CSS Tip

You can customize your list points like this

HTML Tip💡

You can add dark mode to your website with just one line of code.

10 React security best practices
by Ron Perris

Looking for the best ways to secure your React app? Then you’ve come to the right place! We’ve created this checklist of React security best practices to help you and your team find and fix security issues in your React applications. We’ll also show you how to automatically test your React code for security-related issues and automatically fix them.

1-Use default XSS protection with data binding

2-Watch out for dangerous URLs and URL-based script injection

3-Sanitize and render HTML

4-Avoid direct DOM access

5-Secure React server-side rendering

6-Check for known vulnerabilities in dependencies

7-Avoid JSON injection attacks

8-Use non-vulnerable versions of React

9-Use linter configurations

10-Avoid dangerous library code


#REACT #front_end
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Managing Packages with NPM
by FreeCodeCamp

npm (Node Package Manager), is a command line tool to install, create, and share packages of JavaScript code written for Node.js. There are many open source packages available on npm, so before starting a project, take some time to explore so you don't end up recreating the wheel for things like working with dates or fetching data from an API.
In this course, you'll learn the basics of using npm, including how to work with the package.json and how to manage your installed dependencies.

🆓 Free Online Course
⏰ Duration : More than 1 hour
🏃‍♂️ Self paced
✅ Certification available

Course Link

#npm #front_end #freecodecamp
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Answer: 8
since ** is the exponentiation operator and it is like we have 2 * 2 * 2
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📢 Resource Alert: UCI Machine Learning Repository

If you're looking for datasets to practice and experiment with machine learning, check out the UCI Machine Learning Repository!

It's a long-standing resource, widely used by students, educators, and researchers to access a variety of datasets for ML projects.

Explore it here: https://archive.ics.uci.edu/datasets
@javascript_resources
#MachineLearning #DataScience #AI #Resources

👩‍💻 A simple explanation of working with list in Python!

𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 vs 𝐆𝐫𝐚𝐩𝐡 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬

Selecting the right database depends on your data needs—vector databases excel in similarity searches and embeddings, while graph databases are best for managing complex relationships between entities.


𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬:
- Data Encoding: Vector databases encode data into vectors, which are numerical representations of the data.
- Partitioning and Indexing: Data is partitioned into chunks and encoded into vectors, which are then indexed for efficient retrieval.
- Ideal Use Cases: Perfect for tasks involving embedding representations, such as image recognition, natural language processing, and recommendation systems.
- Nearest Neighbor Searches: They excel in performing nearest neighbor searches, finding the most similar data points to a given query efficiently.
- Efficiency: The indexing of vectors enables fast and accurate information retrieval, making these databases suitable for high-dimensional data.

𝐆𝐫𝐚𝐩𝐡 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬:
- Relational Information Management: Graph databases are designed to handle and query relational information between entities.
- Node and Edge Representation: Entities are represented as nodes, and relationships between them as edges, allowing for intricate data modeling.
- Complex Relationships: They excel in scenarios where understanding and navigating complex relationships between data points is crucial.
- Knowledge Extraction: By indexing the resulting knowledge base, they can efficiently extract sub-knowledge bases, helping users focus on specific entities or relationships.
- Use Cases: Ideal for applications like social networks, fraud detection, and knowledge graphs where relationships and connections are the primary focus.

𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧:
Choosing between a vector and a graph database depends on the nature of your data and the type of queries you need to perform. Vector databases are the go-to choice for tasks requiring similarity searches and embedding representations, while graph databases are indispensable for managing and querying complex relationships.