What we're learning while building AI systems that have to work on Monday morning — not just in the demo.
We built a local AI image + video studio on a DGX Spark in a day. Real GB10 benchmarks, the license traps verification caught, and the quality ceiling.
A big release landed for our self-hosted AI agent. We contained it first — and found two systemd directives that don't do what they look like.
Our unlaunched website served 14,000 requests to nobody. How to read pre-launch traffic, pick cookieless web analytics for $0, and the trap we found.
DGX Spark benchmarks for gpt-oss-120B and Laguna-S-2.1 — first independent numbers — plus the silent GPU bug that corrupted our published results.
Real DGX Spark local LLM benchmarks: a 35B MoE at ~100 tok/s, why dense 70Bs are a trap on unified memory — with corrected serving-stack numbers.
Adversarial AI review, applied to our own pitch deck: 29 agents attacked every claim. All six moat claims survived only after being rewritten honest.
We built an AI chief of staff meant to have one tool. AI agent security bit us: 'allowed tools' didn't restrict anything. Here's the fix.
We built a Slack-to-Copilot Studio relay in one night. Five things the docs don't tell you — auth, billing, and one SDK bug that matters.
Multi-agent panels, an adversarial trademark sweep, a licensing-audited pipeline: our mascot, made in an afternoon for $0 in new software spend.
OpenTofu is a drop-in replacement for Terraform — except for one sharp edge in the lock file that our CI found before we did.
How we moved our own site from a template builder to versioned infrastructure on Azure with Cloudflare DNS — practicing what we preach.
The gap between a great demo and a dependable product is wider for AI than for any software we've built before. Here's how we close it.
When retrieval-augmented generation disappoints, teams reach for a bigger model. Nine times out of ten, the fix is upstream — in the data.