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Introduction Every developer who uses an AI coding assistant hits the same wall: the assistant is brilliant for one session, then forgets everything by the next. You re-explain the stack, re-state the conventions, re-warn it about the same trap, and watch it make the same mistake it made last Tuesday. Repeating yourself to a tool that is supposed to save you time is a strange way to work. Claude Code solves this with a single file called CLAUDE.md: a markdown document that lo

Introduction Asking an LLM a question about your data usually means one of two bad options: paste the rows into the chat and hope the model does the arithmetic correctly, or write the analysis code yourself and lose the convenience of just asking. LLMs are unreliable calculators, and hand-written analysis defeats the point of a conversational assistant. In this tutorial we build a CSV data analyst using Microsoft’s AutoGen framework and OpenAI. You upload a CSV and ask a ques

Introduction Most chatbots forget everything the moment a session ends. Ask one for a book recommendation today and tomorrow it has no memory of what you already read, rated, or disliked, so it falls back to generic suggestions that ignore your actual taste. In this tutorial we build a personal book tracker using Mem0’s open-source local memory layer and the OpenAI Agents SDK. You tell it what you have read and how you felt about it, and it remembers that across every future

Introduction Writing product page copy is a task most developers outsource to a human copywriter or a single prompt. Neither approach demonstrates what a multi-agent system can do differently: break the task into focused, independent specialists, run them in parallel, and recombine their output into something no single prompt would produce as reliably. In this tutorial we build a product page copy generator using Google’s open-source Agent Development Kit (ADK). You provide a

Introduction Most browser-automation agent tutorials demo a narrow, single-purpose task and stop there, the agent finds one type of result on one type of site, and the tutorial never has to confront what happens when the page it’s scraping changes shape, or when the search engine itself starts treating the request as a bot. In this tutorial we build a real-time news research agent using GLM-5-Turbo, a tool-calling model from Z.AI, paired with a real Playwright-driven Chromium

Introduction Most “AI memory” tutorials show a single isolated call: add one fact, search for it, print the result. They rarely show a real conversational agent deciding for itself, turn by turn, whether something the user just said should be written to memory, recalled from memory, or neither. In this tutorial we build a reading companion using Supermemory, a hosted memory API, paired with the OpenAI Agents SDK. You chat with it the way you’d chat with a tutor: tell it what

Introduction Most “run this model locally” tutorials stop the moment the model produces any output at all. They download a file, start a server, send one test prompt, and call it done. They rarely cover what happens when that output is technically present but practically useless, because the model spent its entire response budget thinking instead of answering. In this tutorial we build a local writing assistant on top of Bonsai, PrismML’s 1-bit quantized language model, serve

Introduction Most “build an AI agent” tutorials show the happy path: write a skill, register it, call it, done. What they skip is the part where the agent confidently does the wrong thing anyway, in a different way every single time you try again, and you have to figure out why. This tutorial is the version that doesn’t skip that part. We build a customer feedback analyzer using OpenClaw, an orchestration layer that dispatches commands to registered skills, paired with OpenAI

Introduction Most LLM evaluation tutorials check whether a generated answer “sounds right” by asking another LLM to grade it. That works for tone and style, but it falls apart for tasks with an objectively correct answer. SQL generation is exactly that kind of task: a query either returns the right rows or it does not, and no amount of LLM-rubric grading can substitute for actually running the query. In this tutorial we build a promptfoo evaluation for a natural language to S

Business teams sit on huge tables of orders, sales, and profit, yet answering a simple question like “which market is most profitable?” usually means waiting on an analyst or building another pivot table. The gap between having the data and understanding it is where decisions slow down. Chat With Your Data closes that gap. It is a conversational analytics dashboard that pairs interactive charts with an AI assistant, so anyone can explore the numbers by clicking or simply by a

Introduction Content moderation at scale is one of the most operationally demanding problems in AI applications. Rule-based filters miss context and produce too many false positives. Fully manual review does not scale. A large language model can read text the way a human moderator would, understanding tone, context, and intent, and produce structured output that downstream systems can act on automatically. In this tutorial we build a FastAPI content moderation API that passes

Introduction Retrieval-Augmented Generation (RAG) pipelines are widely used to build question-answering systems grounded in private or domain-specific documents. But evaluating whether a RAG pipeline is actually working well is harder than building it. Traditional metrics like BLEU and ROUGE measure surface-level word overlap and miss the semantic quality of answers. Human review is accurate but expensive and slow at any meaningful scale. LLM-as-a-Judge sits between these two

Introduction NVIDIA Nemotron-3 is a family of open models built for reasoning, coding, chat, and agentic workflows. The Nano variant packs strong language understanding into a 4-billion-parameter model that can be fine-tuned on a single 24GB GPU, making it practical for teams who want to adapt a capable base model to their own domain without renting a large training cluster. In this tutorial, we fine-tune Nemotron-3-Nano-4B on a customer support dataset. After training, the m

Introduction Most AI tutorials show you a text box. You type, the model replies, and the whole exchange stays on screen. That covers the mechanics of calling an LLM, but it leaves out what makes voice AI feel genuinely different: the question comes from a microphone, the answer comes back as speech, and the whole thing happens without touching a keyboard. This tutorial builds a working voice AI agent from scratch in Python. Press Enter to start recording, speak your question,

Most readers skip your articles — not because the content is bad, but because reading takes time they don't have. This post breaks down how AI-powered blog-to-audio platforms work, from architecture to cost to rollout, and how a single article can become audio, a podcast episode, and multilingual content automatically. Includes a free downloadable PRD.

Introduction Before you build agents that use tools, remember conversations, or talk to other agents, it helps to start with the simplest possible thing an LLM app can do: take some text in, send it to a model with clear instructions, and return a useful result. In this tutorial, we build a Text Summarizer and Explainer, a terminal application that takes any block of text and processes it in one of three ways: a short summary, a plain language explanation, or a bulleted list

Introduction Most AI chatbot demos are stateless: every message you send is treated as the first. The model has no idea what you said three turns ago, cannot refer back to details you shared earlier, and cannot build a coherent conversation over time. This is the biggest gap between a demo and a real chatbot. In this tutorial, we fix that. We build an AI Chatbot with Memory that maintains the full conversation history across every turn, passes it to the model on each request,

In this guide, you’ll create a fully functional local RAG pipeline in Python that can: Read custom documents Convert them into embeddings Store them in a vector database Retrieve relevant context Generate grounded answers using an LLM By the end, you’ll have a complete command-line RAG application running locally on your machine.

Introduction Understanding how people feel about a product, a service, or an idea is one of the most valuable things a business can do, and it is also one of the tasks where AI consistently outperforms rule-based approaches. A single review can carry joy, frustration, and sarcasm all at once. A rules-based keyword matcher misses this nuance. An LLM does not. In this tutorial, we build a Sentiment Analysis Agent. It is a terminal application that takes any text input, sends it

A Voice AI receptionist is an AI-powered system that answers phone calls — and increasingly, in-app and website voice interactions — on behalf of a clinic, and carries out real conversations with patients in natural, spoken language. It's not an IVR menu ("Press 1 for billing, press 2 for appointments"). It's a system that listens, understands intent, responds conversationally, and — most importantly — takes action on the patient's behalf.