Ai

How a self-assessed, evidence-backed quality program across dozens of teams became the foundation for safe AI-assisted development.

What research shows about AI-assisted development: the METR slowdown, code churn, security vulnerabilities, and mounting maintainability concerns.

How five AI coding agents coordinated 40 merged pull requests in one afternoon using nothing but GitHub Issues, labels, and the gh CLI.

How a local Qwen3.8 27b agent named Echo reverse-engineered two abandoned 2013 Android games — Delver and Flappy Bird — into signed, modern APKs.

AI-generated JIRA tickets and PRs have become word walls. Here's why your automation needs both a human summary and a machine-readable payload.

Practical techniques for giving AI agents the context they need: AGENTS.md files, documentation strategies, and plan mode workflows.

Compare GLM, Qwen, and Gemma local LLMs - benchmarks, context sizing, and when home GPU inference beats cloud token costs.

How Claude Code hooks create mechanical enforcement at commit time — blocking violations, injecting warnings, and coaching better habits before code ever reaches review.

How engineering leaders adopt AI coding tools—from completion to agents—without trading speed for quality.

How to architect, build, and evaluate AI agent systems using orchestration frameworks, protocols like MCP and A2A, and RAG pipelines.