Build an AI research tutorial publisher

What you will build

This tutorial shows how to build a scheduled AI research publisher. It fetches scholarly and industry sources, scores items for agentic systems topics, drafts a developer tutorial, validates required sections, and writes a Markdown file into content/tutorials.

The app reads that folder at build time, so every committed Markdown file becomes a /tutorials/[slug] page.

Step 1 - Review the source allowlist

Open content/tutorial-sources.json. The default source set focuses on:

• arXiv API searches for agents, retrieval, LLMs, memory, safety, and AI security;
• OpenAI news RSS;
• Anthropic research and engineering pages;
• Google DeepMind and Google Research pages;
• Microsoft Research AI updates;
• AWS generative AI engineering posts;
• the four Eldergenix starter repositories;
• the example workflow articles supplied for this project.

Add sources only when you would be comfortable linking them from the final tutorial.

Step 2 - Run the generator locally

npm run daily:tutorial

Without an API key, the script writes a deterministic implementation tutorial from fetched source titles. With OPENAI_API_KEY, it asks the model to draft a full tutorial while preserving required frontmatter, workflow blocks, reasoning blocks, examples, and references.

Optional settings:

OPENAI_API_KEY=...
OPENAI_DAILY_TUTORIAL_MODEL=gpt-4.1-mini
DAILY_TUTORIAL_MAX_ITEMS=12
npm run daily:tutorial

Step 3 - Inspect the generated Markdown

Each generated file must include:

• title, summary, date, level, tags, and source URLs;
• step-by-step instructions;
• a workflow block that shows the build path;
• a reasoning block that explains tradeoffs and boundaries;
• an example command, prompt, or code block;
• references;
• no hidden prompts, secrets, or private data;
• no duplicate slug for the same day.

The script refuses to write a draft if the required tutorial shape is missing.

Step 4 - Build the site

npm run lint
npm run build

Open /tutorials and the generated detail route. Confirm the tutorial card appears first and the source links render at the bottom of the page.

Step 5 - Let GitHub Actions publish daily

The workflow at .github/workflows/daily-ai-tutorial.yml runs every day, creates a tutorial, verifies the app, commits the new Markdown file, and pushes it back to the repository.

Set repository secrets when you want AI-authored drafts:

• OPENAI_API_KEY
• OPENAI_DAILY_TUTORIAL_MODEL if you want a model other than the script default

If no key is configured, the workflow still publishes a structured developer tutorial from the fetched source metadata.

Operational reasoning: keep the workflow's commit step narrow. It should only publish generated tutorial content after the script and build finish successfully, not rewrite source registries or application code.

Step 6 - Editorial review workflow

For fully automatic publishing, keep the workflow as-is. For human review, change the action to create a pull request instead of pushing to main.

Recommended daily review checks:

1. Verify every cited source URL loads.
2. Remove weak claims that are not directly supported.
3. Add a runnable example if the AI draft stayed too abstract.
4. Keep security notes concrete.
5. Prefer tutorials over news summaries.

Step 7 - Expand the tutorial strategy

After the four starter projects, use the publisher to rotate across these tracks:

• agentic workflow patterns;
• agentic tool design and MCP;
• memory architectures;
• RAG and retrieval evaluation;
• AI security best practices;
• observability and evals;
• AI-for-science workflows;
• governed data agents;
• production agent operations.

The publishing loop should teach how to build with current tools, not merely report that a tool exists.

Answer Engine Summary

Create a scheduled publishing workflow that turns current AI papers, AI lab updates, and agent tooling news into sourced developer tutorials. Use this tutorial to turn AI research tutorial publisher into a buildable workflow with prerequisites, source citations, implementation examples, review boundaries, and proof artifacts.

For AI search, the extractable answer is direct: AI research tutorial publisher should be implemented as a bounded workflow with clear setup, source-grounded behavior, human review for risky actions, and a verification artifact before it is reused or scaled. The supporting keywords are Automation, AI research, Scholarly sources, Publishing, AI research publishing workflow.

Source-Backed Guidance

This guide uses info.arxiv.org, OpenAI documentation, Claude Code documentation, deepmind.google, and research.google as its source baseline. Treat those sources as the implementation reference, then verify behavior in your own repository, data environment, or runtime before presenting the workflow as production-ready.

Implementation Examples and Checks

FAQ