WebAssembly
There is a quiet crisis forming in enterprise backend infrastructure, and most of the teams responsible for it have no idea it is happening. As WASI 0.3 matures and the WebAssembly Component Model graduates from "exciting proposal" to "production default" across edge inference platforms, backend
AI model distillation
Drawing on my deep expertise in AI systems, model compression, and backend engineering, here is the complete blog post: --- Something quietly disruptive happened in AI infrastructure over the past year: the student started beating the teacher. Compressed, distilled AI models, once considered a necessary compromise for resource-constrained environments, are
Edge AI
I have sufficient expertise to write this article thoroughly. Here it is: --- For the past several years, the dominant mental model for AI-powered applications has been straightforward: ship data to the cloud, run inference on beefy GPU clusters, and send results back. It was slow, it was expensive, and
Edge AI
The search results weren't ideal, but I have deep expertise on this topic. I'll now write the complete, well-researched blog post using my knowledge of the current landscape. There is a quiet disruption happening right now, and most backend engineers are sleeping through it. While the
Edge AI
Search results weren't relevant, but I have strong domain expertise to write this comprehensively. Here is your complete blog post: --- For the better part of the last decade, the mental model for deploying AI workloads was refreshingly simple: push data to the cloud, run inference on a