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This Python script helps you better understand how embeddings work for SEO. Input a sentence + a query and BERT will calculate a content "Similarity Score": Search engines use embedding models to translate your content into numeric values. This is how they're able to mathematically determine whether a page... on your site is actually relevant to a query someone is searching for. Once both the content and query are run through embeddings - they can calculate the "cosine similarity" or how similar the two entities are to each one. With this Python script, you can actually visualize how cosine similarity works for yourself. To you use you'll simply: 1. Save the Python script to a text file 2. Use your Terminal to run the script 3. You'll be prompted for both a "Sentence" and "Query" 4. Once entered, the script will calculate a "Similarity Score" between the text and query. This is how relevant your sentence is for the target keyword. By running this, you'll see that search engines are able to come with an calculation of content relevance. You'll need to think about the content on your site the same way - which sections have high scores and which ones have low ones? I've linked the Python script in the comments below. No coding knowledge is required and it walks you step by step on how to implement it.show more

Chris Long

13,218 subscribers

13,185 views • 1 year ago •via X (Twitter)

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5 Comments

Chris Long1 year ago

Link to the blog article with the script below:

Daniela Fernandes1 year ago

Olha que legal esse perfil @rodrigowperes

Traxex1 year ago

this a great stuff. Helps a lot for on-page work when we only need to revise specific part of the content and not everything.

Freelancers HUB1 year ago

Is there any way to check longer content?

Suttle1 year ago

Is the chrome extension mentioned in the blog publicly available?

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51,012 views • 1 year ago

Building real-time data pipelines and stream processing is one of the best-paid skills in the market. Apache Kafka and Flink are the industry standards, but they are Java-based and have a Python wrapper. If you are a Python developer, there is an open-source alternative! Watch this video. It's a full tutorial solving a real-world problem using the Quix Streams library. Quix Streams is all Python, and it's open-source! The video shows you how to process GitHub's Firehose API, a constant stream of raw activity. You can use this stream to identify real-time trends with code, issues, and the popularity of public repositories. Here is what the video covers: • How to tap into the GitHub Firehose API using Python and server-sent events (SSE) • How to efficiently stream that data into Kafka • How to optimize Kafka producer settings like batch size and compression for maximum throughput A few other notes about Quix Streams: • You don't need to know any Java • It has a dead-simple API • It integrates seamlessly with your Python stack • It's designed for parallel processing at high velocity You can use Quix Streams to build complete stream processing pipelines in Python, including feature engineering, pre-computations, inference, and real-time machine learning. Thanks to the Quix team for collaborating with me on this post.

Building real-time data pipelines and stream processing is one of the best-paid skills in the market. Apache Kafka and Flink are the industry standards, but they are Java-based and have a Python wrapper. If you are a Python developer, there is an open-source alternative! Watch this video. It's a full tutorial solving a real-world problem using the Quix Streams library. Quix Streams is all Python, and it's open-source! The video shows you how to process GitHub's Firehose API, a constant stream of raw activity. You can use this stream to identify real-time trends with code, issues, and the popularity of public repositories. Here is what the video covers: • How to tap into the GitHub Firehose API using Python and server-sent events (SSE) • How to efficiently stream that data into Kafka • How to optimize Kafka producer settings like batch size and compression for maximum throughput A few other notes about Quix Streams: • You don't need to know any Java • It has a dead-simple API • It integrates seamlessly with your Python stack • It's designed for parallel processing at high velocity You can use Quix Streams to build complete stream processing pipelines in Python, including feature engineering, pre-computations, inference, and real-time machine learning. Thanks to the Quix team for collaborating with me on this post.

Santiago

89,230 views • 1 year ago

Claude Code just got an update nobody is talking about. And it replaced my entire video crew. You type one simple sentence. Claude writes the script. It uses ElevenLabs to clone your voice. It calls Remotion to build the scenes. It grabs HeyGen to make your avatar talk. Ten minutes later, you have a finished video. You never even turn on a camera. This changes how we make content forever.

Julian Goldie SEO

147,795 views • 1 month ago