Anand Kulkarni

So scalability is this question that as soon as you solve it at the level that you are aiming for, your own ambitions take you up to the level where you need to start thinking about it again the same way. I don't think there's any universe where you can avoid working on this problem unless you are not willing to grow or not able to grow.

So scalability is this question that as soon as you solve it at the level that you are aiming for, your own ambitions take you up to the level where you need to start thinking about it again the same way. I don't think there's any universe where you can avoid working on this problem unless you are not willing to grow or not able to grow.

So scalability is this question that as soon as you solve it at the level that you are aiming for, your own ambitions take you up to the level where you need to start thinking about it again the same way. I don't think there's any universe where you can avoid working on this problem unless you are not willing to grow or not able to grow.

We have been in a couple of periods in our growth where we have seen that we were catching up on the ability to support the scale that we were at because our demand suddenly spiked as we were more successful with customers. Good problem to have, but still a real and massive problem. I'm Anand Kulkarni, CEO and founder of Crowdbottom.

We have been in a couple of periods in our growth where we have seen that we were catching up on the ability to support the scale that we were at because our demand suddenly spiked as we were more successful with customers. Good problem to have, but still a real and massive problem. I'm Anand Kulkarni, CEO and founder of Crowdbottom.

We have been in a couple of periods in our growth where we have seen that we were catching up on the ability to support the scale that we were at because our demand suddenly spiked as we were more successful with customers. Good problem to have, but still a real and massive problem. I'm Anand Kulkarni, CEO and founder of Crowdbottom.

We are a modernization company. We use AI systems to turn code bases into human-readable natural language specifications. What that means is we are writing requirements automatically at scale from code bases. You've got a code base, we can reverse engineer that into requirements. Once you've got those requirements, you can flip that back into more code.

We are a modernization company. We use AI systems to turn code bases into human-readable natural language specifications. What that means is we are writing requirements automatically at scale from code bases. You've got a code base, we can reverse engineer that into requirements. Once you've got those requirements, you can flip that back into more code.

We are a modernization company. We use AI systems to turn code bases into human-readable natural language specifications. What that means is we are writing requirements automatically at scale from code bases. You've got a code base, we can reverse engineer that into requirements. Once you've got those requirements, you can flip that back into more code.

We are helping the world deal with this massive problem of updating, fixing, and modernizing all this legacy code. This is something that system integrators, consultants have been helping entities with for a long time. They do an okay job, but there's just too much code.

We are helping the world deal with this massive problem of updating, fixing, and modernizing all this legacy code. This is something that system integrators, consultants have been helping entities with for a long time. They do an okay job, but there's just too much code.

We are helping the world deal with this massive problem of updating, fixing, and modernizing all this legacy code. This is something that system integrators, consultants have been helping entities with for a long time. They do an okay job, but there's just too much code.

What we do in our core insight is that in the LLM era, human written requirements give you this common currency between what machines can understand and what human beings can understand. Crowdbotics exists to support modernization efforts by providing in natural language, human-readable requirements that are also machine-readable, that allow you to go quite a bit faster. So that's what we do.

What we do in our core insight is that in the LLM era, human written requirements give you this common currency between what machines can understand and what human beings can understand. Crowdbotics exists to support modernization efforts by providing in natural language, human-readable requirements that are also machine-readable, that allow you to go quite a bit faster. So that's what we do.

What we do in our core insight is that in the LLM era, human written requirements give you this common currency between what machines can understand and what human beings can understand. Crowdbotics exists to support modernization efforts by providing in natural language, human-readable requirements that are also machine-readable, that allow you to go quite a bit faster. So that's what we do.

Today, AI-based software engineering is a very crowded field. A lot of these folks maybe don't appreciate the complexity and difficulty of this problem. When you look under the hood, you find out it's just a thin wrapper on OpenAI or Anthropic. We've actually been working on this specific problem for a very long time.

Today, AI-based software engineering is a very crowded field. A lot of these folks maybe don't appreciate the complexity and difficulty of this problem. When you look under the hood, you find out it's just a thin wrapper on OpenAI or Anthropic. We've actually been working on this specific problem for a very long time.

Today, AI-based software engineering is a very crowded field. A lot of these folks maybe don't appreciate the complexity and difficulty of this problem. When you look under the hood, you find out it's just a thin wrapper on OpenAI or Anthropic. We've actually been working on this specific problem for a very long time.

In 2016, I was looking at early literature about not large language models, but the precursor technology known as recurrent neural networks. I remember reading this great paper by Andre Carpathy, who was at Stanford at the time. It was called The Unreasonable Effectiveness of Recurrent Neural Networks.

In 2016, I was looking at early literature about not large language models, but the precursor technology known as recurrent neural networks. I remember reading this great paper by Andre Carpathy, who was at Stanford at the time. It was called The Unreasonable Effectiveness of Recurrent Neural Networks.