What does this tool do?

It evaluates operation counts with calculateOperations across O(1), O(log n), O(n), O(n log n), O(n²), O(n³), O(2ⁿ), and O(n!), samples deterministic SVG-ready curves with generateCurvePoints/generateCurvePath, builds comparison-table rows with createComparisonTable, and formats counts for display with formatOperations.

What is n log n at n=1000, and what happens to huge exponentials?

O(n log n) at n=1000 is 1000 × log₂(1000) ≈ 9965.78 (within 1 of 9966). Explosive inputs saturate instead of overflowing: 2^10000 and 1000! both return MAX_OPERATIONS (Number.MAX_SAFE_INTEGER), rendered as “≥ 9.0 quadrillion”.

Is my input uploaded or stored?

No. All math is pure client-side arithmetic — input sizes and counts never leave your browser.

How do log scale and resolution behave?

Pass logScale true to normalize y over the log10 span so all eight classes stay visible; monotonic inputs stay monotonic in y. The sampler returns exactly max(2, floor(pointCount)) points, and the SVG path emits one M/L command per point starting with M 0.0000.

Big-O Complexity Comparator

Compare algorithm growth rates with deterministic, client-side curves and operation counts.

Curve settings

Choose the input range and curves to draw.

Growth curves

Normalized SVG plot from n = 1 to 100 (linear scale).

● O(1)● O(log n)● O(n)● O(n log n)● O(n²)● O(n³)● O(2ⁿ)● O(n!)

Operation comparison

Idealized operation counts for common input sizes; exponential and factorial values are safely capped.

Complexity	n=10	n=100	n=1k	n=1000k
O(1)	1	1	1	1
O(log n)	3.32	6.64	9.97	19.93
O(n)	10	100	1,000	1,000,000
O(n log n)	33.22	664.39	9,965.78	19,931,568.57
O(n²)	100	10,000	1,000,000	1,000,000,000,000
O(n³)	1,000	1,000,000	1,000,000,000	≥ 9.0 quadrillion
O(2ⁿ)	1,024	≥ 9.0 quadrillion	≥ 9.0 quadrillion	≥ 9.0 quadrillion
O(n!)	3,628,800	≥ 9.0 quadrillion	≥ 9.0 quadrillion	≥ 9.0 quadrillion

Algorithms and Safety Nets: Big-O, Graphs, Hashes, Secrets

Compare Big-O growth curves, hash-collision odds, Dijkstra/A*/Bellman-Ford traces, .env secret patterns, and safe batch regex refactors with previews.

.env Secret Safety Linter

Validate .env syntax and spot likely leaked credentials or placeholder secrets.

.htaccess Generator

Build an Apache .htaccess — HTTPS redirects, rewrites, error pages, security headers, and basic auth

2D Fluid Dynamics & Particle Physics Sandbox

A physics playground with two engines sharing one canvas. The fluid mode runs a real Eulerian Navier-Stokes solver — semi-Lagrangian advection, explicit diffusion, Jacobi pressure projection and vorticity confinement — that you paint with dye and velocity using mouse or touch. The particle mode is a damped Verlet N-body engine with gravity wells, Coulomb attractors and repulsors, spring links, wall restitution and elastic circle collisions. Particle counts adapt to the device (100k target on WebGL2 desktop, 2k-5k on CPU fallback), and every configuration exports as runnable code: a Matter.js world, a PixiJS ticker setup, or a standalone vanilla Canvas engine.

AI Function Calling Schema Generator

Generate function calling JSON schemas from TypeScript interfaces

Algorithm Arena & Data Structure Visualizer

Interactive arena for algorithms and data structures: eight sorting algorithms racing on one seeded input, six pathfinding algorithms walking generated mazes, and BST, AVL, Red-Black, B-tree, heap and trie operations replayed step by step with rotations highlighted. Every run shows the Big-O table and the phase-highlighted reference implementation in four languages, and compared values can be played as pitch (sonification) through the shared audio engine. Everything runs client-side; inputs never leave the browser.

Ansible Playbook Generator

Build an Ansible playbook — hosts, vars, and tasks with modules — as YAML