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The Model Context Protocol makes it easy to connect AI assistants to company systems, and that ease is exactly why security needs attention early. An MCP server can expose databases, files, tickets, and actions to a model that reads untrusted text and sometimes follows it. If the server is open, over-permissioned, or silent, one clever message can turn a helpful assistant into a data leak. This blog explains how to secure MCP servers around three controls: authentication, per

Choosing between a small language model and a frontier model is one of the most expensive decisions in a production AI project, and it is often made on reputation rather than evidence. Frontier models are more capable on hard problems. Small models are cheaper, faster, and easier to run privately. The right choice depends on the task, and most businesses have several different tasks. This blog compares the two on five common business tasks, explains where each tends to do wel

Multi-agent systems are an attractive idea. Give each task its own specialist, let a manager agent coordinate them, and the design looks like a small company of experts. In practice, many teams find that the company-of-agents design adds more cost, delay, and confusion than it removes. A growing number of them end up replacing it with something much simpler: one agent that handles the judgment, and ordinary code that handles everything else. This blog explains why that change

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

Most enterprise AI systems still rely on a large language model to make decisions that are, at their core, simple: is this invoice valid, does this application meet the criteria, which department should review this document? Using a chat model for these choices works, but it is slow, expensive at volume, and it returns text that software must parse before anything can happen. In September 2026, TypeSafe introduced Jev, the first model in a new category it calls System One, bu

Every enterprise AI project hits the same wall: the model is capable, but it cannot reach the systems where the work happens. Connecting an assistant to a ticketing tool, a document store, a database, and a CRM has usually meant a custom integration for each pairing, rebuilt whenever anything changes. The Model Context Protocol (MCP) is an open standard that replaces this with one common way for AI applications to discover and use tools and data, which is why it is now a prio

A prompt that sounds better to the person who wrote it is not the same thing as a prompt that performs better in production. Many teams still judge prompt changes by reading a handful of outputs and deciding which one feels right, but that approach cannot tell whether a change improved accuracy, quietly increased hallucinations, or opened the door to a prompt injection attack. Measuring prompt optimization properly replaces opinion with evidence, and for leaders funding AI pr

Most enterprise AI assistants answer questions by finding the passages that look most similar to the question and handing them to a language model. That works well for lookups, but it struggles when the answer depends on how things are connected, such as which systems fail when one service goes down, or what the main themes are across thousands of documents. GraphRAG addresses this by adding a knowledge graph to retrieval, so the system can follow relationships and reason acr

An AI system that reads loan documents, summarizes customer emails, or drafts responses to account queries can save a bank thousands of hours, but only if it can be trusted with sensitive data and cannot be manipulated into doing something it should not. A secure AI banking operations workflow is the set of controls, checkpoints, and monitoring that makes that trust possible, and for banking leaders it is less about the model itself and more about everything built around it.

Picture two lawyers using AI for the same research task. The first receives a confident, well-formatted answer with five case citations and files it without checking. Two of the cases do not exist, and the lawyer ends up explaining the mistake to a judge. The second uses an assistant that finds the relevant cases in minutes, shows the exact passage behind every claim, and flags the one point it could not support. A task that once took hours takes a fraction of the time, and e

Imagine a shopper who buys a washing machine online for S$2,000 and receives a broken one. The seller refuses a refund, so the shopper takes the dispute to a small claims tribunal. Lawyers are not allowed in that room. The evidence is a pile of WhatsApp messages and emails. The forms are confusing, and the documents may not even be in the shopper's first language. For decades, people in this position were largely on their own. Today, courts in several countries are using arti

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

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