Query decomposition doesn’t fix context starvation — it just moves it
I have a small example that would best communicate the message I am trying to convey: say you built a The post Query decomposition doesn’t fix…
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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…
47,000 job listings reveal the engineering roles that AI is creating
Every major transformation in tech has led to roles merging, then new ones emerging. Friction between developers and operations drove The post 47,00…
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…
The systems guide to production token optimization
When enterprise AI applications scale, they inevitably hit a wall. For many engineering teams this wall is initially diagnosed as The post The syste…
Why basic RAG fails at multi-hop reasoning (and how GraphRAG fixes it)
The current approach to designing LLMs within AI engineering is oversimplified. According to the echo chamber’s view, solving LLM hallucinatio…
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…
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…
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…
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…
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 e…
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 th…