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The usual way an AI agent uses tools is simple. The model picks a tool, the system runs it, the result goes back to the model, and the cycle repeats. It works well for a handful of tools and short tasks. It strains as the number of tools grows. Every tool definition sits in the model's context, and every intermediate result passes through the model on its way to the next step. Code Mode MCP takes a different route. Instead of calling tools one at a time, the agent writes a sm

Most AI systems today are built around one idea: a large language model reads a prompt and writes text, one token at a time. That approach is flexible, and it has shaped how teams think about every AI problem. Many decisions inside business software do not need flexibility. They need a fast, reliable answer to a narrow question, such as which queue a request belongs in or whether a case should be escalated. In September 2026, TypeSafe introduced Jev, a model it describes as t

Large language models are impressive, and many teams reach for one first. For a surprising number of business problems, a smaller and older approach does the job better, faster, and for far less money. Classic machine learning means models such as gradient boosted trees, logistic regression, and random forests, trained on structured data for one specific task. They are not glamorous, and in the right setting they are hard to beat. This blog explains when classic ML wins over

When an AI system makes a decision, someone will eventually ask why. A customer disputes a result, a regulator requests evidence, or a team member wants to know why the assistant behaved oddly last Tuesday. Without a good record, the honest answer is that nobody knows. With one, the question can be answered in minutes. This blog explains what to record in audit logs for AI decisions, why each item matters, and how to store the logs safely. It reflects the approach of the Code

A RAG bot that gets only about three answers in ten right is not rare. It is the normal result when a team connects a language model to a pile of documents, sees a few good demos, and ships. The assistant sounds fluent, so the wrong answers are easy to miss until a customer, an auditor, or an employee acts on one. The good news is that low accuracy in retrieval-augmented generation is usually a pipeline problem, not a model problem, and pipeline problems can be measured and f

Most AI assistants stop working the moment the conversation ends. You ask, they answer, and the next step waits until you come back. In September 2026, OpenAI introduced dots, always-on agents inside ChatGPT that keep working toward a goal after the chat is over, using their own cloud computer, a browser, and the apps you connect. This blog explains what OpenAI dots are, how they work, who can use them today, which safety controls come with them, the risks enterprises still n

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 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,

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,

You followed the tutorial. You copied the code. Your AI chatbot answers questions perfectly on your laptop. Then you try to ship it. The API times out under real load. The vector search returns garbage when the query doesn't match training examples exactly. There is no error handling, so one bad request crashes the whole service. You have no idea if it is even working correctly because there is no logging. The chunking strategy that worked on your sample PDF breaks on a scann

A production-ready full-stack inventory management system that eliminates spreadsheet chaos, provides real-time stock visibility, and automatically alerts you when inventory falls below reorder thresholds.

Course: RAG Evaluation Level: Beginner to Medium Type: Individual Duration: 5 to 7 days Objective This assignment tests your ability to build the two foundational components of any RAG evaluation workflow: a golden dataset and a retrieval quality report. Without a golden dataset, no evaluation metric has meaning. Without retrieval evaluation, you cannot tell whether failures come from the retrieval stage or the generation stage. By completing this assignment, you will have a

Course: Docker for AI Apps Level: Medium to Advanced Type: Individual Duration: 7 to 10 days Objective This assignment tests your ability to design and operate a multi-container Docker system for an AI application. You will configure container-to-container networking using a user-defined bridge network, orchestrate a multi-service stack with Docker Compose, build and containerize a FastAPI AI REST API with session management and health checks, apply Docker best practices incl

Course: Docker for AI Apps Level: Beginner to Medium Type: Individual Duration: 5 to 7 days Objective This assignment tests your ability to work with Docker's core building blocks: running and inspecting containers, writing a production-ready Dockerfile, containerizing a Python AI application, and persisting data across container restarts using named volumes. By completing this assignment, you will have built and deployed a fully containerized multi-turn AI chatbot that retai

Course: RAG from Scratch Level: Medium to Advanced Type: Individual Duration: 7 to 10 days Objective This assignment tests your ability to build the retrieval and generation stages of a RAG pipeline from scratch. You will implement cosine similarity without external vector search libraries, build a similarity search function, design a grounding-focused prompt template, and assemble a complete end-to-end RAG system that retrieves context and generates accurate, grounded answer

Course: RAG from Scratch Level: Beginner to Medium Type: Individual Duration: 5 to 7 days Objective This assignment tests your ability to build the foundational stages of a RAG pipeline: loading documents, extracting clean text, attaching metadata, enriching documents with LLM-generated keywords, and splitting them into retrievable chunks. By completing this assignment, you will have built a reusable knowledge base preparation pipeline that you can apply to any document colle

Knowledge workers waste 19% of their time searching for information. Enterprise RAG Systems change that—delivering instant, accurate answers from your documents with verifiable sources. Discover how hybrid search, multi-document intelligence, and hallucination prevention turn scattered institutional knowledge into your competitive advantage.

Executive Summary The Project Research Assistant is a comprehensive multi-agent system designed to streamline the entire research and...

The tech landscape is constantly evolving, and developers are always faced with the question: "What should I learn next?" For MERN stack...