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Building an AI MVP costs more than a standard SaaS MVP — but not for the reason most founders assume. The model itself is cheap. OpenAI, Anthropic, and Google have cut API pricing by 60–80% over the past 18 months. What drives AI MVP cost is everything around the model: the data pipeline, the retrieval architecture, the prompt engineering, the evaluation harness, and the infrastructure to keep it accurate in production. This guide breaks down exactly what an AI MVP costs in 2

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

Sales reps lose deals in the moments they can't see themselves — talking over the prospect, missing a buying signal, going silent when objections hit. A coach whispering in their ear would fix most of that. Most companies can't afford one per rep. An AI agent listening to the live call can be that coach. Here's how to build one using the OpenAI Realtime API for low-latency audio understanding and LangGraph to orchestrate the coaching logic. Why This Stack OpenAI Realtime API

Use Case We Solve: Private Multimodal Search System for Book & Document Libraries The Problem Collectors, archives, law firms, and publishers often hold large scanned libraries (page-by-page JPGs/PDFs) and need to search them like Google — by keyword, concept, or even a photo — without any of that content touching the public web or a third-party index. Typical Requirements We Address Keyword and exact-text search across scanned pages Semantic search for similar quotes / relat

For most of the last decade, the most prestigious job in tech sat inside the research lab. The engineers building the model — the ones pushing benchmark scores, designing new architectures, training larger systems — held the highest status and, often, the highest pay. That has quietly flipped. In 2026, the fastest-growing, highest-leverage role in AI isn't the one building the model. It's the one getting the model to actually work inside a real company, with real data, real l

What Is Loop Engineering? Loop engineering is the discipline of building small automated control systems — loops — that drive AI coding agents on your behalf, instead of you prompting the agent manually, turn by turn. For roughly two years, working with a coding agent meant: write a prompt, read the output, write the next prompt, repeat. You held the steering wheel the entire time. Loop engineering replaces that. You design a system once — a loop with a defined goal, a way to

What Is Loop Engineering? Loop engineering is the practice of building a small automated system — a loop — that drives an AI coding agent for you, instead of you manually prompting it turn by turn. The old way: You write a prompt → Agent responds → You read it → You write the next prompt → repeat The loop engineering way: You write a loop → Loop prompts the agent → Loop reads the output → Loop decides "done or not done?" → Loop re-prompts automatically → repeat until done You

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,

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Every AI team building a product in 2026 eventually hits the same wall. The model works. The demo is good. Investors are happy. And then someone asks the question that changes everything: "How do we know it's actually getting better?" Or worse — six months later: "Why did it get worse after the last fine-tune?" This is the moment a team discovers that building an LLM product and doing LLM research engineering are two different disciplines, staffed by two different kinds of pe