Zod: Runtime Type Validation for TypeScript
TypeScript types only exist at compile time — they vanish at runtime. Zod lets you validate external data at runtime while inferring TypeScript types from the same schema. One definition, two jobs done.
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Zustand: The Lightest Global State Management for React
No Provider, no reducer — global state in just a few lines. NobodyClimb uses it for auth and UI state, paired with TanStack Query for server state.
A One-Person Full-Stack Team: AI-Driven Development Workflow from OpenSpec to Auto-Deploy
Use OpenSpec to break requirements into engineering tasks, Claude Code to implement them, hooks to auto-format and protect, local review before committing, three AI reviewers running in parallel on PR, and auto-deploy after merge. This entire workflow lets one person maintain quality across six sub-projects.
Claude Code Hooks: A Complete Guide to Event-Driven AI Control
Hooks are Claude Code's event system. They trigger shell commands, HTTP requests, MCP tools, or LLM evaluations automatically before/after tool execution, when a prompt is submitted, or when a task ends. Use them to block dangerous operations, run automated reviews, inject context, or write audit logs.
Claude Code Skills: A Complete Guide to Turning Repetitive Workflows into Single Commands
A Skill is an SOP written for AI. Define the steps in a Markdown file and Claude follows them. No coding required, no frameworks to learn — just write down what an experienced person would do.
Turning Debug Sessions into GitHub Issues with a Claude Code Skill: Designing /file-bug-issue
Stuck mid-debug and can't fix it right now? Use /file-bug-issue to package the error analysis, reproduction steps, and attempted fixes from your conversation into a well-structured GitHub issue. Pair it with a Remote Agent to let AI automatically take over the fix.
Let AI Pick Up Issues, Write Code, and Open PRs: Hands-Off Development with Claude Code Remote Agent
Using Claude Code's Scheduled Remote Agent, automatically scan GitHub issues every 2 hours, implement features, open PRs, and address review feedback — no human intervention required. Humans only write issues and click merge. Pair it with the custom /publish-tasks skill to push OpenSpec engineering tasks directly to GitHub issues.
Langfuse Complete Guide: LLM Application Observability from Scratch
Langfuse is currently the most mature open-source LLM Observability platform. This post covers four core capabilities — Tracing, Prompt Management, Evaluation, and Datasets — showing you how to use them in real projects.
Claude Code's Three-Layer Quality Defense: Hooks, Skills, and Instruction Files
Hooks are automated safety nets (blocking bad commits), Skills are interactive workflows (running checks + auto-fixing), and instruction files (CLAUDE.md / AGENTS.md) are behavioral guidelines. Each layer operates independently, but together they enable an AI agent to automatically run lint, typecheck, and build checks before every commit.
How to Classify Code Review Comments? From Conventional Comments to AI Review Tool Taxonomies
Three main classification systems dominate: Conventional Comments (label-based), Google's severity prefixes (Nit/Optional/FYI), and SonarQube's four quadrants (Bug/Vulnerability/Code Smell/Hotspot). AI review tools have each developed their own taxonomies, but the core dimensions consistently converge on four areas: correctness, security, performance, and maintainability.
Context Engineering: Why Your AI Agent's Problem Is Information, Not the Model
Context Engineering is the core concept that replaced Prompt Engineering in 2025: the focus shifted from 'how to ask' to 'what information to provide.' Delivering the right information at the right time into the context window is more effective than upgrading to a stronger model. This post covers the definition, four key strategies, practical techniques, and common failure modes.
From Mock to Real AI: Integrating Cloudflare Workers AI into action-maker
Upgraded action-maker from hardcoded mock data to live Cloudflare Workers AI generation. The architecture splits into Worker (AI only), Server (data storage), and Frontend (orchestration). Hit two gotchas along the way: Qwen3's thinking block and the Workers AI response format.
MCP (Model Context Protocol): The Standardized Protocol for AI Agent Tool Invocation
Every AI tool has its own calling format, making integration costly. MCP (Model Context Protocol) is an open standard proposed by Anthropic that unifies the communication protocol between AI Agents and external tools/data sources, enabling tools to be reused across Agents.
False positives in Node.js image vulnerability scans? Separate app packages from npm built-ins first
When reviewing vulnerability scan results for a Node.js Docker image, you can't just look at package names. First distinguish between project dependencies and the packages bundled with npm inside the base image — otherwise you'll fix the wrong thing.
What Is Vulnerability Scanning? A Quick Intro to Docker and Package Scanning with Trivy
Vulnerability scanning isn't just about generating reports — it helps you discover known risks in your system before they become incidents. This post uses Trivy as a hands-on example to explain what scanners actually look for, how to read the results, and how to get started.
Turning a Scraper Script into an MCP Server for Claude to Use Directly
Wrap a local Python script into an MCP Server using FastMCP so Claude Code can call it directly — no more manually running pipelines.
MCP Tool Returns 1M Characters: The Token Explosion in search_local_jobs
The MCP tool was returning a description field that caused 1,033 job listings to exceed the token limit. The fix: exclude description by default and add pagination.
Agent Memory Systems: From RAG to Read-Write Memory Evolution
RAG is read-only. Agent Memory lets AI not only read but also write and persist information. Three memory types: Procedural (behavior patterns), Episodic (temporal events), and Semantic (factual knowledge) form a complete cognitive memory system.
Complete Guide to AI Agent Architecture Patterns: From Three Pillars to Multi-Agent Systematic Navigation
AI Agent is not a single technology -- it is an entire architecture system. This article is a systematic navigation: starting from the Agent Three Pillars (Context/Cognition/Action), through the three-stage evolution of AI engineering (Prompt -> Context -> Harness), to eight Multi-Agent design patterns and production-grade Harness infrastructure. Each topic links to a dedicated deep-dive article.
The Three Core Pillars of AI Agents: Context, Cognition, Action
An AI agent is not a black box — it is built from three layers: what it knows (Context), how it thinks (Cognition), and what it can do (Action). Understanding these three layers is the key to grasping why agents are sometimes brilliant and sometimes go off the rails, and how to design a truly effective agent system.