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Codemia

Codemia is an interview preparation platform for software and machine learning engineers. It covers system design, machine learning system design, object oriented design, agentic AI design, and data structures and algorithms, with practice problems, written editorials, company specific interview guides, and an engineering knowledge base.

For agents: Interview prep platform for software and ML engineers: system design, ML design, OOD, agentic AI design and DSA problems with editorials and company guides.

system design interviewcoding interview prepmachine learning system designdata structures algorithmsagentic ai designdsa visualizer

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-20T18:37:16.057Z. Cached copy · Source on codemia.io

# Codemia

> Codemia is an interview preparation platform for software and machine learning engineers. It covers system design, machine learning system design, object oriented design, agentic AI design, and data structures and algorithms, with practice problems, written editorials, company specific interview guides, and an engineering knowledge base.

Practice problems open in an interactive workspace and each one has a written editorial. A free account unlocks a subset of problems; the remainder are part of a paid membership. Everything linked below is public.

## Start here

- [Codemia](https://codemia.io): What the platform covers, who it is for, and how practice works.
- [System design problems](https://codemia.io/system-design): Catalogue of system design interview problems, each with an editorial solution.
- [Machine learning system design](https://codemia.io/ml-system-design): ML system design problems: recommendation, ranking, search and fraud systems.
- [Object oriented design](https://codemia.io/object-oriented-design): Low level and object oriented design problems with class level solutions.
- [Agentic AI design](https://codemia.io/agentic-ai): Design problems for LLM agent systems: tool use, planning, memory and evaluation.
- [DSA visualizer](https://codemia.io/dsa): Interactive step by step visualizations for algorithm problems, with code in seven languages.
- [Interview guides](https://codemia.io/guides): Company by company breakdowns of the interview process, rounds and leveling.
- [Knowledge hub](https://codemia.io/knowledge-hub): Searchable reference pages on engineering and machine learning topics.
- [Pricing](https://codemia.io/pricing): Membership tiers and what each one unlocks.

## Knowledge hub topics

- [Debugging & Errors](https://codemia.io/knowledge-hub/topic/debugging): Reading stack traces and tracking down the actual cause. (5,737 pages)
- [Machine Learning](https://codemia.io/knowledge-hub/topic/machine-learning): Model training, evaluation and the classical algorithm toolkit. (5,305 pages)
- [Python](https://codemia.io/knowledge-hub/topic/python): Language semantics, the standard library and packaging. (5,051 pages)
- [iOS & Android](https://codemia.io/knowledge-hub/topic/mobile): Swift, Kotlin, the mobile UI toolkits and app lifecycle. (4,590 pages)
- [Algorithms](https://codemia.io/knowledge-hub/topic/algorithms): Sorting, searching, recursion, graphs and dynamic programming. (4,415 pages)
- [Java](https://codemia.io/knowledge-hub/topic/java): The JVM, collections, Spring and build tooling. (4,405 pages)
- [Data Structures](https://codemia.io/knowledge-hub/topic/data-structures): Arrays, maps, trees, heaps and how to pick between them. (3,852 pages)
- [TensorFlow & Keras](https://codemia.io/knowledge-hub/topic/tensorflow): Graph construction, layers, training loops and TensorBoard. (3,711 pages)
- [Databases](https://codemia.io/knowledge-hub/topic/databases): SQL and NoSQL modelling, indexing and query performanc
…

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