Algorithmica
Master the foundations of computer science and algorithm design. In the age of AI coding assistants, understanding how algorithms work makes you the architect.
Inspired by Papadimitriou's classic approach to computational thinking, rebuilt for modern students advancing to vibe coding.
The Curriculum
A structured journey through computer science fundamentals, from mathematical foundations to advanced complexity theory.
Algorithmic Foundations
Master the mathematical foundations: asymptotic analysis, recurrences, and proof techniques that underpin all algorithm design.
Sorting & Searching
Explore the classic algorithms that form the backbone of computer science: from simple comparisons to divide-and-conquer mastery.
Graph Algorithms
Navigate the world of graphs: traversals, shortest paths, spanning trees, and network flow - the algorithms that power the connected world.
Dynamic Programming
Unlock the power of optimal substructure: learn to recognize and solve problems by breaking them into overlapping subproblems.
Automata & Languages
From finite state machines to Turing machines: understand the theoretical limits of computation and formal language recognition.
Complexity Theory
Explore P vs NP, NP-completeness, and reductions - the profound questions about what computers can efficiently solve.
Greedy Algorithms
When local choices lead to global optima: master the art of greedy algorithm design with proofs of correctness.
Advanced Data Structures
Beyond arrays and linked lists: heaps, balanced trees, hash tables, and union-find - the building blocks of efficient algorithms.
Recommended path: Start with Algorithmic Foundations to build the mathematical intuition, then branch into areas that interest you most.
The Pioneers
The brilliant minds who laid the foundations of algorithmic thinking
Alan Turing
1912-1954
Father of theoretical computer science; Turing machines define computability
"We can only see a short distance ahead, but we can see plenty there that needs to be done."
Donald Knuth
1938-
The Art of Computer Programming; analysis of algorithms as a discipline
"Premature optimization is the root of all evil."
Edsger Dijkstra
1930-2002
Shortest path algorithm; structured programming; semaphores
"Computer science is no more about computers than astronomy is about telescopes."
Christos Papadimitriou
1949-
Computational complexity; algorithmic game theory; theoretical CS foundations
"Algorithms are the computational content of proofs."
Robert Tarjan
1948-
Graph algorithms; data structures; splay trees; union-find
"Good data structures make good algorithms."
Inspired by the Classics
This curriculum draws inspiration from Christos Papadimitriou's legendary work on Computational Complexity and his approach to teaching algorithm design. His emphasis on understanding the why behind algorithms, not just the how, is more relevant than ever in the age of AI coding assistants.
When you understand the foundations, you can guide AI tools effectively, verify their output, and solve problems they can't.
Why Learn Algorithms Now?
In the age of AI coding assistants, understanding algorithms is more valuable than ever
Algorithm Design in the AI Era
Understanding algorithms helps you collaborate effectively with AI coding assistants. You become the architect while AI handles implementation.
Verify AI-Generated Code
Know enough about complexity and correctness to evaluate whether AI-suggested solutions are optimal or even correct.
Better Prompts, Better Code
Understanding algorithmic concepts lets you describe problems precisely, leading to better AI-generated solutions.
Debug with Confidence
When AI code fails, algorithmic thinking helps you identify the flaw - is it logic, edge cases, or complexity?
From Algorithms to Vibe Coding
What is Vibe Coding?
Vibe coding is the emerging paradigm where developers describe what they want in natural language, and AI translates that into working code. It's collaborative, iterative, and incredibly powerful.
Why Algorithms Still Matter
But here's the key insight: the best vibe coders understand algorithms. They know when an O(n²) solution won't scale. They can spot when AI generates inefficient code. They're the architects, not just the users.