From LMMS to Ramp: Scaling Agents with Compute and AI

Join me as I explore the idea that systems that scale with compute beat those that don't, and how this exponential trend can revolutionize the way we build agents and software.

1. Speaker has been working on Learning Management Systems (LMS) for 4 years, focusing on improving chatbot intelligence.
2. Their work began to take off with the release of ChatGPT, which enabled them to build an AI agent company.
3. Early models were frustratingly stupid and required a lot of custom code to function reliably.
4. As models became smarter, less custom code was needed, revealing patterns in effective agent building.
5. Speaker built a structuring extraction library called Json Former to help models understand JSON data.
6. The core idea the speaker wants to convey is that systems that scale with compute beat systems that don't.
7. Rigid, deterministic systems can be outperformed by more flexible systems that utilize greater computational resources.
8. Exponential growth is rare and valuable; when found, it should be embraced and leveraged.
9. Examples from history show that general methods scale better than handcrafted solutions in chess, go, computer vision, and Atari games.
10. The speaker's company, Ramp, is a finance platform that uses AI to automate various tasks.
11. One system at Ramp is the Switching Report, which helps users transfer transaction data from third-party card providers to Ramp.
12. The original solution for the Switching Report was to manually write code for 50 common third-party card vendors, but this approach has limitations.
13. A more general solution involves using LMs to classify column types and map them to a desired schema.
14. An even more flexible approach is to let an LM interpret the CSV and generate the desired output format, which may require significantly more compute power.
15. The speaker argues that engineer time is more scarce than computational resources, justifying the use of high-compute solutions.
16. Different approaches can be visualized as a spectrum, with classical compute on one end and fuzzy LM (neural networks and matrix multiplication) on the other.
17. Modern systems often involve a mix of both classical and fuzzy LM components.
18. The speaker proposes that future systems may rely more heavily on fuzzy LM, allowing for more adaptability and reducing the need for explicit code.
19. In this model, the backend would be an LM that has access to tools, interpreters, and databases, enabling it to handle requests and generate responses more organically.
20. The speaker demonstrates a mail client that uses an LM as its backend, rendering UI elements based on LM interpretations of user interactions.
21. This approach allows for dynamic, real-time adjustments to the UI without requiring explicit code changes or backend calls.
22. While this technology is still in its infancy, it has the potential to revolutionize software development and deployment.
23. The speaker encourages the audience to consider the possibilities of fuzzy LM in their own projects and to embrace exponential trends in technology.
24. As computational resources become cheaper and more powerful, systems that leverage these trends will likely outperform traditional, rigid systems.

Source: AI Engineer via YouTube

❓ What do you think? What are your thoughts on the ideas shared in this video? Feel free to share your thoughts in the comments!