# raullenchai/Rapid-MLX SLOC report

> As of 2026-09-09 (commit ef71b3484f00), raullenchai/Rapid-MLX contains 609,174 total lines: 449,160 code, 112,070 comments, 47,944 blank, across 1,579 files in 17 languages (top: Python 54.3%). Custom configuration (tests excluded, docs excluded, generated excluded). Counted with tokei via OctoCounts.

raullenchai/Rapid-MLX has 449,160 source lines of code out of 609,174 total lines across 1,579 files, counted from the ef71b3484f009987cb37a28682ed52420cf00e03 ref at commit ef71b3484f00 by the OctoCounts tokei engine on 2026-09-09. The report is cached by commit, tokei version, and analysis options, so recounting the same revision returns exactly these numbers.

OctoCounts produced this report by resolving raullenchai/Rapid-MLX to commit ef71b3484f00, downloading the repository source archive, and counting every source file with tokei, the open-source line counter written in Rust. The table below breaks the count down by programming language into files, total lines, code lines, comment lines, and blank lines, so the figures can be compared across languages and projects. Results are cached by commit, tokei version, and analysis options, so counting the same revision again reproduces exactly these numbers.

## Repository size insights

This is a large codebase by counted code lines. Code represents 73.7% of all lines, comments represent 18.4%, and the repository averages 284 code lines per file. Python accounts for 54.3% of counted code.

| Language | Files | Lines | Code | Comments | Blanks |
| --- | ---: | ---: | ---: | ---: | ---: |
| Python | 600 | 321,653 | 243,779 | 49,185 | 28,689 |
| Swift | 564 | 207,187 | 144,008 | 47,121 | 16,058 |
| JSON | 266 | 43,648 | 43,635 | 0 | 13 |
| Shell | 48 | 17,725 | 11,725 | 4,888 | 1,112 |
| JavaScript | 1 | 3,636 | 3,426 | 19 | 191 |
| YAML | 16 | 1,296 | 832 | 380 | 84 |
| Jinja2 | 2 | 792 | 725 | 38 | 29 |
| TOML | 2 | 738 | 323 | 398 | 17 |
| HTML | 1 | 289 | 276 | 0 | 13 |
| Metal Shading Language | 1 | 230 | 149 | 50 | 31 |
| C | 1 | 161 | 104 | 48 | 9 |
| Makefile | 1 | 101 | 56 | 32 | 13 |

Top language (Python 54.3%). Generated at 2026-09-09T19:19:07.996776845+00:00. Canonical report: https://octocounts.com/github/raullenchai/Rapid-MLX

## Reproduce this report

[Reproduce this exact report and configuration](https://octocounts.com/github/raullenchai/Rapid-MLX/commit/ef71b3484f009987cb37a28682ed52420cf00e03?analysis=%7B%22ignoredDirs%22%3A%5B%5D%2C%22ignoredLanguages%22%3A%5B%5D%2C%22profile%22%3A%22default%22%2C%22includeDocs%22%3Afalse%2C%22includeTests%22%3Afalse%2C%22includeGenerated%22%3Afalse%7D).

## Report FAQ

### How many lines of code does raullenchai/Rapid-MLX have?

raullenchai/Rapid-MLX has 609,174 total lines, including 449,160 code lines, 112,070 comment lines, and 47,944 blank lines.

### How was the raullenchai/Rapid-MLX line count measured?

OctoCounts resolved the public GitHub repository to commit ef71b3484f00, downloaded the source archive, counted it with tokei, and cached the report by commit, tokei version, and analysis options.

### What commit was counted for raullenchai/Rapid-MLX?

This OctoCounts report was generated from ef71b3484f009987cb37a28682ed52420cf00e03 at commit ef71b3484f00 on 2026-09-09T19:19:07.996776845+00:00.

## Similar repository reports

- [OpenBB-finance/OpenBB](https://octocounts.com/github/OpenBB-finance/OpenBB) — Python, 450,806 code lines
- [marin-community/marin](https://octocounts.com/github/marin-community/marin) — Python, 446,834 code lines
- [sefcom/oxidizer](https://octocounts.com/github/sefcom/oxidizer) — Python, 457,905 code lines
- [HKUDS/DeepTutor](https://octocounts.com/github/HKUDS/DeepTutor) — Python, 438,547 code lines
- [microsoft/agent-governance-toolkit](https://octocounts.com/github/microsoft/agent-governance-toolkit) — Python, 460,519 code lines
- [numpy/numpy](https://octocounts.com/github/numpy/numpy) — Python, 436,444 code lines

## Related OctoCounts pages

- [Recently analyzed repositories](https://octocounts.com/recent)
- [Popular SLOC reports](https://octocounts.com/popular)
- [Trending GitHub repositories](https://octocounts.com/trending)
- [Hall of Monoliths](https://octocounts.com/hall-of-monoliths)
- [TensorFlow vs PyTorch](https://octocounts.com/compare/tensorflow-vs-pytorch)
- [Django vs Ruby on Rails](https://octocounts.com/compare/django-vs-rails)
- [Laravel vs Django](https://octocounts.com/compare/laravel-vs-django)
- [GitHub SLOC counter guide](https://octocounts.com/docs/github-sloc-counter)
- [Counting methodology](https://octocounts.com/docs/methodology)
- [SLOC and code metrics glossary](https://octocounts.com/docs/glossary)
- [Original research: how filtering changes SLOC counts](https://octocounts.com/research)
- [OctoCounts API docs](https://octocounts.com/docs/api)