Algorithms and data structures
Arrays, linked lists, trees, graphs, sorting and searching, recursion, dynamic programming.
Paste a problem, a function or a screenshot of the handout and work through algorithms, complexity, code and SQL one step at a time.
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Paste the problem, the function you are reading, or a screenshot of the handout. The solver names the structure or algorithm the question is built on and works through it one step at a time — the loop bounds it counts, the pointer it moves, the recursive call it unwinds. If your course is more about writing and shipping code than analysing it, the AI programming tutor and the web development solver go deeper on syntax, frameworks and tooling.
Nothing comes back as a finished file with no reasoning attached. A complexity answer shows which operation dominates and how it scales with the input; a traced function shows what every variable holds at each call; a query shows why the join and the grouping sit where they do. That is the part that transfers to the next question on the problem set, which is usually a variation of the one in front of you.
If a step does not land, ask about that step in the same chat — why is that O(log n)?, what happens when the list is empty? — and it gets re-explained on its own rather than the whole solution being restarted.
Every step names the rule it applies.
The four families of question a computer science course keeps coming back to, from a first programming class through theory and databases.
Arrays, linked lists, trees, graphs, sorting and searching, recursion, dynamic programming.
Big-O analysis, automata and regular languages, computability, proofs of correctness.
Python, Java, C++, JavaScript and SQL: reading unfamiliar code, tracing execution, finding the bug, explaining what a function actually does.
Operating system concepts, concurrency basics, relational design, normalisation and query writing.
The discrete-maths proofs and counting arguments behind a theory course are covered by the AI math problem solver, and the distributions and tests behind a data module by the AI statistics solver.
Paste the assignment brief, the starter code, or the error you're getting. The solver walks through it with you: what the question is actually asking, which structure or algorithm fits, what each line of your code does, and where the logic diverges from the expected output. You get the working, not just a finished file — which is the part you need when the next question on the problem set is a variation of this one. Check your course policy on AI tools before submitting anything.
Being stuck usually means one of three things: the brief is ambiguous, the approach is wrong, or the implementation drifted from the approach. Each one has a different fix, and naming which one you are in is most of the work. A brief gets restated in plain terms; an approach gets compared against the alternatives and the trade-off spelled out; an implementation gets traced against the input that breaks it.
Upload a screenshot of the IDE, a photo of the printed handout, or the PDF of the problem set. The solver reads the brief and the code shown in the image — including the indentation, which is what decides the answer in Python — before working anything through.
That matters when the question lives in a figure rather than a sentence: a linked-list diagram, a binary tree drawn by hand, an ER diagram, a stack trace pasted as an image. Handwritten pseudocode from a lecture works the same way.
The places these problem types usually come from — a first programming class, the core CS sequence, and the theory and systems modules that follow.
Loops, conditionals, functions and scope, arrays and strings, file handling, and the first pass at objects and classes.
Stacks, queues, hash maps, trees and graphs; traversal, sorting and searching; the recursion and dynamic programming that sit on top of them.
Logic and proof, sets and relations, counting, finite automata and regular expressions, decidability and reduction arguments.
ER modelling, keys and normalisation, joins, aggregation and grouping, subqueries and window functions in SQL.
Processes and threads, scheduling, deadlock and race conditions, memory and paging, and the layered model behind a network request.
Exam-style tracing tables, written-response questions and the practice needed to sit them, worked at the level of the course.
Courses differ on conventions — which sorting algorithm counts as the default, whether an average or a worst case is being asked for — so check the working against your own notes, and check your institution's policy on AI tools before you hand anything in.
Working through a specific problem? These free tools solve one step at a time and show the working, so you can check each step against your own.
Flashcards and quizzes are the two that pay off before an exam: turn the term list from a lecture into recall practice, then test whether the definitions actually stuck.
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