How to adopt AI in software development without losing traceability.
- AI code review in practice — checking PRs against your ADRs and incidents
How AI code review differs from a linter or a generic assistant: every pull request checked against your architecture decisions, past incidents, and team rules.
2026-06-14 · 3 min read
- Automating the SDLC with AI — from requirements to release in one context
How to adopt AI in software development without losing traceability: one contextual layer over requirements, code, tests, and releases instead of disconnected assistants.
2026-06-12 · 3 min read
- DORA metrics in practice — what to measure and how to improve delivery
The four DORA metrics in plain terms: deployment frequency, lead time, change failure rate, and MTTR. How to collect them without manual work and what to do with them.
2026-06-10 · 3 min read
- AI security triage — how to stop drowning in scanner findings
Why security scanners produce hundreds of findings nobody fixes, and how AI helps rank vulnerabilities by real business risk instead of abstract CVSS.
2026-06-08 · 3 min read
- Knowledge graphs in software development — why AI without context is useless
How a knowledge graph links commits, PRs, requirements, ADRs, and incidents into one network, and why that turns an AI assistant from a text generator into a useful tool.
2026-06-06 · 3 min read