Why human oversight is shifting from writing code to defining requirements
This walks through the pipeline our agents operate inside—from a recorded scoping meeting through unit specs, spec review, generated code, The…
DevOps
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Stop AI code sprawl before it destroys your software design
While AI code generators help teams ship faster than ever, that speed brings a hidden killer: Comprehension Debt. As soon The post Stop AI code spra…
Why your AI pipeline costs 10x more after the demo
Every token has a price. The problem is that most AI systems don’t reveal the bill until they reach production. The post Why your AI pipeline costs…
Say goodbye to K8s GPU pain: How DRA changes everything
Consider a platform team managing a shared GPU cluster with a mix of B200s, H100s, and recently added B300s. Every The post Say goodbye to K8s GPU p…
The “silent hallucination” loop: how our autonomous data pipeline poisoned its own vector store
Nothing matches the dread of checking a perfectly green observability dashboard with sub-100ms latency, right before an enterprise client emails The…
Why traditional CI/CD fails for LLMs (and the release gates we built to fix it)
This article explains why traditional CI/CD gates are not enough for production AI systems. I share a practical release-gating approach The post Why…
Template-based data extraction is dead. Here’s what comes next.
Modern businesses are in a constant, uphill battle against what to do with unstructured data: PDFs, contracts, scanned images, customer The post Tem…
Your AI pipeline is broken, and your dashboards don’t know it
Recently, a crucial RAG pipeline used by one of our corporate clients began hallucinating about financial numbers without notifying us The post Your…
From Jupyter Notebook to production: How to ship AI systems that actually work
Moving from experimentation to production in AI requires a transformation of mindset, architecture, and engineering discipline. There’s no API…
Debugging the undebuggable: building observability into probabilistic AI systems
Debugging used to be straightforward: A service failed, you checked the logs, followed the stack trace, and fixed the bug. The post Debugging the un…
A nine-point checklist for shipping production-ready AI
Starting from square one Most teams can build an AI prototype. A notebook answers a few prompts, a demo agent The post A nine-point checklist for sh…