Paul 🚀

swe 🇵🇪

[Paul 🚀 @dryorx]

No puedes aspirar grandeza si solo le dedicas 5 horas al día.

[Paul 🚀 @dryorx]

been sitting on this one for a while. finally put it into words. give it a read if you're into this kind of thing. happy hacking :)

https://t.co/hDz8EDBJcp

[Paul 🚀 @dryorx]

@StarPlatinum_ Based on what?

[Paul 🚀 @dryorx]

Just wrote about an interesting problem I've been researching: CSS collisions in microfrontends are actually an ownership election problem in distributed systems. https://t.co/Ik61qP4HGa

[Paul 🚀 @dryorx]

What I learned from a 90s fridge about software design.

https://t.co/oSSLiY0Sm4

[Paul 🚀 @dryorx]

@rickyfm What about flexibility?

[Paul 🚀 @dryorx]

Finally wrote down everything I know about CSS variables—the -- prefix, var(), inheritance, and scoping.
Short read if you want to level up your theming game:

https://t.co/rbgcldoThy

What CSS variable techniques do you use that most devs don't know about?

[Bharat Kara @KaraBharat]

⚡️ JavaScript Set makes state comparison effortless.

#JavaScript

[Paul 🚀 @dryorx]

🚀 New article just dropped: Build Fast, Change Easy.
Why your React apps should depend on interfaces—not concrete implementations.
It’ll save you pain later. Promise.
👇
https://t.co/vaau5xRRHT

[Shalini Goyal @goyalshaliniuk]

System Design Cheatsheet

System design is key to building scalable, reliable, and efficient applications. Here’s a breakdown of essential concepts:

1. Fundamentals of System Design
Scalability, availability, and reliability ensure systems can handle growth and maintain performance. Latency, throughput, and the CAP theorem define system trade-offs.

2. Load Balancing
Techniques like round robin, least connections, and consistent hashing distribute traffic efficiently across servers to optimize performance.

3. Databases
SQL is structured and ACID-compliant, while NoSQL is flexible and scalable. Scaling strategies include vertical scaling, sharding, and replication.

4. Message Queues & Event-Driven Systems
Kafka, RabbitMQ, and Pub/Sub models enable asynchronous communication for high-throughput and real-time processing.

5. Caching Strategies
Write-through and write-back caching improve data access speed, while eviction policies like LRU, LFU, and FIFO manage cache efficiency.

6. API & Communication
REST, GraphQL, gRPC, and WebSockets facilitate data exchange and real-time communication between distributed systems.

Explore more in the image below.

Follow me at @goyalshaliniuk for more such information !

[Paul 🚀 @dryorx]

@IamKyros69 real

[Kyros @IamKyros69]

It’s 4 AM.
No rich parents.
No resources.
No degree.
None of it matters.
I’ll build everything from scratch.

[Fernando @Franc0Fernand0]

If you're a software engineer who wants to know more about databases, read these 10 articles: ↓

[Paul 🚀 @dryorx]

What do you do when you feel you’ve lost the excitement / passion for crafting software?

[Mohit Mishra @chessMan786]

How Compilers Work?

[Paul 🚀 @dryorx]

Just shipped my first article! 🚀

About that moment when your clean React component becomes an analytics management center... and how to fix it without losing your sanity.

https://t.co/PbKWMi7AYw

[Mohit Mishra @chessMan786]

System Calls

[sysxplore @sysxplore]

How DNS works?

[Ashish Pratap Singh @ashishps_1]

7 must-know runtime complexities for coding interviews:

1. 𝐎(1) - 𝐂𝐨𝐧𝐬𝐭𝐚𝐧𝐭 𝐭𝐢𝐦𝐞
- The runtime doesn't change regardless of the input size.
- Example: Accessing an element in an array by its index.

2. 𝐎(𝐥𝐨𝐠 𝐧) - 𝐋𝐨𝐠𝐚𝐫𝐢𝐭𝐡𝐦𝐢𝐜 𝐭𝐢𝐦𝐞
- The runtime grows slowly as the input size increases. Typically seen in algorithms that divide the problem in half with each step.
- Example: Binary search in a sorted array.

3. 𝐎(𝐧) - 𝐋𝐢𝐧𝐞𝐚𝐫 𝐭𝐢𝐦𝐞
- The runtime grows linearly with the input size.
- Example: Finding an element in an array by iterating through each element.

4. 𝐎(𝐧 𝐥𝐨𝐠 𝐧) - 𝐋𝐢𝐧𝐞𝐚𝐫𝐢𝐭𝐡𝐦𝐢𝐜 𝐭𝐢𝐦𝐞
- The runtime grows slightly faster than linear time. It involves a logarithmic number of operations for each element in the input.
- Example: Sorting an array using quick sort or merge sort.

5. 𝐎(𝐧^2) - 𝐐𝐮𝐚𝐝𝐫𝐚𝐭𝐢𝐜 𝐭𝐢𝐦𝐞
- The runtime grows proportionally to the square of the input size.
- Example: Bubble sort algorithm which compares and potentially swaps every pair of elements.

6. 𝐎(2^𝐧) - 𝐄𝐱𝐩𝐨𝐧𝐞𝐧𝐭𝐢𝐚𝐥 𝐭𝐢𝐦𝐞
- The runtime doubles with each addition to the input. These algorithms become impractical for larger input sizes.
- Example: Generating all subsets of a set.

7. 𝐎(𝐧!) - 𝐅𝐚𝐜𝐭𝐨𝐫𝐢𝐚𝐥 𝐭𝐢𝐦𝐞
- Runtime is proportional to the factorial of the input size.
- Example: Generating all permutations of a set.

♻️ Repost to help others in your network.

[Growth Labs @growthhub_]

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