Yup, autocorrect. 😅

Which cos according to you have highest probability of tripling revenue in next 4 years (~30% cagr)?

FY26 revenue is 3x FY22 revenue ?

My guess among cos I track: Sona comstar, saregama, mastek, dynemic products, sastasundar, angel one

I missed one: Mirza india aka red tape. :)

Lets discuss how to invest in equity directly, the end to end picture, taking as an example my largest holding: RACL ⚙️ 🖥️

🧵🧵👇👇

I am not a financial advisor & please don’t treat any of this as financial advice. Also, please dont think of whatever i say here as gospel or the absolute truth. This is only my perception of the truth.

I am not your Guru.

A little bit of framework/paradigm building up-front, please bear with me.

Here is the 1 tweet summary. 1000s of hours of reading, writing & contemplation.
Then, decades of patience. If we get both the steps roughly right, then some 💰💰.

The first step a Direct Equity investor with little knowledge has to do is to perceive their world accurately.

We do this by seeing interviews by experienced investors, books they're writing & reading company communications.

When we perceive our world through reading & listening, we go from being an investor with little knowledge to an investor with some knowledge. We are building mental models by learning from experienced investors

See how many our investor became :D

I sat next to one of the smartest, most successful software engineers in the country for over 10 years.

Here's what I learned.

Fast is 10 times better than good.

9 out of 10 times, a "good enough" solution is all you need to unlock time, money, and attention.

Focus relentlessly on being first. Then take the time to be right.

Fast, then right.

Ship more frequently.

The faster you show your work to your users, the faster you'll hear their feedback, you'll adapt, and you'll ship again.

A question you should ask every day:

What can I do to ship this one day before planned?

Technical debt is a good thing.

Everyone hates technical debt. I think they don't know how to take advantage of it.

When used appropriately, technical debt means working on what truly matters and deferring anything that can wait.

No debt ⟶ You waited too long.

Ask, don't think.

People burn their time every day instead of asking for help.

You don't get extra points for working harder, especially when a little help will make you faster.

Time is your most valuable resource. Don't waste it.

Do you know what's better than a machine learning model?

Two models.

More than one model working together to solve a problem is called "an emsemble." A simple way to build this is having each model vote for an answer.

But there's a problem with this approach: ↓

I'm gonna focus here on image classification.

Let's assume you built two different models:

• Model 1: A ResNet model.
• Model 2: A one-shot model (Siamese network.)

They both solve the same problem, so you want to combine their results to pick the right answer.

The problem is that you have two models, so voting is not trivial.

What happens in this case?

• Model 1's answer: Class A
• Model 2's answer: Class B

Which one do you select?

Notice that this problem is not limited to an even number of models.

You could have 3 models, each giving you a different answer.

How do you decide which answer to choose?

There are multiple ways to approach this problem. I'll mention a few different ideas on this thread.

Important: Some of these ideas might not be feasible depending on your context. They have worked for me before on different situations, but every problem is different.

10 machine learning questions you should be able to answer.

I started experimenting with a questionnaire before interviewing candidates. Applicants submit a video or written answers to apply for the position.

Thread: Let's look at 10 of these questions.

The questionnaire solves a couple of problems:

1. Weeds out candidates that aren't ready for the job.

2. It significantly cuts down the time we spend talking synchronously.

One problem: It requires extra time from the candidate before the phone interview.

Here are some numbers on a job posting before using the questionnaire:

• 249 applicants
• 13 good applications

That's only 5%.

This sucks.

Numbers after using the questionnaire:

• 44 applicants
• 21 good applications

That's 47%.

This is much better.

I look at it this way:

Sooner or later, I'll ask these questions, and the candidate will have to answer them.

I'd rather give them space and the opportunity to think about their answers without any pressure.

Those who can't or don't want to answer won't apply. That's fine.

🧵🧵🧵🧵 👇

Artificial Inteligence = 🖥️ + 🧠

When can you expect your fridge & mobile phone to revolt against you? (Spoiler Alert: Not in the foreseeable future).

Please Do 're-tweet' the Thread & help me spread the knowledge to more people.

1. The Language
In order to understand Artificial intelligence, machine learning, deep learning, we need to start at the very basics.

Humans speak and understand a wide variety of 'natural' languages (this is what computer science community calls them): like English, Hindi, Marathi, Bengali, Tamil etc.

But have you seen a peculiarity of human languages? They do not have a precise meaning associated with a given sentence. On an average, every English word has 2 meanings. This makes human conversations confusing, with the need for multiple turns & clarifications. Example 👇:

Person 1 conversing with Person 2 face to face:

Person 1: Lets go watch The Dark Knight. Its our marriage anniversary!
Person 2: I dont like going out at night. Its too scary & depressing.
Person 1: Oh, what i meant was watching the movie "The dark Knight".