Learn how to apply Clean Architecture in Python without overengineering, using domain entities, use cases, Protocols, and lightweight infrastructure layers.

jeeva-raksha/ ├── backend/ │ ├── server.py # Main Python FastAPI backend server │ ├── medical_dataset.py # ML symptom prediction & dataset indexer │ ├── dataset_cache.pkl # Instant binary cache of ...

The Forefront of EvolutionThe evolution of generative AI knows no bounds and is beginning to deeply permeate our business environments and daily workflows. In this article, we cover the latest trends ...

A deployed agent has three layers: a framework or harness, an agent server, and an agent runtime. See Deploy agents on Azure Databricks. The approach on this page runs on Databricks Apps, the same ...

SDK 2.0 AI libraries are deprecated. For .NET, implement A2A with SDK 2.1 plus an external A2A protocol library. Use Teams SDK handlers for inbound user turns and proactive messaging, and use A2A ...

Aarogya AI is a production-ready medical education chatbot that answers health-related questions using Retrieval-Augmented Generation (RAG) over a MedQuAD-derived knowledge base. ⚠️ Disclaimer: This ...

A new mode includes a study tool, but it still does a user’s homework. And a children’s safety nonprofit says the chatbot ...

Build custom CAD designs with natural language prompts by integrating ChatGPT 6 Astra and FreeCAD. Output precise Python code for 3D printing.

Dots are designed to automate online tasks, like buying furniture. In my initial experience, the always-on agent was a bit buggy and couldn’t complete a captcha.

In one demo example shared by OpenAI, a user’s Dot helps plan dinner. The Dot proactively saw that the user’s calendar showed they were working through dinner and messaged with two GrubHub food ...