From Data Centers to Dorm Rooms

You know that one guy in your DSA class who still aura farms by mentioning that AI model he ran on Ollama three months ago?

Yeah, we’ve all met him.

At first glance, it sounds like a niche flex. Why would anyone want to run a mini-ChatGPT on their laptop when powerful AI models already exist online? Why spend time setting up local models when you can simply open a browser tab and ask ChatGPT whatever you want?

The answer is that something much bigger is happening behind the scenes.

What the personal computer did for computing, local LLMs are beginning to do for artificial intelligence.

A few decades ago, computers occupied entire rooms. They consumed enormous amounts of power, required specialized hardware, and were accessible only to large organizations. Over time, engineers figured out how to squeeze that computing power into desktops, then laptops, and eventually smartphones that now spend most of their lives being used to watch Instagram reels.

AI seems to be following the exact same trajectory.

The most powerful language models today still live inside massive data centers packed with GPUs worth more than a college student’s annual tuition. Yet somehow, people are running surprisingly capable AI models on MacBooks, gaming laptops, and even some smartphones.

That raises an interesting question: how do you fit something as massive as a large language model onto a machine that starts sounding like a jet engine when Chrome has fifteen tabs open?

Think of it like trying to fit an entire cupboard into a backpack. You can’t take everything with you. You need to fold things differently, prioritize what’s important, and maybe leave a few things behind.

That idea is at the heart of one of the biggest trends in AI today: making models smaller without making them feel smaller.

The Shift from Bigger to Smarter

For a long time, the AI industry was obsessed with size. Bigger models. More parameters. More GPUs. More everything. The assumption was simple: if a model is larger, it must be smarter.

But eventually engineers discovered something surprising. Most everyday tasks don’t require the equivalent of an AI superhero.

If you’re summarizing notes before an exam, getting help with code, drafting emails, or explaining a difficult concept, you probably don’t need one of the largest models ever created. A smaller model can often do the job remarkably well.

This realization gave rise to smaller language models designed specifically for efficiency. Instead of carrying the entire cupboard, they only carry what matters most.

Quantization: Packing the Backpack Better

Of course, that still wasn’t enough.

Engineers then turned to a technique called quantization. The easiest way to think about quantization is through image compression. When you upload a photo to social media, some quality is lost, but most people never notice. Quantization does something similar to AI models. It reduces the precision of the numbers stored inside the model, making it significantly smaller and faster while preserving most of its capabilities.

In other words, the model becomes lighter without suddenly forgetting how to do its job.

Distillation: Learning from the Smart Kid’s Notes

Then came model distillation, which might be one of the coolest ideas in modern AI. Imagine it’s the night before an exam and your friend has already studied the entire textbook. Instead of reading all six hundred pages yourself, you ask for their summary notes. You won’t learn every detail, but you’ll probably understand the important concepts.

That’s essentially what happens during distillation. A large, highly capable “teacher” model passes its knowledge to a smaller “student” model. The student learns the important patterns without inheriting all the complexity. The result is a model that is dramatically smaller yet surprisingly capable.

Fine-Tuning: Turning Generalists into Specialists

As AI became more accessible, people wanted models tailored to specific tasks. That’s where fine-tuning enters the story. Think of it like choosing a specialization after learning the fundamentals. A general-purpose model can be further trained for coding, medicine, finance, research, or customer support. Instead of building an entirely new model from scratch, engineers simply adapt an existing one to become exceptionally good at a particular job.

Mixture of Experts: Calling the Right Specialist

Around the same time, researchers realized another important thing: not every part of a model needs to work on every problem. This led to a concept called Mixture of Experts.

Imagine a college project team. One person handles coding, another handles design, another manages research, and someone else takes care of the presentation. If you need a logo designed, you don’t suddenly call the coding lead. You go to the person best suited for that task.

Mixture of Experts models operate similarly. Different parts of the model specialize in different areas, and only the relevant experts are activated when needed. The result is higher efficiency without sacrificing capability.

Why Should We Care?

And this is why local AI matters.

Running models locally isn’t just a fun side project for computer science students. It represents a shift toward personal AI. When models run on your device, they can work offline, offer greater privacy, and be customized to your needs. Your prompts don’t necessarily have to leave your machine. Your AI assistant becomes something you own rather than something you temporarily borrow from a server somewhere across the world.

The bigger picture is even more interesting. For years, the future of AI seemed to be about building larger and larger models. Today, the conversation is increasingly about efficiency, accessibility, and personalization. The goal is no longer just creating the smartest model possible. The goal is creating models that ordinary people can actually use.

The Future of Personal AI

If the last few decades were about putting computers into everyone’s hands, the next decade might be about putting AI into everyone’s pocket.

And if that happens, the guy still talking about the model he ran on Ollama three months ago may have been onto something after all.

The Science Behind Running LLMs Locally was originally published in Mozilla Firefox Club VIT Vellore on Medium, where people are continuing the conversation by highlighting and responding to this story.

You just spent your entire weekend grinding through a 4-hour YouTube tutorial on building a full-stack MERN app. You hooked up your frontend to your backend, hit submit, and… boom! You’re logged in.

You lean back in your chair and think, “Yeah, I am a coding god.”

When a user logs in, your server sends back a security token (a JWT), and you save it straight into your browser’s LocalStorage. You push the code to GitHub and get ready to launch it to the world.

But… oh no.

What if I told you that putting this code on deployment is actually a massive security vulnerability? And what if I told you that this exact mistake is how real organizations and companies get breached by attackers?

Let’s talk about why LocalStorage is a trap, and how you should actually be writing production-grade code.

The Crime Scene: LocalStorage

LocalStorage is super convenient. It lets you save data in the browser so that even if the user refreshes the page, they stay logged in.

But LocalStorage has a very dark secret: It can be read by any JavaScript running on your page.

You might be thinking, “Wait, so what? I wrote all the code on my website. I’m obviously not going to steal my own users’ passwords!”

Yeah, you’re right, you won’t. But modern web apps use tons of external libraries. You probably installed dozens of packages for React, Tailwind, and icons. What happens if one of those packages gets compromised by attackers? Or what if your website has a comment section where a clever hacker sneaks in a malicious script?

This nightmare scenario is called XSS (Cross-Site Scripting).

The Attack: The “Oh No” Moment

If an attacker successfully sneaks their own JavaScript onto your website, stealing your user’s token is laughably easy. They just tell the browser to grab whatever is in LocalStorage and send it silently to their evil server.

Yep, it’s that easy. Once the attacker has the token, they are the user. They don’t need a password. If your app was deployed for a real company, those attackers could now change user emails, delete accounts, or steal sensitive customer data.

The Solution: HTTP-Only Cookies to the Rescue

Okay, so if LocalStorage is basically leaving the front door of your organization wide open, where the heck do we put the token?

The production-grade answer is an HTTP-Only Cookie.

Instead of sending the token for your frontend to handle, your backend should attach the token to a special cookie and send it directly to the browser.

Here is the magic part: When you set a cookie as HTTP-Only, you are acting like a strict bouncer at a VIP club. You're telling the browser: "Under absolutely NO circumstances should you let JavaScript touch this cookie."

If our attacker from earlier tries to run a script to steal the token, the browser will just ignore them. The token is completely invisible to the frontend code!

“But wait, how does the frontend send the token to the backend then?”

That’s the best part! Whenever your app makes an API request to your backend, the browser automatically attaches this cookie under the hood. Your frontend code doesn’t even need to know the token exists. It just magically works.

The “But Wait…” (Handling CSRF)

If you use cookies in a real-world application, you have to be aware of a different threat: “Aren’t cookies vulnerable to CSRF attacks?”

CSRF (Cross-Site Request Forgery) is a different type of attack where a hacker tricks your browser into making a request on a different tab.

But don’t panic, modern web dev has an incredibly easy fix. You just add a SameSite: Strict rule to your cookie. This tells the browser to only send the cookie if the request is actually coming from your exact website. Boom. CSRF attack blocked.

The Production-Grade Mindset

Listen, knowing how to build a basic MERN stack app locally is awesome. But the harsh reality is that thousands of developers can build a CRUD app by following a YouTube tutorial.

To actually stand out and build applications that companies can trust, you need to think about what happens on deployment.

The next time you build a full-stack app, don’t just ask, “Does it work?” Ask yourself: “Is this production-grade? Are my users protected from attackers?”

When you stop treating security as an afterthought and start implementing real defenses like HTTP-Only cookies, you elevate yourself from someone following tutorials to a serious engineer ready for the industry.

Happy coding, and stay secure!

Stop Storing JWTs in LocalStorage! was originally published in Mozilla Firefox Club VIT Vellore on Medium, where people are continuing the conversation by highlighting and responding to this story.

It’s 2AM and you’re still awake staring at you laptop and your body tired . A question comes into your head, a brain storming coding error or the name of a movie you have watched years ago. You typed it, press Enter, and before your finger leaves the key, a list of answers appears.

It feels like magic. It feels instantaneous. But the moment your fingers hit the key, a silent drama begins a high speed relay spanning thousands of miles of networks, completed in very less time than a blink. As members of the Mozilla Firefox Club at VIT Vellore, searching is second nature to us, we search enormous things on web but have you ever peeked behind the curtain? Every query triggers a huge group of networking, security, and data.

Let’s walk through the life cycle of a single click when you searchs everytime .

Step 1: The Invisible Chain Reaction

The moment you enter any query on any browser (let’s say google chrome) and when you press Enter, your browser’s address bar transforms into a launching pad. It converts your plain-text words into a specific digital request. On the surface this looks seamless and very fast , but there’s a language barrier:
The address bar acts as both as a navigation bar and a search interference.
even if you type something other than a web address, the browser recognizes that what you want to search and prepares to send request to the servers.
But here is the twist computers don’t understand what you want to search but understand the specifc IP addresss

Step 2: DNS — Finding Google’s Address
DNS (Domain Name System) is the internet’s phonebook

Humans remember names, but computers need numerical addresses. This is where DNS comes in it helps convert domain names into IP addresses.

The browser first checks its own cache to see if it already knows Google’s address. If not, it checks the operating system cache, then the router cache, and then the ISP’s DNS server.

If the address still isn’t found, the request travels through the DNS hierarchy root servers, top level domain servers (.com), and Google’s authoritative DNS servers until the correct IP address is found.

Step 3: The Digital Handshake: Establishing a secure connection
Devices agree how they will communicate before any search data is moved or before you get results (eg: your favourite movie):

They communicate using TCP which provides reliable communication between your device and google’s server.

The browser sends a SYN ( synchronise ) request , the server responds with SYN-ACK , and the browser acknowledges with ACK (Acknowledge).

Then a secure HTTPS connection is established using TLS (Transport Layer Security).In this process, Google presents its security certificate and the browser verifies it to make sure that your search query can’t be intercepted.

Step 4: Sending the Query Across the Internet
After the communication is establish between the browser and Google’s server, your query begins travelling across the internet in the search of your movie choice.
The request is broken into small packets of data, each containing routing information and parts of your query which you type in the search bar of the browser.
These packets move from your device to your router, then to your Internet Service Provider(ISP),
through the backend networks, and finally to google’s nearest data center.

Step 5: What the Server Sees
When the server reassembles your packets, it gets more than the keywords you typed. It also sees metadata like:

browser type

referred language

approximate location

device information

cookies

login session data

This context personalizes results and make it more relevance to your search query. For example, “best pizza places” will return local recommendations rather than distant top-rated spots.

Step 6: Understanding What You Actually Mean?
Google doesn’t just act like a dumb dictionary looking for exact words. Instead, it tries to understand your intent.It tries to act like a smart friend who can read between the lines

Let’s understand this with an example.

If you type “Apple”, Google looks for clues to decide whether you mean the fruit or the technology company.

It also works with conversational queries like “Should I carry an umbrella?”, interpreting that as “Is it going to rain near me?”

It also works on your search history: For ex: if you searched one movie 100 times , then 101th times when you will search that movie it will be automatically displayed In your suggestion bar.

Essentially, Google has moved beyond simply looking at what you typed and now focuses on why you typed it.

Step 7: Searching the Index, Not the Live Web
A common misconception is that Google searches the entire internet in real time whenever you make a query.

“That would be far too slow.”

Instead, Google searches its own massive index a giant database of web pages that Google has already discovered and stored.

This index is built by crawlers like Googlebot, which continuously visit websites, follow links, analyze content, and update Google’s database.

Think of the internet as a giant library, and Google’s index as the catalog that helps find books quickly.

Step 8: The Balancing Act of Ranking
When relevant pages are pulled from the index, algorithms decide the order you see them by weighing many factors: freshness, domain authority, backlinks, user experience, page speed, mobile-friendliness, HTTPS security, and location.

Example: You search “best wireless headphones under 3,000.”

• Freshness: recent reviews get priority.
• Authority: established tech sites outrank unknown blogs.
• Relevance: results match the 3,000 budget.
• Localisation: shown products and sellers available in your region.
  The algorithm balances these signals to surface results you’ll find useful and actionable.

Step 9: Bringing the Page to Life
After ranking, the server packages the chosen results as HTML, CSS, and JavaScript and sends them back. Your browser parses the HTML, applies styles, runs scripts, and fetches images. In under a second, the search results page appears rendered and ready.

Let’s End A Little Marvel of Engineering

Searching is like a magic trick: You type, you press enter, answers appear. But it’s not magic , it’s a remarkable relay of networking protocols, security checks, massive indexes, and algorithmic decisions all working in concert at lightning speed. And for us web builders and curious users at the Mozilla Firefox Club, this backstage choreography makes everyday clicks small marvels. So next time you type a question, take a moment to wonder at the lightning-fast relay race going on beneath your fingertips.

In our day to day life… we don’t seem to be bothered about the great and wonderful engineering concept behind the thing always…

What Happens Every Time When you Search on Google was originally published in Mozilla Firefox Club VIT Vellore on Medium, where people are continuing the conversation by highlighting and responding to this story.

Modern warfare is no longer fiction, it is a new-age reality. While traditional military operations relied on artillery, conventional rockets, guns, and tanks, modern warfare makes use of advanced technologies to achieve precision and reduce human casualties. Einstein warned that if humanity were ever to engage in a global war, its aftermath could be so devastating that future conflicts would be fought with sticks and stones. Yet today, we live in a different reality, one where the next wars are more likely to be fought with code and algorithms than with crude or even nuclear weapons.

To get a better idea of the importance of AI in modern warfare, consider a hypothetical scenario: State Red and State Blue find themselves engaged in a conflict wherein the former is equipped with modern AI tools and drone systems, while the latter continues to depend on conventional technologies.

Both nations, sharing territorial boundaries, possess access to satellite surveillance and radar intelligence. State Red leverages its technological superiority through AI-driven analysis of data to identify strategic targets, while State Blue manually evaluates potential attack positions. The Red camp efficiently identifies objectives, secures targets, and shares valuable intel with allies through advanced communication networks. It employs advanced autonomous stealth drones for precision strikes, minimizing military casualties. Conversely, the Blue camp experiences significant losses.

This scenario represents modern military dynamics. Technological superiority enables rapid data analysis through pattern recognition and secure cloud-based communication systems, providing significant tactical advantages. While warfare remains inherently destructive, modern technologies-including autonomous drones, AI systems, advanced radar, and cyberwarfare capabilities substantially reduce military casualties. These elements constitute the fundamental components of modern warfare.

This article takes a deep dive into three crucial components helping in military transformation: Drone warfare, Cyberwarfare, and Radar and Imaging technologies.

Drone Warfare: Autonomous Aerial Systems

Unmanned Aerial Vehicles (UAVs), or autonomous drones, are modern drone machines operating without direct human control. These systems have become increasingly important at a time when we are seeing developments in modern defence systems that can down conventional aircrafts. UAVs utilize advanced algorithms, sensors, and artificial intelligence (AI) to navigate, make decisions, and execute tasks autonomously. These systems utilize complex hardware-software integration for sensor data processing, flight plan execution, and environmental adaptation.

Autonomous drone operations involve several critical phases:

• Data Processing: Environmental information acquisition through sensors and LiDAR technologies, incorporating UV, infrared, and optical imaging systems for terrain mapping.
• Flight/Trajectory Path: AI-driven determination of optimal flight paths based on collected data.
• Real-time Decision-making: AI algorithms employing pattern recognition and historical data analysis to adapt to dynamic conditions and potential threats.
• Mission Execution: Flight control systems implementing planned trajectories with real-time adjustments based on sensor inputs and AI analysis.

Autonomous drone platforms exhibit diverse configurations and capabilities, including single-rotor to multi-motor systems optimized for various operational environments. These systems vary in both function and size, with designs enabling high-altitude flight profiles as well as compact models ranging from 2–4 metres in size. Modern drone systems exhibit swarm intelligence, allowing multiple units to act collectively as a single force. Their autonomous technology significantly reduces military casualties. Ongoing technological evolution indicates future integration of advanced microcontrollers (MCUs) in next-generation autonomous vehicles. These microcontrollers allow the vehicle to detect a malfunction and then automatically stop the system safely without any intervention from the operator.

Cyberwarfare: Digital Battlefield Operations

Cyberwarfare refers to conflict conducted through digital infrastructure, where nations employ cyber attacks against hostile systems during military operations. This domain has become increasingly prevalent in modern conflict. Hence, understanding basic cyber attack methodologies is essential as an aware citizen:

• Espionage: Strategic intelligence collection through system infiltration, utilizing botnets and spear phishing techniques to compromise sensitive networks and extract classified information.
• Infrastructure Sabotage: Systematic degradation of adversary digital infrastructure through targeted cyberattacks, including military systems corruption and critical infrastructure disruption (transportation networks, power distribution, healthcare facilities).
• Denial-of-service (DoS) Operations: Network access denial through bot-request spams, making critical military and research platforms inaccessible.
• Propaganda attacks: Strategic narrative control through AI-generated content manipulation, misinformation, social media exploitation, and platform compromise to influence public perception. The goal is to influence minds and destroy or build trust.
• Economic Warfare: Targeting financial systems and institutions to disrupt economic stability and resource accessibility.

Let’s briefly examine two classic examples of cyberwarfare, Stuxnet and the Eternal Blue Vulnerability:

• Stuxnet: Stuxnet was one of the most infamous and sophisticated cyberattacks in history. This malware, though less than a megabyte in size, was secretly embedded into Iranian nuclear centrifuge systems-damaging them over time and weakening Iran’s nuclear abilities. This operation demonstrated the potential impact of self-replicating malware on national infrastructure.
• The Eternal Blue vulnerability: Exposed by a group of hackers called the Shadow Brokers in 2017, the Eternal Blue vulnerability exploited critical Windows operating system vulnerabilities, enabling unauthorized remote access without the necessity of user authentication. Microsoft subsequently implemented security patches. This incident illustrated the extensive economic impact potential of cyber weapons.

Evolving cyber threats necessitate enhanced system security protocols. The cybersecurity sector has experienced significant growth, with white-hat hackers using advanced tools such as Metasploit and Nmap for vulnerability assessment through port scanning, penetration testing, and version control analysis. Machine learning applications facilitate threat detection and mitigation. Major companies and MNCs conduct bug bounty programs to expose threats in their software; all in exchange for prize money.

Increasing cyber warfare activity mandates heightened user awareness. Implementation of multi-factor authentication, regular software updates, and careful evaluation of external communications can minimize cyberattack vulnerability, particularly during periods of conflict.

Radar, Sensors, and Modern Imaging Systems:

RADAR (Radio Detection and Ranging) technology employs electromagnetic waves for spatial positioning and tracking of objects. Military applications continue to drive radar development. AI and ML find their applications in military radar systems:

• Active Electronically Scanned Array (AESA) radar: Advanced phased array systems utilizing electronically steered radio waves, eliminating mechanical components for enhanced scanning efficiency. Modern military radars incorporate AI for target detection and tracking, implementing deep learning for adaptive beamforming through neural network processing.
• ML algorithms can be trained using existing datasets to distinguish between different types of emissions (for example, an advanced missile system vs. a conventional oncoming RPG vs. an oncoming drone). This reduces the time of decision-making.
• Recent and emerging computer vision architectures, such as CLIP (Contrastive Language-Image Pretraining), can potentially be used for military vehicle detection at extended ranges, demonstrating high precision and recall through zero-shot detection.
• Modern military applications integrate machine learning with LiDAR technology, supplementing traditional camera imagery. While optical systems provide two-dimensional data, LiDAR generates three-dimensional environmental mapping through laser reflection, enabling AI systems to analyze terrain features and tactical considerations.

Radar systems remain fundamental to military operations, evolving in parallel with AI and machine learning, incorporating them to enhance detection and tracking capabilities while providing real-time battlefield intelligence.

Military technology continues rapid evolution. While autonomous systems and advanced radar platforms reduce military casualties and operational costs, they call for an increased ethical oversight, and global security awareness. Some of these technologies have already been deployed on the battlefield. For instance, the Russia-Ukraine war saw the advent of swarm drone intelligence; Israel carried out remote pager-triggered attacks on Hezbollah targets; and recent Pakistani drone attacks took place on Indian soil. As we continue to hastily move forward into an AI era, we must pause and think: Are we building tools to protect human lives, or perfecting destruction? No matter how far we progress, advancement holds no value if we forget that the greatest strength lies in peace over war.

Neural Wars: Power Without A Trigger was originally published in Mozilla Firefox Club VIT Vellore on Medium, where people are continuing the conversation by highlighting and responding to this story.

Structure of Blockchain:

1. Block Structure: • Each block inside the diagram contains these elements: A hash: This represents a special block identifier which processes the block contents. Transactions: Every online voting transaction registers an individual voter choice. Transaction N: Each box symbolizes an independent vote recorded within the block.

2. Block Connecting (Blockchain): Arrows connecting blocks represent the connecting process where a block refers to the hash of the previous block. When the block is manipulated, its hash changes resulting in an interruption of linking with the next block thus ensuring data integrity and security.

3.Internet Vote Use: Each vote (transaction) is recorded into a block. Once the block is full (reaches a predetermined count of votes), it is hashed and linked to the previous block. The blockchain ledger — unchangeable chain of cryptographically linked blocks — builds up an open, tamper-free record of votes. This preserves data integrity by preventing votes being changed, deleted or copied without approval, and making it possible to provide an audit trail that can be validated where each voter can individually confirm that their vote has been counted without betraying others’ anonymity.

Core component of blockchain architecture:

1. Node: In an internet vote system on the blockchain, a node is a computer entity or device participating in the vote network by performing central operations at set time intervals. Each node contains a complete copy of the blockchain and helps in recording, verification, and forwarding voting transactions. These operations are carried out within certain time intervals: the node receives votes during the receive interval, verifies and verifies votes during validation process, propagates authenticated transactions to other nodes during forward, and replicates its local version of the blockchain during store. Nodes can be e-voting centre receiving votes and voter authentication or validating stations that verify transactions and achieve consensus. The exact intervals for each function may vary based on the configuration and role of the node within the network.
2. Transaction: A transaction is the record of an action in a ledger, such as a vote being cast.
   For online voting, every time an individual votes, that vote is a transaction. Transactions are propagated to the network for verification.
3. Chain: The chain is a sequence of blocks linked together.
   Every block contains a set of transactions (votes), and the chain links the blocks in chronological order. The chain creates an impenetrable, irreversible history of all the votes that were ever cast.
4. Miners: Miners are tasked with creating new blocks and verifying transactions.
   Mining is the process through which they utilize computational power to decipher difficult mathematical problems; once a miner has validated a block, it is added to the blockchain, and the miner gets rewarded.
5. The agreement between all computers in a blockchain network about valid transactions defines consensus. This system secures itself through consensus while preventing fraud because all users maintain identical blockchain versions. The blockchain network uses two primary consensus methods: Proof of Work and Proof of Stake.
6. Blockchain (Centre): The blockchain itself is at the center, and this is a decentralized and immutable record book which has stored every transaction safely. When new transactions are added, the blockchain is updated and confirmed in real-time.

Transformation in Electronic Voting System:

The picture indicates how voting becomes more secure, transparent, and trustworthy because of blockchain technology compared to the traditional voting system and the blockchain voting system. The voters in the traditional system cast their votes with paper ballots or electronic voting machines powered by a central server. The server stores, gathers, and counts all the votes. This configuration has its disadvantages, nonetheless, as all the data are contained in a single location, making it easier to hack, manipulate, and corrupt. The outcome of the election is at the mercy of hackers if they were able to breach this central server. It is also difficult for voters to verify if their vote was counted. This makes them lose confidence in the results.
But the Blockchain Voting System does things differently. For example, it uses a network of decentralized computers, or nodes, instead of a central server. Voting is recorded securely on a mutual digital ledger across the nodes. This way, the vote is imprinted on the chain once and forever and cannot be destroyed or altered later. It is virtually impossible for the hackers to change or steal the votes in secret since the data is dispersed (decentralized). Additionally, the blockchain employs sophisticated mathematics methods (cryptographic hashing) to secure the data and ensure the entire process very secure.

While traditional voting models offer transparency in the form of open vote counting and paper ballots that can be audited, blockchain voting offers a higher level of digital transparency. Voters can personally verify their vote was recorded correctly — without revealing their identity or getting others’ votes. Such end-to-end verifiability is achieved by employing consensus mechanisms such as Proof of Work or Proof of Stake, which also ensure the correctness of each ballot cast and inserted in the blockchain. The system is far more reliable since there are numerous nodes which share and confirm the data rather than going through a single central authority.

Decentralization: What is it?

Instead of being controlled by a common central authority, decentralization is the spreading of information and decision-making across many locations or participants. With regards to Blockchain voting means making votes on numerous computers (nodes) that are connected to each other by the blockchain network rather than putting them on a single server. As no single party can alter or fake the data by itself, it increases the system security. The remaining nodes have the proper copy of the vote information, making it accurate and transparent even if one of them is hacked.

Working architecture:

The illustration is of how an internet-based voting system with the use of blockchain technology and high-level security measures makes the voting transparent, secure, and reliable. Voter registration is the first step, where individuals register to vote on a secure web-based system. Vote authentication is necessary in order to ensure that only eligible voters can cast their votes. Secure log in and biometric approaches, e.g., fingerprint recognition on a mobile phone, are excellent examples in this regard. HTTPS ensures data confidentiality and integrity in transit during the voting process by establishing an end-to-end secure, encrypted TLS (Transport Layer Security) channel between client and server. It prevents interception, tampering, or unauthorized access to the transmitted information.
The system securely manages digital keys through SSH Key Management and Digital Key Management upon authenticating the voter. The system uses Public Key Infrastructure (PKI) and cryptographic signatures upon authenticating the voter, thereby securely managing digital credentials. This makes each vote be safely encrypted, authenticated, and tamper-proof.
The Blockchain Network documents the vote. Every vote is made to become an irrevocable and non-deletable link in the chain due to blockchain technology. A vote can never be changed or deleted after it has been posted. Blockchain is extremely safe and secure due to the way it connects each vote into a chain.

The system employs a Vote Management platform integrated with a Voter List to facilitate an authentic and trustworthy voting process. In a decentralized system of voting, upon the authentication of a voter’s identity, Public Key Infrastructure (PKI) and digital signatures are employed to securely issue and authenticate digital credentials. The process guarantees votes as independently verifiable, encrypted, and tamper-proof — free from reliance on a central authority.
This voting system solves most of the issues related to conventional online voting using the combination of blockchain technology, advanced methods of secure authentication, and adequate key management. Blockchain’s distributed architecture makes it impossible to have a central control over the information, hence much more secure against tampering or cracking. With its open, safe, and trustworthy method of conducting online voting, this system makes people bring additional trust to the electoral process.

Conclusion:

In conclusion, an online voting system based on blockchain offers a new means of proposing elections based on security, transparency, and data integrity. As compared to other voting systems that are sticked to centralization attacks and tampering, blockchain technology offers the possibility of ensuring that votes are stored securely, irreversibly, and accessible with ease. By using decentralized networks, secure authentication, and robust key management, the system can make it hard to hack or manipulate votes. Moreover, blockchain’s transparency and traceability ensures public trust, so it would be an ideal solution for today’s online voting platforms. Implementing blockchain in elections would ultimately enhance the precision, reliability, and credibility of democratic processes.

Thanks

Research and writing by

Pranjal Sahay

“Click, Vote, Secure: The Blockchain Election” was originally published in Mozilla Firefox Club VIT Vellore on Medium, where people are continuing the conversation by highlighting and responding to this story.

What does open source mean?

Open source refers to a type of software licensing model in which the source code of a program is made publicly available for anyone to view, use, modify, and distribute. The core idea of open source is collaboration and transparency, where developers worldwide can contribute to improving the software, fixing bugs, or creating new features.

Key Features:

1. Free Access to Source Code
   In open-source software, the source code (the code written by developers) is freely available to the public. This allows anyone to examine how the software works, learn from it, and make their own changes.
2. Freedom to Modify and Redistribute
   Users can modify the software to suit their needs and even distribute their modified versions to others. This encourages innovation and customization for various use cases.
3. Community Collaboration
   Open-source projects thrive on community contributions. Multiple developers or even large communities work together to improve the software, submit patches, report bugs, and suggest new features. Successful projects often have hundreds or thousands of contributors.
4. Transparency and Security
   Because the source code is open, anyone can inspect it for security vulnerabilities or issues. This transparency can lead to quicker identification of bugs and stronger security compared to closed-source software, which might rely on fewer developers.
5. No Licensing Fees
   Open-source software is typically free to use. Although some open-source projects offer paid support services, the software itself can be used and modified without any cost.
6. License Requirements
   Open-source software is governed by specific licenses that outline how the code can be used, modified, and shared. Some popular licenses include the MIT License, GNU General Public License (GPL), and Apache License. These licenses ensure that the software remains open and free to use under certain conditions.

Why is open source important?

1. Real-World Experience and Skill Development

Open-source projects provide hands-on experience in solving real-world problems. Unlike classroom assignments, open-source projects are often complex, large-scale, and involve multiple contributors. By participating, you learn:

· Version control (Git, GitHub, GitLab)

· Code reviews: You will get feedback on your contributions from experienced developers.

· Collaboration: Working with developers across the globe, often in different time zones and with different expertise.

· Debugging and testing: These are essential skills in software development, and working on open-source helps you improve them.

2. Exposure to Industry Practices

Contributing to open-source projects exposes you to industry-standard development tools, techniques, and methodologies. It helps bridge the gap between academic learning and the practical skills required in the job market.

For example, you will work on:

· Continuous Integration/Continuous Deployment (CI/CD) Pipelines

· Agile/Scrum frameworks

· Automated testing tools

· This exposure can help you become familiar with the tools and workflows you will encounter in the tech industry.

3. Networking and Community Involvement

Open-source communities are large, active, and global. By contributing, you become a part of a network of like-minded professionals and developers. You can build relationships with mentors and peers, which can lead to collaboration opportunities or job offers.

Prominent open-source contributors are often recognized within their communities, leading to speaking engagements at conferences, meetups, or even opportunities for leadership roles within open-source projects.

4. Building an Impressive Portfolio

Open-source contributions can be showcased on platforms like GitHub, creating a portfolio that demonstrates your skills to potential employers. Unlike personal projects, open-source contributions have real-world significance since they are used by other people or organizations.

Your open-source contributions can reflect:

· Your expertise in a particular language or technology.

· Your ability to work in teams and handle complex software systems.

· Your passion for coding and learning beyond classroom requirements.

5. Enhancing Problem-Solving and Analytical Skills

Open-source projects often deal with solving challenging issues. By working on such projects, you will improve your problem-solving and critical thinking abilities, which are crucial for a successful career in computer science and engineering.

6. Contributing to Social Good and Innovation

Many open-source projects aim to solve global issues, whether it’s improving accessibility for disabled users, building healthcare software for developing countries, or creating educational tools. By contributing, you can be part of a movement that creates social impact through technology.

7. Learning New Technologies

Open-source contributions often require you to work on diverse projects involving different languages, frameworks, and technologies. This constant exposure to new tools helps you stay up-to-date in the fast-evolving tech world.

8. Recognition and Career Opportunities

Many employers value open-source contributions as much as, if not more than, formal work experience. Contributing to well-known projects can serve as proof of your technical skills and dedication. In some cases, companies even hire directly from open-source communities.

Examples of Open-Source Software:

1. Mozilla Firefox: A web browser developed by Mozilla, which is free and open-source.
2. Linux: One of the most popular open-source operating systems, used widely on servers and by developers.
3. VLC Media Player: A versatile media player that can handle a wide variety of audio and video formats.
4. WordPress: An open-source content management system (CMS) used for creating websites and blogs.
5. TensorFlow: An open-source machine learning framework developed by Google, widely used for AI applications.
6. Apache Hadoop: An open-source framework for distributed storage and processing of large data sets.

Examples of Closed-Source Software:

1. Microsoft Windows:
A popular operating system for personal computers, owned by Microsoft. The source code is not available to the public, and users need to purchase a license to use it.

2. Adobe Photoshop:
A leading image editing software developed by Adobe. Users cannot access or modify the source code, and it requires a subscription or one-time purchase.

3. macOS:
Apple’s proprietary operating system for Mac computers. Like Windows, macOS is closed-source, and its code is not available for modification or redistribution.

4. Microsoft Office:
A suite of productivity applications (Word, Excel, PowerPoint, etc.) that is closed-source and available for purchase through licensing or subscriptions (Microsoft 365).

5. Google Chrome:
While Google Chrome is based on an open-source project called Chromium, Chrome itself is closed-source. Some features of Chrome, such as its integration with Google services, are proprietary.

How to get started with open source?

1. Understand What Open Source Is

Before diving in, it is essential to understand the open-source model and the types of projects available. Open-source software is freely available to use, modify, and distribute, typically governed by licenses like MIT, GPL, or Apache License.

2. Set Up Git and GitHub

Most open-source projects are managed using Git (a version control system) and hosted on GitHub, GitLab, or Bitbucket. Here is how to get started:

• Install Git: You will need Git on your computer to manage versions of the code.
• For Linux: sudo apt-get install git
• For macOS: brew install git
• For Windows: Download it from git-scm.com.
• Create a GitHub account: Sign up on GitHub to start interacting with repositories (repos), forking projects, and submitting your contributions.
• Learn Git basics: It is important to understand basic Git commands for contributing to open source. Key concepts include:
• git clone: Clone a project repository to your local machine.
• git pull: Update your local copy with changes from the remote repository.
• git add, git commit, git push: Commands to stage, save, and push your changes to a repository.
• git branch and git merge: Working with different branches and combining changes.

3. Find an Open-Source Project

Start by selecting a project that interests you. There are countless open-source projects in different domains (web development, machine learning, cybersecurity, etc.). Here is how you can find them:

• GitHub Explore: Check out GitHub’s Explore page to find trending or beginner-friendly projects.
• First Contributions: This is a beginner-friendly project that guides you on how to make your first contribution. Visit firstcontributions.github.io for a hands-on tutorial.
• Good First Issues: Many projects on GitHub label beginner-friendly issues with “good first issue” or “help wanted.” These are ideal places to start. Search for these labels at goodfirstissues.com.

Popular Platforms for Open Source Projects:

• GitHub: github.com
• GitLab: gitlab.com
• Bitbucket: bitbucket.org

4. Start with Small Contributions

You don’t need to dive into complex code right away. Start with small tasks like:

• Fixing bugs: Look for minor bugs in a project and submit fixes.
• Improving documentation: Many projects need help with improving or updating their documentation. This is an excellent way to get familiar with the project.
• Testing: Test a project’s functionality, report bugs, or write unit tests.

5. Fork and Clone the Repository

Once you have identified a project to contribute to:

• Fork the repository: This creates a copy of the project under your own GitHub account.
• Clone the repository: Download it to your local machine using git clone <repo-url>.

6. Create a Branch

Before making changes, create a new branch for your contribution.

For example:

git checkout -b my-new-feature

This keeps your changes separate from the main codebase.

7. Make Your Contribution

After creating a branch, make your changes to the code or documentation. When you’re done, commit your changes:

git add .

git commit -m “Add feature X”

git push origin my-new-feature

8. Submit a Pull Request (PR)

Once you’ve made and pushed your changes:

• Go to the original project’s GitHub page.
• You should see an option to Create a Pull Request. This lets the project maintainers review your changes and merge them into the project if approved.

9. Engage with the Community

Open source is as much about collaboration as it is about code. Here’s how to stay engaged:

• Join discussions: Follow the project’s discussion boards or mailing lists.
• Attend community meetings: Some projects have regular calls or meetings for contributors.
• Respond to feedback: When you submit a pull request, maintainers may ask for changes. Engage in the discussion and refine your code.

10. Keep Learning and Contributing

As you grow more comfortable, take on larger and more challenging contributions. Regularly contributing will help you build your skills, portfolio, and reputation within the open-source community.

Open-Source Software Case Studies:

1. Mozilla Firefox:

Mozilla Firefox, often hailed as a pioneer in the world of open-source browsers, began its journey from the ashes of Netscape Navigator, one of the early web browsers. In the late 1990s, Netscape Communications Corporation faced stiff competition from Microsoft’s Internet Explorer. To stay relevant, Netscape made a bold decision in 1998: they released their browser’s source code to the public, birthing the Mozilla Project. This project, originally intended to produce a next-generation browser for Netscape, evolved into something much larger and community-driven.

In 2002, a small group of developers led by Blake Ross and Dave Hyatt within the Mozilla project started working on a lightweight, more user-focused browser, which they named Phoenix (to symbolize rising from the ashes of Netscape). After several name changes due to trademark issues, the browser was finally called Firefox.

Firefox is developed under the Mozilla Foundation, a non-profit organization that coordinates both community and corporate contributions. The development model is community-driven but organized by the Mozilla Foundation to ensure quality control and vision.

Features:

· Community Contributions:

Developers from around the world can contribute to the Firefox codebase. Contributions range from bug fixes and new feature suggestions to localization (translating the browser into different languages).

· Modular Design:

Firefox is built in a modular fashion, with different teams working on specific components such as the Gecko rendering engine (responsible for displaying web content) and the Quantum engine (introduced to improve speed and performance).

· Review and Testing:

Contributions undergo thorough review by Mozilla’s core development team. There’s a strong focus on testing and security, with many beta versions released to the community for testing before rolling out stable releases.

· Innovation:

Mozilla has been instrumental in pushing web standards forward. HTML5, CSS3, and JavaScript advancements owe much to Mozilla’s contributions. Privacy and user-centric features (such as Do Not Track and Enhanced Tracking Protection) have also been Firefox’s hallmark.

Impact:

· Competition with Internet Explorer:

Firefox’s open-source nature helped break Microsoft’s monopoly over the browser market in the 2000s.

· Privacy Advocacy:

Mozilla has been a staunch advocate for user privacy, standing out in a time when other browsers began leveraging user data for ads.

· Open Web Standards:

Firefox’s development paved the way for open web standards, which led to a more interoperable and user-friendly internet experience.

The above diagram showcases the development workflow of Mozilla Firefox.

· Development Branch:

Contributions are made to the codebase by community members.

· Beta Branch:

A testing environment where new features and bug fixes are tested by users and developers.

· Release Branch:

Stable releases are made available to end users after thorough testing.

2. Linux:

The Linux kernel, developed by Linus Torvalds in 1991, has grown to become the backbone of modern technology infrastructure. It all started when Linus, a Finnish computer science student, wanted to create a free operating system kernel for his own use, inspired by MINIX, a Unix-like operating system. He announced the project on an online newsgroup, and soon, developers from around the world started contributing, marking the birth of a global collaborative project.

Linux was created to be a free alternative to proprietary Unix systems, giving users and developers complete control over their system’s code. The decision to make Linux open source under the GNU General Public License (GPL) was pivotal, as it allowed anyone to use, modify, and distribute the kernel freely.

Linux is one of the largest and most successful examples of open-source software development. It thrives on the contributions of thousands of developers, from hobbyists to corporate giants like Google, Intel, and IBM. Here’s how the development of Linux works:

· Distributed Contributions:

Thousands of developers worldwide contribute to Linux, submitting patches (changes) to improve the kernel’s functionality, fix bugs, or add new hardware support.

· Maintainers and Linus Torvalds:

The Linux kernel is divided into several subsystems, each overseen by a maintainer. Maintainers review contributions and forward the approved changes to Linus Torvalds, who ultimately decides which patches get integrated into the mainline kernel.

· Collaborative Governance:

Linux doesn’t have a single central authority dictating its development. Instead, it follows a meritocratic model, where contributors who consistently produce high-quality code become maintainers and lead parts of the kernel.

· Corporate Involvement:

While Linux started as a hobby project, it is now heavily supported by corporations. Companies like Red Hat, Intel, and Google contribute to the Linux kernel because their products depend on it. For example, Android, the world’s most popular mobile operating system, runs on a modified version of the Linux kernel.

Impact:

· Dominance in Servers and Supercomputers:

Linux is the dominant operating system for servers, supercomputers, and cloud infrastructures. Major tech companies, including Google, Facebook, and Amazon, run their servers on Linux due to its flexibility, stability, and security.

· Mobile Revolution:

Android, which powers billions of devices globally, is based on Linux, making Linux a critical part of the smartphone revolution.

· Innovation through Open Source:

The open-source model of Linux has led to constant innovation. New features and security improvements are introduced by a global community of developers and organizations, ensuring that Linux remains cutting-edge.

This picture showcases the development process for the Linux kernel.

· Contributors:

Thousands of developers submit patches to improve the kernel.

· Maintainers:

Subsystem maintainers review patches for specific areas of the kernel.

· Linus Torvalds:

Torvalds decides which patches are included in the final kernel release.

· Distributions:

Various distributions (Ubuntu, Fedora, etc.) take the mainline kernel and integrate it into complete operating systems.

Both Mozilla Firefox and Linux are outstanding examples of how open-source software can drive innovation, stability, and wide adoption through collaboration. Firefox’s focus on privacy and user experience reshaped the browser landscape, while Linux’s flexibility and robustness have made it the backbone of modern computing. Their open-source models show how a global community, unified by shared goals, can build technologies that serve billions of people worldwide.

Useful Resources for Beginners:

First Contributions: First Contributions GitHub Repo — a project that simplifies the contribution process for beginners.

GitHub Learning Lab: GitHub Learning Lab — offers interactive tutorials for Git, GitHub, and open source.

Up for Grabs: Up For Grabs — a list of projects with beginner-friendly issues.

Good First Issues: goodfirstissues.com — lists issues across GitHub repositories suitable for first-time contributors.

Git documentation: Git Documentation — an official resource for learning Git.

Conclusion

Contributing to open-source projects is an excellent way for developers to gain hands-on experience, improve their coding skills, and collaborate with a global community. By following a structured approach — learning Git, finding beginner-friendly projects, and making small contributions — you can gradually become a valuable part of the open-source ecosystem. Whether you’re fixing bugs, enhancing documentation, or building new features, open source offers countless opportunities for personal growth, networking, and giving back to the developer community. Dive in, and start your open-source journey today!

Unlocking the World of Open Source: A Beginner’s Guide to Contributing and Growing was originally published in Mozilla Firefox Club VIT Vellore on Medium, where people are continuing the conversation by highlighting and responding to this story.

What is Red Teaming?

Red teaming involves testing an organization’s security by emulating the tactics, techniques, and procedures (TTPs) used by real-world cyber attackers. Unlike traditional penetration testing, which typically focuses on technical weaknesses, red teaming takes a broader approach. It evaluates not just technology, but also human factors and organizational procedures.

The key goals of red teaming are to:

• Evaluate the effectiveness of security controls: Check if the defenses in place are truly preventing attacks.
• Test incident response capabilities: See how well the organization reacts to a breach.
• Identify gaps in tools, processes, or training: Discover weaknesses that attackers might exploit.

Red teams emulate real-world attackers, such as Advanced Persistent Threats (APTs), using strategies like reconnaissance, exploiting vulnerabilities, escalating privileges, and exfiltrating data. Among the many techniques red teams employ, one of the most powerful is the Golden Ticket Attack.

What is a Golden Ticket Attack?

Kerberos is a widely used authentication protocol in enterprise environments. While it’s generally reliable, if misconfigured or poorly monitored, it can become a significant security vulnerability. The Golden Ticket Attack takes advantage of these weaknesses, allowing attackers to forge Kerberos tickets and gain unrestricted access to the network.

Let’s walk through how a Golden Ticket Attack unfolds, step by step.

Step-by-Step Guide to a Golden Ticket Attack

Step 1: Gaining Initial Access

The first step in a Golden Ticket Attack is gaining access to the target network. This can be achieved through methods like phishing, exploiting system vulnerabilities, or other attack techniques. Once inside, the attacker’s next goal is to escalate privileges and gain domain administrator access.

Step 2: Dumping the KRBTGT Hash

The KRBTGT account is a special account within Active Directory that generates Kerberos tickets. To create a Golden Ticket, the attacker needs to extract the NTLM hash of this account.

1. Gain Domain Admin Access: The attacker escalates privileges to domain admin, often by exploiting weak permissions or leveraging tools like BloodHound.
2. Dump the KRBTGT Hash: Tools like Mimikatz can be used to extract the KRBTGT hash directly from the Domain Controller.

mimikatz.exe "privilege::debug" 

mimikatz.exe "lsadump::dcsync /user:krbtgt"

These command extracts the KRBTGT hash directly from the Domain Controller.

Step 3: Creating the Golden Ticket

Once the attacker has the KRBTGT hash, they can forge a Kerberos ticket that grants them access to any resource within the domain.

1. Create the Golden Ticket: The attacker uses Mimikatz to create the Golden Ticket.

mimikatz.exe "kerberos::golden /user:Administrator /domain:targetdomain.local /sid:S-1-5-21-XXXXXXXX /krbtgt:<KRBTGT Hash> /id:500"

The /id:500 parameter indicates the domain admin account.

2. Inject the Ticket: The attacker injects the forged ticket into memory with the following command:

mimikatz.exe "kerberos::ptt <ticket.kirbi>"

Step 4: Achieving Persistent Access

Once the Golden Ticket is injected into memory, the attacker gains persistent access to the network. This access remains intact until the KRBTGT hash is rotated, which is often neglected or improperly handled by many organizations.

Step 5: Lateral Movement and Exfiltration

With domain admin privileges, the attacker can:

• Access sensitive data
• Deploy backdoors for ongoing persistence
• Disable or bypass security controls
• Move laterally within the network using tools like PsExec or RDP to compromise additional systems.

Defending Against Kerberos Ticket Attacks

While the Golden Ticket Attack is a powerful method for attackers, organizations can take several steps to protect themselves:

1. Monitor KRBTGT Activity: Track any unusual activity related to the KRBTGT account, as this could indicate an ongoing attack.
2. Rotate KRBTGT Hashes Regularly: Periodically change the KRBTGT password to limit the window of opportunity for attackers.
3. Implement Least Privilege Access: Limit Domain Admin access and closely monitor any changes to these accounts.
4. Enable Advanced Logging: Use advanced auditing and logging to detect suspicious activities, such as unexpected Kerberos ticket requests.

Conclusion

Red teaming is an essential practice for organizations looking to strengthen their cybersecurity defenses. By simulating advanced attack techniques like the Golden Ticket Attack, red teams can uncover vulnerabilities that might otherwise remain hidden. These exercises not only bolster technological defenses but also refine incident response strategies, making organizations more resilient to real-world cyber threats.

By staying proactive and addressing potential vulnerabilities, organizations can better prepare themselves to defend against sophisticated attackers and protect their sensitive data and systems.

Red Teaming: The Golden Ticket Attack was originally published in Mozilla Firefox Club VIT Vellore on Medium, where people are continuing the conversation by highlighting and responding to this story.

Introduction

The directory structure in Windows is essential for managing files and folders on a computer system. Understanding Windows directory layout is critical for effective file management and navigation. In this post, we will look at the many components of the Windows directory structure and provide insights into its organization, file paths, and functionality. But firstly, we need to understand File Systems.

What are File Systems?

A file system is how an operating system stores, organizes and manages files and directories on a storage device. It is a mechanism by which operating systems arrange and store data on storage devices such as hard drives, solid-state drives (SSDs), USB drives, and other storage media. There are several file systems for computers, each of which works in its unique way. Some examples include exFAT, ZFS, ReFS, Ext4 (used in Linux), etc.

Some key components of a File System are :
1. Files
2. Directory
3. File Paths
4. Meta Data
5. File Attributes
6. File System Types

A directory is a collection of files. It contains information such as attributes, types, locations, etc about the files stored in it. It can be compared to a Librarian who keeps records of all the books in the library.

(Read more on the directory: https://simeononsecurity.com/articles/windows-directory-structure-guide/)

Directory System of Windows

The directory system in Windows refers to the way files and folders are organized on different components of our computer hardware. Windows uses a hierarchical / tree-like directory structure. Each directory contains subdirectories and files, making a structured and organized system.

Key Directories in Windows System

1. System Directory: One essential part of the Windows operating system is the System Directory. It includes vital system files and libraries required for the operating system to operate correctly. The System Directory’s location varies based on the version of Windows:

• In a 32-bit system, the System directory is located at C:\Windows\System32.
• In a 64-bit system, the System directory is located at C:\SysWOW64.

2. User Directory: The User Directory (also known as the User Profile Folder) contains unique settings and data for each user account on the system. It stores user-specific information like papers, desktop files, downloads, and application settings. The User Directory is placed in C:\Users\username.

Each user’s folder C:\Users is organized to help keep their files easily accessible and neatly categorized.

• Desktop: Holds all files and shortcuts visible on the desktop screen.
• Documents: A default storage location for user-created documents.
• Downloads: The default location for files downloaded from the internet.
• Pictures, Music, and Videos: Default directories for storing multimedia files.
• AppData: A hidden folder that contains application-specific data, further divided into:

1. Local: Stores data specific to the computer.
2. Roaming: Contains data that can “roam” with the user across devices.
3. LocalLow: Stores data with lower privileges, typically for web browsers.

3. Program Files Directory: The Program Records Registry is the default area where applications and programs are introduced on the framework.
It is partitioned into two registries:

• C: Program Records — This registry stores 64-bit applications and programs.
• C: Program Records (x86) — This registry stores 32-bit applications and programs on 64-bit frameworks.
• Program Files stores applications installed on the computer, typically for 64-bit software.
• Program Files (x86) is used for 32-bit applications on 64-bit Windows systems.
  Both directories help keep installed applications organized and ensure smooth functionality across different versions of software.

4. Windows File Directory :
(C:\Windows) The Windows Directory contains core OS and system folders, it includes the Windows Kernel, System libraries, device drivers, and other essential components.

5. ProgramData :
This directory stores shared application data that isn’t specific to a single user.

It contains configurations and data used by installed applications across multiple users, such as settings, license files, and database files.

6. Other Common Directories:

• Downloads, C:\Users\Downloads
• Appdata, C:\Users\AppData
• Dekstop, C:\Users\Desktop

The drives in Windows are assigned drive letters (like C:, D:, E :), out of which usually the C: drive is used as a system directory.

Navigating through Directories

1. The File Explorer is a built-in Windows application that uses a graphical interface to traverse through directory structures. It allows users to explore folders, view files, and execute file management functions. To launch File Explorer, press Win + E or click the folder icon in the taskbar.

2. Command Prompt: The Command Prompt (CMD) is a command-line interface that lets users interact with the system using text commands. It offers a powerful way to navigate the directory structure, with commands like cd (change directory), dir (list directory contents), and mkdir (create new directory).

Here are some common CMD commands:

• cd <directory> — to change the current directory.
• cipher <option> <file or directory> — shows and modifies encryption of files and directories.
• comp <file 1> <file 2> <options> — compare contents of two files.
• copy <options> <source> <destination> — copies content of one file to another file. For example: copy sample1.txt sample2.txt
• dir <drive> <pathname> <options> — the dir (directory) command lists directory contents, including files and subdirectories.

• ftype <file type>=<open command> — shows and changes file type and extension
• hostname — simple command to display the host machine’s name.
• md and mkdir — md and mkdir (make directory) commands create a new directory or subdirectory, syntax is: md <path> or mkdir <path>

These are the few very common CMD commands. We can also learn how to navigate through the “Terminal” via various other platforms.

3. PowerShell: A powerful scripting environment that enables advanced file system management and automation.

File Management Commands in Windows Command Prompt (CMD)

1. Navigating Directories

To manage files, you first need to navigate to the correct directory.

• cd [directory_path]: Changes the current directory.
• Example: cd C:\Users\JohnDoe\Documents
• cd ..: Moves up one level in the directory.
• dir: Lists all files and folders in the current directory.
• Use dir /a to list hidden files and directories as well.

2. Creating Files and Directories

• mkdir [directory_name]: Creates a new directory.
• Example: mkdir NewFolder
• echo [text] > [filename]: Creates a new text file with specified content.
• Example: echo Hello, World! > file.txt Creates a file named file.txt "Hello, World!" inside.
• type nul > [filename]: Creates an empty file.
• Example: type nul > emptyfile.txt

3. Copying Files

• copy [source] [destination]: Copies a file from the source to the destination.
• Example: copy C:\folder1\file.txt C:\folder2\ copies file.txt to folder2.
• xcopy [source] [destination] /s /e: Recursively copies all files and directories, including empty ones.
• Example: xcopy C:\folder1 C:\folder2 /s /e copies everything from folder1 to folder2.

4. Moving Files

• move [source] [destination]: Moves a file or folder to a new location.
• Example: move C:\folder1\file.txt C:\folder2\ moves file.txt to folder2.

5. Deleting Files and Directories

• del [filename]: Deletes a file.
• Example: del file.txt Deletes file.txt in the current directory.
• del /s /q [directory]: Deletes all files within a directory, including subdirectories.
• rmdir [directory]: Deletes an empty directory.
• Example: rmdir OldFolder
• rmdir /s /q [directory]: Deletes a directory and all its contents without prompting for confirmation.
• Example: rmdir /s /q C:\folder1

6. Renaming Files and Directories

• rename [old_name] [new_name]: Renames a file or directory.
• Example: rename oldfile.txt newfile.txt

7. Finding Files and Content in Files

• dir /s [filename]: Searches for a file in the current directory and all subdirectories.
• Example: dir /s file.txt Searches for file.txt all subdirectories.
• find "text" [filename]: Searches for specific text within a file.
• Example: find "error" log.txt Searches for the word "error" within log.txt.
• findstr /s /i /m [text] *.*: Searches for text across multiple files in a directory.
• Example: findstr /s /i /m "error" *.* Finds the word "error" in all files in the current directory and subdirectories.

8. Setting File Permissions

• icacls [file_or_folder] /grant [username]:[permission]: Grants permissions to a file or folder.
• Example: icacls file.txt /grant JohnDoe:F Gives full control to the user JohnDoe.
• icacls [file_or_folder] /deny [username]:[permission]: Denies permissions.
• icacls [file_or_folder] /remove [username]: Removes all permissions for a user.

9. Checking Disk and Folder Sizes

• dir: Shows the total size of files in the current directory.
• dir /s: Displays sizes for files in all subdirectories, including total space used.
• chkdsk [drive_letter]:: Checks disk usage and errors on a specific drive.
• Example: chkdsk C:

10. Other Useful Commands

• attrib [filename]: Displays or changes file attributes (e.g., read-only, hidden).
• Example: attrib +h +r file.txt Makes file.txt hidden and read-only.
• tree: Displays the directory structure in a tree-like format.
• fc [file1] [file2]: Compares two files line by line.
• Example: fc file1.txt file2.txt

Permissions in Windows

Permissions in the Windows file system control which users and groups can access or modify files and folders. Permissions include Read, Write, Execute, Modify, and Full Control. The system administrator can set permissions to secure sensitive data and restrict access as needed.

1. Key Components of Windows Permissions
a. Access Control Lists (ACLs)

• Each file and folder in Windows has an Access Control List (ACL), a list of permissions attached to it.
• An ACL defines the permissions for each user or group, specifying who can read, modify, delete, or execute a file or folder.

b. Security Identifiers (SIDs)

• Windows identifies users and groups with Security Identifiers (SIDs), which are unique values that Windows uses to assign and check permissions.

c Ownership

• Every file or folder has an Owner, the user, or a group with primary control over it.
• Owners can change permissions and control access to the resource. By default, the creator of a file or folder becomes its owner.

2. Types of Permissions

Permissions in Windows can be divided into two main categories: Basic Permissions and Advanced Permissions.

a. Basic Permissions
Basic permissions are straightforward and commonly used, allowing users to set access levels easily:

• Read: Allows viewing or listing the file or folder contents.
• Write: Grants permission to modify and add new content.
• Read & Execute: Allows reading and executing the file, such as running applications.
• Modify: Permits reading, writing, and deleting files or folders.
• Full Control: Provides complete access, including reading, modifying, deleting, and changing permissions.

b. Advanced Permissions
Advanced permissions offer finer control over file and folder access. They can be managed in the Advanced Security Settings and include:

• Traverse Folder / Execute File: Allows moving through folders to reach other files (useful for applications) or executing the file.
• List Folder / Read Data: Let users list folder contents or read file data.
• Read Attributes: Grants permission to view file or folder attributes (like read-only or hidden).
• Read Extended Attributes: Allows viewing additional file or folder metadata.
• Create Files / Write Data: This enables creating new files within a folder or writing data to a file.
• Create Folders / Append Data: Let users create subfolders or add data to files.
• Write Attributes: Allows modifying file or folder attributes.
• Write Extended Attributes: Permits modifying additional metadata associated with files or folders.
• Delete Subfolders and Files: Grants permission to delete content within folders.
• Delete: Allows deleting the file or folder.
• Read Permissions: Permits viewing the permissions of a file or folder.
• Change Permissions: Enables modifying file or folder permissions.
• Take Ownership: Allows a user to take ownership of a file or folder.

3. Permission Conflicts and Resolution

• Deny Overrides Allow: If both “Allow” and “Deny” permissions are set, “Deny” takes precedence.
• Multiple Group Membership: A user in numerous groups will receive the most permissive level of access.
• Conflicts are resolved based on the most restrictive combination, ensuring security.

4. Managing Permissions in Windows

You can manage permissions through File Explorer or via the Command Prompt (using icacls or cacls commands for command-line management).

Steps to Set Permissions via File Explorer

• Right-click the file or folder, select Properties, then go to the Security tab.
• Click Edit to adjust permissions for users or groups.
• Select the user or group, then set the permissions (e.g., Full Control, Read, Write).
• To adjust advanced permissions, click Advanced and modify Special Permissions as needed.

Command-Line Management

• icacls: A command-line tool for viewing and changing permissions.
• For example to grant read and execute permissions to a user

5. Practical Scenarios for Permission Settings

Securing Sensitive Folders

For folders containing sensitive data, restrict access by:

• Setting only Read permissions for most users.
• Granting Modify or Full Control only to trusted administrators.

Publicly Shared Folders

For public folders or files:

• Enable Read & Execute for general access.
• Limit Write and Delete permissions to prevent unwanted modifications.

File Paths in Windows

The File Path is the unique address location of a file. There are two kinds of file paths:

1. Absolute File Path: provides a complete path from the root directory to the target file directory. eg : C:\Yuvi\Username\Docs\file.txt.

2. Relative File Path: it specifies the path of a directory relative to the current directory, thus allowing for shorter file references.

• File Extensions are suffixes (like .txt, .jpg, .exe) that indicate the file type and dictate which application opens it by default.

Conclusion

The Windows directory structure is a critical component of file organization and management in the Windows operating system. Understanding the key directories and how to navigate them is vital for quick file access and system performance. Understanding the directory structure allows you to effectively manage your files, execute programs, and perform system tasks in Windows.

Understanding Windows Directory System was originally published in Mozilla Firefox Club VIT Vellore on Medium, where people are continuing the conversation by highlighting and responding to this story.

Key Metrics that Drive Virality:
Virality of content depends on certain key metrics processed by algorithms that rank the content based on content performance and user engagement. Platforms like Instagram and YouTube use machine learning models like recurrent neural networks and reinforcement learning to track these metrics. These predict how likely it is for the content to become viral. The following are the important metrics that drive content viral:

(i) Engagement metrics: Algorithms are designed to recommend content with higher interaction levels (such as likes, shares, comments, and watch time) to their users. Have you noticed that controversial content, such as rivalries between influencers, is more viral than other useful content? That is because a higher frequency of comments might be one of the key factors that affect the visibility of content compared to the like count. Natural language processing (NLP) models are used to analyze the comments section and categorize the content as positive, negative, or neutral. Long Short-Term Memory (LSTM) models are used to analyze and process all these engagement metrics.

(ii) Virality Coefficient (k): This measures the spread of content formulated as:

k=N/U

Here,
• N denotes the number of new users exposed to content.
• U denotes the number of active users.
An increase in this virality coefficient is beyond one, which denotes exponential growth of content, which induces the algorithm to boost its visibility. Predictive ensemble models like random forests use this metric to push more of the content with virality potential.

(iii) Engagement Velocity: Velocity refers to the pace at which engagements increase. The increase in engagements is monitored by anomaly detection algorithms such as isolation forests. Posts with high velocity are called trending and aggressively promoted in real-time.

(iv) Watch Time and Click-Through Rates (CTR): The total duration for which the content is viewed is called watch time, and the number of times the content is clicked is known as the click-through rate. The videos are ranked according to watch time and click-through rate. The content that prolongs users’ attention is highly promoted by algorithms using Deep Q-Networks. This changes dynamically based on content that has higher engagements.

Algorithmic pathway to virality:
Advanced machine learning models and algorithms are involved from uploading to going viral. These are the steps to determine if content will become viral:

Step 1: Content Upload and Initial Distribution:
The digital footprint is the data collected from users’ histories, interactions, online behavior, and engagement with content. When content is uploaded, the content is shared with a minimal audience based on users’ interaction history collected. A minimum number of users who might likely engage with this content are identified by deep learning models like user embedding. This first phase of content distribution determines how probable it is for the content to go viral. Have you noticed YouTube’s shorts feed recommending a recently uploaded video with very few views? That is because the video recommended falls in the same genre of content that you interact with the most. At this initial distribution stage, algorithms are probably testing how well the content is engaging with a smaller audience. Based on your interaction with the video, it may be recommended to a larger audience if it shows great engagement.

Step 2: Measuring Early Engagement:
Algorithms measure early engagement metrics, such as click-through rate and watch time. If content shows robust performance in the early stages, it is pushed to the audience. Platforms such as YouTube and Instagram monitor this early engagement to see if the content is worth further promotion.

Step 3: Real-time trending detection:
Algorithms are always tracing engagement to know what content is gaining popularity faster. The posts containing frequently used hashtags or sudden rises in user engagements are flagged by the system using K-means clustering. This makes the algorithms push them even more and hence increases their visibility. This is the reason that the content using a recently released song in the background is likely to go viral.
Since there are comments and hashtags in multiple languages, natural language processing is used to detect viral content in a particular region. Algorithms use anomaly detection techniques to identify content that differs from regular interaction patterns, which in turn increases their popularity. This real-time engagement detection system makes it possible for platforms to be able to highlight information that corresponds with users’ current behaviors and trends. Moreover, do you notice the evolution of internet slang like ‘cringe’ to more recent ones like ‘broski,’ ‘delulu,’ ‘Brainrot,’ and ‘Pookie’? Word embedding models are used to make algorithms understand the meaning and usage of these words in the posts, ultimately trending them.

Step 4: Personalization via collaborative filtering:
Algorithms tailor user feeds based on the engagement patterns of users with similar interests. Collaborative filtering techniques are used by the algorithms to predict content that will be appealing to users based on users’ online activities. Have you seen Instagram posts that say “Comment to get more details in your direct message”? The reason is that Instagram’s ranking system prioritizes posts from accounts with which users have had private interactions using embedding-based models. This helps better personalization of their feeds.
Instagram and YouTube also track the interaction history of users and group them according to their similar behaviors into several clusters. Therefore, if some users belonging to a specific cluster start interacting more with specific content, then the algorithm will push such content to other users within the same cluster. This is most likely to increase the probability of virality while personalizing the feed that the users consume, thus making it more relevant to them.

Step 5: Reinforcement Learning for Content Ranking:
The reinforcement learning models update the ranking of the content in real-time with respect to its ongoing performance. Better engagement across velocity, comments, and shares generally results in greater visibility, which ensures that the content stays prominent in users’ feeds.

Content Format, Timing, and Behavioural Triggers:

1. Role of Content Format in Virality
Different content formats, like long-form videos, images, texts, and short-form videos influence users’ engagement uniquely. TikTok and YouTube use short-form completion-rate algorithms for shorter videos and long-form watch-time models for longer videos. These predict the relevance of content to the user by evaluating the users’ behavior in a specific video, and they will display content that keeps a user engaged. Based on these, the algorithms rank each form of content separately. How often have you stumbled upon quizzes and poll sorts of content in your feed? This is because algorithms rank them higher and recommend them better to users, as they engage users more than just letting them consume the content passively.

2. Timing and predictive models
Why are new movies’ and series’ announcements or updates mostly released in the evening around 6:00 p.m.? This is because the virality of content also depends on the timing of the content upload, and the evening time is generally regarded as the prime time. Algorithms use predictive analytics and time series models to find the time during which the users are most active on social media platforms. Algorithms maximize the initial engagement by scheduling those times for such content releases. Then reinforcement learning amplifies the content spreading during those optimal times quickly.

3. Emotional triggers
Why do we get more “then vs. now” content or prank content in our feeds? This is because emotional triggers such as nostalgia, joy, anger, fear, shock, and surprise play a critical role in increasing users’ engagement. Contents having a stronger emotional connection are more likely to be shared by users, as humans are evoked by such feelings. So, algorithms use sentiment analysis based on NLP to evaluate the emotional responses of users in the comments and boost such highly emotional content. This is the primary reason that many humorous memes are more viral. This is again the reason for food content to become viral, as it triggers a sense of hunger.

Case Study: The Virality of Johnny Depp versus Amber Heard Trial:

The 2022 Johnny Depp versus Amber Heard defamation case went so viral around the world that it was still trending on social media until 2023. Not only did the trial gain immense public interest, but also algorithms exploited the case to gain engagement through live streams and meme trends. That is what made the case accurately a perfect example of how virality on social media can work.

The trial gained widespread public interest causing the algorithms on platforms like YouTube and Twitter to boost related content as there were higher user engagements. Machine learning models analyzed engagement metrics and pushed live streams of relevant content into users’ feeds based on real-time interactions. Trending hashtags such as #JusticeForJohnnyDepp were prioritized by algorithms to increase visibility.

As we saw that content with more emotional connection is more likely to become viral, the “My Dog Stepped on a Bee” meme is one of the finest examples in this regard. Algorithms categorized this into humorous content using sentiment analysis from users’ comments. Then algorithms predicted this as trending using anomaly detection, boosted more posts having this meme template, and recommended more content regarding this trial.

Additionally, collaborative filtering blew the information on the case through social media. Users interested in celebrity gossip, legal proceedings, or even memes find more content on the case. YouTube and Instagram use machine learning to enable customizing feeds related to this content. This set up a feedback cycle of continuous engagement and amplification.

Conclusion:
In this blog, we have observed that social media decides what is the best content by analyzing user interactions and trends in real-time. We have grasped that even simple things such as the amount of time spent viewing a video, how often people share the post, and basic memes can boost the visibility of the post. However, it is not just about creating interesting content rather, knowing how to work with the algorithms behind the scenes can make a big difference. Going forward, understanding these dynamics can help content creators reach more people and stand out in a crowded digital space.

Algorithmic Secrets of Viral Content was originally published in Mozilla Firefox Club VIT Vellore on Medium, where people are continuing the conversation by highlighting and responding to this story.

What Exactly is AI-Powered Code Generation?

AI-powered code generation refers to tools that can automatically produce code snippets, functions, or even entire programs. These models are trained on vast datasets of publicly available code, gaining an understanding of languages and common coding patterns. GitHub Copilot, for example, is one of the most popular tools in this category, seamlessly integrating with IDEs like Visual Studio Code and offering context-aware suggestions based on comments or partial code.

Unlike traditional autocompletion, these tools go beyond filling in variable names — they provide entire code snippets or even full functions, making them a valuable assistant for developers. In short, AI-powered code generation is redefining the scope of developer productivity.

How Do Tools Like Codex Actually Work?

The core of AI-driven tools like Copilot is OpenAI’s Codex, trained on billions of lines of code across numerous programming languages. Codex interprets natural language and translates it into functional code. For instance, a comment like “create a function to sort an array” prompts the model to generate code in Python or other languages, allowing programmers to work faster and more intuitively.

Codex’s ability to translate plain English into code opens up new possibilities for non-developers and experienced developers alike. By bridging the gap between language and code, these tools are making programming more accessible and inspiring creativity in new ways.

What Impact Do These Tools Have on Developer Workflows?

AI-powered code generation is transforming the development workflow, especially when it comes to repetitive tasks. By automating routine coding, developers can shift their focus to more strategic areas of software design.

Some of the main benefits include:

• Faster Prototyping: Boilerplate code for functions, APIs, or classes can be generated quickly, speeding up prototyping.
• Learning Aid for Beginners: These tools offer real-time syntax and structure guidance, making them great for beginners.
• Enhanced Code Reviews: AI can suggest improvements or catch potential bugs, adding an extra layer of quality control.

Even with these advantages, developers must remain vigilant, verifying generated code to ensure it meets quality and security standards. Ultimately, AI code generation acts as a powerful extension of the developer’s toolkit, enhancing productivity while requiring thoughtful oversight.

Are There Ethical and Practical Challenges?

While promising, AI code generation raises some ethical and practical challenges. One major concern is code quality and security, as AI-generated code may introduce vulnerabilities if not reviewed thoroughly.

Intellectual property is another consideration. With models trained on publicly available code, questions arise around whether AI might inadvertently reproduce copyrighted snippets. It’s essential for developers and companies to be mindful of the legal implications when using AI-generated code.

Furthermore, there’s concern over job displacement, as automation could reduce demand for entry-level developers who usually handle simpler coding tasks. However, many believe that these tools will create new opportunities, allowing developers to tackle more complex problems. As with any powerful tool, AI code generation demands a balance between utility and responsibility.

What Does the Future Hold for AI in Programming?

Looking ahead, AI is expected to take on an even bigger role in software development. Future iterations of Codex-like tools may improve debugging, full project generation, and AI-driven code refactoring for more efficient codebases.

In addition, more advanced models could assist in system architecture or performance optimization, analyzing code efficiency and making recommendations. The goal isn’t to replace developers but to provide tools that enhance their capabilities, pushing software development forward. The future of programming with AI isn’t just about automation; it’s about empowering developers to build smarter, faster, and more innovative solutions.

Conclusion: A New Era for Developers

AI-powered code generation represents a transformative shift in programming, democratizing software development, speeding up innovation, and reshaping traditional development roles. But like any new technology, it requires careful thought around limitations and ethical implications.

As the field evolves, developers who embrace these tools will find themselves leading a new era of software development, where AI becomes a collaborative partner in creating efficient and imaginative solutions. With AI as a creative partner, developers are poised to unlock new levels of productivity and innovation.

The Rise of AI Powered Code Generation: What it means for developers was originally published in Mozilla Firefox Club VIT Vellore on Medium, where people are continuing the conversation by highlighting and responding to this story.