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.
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.
Performance testing done once before launch catches problems too late. How to make it continuous, with baselines, budgets, and regression detection that catch slowdowns as they happen.
Test data is the quiet bottleneck behind slow, flaky, non-compliant testing. How to make test data realistic, compliant, and available on demand, with a clear owner.
Fault injection testing deliberately breaks things to see how the system responds, so you rehearse failure before customers experience it. How to run it safely with hypotheses and blast-radius…
End-to-end tests are valuable and easy to overdo. Why fewer, deeper, more stable E2E tests beat a sprawling flaky suite, and how to build the ones that earn their cost.
API tests are the fastest, most stable quality signal you can build: below the flaky UI, above the isolated unit. How to automate schema, contract, functional, and boundary checks.
Exploratory testing is where human curiosity and judgment find the bugs scripts and agents miss. Why it stays human, how to run it with charters, and where AI helps around it.
Visual regression testing catches UI breakage that functional tests miss. From brittle pixel diffs to perceptual AI checks, how to catch real visual bugs without drowning in noise.
Flaky tests have specific, diagnosable root causes: timing, shared state, environment, and nondeterminism. A field guide to finding the real cause and the fixes that stick.
Test maintenance is the hidden cost that quietly eats your QA budget. Where it comes from, why it stays invisible, and how agentic tools and better design cut it.
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.
