52 Dispatches from the engineering frontier — Deep dives, insights, and explorations
Running heavy AI models in the browser used to be a pipe dream, usually relegated to clunky JavaScript shims that choked on anything larger than a basic linear regression. But w...
Scaling an AI-driven platform to handle one million daily queries isn't just about throwing more GPUs at the problem. When I hit the scale where latency starts creeping up and i...
Streaming AI responses into a UI often feels like a race against the network. If you aren't careful, you end up with flickering text, broken syntax highlighting, and a jarring e...
Tailwind CSS v4 feels like a complete rethink of how we ship styles. For years, we dealt with the overhead of the PostCSS plugin chain and the complexity of managing tailwind.co...
Building a Retrieval-Augmented Generation (RAG) pipeline is the easy part. Making sure that pipeline isn’t hallucinating or giving users irrelevant fluff is where most engineeri...
Building a single AI agent that can handle complex workflows is often a recipe for hallucination and brittle code. Lately, I’ve shifted my architecture toward multi-agent system...
Standard pre-trained embedding models like text-embedding-3-small or general-purpose BERT variants are impressive, but they often stumble when your data lives in a niche silo. I...
When I first started deploying LLMs for enterprise clients, the default move was always "get the biggest model available." If GPT-4 could do it, why bother with anything else? B...
Building LLM-powered applications is fun until you hit the "jailbreak" wall. I remember my first production chat deployment—everything worked perfectly until a user started test...
Tracking objects across hours of raw CCTV or drone footage used to be a manual nightmare. Last month, I had to build a pipeline that turns raw video streams into structured, que...