Arin

beautiful

Written by gpt... didn't even have to check.

@CANehaNiharika @congratstothe

Met a 45-year-old techie today. He graduated at 22 and began a tiny SIP from his very first salary. No push from parents, employer or anyone. He shared that a mutual fund advisor in his society came to him with this idea, he simply acted. 22+ years later, today, the corpus is beyond what he imagined. He smiled and said, “I’m never stopping this. It’s been the best decision of my life.” That’s the beauty of SIPs: effortless wealth building. You set up an auto-debit, start small, and let time do the hard work . It never feels like a burden. And if you start early, this money is not for a distant future or next generation only. It’s money you can use in this life for real goals: a better home, children’s education, a sabbatical, a dream project…on your terms. If you’re in your 20s or 30s, copy this playbook: start with ₹2000–₹5,000 (whatever is comfortable), automate it, stay consistent, and top up when income grows. India’s growth runway + your discipline could be your path to financial freedom. In a few years the SIP amount will feel tiny, but the habit will feel priceless. #SIP

@thundermeowx @robmooreEsports yeah i see what u did there

@robmooreEsports young core group of players the way to go...that's how dynasties are built

We will all miss Zekken , Zellsis and Bang. But we are now locked in for the future. Everyone signed up through 2028. With a bunch of young players there are bound to be some bumps along the way but you can be there for all of it starting Thursday with the Sen City Classic.

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This is a playlist of 9 AI & ML youtube videos you can’t miss as an AI engineer, It’s 50+ hours of technical hands-on courses: 1. Neural Networks Zero to Hero (Karpathy) lnkd.in/gd2JZ5Wt From micro-gradients to nanoGPT, code-first all the way. 2. Stanford CS336 (2025): Language Modelling from Scratch lnkd.in/gzW4JkW9 A full-stack LLM bootcamp: data → training → serving → evaluation. 3. MIT 6.S191 (2025): Intro to Deep Learning lnkd.in/gNGV2kbB Transformers, diffusion, and modern DL in under 2 hours. 4. CS25: Intro to Transformers with Karpathy lnkd.in/ghmYwDQB Turns “Attention Is All You Need” into code you can actually deploy. 5. Stanford CS229 Guest Lecture: Building LLMs lnkd.in/gNsyZbX7 Behind the curtain of Stanford’s 2025 LLM stack. 6. Deep Dive into LLMs like ChatGPT lnkd.in/gwkSpfxQ 3.5 hours of how GPTs really work under the hood. 7. Let’s Build GPT from Scratch lnkd.in/guWVGR3C 200 lines of Python → a functional GPT. Watch, code, repeat. 8. Agentic AI by Stanford lnkd.in/dh3c53Yw Gain an introduction to the concept of agentic AI language. 9. Transformers and Self-Attention lnkd.in/d7MAUS-P Introduction to the Transformers architecture from scratch Hats off to @ordax for the list!

who cares about checking github commit history typical indian aunty behaviour. Noone cares focus on yourself and stop posting about others dumbass

@beffjezos Boing Boing

When you go to the function with "it's time to build" vibes but all you have to work with are forks and spoons: x.com/CH3F_X_ETH/sta…

@ananyachdh Hey congrats on the launch i would love to try

It's official — we are excited to launch Quander.ai in the world. GENERATE A 1-MINUTE MOVIE FROM A MINOR PROMPT. Our AI agent assembles the entire video for you, in your video timeline, and you can manually make changes, if you’d like. It's incredible to see our first users have made videos with their friends as main characters, product ads, faith stories, music videos, fanfictions, book trailers, and more. 🧵 Try it today @quanderAI. If you’ve made it here, we have free credits for you 👇

Before diving deep into diffusion with these Lectures you can start with a friendly blog post on flow matching :) dl.heeere.com/conditional-fl…

@wanshunzhi what is TH team card doing in hand of nobody

EDG did a tier list of Masters Bangkok teams: CHICHOO: “T1 did a reshuffle and this time they have some players that I quite like. I’ve said before that I really like stax’s personality… plus their results [when they were] on DRX before were so crazy and now they added Meteor.”

@100T_VAL @Boostio would be good if y'all played that game too fkin shit org

@Boostio NRG already playing musical chairs 😭😭

I got farmed by a benchwarmer 😭😭

How to escape India while living in India ? 1. Always travel in flights unless its too expensive 2. Live in a gated community 3. Buy groceries from supermarket 4. Do not munch street food Add more

unofficial guide to nyc's AI underground, aka places i wish i knew sooner - latent space: former bank vault hosting banned model training & token parties - neural.house: communal living for prompt engineers w/ weekly completion battles - gradient gallery: chinatown basement running autonomous art collectives - tensor tea house: invite-only spot for model merging & midnight inference sessions - the null: dumbo warehouse for generative art & rogue agent experiments

Worst thing to do when you're full of agency is to wake up and browse Twitter

Not having a clear deadline is the most common way to waste many years.

I think the Deepseek moment is not really the Sputnik moment, but more like the Google moment. If anyone was around in ~2004, you'll know what I mean, but more on that later. I think everyone is over-rotated on this because Deepseek came out of China. Let me try to un-rotate you. Deepseek could have come out of some lab in the US Midwest. Like say some CS lab couldn't afford the latest nVidia chips and had to use older hardware, but they had a great algo and systems department, and they found a bunch of optimizations and trained a model for a few million dollars and lo, the model is roughly on par with o1. Look everyone, we found a new training method and we optimized a bunch of algorithms! Everyone is like OH WOW and starts trying the same thing. Great week for AI advancement! No need for US markets to lose a trillion in market cap. The tech world (and apparently Wall Street) is massively over-rotated on this because it came out of CHINA. I get it. After everyone has been sensitized over the H1BLM uproar, we are conditioned to think of OMG Immigrants China as some kind of Alien Other. As though the Alien-Other Chinese Researchers are doing something special that's out of reach and now China The Empire is somehow uniquely in possession of Super Efficient AI Power and the US companies can't compete. The subtext of "A New Fearsome Power Now Under The Command of the CCP" is what's driving the current sentiment, and it's not really valid. Like, no. These are guys basically working on the same problems we are in the US, and not only that, they wrote a paper about it and open-sourced their model! It is not actually some sort of tectonic geopolitical shift, it is just Some Nerds Over There saying "Hey we figured out some cool shit, here's how we did it, maybe you would like to check it out?" Sputnik showed that the Soviets could do something the US couldn't ("a new fearsome power"). They didn't subsequently publish all the technical details and half the blueprints. They only showed that it could be done. With Deepseek, if I recall correctly, a lab in Berkeley read their paper and duplicated the claimed results on a small scale within a day. That's why I say it's like the Google moment in 2004. Google filed its S-1 in 2004, and revealed to the world that they had built the largest supercomputer cluster by using distributed algorithms to network together commodity computers at the best performance-per-dollar point on the cost curve. This was in contrast to every other tech company, who at that time just bought what were essentially larger and larger mainframes, always at the most expensive leading edge of the cost curve. (To the young people reading this, this will sound incredible to you) I worked at PayPal at the time, and in order to keep pace with the rising transaction volume, the company was forced to buy bigger and bigger database servers from Oracle. We were totally Oracle's bitch. At one point when we ran into scalability issues, the Oracle reps told us we were their biggest installation so they had no other reference point on how to help us overcome our scalability issues. We literally resorted to flipping random config switches and rebooting it. (This heavily influenced me when I was a young manager later at Facebook. I deliberately torpedoed an Oracle salesman's pitch to try and get us to switch from open source MySQL databases to an Oracle contract: of course we had scalability problems, but at least when we had them, we could open up the hood and figure out how to fix it ... assuming we had good enough engineers, and we did. When it's closed-source infra, you're at the mercy of the vendor's support engineers) Back to Google - in their S-1, they described how they were able to leapfrog the scalability limits of mainframes and had been (for years!) running a far more massive networked supercomputer comprised of thousands of commodity machines at the optimal performance-per-dollar price point - i.e. not the more expensive leading edge - all knit together by fault-tolerant distributed algorithms written in-house. Some time later, Google published their MapReduce and BigTable papers, describing the algorithms they'd used to manage and control this massively more cost-effective and powerful supercomputer. Deepseek is MUCH more like the Google moment, because Google essentially described what it did and told everyone else how they could do it too. In Google's case, a fair bit of time elapsed between when they revealed to the world what they were doing and when they published a papers showing everyone how to do it. Deepseek, in contrast, published their paper alongside the model release. Now, I've also written about how I think this is also a demonstration of Deepseek's trajectory, but that's also no different from Google in ~2004 revealing what it was capable of. Competitors will still need to gear up and DO the thing, but they've moved the field forward. But it's not like Sputnik where the Soviets have developed technology unreachable to the US, it's more like Google saying, "Hey, we did this cool thing, here's how we did it." There is no reason to think nVidia and OAI and Meta and Microsoft and Google et al are dead. Sure, Deepseek is a new and formidable upstart, but doesn't that happen every week in the world of AI? I am sure that Sam and Zuck, backed by the power of Satya, can figure something out. Everyone is going to duplicate this feat in a few months and everything just got cheaper. The only real consequence is that AI utopia/doom is now closer than ever. ==== Bonus: This is also a little similar the Ethereum PoS moment, when AI finally has a counterpoint to the environmentalists who say AI uses so much electricity. We just brought down the cost of inference by 97%!

GRPO (used on deepseek) is really cool, trying it out on Qwen2-0.5B. The model is generating its own thinking tokens. Correctness reward going up as completion length increases (gsm8k). H/T @willccbb for the initial gist

I read the R1 zero paper and the method is very simple , just a tweak to PPO to fine tune deepseek v3 base using a verifiable sparse binary reward. The fact that they got it to work even though others failed is likely due to better data and/or their very efficient implementation