Long-form essays from the engineers shipping AI inside payers, hospitals, energy operators and proptech platforms. Written for technology leaders who care more about what runs in production than what trended last week.
How energy teams use AI to migrate legacy operational code without changing behavior that runs the grid. Capture behavior, migrate incrementally, and prove equivalence safely.
How healthcare teams use AI to migrate legacy code without changing clinical logic. Capture behavior, migrate incrementally, and prove equivalence for safety and compliance.
How fintech teams use AI to migrate legacy code without changing the money logic. Capture behavior, migrate incrementally, and prove equivalence for correctness and compliance.
AI does not just speed up coding in energy; it reweights the whole lifecycle toward verification and operational safety. What changes in plan, spec, generate, verify, ship, and operate.
AI does not just speed up coding in healthcare; it reweights the whole lifecycle toward verification, safety, and compliance. What changes in plan, spec, generate, verify, ship, and operate.
AI does not just speed up coding in SaaS; it reweights the whole lifecycle. What changes in plan, spec, generate, verify, ship, and operate for a multi-tenant product that ships constantly.
What senior SaaS engineers delegate to an AI pair and what they never do. A division of labor that keeps multi-tenant architecture and quality decisions in expert hands.
When AI writes half the diff in fintech, review breaks, and a missed defect is real money. How to layer automation, AI triage, and human sign-off so quality and compliance hold.
When AI writes half the diff in a shared SaaS codebase, review breaks. How to layer automation, AI triage, and human gates so quality holds across every tenant.
In fintech, AI speeds code generation and moves the bottleneck to review, testing, and specs, where money correctness and compliance raise the bar. How to invest where the constraint lands.
In healthcare, AI speeds code generation and moves the bottleneck to review, testing, and specs, where safety and compliance raise the bar. How to invest where the constraint lands.
In SaaS, AI speeds up code generation and moves the bottleneck to review, testing, and specs, across a shared codebase that ships constantly. How to find the real constraint and invest there.
Why vibe-coded SaaS prototypes die in production, and what production engineering adds for multi-tenant, always-on products: reliability, security, and operability at scale.
How fintech teams give AI coding tools persistent context and quality bars through durable specs, so AI-assisted code meets the correctness, compliance, and auditability money demands.
How SaaS teams give AI coding tools persistent context and quality bars through durable specs, so fast-shipping multi-tenant products get AI-assisted code that lasts.
How hospitality teams build products where intelligence is a layer, not a bolted-on feature: guest-facing accuracy, 24/7 reliability, and personalization designed into the architecture.
How retail teams build products where intelligence is a layer, not a bolted-on feature: personalization, seasonal peaks, and cost control designed into the architecture.
How SaaS teams build products where intelligence is a layer, not a bolted-on feature: model abstraction, evaluation, and cost control across multi-tenant, usage-priced products.
Most QA dashboards bury the signal executives need under charts nobody acts on. How to report quality upward with a few decision-driving numbers: risk, coverage, and escape rate.
In an AI system the model, the prompt, and the data can all change independently, and any one can cause a regression. How to catch it with one quality baseline and change attribution.
AI applications do not give the same output twice, so pass-or-fail testing breaks. How to test non-deterministic systems with evaluations, baselines, and quality distributions.
Canary releases turn every deploy into a small, measured experiment with automatic rollback. How to use them as a quality instrument, with the right metrics, gates, and abort criteria.
Testing across every device, OS, and screen size is impossible to do exhaustively. How to automate mobile testing against a risk-based device matrix that covers real users without the madness.
Automated accessibility checks catch a fraction of real barriers. How to combine automated and manual testing into accessibility coverage that actually passes an audit.
One long-form essay every other Wednesday. Written by the engineers shipping production AI for our clients, not by a content team. No promotional emails. Unsubscribe in one click.
