Live workshop
A mentor introduces or connects concepts through worked examples and student questions.
Curriculum field guide
Four levels. One progression.
Choose a starting point by what you can already do—not by age, hype, or how many tutorials you have watched.
The weekly model
Learn, practice, explain, improve.
Exact meeting days, time zone, and workload are cohort-specific and will be published before an offer is accepted.
Deliberate practice
Students complete a problem set, mini-project, experiment, or project milestone with clear prerequisites.
Feedback loop
Code review, office hours, peer explanation, or a mock contest turns mistakes into the next learning target.
Level 1
Python Foundations
Who it is for
Absolute beginners. No prior programming experience is required.
Prerequisite check
You should be comfortable using a browser, typing, saving files, and asking for help. That is enough.
Learning sequence
- Values, variables, types, and input/output
- Conditionals, loops, and debugging
- Functions and decomposition
- Lists, dictionaries, strings, and files
- Small object-oriented patterns where useful
- A capstone program that solves a real problem
Evidence of readiness to progress
Write and explain a multi-function Python program, debug common errors, use core collections, and complete a project without copying a walkthrough.
Expected outcome
Computational thinking and enough practical Python fluency to enter Level 2 or begin structured algorithm practice.
Level 2
Applied AI & Machine Learning
Who it is for
Students who completed Level 1 or can independently write Python functions, loops, and collection-based programs.
Prerequisite check
Given a CSV file, you can read it in Python, inspect values, and write functions that transform the data.
Learning sequence
- Arrays and numerical thinking with NumPy
- Cleaning and exploring tabular data with pandas
- Features, labels, training, and testing
- Regression and classification with scikit-learn
- Evaluation, overfitting, bias, and responsible interpretation
- An introduction to neural-network concepts and tools
Evidence of readiness to progress
Build and explain a small end-to-end ML project, justify an evaluation metric, and communicate limitations rather than presenting a model as magic.
Expected outcome
Practical literacy with the modern Python data stack and a portfolio-ready applied mini-project.
Level 3
Algorithms & Competitive Programming
Who it is for
Intermediate students ready for sustained problem solving and USACO Bronze through Silver preparation.
Prerequisite check
You can implement programs from a written specification, reason about loops, and debug without needing the final code.
Learning sequence
- Complexity and constraints
- Sorting, searching, prefix sums, and two pointers
- Stacks, queues, sets, maps, trees, and graphs
- Greedy reasoning and binary search
- Graph traversal and introductory dynamic programming
- Timed practice, post-contest review, and mock contests
Evidence of readiness to progress
Recognize common patterns, justify complexity, implement under time pressure, and conduct an honest post-solution review.
Expected outcome
A repeatable contest process and stronger readiness for Bronze-to-Silver competition problems. Promotion is never guaranteed.
Level 4
Passion Project
Who it is for
Students who completed prior levels or can demonstrate the independence needed to own an ambitious technical project.
Possible outputs
An AI application, startup prototype, developer tool, data investigation, or another technically serious portfolio project.
Project sequence
- Problem framing and scope
- Prior work and technical plan
- Milestones, risks, and experiment design
- Implementation with review checkpoints
- Testing, documentation, and revision
- Public demo, project write-up, or portfolio presentation where appropriate
Evidence of completion
A working and documented artifact, an explanation of design decisions, and an honest account of limitations and next work.
Mentorship boundary
Mentors guide planning, reasoning, review, and accountability. The student remains the author of the work.
Placement guide
Choose the lowest level that still challenges you.
Placement guidance supports a conversation; it is not an automated admissions decision.
Start at Level 1 if…
You are new to coding or still need support turning a problem into functions, loops, and data structures.
Start at Level 2 if…
You can independently build small Python programs and want to use data and models responsibly.
Start at Level 3 if…
You can implement from specifications and want systematic algorithm and contest practice.
Level 4 placement requires a project idea or demonstrated independent readiness. If uncertain, select the closest level; a founder will review fit.
Representative sample
A Level 1 problem is more than syntax.
This short sample shows the expected thinking loop without publishing a full solution.
Transit delay analyzer
Prerequisites: variables, lists, loops, conditionals, functions. Expected time: 45–75 minutes after instruction.
Given a list of scheduled and actual arrival times, write functions that calculate each delay, identify the longest delay, and summarize how many arrivals were on time. Then add input validation and explain the complexity of your approach.
Review questions: What assumptions did you make? Which cases break the program? How would your representation change if trips crossed midnight?