🔥Turn ANY website into LLM-ready data (37k stars)
AI systems love neatly formatted data—Markdown, Structured data, HTML, etc.
And now it is easier than ever to produce LLM-digestible data!
Firecrawl is a framework that takes a URL, crawls it, and converts it into a clean markdown or structured format.
LLM-ready formats → Markdown, HTML, Structured data, metadata.
Handles the hard stuff → proxies, anti-bots, dynamic content.
Customizable → exclude tags, custom headers, max depth.
Reliable → gets the data you need, no matter what.
Batching → scrape thousands of URLs at once
Media parsing → PDFs, DOCX, images
Actions → click, scroll, input, wait.
If you prefer FireCrawl’s managed service, you can use the code “DDODS” for a 10% discount code here →
Thanks to Firecrawl for partnering with us today!
Build an MCP Server in 3 Steps
We found the easiest way to build an MCP server.
Just follow these 3 steps:
Use Gitingest to convert the entire FastMCP repo into LLM-ready text.
Download the text file.
Upload it to Google AI Studio and specify the type of MCP server you want to build.
That's all!
Gemini 2.5 Pro builds it for you.
We have attached a video walkthrough at the top!
If you don't know about MCP servers, we covered them recently in the newsletter here:
Thanks for reading!
P.S. For those wanting to develop “Industry ML” expertise:
At the end of the day, all businesses care about impact. That’s it!
Can you reduce costs?
Drive revenue?
Can you scale ML models?
Predict trends before they happen?
We have discussed several other topics (with implementations) that align with such topics.
Here are some of them:
Learn how to build Agentic systems in an ongoing crash course with 13 parts.
Learn how to build real-world RAG apps and evaluate and scale them in this crash course.
Learn sophisticated graph architectures and how to train them on graph data.
So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here.
Learn how to run large models on small devices using Quantization techniques.
Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust using Conformal Predictions.
Learn how to identify causal relationships and answer business questions using causal inference in this crash course.
Learn how to scale and implement ML model training in this practical guide.
Learn techniques to reliably test new models in production.
Learn how to build privacy-first ML systems using Federated Learning.
Learn 6 techniques with implementation to compress ML models.
All these resources will help you cultivate key skills that businesses and companies care about the most.
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