#llmengineering

[capestart]

Clinical PDFs look structured, until you try to extract data from them.

Tables, symbols, layout, and source highlights all matter. That is why we moved our Summary-Level Extraction workflow from a LangChain + OCR-based setup to Anthropic’s native PDF understanding.

The bigger lesson: in Document AI, the answer matters, but the source behind the answer matters just as much.

[deepgriffineffigy]

AI Agent Development Services A futuristic workspace showcasing intelligent AI agents interacting across multiple dashboards—automating workflows, analyzing data, making decisions, and collaborating with users. Visual elements include neural networks, multi-agent systems, LLM pipelines, automation flows, API integrations, and real-time analytics. The image represents how AI agents streamline business processes, enhance productivity, and deliver autonomous, intelligent task execution.

[ianfulgar]

Taking my first real steps in customizing my own AI agent. It needs to sync across multiple devices and still function offline when needed. To build that foundation, I’m starting with a VectorDB on Supabase to handle long-term memory, then connecting it with a Render-hosted engine as the main brain. This is where function meets autonomy—and the architecture begins.