The Test Pyramid That Actually Works in 2026
Unit, contract, integration, and a thin layer of high-value E2E — the proportions, tooling, and CI rules that keep a 10k-test suite under 8 minutes.
Test automationPlaywrightPact
What's inside
- 1Why most pyramids are inverted in practice
- 2Contract testing as the load-bearing layer
- 3E2E: the thin top layer, ruthlessly curated
- 4CI rules: shard, parallel, retry-once, fail-fast
- 5Flaky tests as outages — the policy that fixes them
Performance Budgets in CI: How to Catch Regressions Before Users Do
Core Web Vitals, k6 scenarios, and budget-as-code — the practice that turns performance from a heroic firefight into a graph that doesn't move.
Performancek6Core Web Vitals
What's inside
- 1The performance regressions you ship and never see
- 2Synthetic vs. RUM: pairing the two correctly
- 3k6 scenarios that mirror real user journeys
- 4Budget-as-code: failing the build for a 200ms regression
- 5Communicating performance wins to non-technical leaders
Security as Code: Catching the OWASP Top 10 in the PR, Not in Pen-Test
SAST, DAST, dependency scanning, container scanning, IaC scanning — sequenced into the PR so security findings arrive while the developer still has context.
SecurityDevSecOpsOWASP
What's inside
- 1Why pen-test-only security models keep losing
- 2The five scans every PR should run (and the ones that should not)
- 3Triage in the PR: severity, exploitability, and the rules engine
- 4Secrets management and the build provenance chain
- 5Threat modeling for the highest-risk surfaces
SLOs and Error Budgets That Actually Mean Something
How to write SLOs leaders defend, error budgets that gate releases, and observability stacks that turn 3am pages into 9am dashboards.
SRESLOObservability
What's inside
- 1The SLO antipattern: copying numbers from a Google book
- 2Writing SLOs from customer journeys, not infra metrics
- 3Error budgets as a release-cadence lever
- 4Observability: traces, metrics, logs — and the one thing missing
- 5On-call rotations that don't burn the team out
Quality Engineering for AI Systems Is a Different Discipline
Eval harnesses, golden sets, regression suites for prompts, and the canary patterns that catch model-drift before the customer does.
AI qualityEvaluationMLOps
What's inside
- 1Why traditional QA misses 90% of AI failure modes
- 2Eval harnesses: golden sets, faithfulness, answer relevance
- 3Prompt regression suites and the registry that holds them
- 4Canary deployments and shadow scoring for model swaps
- 5Closing the loop: production feedback → eval set