Overview
People usually come to us with a concrete symptom: a Python script that runs on one laptop and not another, a Java class hierarchy that has become impossible to change, a C program that segfaults only on large inputs, a MATLAB loop that takes an hour, an SQL query that returns duplicate rows. Each has a cause in a specific language mechanism (mutable default arguments, reference semantics, pointer arithmetic, vectorization, join cardinality) and naming that mechanism is most of the fix.
Our help takes three forms. Explanation sessions cover a concept or library you need to understand, using small examples we write together. Code review looks at code you wrote and returns line-level comments on correctness, readability, structure and performance. Research code support is for thesis students, researchers and professionals who need working scripts, data pipelines or prototypes that become their own documented property.
For students on taught courses the line is clear: we teach and review, you write the code you submit. We will explain the idea behind an assignment, work through a different problem that uses the same technique and comment on your draft, but we do not produce solutions to graded exercises, coding tests or exams.
Who it’s for and the problems it solves
This is for you if you are…
- Undergraduates and master's students taking programming modules who need concepts explained and their own code reviewed
- Thesis and PhD researchers writing analysis scripts, simulations or data pipelines in Python, R or MATLAB
- Engineers and scientists moving code from MATLAB to Python, or from notebooks to a reusable package
- Professionals learning SQL or JavaScript for data or web work who want structured feedback
- Self-taught programmers who can make code run but want it reviewed for clarity, efficiency and good practice
Common problems we help with
- The code runs but you cannot explain why, which worries you before a demo or viva
- Error messages and stack traces are unreadable, so you change things at random until something works
- Your research script has grown into one long file that nobody else could run
- Code is correct on small inputs but far too slow on your real data
- You were told to learn a new language (for example Java after Python) and the concepts do not map cleanly
What’s included — and what isn’t
Included
- Concept explanations with small runnable examples: types, control flow, functions, scope, recursion, object-oriented design, memory and references
- Python: idiomatic code, virtual environments, packaging, NumPy/pandas vectorization, type hints and pytest
- R: data frames and the tidyverse, functions and vectorization, package management with renv, R Markdown or Quarto
- Java: classes, interfaces and inheritance, collections, exceptions, build tools such as Maven or Gradle, JUnit tests
- C and C++: pointers, memory management, undefined behavior, compilation and linking, CMake basics, using debuggers and sanitizers
- MATLAB: matrix-oriented code, preallocation, vectorization, functions versus scripts, toolbox dependencies
- SQL: SELECT with joins, grouping and window functions, subqueries versus CTEs, transactions and query plans
- JavaScript: the event loop, asynchronous code with promises and async/await, DOM manipulation, Node.js basics
- Code review of your own work with written comments on correctness, readability, structure and performance
- For researchers and professionals: bespoke scripts, data pipelines and prototypes, delivered with documentation and tests
Not included
- Writing or completing graded programming assignments, lab exercises or projects that will be submitted as your own work
- Taking coding tests, timed programming exams or online assessments for anyone
- Production software development for commercial products, security-critical systems or regulated medical devices
- Circumventing software licenses, academic plagiarism checkers such as MOSS, or course platform restrictions
What you receive
- Session notes and example code
- A short written summary of what we covered, with the example snippets we built together, so you can revisit the idea without replaying the session.
- Code review report
- Line-referenced comments on your code grouped by severity (bugs, risky patterns, style), with an explanation of each suggestion and links to official documentation.
- Learning plan
- For multi-session help, a sequence of topics and small practice tasks matched to your course outline or research need.
- Research code package
- For researcher and professional clients: source code in a Git repository, a README, dependency file, basic tests and comments, owned by you.
- Handover walkthrough
- A live session explaining how the delivered or reviewed code works so you can modify and defend it yourself.
How it works
Describe the goal
Tell us the language, what the code should do, what it actually does and whether this is coursework, research or professional work. The context decides which form of help is appropriate.
Share code or a minimal example
Send the relevant files, the exact error output and your environment details (language version, operating system, key packages). For course code, a stripped-down example is often enough.
Diagnose and agree a format
We identify the underlying concept or defect and propose explanation sessions, a written review, or (for research code) a small scoped build.
Work through it
Sessions use screen sharing or a shared editor, with you typing where possible. Reviews and builds are delivered in writing with a follow-up call.
Consolidate
You receive notes, examples and suggested practice, and one round of follow-up questions.
Languages, tools and practices we use
- Minimal reproducible examples
- We cut a failing program down to the smallest code and data that still show the problem. This isolates the cause and is the same skill you need for asking good questions on forums or issue trackers.
- Debuggers and sanitizers
- pdb and IDE debuggers for Python, browser()/debug() in R, jdb or IDE breakpoints for Java, gdb or lldb with AddressSanitizer for C/C++, and MATLAB breakpoints.
- Testing frameworks
- pytest, testthat, JUnit, GoogleTest or Catch2, MATLAB unit tests and Jest, used to pin down expected behavior before changing code.
- Profiling
- cProfile and line_profiler, Rprof and profvis, Java profilers, the MATLAB profiler and EXPLAIN plans for SQL, so optimization targets real bottlenecks.
- Version control
- Git basics (commits, branches, meaningful messages, .gitignore) so you can experiment safely and show how your work evolved.
- Linters and formatters
- Ruff or flake8 and Black for Python, lintr and styler for R, Checkstyle for Java, clang-tidy and clang-format for C/C++, ESLint and Prettier for JavaScript.
What each language tends to trip people up on
Every language has characteristic failure modes, and knowing them speeds up diagnosis. We often start by checking the usual suspects for your language before reading the code in detail.
- Python: mutable default arguments, shallow versus deep copies, pandas chained assignment warnings, mixing environments
- R: factors versus character vectors, recycling rules, non-standard evaluation inside dplyr functions
- Java: == versus equals(), NullPointerException from uninitialized fields, integer overflow, checked exceptions
- C/C++: dangling pointers, off-by-one buffer writes, uninitialized variables, header and linker errors
- MATLAB: growing arrays inside loops, element-wise (.*) versus matrix (*) operators, path shadowing of functions
- SQL: NULL comparisons, many-to-many joins that multiply rows, GROUP BY with non-aggregated columns
- JavaScript: this binding, floating promises that are never awaited, type coercion with ==
Research code that someone else can run
Research scripts often start as a single notebook and grow until only their author can run them, and only on one machine. When we build or refactor research code, we separate configuration from logic, pin dependencies (requirements.txt, environment.yml, renv.lock), write a README that explains how to reproduce each result, and add tests for the calculations that matter most.
You own the code and the repository. We recommend that you acknowledge programming assistance in your thesis or paper according to your institution's and publisher's rules, and we can document which parts were written by whom.
Learning with AI coding assistants
Tools such as GitHub Copilot and chat assistants can produce plausible code that hides subtle bugs. We show you how to read generated code critically, write tests that expose its assumptions and check it against official documentation. If your course restricts AI use, we work within that policy.
What the work can look like
Illustrative example — fictional data
Sample code review comment: slow Python loop in a thesis script
A master's student analyzing sensor logs shared a Python script that took about 40 minutes to process 2 million rows. The details are invented to illustrate the format of a review.
Finding
Lines 58–74 iterate over DataFrame rows with iterrows() and append each result to a list, then build a new DataFrame. iterrows() creates a Series object for every row, which is slow, and it also silently converts integer columns to floats.
Suggestion
Replace the loop with vectorized column operations: compute the moving average with df['value'].rolling(window=10).mean() and the threshold flag with a boolean comparison. On the student's sample file this reduced run time from minutes to seconds; exact timings will differ on other hardware.
What to check yourself
Add a pytest test that runs the old and new functions on a 1,000-row sample and asserts that the outputs are equal (pandas.testing.assert_frame_equal), so the speed-up does not change your results.
Illustrative example with invented details. Real reviews are based on your code, your data and your course or project requirements.
What you’ll need to provide
- The code files or repository link, and the exact error messages or unexpected output
- Language and library versions, operating system and how you run the code (IDE, terminal, notebook)
- The assignment brief or course policy on outside help, if the work is for a course
- For research code: a description of inputs, expected outputs and a small sample dataset
- Your deadline and whether you prefer live sessions or written feedback
You don’t need everything ready to ask for a quote — tell us what you have and we’ll say what’s missing.
What affects price and turnaround
Price depends on
- Format: live explanation sessions, written code review or a scoped research code build
- Size and state of the codebase, and how many files or languages are involved
- Specialized libraries or toolchains (for example CUDA, MPI, embedded C) that need setup time
- Whether tests, documentation and a handover walkthrough are part of a research build
- Number of sessions or review rounds requested
Turnaround depends on
- Whether we receive complete code, error output and environment details at the start
- Complexity of reproducing your environment, especially for GPU, cluster or licensed toolboxes
- Your availability for live sessions and for answering clarification questions
Every project is quoted individually. How we price our work.
Questions people ask before starting
Can you help with my programming assignment?
We can explain the concepts it tests, work through a different problem that uses the same technique and review code you have written, pointing out bugs and explaining why they happen. We will not write the solution you submit. If your course has rules on outside help or collaboration, share them and we will work within them.
Which language versions and IDEs do you support?
Current mainstream versions of Python 3, R, Java (long-term support releases), C11/C17 and modern C++ standards, recent MATLAB releases, standard SQL with PostgreSQL, MySQL, SQLite or SQL Server dialects, and modern JavaScript and Node.js. We are comfortable with VS Code, PyCharm, RStudio, IntelliJ, Eclipse, CLion and the MATLAB desktop. Tell us your setup so our examples match it.
Is my code kept confidential?
Yes. Code, data and repository access are used only for your request, not shared or reused in other work. If your research code is under a non-disclosure agreement or contains sensitive data, send a synthetic or anonymized sample instead, and we can sign a confidentiality agreement if needed.
I am a researcher. Who owns code you write for my project?
You do. We deliver it in your repository or as an archive with a README and license of your choice. We recommend acknowledging programming assistance in line with your institution's and publisher's policies.
Can you teach me to debug instead of just fixing things?
That is our preferred format. We work through the problem with you driving, show how to read the stack trace, set breakpoints, write a failing test and narrow the cause, so the next bug takes less time.
Do you help with competitive programming or technical interview preparation?
We can teach data structures, algorithms and problem-solving patterns and give feedback on your practice solutions. We do not take part in live contests or interviews on anyone's behalf.