Concept

AI Alignment

The effort to ensure AI systems behave according to human values and intentions. As models become more capable and autonomous, alignment has evolved from a theoretical concern to a practical engineering discipline. Key approaches include Constitutional AI (Anthropic), RLHF (Reinforcement Learning from Human Feedback), and red-teaming. The 2026 landscape features companies like Anthropic making alignment their core mission while competing labs invest varying levels of resources into safety research.

AI Memory Systems

AI Video Generation

The creation of video content from text prompts, images, or other video. In 2026, AI video generation has progressed from novelty to professional-grade tool. Models like Runway Gen-4.5, Sora, and Wan 2.2 produce minutes-long clips with consistent characters, realistic physics, and cinematic camera movements. The technology is beginning to reshape filmmaking, advertising, and content creation at every level.

AI-Powered Coding

The use of AI models to write, debug, refactor, and review code. In 2026, AI-powered coding has matured from autocomplete suggestions to full autonomous software engineering agents. Tools like Cursor and Devin can understand entire codebases, implement features from natural language descriptions, and iterate on their own output. Benchmarks like SWE-Bench measure models' ability to solve real GitHub issues, with top models resolving over 50% of production bugs autonomously.

Agentic AI

AI systems that go beyond simple prompt-response cycles to autonomously plan, execute multi-step tasks, use tools, and adapt their approach based on intermediate results. In 2026, agentic AI has become the dominant paradigm, with frameworks like OpenAI's Agents SDK, LangGraph, and Google's ADK enabling developers to build AI workflows that can browse the web, write code, manage databases, and orchestrate other AI models — all with minimal human intervention.

Chain-of-Thought

Context Windows

The amount of text (measured in tokens) a model can process in a single interaction. Context window size has exploded from thousands to millions of tokens in 2026, fundamentally changing what's possible. Models can now ingest entire codebases, legal document archives, or hours of transcripts in one go. Llama 4 Scout leads with a 10-million-token window, while Gemini 3 Pro offers 1 million tokens natively integrated into Google Workspace.

Fine-Tuning

Founder Interview

Hallucination

Mixture-of-Experts

An architecture where a model contains multiple specialized 'expert' sub-networks, but only activates a subset of them for any given input. This allows models to have massive total parameter counts while keeping inference costs manageable — only the relevant experts fire for each token. MoE is the architecture behind Llama 4's ability to pack 2 trillion parameters while only using 17-288 billion per forward pass, enabling frontier-class performance at a fraction of the compute cost of dense models.

Model Context Protocol (MCP)

The Model Context Protocol — an open standard for connecting AI models to external tools, data sources, and services. Originally created by Anthropic, MCP has become the universal integration layer for AI applications in 2026, enabling models to interact with databases, APIs, code editors, and more through a standardized interface.

Open-Source AI

AI models whose weights are publicly released, enabling anyone to download, run, fine-tune, and deploy them. The open-source AI movement — led by Meta's Llama, DeepSeek, and Stability AI — has democratized access to frontier-class capabilities. Open models run on consumer hardware, allow data sovereignty, and spawn vibrant ecosystems of community-built fine-tunes. The tension between open and closed approaches remains one of the defining debates in AI development.

Prompt Engineering

Prompt Injection

RAG (Retrieval-Augmented Generation)

Synthetic Data

Temperature (AI)

Tokens

Tool Use & MCP

The ability of AI models to interact with external software, APIs, databases, and services. Tool use transforms LLMs from text generators into action-taking agents. Anthropic's Model Context Protocol (MCP) has emerged as the leading standard for connecting AI systems with external tools, providing a universal interface that works across models and platforms. This capability is foundational to agentic AI workflows.

Vibe Coding