MIVORA Start learning free

Get genuinely good at AI — and the frontier beyond it

Free, plain-language lessons, beginner to expert. The flagship path makes you genuinely good at the AI you already use — with experiments you run in your own ChatGPT or Claude — plus a full library on quantum, crypto, finance, and the future of tech. Read here; master it (with quizzes and hands-on practice) in the app.

Building with AI

Design, build, evaluate, and ship a real AI application — reasoning from first principles about why each piece works and where it fails.

  1. What Is AI, Really?
  2. How LLMs Actually Work
  3. Choosing a Model
  4. Your First Real Prompt
  5. Context Is Everything
  6. Prompt Engineering Core
  7. Controlling the Output
  8. Debugging a Prompt
  9. Why Evaluation Is the Real Skill
  10. Build Your First Eval
  11. LLM as a Judge
  12. The Knowledge Problem
  13. Embeddings & Meaning
  14. Vector Databases (pgvector)
  15. Build a RAG Pipeline
  16. Making RAG Good
  17. Function Calling
  18. MCP: The Universal Plug
  19. Your First Agent
  20. When Agents Break
  21. Agent Frameworks
  22. A (Paper) Trading Agent
  23. Multi-Agent Workflows
  24. Guardrails & Safety
  25. The Builder's Stack
  26. Version Control with GitHub
  27. Frontend for AI Apps
  28. Backend & APIs
  29. Database with Supabase
  30. Deploy with Vercel
  31. Capstone: Ship a Real AI App
  32. Cost Optimization
  33. Observability & Monitoring
  34. Reliability at Scale
  35. Security Deep-Dive
  36. The Agent Loop, in Depth
  37. Goals & Planning
  38. How the Model Chooses a Tool
  39. Designing Tools an Agent Can Use
  40. Decomposition: Break the Task Down
  41. Chain-of-Thought & Tree-of-Thought
  42. Self-Consistency: Ask, Then Vote
  43. Prompt Anti-Patterns: How Prompts Go Wrong
  44. Chunking Strategies in Depth
  45. Embedding Quality: The Retrieval Bottleneck
  46. Query Rewriting & Expansion
  47. Multi-Hop Retrieval
  48. Context Windows: The Working-Memory Budget
  49. Short-Term vs Long-Term Memory
  50. Summarization & Compaction
  51. Memory Retrieval, Personalization & Privacy
  52. Testing & CI for AI
  53. Prompt & Version Management
  54. Safety & Red-Teaming
  55. Human-in-the-Loop & Shipping Responsibly

Quantum Computing

Explain superposition, entanglement, and what quantum computing does and does not speed up — and tell hype from reality.

  1. Bit vs. Qubit
  2. Superposition: A Real Blend, Not Ignorance
  3. Measurement & Collapse
  4. Why Qubits Scale (and the Catch)
  5. Entanglement
  6. Interference
  7. Quantum Gates
  8. What a Quantum Algorithm Even Is
  9. Grover's Search
  10. Shor's Factoring
  11. Quantum Simulation
  12. What Quantum Computers Are Actually Good At
  13. Physical Qubits
  14. Decoherence & Noise
  15. Quantum Error Correction
  16. What Today's Machines Actually Do
  17. Hype vs. Reality
  18. Post-Quantum Cryptography
  19. The Open Problems
  20. Quantum and the World
  21. How to Think About Quantum Computing
  22. The Quantum Fourier Transform
  23. Phase Estimation
  24. Amplitude Amplification
  25. Variational Algorithms (VQE & QAOA)
  26. The No-Cloning Theorem
  27. Quantum Teleportation
  28. Superdense Coding
  29. Bell Tests: Proving Entanglement Is Real
  30. Two Kinds of Quantum Error
  31. The Surface Code
  32. Logical vs Physical Qubits
  33. The Hardware Race: Four Ways to Build a Qubit
  34. Chemistry & Materials: The Flagship Application
  35. Optimization: The Honest Take
  36. Quantum Machine Learning: The Honest Take
  37. Quantum Sensing: Fragility as a Feature
  38. Quantum Advantage & Benchmarks
  39. Reading Roadmaps: The Milestones That Matter
  40. The Algorithm Gap
  41. When Will Quantum Matter?

Crypto & Tokenization

Derive why a blockchain works from first principles and tell a scam from a real use case — always with “here’s how to check it yourself.”

  1. What Is a Ledger?
  2. Keys & Wallets: How You Own Crypto
  3. Tokens: What They Are and Aren’t
  4. Blocks & Hashes: Why You Can’t Quietly Rewrite History
  5. Reading the Chain Yourself
  6. Consensus: Agreeing Without a Referee
  7. Mining vs. Staking
  8. Smart Contracts: Code That Runs Itself
  9. Gas & Fees: Paying for Shared Computation
  10. Nodes: Who Keeps the Rules Honest
  11. Stablecoins: Holding a Peg
  12. Swapping Without a Middleman: AMMs
  13. Lending, Borrowing & Liquidations
  14. Tokenizing Real-World Assets
  15. The Oracle Problem
  16. Scaling & the Blockchain Trilemma
  17. Security in Practice: How Funds Actually Get Lost
  18. Custody: Who Holds the Keys
  19. The Regulation Reality
  20. Failure Modes: Where Crypto Actually Breaks
  21. Common Scams & How to Spot Them
  22. Where the Trust Actually Lives
  23. Evaluating a Project From First Principles
  24. The Honest Balance Sheet: What Crypto Is Really For
  25. Order Books vs AMMs
  26. Lending Mechanics in Depth
  27. Perpetuals: The Contract That Never Expires
  28. Where Yield Really Comes From
  29. MEV: The Value Hidden in Ordering
  30. Liquidations & Cascades
  31. Optimistic Rollups
  32. ZK Rollups
  33. Data Availability: The Hidden Pillar
  34. Bridges & Their Risks
  35. Modular vs Monolithic Chains
  36. The L2 Tradeoffs: What You Really Give Up
  37. Smart-Contract Vulnerabilities: Reentrancy
  38. Oracle Manipulation & Flash Loans
  39. What an Audit Does — and Doesn’t
  40. Key Management: Beyond a Single Key
  41. Formal Verification: Proving Code Correct
  42. Exploit Case Studies: The Recurring Patterns
  43. Tokenomics: Supply, Demand & Incentives
  44. DAOs & Governance: Voting With Tokens
  45. Emissions & Vesting: The Supply Schedule
  46. Treasuries & Mechanism Design
  47. Real-World Assets: The Last-Mile Problem
  48. Payments & Stable Systems: The Honest Scorecard
  49. Decentralized Identity
  50. NFTs & Digital Ownership
  51. The Adoption Scorecard: Promise vs Reality
  52. Does It Actually Need a Blockchain?

Finance, Trading & Markets

Reason about risk, value, and market mechanics from fundamentals — not tips. Empower, never give financial advice.

  1. What Is Money, Really?
  2. Price Is Not Value
  3. Risk vs. Reward
  4. What Is a Market?
  5. Opportunity Cost
  6. The Time Value of Money
  7. Compounding: Returns on Your Returns
  8. What Is a Stock?
  9. What Is a Bond?
  10. Diversification: The Only Free Lunch
  11. How Prices Actually Move
  12. What Is a Business Actually Worth?
  13. Why Beating the Market Is Hard
  14. Markets Move on Surprises, Not News
  15. Building a Portfolio
  16. Derivatives in Spirit: Insurance and Leverage
  17. Inflation & Interest Rates: The Big Levers
  18. The Business Cycle: Booms and Busts
  19. Bubbles & Crashes
  20. Behavioral Traps: How We Fool Ourselves
  21. Too Good to Be True: Spotting Scams
  22. Luck vs. Skill: The Survivors You See
  23. Thinking Clearly About Money
  24. Intrinsic Value & Discounted Cash Flow
  25. Valuation Multiples (P/E & Friends)
  26. Growth vs Value: Two Lenses
  27. Moats: Why Profits Survive
  28. Margin of Safety
  29. Quality at a Fair Price
  30. Why Diversification Works (the Math)
  31. Correlation: When Diversification Fails
  32. Position Sizing: How Much, Not Just What
  33. Drawdowns & Volatility
  34. Risk-Adjusted Return
  35. Rebalancing: Keeping the Plan
  36. Calls & Puts: The Anatomy of an Option
  37. Option Premiums: Intrinsic Value & Time Value
  38. Futures & Hedging: The Price of Certainty
  39. Leverage & Margin: The Amplifier
  40. Hedging Strategies: Putting the Pieces Together
  41. Structured Products & Their Dangers
  42. Interest Rates: The Price of Money
  43. Inflation: The Hidden Tax
  44. The Credit Cycle: Debt as the Amplifier
  45. Central Banks: Hands on the Dials
  46. Currencies & Exchange Rates
  47. How Macro Moves Asset Prices
  48. Decision-Making Under Uncertainty
  49. Expected Value: Weighing the Bet
  50. Process Over Outcome
  51. The Traps That Fool Even Pros
  52. Second-Order Thinking: “And Then What?”
  53. Your Money Philosophy

The Future of Technology

Reason about emerging technology and the future without hype or fear — the most explicitly optimistic-but-grounded track.

  1. Why the Future Feels Sudden
  2. Signal vs Noise
  3. Curves, Not Lines
  4. Too Soon, Then Too Late
  5. Forecast Like a Weather Report
  6. A Few Deep Forces
  7. Compute: The Cost of Thinking-by-Machine
  8. Energy: The Master Resource
  9. Biology Becomes Engineering
  10. AI: Cheap Intelligence
  11. Loops, Not Levers
  12. Why Making More Makes It Cheaper
  13. The Moving Bottleneck
  14. Old Parts, New Inventions
  15. And Then What?
  16. Tasks, Not Jobs
  17. Efficiency Can Backfire
  18. Who Gets It, and When
  19. Why Doom and Hype Both Sell
  20. Seeing Through Hype
  21. Taking Risk Seriously, Without Doom
  22. The Honest Optimist
  23. Base Rates: Start With the Odds
  24. Scenario Planning
  25. Calibration: Being Right About Being Unsure
  26. Prediction Markets & the Crowd
  27. The AI Frontier: Where It’s Actually Heading
  28. The Biotech Frontier: Biology Becomes Engineering
  29. The Energy Frontier: Toward Cheap Abundance
  30. The Robotics Frontier: AI Meets the Physical World
  31. The Space Frontier: The Cost of Getting There
  32. The Materials Frontier: The Hidden Foundation
  33. Networks & Network Effects
  34. Emergence: The Whole Is More Than the Parts
  35. Tipping Points
  36. Path Dependence: Why History Sticks
  37. Coordination Problems
  38. Resilience & Fragility
  39. The Transition Problem
  40. Institutions Under Pressure
  41. Technology & Power
  42. Who Gains, Who Loses
  43. Technology & Human Behavior
  44. Reasoning Past the First Effect
  45. The Mental Models for the Future
  46. Thinking Clearly Forward