AutoGen is a framework for building AI agents and multi-agent systems using large language models (LLMs). It started as a research project at Microsoft Research and pioneered several concepts in multi ...

A sample Databricks App (FastAPI + React) that scores credit card transactions for fraud in real time using Model Serving route optimization for low-latency inference and Lakebase Postgres for online ...

Building the agentic software factory with Anthropic and OpenAI. SonarQube World Tour NYC, Oct. 15 Sonar Vortex with semantic code navigation lowers coding agent costs by mapping structural code ...

The Responses API is the newer generation API shape for OpenAI-compatible model calls. Chat Completions remains supported, but Responses is designed to handle more of the patterns developers now build ...

Your browser does not support the audio element. Coding geometric patterns is a classic milestone for any programmer. It transitions your mind from thinking in linear ...

If you silently fail here, inertia becomes NaN and convergence never happens. Common Mistake: Using a list comprehension inside a tight loop without broadcasting. The vectorized distance computation X ...

In this tutorial, we explore tqdm in depth and demonstrate how we build powerful, real-time progress tracking into modern Python workflows. We begin with nested progress bars and manual progress ...

In this tutorial, we build an end-to-end cognitive complexity analysis workflow using complexipy. We start by measuring complexity directly from raw code strings, then scale the same analysis to ...

This blog was written in collaboration with Fan Bu, Jason Mackay, Borya Sobolev, Dev Khanolkar, Ali Dabir, Puneet Kamal, Li Zhang, and Lei Jin. Anthropic successfully popularized the notion that “bash ...

Working with JSON in Python is often challenging. The basic json.loads() only gets you so far. API responses, configuration files, and data exports often contain JSON that is messy or poorly ...