We take AI from research papers to industrial plants. Computer vision, agents, speech, generative AI. Bangalore | Stuttgart | Abu Dhabi.
Joined May 2025
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Fast Code AI retweeted
๐ ๐ฆ๐ฝ๐ฎ๐บ๐บ๐ฒ๐ฑ ๐ญ,๐ด๐ฌ๐ฌ ๐ฃ๐ฟ๐ผ๐ณ๐ฒ๐๐๐ผ๐ฟ๐ ๐๐ถ๐ธ๐ฒ ๐ฎ ๐ก๐ถ๐ด๐ฒ๐ฟ๐ถ๐ฎ๐ป ๐ฃ๐ฟ๐ถ๐ป๐ฐ๐ฒ. ๐ข๐ป๐ฒ ๐ฅ๐ฒ๐ฝ๐น๐ถ๐ฒ๐ฑ.
2007. I could do my Yahoo! job at 20% capacity, and when you're not giving your 100%, something in you withers.
๐ง๐ต๐ฎ๐'๐ ๐๐ต๐ฒ๐ป ๐ ๐ฑ๐ถ๐๐ฐ๐ผ๐๐ฒ๐ฟ๐ฒ๐ฑ ๐๐ต๐ฎ๐ ๐ฎ๐ด๐ฒ๐ป๐ฐ๐ ๐ฟ๐ฒ๐ฎ๐น๐น๐ ๐บ๐ฒ๐ฎ๐ป๐: finding the simple, non-default solution when you're pushed against the wall.
At 2 AM in my #Bangalore apartment, I wrote a PHP script. Scraped email addresses from university websites across Europe. Sent emails to 1,800 professors.
My friends thought I'd lost it. "Apply properly," they said. But proper channels weren't built for RV passouts dreaming beyond their station.
Prof. Alberto Del Bimbo and Prof. Marco Bertini from Florence took a chance. โฌ1,400/month at MICC.
My mother panicked - I was supporting my family. My father though? IIT KGP 1969 batch dropout who chose the mountains and music over engineering - he simply said: "๐๐ฆ ๐ธ๐ช๐ญ๐ญ ๐ฎ๐ข๐ฏ๐ข๐จ๐ฆ. ๐ ๐ฐ๐ถ ๐ง๐ญ๐บ."
#Florence broke me financially. Vegetarian in Italy, no kitchen, surviving on fumes. One desperate night, I bought a chicken burger thinking meat would kill hunger longer. Sat there trying to force one bite. Couldn't. My body rejected it completely. Slept hungry that night, having blown my food money on something I couldn't swallow.
But I stayed. MPII followed. Then NYU. I was there when deep learning took off. Papers, patents, Apple, Mercedes acquiring my IP, teaching at IIT & IISc.
๐ก๐ผ๐ ๐ฏ๐ฒ๐ฐ๐ฎ๐๐๐ฒ ๐ ๐๐ฎ๐ ๐ฏ๐ฟ๐ถ๐น๐น๐ถ๐ฎ๐ป๐. ๐๐ฒ๐ฐ๐ฎ๐๐๐ฒ ๐ ๐ฟ๐ฒ๐ณ๐๐๐ฒ๐ฑ ๐๐ต๐ฒ ๐น๐ถ๐ณ๐ฒ ๐๐ต๐ฒ๐ ๐ต๐ฎ๐ป๐ฑ๐ฒ๐ฑ ๐บ๐ฒ.
"๐๐ฐ๐ญ๐ญ ๐ถ๐ฑ ๐บ๐ฐ๐ถ๐ณ ๐ด๐ญ๐ฆ๐ฆ๐ท๐ฆ๐ด ๐ข๐ฏ๐ฅ ๐ธ๐ฐ๐ณ๐ฌ ๐ข๐ด ๐ข ๐ฃ๐ฐ๐ถ๐ฏ๐ค๐ฆ๐ณ ๐ช๐ฏ ๐ข ๐ฌ๐๐ฑ ๐๐ฅ๐ฎ๐ ๐ฐ๐ณ ๐ข ๐ธ๐ข๐ณ๐ฅ๐ฆ๐ฏ ๐ช๐ฏ ๐ข ๐ฅ๐ฎ๐ง๐๐ญ๐ข๐ ๐๐ฌ๐ฒ๐ฅ๐ฎ๐ฆ. ๐๐ณ๐ช๐ท๐ฆ ๐ข ๐ต๐ข๐น๐ช ๐ง๐ฐ๐ณ ๐ด๐ช๐น ๐ฎ๐ฐ๐ฏ๐ต๐ฉ๐ด ๐ข๐ฏ๐ฅ ๐บ๐ฐ๐ถ'๐ญ๐ญ ๐ฉ๐ข๐ท๐ฆ ๐ฆ๐ฏ๐ฐ๐ถ๐จ๐ฉ ๐ฎ๐ฐ๐ฏ๐ฆ๐บ ๐ต๐ฐ ๐ฎ๐ข๐ฌ๐ฆ ๐ข ๐ง๐ช๐ญ๐ฎ."
Werner Herzog says people with high agency find solutions so simple, others think they're idiots. Like mass-emailing 1,800 professors. Crude? Yes. Effective? Also yes.
๐๐ฒ๐ฟ๐ฒ'๐ ๐๐ต๐ฎ๐ ๐ป๐ผ๐ฏ๐ผ๐ฑ๐ ๐๐ฒ๐น๐น๐ ๐๐ผ๐ ๐ฎ๐ฏ๐ผ๐๐ ๐ฎ๐ด๐ฒ๐ป๐ฐ๐: it's not heroic. It's desperate PHP scripts at midnight. It's โฌ300 sent home from โฌ1,400. It's grinding while everyone thinks you've lost it.
Today, @FastCodeAI is my latest rebellion against the default path. Still believing every wall has a door.
Don't let anyone tell you to ๐ฅ๐๐จ๐จ๐๐ซ๐๐ก๐ฎ ๐๐๐๐๐ฅ๐ฉ ๐ฌ๐๐๐ฉ ๐๐จ ๐๐๐ฃ๐๐๐ ๐ฉ๐ค ๐ฎ๐ค๐ช.
That's what kills dreams. Not rejection. Not failure. Acceptance.
๐ฆ๐๐ณ๐ณ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ถ๐ ๐ถ๐ป๐ฒ๐๐ถ๐๐ฎ๐ฏ๐น๐ฒ. ๐ฌ๐ผ๐ ๐ท๐๐๐ ๐ด๐ฒ๐ ๐๐ผ ๐ฐ๐ต๐ผ๐ผ๐๐ฒ ๐๐ต๐ฎ๐ ๐ธ๐ถ๐ป๐ฑ.
You can suffer doing work at 20% capacity, or you can suffer sleeping hungry in Florence.
๐ฌ๐ผ๐ ๐ฐ๐ต๐ผ๐ผ๐๐ฒ.
That email you're afraid to send? Send 1,800 of them.
Sydney Sweeney reveals in an interview that she's investing heavily in digital twins for industrial plants.
"Everyone keeps asking when we'll have a photorealistic 3D model of the factory. I just want to know which valves I have to close before taking a pump offline."
A cybersecurity company used certified cryptographic hardware, passed independent audits, and somehow ended up protecting critical certificates with a private key that was literally the number 4.
The ongoing eMudhra controversy is a fascinating case study in how human error can undermine even the most sophisticated engineering systems.
Eight private keys associated with eMudhra's certificate authorities were discovered to have values smaller than 256, despite using P-384 cryptography, which ordinarily involves an astronomically large key space.
Researchers recovered all eight keys in less than a second.
But the most interesting part is how this apparently happened.
According to eMudhra's preliminary explanation, the key-generation process produced two sets of keys: one cryptographically strong, the other extraordinarily weak.
The keys had similar labels, and the wrong ones were selected during the process of creating certificate signing requests.
The company has acknowledged human errors at multiple stages and is investigating failures in its procedures and verification controls.
Consider the extraordinary chain of engineering involved here.
Mathematically sophisticated cryptographic algorithms, certified hardware security modules, established operational procedures, and independent audits.
Yet the security of the entire system ultimately depended on something as mundane as selecting the correct keys.
And the mistake went undetected for approximately 17 months.
This illustrates a fundamental challenge in engineering complex systems.
As systems become more sophisticated, the consequences of relatively simple human mistakes can become disproportionately large.
We invest enormous resources in making individual components reliable, but failures frequently emerge from the way humans configure, connect, and operate those components.
The lesson is particularly relevant as AI systems become increasingly autonomous and are entrusted with critical industrial and enterprise operations.
The challenge extends beyond building technically capable systems. It includes designing workflows where predictable human mistakes are detected, contained, or prevented from becoming catastrophic.
Because even the most sophisticated technology cannot compensate for missing safeguards around the people operating it.
This is very interesting career advice from an Anthropic researcher.
He is encouraging people to be independent thinkers and asking people to leverage AI to stay on top of the exponential.
We are seeing this in our organization as well. Physicists and mathematicians who know how to think well from first principles are now writing code. Coders are learning how to sell.
If you are a dynamic person and enjoy problem solving, reach out! We always have room for the exceptionally talented.
We build air-gapped, custom trained AI models for organizations across oil and gas, automobile and even law.
Again, reach out if you are impatient, exceptional and dynamic.
An Anthropic researcher just told a room full of PhD students that their career plan no longer exists
His 3 pieces of advice for students:
1. Use AI every day. If you're on the exponential, you'll never fall behind it.
2. Choose projects that get better when compute scales. The rest will be solved before you finish them.
3. Stop planning your career as if it were stable. The safe option no longer buys safety; it no longer exists.
And a phrase that sums it all up:
"Writing is thinking. If you outsource your writing, you outsource your thinking."
The talk is titled "The End of the Anthropocene"
Fast Code AI retweeted
I think people are underestimating just how much industrial knowledge Germany is sitting on, and how easily it could lose its competitive advantage over the next decade.
Germany has spent generations building some of the most sophisticated industrial companies in the world. Many are family-owned Mittelstand businesses, operating in obscure niches, making products that most people have never heard of, but which entire global supply chains depend on.
Their competitive advantage was built over decades. It lives in the accumulated experience of their engineers, the intricacies of their manufacturing processes, and an intimate understanding of problems that competitors may not even know exist.
And yet, these companies face an increasingly uncomfortable future.
Energy is expensive, regulation is burdensome, skilled workers are difficult to find, and competition from China is intensifying. Meanwhile, technological change threatens to erode advantages that took generations to build.
I find it troubling how casually we discuss Germany's industrial decline, as though we are talking about abstract economic statistics rather than businesses that families have spent their lives building.
But I also believe AI offers these companies an extraordinary opportunity.
Consider the sheer quantity of knowledge accumulated inside a fifty-year-old manufacturing business. Engineering drawings, maintenance records, production data, and decades of experience troubleshooting problems on the factory floor.
Much of this knowledge remains fragmented, difficult to access, and dependent on a handful of experienced engineers.
Imagine making that accumulated expertise accessible across the organization, allowing engineers to understand complex dependencies and make better decisions in a fraction of the time.
This is precisely the kind of problem we're working on at @FastCodeAI.
Germany's greatest industrial strength is its accumulated engineering knowledge. Air-gapped secure customized AI offers an opportunity to make that knowledge considerably more valuable.
And I believe preserving this advantage should be a national priority.
Arjun, our CEO, has deep connections with Germany. PhD from @maxplanckpress and his first company sold to @MercedesBenz.
We have experience building AI that is secure, air-gapped and specifically trained for you. We have worked across oil, gas, automobile and manufacturing.
We are expanding in Germany now!
I think people are underestimating just how much industrial knowledge Germany is sitting on, and how easily it could lose its competitive advantage over the next decade.
Germany has spent generations building some of the most sophisticated industrial companies in the world. Many are family-owned Mittelstand businesses, operating in obscure niches, making products that most people have never heard of, but which entire global supply chains depend on.
Their competitive advantage was built over decades. It lives in the accumulated experience of their engineers, the intricacies of their manufacturing processes, and an intimate understanding of problems that competitors may not even know exist.
And yet, these companies face an increasingly uncomfortable future.
Energy is expensive, regulation is burdensome, skilled workers are difficult to find, and competition from China is intensifying. Meanwhile, technological change threatens to erode advantages that took generations to build.
I find it troubling how casually we discuss Germany's industrial decline, as though we are talking about abstract economic statistics rather than businesses that families have spent their lives building.
But I also believe AI offers these companies an extraordinary opportunity.
Consider the sheer quantity of knowledge accumulated inside a fifty-year-old manufacturing business. Engineering drawings, maintenance records, production data, and decades of experience troubleshooting problems on the factory floor.
Much of this knowledge remains fragmented, difficult to access, and dependent on a handful of experienced engineers.
Imagine making that accumulated expertise accessible across the organization, allowing engineers to understand complex dependencies and make better decisions in a fraction of the time.
This is precisely the kind of problem we're working on at @FastCodeAI.
Germany's greatest industrial strength is its accumulated engineering knowledge. Air-gapped secure customized AI offers an opportunity to make that knowledge considerably more valuable.
And I believe preserving this advantage should be a national priority.
We got a lot of heat for promoting Research as a Service.
Questions were asked: Can we just buy papers? Is thus a paper mill?
Unfortunately, what most people don't know is that this has existed as a business model for decades. Especially in pharma, where drug development is painful and often the R&D is outsourced away to smaller, leaner companies that can iterate faster.
We are simply doing this for ML and AI. And it absolutely works!
There is one part that deserves particular clarity: you cannot buy a peer-reviewed paper. We cannot and don't guarantee that NeurIPS, CVPR, ICLR, AAAI, or any other venue will accept a piece of work, and nobody credible can. Peer review exists precisely because an independent community decides whether the research meets its standards.
Shared authorship makes sense when both parties make substantive research contributions and must follow the authorship and disclosure rules of the relevant venue.
And this is exactly what we did and our Research-as-a-Service collab with Renesas went in NeurIPS 2026. Both names on the paper, like the card says.
We established CausalDriveBench, which tests whether modern vision-language-action models used for autonomous driving actually reason about cause and effect, rather than simply producing plausible explanations alongside plausible driving trajectories.
Oh btw, it works!
Our Research-as-a-Service collab with Renesas went in. NeurIPS 2026. Both names on the paper, like the card says. ๐
CausalDriveBench: AI driving models explain their decisions fluently. The reasoning is mostly for show.
arxiv.org/abs/2609.32157
Lessgo ๐
Fast Code AI retweeted
Sound on. ๐
Beep. Hum. Pour. Flip. Click. Clack. Strum.
A day at @FastCodeAI, in 35 seconds.
We are terribly impatient as a company and absolutely hate mulling over things for six months before they exist.
We would rather start building the thing, and course-correct on the way.
If you want to automate part of your physical factory, call us.
If you are a fast-growing startup with a difficult research problem but cannot justify building an entire research team, call us.
If your company has decades of knowledge scattered across drawings, PDFs, databases and peopleโs heads, and you want to turn it into something your machines can actually understand, call us.
Or just talk to @SanjaySuriya6 who is in SF now!
Fast Code AI retweeted
Today, it is expected that most successful people should have a modicum of financial literacy. We may be heading towards a world where AI literacy is expected too, if not at the depth of Ben Affleck, then at least at the level of understanding the basic machinery.
Most people do not need to train neural networks or write Python. Financial literacy does not require you to price derivatives either. It requires enough understanding to follow what is happening, ask sensible questions, and recognize when someone is talking nonsense.
AI is moving in that direction.
A lawyer will increasingly benefit from knowing how language models arrive at answers, where they are likely to fail, and what happens to sensitive information passed through them. A filmmaker may need to understand generative video, segmentation, compositing, and the limits of synthetic footage. A CEO should probably know what inference costs, why proprietary data matters, and why a model that sounds extremely confident may still be wrong.
What makes the Ben Affleck clip striking is how naturally this knowledge seems to have entered his working life. He ran a visual-effects company, worked with GPUs, encountered convolutional neural networks through VFX, and eventually found himself curious enough to call Google and OpenAI to see what they were building.
That trajectory is going to become increasingly common. As AI works its way into law, finance, medicine, engineering, media, education, and management, some knowledge of how these systems behave will become part of being competent in the field.
A decade from now, โIโm not technicalโ may sound increasingly dated. You will not need to understand every layer of the stack, but if AI is shaping decisions inside your profession, understanding its basic logic will probably become part of understanding your profession itself.
Ben Affleck reveals he writes Python, understands convolutional neural networks, worked extensively with GPUs, and used his celebrity status to get private looks at Google and OpenAIโs video models
โIโve always been kind of into computers since I was young. Then, when film started to move from analog film to digital, I became more interested in that aspect of it. The visual-effects workflow for many years has included machine learning, so I can write pretty shitty Python scripts and stuff like that.
โWith convolutional neural networks, which were the precursors to what the transformer can do, which is much more computation simultaneously, you would do things like look at whatโs called a tensor. Thatโs the numerical translation of a visual image in numbers, like the batch number, the frame number and the red, green and blue values of each pixel in each frame. Itโs just that simple. That numeric is called a tensor.
โYouโd use a convolutional neural network to identify patterns that reveal whatโs called edge detection or feature extraction, which is identifying patterns well enough to know, this is where the window ledge is, so we can more easily take the green-screen image out and replace it with something.
โThat was familiar to me early on because, prior to Artists Equity, I had a small visual-effects company. Iโve worked with GPUs a lot too. The visual-effects guys said, โHey, you should see. There are a couple: Google and this other company, OpenAI, are doing really interesting stuff with transformers in video.โ
โIโve learned that I can actually just call up and go, โHey, itโs Ben Affleck. Can I come see what youโre doing?โ Sometimes people say yes, to my astonishment.โ
Today, it is expected that most successful people should have a modicum of financial literacy. We may be heading towards a world where AI literacy is expected too, if not at the depth of Ben Affleck, then at least at the level of understanding the basic machinery.
Most people do not need to train neural networks or write Python. Financial literacy does not require you to price derivatives either. It requires enough understanding to follow what is happening, ask sensible questions, and recognize when someone is talking nonsense.
AI is moving in that direction.
A lawyer will increasingly benefit from knowing how language models arrive at answers, where they are likely to fail, and what happens to sensitive information passed through them. A filmmaker may need to understand generative video, segmentation, compositing, and the limits of synthetic footage. A CEO should probably know what inference costs, why proprietary data matters, and why a model that sounds extremely confident may still be wrong.
What makes the Ben Affleck clip striking is how naturally this knowledge seems to have entered his working life. He ran a visual-effects company, worked with GPUs, encountered convolutional neural networks through VFX, and eventually found himself curious enough to call Google and OpenAI to see what they were building.
That trajectory is going to become increasingly common. As AI works its way into law, finance, medicine, engineering, media, education, and management, some knowledge of how these systems behave will become part of being competent in the field.
A decade from now, โIโm not technicalโ may sound increasingly dated. You will not need to understand every layer of the stack, but if AI is shaping decisions inside your profession, understanding its basic logic will probably become part of understanding your profession itself.
Ben Affleck reveals he writes Python, understands convolutional neural networks, worked extensively with GPUs, and used his celebrity status to get private looks at Google and OpenAIโs video models
โIโve always been kind of into computers since I was young. Then, when film started to move from analog film to digital, I became more interested in that aspect of it. The visual-effects workflow for many years has included machine learning, so I can write pretty shitty Python scripts and stuff like that.
โWith convolutional neural networks, which were the precursors to what the transformer can do, which is much more computation simultaneously, you would do things like look at whatโs called a tensor. Thatโs the numerical translation of a visual image in numbers, like the batch number, the frame number and the red, green and blue values of each pixel in each frame. Itโs just that simple. That numeric is called a tensor.
โYouโd use a convolutional neural network to identify patterns that reveal whatโs called edge detection or feature extraction, which is identifying patterns well enough to know, this is where the window ledge is, so we can more easily take the green-screen image out and replace it with something.
โThat was familiar to me early on because, prior to Artists Equity, I had a small visual-effects company. Iโve worked with GPUs a lot too. The visual-effects guys said, โHey, you should see. There are a couple: Google and this other company, OpenAI, are doing really interesting stuff with transformers in video.โ
โIโve learned that I can actually just call up and go, โHey, itโs Ben Affleck. Can I come see what youโre doing?โ Sometimes people say yes, to my astonishment.โ
