# cbamls/AI_Tutorial

大厂发布的AI落地实践、顶尖实验室的最新论文、工业界的真实踩坑记录

Repository: https://github.com/cbamls/AI_Tutorial
Canonical: https://ross.abutalabs.com/products/ai_tutorial
Homepage: https://www.6aiq.com
License Family: other
Topics: machine-learning, search-system, recommender-systems, artificial-intelligence, artificial-intelligence-algorithms, ai-agent, llm
Last push: 2026-06-09T14:59:50+00:00

## Health v2 (maintenance only)
Score: 71/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 86, release rhythm 35, longevity 100
- inputs: {"age_days": 2829, "days_push": 85, "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 3688, forks 511 (observed 2026-08-28T04:08:14.718783+00:00)

## What it is
AIQ is a curated knowledge base and companion website aggregating high-quality AI/ML/big-data engineering content from major tech companies (FAANG, Alibaba, Meituan, ByteDance), research labs, and technical communities, updated daily. It focuses on real-world production experience, latest papers, and industry lessons learned rather than auto-generated encyclopedic content.

## Use cases
- find real-world LLM deployment case studies from big tech
- keep up with the latest AI papers and model releases
- learn recommender system engineering practices from industry
- read production incident postmortems and lessons learned in AI
- discover curated AI tools and product directories
- study agent infrastructure and RAG engineering practices

## When to choose
- you want curated, experience-driven AI engineering articles instead of raw tutorials
- you follow Chinese-language tech blogs from Alibaba, Meituan, Tencent, ByteDance and FAANG
- you need a daily-updated feed of AI industry practice and paper digests

## When to avoid
- you need runnable code, libraries, or a software tool rather than reading material
- you require content under an open-source license (no license is provided)
- you need English-only resources or structured datasets

## Facets
- artifact type: learning-resource
- maturity: active
- function: developer-tools, search-engine, data-science
- domain: artificial-intelligence, machine-learning, large-language-models, tutorials, awesome-lists
- platform: cross-platform
- tags: curated-resources, engineering-blogs, llm, recommender-systems, search-systems, chinese-content, daily-updates, industry-practice, ai-agents, web-server

## Member repositories
- cbamls/AI_Tutorial (main) score 71

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:14.718783+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 2026-08-29T18:31:08.720708+00:00, confidence not recorded.
  - readme: https://github.com/cbamls/AI_Tutorial (fetched 2026-08-28T04:08:14.718783+00:00, sha aabc6fdf7a8e)
  - homepage: https://www.6aiq.com (fetched 2026-08-29T09:25:14.066914+00:00, sha e1c7d1c19c57)
  - site_page: https://www.6aiq.com/about (fetched 2026-08-29T09:25:14.076043+00:00, sha e9d46e0d3644)
- Data as of 2026-08-30T08:39:29.467469+00:00.