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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 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 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,

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

Introduction Most AI assistants are good at answering questions but poor at remembering what you asked them to do yesterday. They have no persistent state across conversations — every session starts fresh. The Model Context Protocol (MCP) solves this by letting you build external tools that Claude (or any MCP-compatible host) can call during a conversation, with results persisted wherever you choose. In this tutorial, we build an MCP To-Do List Manager — a local server that g

Introduction Not every piece of text needs the most powerful language model to check it. A short sentence with a grammar error can be caught by a fast, cheap model in under a second. Only long, complex writing with structural and coherence problems genuinely benefits from the most capable model available. Tiered model routing applies this logic systematically. Short to medium text (up to 100 words) goes to GPT-4o-mini for grammar and clarity. If it detects structural or coher

Introduction Finding the next great book is harder than it sounds. Generic bestseller lists ignore your taste, and search engines return the same ten titles for every query. What most readers need is a recommendation that actually understands them — their preferred themes, emotional tone, narrative pace, and the books they already love. In this tutorial, we build an AI-powered Book Recommender using Kimi K2, Moonshot AI’s flagship agentic model. The user describes their readi

The AI Analytics and Reporting Platform is a self-contained web application that solves all of this in one pipeline. Users upload a dataset, receive automatic schema profiling, create an analysis plan (manually or via a natural language prompt), execute it through a deterministic engine, and get professional DOCX and PPTX reports — with every step logged and reproducible.

AI Constraint Optimizer is a full-stack application that accepts natural language descriptions of optimization problems, uses GPT-4o to parse them into a structured representation, selects the best algorithm from a library of eight solvers, runs the solver in real-time while streaming a live narrated explanation to the browser, and then provides an interactive Q&A interface so users can interrogate the solution.

Introduction Modern satellite constellations generate petabytes of multispectral, hyperspectral, SAR, and LiDAR data every day, far outpacing the capacity of traditional analysis methods. Remote sensing professionals must interpret this imagery against historical baselines, evolving scientific literature, environmental benchmarks, and mission-specific requirements simultaneously. Satellite Data Analysis Systems powered by Retrieval-Augmented Generation (RAG) address this by d

Introduction Loan underwriting requires the rapid processing of vast financial documents, regulatory guidelines, and market data under tight deadlines, a challenge that rigid scoring models and manual review workflows are ill-equipped to handle. Underwriters must assess creditworthiness, collateral quality, and compliance requirements while keeping pace with constantly shifting lending regulations and economic conditions. Loan Underwriting Systems powered by Retrieval-Augment

Introduction Veterinary professionals must deliver accurate diagnoses across many species with unique biological differences, while keeping up with constantly evolving research and treatment guidelines. Retrieval Augmented Generation powered diagnostic systems provide real time access to veterinary literature, species specific protocols, diagnostic data, and patient history. By retrieving and synthesizing the most relevant and up to date evidence, these systems deliver contex

Introduction Traditional AI interactions require rigid command structures limiting natural communication. Developers struggle to extract optimal responses from language models without specialized knowledge. Manual experimentation with different prompting approaches consumes significant development time. Inconsistent model outputs complicate production deployment and user experience. Llama 3:8B Chat transforms AI interactions through instruction-tuned conversational capabiliti

Introduction Traditional AI models process either text or images separately requiring multiple systems for comprehensive analysis. Businesses need solutions understanding visual content and answering questions about images. Manual image description and analysis consume significant time and resources. Language barriers complicate global image understanding applications. Gemma 3:4B Multimodal Model transforms visual understanding through combined vision and language processing.