AI Knowledge Hub

Learn AI

Your comprehensive guide to understanding artificial intelligence. From benchmarks and techniques to research and the mathematics that power modern AI systems.

Build a Frontier Model

The full 11-chapter pipeline used by frontier labs in 2026 — data, architecture, pretraining, RLVR, reasoning, agents, multimodal, inference, infrastructure, evaluation. References 100+ papers from 2017 through 2026.

PretrainingRLVRMoEStargateMCP
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Benchmarks & Metrics

Understand how AI models are evaluated and compared. From MMLU to HumanEval, learn about standardized tests, ELO ratings, and leaderboards that measure LLM performance.

MMLUHumanEvalELO RatingsChatbot Arena
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Improvement Techniques

Master the methods to enhance AI model performance. From prompt engineering to RAG, fine-tuning, and RLHF—learn when and how to apply each optimization strategy.

Prompt EngineeringRAGFine-TuningRLHF
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AI Modalities

Explore the different ways AI can perceive and generate content. Text, vision, audio, video—and emerging modalities like touch and brain-computer interfaces.

MultimodalVisionAudioVideo
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Research Papers

Dive into the foundational and cutting-edge research shaping AI. From "Attention Is All You Need" to the latest 2025 breakthroughs in reasoning and architecture.

TransformersRLHFMambaDiffusion
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Companies & People

Meet the organizations and researchers driving AI forward. OpenAI, Anthropic, DeepMind, and the brilliant minds behind today's most powerful models.

LabsResearchersStartupsHardware
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Math for AI

Build the mathematical foundation for understanding AI. Linear algebra, calculus, probability, and optimization—the essential concepts behind machine learning.

Linear AlgebraCalculusProbabilityOptimization
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Suggested Learning Path

New to AI? Follow this recommended progression to build your understanding from the ground up.

1
Math for AI

Start with the mathematical foundations. Understanding linear algebra, calculus, and probability is essential for grasping how AI models work.

2
Research Papers

Read the foundational papers like "Attention Is All You Need" to understand the transformer architecture that powers modern AI.

3
Benchmarks & Metrics

Learn how AI models are evaluated and compared. Understanding benchmarks helps you assess model capabilities for your use cases.

4
Improvement Techniques

Master practical methods like RAG, fine-tuning, RLVR, and MCP tool-use for your specific applications.

5
Build a Frontier Model

Capstone. Walk through the actual pipeline a frontier lab uses — data, architecture, pretraining, RLVR, reasoning, agents, multimodal, inference, infrastructure, and safety.

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