Original comparisons, architecture patterns, and practitioner frameworks. Each resource states how it was assembled and includes a stable citation.
A current taxonomy of VS Code forks and the decision criteria that distinguish them.
Method: Product documentation and public product behaviour reviewed against a consistent comparison framework.
Read and citeDownload comparison dataCSVTen concrete rules for creating and maintaining evaluation datasets for production AI systems.
Method: A practice-derived framework based on production evaluation and regression-testing workflows.
Read and citeDownload common mistakes dataCSVA side-by-side review of AWS, Microsoft, and Google code-modernisation services.
Method: First-party vendor documentation compared by supported workloads, transformation approach, and delivery constraints.
Read and citeDownload provider comparisonCSVA practical comparison of managed private endpoints, dedicated deployments, and self-hosted LLMs.
Method: Architecture patterns evaluated across data control, cost, compliance, and operational complexity.
Read and citeDownload governance matrixCSVA requirements framework for evaluating coding agents in regulated and enterprise environments.
Method: Control requirements grouped by data handling, access, auditability, retention, and enterprise administration.
Read and citeA staged model for moving software teams from assisted coding to agentic delivery.
Method: A four-phase operating model covering workflow design, controls, evaluation, and incident handling.
Read and citeDownload maturity modelCSVWe prefer first-party documentation and clearly identify practitioner judgement. Product landscapes are time-stamped, comparisons use the same criteria across vendors, and material revisions receive an updated date. Corrections are made in place so citations keep a stable URL.
Ask for a methodology clarification, a source interview, or a comment on AI engineering and governance in APAC.
alex@vgtc.io