https://www.uber.com/en-CA/blog/ubers-next-gen-push-platform-on-grpc/
Essential reading for individuals interested in large-scale and distributed systems 👆
Essential reading for individuals interested in large-scale and distributed systems 👆
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When you need to pass data between a new tab that you open in a browser and the parent tab (the one that opened the new tab), you can use a combination of techniques, such as:
- URL Parameters
- localStorage or sessionStorage
- window.opener
-window.postMessage
- URL Parameters
- localStorage or sessionStorage
- window.opener
-window.postMessage
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🚀 Sync Airflow DAGs from Git and deploy in one click! The popular Git sync tool is now available as a standalone docker image - just run it and your DAGs auto-sync. 🐳
https://github.com/data-burst/airflow-git-sync
https://github.com/data-burst/airflow-git-sync
GitHub
GitHub - data-burst/airflow-git-sync: Sync DAG changes from Git to Airflow
Sync DAG changes from Git to Airflow. Contribute to data-burst/airflow-git-sync development by creating an account on GitHub.
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Has anyone had experience with Edge Network and Edge Runtime and would like to share their own experience?
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package main
import (
"fmt"
"time"
)
func main() {
ch := make(chan int)
// Start a goroutine to receive data from the channel
go func() {
data := <-ch
fmt.Println("Received data:", data)
}()
// Sleep for a while to simulate some delay
time.Sleep(2 * time.Second)
// Send data to the channel
ch <- 42
fmt.Println("Main goroutine exiting...")
}
what’s the problem of this code?
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Twitter Cards are a set of metadata tags that you can add to your web pages to enhance how your content appears when shared on Twitter. Similar to Open Graph tags, Twitter Cards provide structured information about your web pages, allowing you to control the way your content is displayed in Twitter's timeline and tweets.
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<!-- Twitter Card meta tags -->
<meta name="twitter:card" content="summary">
<meta name="twitter:title" content="Your Page Title">
<meta name="twitter:description" content="A brief description of your page content.">
<meta name="twitter:image" content="https://example.com/your-image.jpg">
<!-- Other Twitter Card meta tags can be added as needed -->
<title>Your Page Title</title>
</head>
<body>
<!-- Your web page content goes here -->
</body>
</html>
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Pymilo is an open-source Python package that provides a simple, efficient, and safe way for users to export pre-trained machine-learning models in a transparent way. By this, the exported model can be used in other environments, transferred across different platforms, and shared with others. PyMilo allows the users to export the models that are trained using popular Python libraries like scikit-learn, and then use them in deployment environments, or share them without exposing the underlying code or dependencies. The transparency of the exported models ensures reliability and safety for the end users, as it eliminates the risks of binary or pickle formats.
As this library is still in its early stages of development, it currently supports a limited number of machine learning models provided by Scikit-learn. Nevertheless, we believe it would be immensely valuable if we ask the community to utilize this library and provide us with their feedbacks about how to improve the package's interface and prioritize future developments. Your cooperation would be invaluable to us.
As this library is still in its early stages of development, it currently supports a limited number of machine learning models provided by Scikit-learn. Nevertheless, we believe it would be immensely valuable if we ask the community to utilize this library and provide us with their feedbacks about how to improve the package's interface and prioritize future developments. Your cooperation would be invaluable to us.
GitHub
GitHub - openscilab/pymilo: PyMilo: Python for ML I/O
PyMilo: Python for ML I/O. Contribute to openscilab/pymilo development by creating an account on GitHub.
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دیدگاه من:
به گمانم مسیر هوشمصنوعی بدون گره خوردن یه دنیای متن باز میتواند ترسناک باشد ولی با متن باز شدن نظارت عمومی این مسیر میتوان جاهای خوب هدایت شود. در این مسیر متا (فیسبوک) تا حدودی با متن باز کردن برخی محصولات و مدلهایش دارد گام برمیدارد.
از این رو دارم روی یک به شبکه بلاکچین فکر میکنم که بلاکهای آن نسخههای مختلف یک llm است. مثل یک git history روند هوشمندی در هر بلاک مشخص است. به طوری که smart contacts برای نوشتن plug-in به منظور تقویت این هوش اصلی باشد و فردی بتواند دادههای خودش را به این مدل متن باز بدهد (بدون به اشتراک گذاری دادههایی که نمیخواهد) و مدل هوشمندتری ایجاد کند. همه شبکه هم متن باز باشد.
به گمانم مسیر هوشمصنوعی بدون گره خوردن یه دنیای متن باز میتواند ترسناک باشد ولی با متن باز شدن نظارت عمومی این مسیر میتوان جاهای خوب هدایت شود. در این مسیر متا (فیسبوک) تا حدودی با متن باز کردن برخی محصولات و مدلهایش دارد گام برمیدارد.
از این رو دارم روی یک به شبکه بلاکچین فکر میکنم که بلاکهای آن نسخههای مختلف یک llm است. مثل یک git history روند هوشمندی در هر بلاک مشخص است. به طوری که smart contacts برای نوشتن plug-in به منظور تقویت این هوش اصلی باشد و فردی بتواند دادههای خودش را به این مدل متن باز بدهد (بدون به اشتراک گذاری دادههایی که نمیخواهد) و مدل هوشمندتری ایجاد کند. همه شبکه هم متن باز باشد.
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