Engineering at https://nitter.gq/t.co/6mNfExOwSX | Side projects: https://nitter.gq/t.co/UYK9Us8OiD | Prev founder of @supertokensio | Y Combinator S20 | @imperialcollege
Joined March 2019
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Rishabh Poddar retweeted
scaling realism is the most important aspect of abundant intelligence improvement.
work with us at micro1.ai/data
I asked 36 people in AI who to talk to if you're selling a private dataset, like a shut-down startup's data:
19 votes: micro1 (@aliansarinik)
9 votes: Mercor (@BrendanFoody, @adarsh_exe, @suryamidha)
5 votes each:
- Handshake (@GarrettLord, @benmchristensen, @scottringwelski)
- SF Data (@brandonguo, @faizsiddiqii)
4 votes: Surge (@echen)
3 votes each:
- AfterQuery (@CarlosGeorgescu, @spencermateega)
- Ooak Data (@PLouisV, @ThomAub, @adeRL_g)
2 votes each:
- DataVendor (@seeklis, @parth220, @jayendra_ram)
- Grepped (@eriknewsha)
- Nyne (@michaelfanous1, Emad Fanous)
- Standard Data
36 people voted. People couldn't pick a company they work at or founded. Grepped received one negative vote as well. Disclosure: on this list, I'm a small investor in AfterQuery, Fleet, Ionic, Nyne, Ooak Data, Patronus, and SF Data. I didn't vote.
Continued:
Rishabh Poddar retweeted
Did a very different format with @reinerpope – a blackboard lecture where he walks through how frontier LLMs are trained and served.
It's shocking how much you can deduce about what the labs are doing from a handful of equations, public API prices, and some chalk.
It’s a bit technical, but I encourage you to hang in there - it’s really worth it.
There are less than a handful of people who understand the full stack of AI, from chip design to model architecture, as well as Reiner. It was a real delight to learn from him.
Recommend watching this one on YouTube so you can see the chalkboard.
0:00:00 – How batch size affects token cost and speed
0:31:59 – How MoE models are laid out across GPU racks
0:47:02 – How pipeline parallelism spreads model layers across racks
1:03:27 – Why Ilya said, “As we now know, pipelining is not wise.”
1:18:49 – Because of RL, models may be 100x over-trained beyond Chinchilla-optimal
1:32:52 – Deducing long context memory costs from API pricing
2:03:52 – Convergent evolution between neural nets and cryptography
Rishabh Poddar retweeted
Here's my conversation with Jensen Huang, CEO of NVIDIA, the most valuable & one of the most influential companies in the history of human civilization. It is the engine powering the AI revolution.
This was a fascinating & inspiring conversation, in parts super-technical on engineering of every part of the AI stack, memory, power, supply chain (TSMC, ASML, etc), in parts about leadership & psychology, and in parts personal & philosophical about life, consciousness, mortality, and human nature.
It's here on X in full and is up everywhere else (see comment).
Timestamps:
0:00 - Introduction
0:33 - Extreme co-design and rack-scale engineering
3:18 - How Jensen runs NVIDIA
22:40 - AI scaling laws
37:40 - Biggest blockers to AI scaling laws
39:23 - Supply chain
41:18 - Memory
47:24 - Power
52:43 - Elon and Colossus
56:11 - Jensen's approach to engineering and leadership
1:01:37 - China
1:09:50 - TSMC and Taiwan
1:15:04 - NVIDIA's moat
1:20:41 - AI data centers in space
1:24:30 - Will NVIDIA be worth $10 trillion?
1:34:39 - Leadership under pressure
1:48:25 - Video games
1:55:16 - AGI timeline
1:57:29 - Future of programming
2:11:01 - Consciousness
2:17:22 - Mortality
Rishabh Poddar retweeted
In 2010, Stanford professor Robert Sapolsky gave a 1-hour masterclass on "Human Behavioral Biology."
He broke down how:
- Biology collapses moral
- Behavior changes w/o intention
- Mind & body rewrite each other
10 lessons that'll make you a master at human behavior:
Rishabh Poddar retweeted
New podcast on AI (full episode). Links below.
A Motorcycle for the Mind
0:00 If you want to learn, do
2:13 Vibe coding is the new product management
6:49 Training models is the new coding
10:13 Is traditional software engineering dead?
13:07 There is no demand for average
14:12 The hottest new programming language is English
18:36 AI is adapting to us faster than we are adapting to it
22:56 No entrepreneur is worried about AI taking their job
26:46 The goal is not to have a job
29:49 AIs are not alive
32:55 AI fails the only true test of intelligence
36:49 Early adopters of AI have an enormous edge
39:37 AI meets you exactly where you are
43:02 Always leverage the best intelligence
44:37 If you can't define it, you can't program it
49:37 The solution to AI anxiety is action
Rishabh Poddar retweeted
New art project.
Train and inference GPT in 243 lines of pure, dependency-free Python. This is the *full* algorithmic content of what is needed. Everything else is just for efficiency. I cannot simplify this any further.
gist.github.com/karpathy/862…
Rishabh Poddar retweeted
Performance Hints
Over the years, my colleague Sanjay Ghemawat and I have done a fair bit of diving into performance tuning of various pieces of code. We wrote an internal Performance Hints document a couple of years ago as a way of identifying some general principles and we've recently published a version of it externally.
We'd love any feedback you might have!
Read the full doc at: abseil.io/fast/hints.html
Rishabh Poddar retweeted
Alexandr Wang (@alexandr_wang) started Scale AI to help machine learning teams label data faster.
It started as a simple API for human labor, but behind the scenes, he was tackling a much bigger problem: how to turn messy, real-world data into something AI could learn from.
Today, that early idea powers a multi-hundred-million-dollar engine behind America's AI infrastructure—fueling everything from Fortune 500 workflows to real-time military planning.
Just last week, Meta agreed to invest over $14 billion in Scale, valuing the company at $29 billion.
Alexandr joined us on @LightconePod to share how Scale evolved from a scrappy YC startup into the backbone of some of the world's most advanced AI systems, how he thinks about competition with Chinese AI labs, and what it takes to build infrastructure that shapes the frontier.
01:15 - Alexandr’s early days at YC
07:25 - Dialing in on what worked
10:24 - Model improvements, evals
19:18 - The techno-optimist view of work
27:47 - The turning points for Scale AI
37:37 - Agentic workflows
41:55 - “Humanity’s Last Exam”
47:48 - U.S. vs China in AI and hard tech
56:57 - How to be hardcore
Rishabh Poddar retweeted
If you use "AI agents" (LLMs that call tools) you need to be aware of the Lethal Trifecta
Any time you combine access to private data with exposure to untrusted content and the ability to externally communicate an attacker can trick the system into stealing your data!
ALT The lethal trifecta (diagram with three circles): access to private data, ability to externally communicate, exposure to untrusted content
Rishabh Poddar retweeted
My notes on Anthropic's substantial essay about how they built their multi-agent research system, which has finally talked me around to taking multi-agent LLM prompt engineering seriously simonwillison.net/2025/Jun/1…
Rishabh Poddar retweeted
Every parent wants to give their children more than they had. For the first time in human history, Nucleus adds a new tool to that commitment.
Welcome to Nucleus Embryo.
mynucleus.com/embryo/press
Rishabh Poddar retweeted
An attempt to explain (current) ChatGPT versions.
I still run into many, many people who don't know that:
- o3 is the obvious best thing for important/hard things. It is a reasoning model that is much stronger than 4o and if you are using ChatGPT professionally and not using o3 you're ngmi.
- 4o is different from o4. Yes I know lol. 4o is a good "daily driver" for many easy-medium questions. o4 is only available as mini for now, and is not as good as o3, and I'm not super sure why it's out right now.
Example basic "router" in my own personal use:
- Any simple query (e.g. "what foods are high in fiber"?) => 4o (about ~40% of my use)
- Any hard/important enough query where I am willing to wait a bit (e.g. "help me understand this tax thing...") => o3 (about ~40% of my use)
- I am vibe coding (e.g. "change this code so that...") => 4.1 (about ~10% of my use)
- I want to deeply understand one topic - I want GPT to go off for 10 minutes, look at many, many links and summarize a topic for me. (e.g. "help me understand the rise and fall of Luminar"). => Deep Research (about ~10% of my use). Note that Deep Research is not a model version to be picked from the model picker (!!!), it is a toggle inside the Tools. Under the hood it is based on o3, but I believe is not fully equivalent of just asking o3 the same query, but I am not sure.
All of this is only within the ChatGPT universe of models. In practice my use is more complicated because I like to bounce between all of ChatGPT, Claude, Gemini, Grok and Perplexity depending on the task and out of research interest.
Rishabh Poddar retweeted
Hacker news has some of the smartest minds in tech.
I've been browsing HN and separating the cracked from the mid, to collect all the best comments into one super-dump of pure knowledge.
The best comments on physics, ML, music, and productivity. 🧵 👇
Rishabh Poddar retweeted
i've been trying to understand the transformer architecture for ages but it never clicked. so recently i started trying to learn its history and that has helped a LOT. to try to cement what i learned imma write up a essay.
Undefined Behavior presents: The Evolution of Attention