Imhimanshu

i guess fine-tuning is fun!🤖

[Imhimanshu @mlgeeks_18]

Goldman Sachs just dropped a BIG piece of research- as many as 300 million jobs could be affected by AI.

The following job sectors are at the greatest risk:
-Administrative: 46% at risk
-Legal: 44% at risk
-Architecture and Engineering: 37% risk.

@drriteshmalik @VarunMayya

[Imhimanshu @mlgeeks_18]

@Priyansh_31Dec Bhaiya sabko ke liye kar do na

[Yun-Ta Tsai @yunta_tsai]

Getting used to being liked likely means you are overfit to RLHF.

The problem with overfitting is that the pain overwhelms the limbic system once you try to sample trajectories outside the known distribution.

As more people like you, your sampling regime becomes smaller and smaller to avoid negative feedback. Eventually you get stuck and become a slave to your own feelings.

That’s why I have never seen a model student happy once they become a “model”. Their weights are frozen and cannot be updated anymore. They cannot risk being better than their own SOTA.

[Imhimanshu @mlgeeks_18]

@Normal_2610 @ManjuDeshpande drop the list of resources that cover different sectors beneficiary of India. (AI, chips, energy.......)

[Imhimanshu @mlgeeks_18]

@arpit_bhayani too expensive for students

[Imhimanshu @mlgeeks_18]

@justinskycak +physics

I guarantee you will become untouchable.

[Imhimanshu @mlgeeks_18]

@signulll I would rate over noam over karpathy.

What do you think @signulll ?

[Imhimanshu @mlgeeks_18]

@teortaxesTex "izzat"

[Imhimanshu @mlgeeks_18]

@TheAhmadOsman I can't process "build thick skin" why not you man?

[Ishaan @auto_grad_]

its high time that india starts to embrace the need of "working together" and start building towards the sovereign ecosystem. there will be a shortage of gpu in future, and you will be deprieved of large model's usage in that case, what do you do then?

the only feasible solution is to build a self-sustaining ecosystem for ai, where different start-ups work in co-operation for mutual growth (this is the need of the hour). for instance, i would like to cite an example of how @PrimeIntellect, @arcee_ai and @datologyai works in sync while achieving their own set of goals. datology builds and works on coming up with excellent synthetic data pipelines and flows, prime invests heavily around the ai-infra/post-training research and development, arcee works on frontier models from scratch. they're improving steadily, they're not wanting to go more than others in valuation but accelerating research and making it accessible.

we need to have the same ecosystem in india as well. you cannot expect @sarvamai to carry all the heavy-weight and deliver a model which out-performs opus/gpt, its an extremely impracticle demand. instead, i ask the vcs to invest more in companies which can accelarate other domains of research (small models, ai infra, hardware accelaration, data curation (india is a data mine note that), post-training etc). in this way, we're investing in the ecosystem not the immediate results and investing in a process will eventually result in long term benefits, its bound to happen.

so instead of being dependent on 1-2 big labs and work according to their operation strategies, why don't we have something for ourselves which would help us even in times of adversities? research has to be democratized not concentrated in the hands of few.

think.

[Imhimanshu @mlgeeks_18]

@archiexzzz LFG 🚀🚀🚀

[Imhimanshu @mlgeeks_18]

@drummatick Codeforce over leetcode

[Redpoint @Redpoint]

The Redpoint InfraRed 100 is now live.

These are the companies building the infrastructure that powers everything happening in AI right now, from world models and agent runtimes to the sandboxes, databases, and security tools agents depend on.

Congratulations to this year's honorees!

Read the full 2026 InfraRed Report: our state of the union on AI and cloud infrastructure 👉 https://t.co/Y1y94ZwI5B

[Imhimanshu @mlgeeks_18]

@karpathy Why did you choose Anthropic over OpenAI?

[Richard Sutton @RichardSSutton]

The bitter lesson in 26 words:

Don’t be distracted by human knowledge, as AI has been historically.
Instead focus on methods for creating knowledge that scale with computation, like search and learning.

[Imhimanshu @mlgeeks_18]

@aravind @minimaaIist Sir, have you deleted your recent post regarding #NEET exam paper leak?

[Imhimanshu @mlgeeks_18]

Energy>>compute>>data>>AI talent

[Imhimanshu @mlgeeks_18]

In the future, the biggest bottleneck for AI will not be data or compute, but definitely "ENERGY".

[Archie Sengupta @archiexzzz]

i love capitalism.

[Jiaqi Ma @Jiaqi_Ma_]

This is the best slides I've seen so far about claude code pro tips (esp. for AI researchers): https://t.co/BcGAgmAwmy

By @_Jimmy_Zhang_

[Imhimanshu @mlgeeks_18]

@TheAhmadOsman Are you hating Dario?