Somebody opens a photo in Photoshop, erase a tiny scratch in the background, save, and post it online thinking nobody will notice, and nobody notices, looking at it with the eye. Except the camera noise noticed, and it is going to tell. This post is about how a classical signal-processing technique, with zero lines of … Read more
In this post I describe how I create a on-premise solution to handle with videos that needs scenes segmented, faces identified, subtitles generated, thumbnails picked, metadata pulled into the catalog. This post is about how we stopped trying to win that math with people and started winning it with our own AI. TL;DR: How SOFA … Read more
You spent three sprints building the screen. Forms, dropdowns, a five-step wizard with a progress bar you were weirdly proud of. Then a user ignored all of it, pointed an AI agent at the same API your screen sits on, and finished the job in an hour while you were still in standup. This post … Read more
Today, it’s entirely possible for (almost) anyone to build an AI agent that pulls data from an MCP, reshapes a JSON, and earns applause as quickly as it breaks in production. The person is left staring at a workflow they built but never truly understood, supported by a tool whose limitations they never learned. The … Read more
You are handed a brain MRI and a deceptively simple question: how much multiple sclerosis is in here? The lesions are right there on the FLAIR scan, bright little blobs scattered through the white matter, but “right there” is not a number. Counting them by hand across 181 slices, for five patients, is how you … Read more
You hit a concept you do not understand, paste it into an AI, read the clean answer, nod, and move on. Twenty minutes later you cannot reproduce a single line of it. The explanation was right there, perfectly worded, and somehow none of it stuck. That is because you did not learn it. You rented … Read more
You want an AI agent that actually reasons, queries databases, and uses tools intelligently, not a chatbot that rephrases your question back with a different tone. The ReAct pattern with LangGraph and Azure OpenAI is how you build that. TL;DR: Build a production-ready AI agent using LangGraph’s ReAct pattern with Elasticsearch (vector search), Neo4j (graph … Read more
SNOMED CT provides a structured vocabulary of millions of clinical concepts. FHIR provides the exchange format for healthcare data. Combining them via the Snowstorm server gives you a standards-compliant terminology service that healthcare systems can query. TL;DR: Query the Snowstorm SNOMED CT terminology server via its FHIR-compatible API to look up clinical concepts and code … Read more