Blog
- 01
Automated regression testing for Acumatica configuration-as-code (GitOps)
Acumatica configuration ships after manual click-through with no CI gate. With configuration-as-code in git, the GitOps gate is acu apply, acu run, and acu diff — three exit codes a pipeline can enforce.
tags: acumatica · erp-gitops · automated-testing · acumatica-cli · configuration-as-code
- 02
Configuration-as-code for Acumatica: a tenant built from YAML in git
Acumatica configuration usually lives only in the web UI. YAML in git plus acu apply and acu diff turns a credit-terms edit into a pull request, an idempotent upsert, and a zero-drift proof with an exit code.
tags: acumatica · configuration-as-code · erp-gitops · acumatica-cli · yaml
- 03
Scheduled ERP backups in two layers: database dumps and storage snapshots
Every self-hosted Acumatica instance is yours to back up, and the vendor guidance converges on scheduled SQL-native backups — but backups end up ad hoc. Two independent layers provisioned by Ansible, a SQL `.bak` per database and a nightly whole-VM ZFS snapshot, run on a schedule with no manual routine.
tags: acumatica · backups · erp-devops · sql-server · zfs · ansible
- 04
Automated DEV/TEST environments for Acumatica with Ansible
Most Acumatica customers run production on Acumatica's SaaS cloud, but customization and upgrade testing need self-hosted instances. One Ansible command builds one on a Linux KVM host — golden image to login page, unattended.
tags: acumatica · ansible · erp-devops · infrastructure-as-code · kvm · zfs
also
Engineering notes and a live AI demo — method proof, not the Acumatica offer catalog.
- 05
The 90/10 software rule small businesses can use
For decades only big companies could afford to build their differentiated software. AI collapsed the cost of that slice to where one developer can deliver it — which is exactly what a small business can now afford.
tags: ai-agents · llm-engineering · build-vs-buy · small-business · cost
- 06
What one AI email actually costs
Most AI ROI talk is hand-waving. Here is a traced, per-email cost from a production agent — and the risk/reward math a CFO can check.
tags: ai-agents · llm-engineering · roi · cost · observability · pydantic-ai
- 07
Troubleshooting application failures with Logfire
A repeatable workflow for using Logfire span trees, SQL-over-traces, and OpenTelemetry semantic conventions to turn opaque application failures into one-line diagnoses — walked through a real production bug.
tags: logfire · opentelemetry · observability · debugging · pydantic-ai
- 08
Smoke-testing an LLM agent with Claude Code skills
Mocked pytest stays green while a live agent fabricates product specs. Lint and pytest cover the machinery; Claude Code skills drive real Gmail, real Drive, and the real model — with deterministic gates where possible and an LLM judge where natural language is the answer.
tags: testing · claude-code · ai-coding · smoke-test
- 09
Code consistency is the casualty of agent velocity
Agents ship inconsistent code that tests still pass. One small SPEC.md re-read every turn, telegraph-encoded to a 41% token cut, is the defense — with backprop turning each under-specified failure into a permanent invariant.
tags: spec-driven-development · claude-code · ai-coding · benchmarks