Yash Saini

about 1 month ago

Work so fucking hard that your parents get scared of your routine… Work until your own house is afraid of you. Until your mother stops at your door not to praise you, to plead. Beta, ab ruk jaa. So jaa. Bas. That’s not concern. That’s respect she doesn’t have the words for yet. Your hostel friends should roast you: Ye goonchu poora din padhta rehta hai. Let them do it, because they haven’t seen what desperation + discipline can do to a person. You don’t need everyone to understand your obsession. You need results. And even if you fail after giving it everything, you’ll still have the discipline, pain tolerance and experience that most people will only start building when life finally kicks their ass. So stop trying to look normal. For the next few years, be unreasonable be obsessed. Work when others are sleeping. Study when others are scrolling. Make people around you genuinely wonder, Bhai, isko itna karna hi kyun hai? Because you know exactly WHY. You’re not here to live the same fucking life as everyone else.

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yashhsaini retweeted

about 2 months ago

Get ready to become Forward deployed Engineer… Launching tomorrow on youtube for Free….🇮🇳🇮🇳🇮🇳

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yashhsaini retweeted

3 months ago

Companies are discovering that AI isn't just powerful it's expensive. Uber reportedly exhausted its annual AI budget in just 4 months, while Microsoft, Salesforce, and GitHub are tightening AI usage to control costs.

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4 months ago

Telegram is a platform of the frauds, by the frauds, for the frauds....

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yashhsaini retweeted

4 months ago

Springboot Start hogya hai doston on youtube... Attendance laga do... In Depth hoga sab ispe kisko doubt nahi hoga....

yashhsaini retweeted

4 months ago

Jaldi hi milte hai guys 🙌🏻 Spring Boot ko Next level le jaana hai 🤍

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4 months ago

Don't use AI to build everything. Some skills need to be learned the hard way by coding, debugging, and figuring things out yourself. Use AI to move faster, not to skip the learning.

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4 months ago

The only one stop solution you need🙌🏻

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Aditya Tandon

@adityatandon02

4 months ago

When we watch a live cricket match on Hotstar, it feels simple, open the app, click play, and enjoy the game. But behind the scenes, delivering the same ball to millions of viewers at exactly the same time is one of the toughest system design challenges in the world. Think about an IPL final or a World Cup match. The moment Virat Kohli reaches a century or a batter hits a match-winning six, millions of people open the app simultaneously. If every user request went directly to a central server, the entire system would crash within seconds. That's why streaming platforms rely heavily on CDNs (Content Delivery Networks). Instead of serving video from one location, video segments are distributed across CDN edge servers around the world. Users are connected to the nearest CDN node, reducing latency and preventing backend servers from being overwhelmed. Another interesting fact is that live video isn't streamed as one huge file. The live feed is broken into thousands of small video segments, each lasting a few seconds. Viewers continuously download Segment 1, Segment 2, Segment 3, and so on. Since millions of users request the same segments, CDN nodes cache them and serve them repeatedly without contacting the origin servers every time. During major sporting events, CDNs often become more important than the application servers themselves. Behind the CDN sits a load balancer. Its job is to distribute incoming traffic across multiple backend servers so that no single machine gets overloaded. If one server fails, traffic is automatically redirected to healthy servers, ensuring uninterrupted streaming for viewers. Traffic during live events is highly unpredictable, which is why platforms use Auto Scaling Groups. On a normal day, a few hundred thousand users may be watching. During an IPL final, that number can suddenly jump to tens of millions. Cloud infrastructure continuously monitors CPU usage, memory consumption, network throughput, active connections, and request rates. As traffic increases, new servers are launched automatically. Once the match ends and traffic drops, unnecessary servers are terminated, helping control infrastructure costs. Bandwidth is another massive challenge. A single HD stream consumes several megabits per second. Multiply that by millions of concurrent viewers, and the bandwidth requirements become enormous. To solve this, streaming platforms use adaptive bitrate streaming. Users with fast internet connections receive higher-quality video, while users on slower networks receive lower-quality streams. The system dynamically adjusts video quality based on network conditions to provide the best possible viewing experience. One of the toughest challenges is the "thundering herd problem." Imagine a match-winning six. Millions of users may refresh the stream, replay highlights, check scorecards, share clips, or open the app within seconds. These synchronized actions create sudden traffic spikes far larger than normal patterns. To handle such bursts, platforms depend on distributed caching, edge delivery, traffic shaping, queue-based processing, and rate limiting. A common question is: why not simply buy one extremely powerful server? The answer is that computing power is only a small part of the challenge. The real problems are geographic distribution, bandwidth, fault tolerance, latency, and handling millions of simultaneous connections worldwide. No single server, regardless of its power, can solve these distributed system challenges. The most fascinating part of live streaming architecture is that encoding and storing the video is often the easy part. The real challenge is delivering the same content reliably, at scale, with low latency, to millions of users simultaneously without the entire system collapsing. That's why platforms like Hotstar are not just video applications - they are some of the largest and most sophisticated distributed systems ever built. Happy designing ❤️

adityatandon02's tweet photo. When we watch a live cricket match on Hotstar, it feels simple, open the app, click play, and enjoy the game. But behind the scenes, delivering the same ball to millions of viewers at exactly the same time is one of the toughest system design challenges in the world.

Think about an IPL final or a World Cup match. The moment Virat Kohli reaches a century or a batter hits a match-winning six, millions of people open the app simultaneously. If every user request went directly to a central server, the entire system would crash within seconds. That's why streaming platforms rely heavily on CDNs (Content Delivery Networks). Instead of serving video from one location, video segments are distributed across CDN edge servers around the world. Users are connected to the nearest CDN node, reducing latency and preventing backend servers from being overwhelmed.

Another interesting fact is that live video isn't streamed as one huge file. The live feed is broken into thousands of small video segments, each lasting a few seconds. Viewers continuously download Segment 1, Segment 2, Segment 3, and so on. Since millions of users request the same segments, CDN nodes cache them and serve them repeatedly without contacting the origin servers every time. During major sporting events, CDNs often become more important than the application servers themselves.

Behind the CDN sits a load balancer. Its job is to distribute incoming traffic across multiple backend servers so that no single machine gets overloaded. If one server fails, traffic is automatically redirected to healthy servers, ensuring uninterrupted streaming for viewers.

Traffic during live events is highly unpredictable, which is why platforms use Auto Scaling Groups. On a normal day, a few hundred thousand users may be watching. During an IPL final, that number can suddenly jump to tens of millions. Cloud infrastructure continuously monitors CPU usage, memory consumption, network throughput, active connections, and request rates. As traffic increases, new servers are launched automatically. Once the match ends and traffic drops, unnecessary servers are terminated, helping control infrastructure costs.

Bandwidth is another massive challenge. A single HD stream consumes several megabits per second. Multiply that by millions of concurrent viewers, and the bandwidth requirements become enormous. To solve this, streaming platforms use adaptive bitrate streaming. Users with fast internet connections receive higher-quality video, while users on slower networks receive lower-quality streams. The system dynamically adjusts video quality based on network conditions to provide the best possible viewing experience.

One of the toughest challenges is the "thundering herd problem." Imagine a match-winning six. Millions of users may refresh the stream, replay highlights, check scorecards, share clips, or open the app within seconds. These synchronized actions create sudden traffic spikes far larger than normal patterns. To handle such bursts, platforms depend on distributed caching, edge delivery, traffic shaping, queue-based processing, and rate limiting.

A common question is: why not simply buy one extremely powerful server? The answer is that computing power is only a small part of the challenge. The real problems are geographic distribution, bandwidth, fault tolerance, latency, and handling millions of simultaneous connections worldwide. No single server, regardless of its power, can solve these distributed system challenges.

The most fascinating part of live streaming architecture is that encoding and storing the video is often the easy part. The real challenge is delivering the same content reliably, at scale, with low latency, to millions of users simultaneously without the entire system collapsing. That's why platforms like Hotstar are not just video applications - they are some of the largest and most sophisticated distributed systems ever built.

Happy designing ❤️

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yashhsaini retweeted

Aditya Tandon

@adityatandon02

4 months ago

How WhatsApp ensures end-to-end Encryption? The goal of end-to-end encryption is surprisingly straightforward. The message should only be readable by the sender and the intended receiver. Not WhatsApp. Not internet providers. Not hackers. Not even the servers that carry the message across the world. The process starts when the sender wants to send a message. Before any message leaves the sender's device, it is encrypted using a public key associated with the receiver. A public key can be safely shared with anyone and is usually distributed through the messaging platform's servers. The important detail is that while anyone can encrypt data using the public key, nobody can decrypt it using that same key. Once encrypted, the message travels through WhatsApp's servers. This is where many people have a common misconception. The servers absolutely do handle the message, route it, store it temporarily if the receiver is offline, and ensure delivery. However, the servers only see encrypted ciphertext. When the encrypted message reaches the receiver, the receiver's private key is used to decrypt the content. Unlike the public key, the private key never leaves the receiver's device and is kept secret. Since only the matching private key can decrypt data encrypted with the corresponding public key, only the intended recipient can read the original message. If WhatsApp's servers help deliver the message, why can't WhatsApp simply decrypt it? Because the server only has access to public keys, which are useful for encryption but useless for decryption. The private key required to unlock the message never resides on WhatsApp's infrastructure. Even if the server wanted to read the message, it lacks the necessary cryptographic material. Another interesting challenge appears when millions of users are continuously exchanging messages. Constantly encrypting every message with asymmetric encryption alone would be computationally expensive. Modern messaging applications therefore use a hybrid approach. Public and private keys are primarily used to establish a secure session, after which much faster symmetric encryption keys are generated for actual message exchange. This provides both strong security and high performance. What happens if someone intercepts the message while it is traveling across the internet? Without encryption, the attacker could immediately read the contents. With end-to-end encryption, the attacker only sees random encrypted bytes. Since they do not possess the receiver's private key, the intercepted data is practically useless. The system also protects against many server-side breaches. Imagine a messaging company's database being compromised. Attackers may gain access to stored encrypted messages, but without the corresponding private keys, those messages remain unreadable. This significantly reduces the impact of infrastructure compromises. Messages are end-to-end encrypted, but WhatsApp know who is talking to whom because encryption protects message content, not necessarily metadata. The platform may still know information such as sender IDs, receiver IDs, timestamps, delivery status, and device information. The actual message body, remains encrypted and inaccessible to the platform. End-to-end encryption becomes even more important when messages cross multiple networks, countries, cloud providers, and data centers. Every intermediate system involved in routing the message becomes untrusted by design. Security does not depend on trusting the network. Security depends on cryptography. The fascinating part is that the internet itself was never designed to be trustworthy. Data passes through routers, ISPs, cloud infrastructure, and countless intermediate systems. End-to-end encryption solves this problem by ensuring that even if every component in the middle can see the message packet, none of them can understand its contents.

adityatandon02's tweet photo. How WhatsApp ensures end-to-end Encryption?

The goal of end-to-end encryption is surprisingly straightforward. The message should only be readable by the sender and the intended receiver. Not WhatsApp. Not internet providers. Not hackers. Not even the servers that carry the message across the world.

The process starts when the sender wants to send a message. Before any message leaves the sender's device, it is encrypted using a public key associated with the receiver. A public key can be safely shared with anyone and is usually distributed through the messaging platform's servers. The important detail is that while anyone can encrypt data using the public key, nobody can decrypt it using that same key.

Once encrypted, the message travels through WhatsApp's servers. This is where many people have a common misconception. The servers absolutely do handle the message, route it, store it temporarily if the receiver is offline, and ensure delivery. However, the servers only see encrypted ciphertext.

When the encrypted message reaches the receiver, the receiver's private key is used to decrypt the content. Unlike the public key, the private key never leaves the receiver's device and is kept secret. Since only the matching private key can decrypt data encrypted with the corresponding public key, only the intended recipient can read the original message.

If WhatsApp's servers help deliver the message, why can't WhatsApp simply decrypt it?

Because the server only has access to public keys, which are useful for encryption but useless for decryption. The private key required to unlock the message never resides on WhatsApp's infrastructure. Even if the server wanted to read the message, it lacks the necessary cryptographic material.

Another interesting challenge appears when millions of users are continuously exchanging messages. Constantly encrypting every message with asymmetric encryption alone would be computationally expensive. Modern messaging applications therefore use a hybrid approach. Public and private keys are primarily used to establish a secure session, after which much faster symmetric encryption keys are generated for actual message exchange. This provides both strong security and high performance.

What happens if someone intercepts the message while it is traveling across the internet?

Without encryption, the attacker could immediately read the contents. With end-to-end encryption, the attacker only sees random encrypted bytes. Since they do not possess the receiver's private key, the intercepted data is practically useless.

The system also protects against many server-side breaches. Imagine a messaging company's database being compromised. Attackers may gain access to stored encrypted messages, but without the corresponding private keys, those messages remain unreadable. This significantly reduces the impact of infrastructure compromises.

Messages are end-to-end encrypted, but WhatsApp know who is talking to whom because encryption protects message content, not necessarily metadata. The platform may still know information such as sender IDs, receiver IDs, timestamps, delivery status, and device information. The actual message body, remains encrypted and inaccessible to the platform.

End-to-end encryption becomes even more important when messages cross multiple networks, countries, cloud providers, and data centers. Every intermediate system involved in routing the message becomes untrusted by design. Security does not depend on trusting the network. Security depends on cryptography.

The fascinating part is that the internet itself was never designed to be trustworthy. Data passes through routers, ISPs, cloud infrastructure, and countless intermediate systems. End-to-end encryption solves this problem by ensuring that even if every component in the middle can see the message packet, none of them can understand its contents.

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Harshal Chauhan @HarshalCh8411

4 months ago

We're immune😎

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4 months ago

💔🥺 2026-27 grads: Truefoundry is hiring Frontend Interns in Bangalore. Work on AI platforms used by Mastercard, Siemens & CVS. Series A startup backed by Intel Capital & Sequoia. Tech: React, Next.js, TS. 📌 APPLY: https://t.co/1RiJnjdgjJ 💬 Comment "DONE" 🔁 RT to help me

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