Ideas, tutorials, and stories from the world of code.
Hand-picked deep dives on programming, AI tooling, and software craft — written by the CoddyKit team for curious learners.
7 AI Coding Agents Compared in 2026: Which One Should You Actually Use?
A comprehensive comparison of the 7 leading AI coding agents in 2026 — Claude Code, Codex, Cursor, OpenClaw, CLI-Anything, Gemini…
KV Cache Is the Next Bottleneck — And New Tools Like KVBoost Are Solving It
KV cache optimization is emerging as the critical bottleneck for LLM production systems. KVBoost, a new open-source project trend…
How to Use Chrome DevTools MCP with AI Coding Agents — A Step-by-Step Tutorial
A step-by-step tutorial on connecting Chrome DevTools to AI coding agents via the Model Context Protocol (MCP), enabling natural-…
Why the Best Developers in 2026 Won't Just Write Code — They'll Solve Systems
AI can write code now. The developers who thrive in 2026 will be the ones who understand entire systems — data flow, architecture…
Building a Production-Ready Speculative Decoding Pipeline for LLM Inference
A deep technical guide to building speculative decoding pipelines for LLM inference — covering draft/target model pairing, stocha…
7 Best AI Coding Assistants for Developers in 2026 — Compared
A comprehensive comparison of the 7 best AI coding assistants in 2026 — GitHub Copilot, Claude Code, Cursor, Codeium, Amazon Q De…
Google I/O 2026: The Agent Revolution — What Every Developer Needs to Know
Google I/O 2026 introduced Gemini Omni, Gemini 3.5 Flash, and expanded Antigravity — shifting AI from assistant to autonomous age…
How to Build Persistent Memory for AI Coding Agents: A Step-by-Step Guide
Build a file-based persistent memory system that gives your AI coding agent project awareness across sessions. Covers auto-indexi…
The New Senior Engineer: Why AI Orchestration Is Replacing Pure Coding Skills
The definition of seniority in software engineering is shifting in 2026. AI coding tools have democratized code production, makin…
Speculative Decoding: 2-3x Faster LLM Inference Without Quality Loss
Speculative decoding breaks the autoregressive bottleneck: a small draft model proposes tokens in parallel, a large target model…