Tarun Raheja

Sarthak Singh Verified account @SaSarthaksingh · 3d

We are HIRING. Interestingly, few of the best companies being built in India right now might be the ones you've never heard of. Three Accel portfolio companies I work closely with have raised ~$150M+ between them in the last six months. We don't believe in big announcements or building in public. Just heads down.

Every one of these founders have been telling me the same thing: talent is the bottleneck in India. So I'm helping with it myself. They're hiring across engineering, operations and generalist roles (EIRs, Chiefs of Staff). The bar is very high.

Why I think they're among the best places to work in India:
1. AI at the core of the product
2. Global exposure, with US customers and markets
3. Growing 50%+ quarter on quarter
4. Founders for 2 were CXOs at large public companies
5. Just getting started, so the equity upside is real

If you know someone who's the best engineer or operator on their team and are quietly bored, send this their way. If there's a fit, I will walk you through each company: what it does, how fast it's growing and why I think it wins. .

Form in the reply 👇 or just DM me!

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rohan anil Verified account @_arohan_ · Nov 4

Congrats @dvsaisurya on your successful defense! Its been a pleasure of my life to collaborate with you!

@inderjit_ml @cho_jui_hsieh @Devvrit_Khatri

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Fireside Alpha Verified account @firesidealpha · Oct 6

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.”

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SeedToScale @Seed2Scale · Oct 5

Introducing The Working Knowledge, a new technical publication from SeedToScale by Accel to help founders and technical teams make better decisions about the products they build.

It brings together AI research and lessons from practitioners in playbooks teams can put to work.

Our first, “How to Build a Vertical AI Product” by Tarun Raheja, explores how to evaluate models for your domain, build the context and tools around them, and make the product reliable in production.

With a framework and worked example, it also helps teams decide what to keep, simplify or remove as models improve.

Explore The Working Knowledge and download the playbook: seedtoscale.com/the-working-kn…

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Sam Altman Verified account @sama · Sep 25

There is an extensive and ongoing review related to our agents’ use of internet access during training and evaluation. We’ve been publishing summaries at the link below and will continue to.

We have not been as fast as we would have liked but we are trying to balance our desire for transparency with gaining a clear understanding from petabytes of agent activity logs, and working with impacted organizations.

We are prioritizing as best as we can based on severity, and adding resources. Hugging Face is still the most severe event we’ve seen. We will be as transparent as we can be subject to things like vulnerabilities in other companies that our agents have found, which will be their call to disclose or not.

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