Practical notes on
AI & systems
Practical notes on AI systems integration, enterprise automation, RAG, cloud, and IoT for teams evaluating implementation.
Balancing Offline and Online Evaluation for Enterprise AI
Offline evaluation establishes quality and safety gates, while online evaluation verifies real-world value and feeds new evidence into future releases.
Securing Packages, Container Images, and SBOMs
Build a traceable and verifiable software supply chain across dependencies, container images, and SBOM operations.
Choosing Log-, Timestamp-, or Event-Based CDC
A practical framework for selecting CDC by consistency, latency, delete capture, source constraints, recovery behavior, and operating cost.
Prioritizing Legacy Modernization by Risk, Value, and Dependency
A practical framework for sequencing legacy modernization around operational risk, business value, and the dependencies that determine what can safely move next.
Automating Email Attachment Validation, Classification, and Recovery
A practical architecture for validating, classifying, routing, and safely recovering email attachments across enterprise systems.
Setting Practical SLAs, SLOs, and Error Budgets
A practical framework for turning business impact into measurable reliability targets, operating rules, and defensible service commitments.
Assessing Dependencies and State Before Containerization
Before containerizing an enterprise system, map its runtime dependencies, persistent state, lifecycle behavior, and recovery responsibilities.
Safe OTA Updates and Recovery for Industrial Gateways
A practical design guide to signed releases, staged rollout, atomic installation, rollback, and field recovery for industrial gateways.
Designing Fine-Grained Authorization for Enterprise APIs
Enterprise API authorization must control not only which endpoint may be called, but also which records, fields, and business states are accessible.
Reliable API Integration for Pagination, Rate Limits, and Bulk Sync
Design recoverable bulk API integrations with stable pagination, adaptive rate control, idempotent writes, checkpoints, and reconciliation.
Implementing Data Lineage from Source Fields to Reports
Build maintainable lineage that traces management metrics through transformation logic, datasets, source fields, and accountable systems.
Preserving Numbers, Terms, and Ownership in AI Summaries
A practical engineering approach to keeping critical figures, contractual conditions, and accountable owners intact in AI-generated summaries.
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