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Fri 04 Sept 23:13 UTC
Dev Toolsevaluationupdated 04 Sept 2026

leetcode review

doocs/leetcode is a Chinese-first library of LeetCode and programming-interview solutions, and an English README is available. It helps developers study algorithms, compare implementations across several languages, and find worked answers by problem or technique.

trackingstars / 7d
Verdict

Our pnpm install completed in 13 seconds, but there was no build or test target to validate the 610,402-line checkout. Use doocs/leetcode as a broad, actively maintained study reference, especially if you read Chinese and like comparing languages. Do not treat its popularity or zero open issues as proof that every solution has been independently verified.

We ran it

Install✓ · 13s86 packages · 92 MB
Buildn/ano build script
Testsn/ano test script
Repo35982 files~610,402 lines of source · 228.2 MB · 13 CI workflows

Answers from our run

Does leetcode build from source?

Dependencies installed in 13 seconds (86 packages), and the project has no separate build step. We cloned commit 60ada74 into a clean Debian container with 3 CPUs and no project-specific setup.

Does leetcode have tests you can run?

Not through a standard command: the project exposes no test script or target that our harness could run.

Who should not use leetcode?

Learners who want an English-first course with guided lessons and exercises

What are the alternatives to leetcode?

LeetCode Solutions by walkccc, LeetCode-Solutions, fucking-algorithm. Our pnpm install completed in 13 seconds, but there was no build or test target to validate the 610,402-line checkout.

Setup4/5Install took 13s, though no build or test target exists
Docs4/5Strong topic indexing, but the main material is Chinese-first
Community4/536,546 stars and a same-day push, with no open issues
Maturity4/5Established catalog, but local validation hooks are limited

Who it’s for

Developers preparing for coding interviews who want a large searchable reference
Chinese-reading learners who prefer explanations organized by problem and technique
Polyglot programmers comparing Java, Python, C++, Go, TypeScript, and Rust approaches
Study groups that want a shared, actively updated solution catalog

Who it’s NOT for

Learners who want an English-first course with guided lessons and exercises
Teams seeking a small dependency or reusable algorithm package
Anyone who needs an independently tested answer set with a conventional test command
Publishers who cannot comply with the CC-BY-SA-4.0 license

Setup reality

In our run, pnpm installation succeeded in 13 seconds, adding 86 packages and using 92 MB on disk. We could not run a build or test suite because the repository exposes neither a build target nor a test target; both steps were skipped, not passed. The 228.2 MB checkout is primarily a large content collection rather than an application that needs conventional deployment, so browsing the hosted site or reading files directly is more representative than expecting a one-command local app.

It is a reference library, not a coding platform

doocs/leetcode is best understood as a very large answer shelf for algorithm practice. The repository has 36,546 stars and centers on LeetCode, while also covering the second edition of Sword Offer, its focused edition, and the sixth edition of Cracking the Coding Interview. Its main documentation is Chinese, although an English README exists. That language choice is central to the experience: English readers can navigate the project, but the strongest fit is someone comfortable studying explanations and indexes in Chinese.

The project addresses a familiar interview-preparation problem: one site may give you a prompt, but you still need a clear implementation and a way to connect that problem to a reusable technique. The README does this through links grouped under basic algorithms, data structures, and search. It names at least 6 implementation languages, including Java, Python, C++, Go, TypeScript, and Rust, so it is useful when a concept makes sense in one language but you want to see how another expresses it.

Our install worked, but there was nothing to build or test

We cloned commit 60ada74 into an unprivileged Debian sandbox with 3 CPUs and 8 GB of RAM. In our run, pnpm installation succeeded in 13 seconds, installed 86 packages, and occupied 92 MB. That is a clean result for dependency setup. The more important finding is what came next: the repository provides no build script or target and no test script or target, so both stages were skipped. A skipped test step is not a passing test suite.

The checkout itself contained 35,982 files, about 610,402 lines of source, and consumed 228.2 MB. We also found 13 CI workflow files, no Dockerfile, and no tests directory. Those facts make sense for a content-heavy repository, but they change what “running it” means. This is not a typical Java or Node application where installation leads to a server and a green test command. The hosted documentation is the product; the local tree is mainly the underlying corpus and tooling.

The strongest feature is practical indexing

The README does more than dump links by problem number. It creates routes into concepts such as binary search, prefix sums, difference arrays, two pointers, monotonic stacks, monotonic queues, hashing, breadth-first search, depth-first search, backtracking, pruning, and A*. That is genuinely helpful because interview study rarely proceeds neatly from problem 1 upward. A learner can identify a weak technique, select several examples, and compare how the pattern changes across constraints.

Specific entries show the value of that organization. The basics list connects problem 34 to binary search, problem 912 to quicksort and merge sort, and problems 303 and 304 to one-dimensional and two-dimensional prefix sums. The data-structure section groups Daily Temperatures, Sum of Subarray Minimums, and other problems under monotonic stacks. The search section distinguishes ordinary BFS, multi-source BFS, bidirectional BFS, deque-based BFS, and A*. Those labels turn a giant archive into a usable revision map.

Verification is the biggest rough edge

The absence of a test target matters. A solution repository can contain code that was accepted under a particular judge, language version, or input contract, yet still be awkward to validate as a whole. Our run found no tests directory and could not execute a repository-level suite. That does not show the answers are wrong; it means users should not confuse a large catalog with a centrally verified software package. For serious teaching or reuse, run the selected solution against the original problem cases yourself.

The repository is also heavy for someone who only needs a dozen patterns. At 228.2 MB checked out, cloning everything is less convenient than using the hosted site. There is no Dockerfile, and installation does not culminate in a documented build artifact in the measured workflow. The CC-BY-SA-4.0 license is friendly to sharing, but its attribution and share-alike terms deserve attention if you plan to republish explanations or build commercial course material from them.

Current activity outweighs the old release tag

The latest tagged release is v0.3.0 from February 20, 2025, which is old relative to this review. That alone is not a reason to call the project abandoned. The repository was pushed on September 4, 2026, the day of our review, and has 36,546 stars. For a documentation corpus, ongoing commits can be a better health signal than frequent packaged releases because new problems and translations may land without needing a formal version.

The reported open-issue count is 0. That looks tidy, but it is not enough evidence to claim flawless maintenance or unusually fast support. Zero can reflect prompt issue handling, a preference for pull requests or discussions, or simply the current snapshot. Taken together, same-day code activity and substantial adoption indicate an active project, while the February 2025 release date suggests consumers should follow the main branch or hosted site rather than wait for frequent versioned bundles.

It belongs beside your practice workflow

In a real study stack, doocs/leetcode fits after the problem statement and before your own final implementation. Read the original prompt, attempt a solution, then use this repository to compare techniques or language syntax. Keep your own notes and executable tests elsewhere. It should not be wired into production as a runtime dependency, and the 610,402-line corpus is not a substitute for measuring the complexity and correctness of the specific code you adopt.

Choose walkccc/LeetCode when you want a more English-first companion site, kamyu104/LeetCode-Solutions when terse accepted implementations are the priority, or labuladong/fucking-algorithm when pattern-oriented Chinese teaching is more valuable than exhaustive problem lookup. doocs/leetcode wins on breadth, multilingual comparison, and structured topic lists. Its tradeoff is equally clear: our 13-second install succeeded, but the repository offered no single build or test gate that could certify the collection as a whole.

Alternatives

ProjectWhat it isPick it when
LeetCode Solutions by walkcccA polished collection of LeetCode solutions with a companion website.Pick this instead when you want an English-first reference with concise complexity notes.
LeetCode-SolutionsA long-running repository of accepted solutions organized by language.Pick this instead when compact implementation examples matter more than a topic-led study guide.
fucking-algorithmA Chinese-language collection that teaches reusable algorithm patterns.Pick this instead when you want explanatory pattern lessons rather than a problem-by-problem catalog.

What people are saying

  1. [velocity-scout] doocs/leetcode

Sources

  1. doocs/leetcode on GitHub
  2. LeetCode solutions documentation

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