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Voice AI has a dirty secret: most of it was never designed for conversation. The dominant paradigm — feed text in, get audio out — traces its lineage to audiobook narration and voiceover production, where the model never hears the person on the other end. That’s fine when you’re generating a podcast intro. It’s not […]
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
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.
Build a Modular Skill-Based Agent System for LLMs with Dynamic Tool Routing in Python Read More »
Gradient descent has a fundamental limitation: on most real-world loss surfaces, it is inefficient. When the surface has uneven curvature—steep in one direction and flat in another, which is common in practice—the algorithm struggles to make consistent progress. A high learning rate helps move faster along the flat direction but causes overshooting and oscillations along
Why Gradient Descent Zigzags and How Momentum Fixes It Read More »
If you’ve ever built a production AI pipeline that runs long jobs — processing thousands of prompts overnight, kicking off a Deep Research agent, or generating a long video — you’ve almost certainly dealt with the polling problem. Your code sits in a loop, firing GET requests every few seconds asking, “Is the job done
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),
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
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
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
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
