Distributed Systems:


A distributed system is a system of multiple nodes that are physically separated but linked together using the network. Each of these nodes includes a small amount of the distributed operating system software. Every node in this system communicates and shares resources with each other and handles processes in a team. Distributed systems are a growing trend as more and more applications migrate to the cloud. They allow improved performance, scalability, and fault tolerance compared to a traditional centralized system. This Distributed System blog will cover all the basic to advanced topics of distributed systems like communication, remote procedure calls, Distributed File Systems, Distributed shared memory, etc. to provide you with a thorough overview of distributed systems.


Why do we need a distributed system

Broadly speaking, they allow us to achieve following
1. Horizontal scalability
2. High efficiency for given infra costs
3. High availability

from medium.com

The net/http package in Go remains one of the most elegant and powerful ways to build web servers. ⚙️

It gives full control, from defining routes to handling requests and crafting responses, without hiding the details behind abstractions.
That simplicity is intentional: you understand every layer of the HTTP workflow while still leveraging Go’s speed and concurrency model.

It’s a great reminder that strong fundamentals often outperform heavy frameworks.
Sometimes, minimalism is the best architecture.

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Progress doesn’t need approval
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📌 Machine Learning Basics: Classification
Hello everyone 👋
Today I want to share one of the core concepts of Machine Learning — Classification.
🔹 What is Classification?
Classification is a type of supervised learning where the goal is to predict a category (label) instead of a number.
The model learns from labeled data and uses that knowledge to classify new, unseen data.
🔹 Common Examples

* Email → Spam or Not Spam
* Tumor → Malignant or Benign
* Transaction → Fraud or Normal
* Student result → Pass or Fail

🔹 Binary Classification
Most beginner problems are binary classification, meaning there are only two possible outcomes:

* Yes / No
* True / False
* Positive / Negative
* 1 / 0

In ML:

* 1 usually represents the positive class
* 0 represents the negative class

Example:
Malignant → 1
Benign → 0
🔹 Why Classification Matters
Classification is widely used in:

* Healthcare 🏥
* Cybersecurity 🔐
* Finance 💳
* Email filtering 📧
* Image & text recognition 🤖

Sometimes we wonder why things don’t happen instantly, but maybe the waiting is where life finds its meaning

I am open for new perspectives but how I see it is some of the things we once wished and prayed for have already happened. And that becomes hope that the other things we still are dreaming of will happen too.

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And the things we are praying for now, they are also waiting for their own time. While we wait, we grow. While we wait, we become.

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Jessica, co-founder and CEO of Sol, shares her journey from being the youngest quantitative researcher at a major hedge fund to leading a startup focused on agentic process automation. Sol is an AI-driven platform designed to automate critical and operational workflows for enterprises much more efficiently than traditional Robotic Process Automation (RPA) tools. She is 22 years old btw🫡 YouTube

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Moral of the story: just post your work, guys. You never know who’s watching.

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@natyiu0

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