Introduction The promise of large language models (LLMs) is compelling: intelligent agents that can handle customer interactions, provide guidance, and automate complex tasks. Yet in practice, developers face a critical challenge that no amount of prompt engineering can fully solve. An AI agent that performs flawlessly in testing often fails spectacularly in production—ignoring business rules, hallucinating information, and delivering inconsistent responses that damage brand reputation and customer trust.[3] This gap between prototype and production is where Parlant enters the picture. Built by Emcie, a startup founded by Yam Marcovitz and staffed by engineers and NLP researchers from Microsoft, Check Point, and the Weizmann Institute of Science, Parlant is an open-source framework that fundamentally rethinks how developers build conversational AI agents.[3] Rather than fighting with prompts, Parlant teaches agents how to behave through structured, programmable guidelines, journeys, and guardrails—making it possible to deploy agents at scale without sacrificing control or compliance.[3] ...

Large Language Models (LLMs) like GPT-2 and BERT have revolutionized natural language processing, and TensorFlow provides powerful tools to build, train, and deploy them. This detailed guide walks you through using TensorFlow and Keras for LLMs—from basics to advanced transformer architectures, fine-tuning pipelines, and on-device deployment.[1][2][4] Whether you’re prototyping a sentiment analyzer or fine-tuning GPT-2 for custom tasks, TensorFlow’s high-level Keras API simplifies complex workflows while offering low-level control for optimization.[1][2] ...

Vector search in PostgreSQL has gone from “experimental hack” to a serious production option, largely thanks to the pgvector extension. But as teams push from thousands to tens or hundreds of millions of embeddings, a natural question emerges: How do you keep vector search fast and cost‑effective as the dataset grows, without adding yet another external database? This is exactly the problem pgvectorscale is designed to address. In this article, we’ll take a detailed look at pgvectorscale: what it is, how it fits into the Postgres ecosystem, how it scales vector search, and what trade‑offs you should understand before using it. ...

Large Language Models (LLMs) power everything from chatbots to code generation, but scaling them for training, fine-tuning, and inference demands distributed computing expertise. Ray, an open-source framework, simplifies this with libraries like Ray LLM, Ray Serve, Ray Train, and Ray Data, enabling efficient handling of massive workloads across GPU clusters.[1][5] This guide takes you from zero knowledge to hero status, covering installation, core concepts, hands-on examples, and production deployment. What is Ray and Why Use It for LLMs? Ray is a unified framework for scaling AI and Python workloads, eliminating the need for multiple tools across your ML pipeline.[5] For LLMs, Ray LLM builds on Ray to optimize training and serving through distributed execution, model parallelism, and high-performance inference.[1] ...

Large Language Models (LLMs) power tools like ChatGPT, revolutionizing how we interact with AI. This zero-to-hero guide takes you from foundational machine learning concepts to building, fine-tuning, and deploying LLMs, with curated link resources for hands-on learning.[1][2][3] Whether you’re a beginner with basic Python skills or an intermediate learner aiming for expertise, this post provides a structured path. We’ll cover theory, practical implementations, and pitfalls, drawing from top courses and tutorials. ...