Pramit Majumder

DJ | Currently photographing Brazil | MBA @iim_bangalore | Viral Script Writer 150 million views | Eternally & infinitely curious

[Pramit Majumder @sedotonin]

For love and for adventure

[Pramit Majumder @sedotonin]

Brazil

[Pramit Majumder @sedotonin]

During the end of my first year at IIM Bangalore, I started teaching myself photography

I am right now in Brazil on my student exchange

Here are some photos that I have taken

[Pramit Majumder @sedotonin]

Not able to access polymarket in Brazil

Today is election day here and wanted to use it to follow the odds

[Pramit Majumder @sedotonin]

Uploaded from Starlink in the middle of a desert

[Pramit Majumder @sedotonin]

@MaxTorchbearer The blog could have been a tweet. That tweet would have been better left unsaid

[Pramit Majumder @sedotonin]

@theamitnikhade @elonmusk what new stuff people are creating with the latest models. basically human ingenuity with model testing

[Pramit Majumder @sedotonin]

I come to twitter for AI education content, instead all I get these days is AI evangelists and their opinions.

@elonmusk can you fix this. I don’t another opinion. I want education

[Pramit Majumder @sedotonin]

São Paulo

[Pramit Majumder @sedotonin]

It’s incredible that there is starlink on international flights.

Had network access all over the Atlantic.

Tweeting this from the Atlantic on the way to Brazil

[Andrew Ho @andrewho03]

Today is my last day at @OpenAI. I'm glad to have spent the last eight months of my life working here!

I'm starting a new company focused on the production of high-quality reinforcement learning datasets:

1. The generalization ability of LLMs is clearly very poor, with "spiky" capabilities even in areas that have received tremendous amounts of investment and attention. For example, despite multiple years with tens (if not hundreds) of billions invested, even coding capabilities don't demonstrate "generality" -- even if every model can solve Codeforces questions or port C++ to Rust better than I can, I still have to manually "deslop" pull requests.

2. The vast majority of economically productive capabilities are not well represented in existing data offerings. First, there's a certain art to the design of an RL dataset which most vendors, not having upstreamed data into large training runs themselves, don't really understand. Second, and more importantly, most work is highly contextual and not easily encoded into a gradable environment; even if we can observe a "golden path" taken by a human which we believe to be good, it's challenging to understand whether alternate, counterfactual paths produce good or bad outcomes.

The basic premise here is that I have a clear understanding of what labs need/want, having explicitly been on the other side and having been involved at every level from procurement all the way through training, and I'm able to provide it. I also believe that data needs will grow tremendously in the coming years, especially as frontier labs face increasing pressure toward profitability, and that they won't get the relevant capabilities "for free" through scaling alone; instead, they'll need to spend >$100B on precise, well-targeted data acquisition.

Our first products will be focused on biology and statistical reasoning:

1. First, datasets that address long-horizon scientific reasoning, drawing on my work on GeneBench-Pro with @jeremyli__. Frontier models are still unable to reliably execute "messy" data analyses that require judgment, exploration, and adaptive revision (GB-Pro passrate on GPT-5.6 Sol scarcely exceeds 30%); to address this, we have the ability to generate thousands of high-quality problems with known ground truths which can be reliably graded. (In contrast, most existing RL data for bioinformatics is either massively over- or under-specified, and will probably break your model when you train on it.) Moving the "reliability gap" from 30% to >90% is obviously required for scientific acceleration, and -- despite my skepticism about generalization of RL -- is one of the *most promising datasets* conceivable when it comes to yielding generalization benefits for models' overall reasoning capabilities.

2. Second, datasets that address capabilities relevant to day-to-day workflows. Imagine a scientist snapping a picture of some experimental process or result -- say, a cell culture plate or a Western blot -- and asking Claude a question. Frontier models remain quite bad at these questions, especially those with multimodal components. But they're obviously required for acceleration of scientific discovery; before we can dream about automating science, we have to begin with shoring up these basic, generalist capabilities.

Beyond these two, we hope to expand to adjacent fields (chemistry, materials science, etc.), and then even further into fields with more direct economic applicability like healthcare and white-collar office work.

I strongly encourage labs with data needs to reach out. We offer industry-standard pricing and terms, and like I said -- I know how this process works, what good data looks like, and how to demonstrate to you, convincingly, that you'll be able to upstream our data into your training processes without issue. My DMs are open!

[The Kaipullai @thekaipullai]

On this auspicious day of Ashada Ekadashi, I am very happy to announce the launch of my humble attempt to make history interesting.

https://t.co/SGfD2ebBpO

The idea is actually very very simple

You go to the site, select a year, and It will show you who ruled which part of India at that point in time.

[Brian Roemmele @BrianRoemmele]

The microwave de-droner.

[Pramit Majumder @sedotonin]

Curiosity is the biggest determiner of success

[Pramit Majumder @sedotonin]

The birth of 0 is a very interesting story

[Pramit Majumder @sedotonin]

Good businesses reduce human suffering

[Pramit Majumder @sedotonin]

Curiousity is contagious

[Trung Phan @TrungTPhan]

the Mexico fans outside of Team England’s hotel trying to disrupt their sleep at 2am went about 1,000x harder than the Mexico fans outside of Team Ecuador’s hotel for the previous match

[Pramit Majumder @sedotonin]

It’s insane to me that Salvador Dali painted persistence of time at 27

[Pramit Majumder @sedotonin]

Originality is plagiarism at scale

[Pramit Majumder @sedotonin]

You can truly understand or wield a language when you can enjoy poetry in that language