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AI Foundations0%

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← AI Foundations

0 of 173 complete

○Lesson 301What Is Intelligence, Really? A Working Framework
○Lesson 302The Full Machine Learning Pipeline
○Lesson 303Transformers Under the Hood
○Lesson 304The Economics and Ethics of Training Data
○Lesson 305Scaling Laws and Compute-Optimal Training
○Lesson 306Emergence, Capability Forecasting, and Safety
○Lesson 307Narrow, General, AGI, ASI: What We Mean and Why It Matters
○Lesson 308Probabilistic Systems: Why LLMs Do Not Act Like Code
○Lesson 309Open vs. Closed Models: Philosophy and Strategy
○Lesson 310The Three Ingredients: Data, Compute, Algorithms (Capstone)
○Lesson 591Calculus with AI: Limits, Derivatives, and Not Getting Lost
○Lesson 594AP Biology: Using AI to Survive the Vocab Tsunami
○Lesson 595AP Chemistry: Stoichiometry Without the Tears
○Lesson 596AP Physics: Free-Body Diagrams and Walkthroughs
○Lesson 600Debate Prep: Researching Both Sides Fast
○Lesson 860MMLU, GPQA, HumanEval, SWE-bench: The Core Four
○Lesson 861How Chatbot Arena Works
○Lesson 862Elo Ratings for AI
○Lesson 863Benchmark Saturation
○Lesson 864Benchmark Contamination
○Lesson 865Private vs. Public Evaluations
○Lesson 866Agent Benchmarks: WebArena, GAIA, OSWorld
○Lesson 867Multimodal Benchmarks
○Lesson 868Why You Should Not Trust the Leaderboard
○Lesson 872LLM-as-Judge: Promise and Pitfalls
○Lesson 873Designing Your Own Eval
○Lesson 874Golden-Dataset Curation
○Lesson 875Regression Testing for Prompts
○Lesson 876Uncertainty Quantification in LLMs
○Lesson 877Calibration
○Lesson 878Red-Team Evals
○Lesson 887Capability Evaluation vs. Safety Evaluation
○Lesson 888The Jagged Frontier of AI Capabilities
○Lesson 889Grokking: Learning That Snaps Into Place
○Lesson 890Emergence vs. Scaling
○Lesson 891Transfer Learning
○Lesson 892In-Context Learning
○Lesson 893Chain-of-Thought Mechanics
○Lesson 894Why Models Are Hard to Reason About
○Lesson 895Running a Literature Review With AI
○Lesson 896Keeping Current: Newsletters, Feeds, and Lists
○Lesson 897Taking Good Notes With NotebookLM
○Lesson 898Citing AI-Assisted Work Honestly
○Lesson 899Running Your Own Small Experiment
○Lesson 900Writing Up Your Findings
○Lesson 915Synthetic Data: When AI Trains on AI
○Lesson 916Labeling at Scale: The Hidden Human Layer
○Lesson 917Big Data vs. Good Data: The Tradeoff
○Lesson 918Data Cards: The Label on Your Dataset
○Lesson 919Representation Bias: Who Is in the Data?
○Lesson 920Measurement Bias: When the Ruler Is Bent
○Lesson 921Historical Bias: The COMPAS Case Study
○Lesson 922Label Noise: When Your Ground Truth Is Wrong
○Lesson 923Inter-Annotator Agreement: Measuring Reality
○Lesson 924Underrepresented Groups: Building Inclusive Datasets
○Lesson 925Geographic Bias: The West Dominates
○Lesson 926Language Bias: Why English Dominates AI
○Lesson 928Debiasing: What Actually Works and What Does Not
○Lesson 929Mean, Median, Mode: Three Kinds of Average
○Lesson 930Variance and Standard Deviation: How Spread Out?
○Lesson 931Distributions: Normal, Power-Law, and Bimodal
○Lesson 932Log-Scale Thinking: When Linear Lies
○Lesson 933Simpson's Paradox: When Aggregated Data Lies
○Lesson 934Outliers: Keep Them, Remove Them, or Investigate?
○Lesson 935Resampling: Making Data Work Harder
○Lesson 936Bootstrapping: Confidence Without a Formula
○Lesson 937Who Owns the Data in a Dataset?
○Lesson 938Copyright vs. Terms of Service: Two Different Fights
○Lesson 940The Data Broker Ecosystem: The Shadow Industry
○Lesson 942robots.txt and ai.txt: The Web's Consent Signals
○Lesson 943Licensing Your Own Datasets
○Lesson 944Anonymization and Why It Often Fails
○Lesson 945Your First Dataset Project, End to End
○Lesson 946Jupyter Notebook Basics
○Lesson 947Pandas Fundamentals in 40 Minutes
○Lesson 948Reading and Writing CSV and JSON in Python
○Lesson 949Creating Your First Small Labeled Dataset
○Lesson 950Sharing Datasets on Hugging Face Hub
○Lesson 954Shannon and the Birth of Information
○Lesson 959The Lighthill Report and the First Winter
○Lesson 962Backpropagation Rediscovered, 1986
○Lesson 965AlexNet and the Deep Learning Revolution
○Lesson 968ResNets and the Depth Breakthrough
○Lesson 969Attention Is All You Need, 2017
○Lesson 971GPT-3 and the Scaling Laws
○Lesson 974Searle's Chinese Room: Understanding Without Meaning?
○Lesson 975The Arc of AI: Patterns Across Seventy Years
○Lesson 1597College Admissions Essays Without Lying
○Lesson 1599SAT/ACT Prep — Drilling Weak Spots
○Lesson 1602AI For College Research (Beyond ChatGPT)
○Lesson 1607AI For Fitness And Nutrition Planning
○Lesson 1618AI Literacy On A Tight Budget — Free Tools
○Lesson 2508Civics and Government: AI for Understanding the News
○Lesson 2509AP Computer Science A: Learning Java Without Cheating
○Lesson 41900Attention deep dive: queries, keys, values, and why it works
○Lesson 41901Tokenization economics: why your bill depends on the tokenizer
○Lesson 41902RLHF vs DPO: aligning models without breaking them
○Lesson 41903Context window engineering: more is not always better
○Lesson 41904Fine-tuning vs RAG: choosing the right knob
○Lesson 41905Evaluation suite fundamentals: what to measure and how
○Lesson 41906Model distillation fundamentals: smaller, faster, mostly as good
○Lesson 41907Quantization fundamentals: bits, accuracy, and serving cost
○Lesson 41908Prompt injection fundamentals: trust boundaries in agent systems
○Lesson 41909Agent loop fundamentals: planning, tools, and stop conditions
○Lesson 43800Mixture-of-Experts: Why MoE Models Behave Differently
○Lesson 43801Speculative Decoding: Latency Wins Without Quality Loss
○Lesson 43802FlashAttention: Why Memory Layout Beat Math
○Lesson 43803Context Rot: Why Long-Context Models Still Lose Information
○Lesson 43804Instruction-Following Evaluation: Beyond Single-Turn Tests
○Lesson 43805Tool-Use Evaluation: Building Reliable Agent Benchmarks
○Lesson 43806RAG Failure Mode Taxonomy: A Diagnostic Framework
○Lesson 43807Jailbreak Categories: Mapping the Adversarial Surface
○Lesson 43808Tokenizer Impact: Why Two Models Read the Same Text Differently
○Lesson 43809Distillation Tradeoffs: When Smaller Models Quietly Lose
○Lesson 45720Grouped-Query Attention: Why Modern Models Use It
○Lesson 45721RoPE Scaling: How Long-Context Models Get Their Reach
○Lesson 45722Constitutional AI: Self-Critique as a Training Signal
○Lesson 45723DPO vs PPO: Why Direct Preference Optimization Won
○Lesson 45724Tool-Call Grammars: Constrained Decoding for Reliability
○Lesson 45725Batch-Inference Economics: Why Async Costs Half
○Lesson 45726KV-Cache Eviction: The Hidden Quality Knob
○Lesson 45727Quantization: Where the Quality Cliff Hides
○Lesson 45728Multi-Token Prediction: Faster Decoding Without Drafts
○Lesson 45729Process Reward Models: Grading the Steps, Not the Answer
○Lesson 47704Chinchilla Scaling Laws: How Much Data Does an AI Model Need
○Lesson 47705Flash Attention: How AI Models Hit Long Context Without Running Out of Memory
○Lesson 47707Tool Calling Grammars: How AI Models Produce Reliable Structured Output
○Lesson 47708Context Compaction: How AI Agents Survive Long Sessions
○Lesson 47709Sparse Autoencoders: Looking Inside an AI Model's Brain
Lesson 49700FlashAttention Trade-offs: Why AI Models Run Faster on the Same GPU
○Lesson 49701PagedAttention KV-Cache Management: How AI Servers Pack More Requests
○Lesson 49703Extending Rotary Position Embeddings: How AI Context Windows Grow
○Lesson 49707Mixture of Depths: How AI Models Spend Compute Per Token
○Lesson 49708Jailbreak Mechanisms and Defenses: How Adversaries Bypass AI Safety
○Lesson 49709Test-Time Compute Scaling: How AI Models Trade Inference Cost for Quality
○Lesson 51708AI Process Reward Models: Grading Steps Instead of Outcomes
○Lesson 51709AI Tokenization Byte Fallback: How Vocabularies Handle the Unknown
○Lesson 53700AI Foundations: Attention Sink Tokens
○Lesson 53704AI Foundations: Grouped-Query Attention Tradeoffs
○Lesson 53705AI Foundations: Ring Attention for Distributed Long Context
○Lesson 53707AI Foundations: KTO with Binary Feedback
○Lesson 53709AI Foundations: Mamba and Selective State-Space Models
○Lesson 55700AI and Eval Harness Design: Building Your Own Test Set
○Lesson 55701AI and Context Window Budgeting: Spending Tokens Wisely
○Lesson 55702AI and Temperature Tuning Method: Calibrating Creativity
○Lesson 55703AI and System Prompt Architecture: Layered Instruction Design
○Lesson 55704AI and RAG Chunk Strategy: Picking the Right Slice Size
○Lesson 55705AI and Embedding Model Selection: Beyond OpenAI Defaults
○Lesson 55707AI and Output Schema Validation: Trusting Structured Generation
○Lesson 55708AI and Prompt Versioning Discipline: Treating Prompts as Code
○Lesson 55709AI and Streaming UX Tradeoffs: When to Stream and When Not To
○Lesson 60100How AI Models See Text: Tokens, Context, and Why It Matters
○Lesson 60102System Prompts vs User Prompts and Why the Distinction Matters
○Lesson 60103Context Windows, Lost in the Middle, and Practical Limits
○Lesson 60104RAG Explained: Retrieval-Augmented Generation Without the Buzzwords
○Lesson 60105Embeddings: Why AI Knows Bank and Bank Are Different
○Lesson 60106Fine-Tuning vs Prompting vs RAG: Choosing the Right Tool
○Lesson 60108Agents Demystified: What They Are and Are Not
○Lesson 60109Why AI Hallucinates and What Actually Reduces It
○Lesson 60110Multimodal Models: Vision, Audio, and What They Cannot See
○Lesson 60111Prompt Injection: The Top Security Issue in AI Apps
○Lesson 60112Evals: How You Actually Know if Your AI Feature Works
○Lesson 60113AI Cost Engineering: Where the Money Actually Goes
○Lesson 60114Streaming Responses: Why AI Apps Feel Different
○Lesson 60115Structured Output: Getting JSON You Can Actually Parse
○Lesson 60116Choosing Between AI Models: Capability, Cost, Latency
○Lesson 60117How AI Models Get Safety Training: RLHF in Plain Words
○Lesson 60118The AI Data Flywheel: Why Some Products Get Better Faster
○Lesson 60119Distillation: Making Big Models Cheap
○Lesson 60120Model Context Protocol: A Shared Language for AI Tools
○Lesson 60121How AI Coding Assistants Actually Work
○Lesson 60122On-Device AI: Running Models on Your Phone and Laptop
○Lesson 60124AI Literacy: Staying Sharp as the Field Moves

Curriculum
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AI Foundations
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FlashAttention Trade-offs: Why AI Models Run Faster on the Same GPU

Lesson 1324 of 1596

FlashAttention Trade-offs: Why AI Models Run Faster on the Same GPU

FlashAttention reorders memory access to make attention faster and lower-memory; understand the trade-offs to debug throughput surprises.

Creators · AI Foundations · ~19 min read

The premise

FlashAttention reorders attention computation against the GPU memory hierarchy to cut HBM reads, raising throughput at the same accuracy.

What AI does well here

Reduce HBM reads and writes by tiling attention against SRAM
Enable longer context windows on the same GPU memory budget
Match dense attention numerics within tight tolerances

Profile before you blame the kernel

When throughput plateaus, profile attention versus FFN time before swapping kernels. FlashAttention helps attention-bound workloads more than FFN-bound ones.

What AI cannot do

Eliminate every attention-cost regime on small sequence lengths
Match exotic numerically modified attention variants without porting work
Replace algorithmic improvements like sparse or linear attention

Kernel choice affects determinism

Some FlashAttention variants change reduction order. Lock in a kernel for reproducibility-critical evaluations and document the choice.

Key terms in this lesson

flash attention
memory hierarchy
throughput
GPU

Ground your practice in fundamentals

Every AI capability has an underlying mechanism. Understanding that mechanism tells you where it'll fail — which is more valuable than knowing where it succeeds.

Lesson complete

You've completed "FlashAttention Trade-offs: Why AI Models Run Faster on the Same GPU". Mark this lesson done and keep going — every lesson builds on the last.

Practice this safely

Use a small project example from your own work. The useful move is to compare the AI's draft against your goal, sources, and constraints before you trust it.

1Ask AI to explain flash attention in plain language, then underline anything that sounds uncertain or too broad.
2Give it one detail from "FlashAttention Trade-offs: Why AI Models Run Faster on the Same GPU" and ask for two possible next steps plus one reason each step might be wrong.
3Check memory hierarchy against a trusted source, teacher, adult, expert, or original document before you use it.

If the answer affects another person, a grade, money, safety, or a public post, slow down and verify it with a reliable source.

End-of-lesson quiz

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Creators · 40 min
FlashAttention: Why Memory Layout Beat Math
FlashAttention rewrote attention computation around GPU memory hierarchy — the lesson is that hardware-aware engineering can beat algorithmic novelty.
Creators · 11 min
Batch-Inference Economics: Why Async Costs Half
Batch-Inference Economics reshapes serving and quality tradeoffs. This lesson covers why it matters and how to evaluate adoption.
Creators · 29 min
PagedAttention KV-Cache Management: How AI Servers Pack More Requests
PagedAttention treats KV cache like virtual memory pages, raising serving throughput; understand the mechanism to debug eviction storms.

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