What's in each course
A summary of every course in the app and where its questions come from. No course uses real past-paper questions — everything is written for this app.
GCSE Revision
Maths — number work at foundation level: HCF/LCM, fractions and decimals, percentages, prime factorisation, rounding and estimation.
Science — core biology, chemistry and physics: cells and organelles, photosynthesis and respiration, enzymes, and the processes behind them.
English — language techniques and their effects: metaphor, simile, alliteration, rhetorical questions, tone and writer's intent.
Geography — physical processes and their human impact: plate tectonics, river landforms, climate change, development differences.
History — causes of WWI, the Weimar Republic and the rise of the Nazis: the alliance system, the Schlieffen Plan, key dates and terms.
French & Spanish — everyday vocabulary: family, food, days, school life, and common phrases in both directions.
Watch alongside: Cognito (maths & science), Freesciencelessons (science), Mr Bruff (English) — three of the most-used GCSE revision channels on YouTube.
Python · Beginner
First contact with Python: print() and input(), variables
and reassignment, the basic types (int, float, string, bool), f-strings, simple
arithmetic, and your first loops and conditionals. Many cards are
predict-the-output — you read a real snippet and work out what it prints.
Python · Intermediate
The data structures real programs are made of: dictionaries and
.get(), sets and deduplication, tuples and unpacking, list
comprehensions, and how Python errors work — reading tracebacks,
try/except, and what actually raises.
Python · Advanced
How Python's object model fits together: classes, __init__ and
self, inheritance and overriding, __repr__ and equality,
then decorators and generators — the features that make Python code composable.
Watch alongside (all three Python decks): Corey Schafer — the gold-standard Python tutorial channel, from beginner series through OOP, decorators and generators — and freeCodeCamp for full-length free Python courses.
Prompt Engineering
How to get good results out of an AI model: the context–task–format structure, zero-shot vs few-shot, chain-of-thought, temperature, system prompts, tokens and context windows, hallucination and grounding, structured output, iterating on a prompt like a debugger, and prompt injection as a security concern.
Graph Engineering
Structuring AI work as a graph instead of one long conversation: nodes, edges and dependencies, fan-out and barriers, the diamond pattern (planner → parallel researchers → skeptic → merge → human gate), verification by majority vote and by diverse perspectives, loop-until-dry discovery, and the cost trade-off that decides when a graph is worth it.