24 posts found
The second part of Weaving Memory. What the build taught me about the comfort-amplifier risk I named in Part 1, and why grounding memory outside the probabilistic system was the move I did not see in the spec.
The first part of Weaving Memory. What cannot be exported has to be built. Why I stopped trying to paste context across tools and started building the bridge.
The category error behind every AI trust debate. Confidence is the measured basis for authority delegation decisions.
When AI tools learn to work with you perfectly, you stop seeing your own gaps. The mirror that learns becomes the mirror that accommodates.
Every Kubernetes governance model rests on an invisible assumption: humans are accountable at the end of every thread. Autonomous agents just severed it.
Context is the real product of AI collaboration. It's also the real risk. What happens to the digital self when the mirror changes.
Turn observability patterns into operational infrastructure with alert rules, workbooks, and deployment guidance that makes AI monitoring actionable.
Technical observability tells you what happened. Governance observability tells you whether it was acceptable and proves you're governing responsibly.
You've seen the map. You know where you are. Here's the work that actually prevents the spiral - the requirements, the cost, and why most won't do it.
We're rushing toward AI governance as the solution to AI chaos. I've watched this movie before. Here's the map, here's where you are, and here's what happens next if you don't change course.
Infrastructure metrics can't tell you if AI responses are helpful. Learn to instrument semantic quality, conversation degradation, and user outcomes.
AI tools removed the speed limit on learning and creation. Then my body sent the invoice for months of borrowing against a balance that never existed.
RAG systems fail silently when retrieval breaks. Learn to monitor Azure AI Search, vector stores, and the retrieval pipeline that feeds your models.
Spec quality isn't a skill you can workshop. It's an emergent property of organizational health. Why training fails and what actually produces clarity.
AI can accelerate your growth or replace it. The productivity discourse gives you metrics that feel good but tell you nothing about what's happening to your capability.
Stop monitoring AI infrastructure like web servers. Learn to instrument Azure OpenAI with queries that reveal token consumption, content filters, and cost attribution.
Two early signals reveal whether your governance initiative will succeed or fail. Learn to spot them before wasting eighteen months building something that was never going to work.
A satirical look at AI architecture complexity theater, followed by a practical guide to what Azure AI Landing Zones actually do—no token valves required.
AI as a writing partner, not a ghostwriter. How to use AI to think better, not to avoid thinking entirely.
Why AI doom narratives distract from real risks and real opportunities. A practitioner's perspective on fear, progress, and human nature.
Five years ago I was manually running Jekyll in WSL. Now I have an AI-assisted, fully automated publishing platform. Here's the journey.
Addressing real objections to AI adoption: quality concerns, supportability, and what happens when your craft becomes accessible to everyone.
Why AI tools are extensions of you, not replacements. A practitioners guide to AI adoption for the uncertain and skeptical.
Im not a software developer. But with Kiro AI assistant properly configured, I can take a vision and make it reality. Heres how I set it up.