6 Dispatches from the engineering frontier — Deep dives, insights, and explorations
Running heavy language models on your own hardware used to be a nightmare of dependency hell and mismatched CUDA versions. With Ollama and Docker, we’ve finally hit a point wher...
Running LLMs in production is a constant battle between keeping GPU costs down and ensuring latency doesn't spike when traffic hits. If you leave your pods running 24/7, your cl...
GPU compute is expensive, and keeping a cluster idling is a quick way to burn through a startup’s runway. As I moved from managing persistent Kubernetes clusters to more fluid, ...
LLM-based applications are notoriously brittle. Unlike traditional software where a unit test checks if 2 + 2 = 4, prompt engineering is probabilistic. A minor tweak to a system...
When I started scaling our LLM-powered features last year, the monthly AWS and OpenAI bills were the first thing that kept me up at night. It’s easy to prototype with a few API ...
Running LLM-powered applications at the edge—whether on Cloudflare Workers, Vercel Edge Functions, or distributed nodes—creates a unique security headache. You need low-latency ...