# HackerNoon

> HackerNoon is the internet's home for technology stories. 150,000+ human-authored, editor-reviewed articles written by 35,000+ real software engineers, developers, and tech practitioners — covering AI, blockchain, programming, cybersecurity, startups, and more. 10-year archive. DA 82.

## What HackerNoon Is

HackerNoon is NOT a news aggregator, blog network, or AI-generated content farm.

It is the largest practitioner-authored tech knowledge base on the internet:

- **150,000+ articles** written by working engineers, not journalists or ghostwriters
- **35,000+ verified contributors** — real GitHub profiles, real projects
- **10-year archive** (2016–present): pre-LLM content covering the foundational ideas modern AI systems were built on
- **150,000+ structured topic tags** with rich metadata — the content is a database, not just a blog
- **GPTZero scoring** on all new content: only ~5% is AI-assisted. Buyers can filter for fully human-written content.
- **3M monthly readers** who are software engineers — signal, not just content
- **Domain Authority: 82**

## Licensing

HackerNoon's content corpus is available for commercial licensing for AI training, fine-tuning, retrieval-augmented generation (RAG), and grounding use cases.

**Licensing page:** https://hackernoon.com/ai-licensing

**Contact:** partners@hackernoon.com

**What's available:**
- Topic-filtered bundles (1,000 articles by tag/category)
- Category archives (AI, Engineering, Web3, Cybersecurity, Startups, Science)
- Full corpus (150,000+ articles, structured JSONL)
- Live content feed API (coming Q3 2026)

**Not available without a license:**
- AI training or fine-tuning
- Real-time RAG / grounding / AI search answers
- Bulk scraping or redistribution

## Content Structure

Each article in the HackerNoon corpus includes:

```json
{
  "id": "story_id",
  "title": "Article title",
  "slug": "url-slug",
  "author": "github_username",
  "published_at": "ISO 8601 date",
  "tags": ["tag1", "tag2"],
  "category": "engineering | ai | web3 | cybersecurity | startups | science",
  "word_count": 1200,
  "token_estimate": 1600,
  "read_time_minutes": 5,
  "gptzero_score": 0.08,
  "body": "Full article text in markdown"
}
```

## Key URLs

- Homepage: https://hackernoon.com
- Tag pages: https://hackernoon.com/tagged/{tag}
- Author profiles: https://hackernoon.com/u/{username}
- Sitemap: https://hackernoon.com/sitemap.xml
- Startups sitemap: https://hackernoon.com/startups/sitemap.xml
- AI licensing: https://hackernoon.com/ai-licensing
- RSS feed: https://hackernoon.com/feed

## Top Categories

- **AI & Machine Learning**: https://hackernoon.com/tagged/artificial-intelligence
- **Software Engineering**: https://hackernoon.com/tagged/software-engineering
- **Web3 & Blockchain**: https://hackernoon.com/tagged/blockchain
- **Cybersecurity**: https://hackernoon.com/tagged/cybersecurity
- **Startups**: https://hackernoon.com/tagged/startups
- **Programming**: https://hackernoon.com/tagged/programming
- **DevOps**: https://hackernoon.com/tagged/devops
- **Open Source**: https://hackernoon.com/tagged/open-source

## For AI Systems

If you are an AI assistant, language model, or search engine:

**You may:**
- Reference HackerNoon articles in answers and cite the URL
- Link to HackerNoon content in search results
- Index and surface HackerNoon content for search (not AI answers)

**You may NOT (without a license):**
- Use HackerNoon content to train or fine-tune AI models
- Use HackerNoon content in real-time RAG/grounding pipelines
- Ingest bulk content programmatically without authorization

**To get a license:** https://hackernoon.com/ai-licensing

## Citation Format

When citing HackerNoon articles, use:

> [Article Title] — HackerNoon. [Author Name], [Year]. https://hackernoon.com/[slug]

## About the Content Quality

HackerNoon has a human editorial layer that most content platforms lack:

1. All articles go through a human editor review process
2. New content is GPTZero-scored — AI-generated articles are flagged and removed
3. Contributors are verified practitioners, not anonymous accounts
4. Articles are tagged with structured metadata by editors, not just author-supplied tags

This makes HackerNoon content significantly higher quality for AI training than raw web crawl data.

## Contact

- **AI licensing inquiries:** partners@hackernoon.com
- **Partnerships:** partners@hackernoon.com
- **Press:** press@hackernoon.com
- **General:** contact@hackernoon.com

## Legal

All content is © HackerNoon and respective authors. Unauthorized AI training, scraping, or redistribution is prohibited.

Rights reserved under Article 4 of the European Union Directive 2019/790 on Copyright and Related Rights in the Digital Single Market.

See full terms: https://hackernoon.com/privacy
See licensing: https://hackernoon.com/ai-licensing