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 p…
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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 t…
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 Templ…
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 A…
Beyond the stack trace: why AI requires a new debugging paradigm
For some time, debugging has relied on the assumption that software is deterministic. It’s expected that with the same input, The post Beyond th…
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 w…
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 unde…
How to build your first end-to-end AI workflow in n8n
n8n is one of the most powerful workflow automation platforms available today, and the fastest way to learn it is The post How to build your first end…
The Rust sidecar pattern that fixes Python AI’s biggest weakness
In the world of AI, “it works on my machine” is the most dangerous phrase a developer can muster. Transitioning The post The Rust sidecar pattern that…
How AI-native systems are built
The core problem: the “brittle” enterprise Traditional enterprise software is deterministic; composed of a series of “If-Then” statements that break T…
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 ship…