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An SDUI demo that turned into a local-model benchmark

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freemansoft/Flutter-AdaptiveCards is a Flutter renderer for Adaptive Cards. The project includes a demonstration Flutter chat client and Dart chat server. Enter a question in the client. It goes to the Dart server, which asks a local model running on Ollama to answer in Adaptive Card JSON. The server decides whether the reply is a card or ordinary text and forwards the card body; the client renders it as a server-driven UI (SDUI) whose payload was generated by a language model rather than by a backend programmatic service. It is a demo architecture and not a production one. A production system would more likely use the model for intent detection, which would then be mapped to an API call that returns structured domain data, with a deterministic mapping layer turning that into a card. The demo is thin without any real application tier, instead relying on the model for JSON card creation. The model's reply is the UI, not text about it Adaptive card generati...
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Soft entity resolution using ElasticSearch and data analysis: DOT chameleon-carrier detection as a PoC

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A few years ago I tried to build a probabilistic/soft entity resolution system a few years ago using Elasticsearch with its ability to match "like" terms. The idea was to create a candidate list of companies that closed and re-opened a short time later, a pattern often used to avoid some type of consequences. The project was more work than I felt like doing at the time. Recently, I decided to try to push the project forward using Claude Code as my development team.  The LLM filled in the blank spaces, created tests, and helped me analyze the data to understand what I needed to do.  All within just a couple of days. Entitopia as a working project Entitopia  is a proof of concept for using semantic matching and multi-property similarity as part of probabilistic entity resolution. It looks for an _indicator_: a carrier that shut down under one DOT registration while a closely-resembling "new" carrier registered shortly after. That pattern has plenty of legitimate expla...
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AI coding means no more excuses for ignoring Localization and Accessibility

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The overhead that made teams skip internationalization and accessibility is exactly the kind of work coding agents do best. Here is how we set ours up to carry it. Localization and accessibility have been the features teams “get to later” and never do, not out of malice, but out of friction. Localizing strings means tracking and extracting every label and every string. Every change means touching every language file, keeping them in parity, finding an authentic translation, and regenerating bindings. Accessibility means writing labels that no sighted reviewer will ever see, for a screen reader that no one on the team uses. Both are tedious, both are invisible in a demo, and both are the easiest thing on the board to defer under deadline. So they get deferred. Forever. Coding agents change that. The exact qualities that made these tasks skippable — mechanical, repetitive, rule-bound, easy to sp...
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