What is the Machine Payments Protocol (MPP)? The Machine Payments Protocol (MPP) is a payment infrastructure protocol developed by Stripe and Tempo that enables AI agents and automated systems to execute payments programmatically across multiple payment rails. Launched on March 18, 2026, MPP provides a unified interface for agent-driven commerce, allowing AI systems to initiate,…
Defining the Agentic Economy Imagine you are planning a complex business trip. Today, you might use a chatbot to find flight options, but you still have to manually book the tickets, reserve the hotel, and update your calendar. In the emerging agentic economy, you don’t do the booking. Instead, you delegate the entire workflow to…
Reinforcement learning teaches AI to align with human preferences through rewards and penalties. Learn how RLHF, DPO, GRPO, and RLVR work, why reward hacking matters, and how these techniques power the agentic economy.
What is a blockchain? A blockchain is a database maintained by a network of independent computers — called nodes — that agree on every update through a shared protocol, rather than relying on a single central authority [1]. Data is stored in blocks that are cryptographically linked to each other in sequence, making the history…
Prompt engineering is the practice of designing and refining the instructions, context, and examples given to a large language model to produce accurate, relevant, and useful outputs—bridging human intent and model behavior through structured communication.
What is the Transformer Architecture? The Transformer architecture is the engine room behind the current wave of artificial intelligence. Introduced in the seminal paper ‘Attention Is All You Need’ by Vaswani et al., this design fundamentally changed how machines process language. Before this, models relied on sequential processing, which was slow and struggled to remember…
Understanding Embeddings and Vector Databases In the world of modern AI, computers need a way to understand the ‘meaning’ behind human language, images, and audio. Embeddings are the bridge between raw data and machine understanding. They are numerical vector representations—essentially long lists of numbers—produced by machine learning models. In this mathematical space, items that share…
Think of training data like the textbooks and practice problems a student works through before an exam. The student doesn’t memorize every answer—they learn underlying patterns, relationships, and problem-solving strategies that they can apply to new questions they’ve never seen before. Training data plays the same role for an AI model: it provides the examples…
Context window The context window (also referred to as context length, context size, or context limit) is the maximum amount of information—measured in tokens—that a large language model can process in a single request. Think of it as the model’s “working memory” for a specific conversation or task. Everything you provide as input, plus everything…
A Large Language Model (LLM) is a neural network trained on massive text data to understand and generate human language. Learn how transformers, tokens, pre-training, and alignment work—with examples like GPT, Claude, and Gemini.