# MarkTechStation/VideoCode

Repository: https://github.com/MarkTechStation/VideoCode
Canonical: https://ross.abutalabs.com/products/videocode
Language: Jupyter Notebook
License Family: other
Last push: 2025-08-13T14:02:31+00:00

## Health v2 (maintenance only)
Score: 35/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 36, release rhythm 35, longevity 35
- inputs: {"age_days": 502, "days_push": 385, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4076, forks 1055 (observed 2026-08-28T04:08:34.318807+00:00)

## What it is
A companion repository of example code for the 'Mark's Tech Workshop' (马克的技术工作坊) video tutorial channel, written primarily in Jupyter Notebooks. It stores sample code referenced in the creator's videos on YouTube, BiliBili, Xiaohongshu, and Douyin.

## Use cases
- find code examples from Mark's Tech Workshop tutorial videos
- download companion notebooks for a video lesson
- follow along with video tutorials by running the sample code
- learn programming from a Chinese-language tech channel
- verify a video is the original creator's content

## When to choose
- you are following one of the channel's video tutorials and want the matching code
- you prefer learning by reading and running notebook examples alongside videos

## When to avoid
- you need a standalone tool or library with its own functionality
- you want documented, production-ready software rather than educational snippets
- you have not watched the associated videos, since the code lacks standalone documentation

## Facets
- artifact type: learning-resource
- maturity: active
- function: developer-tools
- domain: tutorials, programming-languages, developer-tools
- platform: python, cross-platform
- tags: tutorial-code, video-course-companion, jupyter-notebooks, chinese-content, example-code

## Member repositories
- MarkTechStation/VideoCode (main) score 35

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:34.318807+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:23:27.428274+00:00, confidence not recorded.
  - readme: https://github.com/MarkTechStation/VideoCode (fetched 2026-08-28T04:08:34.318807+00:00, sha 99a7858f6e92)
- Data as of 2026-08-30T08:39:29.467469+00:00.