In this article, you will learn how to build multimodal AI capabilities — image classification, image captioning, and speech transcription — that run entirely in the browser using Transformers.js, with no server, no API key, and no data leaving the user’s device.
In this article, you will learn what AgentOps is, how it differs from traditional LLM monitoring, and how to build a production-ready observability stack for autonomous AI agents.
In this article, you will learn how sentence embeddings work and how to build a fully client-side semantic search engine using Transformers.js, with no server, no API key, and no backend infrastructure required.
In this article, you will learn how to build production-grade LLM systems by following a structured six-step LLMOps roadmap covering observability, evaluation, cost control, and agent orchestration. Topics we will cover include: How LLMOps differs from traditional MLOps, and what foundational skills you need before touching any LLMOps tooling. How to instrument LLM calls with […]
In this article, you will learn what agentic programming is, how production-grade AI agents are built from the ground up, and what it takes to go from zero experience to shipping a real agent in production.
In this article, you will learn how prompt engineering changes fundamentally when applied to agentic AI systems, and what principles and patterns enable reliable agent behavior at scale.
In this article, you will learn how to build a fully functional AI agent that runs entirely on your own machine using small language models, with no internet connection and no API costs required. Topics we will cover include: What AI agents and small language models are, and why running them locally is a practical […]
In this article, you will learn how to build efficient long-context retrieval-augmented generation (RAG) systems using modern techniques that address attention limitations and cost challenges.
In this article, you will learn how machine learning is evolving in 2026 from prediction-focused systems into deeply integrated, action-oriented systems that drive real-world workflows.
In this article, you will learn why large language model hallucinations happen and how to reduce them using system-level techniques that go beyond prompt engineering.
