Field notes.
Real opinions about what AI is getting right and wrong for operators in 2026, written by us, in our own voice, not a content calendar. Plus the occasional teardown of something Izzy built and what it taught us.
Articles
Claude Opus 4.6 Fast Mode: The New Frontier for Production AI
Claude Opus 4.6 Fast Mode shifts LLM deployment from model selection to inference configuration. A deep dive into latency and cost tradeoffs.
Falling Token Prices Won't Save the Budget You Now Own
AI spend grew 16x. If you inherited the AI mandate, here's why visibility, not price, is the fix.
The 5 Biggest Engineering Problems with GDPR-Compliant AI
GDPR fines have hit EUR 6.11 billion. Legal policies don't stop data leaks, architecture does. Five engineering failures in 'compliant' AI, and the fixes.
AI Infrastructure Costs 2026: A Build vs. Buy Decision Guide
Stop optimizing blindly. Learn the true TCO of enterprise AI in 2026. We break down costs for vector DBs, tokens, and observability to help you avoid the Danger Zone.
How to Reduce LLM Evaluation Costs by 90% (Without Losing Quality)
Stop running exhaustive evaluations. Discover the three-tier monitoring strategy that delivers 95% of the insight for just 5% of the cost.
From €115 to €43,000: Preventing LLM Cost Catastrophes
A single AI agent caused a €43,000 bill in 4 weeks. Learn the 5 behavioral failure modes driving runaway LLM costs and the guardrails to stop them.
FinOps for AI: How to Track & Reduce LLM Costs Per Feature
Spending over €5K/month on LLMs? Learn why per-feature cost tracking is critical for AI FinOps, EU compliance, and cutting token waste by up to 50%.
The 95% Accuracy Trap: Why Multi-Step AI Agents Fail
A 95% per-step accuracy means your 10-step AI agent fails 40% of the time. Discover the math behind cascading errors and how to fix agent reliability.
Your RAG System Is Silently Failing: Why Traditional Metrics Miss It
Is your RAG system returning "200 OK" but hallucinating? Learn why traditional metrics fail to catch silent degradation and how to monitor drift in production.
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