10 Python Libraries for Building LLM Applications

A practical guide to modern type annotations in Python for data science The post The Joy of Typing appeared first on Towards Data Science.

Feature engineering is the foundation of strong machine learning systems, but the traditional process is often manual, time-consuming, and dependent on domain expertise. While effective, it can miss deeper signals hidden in unstructured data such as text, logs, and user interactions. Large Language Models change this by helping machines understand language, extract meaning, and generate […] The post Feature Engineering with LLMs: Techniques & Python Examples appeared first on Analytics Vidhya.

Front-end development has never been more capable. Modern frameworks offer fast rendering pipelines, component composition, powerful tooling, and a growing ecosystem of libraries that promise to make building sophisticated applications easier than ever. Yet many teams experience exactly the opposite — increasing difficulty. Applications grow harder to reason about. Features interact in unexpected ways. Simple changes ripple through unrelated parts of the system. Debugging becomes an exercise in tracing invisible dependencies across the application. The tools improved, but the complexity remained. Front-end complexity never ends For many years, front-end complexity was blamed on frameworks. Each generation of tooling promised to fix the limitations of the previous one. The transition from server-rendered pages to client-side frameworks introduced a wave of architectural experimentation. Then came virtual DOM engines, reactive libraries, and increasingly sophisticated component systems.

Stop shifting elements in lists! Discover why collections.deque is the secret to high-performance sliding windows, thread-safe queues, and efficient data streams in your next Python project. The post Beyond Lists: Using Python Deque for Real-Time Sliding Windows appeared first on Towards Data Science.

In this tutorial, we build a complete skill-based agent system for large language models and explore how modular capabilities can be structured like an operating system for AI agents. We define reusable skills, attach metadata and schemas to them, register them in a central registry, and enable dynamic orchestration through tool calling and multi-step reasoning. […] The post Build a Modular Skill-Based Agent System for LLMs with Dynamic Tool Routing in Python appeared first on MarkTechPost.

Armed with some Python and a white-hot sense of injustice, one medical student spent six months trying to figure out whether an algorithm trashed his job application.

Learn how the Voxtral TTS model works, what makes its voice cloning and low‑latency performance special, and how to start generating speech with just a few lines of Python code.

In this tutorial, we build the entire Agentic UI stack from the ground up using plain Python, without relying on external frameworks to abstract away the core ideas. We implement the AG-UI event stream to make agent behavior observable in real time, and we bring in A2UI as a declarative layer that allows interfaces to […] The post A Coding Deep Dive into Agentic UI, Generative UI, State Synchronization, and Interrupt-Driven Approval Flows appeared first on MarkTechPost.