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How to Build a Fully Interactive Multi-Page NiceGUI Application with Real-Time Dashboard, CRUD Operations, File Upload, and Async Chat

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In this tutorial, we build a fully interactive, multi-page web application using NiceGUI. We start by setting up the environment and designing a reusable layout that includes navigation, theming, and dark mode support. As we move forward, we implement a live dashboard with real-time metrics and charts, demonstrating reactive bindings and timed updates. We then […]

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Closing the ‘Expressivity Gap’: How Mistral’s Voxtral TTS is Redefining Multilingual Voice Cloning with a Hybrid Autoregressive and Flow-Matching Architecture

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Voice AI has a dirty secret. Most text-to-speech systems sound fine — until they don’t. They can read a sentence. What they cannot do is mean it. The rhythm is off. The emotion is flat. The speaker sounds like themselves for two seconds, then drifts into generic synthetic territory. That gap between intelligible audio and

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Build a Modular Skill-Based Agent System for LLMs with Dynamic Tool Routing in Python

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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.

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A Coding Guide to Survey Bias Correction Using Facebook Research Balance with IPW CBPS Ranking and Post Stratification Methods

ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, Data Science, deep learning, Editors Pick, Machine Learning, Software engineering, Staff, Technology, Tutorials

In this tutorial, we walk through a complete, end-to-end workflow for correcting bias in survey data using the balance library. We simulate a realistic population, deliberately introduce sampling bias, and then apply multiple re-weighting techniques to recover unbiased estimates. We focus on four widely used methods: Inverse Probability Weighting (IPW), Covariate Balancing Propensity Scores (CBPS),

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Zyphra Introduces Tensor and Sequence Parallelism (TSP): A Hardware-Aware Training and Inference Strategy That Delivers 2.6x Throughput Over Matched TP+SP Baselines

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Training and serving large transformer models at scale is fundamentally a memory management problem. Every GPU in a cluster has a fixed amount of VRAM, and as model sizes and context lengths grow, engineers constantly have to make trade-offs about how to distribute work across hardware. A new technique from Zyphra, called Tensor and Sequence

Zyphra Introduces Tensor and Sequence Parallelism (TSP): A Hardware-Aware Training and Inference Strategy That Delivers 2.6x Throughput Over Matched TP+SP Baselines Read More »

How to Build an End-to-End Production Grade Machine Learning Pipeline with ZenML, Including Custom Materializers, Metadata Tracking, and Hyperparameter Optimization

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In this tutorial, we walk through an end-to-end implementation of an advanced machine learning pipeline using ZenML. We begin by setting up the environment and initializing a ZenML project, then define a custom materializer that enables seamless serialization and metadata extraction for a domain-specific dataset object. As we progress, we build a modular pipeline that

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Top Search and Fetch APIs for Building AI Agents in 2026: Tools, Tradeoffs, and Free Tiers

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Web search and content retrieval have quietly become the most critical infrastructure decisions in AI agent development. An agent without reliable access to live web data is effectively operating on stale knowledge — a hard limitation for any production deployment handling research, lead enrichment, competitive intelligence, or real-time monitoring. In 2026, the ecosystem of search

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A Developer’s Guide to Systematic Prompting: Mastering Negative Constraints, Structured JSON Outputs, and Multi-Hypothesis Verbalized Sampling

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Most developers treat prompting as an afterthought—write something reasonable, observe the output, and iterate if needed. That approach works until reliability becomes critical. As LLMs move into production systems, the difference between a prompt that usually works and one that works consistently becomes an engineering concern. In response, the research community has formalized prompting into

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A Coding Implementation to Explore and Analyze the TaskTrove Dataset with Streaming Parsing Visualization and Verifier Detection

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In this tutorial, we take a deep dive into the TaskTrove dataset on Hugging Face and build a complete, practical workflow to efficiently explore it. Instead of downloading the full multi-gigabyte dataset, we stream it directly and work with individual samples in real time. We begin by setting up the environment and inspecting the raw

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Sakana AI Introduces KAME: A Tandem Speech-to-Speech Architecture That Injects LLM Knowledge in Real Time

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The fundamental tension in conversational AI has always been a binary choice: respond fast or respond smart. Real-time speech-to-speech (S2S) models — the kind that power natural-feeling voice assistants — start talking almost instantly, but their answers tend to be shallow. Cascaded systems that route speech through a large language model (LLM) are far more

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