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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

13,047 subscribers

13,185 просмотров • 2 лет назад •via X (Twitter)

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Комментарии: 5

Фото профиля Chris Long

Chris Long2 лет назад

Link to the blog article with the script below:

Фото профиля Daniela Fernandes

Daniela Fernandes2 лет назад

Olha que legal esse perfil @rodrigowperes

Фото профиля Traxex

Traxex2 лет назад

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.

Фото профиля Freelancers HUB

Freelancers HUB2 лет назад

Is there any way to check longer content?

Фото профиля Suttle

Suttle1 год назад

Is the chrome extension mentioned in the blog publicly available?

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Chris Long

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Andrew Ng

384,282 просмотров • 3 лет назад

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Santiago

51,012 просмотров • 1 год назад

Power has a secret. It doesn't come from armies. It doesn't come from money. It comes from three things. Secrecy. Trust. Coordination. The people who understand this form secret societies. Not because they are evil. Because they are rational. If you have information others don't have you have power others don't have. If you trust your allies completely and your enemies trust no one you will always win. If you can coordinate across governments across militaries across banks without anyone knowing you can move history itself. And the mechanism that allows all three to work together is eschatology. A shared script. A shared vision of how the world ends. They don't need to communicate. They don't need to agree. They just follow the script. And the script moves history forward. That is how secret societies work. That is how they always have.

Power has a secret. It doesn't come from armies. It doesn't come from money. It comes from three things. Secrecy. Trust. Coordination. The people who understand this form secret societies. Not because they are evil. Because they are rational. If you have information others don't have you have power others don't have. If you trust your allies completely and your enemies trust no one you will always win. If you can coordinate across governments across militaries across banks without anyone knowing you can move history itself. And the mechanism that allows all three to work together is eschatology. A shared script. A shared vision of how the world ends. They don't need to communicate. They don't need to agree. They just follow the script. And the script moves history forward. That is how secret societies work. That is how they always have.

Predictive History

10,283 просмотров • 2 месяцев назад

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 просмотров • 2 лет назад

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

148,116 просмотров • 2 месяцев назад