We take AI from research papers to industrial plants. Computer vision, agents, speech, generative AI. Bangalore | Stuttgart | Abu Dhabi.
Joined May 2025
- Tweets335
- Following115
- Followers476
- Likes247
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. 👇
CausalDriveBench: AI driving models explain their decisions fluently. The reasoning is mostly for show.
arxiv.org/abs/2609.32157
Lessgo 🚀
Fast Code AI retweeted
Sound on. 🔊
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 people’s 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, “I’m not technical” 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 OpenAI’s video models
“I’ve 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.
“With 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 what’s called a tensor. That’s 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. It’s just that simple. That numeric is called a tensor.
“You’d use a convolutional neural network to identify patterns that reveal what’s 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.
“That was familiar to me early on because, prior to Artists Equity, I had a small visual-effects company. I’ve worked with GPUs a lot too. The visual-effects guys said, ‘Hey, you should see. There are a couple: Google and this other company, OpenAI, are doing really interesting stuff with transformers in video.’
“I’ve learned that I can actually just call up and go, ‘Hey, it’s Ben Affleck. Can I come see what you’re doing?’ Sometimes people say yes, to my astonishment.”
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, “I’m not technical” 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 OpenAI’s video models
“I’ve 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.
“With 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 what’s called a tensor. That’s 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. It’s just that simple. That numeric is called a tensor.
“You’d use a convolutional neural network to identify patterns that reveal what’s 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.
“That was familiar to me early on because, prior to Artists Equity, I had a small visual-effects company. I’ve worked with GPUs a lot too. The visual-effects guys said, ‘Hey, you should see. There are a couple: Google and this other company, OpenAI, are doing really interesting stuff with transformers in video.’
“I’ve learned that I can actually just call up and go, ‘Hey, it’s Ben Affleck. Can I come see what you’re doing?’ Sometimes people say yes, to my astonishment.”
Fast Code AI retweeted
A self-driving car can make the right decision for the wrong reason.
For an LLM, that can be annoying. For a self-driving car, where the decision can literally be a matter of life and death, being consistently causally wrong is really fucking dangerous.
So we built a benchmark to test whether autonomous driving models actually understand why they should act, not just whether they produce the right action.
That work has now been accepted at NeurIPS.
Here is a brief explainer: