# software-engineering

Google Engineering Practices frames code review as code-health work, not a perfection ritual: approve CLs that improve the system, while aligning design, tests, speed, comments, and author habits around maintainability.

Addy Osmani warns that default AI coding workflows help people close tasks, but do not automatically make them sharper. The difference is not whether engineers use AI; it is whether they use it to test and grow their own mental models.

Mitchell Hashimoto's point about Bun moving from Zig to Rust is not that Rust won and Zig lost. The more useful lesson is that programming languages are becoming more replaceable, and developer-tool companies need to manage technical narratives before the internet turns them into faction wars.

Peter Steinberger says OpenClaw often runs about a hundred Codex instances in the cloud. The point is not showing off AI spend. It is testing what software work looks like when review, triage, security, reproduction, benchmarks, and meeting follow-up become always-on agent work.

Garry Tan argues the real breakthrough in AI coding is not speed. It's turning tests, docs, and evals into a forward-only quality ratchet, so every change locks in what the team learned and makes the codebase harder to silently degrade.

Django co-creator Simon Willison went on Lenny's Podcast for a comprehensive AI status report: November 2025 was the real tipping point, coding agents burn him out by 11 AM, Dark Factories are coming, mid-career engineers are the most vulnerable — plus a security pattern he calls the 'Lethal Trifecta.'

Mario Zechner wrote a sharp critique of how coding agents are being used in production — compounding errors, zero learning, runaway complexity, and low search recall. His conclusion isn't 'stop using agents' but 'slow down and put human judgment back in the loop.'

Dan McAteer's tweet questions a common leap in logic: if AI automates software engineering, software demand keeps growing, and trained engineers are best positioned to ride the wave — how does anyone conclude that engineers are screwed?

The author benchmarked system SQLite against an LLM-generated Rust rewrite. Even though it compiled and passed all tests, primary key lookups were ~20,000x slower. The takeaway: define acceptance criteria before you talk about AI productivity.

AI writing better code? That "Deep Blue" feeling, coined by Simon Willison & Adam Leventhal (Oxide & Friends), means IBM's chess computer & the color of sadness. It's not just a tech problem, but a psychological crisis for engineers.

Technical debt lives in code, cognitive debt in your brain. As AI writes 80% of code, system understanding drops to 20%. UVic's Margaret-Anne Storey, Simon Willison, & Martin Fowler confirm this isn't a hypothetical future—it's happening now.

Thoughtworks' AI in software retreat: Juniors more valuable, mid-level devs at risk, source code transient, AI agents on org charts. Humans too slow for AI's speed.

Anthropic published its 2026 Agentic Coding Trends Report, revealing 8 key trends: Multi-Agent Systems becoming standard (57% org adoption), Papercut Revolution for clearing tech debt at low cost, Self-Healing Code with autonomous debug loops, and Claude Code hitting $1B annualized revenue. TELUS saved 500K hours, Rakuten achieved 99.9% accuracy on 12.5M lines. Developer roles are shifting from Code Writer to System Orchestrator.

OpenAI co-founder Greg Brockman publicly reveals how OpenAI is transforming to agentic software development internally. By March 31st, agents should become the first resort for all technical tasks. Includes six concrete recommendations, including 'Say no to slop' on code quality.