Engineering In The Age of AI Insights & Best Practices
Learn how to improve code quality, boost developer productivity, and build better software with AI-powered development workflows.
AI Models
AI and LLM Breakthroughs in 2026: What Actually Changed
The biggest AI and LLM breakthroughs in 2026 come from the whole stack: agent loops, hybrid architectures, cheaper inference, and runtime controls that make models usable in production.
Best Practices
Background Agents in Engineering: Use Cases, Tradeoffs, and When to Use Them
Background agents run async work, keep context over time, and return with PRs or evidence bundles. This guide covers use cases, tradeoffs, and how to deploy them safely.
Best Practices
The New SDLC: Spec-to-PR Workflows with Coding Agents
Coding agents are collapsing SDLC phases. Teams can go from spec to PR in one session. This guide covers how to redesign handoffs so that speed and quality improve together.
Security
AI Open Source Rewrites: A Code Review Playbook for Relicensing Risk
AI-assisted rewrites are moving to production. A faster rewrite creates a harder review problem: provenance, licensing, and legal risk are now core code review concerns. This playbook covers how to manage them.
AI Models
Code Arena vs SWE-bench Verified: Which Benchmark Should Developers Trust in 2026?
Code Arena measures pairwise human preference while SWE-bench Verified measures issue-resolution pass rate. This guide explains when to use each benchmark and how to combine them for production decisions.
AI Models
How to Read LM Arena Rank Spread: Confidence Intervals, Vote Depth, and Decision Thresholds
Most teams misread LM Arena by focusing on rank number alone. The better signal is rank spread: score gaps, confidence intervals, and vote depth together. This guide shows how to read them correctly.
Best Practices
AI Code Review Needs Session Provenance: What to Store in Every PR
Coding agents ship multi-file PRs in minutes. Reviewers often receive only a diff and a passing CI badge. Session provenance fills the gap: a compact record of what the agent was asked, what tools it used, and what assumptions shaped the code.
Best Practices
Parallel Coding Agents: Code Review Guardrails for Branch Chaos
Learn how to review parallel coding agent output with branch budgets, risk routing, and evidence packs that prevent merge chaos and protect delivery quality.
Best Practices
AI Coding Agent Stack Policy: Keep Build vs Buy Decisions Reviewable
Build stack policy for AI coding agents with risk routing, decision artifacts, and review gates that keep build versus buy choices visible and controlled.