Rescue work, automation that earns its keep, and staying with a system once it is live.
Why the first move on a fragile live system is often uptime and releases, not a rewrite — and how to know when a rebuild actually earns its keep.
Read moreA practical checklist for non-technical founders who shipped an AI-built prototype and now need to make it dependable. The things that quietly fail under real users, and how to fix them without throwing the work away.
Read moreA small framework for picking automation candidates inside a business: frequency, variability, and blast radius. Where automation pays off, where it quietly costs you more than it saves, and how to tell the difference before you start.
Read morePatterns for adding AI features to ERP and back-office systems without putting your operations at risk: read-only first, human-in-the-loop, audit trails, and the rollbacks you hope you never need.
Read moreReact Server Components have made server-first architecture genuinely viable for most web applications. The performance case is real. The organizational and debugging tradeoffs are less discussed, and those are the ones that tend to bite.
Read moreA harness is the structured context around a coding agent — conventions, dependencies, past decisions — so the model does the right thing for your project, not a generic sensible guess. Related: our automation pipeline work.
Read moreStart with the workflow that already hurts, then decide where a model earns its keep — ownership, intake, and what happens when the answer is wrong.
Read moreOne concrete decision on every engagement: which steps an agent may own, which stay with a person, and how you tell when the split is wrong.
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