Curious about systems, code, and consciousness. Notes to self.
Bengaluru, India
Joined July 2009
- Tweets3.9K
- Following1.5K
- Followers481
- Likes15.3K
aman retweeted
PSA: Find your voter status (in Bangalore) easily. Pls RT.
Final SIR data is out!
But it's all a myriad of PDFs to comb through, captchas, slow sites, and changed part numbers so it is hard to find out your status as a voter!
Over 4 days, I painstakingly reverse engineered the captcha, downloaded 10,000+ PDFs (over 100GB), OCR'ed them, converted to structured data, and then setup sir-bangalore.shantanugoel.c… so you can search for your status through name, voter id, house number etc in an instant without any captcha.
aman retweeted
Elon Musk on why his companies move faster than anyone else.
"I'm constantly addressing the limiting factor. Whatever the limiting factor is on speed, I'm going to tackle that. If capital is the limiting factor, I'll solve for capital. If it's not the limiting factor, I'll solve for something else."
He then said something most managers never figure out:
"If something is going really well and making good progress, there's no point in me spending time on it.The irony is if something's going really well, they don't see much of me. But if something is the limiting factor, they'll see a lot of me."
He spends his time entirely on whatever is blocking the next step. Not on what's interesting or what he's best at. But on whatever is the bottleneck right now.
Most leaders do the opposite... They gravitate toward what they're comfortable with and away from the hard problem.
aman retweeted
If re-entry is your problem, @pre6ai can help!
We can predict the following at hypersonic and supersonic -
- Mach
- Pressure
- Temperature
- Schlieren
- Shock Surface
completely indigenous software!
IN-SPACe has selected six Indian companies for up to ₹1 crore each to develop AI technologies for space.
* Agnikul: Precision landing
* Digantara: Space object analysis
* EON Space Labs: Debris detection
* OrbitAID: On-orbit servicing
* Omnipresent Robot Tech: Target recognition
* TakeMe2Space: Spacecraft autonomy
IN-SPACe began its Seed Fund Scheme in 2023 with agriculture-focused space applications. It has since funded work in disaster management, urban development, and other sectors.
aman retweeted
PSA: If you use agents heavily, I'd recommend being very cautious next few days as things get patched because even if you updated your browser or system, the agents may not have yet!
Been operating this way without knowing it had a name.
“All green lights” thinking - stop assuming things have to take as long as they usually do. Start with what's actually possible.
Especially true in startups. Probably one of the highest-leverage ways to think. – at Suratgarh, India
Neuralink president DJ Seo says Elon’s aggressive schedules are rigorous first-principles engineering, and an all-green-light schedule shows 80 to 90% of delays are just assumed
“What I’ve learned is that it’s not really some arbitrary management tactics, but it’s really used as a rigorous engineering and first principle, you know. Again, this is more applicable in the world of atoms, but we have this concept called all green light schedule, which is kind of forcing us to think about if every single light is green, there’s no administrative burdens or delays or no shipping delays or risk or no legacy bottlenecks, how fast can you build it?”
“And when you think from that point of view and vantage point and literally strip away any man-made bottlenecks and you ask a question of how fast can you actually manufacture a chip, how fast can you design a robot and test it. It turns out like, I don’t know what the right number is, but at least 80-90% of it is things that you think are the way it is, just because you don’t really think about it from that perspective.”
Exactly! – at Hanumangarh, India
J'ai vu une Tesla décoller. Et j'ai entendu beaucoup de bêtises.
« Ça ne sert à rien. » « Gadget de milliardaire. » « Qui a besoin d'une voiture qui vole ? »
Personne. Et c'est exactement pour ça que c'est important.
En 1978, Brabham a aligné en Formule 1 une voiture équipée d'un ventilateur géant à l'arrière. Il aspirait l'air sous le châssis et plaquait la voiture au sol. Elle a gagné sa première course avec une avance insolente. Elle a été interdite immédiatement.
Quarante-huit ans plus tard, on y revient. Un système de succion qui colle la voiture à la route et lui permet de passer sous la seconde au 0 à 100.
Personne n'a « besoin » de faire le 0 à 100 en moins d'une seconde. Comme personne n'a besoin d'une Formule 1. Et pourtant : les freins carbone de votre berline, l'hybridation de votre moteur, l'aérodynamique de votre SUV, les palettes au volant — tout ça vient de voitures de course que personne n'utilisera jamais pour aller au travail.
La Formule 1 ne sert à rien. Pourtant elle sert à tout.
En 1973, quand la France a lancé Ariane, l'Europe sortait de onze échecs consécutifs du programme Europa. La presse parlait d'argent jeté dans l'espace. Vingt ans plus tard, Ariane détenait la moitié du marché mondial des lancements commerciaux, et chaque satellite de télévision, de météo ou de GPS que vous utilisez a bénéficié de cette obstination.
Le progrès a toujours la même tête au début : un truc inutile, cher, ridicule, construit par des gens qu'on prend pour des fous.
Ce que j'ai vu à Dallas, ce n'est pas une voiture volante. C'est un châssis, une gestion de l'énergie et une maîtrise des flux d'air que l'industrie automobile mettra dix ans à digérer.
Les applications viendront : sécurité, freinage, stabilité, consommation. Elles finiront dans des voitures ordinaires conduites par des gens qui expliqueront, en 2036, que tout ça était évident.
La question n'est jamais « à quoi ça sert ».
La question est : qui ose encore construire des choses qui ne servent à rien ?
Il y a cinquante ans, c'était nous. La fusée, le TGV, le Concorde, le Minitel.
L'Europe construisait des choses absurdes et magnifiques.
Aujourd'hui, on commente depuis le siège arrière car nous sommes hors course.
Merci @elonmusk de nous faire rêver
aman retweeted
I built Universal Prompt Enhancer: a tiny qwen based 0.8B model that turns rough ideas into prompts for image, video, sound & music generators.
Runs locally. Accepts images for edits. Q6_K: ~630 MB.
Weights + 44K examples, released for research.
Try your messiest prompt 👇
I doubt the thrusters will make it to roads, but the way the car handles and climbs that curve is seriously impressive nonetheless.
aman retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
This was a fun stress test of what we're building at Pre6.
Orion re-entry is one of the nastiest physics regimes you can throw at a simulator. We took the same GPU-accelerated simulation stack we're building for manufacturing and pushed it to Mach 25 hypersonic flow.
The exciting part isn't just demoing this nut how far a general physics engine can stretch. – at Anekal Bangaluru, India
Hypersonic (Mach 25) re-entry of Orion crew module
We have been working hard on our GPU-accelerated physics simulation engines that powers Pre6's manufacturing process simulations, we repurposed that tech to simulate it for Bengaluru Space Expo 2026: pre6.dev/costing/aero/hypers…
massively scalable and agent-native simulation is the future!
aman retweeted
If you have completed this 👇 course (MIT 18.337J / 6.338J), and enjoyed it, I have a job for you. It's pretty cool.
✉️ me.
aman retweeted
We’re hiring an Engineer - Scientific Computing (C++/Rust) at @pre6ai in Bengaluru.
If you can pick up a CFD/simulation paper, understand the math, and implement it for a new problem from first principles - without vibe coding - we want to talk.
You’ll build numerical compute systems, production solvers, and eventually push them hard on GPUs.
Deep ownership. Small team. 100th percentile salary.
apply - [email protected]
aman retweeted
I am planning to offer AI credit free for power users of @proofzme .
Proof is used by 2200+ professionals including college students.
Please tag anyone who can help me here.
