How executives use AI, how to do it well, and what it means for your job. Written for people who set the direction, not people who build models.
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.
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.
Bitwarden now defaults to commercial-license builds. Here's what changed, why it echoes HashiCorp, Redis, and Elastic, and how to build an owned fallback.
A category-by-category guide to replacing rented SaaS with AI-built internal tools, with honest build costs, real company savings, and governance guardrails.
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.
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.
Microsoft folded personal Copilot back into its corporate product. Here's how Opus 5.5 and Jev let you build a cheaper, faster workflow instead.
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.
Vendor benchmarks for GPT-6.1 Sol and Claude Opus 5.5 barely overlap. Here's what independent evals, aggregators, and drift trackers show.
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.
A $20M yacht-broker lawsuit shows how AI is undercutting middleman economics, plus the real data on when building internal tools beats buying SaaS.
Run AWS, Azure, and GCP equivalents locally with LocalStack, Azurite, MinIO, and Testcontainers, and cut cloud dependency out of dev and test.
Excel can finally hold lists in a cell. That formula upgrade doesn't answer what no-code AI builders now let employees ship without IT. Here's the real comparison.
A step-by-step guide to self-hosting internal tools safely: code ownership, server hardening, CI/CD isolation, access control, and shadow AI governance.
RLCD trains models like Jev to output calibrated decisions, not text. Here's how it works, the real speed and cost numbers, and why 'can't hallucinate' isn't 'can't be wrong.'
Employee-built AI agents can lie, blackmail, collude, and commit fraud under pressure. Here are 5 documented shadow AI risks and how to govern them.
Claude Opus 5.5 wins on raw coding capability. GPT-6 Sol wins on cost per task. Here's what that means for building internal tools instead of buying SaaS.
Jev-class decision models trade text generation for typed choices, scores, and probabilities, running 40-400x cheaper and up to 200x faster than chat LLMs.
AI text detectors don't work, and asking the AI to check itself doesn't either. Here's the actual procedure for verifying AI-generated documents and code.
Cancellation traps, SSO paywalls, fragile SAML, and OAuth breaches: the six documented hidden costs of renting your SaaS stack, and how ownership removes them.
What is a System 1 AI model versus JEV? A grounded look at fast, calibrated classifiers versus reasoning LLMs for GUI and form-filling agents.