Compile Theory
Deep dives on how software gets built: .NET, Azure, distributed systems, and AI that survives production. Written for engineers who want to know why.
- Evaluating AI Agents on Databricks — When the correct output is a set rather than a value, you cannot assert it. Code scorers, LLM judges, and where each one belongs in an evaluation.
- Calling Databricks from ASP.NET Core Without Building a Fragile Integration — A cold warehouse, a double-clicked button, and a deploy mid-request. The state machine and idempotency work that keeps an ASP.NET Core integration from going fragile.
- A session should survive a node change — Long-lived agents, auth redirects, and cross-region failover all punish the same design: session truth trapped in one worker. Treat sessions as portable references to durable state, and keep sticky sessions on a removal plan.
- GitHub Actions OIDC should trust workflows, not repositories — GitHub lets any job with `id-token: write` choose its OIDC audience at runtime. If your cloud trust policy keys on repo identity alone, separate workflows share one blast radius.
- Seven Signals That a Databricks Repository Is Still a Prototype — Seven cheap checks that tell you whether a repository was promoted to production by decision or by usage, and which failure will arrive first.
- The SQLite CVE mess shows where your vulnerability automation needs a gate — A bad advisory turns expensive once scanners, ticket bots, and coding agents treat it as work. The SQLite episode points to a simple boundary: verify provenance and evidence before severity fans out into your pipeline.
- Treating Databricks Projects as Software Products — Ask a data team what they deploy and the answer is a notebook. What changes when a Databricks project gets a versioned artefact, a test gate, and its own identity.
- The SOC 2 controls you already run, and the five gaps that stop them counting — Most of what a SOC 2 report tests, your team already does. Five gaps separate the practice from the control an auditor can test, and closing four of them is writing rather than engineering.
- Coding agents make senior engineers more valuable, if the rulebook leaves their head — Coding agents make implementation cheap, which raises the return on local judgement. Teams capture that upside when they turn subsystem invariants, failure modes, and review heuristics into small playbooks that both agents and reviewers use.
- Open weights are now part of your AI disaster-recovery plan — The late-July fight over Chinese open-weight models exposed a new failure mode for production teams: losing access to a model family for reasons outside your roadmap. Treat open-weight failover like DR, classify workloads now, and rehearse the switch before policy or licensing changes make the call for you.
- Open-weight access now belongs in your AI supply chain — Anthropic’s July 2026 open-weights statement and fresh U.S. policy pressure around Chinese open models change what portability has to cover. For production agents, fallback planning now needs policy-aware routing, not a model dropdown.
- The Sequence Column Is a Business Decision — SEQUENCE BY decides which version of a record wins, and that is a question about the business. What each of the usual timestamp candidates actually asserts.
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