“the best prize life has to offer is a chance to work hard at work worth doing” | “... specialization is for insects”
Bengaluru, India
Joined October 2008
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I'm using this (the paid ZDR version) for a slice of my token needs and it's been very pleasant so far!
We're really, really excited: we're now serving DeepSeek V4.1 Flash at Gödel Machines, hosted in India.
1M-token context, tool calling, reasoning, and it's still free to use.
That does mean we can no longer run Qwen 3.8 27B. We don't think you'll miss it. DeepSeek is something else, and we can't wait to see what you build with it.
It's interesting the kind of corpses that lie buried in ML algorithms, because the field incentivises scaling up more than getting the basics right.
Siva retweeted
Meanwhile Bret Victor on mechanistically generated interfaces — “Is this really the civilization we want?”
ALT Screenshot of https://dynamicland.org/archive/2017/Is_this_the_civilization_we_really_want excerpting “Aspirationally, our … Is this the civilzation we really want?” highlighting “And what happens … in the world?”
It's great that the Indian govt is sponsoring compute capacity, but why is the last-mile UX a govt portal?
Taking lessons from UPI, govt could provide the subsidy rail as an open protocol, with private players solving the UX problem to induce rapid adoption. Wrappers could range from compute rental to tokens via hosted APIs -- and would make a competitive market for innovation.
Currently, we have precious compute locked behind hurdles, while Indian service providers innovating on UX and applications are starved of capacity.
We reported on an important story around AI and compute demand in India earlier this week.
Question to academics, founders and VCs- why is there no demand for compute in the domestic market? So much so that govt is now allwping those empanelled with IndiaAI to sell it overseas.
Is it a question of red tape (access), paucity of capital or lack of training/consumer AI startups that need compute
Excited to try Jarvislabs next time around. Hearing multiple recommendations -- they must be doing something right!
Fair point on usability. That's what we've been obsessing over at Jarvislabs.
Containers launch in under 2s, VMs in under 90s. InfiniBand clusters, serverless endpoints and token factories too, all built in India.
We're seeing a big jump in Indian customers on GPUs. Honestly, our biggest problem right now is compute supply, and we're actively adding more.
Try our CLI with Claude Code, Codex or OpenCode. Stuck anywhere? Email [email protected], real engineers reply.
Siva retweeted
I like the ending where there is a suggestion of moving from "shut up and calculate" to "slow down and think". This aligns very closely with how (and I suspsect many others) think about AI in Physics
Siva retweeted
My personal experience with this is i submitted a form for compute. Nothing too significant. The total budget was under 10k.
I had to fill form for almost 30 minutes to finish the process. After all that i got a mail where i had to share financials such as annual turnover and meet their thresholds.
I was just trying to do a PoC and explore the compute ecosystem. Since it ended up being too much hassle, i moved on to AWS and only thing i had to do was to create a ticket for quota increase.
There is definitely a lot of good initiatives like subsidy, free notebook usage etc. But the amount of processes and approvals needed for getting like a GPU node is more than what most people have patience for in this current day and age.
That’s probably the reason why this is happening. Unfortunate.
🚨 Imp: IndiaAI GPU providers may offer services to overseas clients to keep investments viable
The move follows concerns that demand for computing power in India is not yet sufficient to absorb the capacity being added under the government-backed programme.
Allowing companies to serve customers abroad would give them another source of revenue while the domestic market develops, a senior government official said.
"There is not enough uptake of GPUs in India right now. It is, therefore, important that these companies are able to sell the compute for the right price to anyone globally," the official said on condition of anonymity .
Under the Rs 10,372-crore IndiaAI Mission, the government has empanelled 14 companies to provide computing capacity using GPUs sourced from manufacturers including Nvidia, Intel and AMD.
The selected companies include Jio Platforms, Tata Communications and Yotta Data Services, alongside other data centre and cloud service providers.
By @AihikS
moneycontrol.com/news/busine…
I find this to be a far more exciting exploration than the math slopocalypse from OpenAI; left to themselves mathematicians would have come up with something similar and we could've had very interesting discussions about how to use AI to accelerate theoretical research.
Hope to see more along these lines!
Siva retweeted
It's a very peculiar situation. This is how I understand under utilisation.
These are fragmented (accross multiple providers) and not enough for frontier model training.
Application builder startups typically use LLM models in two ways :
1. Either host an open source models (Chinese) on these GPUs and serve customers.
2. Or Use APIs directly from openAI or anthropic.
Typically most startups chose option 2, because (a) most non Chinese frontier modles are closed and can't be hosted on these, (b) lot of customers are not comfortable with using Chinese models (even if they are hosted), (c)
it works out cheaper for startups to pay via API usage than paying for GPU hours.
🚨 Imp: IndiaAI GPU providers may offer services to overseas clients to keep investments viable
The move follows concerns that demand for computing power in India is not yet sufficient to absorb the capacity being added under the government-backed programme.
Allowing companies to serve customers abroad would give them another source of revenue while the domestic market develops, a senior government official said.
"There is not enough uptake of GPUs in India right now. It is, therefore, important that these companies are able to sell the compute for the right price to anyone globally," the official said on condition of anonymity .
Under the Rs 10,372-crore IndiaAI Mission, the government has empanelled 14 companies to provide computing capacity using GPUs sourced from manufacturers including Nvidia, Intel and AMD.
The selected companies include Jio Platforms, Tata Communications and Yotta Data Services, alongside other data centre and cloud service providers.
By @AihikS
moneycontrol.com/news/busine…
Batshit crazy if true...
Sclerotic bureaucracy would rather swallow depreciation than allow use at marginal cost. Not to mention the opportunity cost of missing out on the whole point of the AI mission!
Parallel to Neelkanth Mishra's complaint that we supposedly don't have the talent to deploy RDI funds 🤪
I've gotten multiple people (in BLR) up and running from scratch in 5mins with Runpod, Baseten, etc. Spending $$$ on GPU machines, API tokens, etc.
Haven't found any Indian service that comes close in usability.
Double whammy for the Indian ecosystem -- sucks for both builders and providers!
One other aspect one has to keep in mind, let’s assume that the GPU compute hours for AI mission is a given and the rest was always for Indian commercial needs, so why isn’t there commerical demand from India Inc for GPU compute? Either for AI or otherwise like engineering or biotech simulations which all today have GPU based algorithms thriving. My experience from the engineering simulation industry is that Indian engineering budgets for Indian OEMs and services hourly rates for engineering services industry are too low and prohibitive for the use of commerical GPUs at decent scale.
Anecdotally I saw an Indian auto OEM try to run a CFD simulation on 2 GPUs(A-100s) what a typical western OEM runs on 2 nodes-16 GPUs(H-100s). When we recommended to just buy a single server they were flabbergasted at the rates. Again a function of the OEM’s R&D budget.
Work on developing training and deploying ROMs, surrogate models are all a question too far away, when low cost product development is your USP.
Overall point being, either the govt should purchase more compute hours to fund more foundational or applied AI research or just allow companies to sell compute hours abroad to have a business.
Siva retweeted
Gentle reminder that the sign error that caused 3 withdrawals in the mathematical findings compilation by @OpenAI could not be probed by just asking Astra to audit the article, even specifying the section. Indeed, the correction came from humans who found the entire proof strategy suspect.
The proof strategy involves constructing "flexible" Lagrangians inside symplectic manifolds that are obstructed in Lagrangian Floer homology. This immediately fires up red flags in any experts' mind: loose Legendrians having vanishing Floer invariants.
Cutting humans out of the loop gives way for proof-destroying errors of such magnitude to go unchecked. Now extrapolate such blunders to biotech research, where formal verifications aren't available (even in math, they had Lean, but sheer negligence made them assume a Bayesian epsitemology precludes major errors).
Interesting: one of the OpenAI papers has an error. It’s a sign error—which is oddly common.
Astra Pro finds the error when asked (without further hints) to “Audit lemma 3.6” but not with less focus, eg, Audit Section 3” didn’t work when I tried.
It’s interesting because it suggests something about the depth and intensity of the proof and audit workflow by OpenAI (of course, very weakly so).
Siva retweeted
Talked to a finance guy today who said his firm has not only totally stopped buying software in favor of building it all in-house, they've actually hired multiple SWEs to do the buildouts, even though their salaries cost more than the subscription fees they were paying. Having tools custom-built for their specific business is just that valuable. So they're not spending less money or saving time, versus the pre-AI world, but they're getting a much higher return on the money and time they are spending. Much to ponder
Siva retweeted
reading the pained responses of mathematicians, there’s a real cruelty in the way the labs have pursued this. not least because the attempted outcome in dropping so much unvalidated and poorly-written work (per the comments) is to push some of the world’s most passionate problem-solvers to be fact-checkers to a machine, as a kind of cynical cheap-as-free RL environment-by-nerdsnipe. dump a bunch of shit in public, call it solutions, get the experts to pore over it, painstakingly review and critique, scrape their efforts and use it to tweak the model, all while stoking terror about the future of the profession. it’s fucked up.
