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TinyWow is 100% free to use (even OCR), with no pesky registration required.

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Free online video editor.

Basic editor but it does quick edits for me perfectly. No sign up required.

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You're in a web developer internship interview.

The interviewer asked:

"What are the key differences between GET and POST HTTP methods in web development?"

How would you answer? Here's how :πŸ‘‡πŸ‘‡

Key Differences Between GET and POST HTTP Methods in Web Development

1/ Purpose

GET: Retrieve data, no server-side impact.
POST: Submit data, may affect the server.

2/ Data Transmission

GET: Data in URL (query params).

POST: Data in request body (not visible)

.3/ Caching
GET: Cacheable for performance.
POST: Not cacheable, potential side effects.

4/ Bookmarks and Back Button
GET: Bookmarkable, back button friendly.

POST: Not bookmarkable, avoid back button.

5/ Security
GET: Less secure, data in URL.
POST: More secure, data hidden in body.
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12 types of Databases and when to use them:
1) Relational Databases (SQL):
- Use when your data is structured and consistent.
- Supports ACID transactions and complex queries.
- Examples: MySQL, PostgreSQL, Oracle.

2) Key-Value Store:
- Use when data model is based on key-value pairs and requires high scalability and availability.
- Provides lightning-fast data retrieval and high throughput.
- Examples: Aerospike, DynamoDB

3) Document Databases:
- Handles semi-structured data with varying fields.
- Provides schema flexibility and horizontal scaling.
- Examples: MongoDB, Couchbase

4) Graph Databases:
- Perfect for data with complex relationships.
- Used in applications like social networks and recommendation engines.
- Examples: Neo4j, Amazon Neptune.

5) Columnar Databases:
- Data is stored by columns instead of rows to optimize reading from a column.
- Great for applications that involve storing massive data sets and running analytical queries.
- Examples: HBase, Redshift.

6) In-Memory Databases:
- When speed is of the essence, and you can afford to sacrifice persistence.
- Ideal for caching, real-time analytics, and high-frequency trading.
- Redis and Memcached are popular choices.

6) In-Memory Databases:
- When speed is of the essence, and you can afford to sacrifice persistence.
- Ideal for caching, real-time analytics, and high-frequency trading.
- Redis and Memcached are popular choices.

7) Time-Series Databases:
- Opt for this when dealing with time-series data like IoT sensor readings or server logs.
- Great for efficient storage and retrieval of time-stamped data.
- Examples: InfluxDB, Prometheus.

8) Wide-Column Stores:
- Use in applications with large volumes of data and high write throughput.
- Best suited for analytical workloads.
- Apache Cassandra is a prominent example.

9) Search Engines:
- When your primary use case revolves around full-text search.
- Essential for applications that require searching of data content.
- Elasticsearch and Solr are popular choices.

10) Spatial Databases:
- Used to store geographical and location-based data.
- Choose for applications that require Spatial indexing, geospatial analytics.
- Examples include PostGIS, CartoDB

11) Blob Datastore:
- Use in applications that requires storing large documents, images, audio and video files.
- Provides high availability, durability and cost effective storage.
- Examples include HDFS, Amazon S3

12) Ledger Databases:
- Used for maintaining a transparent, immutable, and cryptographically verifiable transaction log.
- Useful for applications dealing with financial transactions and supply chain systems
- Examples: Amazon QLDB, Azure SQL Ledger


There's no one-size-fits-all solution.
The choice of a database depends on your specific use case, data model, scalability needs, and budget
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Learn about CSS grid alignments with this overview πŸ‘¨β€πŸ’»πŸš€
This javascript question is a bit advanced. If you get it correctly without thinking twice, you are a PRO🫑.
Hint: mapping over an array
#programming
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1. Open Dev Tools
2. Right-click on the <body> tag, and select "Node Screenshot"

Works in both Chrome and FF.
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Mastering data structures and algorithms can help you crack top programming jobs

Here’s 5 most popular DSA patterns asked in interviews:

Sliding Window:

Explanation: Sliding window problems involve maintaining a dynamic window over a sequential data structure (e.g., an array or string) and efficiently processing elements as the window "slides" from one position to another.

Sample Problem: Given an array of integers, find the maximum sum of any contiguous subarray of a fixed size k.


Binary Search:

Explanation: These questions involve efficiently searching for an element in a sorted data structure.

Sample Problem: Implement a binary search algorithm to find an element in a sorted array.


Two Heaps (or Priority Queues):

Explanation: This pattern involves using two heaps, typically a max-heap and a min-heap, to efficiently maintain and retrieve certain elements while satisfying specific conditions.

Sample Problem: Implement a data structure that supports adding elements and finding the median of the elements in constant time.


Graph Traversal

Explanation: These questions involve navigating through graphs or trees.

Sample Problem: Perform a breadth-first search (BFS) to find the shortest path between two nodes in an undirected graph.

Dynamic Programming:

Explanation: These questions require solving problems by breaking them down into smaller overlapping subproblems.

Sample Problem: Calculate the nth Fibonacci number using dynamic programming.
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Which function is used to add an event listener in JavaScript?

A) addEventListener()
B) attachEventListener()
C) listenEvent()
D) eventHandler()