Dive into poolside: AI models just for software engineering (AIM133)

Summary of the Video Transcription

Introduction

  • The speakers are Isaac and VTor, who lead the Solutions Engineering team at Poolside, a company building the Next Generation AI for software engineering.
  • Poolside is a full-stack solution that includes a new foundation model specific to software development, fine-tuned on code and developer interactions.

Why Poolside Exists

Poolside was founded on three core convictions:

  1. The future belongs to domain-specific models, as opposed to general language models.
  2. General language models are not relevant to customers' older code bases, and are only helpful for greenfield projects.
  3. As AI adoption has increased, more data is being sent to the cloud for inference, which raises security and privacy concerns.

Limitations of General Language Models

  • General language models understand software development just as much as they understand French literature or car maintenance.
  • They are "taught about code" but do not know how to code, which is a significant limitation.
  • Poolside's focus is solely on software development, allowing them to make different architectural decisions in building their language model.

Addressing Challenges in Building Language Models

  • Compute: Poolside's recent Series B funding allowed them to procure GPUs for the next 3 years, enabling them to be at the forefront of building frontier models.
  • Data: The challenge is that not all available code data is useful for training. Poolside has developed a unique solution called "Reinforcement Learning via Code Execution Feedback" to address this.

Reinforcement Learning via Code Execution Feedback

  • Poolside looks at the top open-source repositories, hides the code, and has their model attempt to code against the repositories.
  • They can then evaluate whether the model's code compiled, passed tests, and was secure, providing feedback to the model.
  • This process has been automated and scaled to over 500,000 open-source repositories, teaching the model to think like a developer.

Relevance and Fine-tuning

  • General language models often provide the same generic results, lacking the context and understanding of a specific business.
  • Poolside's models are fine-tuned on the customer's code, data, and user behavior to become highly relevant and tailored to their needs.
  • The goal is to create a "Software Intelligence Layer" that knows more about a company's software business than any single human developer.

Security and Privacy

  • As models demand more capability and data, there is a tradeoff between performance and the amount of data sent to the cloud.
  • Poolside's solution is to provide the model within the customer's security boundary, allowing all data circulation and fine-tuning to happen within their network.

Conclusion

  • Poolside is building the most capable models for software development through their unique approach of reinforcement learning via code execution feedback.
  • The fine-tuned models are tailored to the customer's needs and can be deployed within their security boundary, addressing privacy and security concerns.

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