Paras Chopra Profile picture
Jul 25, 2020 20 tweets 4 min read Read on X
Reading this #book by @paulg.

Will keep sharing notes in this thread as I go along. Image
1/ The first chapter is on why nerds are unpopular.

The key insight: groups with a clearly well-defined adversary (sports teams, companies) form hierarchy based on ability, while groups without an external adversary (teenagers in school) form hierarchy based on who can rise more
2/ Because teenagers yet don’t have economic value, they’re kept in schools so that adults can go about their day.

In such an empty place, being popular is a full time job and has less to do with what you can do but rather your ability to do things that “popular” kids do.
3/ Nerds are more concerned about doing things and since being popular is a full time job in purposeless groups, they naturally end up becoming unpopular.

While cool kids are spending hours curating Instagram profiles, nerds are obsessing about random projects.
4/ The next chapter is about how hackers and painters are similar to each other in the sense of both being makers.

@paulg describes the hacker ethic beautifully and how calling it “computer science” discourages and confuses would-be hackers.
5/ Science is about discovering something new while hacking and painting is about creating something new.

In that sense, hacking computer programs is like tinkering. You make something and iterate on it.

Unlike science, there’s no “correct” way of painting and making things.
6/ But.. painters don’t paid much because what they’re doing is fun.

Similarly, hacking cool software is not going to make you money.

People pay you money when you solve problems for them, which usually requires a ton of grunt work.
7/ But that’s not to say you shouldn’t hack.

Like musicians, painters, writers, keep a day job to pay bills and hack your heart out during evenings and weekends.

Incidentally: reason for the rise of open source was precisely this.
8/ The crux of the chapter is that hackers shouldn’t feel guilty of not doing things “properly” or knowing theory.

They should learn from painters who often don’t have complete picture upfront, regularly change direction mid way, and don’t care much about how paints are made.
9/ The third chapter is on what we ideas we can’t say out loud.

The key idea is to notice what labels people use to suppress ideas. If they use anything other than “untrue”, you know what you’re not allowed to say.

E.g. “sexist”, “politically incorrect”, “anti-national”
10/ Why is it important to unearth ideas you can’t say aloud?

Because otherwise you’d never recognise that some of them might be true and that the only reason they’re not allowed because some group might feel threatened.
11/ E.g. Galileo’s ideas were considered “heretical”, and not untrue.

Similarly, what ideas today are considered X-ist? Play with those ideas in your head and judge their truthfulness in private comfort of your thoughts.
12/ While it may not be worth it to pick a fight with a powerful group on a true idea that you’re not supposed to say aloud, at least such recognition gives you mental clarity which otherwise you won’t have.
13/ Thought suppression by different groups happens all the time.

It will benefit you to actively try finding what you’re not allowed to say and why you’re not allowed to say that? (Most likely it’s because of fear of idea’s consequences and not because the idea is false)
14/ The next chapter is my all time favorite: how to make wealth.

In it, Paul talks about difference between wealth (stuff we want) and money (compressed storage of the stuff we want).

The way to make wealth is to build things that others care about.
15/ There are two requirements for increasing your chances of becoming wealthy: measurement and leverage.

Measurement = being able to attribute outcomes to your effort.

Leverage = potential for having an impact even while you’re sleeping
16/ Leverage typically is had with technology. It’s what you call “create once, sell infinite times”.

Artisans who work on one piece at a time don’t have any leverage while inventor of a machine that makes the same thing has a lot of leverage.
17/ Measurement is important to get wealthy because if your output is averaged with all others (like it typically happens in a big company), you don’t have incentive to work 10 times hard in order to have a chance to get paid 10 times more.
18/ Only when you’re working by yourself or in a small team of a startup, you have a shot at working harder and at difficult problems because you know you’ll get paid more

This is why big companies are slow in innovation. There are not sufficient incentives for anyone to do that
19/ Without a hope of having a real chance to make a lot of money, people will naturally work on either easy problems or interesting problems.

But a lot of money is usually made by doing work that nobody else is willing to do (because it’s hard or unsexy).

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More from @paraschopra

Jun 4, 2025
Recently, I read this beautiful #book, named Life as No One Knows It, by @Sara_Imari and had a minor revelation that maybe we’ve been looking at life from a completely wrong lens.

My notes 👇 Image
1/ It’s always hard to define life. Everyone has their favorite definition – some describe it as a struggle against entropy, while others describe it as an emergent property of chemicals.

Countless books have been written on the topic, yet we’re far from a consensus.
2/ Against the backdrop of the second law of thermodynamics, life seems like an improbable accident.

When everything tends to go towards disorder, how come life is able to create cities, computers and space ships?

How do we reconcile all the beautiful complexity we see around us with the stupidly simple laws we observe in physics?
Read 25 tweets
Mar 23, 2025
Don't compete.

🧵
1/ The Internet is full of people winning all the time. Someone is traveling to exotic locations, someone else is raising funds, and another person is winning awards.
2/ Essentially, everyone around you is succeeding while you do spend your days as the nature intended – sleeping, eating, smiling, chatting with friends, and spending time with your cat.
Read 33 tweets
Mar 4, 2025
Slides from the projects done during Batch 3 of @lossfunk residency.

🧵 Image
1/ Naveen Arulselvan talked about

Can Robots Learn to Drive

drive.google.com/file/d/1fJelK6…

His linkedin: linkedin.com/in/naveen-arul… Image
2/ Sibesh Kar @sibeshkar talked about how we build tiny programs in our head when we're thinking.

He explained how general intelligence should require something akin to program search (which I fully believe in)

He also blew my mind with reference to Panini's attempt at synthesizing Sanskrit grammar into its core essence

drive.google.com/file/d/17SoYZu…Image
Read 12 tweets
Jan 8, 2025
Kickstarted Batch 3 of Turing's Dream 6 week AI residency!

Profiles of the current residents 👇 Image
1/ Jayesh @wjayesh is exploring multi-agent systems.

For example, how can you have Devin talk to your cursor agent, and both of them talk to marketing agent.

During the next 6 weeks, he will aim to add one major feature to his open source project

github.com/wjayesh/mahiloImage
@wjayesh 2/ Vijit @SinghVijit14477 spent last 13 years aT Amazon, most recently doing R&D for Alexa.

He will be exploring multi-modal content generation and personalized AI! Image
Read 14 tweets
Nov 10, 2024
Kicked off the 2nd batch of Turing’s Dream, the AI residency that I run in Bangalore!

Here’s what they’re upto… Image
1/ Adithya S Kolavi @adithya_s_k is a 4th year engineering student at PES.

In 2024, he set a target to achieve 10k stars across his github repositories

His most famous one is Omniparse and has 5.5k stars, it's a library that converts unstructured data into structured data for LLMs github.com/adithya-s-k/om…
@adithya_s_k 2/ Arjun Balaji @kaizen797 - 4th year engineering.

He's working with UPI team to detect money laundering using graph NNs. (it has trillion edges, so fun problem!)

He's also working with a Harvard team to map MRI images over time to 3D space to see how brain structures change!
Read 13 tweets
Oct 30, 2024
Turing’s Dream first batch is over.

10 people, 10 projects, 6 weeks!

Here's what everyone explored in AI...

🧵
1/ First a teaser of the atmosphere in the hackhouse!

A 3 minute video featuring @NirantK, @dementorSam, @__hsuya and myself (made by @mistrymm7)

@NirantK @dementorSam @__hsuya @mistrymm7 2/ Now, to the projects..

@py_parrot worked on replicating Anthropic's toy models of superposition, and extended it by trying to see how sparsity and importance of features impact representation of features.

Slides: docs.google.com/presentation/d…Image
Read 14 tweets

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