AI System Design

Tired of AI making things up? Let's go beyond basic chatbots. In this guide, we'll build an agent that retrieves evidence, answers questions, and then scores its own work for accuracy.

AI agents are powerful, but they can be incredibly expensive. Let's walk through how to build a smarter agent that balances quality with real-world constraints like token limits, latency, and API costs.

Tired of simple word filters failing? Let's walk through how to build a smarter, multi-layered defense for your LLM that can spot clever jailbreaks, paraphrased attacks, and adversarial prompts that other systems miss.

Ever feel that awkward pause when talking to a voice assistant? We're breaking down the tech that eliminates that lag, showing you how to build a fully streaming voice agent that feels truly conversational.

Billions are being spent on enterprise AI, yet nearly half of all projects are abandoned. The culprit isn't the AI—it's the "successful" pilot projects that create a false sense of security and set you up for failure in the real world.

AI agents need a reliable way to communicate. Let's ditch the complex, stateful sessions and build a clean, secure, and asynchronous protocol that lets our agents work together, scale effortlessly, and handle long-running tasks without breaking a sweat.

Your LLM-powered app feels like a black box—when it fails, you're left guessing why. Let's pull back the curtain on AI observability and show you how to finally understand, monitor, and debug what's really happening inside your AI.

We've all been obsessed with bigger, more powerful AI models. But what if that's the wrong race? A new AI software engineer from Meta and Harvard shows that the system *around* the model is what truly matters, and the results are pretty stunning.

Go beyond simple AI agents. Let's walk through how to build a sophisticated system with LangGraph and OpenAI that can deliberate, remember past experiences, and learn from its actions.

Ever wonder why training massive AI models can be so difficult? DeepSeek AI found a major source of instability and fixed it by dusting off a clever matrix normalization algorithm from 1967. Here's how it works.

AI agents with powerful tools are amazing, but how do you stop them from going rogue? We built an AI "fight club"—a system where agents test, attack, and judge each other to find security flaws before they become disasters.

We've all hit the context window limit with LLMs. A new approach called Recursive Language Models (RLMs) flips the script, treating massive prompts not as a wall of text to read, but as an environment to explore with code. Here’s how it works and why it’s a huge deal.