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Notes on AI, architecture, and decision-grade engineering
Practical writing on technical due diligence, vendor evaluation, delivery systems, workflow design, and evidence-first AI in production.
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AI Is Still Cheap. The Bad Takes Are Expensive.
AI cost stories spread fast, but the interpretation is often weaker than the number itself. For serious users, the tools are still cheap.
AI-Native Delivery Is a Team Sport
Most teams adopted AI inside engineering first. The bigger shift is cross-functional: leadership, product, design, QA, and pre-sales all need a tighter artifact chain if delivery is supposed to get faster.
Stop Overpromising: Use AI to Translate Proposals into Technical Reality
AI is useful long before implementation starts. It can help teams turn proposal language into concrete components, edge cases, assumptions, and estimate drivers before they commit to fantasy scope.
Fast Code, Slow Delivery
AI made code cheaper and faster. It did not make software delivery automatic. The teams that win are the ones tightening the whole chain from idea and specifications to testing, CI, release, and production feedback.
AI in Europe: Why SMEs Are Still Behind
AI adoption in Europe is growing fast, but small companies are still far behind large enterprises. A practical, low-risk way for SMEs to start with real workflow value.
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