For people building with AI: new models, local AI, coding tools, pricing and security, explained plainly.
Learn how to monitor AI agents at work with a six-step framework: agent inventory, non-human identity, autonomy tiers, runtime logging, kill switches, and NIST/OWASP alignment.
DHS banned DeepSeek from 100+ federal agencies. See what running AI models locally costs, from a $6,000 CPU build to H100 clusters, and when it beats APIs.
Decision models return typed choices and scores; execution models and sandboxed code act on them. Compare latency, cost, and real benchmarks.
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.
Employees are pasting trade secrets into ChatGPT. Here's how to build a locally hosted, permission-aware document scanner that stops the leak without a ban.
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.
AI math breakthroughs are compressing encryption's safety margin. Here's why enterprises renting security from opaque SaaS vendors can't keep pace.
New research shows AI models can provably hide capabilities, and the labs that build them call breaches inevitable. Here's why that makes building internally a security control.
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.
AI coding assistants cut keystrokes and seem to ease RSI for some developers, but burnout keeps rising. Here's what the data actually shows.
Microsoft's Windows and Excel failures, plus the CrowdStrike outage, show the real risks of forced SaaS updates: no control over timing, no way to opt out.
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.
Securing AI agents in production takes four controls: unique non-human identities, least-privilege access, sandboxed execution, and tamper-proof logging.
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.
We secretly tested our team on spotting AI vs. human content. Accuracy barely beat a coin flip, matching research from 19% to 76%. Here's what to build instead.
"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.
The Danfoss refrigeration hack spoiled millions in food across European supermarkets. Here's what happened, why patching won't fix it, and how to prevent it.
AI models optimize to look correct, not to be correct. Here's how to test AI-generated code and sandbox employee-built tools before they fail in production.
ChatGPT can read a 1040 or W-2, but accuracy is spotty and your data's fate isn't yours to control. Here's what uploading your return really risks.
Diagrams and canvases now generate working code. See how tldraw, Figma, and Coinbase's own agent turn sketches into shipped internal tools.
Open source can't agree on AI code policy. Here's the 6-step framework enterprise IT leaders can use to audit AI-generated and shadow AI code.
AI isn't replacing web developers. It's automating boilerplate. Here's what BLS, Anthropic, and SignalFire data show about the real job shift.