding AI often cuts corners — placeholder comments, empty stubs, mock data, and skipped logic that looks complete but can't run. Practical prompt constraints, a reusable self-check snippet, and a manual validation workflow to catch unfinished code before it wastes your debugging time.
A bottom-up overview of the AI industry chain — energy (solar/wind/nuclear), chips (ASML lithography, semiconductor manufacturing), PCB & MLCC, large models (training pipeline), and smart robots — plus investment, career, and skill-building opportunities in each layer.
A practical guide to effective AI collaboration using a 3-step framework — Put It Forward (start with real tasks, not tutorials), Explain It Clearly (break vague feelings into specific parameters), and Keep Asking (build a two-way question loop) — embracing 'learning by doing' over 'learn first, use later.'
A deep dive into why most AI knowledge bases fail — covering RAG fundamentals, ingestion pipeline (cleaning, semantic chunking, vectorization), QA pipeline (question understanding, reranking, context assembly), four-layer implementation, hybrid retrieval, and practical lessons from real enterprise projects.
A 30-day, $4,185 hardware deep dive testing 13 open-source LLMs (Qwen3.6, Gemma 4, Ornith 1.0) for local deployment — covering quantization, MTP, Dense vs MoE, OCR, long-context reasoning, full-stack coding, speed benchmarks, and llama.cpp deployment with GGUF.
A systematic breakdown of 7 Agent architectures — Single Agent, ReAct, Plan & Execute, Multi-Agent, Router+Skill, Blackboard, and Graph/Workflow — with pros, cons, best-use cases, and a recommended evolution path from beginner to enterprise.
A comprehensive guide breaking down LLM core principles, Transformer architecture with PyTorch implementation, common limitations (hallucinations, context decay, math errors), and practical tips for choosing and using models effectively in real-world tasks.