Your AI Architecture is Bleeding Money
Cost-per-token is the wrong metric. The real savings come from architectural decisions most teams get wrong.
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Blog
Insights on AI trust
Practical perspectives on agentic systems, data architecture, and building AI that works in production.
Posts - Page 2
The AI Production Readiness Checklist
The comprehensive checklist for launching LLM-powered features. Evaluation, monitoring, fallbacks, cost controls, and incident response.
Read more →Prompt Injection is an Unsolved Problem (Here's How to Mitigate Anyway)
There's no complete solution to prompt injection. Here's the defense-in-depth playbook for production AI systems.
Read more →When to Use Agents vs Deterministic Workflows: A Decision Framework
A concrete decision tree for when to reach for AI agents vs traditional orchestration. Cost, latency, reliability, and compliance dimensions.
Read more →From 11% to 88% GPU Utilization: How We Built 8x Faster LLM Inference
PyTorch leaves 89% of GPU bandwidth on the table. We fixed it with custom Triton kernels. Here's what we learned building Accelerate.
Read more →Eval Debt Will End Careers
Tech debt is slow. Eval debt is sudden. The teams that survive will treat evals like unit tests: written first, run always.
Read more →Agentic AI Is a Cost Center, Not a Strategy
Everyone's racing to deploy AI agents. Most will waste millions. The question isn't 'how do we use more AI?' - it's 'how do we use AI sustainably?'
Read more →AI Observability is Expensive Voyeurism
The observability market is selling you dashboards to watch your AI fail in high resolution. What you need is controllability.
Read more →Your Data Team is Building an AI Graveyard
Every transformation in your data pipeline destroys information AI needs. Traditional data engineering is a lossy compression algorithm.
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