Falak Shah

not to sound like a potted plant but sunlight and fresh air really makes a difference

you pity the moth confusing a lamp for the moon, yet here you are confusing a screen for the world

@LnTFinance : scammer claiming to be from L&T calling and messaging about non existent loans to my number. Please raise alerts

A giant clear plastic bag was used to show just how much pollution and environmental harm comes from burning a single car tire.

@jjuulliiaallii @photon_hq @daniel_tian1 Link

I will fly you out to sf all expenses paid. You’ll get: > 3 days in sf  > fully covered flights, food and housing  > a chance to build with @photon_hq photon residency is for TOP growth, engineers and designers that reimagine agent-human interaction Reply “link” for the link to join us in sf

@ludocomito github.com/infocusp/diffu… Was on a very similar journey when diffusion models were introduced and had made a notebook. Thanks for covering the many variations that came after it.

Sharing a collection of notebooks I implemented from scratch to learn step-by-step major concepts of diffusion models. In this project I opted for simple implementations, derived from first principles. Starting from the basics with an unconditional DDPM to reconstruct spiral-shaped data, I go through image generation, classifier-free guidance, flow matching, latent diffusion and gradient-based optimization. I built this over time as an excuse to learn diffusion from the basics, hopefully it’ll be helpful to someone else as well! github.com/ludocomito/lea…

@gabrield_df @mtlushan Thank you!

@FalakShah5 @mtlushan - Voet's Principles of Biochemistry - Albert's Molecular Biology of the Cell - Robbins and Cotran Atlas of Pathology My three favorite books when I was a student.

I would say my biggest takesway from spending the last 8 months singlemindedly studying bioML is that understanding the biology actually is important

You should always be ready to question even your most foundational assumptions if you encounter credible evidence that they might be questionable. You can pick your beliefs but you can't pick your reality.

there has to be a psychological study on the “Karpathy effect” and why literally everyone in the field loves this guy by heart. he def has the knowledge, he coins lifelong terms by a simple tweet, he drops fact bombs without sounding arrogant. there’s a thing about how he delivers his thoughts, and he doesn’t make it sound smart or complicated without any reason. i see people here in the tpot speaking a complete different language nobody understands to make it look genuis, while in fact it’s a beautifully packaged garbage. Karpathy though, he’s with the community. he’s with the most ordinary guys, dropping videos to help people who are starting to learn about AI. that why when he says something or drops a comment, you respectfully hear it out even if you may disagree with it. the Karpathy effect is real.

The greatest feeling you can have as a scientist is realizing you’ve discovered something that no one else in the world knows yet When this happens, put your pencil down, take a deep breath and savor those three minutes before someone points out it was published fifty yrs ago

Never forget this: If you half-ass the small stuff, no one will trust you with the big stuff. Attention to detail isn't optional. Everything you do matters—act accordingly.

Often when trying to decide between option A and option B, the best answer actually turns out to be an option C.

60 years ago this month, the Fast Fourier Transform was created, a powerful tool for image compression & data analysis. Watch a classic MIT breakdown of FFT, perhaps the most-taught algorithm at the Institute: bit.ly/4cNMbPm v/@MITOCW

RL is not all you need, nor attention nor Bayesianism nor free energy minimisation, nor an age of first person experience. Such statements are propaganda. You need thousands of people working hard on data pipelines, scaling infrastructure, HPC, apps with feedback to drive benchmarks and data, tons of research and engineering on generative models, data mixtures, ablations, RL/selftraining, etc etc and we will probably need lots of people working hard to figure out safety, causal world models, awareness, models that create abstractions comparable to infinity and zero and use these to predict the existence of things like black holes and suggest experiments to verify such hypothesis, or come up with novel engineering designs to generate energy more efficiently, robotics, etc etc. It takes thousands of people and many ideas. In the end some simple ideas might become obvious but such obviousness only happens in retrospect. Yes, there is a bitter lesson but if we had followed it, we’d still be doing linear regression with RL. Let’s not oversimplify, but rather honour the research and engineering of thousands of people. Also, people keep rewriting history. When our language understanding start up (darkbluelabs) was acquired by Google about 10 years ago, we joined DeepMind, where the AGI documents were all about concepts, RL, episodic memories and made it clear that there was no room for language. To be honest, back then such a position wasn’t so crazy. Now it seems silly, but only because of the benefit of hindsight. There’s no 1 or 10 heroes in the history of AI. There’s many 1000s of hard working students, profs, engineers, operations and support people, product folks, managers, even hedge funds among others. Let’s honour the whole community and not just ceos or the philosophers of Bayes, RL, deep learning, etc. I look forward to learning from the next generation and seeing what they will achieve. To them: Don’t buy the existing narratives blindly, innovate. Remember that just like mathematics, AI will advance one grave at the time.

The irony

from 2017–2020 i was learning ML. i didn't publish any research (and hadn't yet); i just trained a lot of tiny models and read a paper every single day i maintained a giant spreadsheet with notes about each paper along with random thoughts. was a great way to learn

@prajnaprabhuu Awesome! :) I'll try and add customization of font

built something createchillguy.vercel.app

R&D as percentage of revenue. - Google: 14.8% - NVIDIA: 12.2% - Apple: 7.8% - Tesla: 5.6% ... ... - Infosys: 1.5% - L&T: 1.5% - Reliance: 0.4% - Maruti Suzuki: 1.5%