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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.

The protocol is designed to improve GPU performance as AI compute ramps up.

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.

Three key advantages of SLMs Division of labor: Modern AI architecture uses routers to send routine tasks to 7B-parameter SLMs, reserving trillion-parameter LLMs only for complex reasoning. Economic efficiency: For high-volume, repetitive tasks, SLMs can reduce cloud inference costs by up to 90% while providing near-instant latency. Privacy at the edge: Because SLMs can run locally on-device or on-premises, they reduce the data leakage risks inherent in sending sensitive telemetry to the public cloud. Large language models (LLMs) are the workhorses of AI, supporting ever more sophisticated capabilities and workflows, and approaching near-human level performance. But sometimes more isn’t always better — it’s just more. Specialized data and limited capabilities are just fine for some workflows. This realization is driving the evolution of small language models (SLMs), rather than one-size-fits-all LLMs. SLMs — coming in the form of domain-specific models, statistical langua

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.