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Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 3525 posts
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Zhuangzi's Free Wandering: How Wu Wei Unlocks a Developer's Creativity
Zhuangzi's 4th-century BCE philosophy of effortless action (Wu Wei) offers a surprisingly precise diagnosis of modern developer burnout, over-engineering, and creative blocks. A deep dive into the Peng bird, Cook Ding, a
2026-03-18 · 10 min read #zhuangzi#taoism#wu-wei#creativity#philosophyVibe Coding: The New Paradigm of AI-Assisted Development — Potential and Pitfalls
A deep dive into Andrej Karpathy's vibe coding concept from 2025. Is it a genuine revolution, or a technical debt machine? An honest look based on real experience.
2026-03-18 · 7 min read #vibe-coding#ai-coding#cursor#github-copilot#ai-developmentThe Champion's Ritual: Nadal, Ichiro, and Your Developer Kata
Rafael Nadal's water bottle ritual, Ichiro Suzuki's 3-hour pre-game preparation, and the martial arts concept of kata — why champions use pre-performance routines, and how developers can build their own to unlock focus,
2026-03-18 · 10 min read #routine#habits#nadal#ichiro#sports-psychologyRAGAS Complete Guide: How to Quantitatively Evaluate Your RAG System
Learn to use RAGAS — the gold standard for RAG evaluation — to measure Faithfulness, Answer Relevancy, Context Precision, and Context Recall. Includes complete Python code, automated CI/CD evaluation pipelines, synthetic
2026-03-18 · 9 min read #ragas#rag-evaluation#llm-evaluation#ai-development#qualityPrompt Engineering 2025: Getting Maximum Performance from Modern LLMs
Even the best model gives poor results with a poor prompt. Six prompt engineering techniques proven in production, with working code for GPT-4o, Claude, and open-source models.
2026-03-18 · 7 min read #prompt-engineering#llm#ai-development#gpt-4o#claudeNPU Deep Dive: How Transformer Architecture Runs Directly on Silicon ♪ Listenable
A complete technical breakdown of how NPUs differ from CPUs and GPUs, how every transformer operation maps to hardware, and why LLM inference is memory-bound — not compute-bound. From Apple ANE to Qualcomm Hexagon, Groq
2026-03-18 · 22 min read #npu#transformer#ai-hardware#quantization#kv-cacheThree Thousand Kilometers: What the Monarch Butterfly Teaches Developers About Legacy
No single monarch butterfly completes the round trip from Canada to Mexico. The migration takes four generations — yet each generation carries knowledge it has never personally experienced. Developers carry the same kind
2026-03-18 · 15 min read #monarch-butterfly#migration#collective-knowledge#purpose#natureHow Matrices Fly on GPU: Complete Deep Dive from GEMM to FlashAttention
Matrix multiplication accounts for over 80% of deep learning compute. This post dissects every layer of optimization from naive O(n³) code to cache blocking, cuBLAS, Tensor Cores, and the IO-aware revolution of FlashAtte
2026-03-18 · 15 min read #gemm#matrix-multiplication#flash-attention#gpu#parallel-computingLLM Hallucination: Why AI Makes Things Up and 5 Strategies to Prevent It
A technical deep-dive into why LLMs hallucinate, covering all four hallucination types and five practical prevention strategies including RAG, self-critique pipelines, Chain of Verification, and temperature tuning — all
2026-03-18 · 9 min read #hallucination#llm#ai-reliability#ai-development#rag1 Million Token Context Windows: Is RAG Becoming Obsolete?
With Gemini 1.5 Pro's 1M token context and Claude's 200K, many developers ask: why bother with RAG? We compare real costs, latency, and quality to give you an honest answer.
2026-03-18 · 6 min read #context-window#rag#llm#long-context#ai-developmentGraphRAG Complete Guide: How Knowledge Graphs Overcome RAG Limitations
Standard RAG fails on global queries like "summarize the key risk themes across all these documents." We walk through how Microsoft Research's GraphRAG solves this, with real code and honest cost analysis.
2026-03-18 · 8 min read #graphrag#knowledge-graph#rag#microsoft#ai-developmentFine-tuning in Practice: Building Your Own Model with LoRA and QLoRA
You don't need 7 H100s. With LoRA and QLoRA, you can fine-tune a 70B model on a single consumer GPU. A practical end-to-end guide with working code.
2026-03-18 · 7 min read #fine-tuning#lora#qlora#llm#ai-developmentDeveloper Career Strategy in the AI Era: A Realistic 5-Year Roadmap
Moving past the fear of AI replacing developers, this post offers an honest look at the real situation and concrete career strategies to prepare for the next five years.
2026-03-18 · 7 min read #developer-career#ai-era#career-strategy#developer-growth#ai-developmentOne Must Imagine the Developer Happy: Camus, Sisyphus, and Repetitive Work
Albert Camus' 1942 essay on Sisyphus offers the most honest philosophy of repetitive work ever written. The bug queue that never empties, the codebase that always needs refactoring, the sprint that never truly ends — and
2026-03-18 · 10 min read #camus#sisyphus#absurdism#repetitive-work#philosophyAristotle's Eudaimonia: A Philosophy of Flourishing for Developers
Aristotle's concept of Eudaimonia (εὐδαιμονία) offers developers a framework for genuine flourishing — not just pleasure, but a life of excellence, meaningful relationships, and purposeful work.
2026-03-18 · 15 min read #aristotle#eudaimonia#happiness#philosophy#developerAMD GPU & ROCm Deep Dive: Can It Challenge CUDA for LLM Inference?
A thorough technical analysis of AMD MI300X with 192GB HBM3, the ROCm software stack, and HIP programming model. Includes real LLM serving benchmarks with vLLM and llama.cpp, and an honest assessment of strengths and wea
2026-03-18 · 14 min read #amd#rocm#gpu#mi300x#model-servingFAANG Behavioral Interview Complete Guide: STAR Method and Leadership Principles ♪ Listenable
A comprehensive guide to mastering FAANG behavioral interviews. Covers the STAR method, all 14 Amazon Leadership Principles, Google and Meta interview characteristics, Top 30 frequently asked questions, and elevator pitc
2026-03-17 · 17 min read #faang#behavioral-interview#star-method#아마존리더십원칙#구글면접MLOps Complete Guide: From ML Pipeline to Production Deployment ♪ Listenable
The complete guide to MLOps. Master ML pipeline design, experiment tracking (MLflow, W&B), model registry, CI/CD, model serving, and monitoring with real-world examples.
2026-03-17 · 22 min read #mlops#ml-pipeline#kubeflow#mlflow#wandbAI Model Serving and Inference Optimization Complete Guide: vLLM, TensorRT, Triton, Ollama ♪ Listenable
The complete guide to efficiently serving AI models in production. Master vLLM, TensorRT, NVIDIA Triton Inference Server, Ollama, quantization (INT8/INT4), batch processing, and latency optimization with real-world examp
2026-03-17 · 19 min read #mlops#model-serving#vllm#tensorrt#tritonComplete Guide to AI Model Deployment & Serving: Triton, vLLM, BentoML, and Kubernetes
A production-ready guide covering Docker GPU containers, Kubernetes HPA, NVIDIA Triton, vLLM LLM serving, BentoML, and Ray Serve for deploying AI models at scale.
2026-03-17 · 12 min read #modelserving#triton#vllm#bentoml#kubernetes