
How to Review AI-Generated Code: A Checklist for Developers
Learn how to review AI-generated code: verify behavior, check dependencies, scan for security flaws, and add monitoring. Includes data on why standard PR review fails.
How engineers use AI, how to do it well, and what it means for your job. Written for people who ship and run the software, not people who build models.

Learn how to review AI-generated code: verify behavior, check dependencies, scan for security flaws, and add monitoring. Includes data on why standard PR review fails.

Agent-native software means AI agents and humans share the same callable actions, data, and permissions. Here's the architecture, the jargon, and a checklist.

Court orders, training opt-outs, and leaks at OpenAI, Anthropic, and xAI show renting AI exposes your data. Here's the case for running open weights yourself.

A practical guide to setting up Claude Code auto mode, subagents, and verification loops so agents research and fix bugs in parallel before a human merges.

Electric bills, water tables, bot traffic, and egress fees: who actually pays for the cloud. A data-backed look at cloud computing's externalized costs.

Human review can't keep up with AI-generated code and math. Here's why formal, machine-checkable verification is becoming the real governance control.

Omarchy ships 10 AI coding agents built into the desktop. A hands-on look at what works, what's shaky, and who should actually run it.

"ChatGPT dots" means two different OpenAI features: a camera Scan menu for PDFs and a separate always-on agent. Here's how each works and where each falls short.

Diagrams and canvases now generate working code. See how tldraw, Figma, and Coinbase's own agent turn sketches into shipped internal tools.

Enterprises spend $12.5B a year renting frontier AI APIs. Here's the distillation pipeline that turns that spend into owned models, 75-98% cheaper to run.

Copilot+ PCs launched in 2024 with big claims. Two years of weak sales, Recall's security backlash, and enterprise indifference later, here's what actually happened.

AI vendors need trillions in new revenue, so agent pricing is shifting from flat seats to metered billing you can't forecast. Here's why that's a bad bet.

OpenAI's Dots turn ChatGPT into a standing, always-on agent with its own cloud computer. Here's how it works, what it can't do alone, and what it costs.

Vendor benchmarks for GPT-6.1 Sol and Claude Opus 5.5 barely overlap. Here's what independent evals, aggregators, and drift trackers show.

Step-by-step KVM/VFIO GPU passthrough guide: IOMMU, VFIO binding, libvirt/Proxmox config, and model serving for near-native local AI coding agents.

GLM-5.3-Flash and 512GB unified-memory hardware make local AI practical in 2026. Here's the setup, the real cost math, and when renting still wins.

Open-weight models now rival GPT-4o and o1. Here's how to run an AI agent locally: hardware, quantization, MCP tool access, and the real cost math.

Jev made fast, cheap decision models mainstream. CLM makes them self-hostable and fine-tunable. A numbers-first comparison of speed, cost, accuracy, and ownership.

Developers are burned out by shallow AI prompting. The fix: redirect AI toward boilerplate and owned internal tools, not another rented SaaS subscription.

Jev, Julia-1, Ember-1, Bonsai 2, and OrcaSAQ2: five tiny, permissive models that run classification and reasoning on local hardware for near-zero cost.

How to use DSPy (and its Elixir port Imp) to compile internal AI workflows into versioned, testable software instead of renting another SaaS AI feature.

n8n, Cloudflare's cf CLI, Make, Zapier, and Retool ranked for building AI automations, plus the governance tradeoffs each one forces you to make.

Quantized GGUF models, BitNet 1-bit weights, and P2P clusters like exo and Petals let you run real AI on a Raspberry Pi or old laptop. Here's the playbook.

Bao and LLM-QO beat Postgres's planner by 20-70%. Here's whether a cheap, fine-tuned 4B model can be owned outright instead of rented from a vendor.