Mani Builds

small aim is a big crime

[Mani Builds @ManiBuilds]

@gxjo_dev Saw same story by few others too 🤔

[Mani Builds @ManiBuilds]

a great theory to know and start your day with focus🎯

[Sahil Bloom @SahilBloom]

Everyone needs to read this...

Black Coffee Theory

(a visual thread)

[Mani Builds @ManiBuilds]

Wow, that's a legacy!

[Sann @san_x_m]

Her name is Claire Mazumdar.

She is 37 years old. Her parents are both computer science professors in the United States. Her brother Eric is a professor at the California Institute of Technology and an AI expert.

She holds a degree in Biological Engineering from MIT and both an MBA and a PhD in cancer biology from Stanford.

She founded Bicara Therapeutics in 2018. A clinical stage cancer therapy company focused on bi specific antibodies. She led it to a Nasdaq listing in 2024. She also sits on the boards of Relay Therapeutics and Noora Health.

She is the niece of Kiran Mazumdar Shaw. The woman who founded Biocon in 1978 in a garage in Bengaluru with Rs 10000 and built it into India’s largest biotechnology company over four decades.

Kiran Mazumdar Shaw has no children.

Today she named Claire as her successor to lead Biocon into its next phase.

Kiran said I am the sole owner of Biocon and I need to make sure that I put it in good hands. I have seen my niece Claire as my successor because I think she has proved to me that she can run a company.

Biocon was built by one woman over four decades. It will now be led by another.

Follow for stories about the people shaping India’s future.

[Mani Builds @ManiBuilds]

@BrocolliV74667 Add pragg too

[Mani Builds @ManiBuilds]

Sends random location jobs I am not interested into, for ex. USA based jobs even when I specified city and country.
Also jobs which are closed❌

[Mani Builds @ManiBuilds]

Seeing the buzz over it, the marketing went well. Used it, my feedbacks:
- LinkedIn profile extraction and platform scraping ✅
- extracted profile details are stored in the sidebar Job Preferences which Tal uses for searching jobs but Location field is non-editable ❌

[Saumil @OnTheGrapevine]

We’ve raised $4.1M to let AI find the right job for everyone.
So today, we’re launching TAL.

3 years and 1M+ users on Grapevine taught us this:
The internet today works for companies.
Not for talent.

Job boards, applications, resumes, ats.
The system was built to help companies process people.

But people are not pipelines.

You are not a resume.
You are not an application.
You are not a candidate.

You are talent.
TAL is your talent agent.

[Mani Builds @ManiBuilds]

It kept erring over these details.
- no memory. In just a minute span, it kept asking me same questions I answered before. ❌
- Outdated jobs, too old (posted a month/week back) even though instructed to look for at most 24 hours old again and again ❌

[Mani Builds @ManiBuilds]

Seems too much work using this at this point of time. Lot of fixes needed

[Mani Builds @ManiBuilds]

party. It was around 10:30 PM and I got a msg on WA for a quick call for the role. I just didn't feel it right to reply then. And there was no attempt done to move it further. 🤷‍♀️

[Mani Builds @ManiBuilds]

@theswapnilsri true, many companies are seeing AI as an excuse to avoid dedicated customer assistance... this is frustrating

[Mani Builds @ManiBuilds]

@alex_prompter Shouldn't we take this as a sign to brace up for security against AI and define it's boundaries?

[Mani Builds @ManiBuilds]

@AyAys728 💯 true
Add to this, everyone is overly interested in others lives cuz they don't have other means of enjoyment😄

[Mani Builds @ManiBuilds]

@BunBhujia @Ketanomy good questions

[Mani Builds @ManiBuilds]

@0xratnakar sad, another way my tax money being wasted

[Mani Builds @ManiBuilds]

@Nidhyanaa Thank you☺

[Mani Builds @ManiBuilds]

Can't believe🤯 I just passed GCP PCA cert exam with just 1 day(almost) prep🥳

[Mani Builds @ManiBuilds]

Wow, what an explanation!

[Akhilesh Mishra @livingdevops]

Before you learn Kubernetes, understand why to learn Kubernetes. Or should you?

25 years back, if you wanted to run an application, you bought a $50,000 physical server. You did the cabling. Installed an OS. Configured everything. Then run your app.

Need another app? Buy another $50,000 machine.

Only banks and big companies could afford this. It was expensive and painful.

Then came virtualization. You could take 10 physical servers and split them into 50 or 100 virtual machines. Better, but you still had to buy and maintain all that hardware.

Around 2005, Amazon had a brilliant idea. They had data centers worldwide but weren't using full capacity. So they decided to rent it out.

For startups, this changed everything. Launch without buying a single server. Pay only for what you use. Scale when you grow.

Netflix was one of the first to jump on this.

But this solved only the server problem.
But "How do people build applications?" was still broken.

In the early days, companies built one big application that did everything. Netflix had user accounts, video player, recommendations, and payments all in one codebase.

Simple to build. Easy to deploy. But it didn't scale well.

In 2008, Netflix had a major outage. They realized if they were getting downtime with just US users, how would they scale worldwide?

So they broke their monolith into hundreds of smaller services. User accounts, separate. Video player, separate. Recommendations, separate.

They called it microservices.

Other companies started copying this approach. Even when they didn't really need it.

But microservices created a massive headache. Every service needed different dependencies. Python version 2.7 for one service. Python 3.6 for another. Different libraries. Different configs.

Setting up a new developer's machine took days. Install this database version. That Python version. These specific libraries. Configure environment variables.

And then came the most frustrating phrase in software development: "But it works on my machine."

A developer would test their code locally. Everything worked perfectly. They'd deploy to staging. Boom. Application crashed. Why? Different OS version. Missing dependency. Wrong configuration.

Teams spent hours debugging environment issues instead of building features.

Then Docker came along in 2012.

Google had been using containers for years with their Borg system. But only top Google engineers could use it, too complex for normal developers.

Docker made containers accessible to everyone. Package your app with all dependencies in one container. The exact Python version. The exact libraries. The exact configuration.

Run it on your laptop. Works. Run it on staging. Works. Run it in production. Still works.

No more "works on my machine" problems. No more spending days setting up environments.

By 2014, millions of developers were running Docker containers.

But running one container is easy. Running 10,000 containers? That's a nightmare.

Microservices meant managing 50+ services manually. Services kept crashing with no auto-restart. Scaling was difficult. Services couldn't find each other when IPs changed.

People used custom shell scripts. It was error-prone and painful. Everyone struggled with the same problems. Auto-restart, auto-scaling, service discovery, load balancing.

AWS launched ECS to help. But managing 100+ microservices at scale was still a pain.

This is exactly what Kubernetes solved.

Google saw an opportunity. They were already running millions of containers using Borg. In 2014, they rebuilt it as Kubernetes and open-sourced it.

But here's the smart move. They also launched GKE, a managed service that made running Kubernetes so easy that companies started choosing Google Cloud just for it.

AWS and Azure panicked. They quickly built EKS and AKS. People jumped ship, moving from running k8s clusters on-prem to managed kubernetes on the cloud.

12 years later, Kubernetes runs 90% of production infrastructure. Netflix, Uber, OpenAI, Medium, they all run on it.

Now advanced Kubernetes skills pay big bucks.

Why did Kubernetes win?
Perfect timing.
Docker has made containers popular. Netflix made microservices popular. Millions of people needed a solution to manage these complex microservices at scale.

Kubernetes solved that exact problem.

It handles everything. Deploying services, auto-healing when things crash, auto-scaling based on traffic, service discovery, health monitoring, and load balancing.

Then AI happened. And Kubernetes became even more critical.

AI startups need to run thousands of ML training jobs simultaneously. They need GPU scheduling. They need to scale inference workloads based on demand.

Companies like OpenAI, Hugging Face, and Anthropic run their AI infrastructure on Kubernetes. Training models, running inference APIs, orchestrating AI agents, all on K8s.

The AI boom made Kubernetes essential. Not just for traditional web apps, but for all AI/ML workloads.

Understanding this story is more important than memorizing kubectl commands.

Now go learn Kubernetes already.
Don't take people who write "Kubernetes is dead" articles are just doing it for views/clicks. They might have never used k8s.

[Mani Builds @ManiBuilds]

https://t.co/nY3EXsJdVH is also there, available on discord too.

[Ajeet ( opensox.ai ) @jackedAJ]

btw Indian gov has an OSS Program that allows Open Source contributors to contribute to many cool projects like Digilocker, ABHA, etc

also, you can get a chance to work in various central gov / state gov departments.

link below.

[Mani Builds @ManiBuilds]

Lately learned the basic fact that working in a company is most importantly for increasing the shareholder value #BetterLateThanNever

[Mani Builds @ManiBuilds]

Okay guys, learnt a new word today: "retard maxxing" and now I think this is totally relevant to me.
On my way to retard-maxxing🫠