Let's discuss about fascinating concept in computer science, P and NP problems

What are P and NP?

P (Polynomial Time): This class contains problems that can be solved quickly (in polynomial time) by an algorithm. In simpler terms, if you can find a solution to a problem efficiently, it belongs to P.
Examples include:

Sorting a list of numbers.
Finding the shortest path in a graph using Dijkstra's algorithm.

NP (Nondeterministic Polynomial Time): This class contains problems for which a proposed solution can be verified quickly (in polynomial time). However, finding that solution may not be efficient. Examples include:

The Traveling Salesman Problem.
The Knapsack Problem.
The Big Question

The core question of the P vs NP problem is: Is every problem whose solution can be verified quickly (NP) also solvable quickly (P)? In other words, is P equal to NP?

If P = NP: This would mean that every problem for which we can check a solution quickly can also be solved quickly. This would have profound implications for fields like cryptography, optimization, and artificial intelligence.
If P ≠ NP: This would indicate that there are some problems that can be verified quickly but cannot be solved quickly. This is the widely held belief among computer scientists.

Importance of the P vs NP Problem

Theoretical Implications: Understanding whether P equals NP could reshape our understanding of computational complexity and algorithms.

Practical Applications: Many real-world problems, such as scheduling, routing, and resource allocation, fall into the NP category. Solving these problems efficiently is crucial in various industries.

Cryptography: Many encryption methods rely on the assumption that certain problems (like factoring large numbers) are hard to solve, which ties back to the P vs NP question.

Use these resources for any soft tech .....


https://t.me/zero_to_mastery

https://www.loom.com/share/6839007a9840449b82c83be7c7ce15ce


Surely it is the AI-integrated work era

https://education.github.com/pack


Do you know about the github education pack explore it, it is all fascinating

The github ed pack is awesome but can be challenging to set up for many high school students as it needs the website of your school but if anyone here is AAU stud it says can use the university provided email, I am contacting the students service team in how to set that up and will inform you soon

https://t.me/TechInEthio/1897

A really great work👏👏👏👏

Hi fans, long time no see 🙈🙉 how is everything, I was AFK

I read about something interesting and it was about personhood credentials. In the near future we might need a method other than recaptcha to distinguish between a real person and digital imposters and it was fascinating that these are being thoroughly studied and will appear in the near future in which we should have a personhood I'd which can be issued by government and can be provided for the specific service we are using to verify we are human, XD 😄, (u don't need personhood id to proof you are human for your self until now, lol 😆) and it is insane, 😭 want to read more 👇

https://news.mit.edu/2024/3-questions-proving-humanity-online-0816

Will help of anybody here is planning to include face detection feature in a project
https://justadudewhohacks.github.io/face-api.js/docs/index.html?ref=dailydev

And recently ui templates are out of bounds, it is very resourceful for us who rush to the functionalities but I reckon about the front end gurus

https://github.com/nvbn/thefuck

Check this out

DevDojo free customizable Laravel starter kits with authentications and socialities

https://laravel-news.com/laravel-auth-screens?ref=dailydev

መስቀል ብርሃን ለኵሉ ዓለም።

እንኳን ለብርሃነ መስቀሉ በሰላም አደረሳችሁ አደረሰን

በዓሉ የሰላም የፍቅር ያድርግልን።

@software_nexus_focus

The AR race is being Inclined to Meta

https://t.me/deep_machine_learning_future/2243

Been quiet for a while — not idle though. Got a lot cooking. But yeah, I think I'm back. I'll be posting about my journey — learning new things, building, and just the real ups and downs of thriving in tech, especially AI.

Hi fans 🙌

been diving into continual learning lately and honestly... it's fascinating 🧠

basically the problem is — how do you teach a model new stuff without it forgetting everything it already knows? turns out it's really hard lol

found this paper on sparse memory finetuning and the approach is kinda crazy. instead of updating the whole model (which wrecks it), you give it millions of tiny memory slots and only touch the relevant ones. like... surgical precision 🔬

the numbers: full finetuning forgets 89%. LoRA still forgets 71%. this method? only 11%. that's wild

still early research (1.3B scale) but the direction feels right. this is where always-learning AI actually starts to look possible

also I started google's nested learning path a while back but never finished it... getting back to that too 😅

https://jessylin.com/2025/10/20/continual-learning/