9. VidMix
Free online video editor.
Basic editor but it does quick edits for me perfectly. No sign up required.
vidmix.app
Free online video editor.
Basic editor but it does quick edits for me perfectly. No sign up required.
vidmix.app
👍1
10. RemoveBG
Remove the background from any image with just a few clicks.
No need to spend 2 hours in Photoshop.
remove.bg
Remove the background from any image with just a few clicks.
No need to spend 2 hours in Photoshop.
remove.bg
👍1
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 :👇👇
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.
The interviewer asked:
"What are the key differences between GET and POST HTTP methods in web development?"
How would you answer? Here's how :👇👇
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.
👍1
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
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
👍5
Websites to practice programming:
1. LeetCode
leetcode.com
2. HackerRank
hackerrank.com
3. Codewars
codewars.com
4. CodinGame
codingame.com
5. CSSBattle
cssbattle.dev
6. Coding Challenges
codingchallenges.fyi
1. LeetCode
leetcode.com
2. HackerRank
hackerrank.com
3. Codewars
codewars.com
4. CodinGame
codingame.com
5. CSSBattle
cssbattle.dev
6. Coding Challenges
codingchallenges.fyi
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
Hint: mapping over an array
#programming
Tip:
Take full-size screenshots of websites.
1. Open Dev Tools
2. Right-click on the
Works in both Chrome and FF.
Take full-size screenshots of websites.
1. Open Dev Tools
2. Right-click on the
<body> tag, and select "Node Screenshot"Works in both Chrome and FF.
Sites for FREE HTML/CSS Templates🔥
🎯html5up .net
🎯htmlrev .com
🎯free-css .com
🎯templated .co
🎯freehtml5 .co
🎯startbootstrap .com
🎯bootstrapmade .com
🎯bootswatch .com
🎯bootstraptaste .com
🎯cruip .com
🎯styleshout .com
🎯tooplate .com
🎯html5xcss3 .com
🎯html5up .net
🎯htmlrev .com
🎯free-css .com
🎯templated .co
🎯freehtml5 .co
🎯startbootstrap .com
🎯bootstrapmade .com
🎯bootswatch .com
🎯bootstraptaste .com
🎯cruip .com
🎯styleshout .com
🎯tooplate .com
🎯html5xcss3 .com
👍4
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.
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.
❤1👍1
Which function is used to add an event listener in JavaScript?
A) addEventListener()
B) attachEventListener()
C) listenEvent()
D) eventHandler()
A) addEventListener()
B) attachEventListener()
C) listenEvent()
D) eventHandler()
Books and other copyrighted content on various topics is available on private group -
https://t.me/+eweUkdFwlho4YjRl
https://t.me/+eweUkdFwlho4YjRl
Telegram
Resources
Miguel P. invites you to join this group on Telegram.
Give some suggestion.. Should I start blog or youtube channel?