Learn by doing.

Learn ARM64 assembly language from scratch. Understand how processors execute code with registers, memory, branches, and syscalls.

Learn x86_64 assembly language from scratch. Understand how desktop and server processors execute code with registers, memory, branches, and syscalls.

Learn the C programming language from scratch. See the ARM64 assembly your code compiles to, compiled and executed entirely in your browser.

Learn Linux and the Bash shell from scratch. Master the command line: navigate the filesystem, manage files, process text with pipes, and write shell scripts.

Understand how the Linux kernel works by implementing its core data structures in C. Build a process scheduler, page table, slab allocator, ring buffer, semaphore, signal table, and syscall dispatcher.

Reimplement the classic Unix command-line tools in C. Build echo, cat, grep, wc, head, tail, tr, uniq, and more from scratch — and understand how they work.

Learn Calculus 1 by implementing the algorithms in C. Compute limits, derivatives, and integrals numerically — Euler's method, Newton's method, Riemann sums, Simpson's rule, and volumes of revolution.

Learn Calculus 2 by implementing the algorithms in C. Compute arc lengths, surface areas, improper integrals, partial sums, geometric series, Taylor polynomials, parametric arc length, polar area, and curvature.

Learn Multivariable Calculus by implementing the algorithms in C. Compute dot and cross products, partial derivatives, gradients, tangent planes, the Laplacian, double integrals, polar integrals, and triple integrals.

Master the mathematics of finance from scratch in pure Python. Implement present and future value, annuities, NPV, IRR, bond pricing, Macaulay and modified duration, convexity, yield to maturity, spot rates, forward rates, bootstrapping, continuous compounding, discount factors, the Vasicek interest rate model, and par/swap rates.

Build the statistical toolkit of quantitative finance from scratch in pure Python. Implement log and arithmetic returns, moments (mean, variance, skewness, kurtosis), rolling statistics, covariance and correlation, z-scores, historical volatility, drawdown, Sharpe and Sortino ratios, beta and alpha, t-tests, OLS regression, R-squared, the Jarque-Bera test, Monte Carlo sampling, and bootstrapping.

Implement time series analysis for financial data from scratch in pure Python. Build the autocorrelation function (ACF), partial autocorrelation (PACF), stationarity tests, differencing, MA(q) and AR(p) models, ARMA, ARIMA, exponential smoothing, Holt-Winters, volatility clustering, ARCH, GARCH(1,1), realized volatility, and a 1D Kalman filter.

Implement modern portfolio theory from scratch in pure Python. Build portfolio expected return, 2-asset variance, N-asset covariance matrices, the minimum variance portfolio, efficient frontier, tangency portfolio, capital market line, CAPM, security market line, Jensen's alpha, risk decomposition, single-factor models, diversification ratios, Black-Litterman views, and risk parity weights.

Implement options pricing from scratch in pure Python. Build call and put payoffs, put-call parity, intrinsic and time value, the Black-Scholes formula, implied volatility via bisection, all five Greeks (delta, gamma, theta, vega, rho), 1-step and N-step CRR binomial trees, American options with early exercise, Monte Carlo pricing, Asian options, and barrier options.

Build algorithmic trading strategies from scratch in pure Python. Implement moving averages, Bollinger Bands, RSI, MACD, SMA crossover, momentum signals, z-score mean reversion, pairs trading, portfolio rebalancing, a backtest engine, slippage modeling, Sharpe ratio, walk-forward validation, and Kelly criterion position sizing.

Implement quantitative risk management from scratch in pure Python. Build historical VaR, parametric VaR, Monte Carlo VaR, CVaR (expected shortfall), marginal and component VaR, stress testing, scenario P&L, drawdown analysis, recovery time, Kelly criterion, fixed fractional sizing, volatility targeting, delta hedging, and risk-adjusted performance metrics.