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Hackernoon
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Our AI Coding Tool Went Viral, Then Everything Broke. This is What We Learned.
- A VS Code extension to help developers search their codebases launched on March 2, 2023, had over 7,000 users within weeks, and attracted an unexpectedly high level of traffic. The backend architecture was not able to handle the surge in demand, and the team needed to rebuild the system's architecture, which relied on LLM, to deliver the same functionality without LLM. The team developed a custom matrix to bias embeddings for the specific use case, which improved matching code snippets to user queries and reduced costs and computational overhead. The team learned valuable lessons about pivoting, monitoring, cost structure, communication, and data quality.
- The team's journey from a simple VS Code extension led to the development of Genie, a fully autonomous AI software engineer that embodies the vision of the original product. The team built with what they could while waiting for AI technology to catch up to their vision, such as their initial code search and retrieval system, cloud-based indexing platform, and experiments with emerging AI models, leading to Genie's development.
- The team developed a proprietary technique to generate datasets that truly captured how human developers think, which caught the attention of OpenAI, who granted the team early access to fine-tune GPT-4 beyond public availability.
- In August 2024, the team achieved a major milestone when Genie scored 43.8% on the industry-standard SWE-Bench Verified, significantly outperforming previous records held by major tech companies.
- The sudden surge in traffic taught the team valuable lessons, including the importance of pivoting, monitoring, cost structure, communication, and data quality, which helped the team to rebuild the system's architecture.
- The custom matrix, which helps find the right code snippet faster and more accurately, reduced costs and computational overhead, and improved matching code snippets to user queries, allowed the team to maintain the core functionality of their product.
- A willingness to pivot allowed the team to survive, while their initial approach did not hold up under real-world conditions.
- Implementing better monitoring and alerting systems could have helped the team respond more quickly to the sudden surge in usage.
- The team did not fully grasp how their costs would scale with usage, leading to painful financial realisations in those early days.
- Managing expectations during the rebuilding phase was challenging for the team and underscored the importance of transparent communication, especially when dealing with technical users.
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