"Providing code data vetted by the best engineers, so you can build the most capable model or application"
Datacurve provides expert quality code data at scale from highly skilled software engineers.
Founded by Serena Ge and Charley Lee
The Problem: Why getting high-quality code data is so hard
From their experience training models, the Datacurve team believes the biggest bottleneck of progressing vertical LLM capabilities is the lack of curated, high-quality training data.
Acquiring this high-quality data is difficult because:
- Consistent, high-quality code data cannot be synthetically generated or scraped. Tasks are often too challenging or specific for even the most capable models, and even a few incorrect samples can noticeably worsen the final training results.
- Hiring human annotators is tricky. Manual data labeling en masse tends towards low-skill gig work; it’s difficult to hire and retain highly competent engineers as annotators.
The Solution
Datacurve solves the data problem with their gamified annotation platform that attracts the best engineers to come and solve fun coding problems. The startup has already brought on top competitive programmers, as well as highly competent engineers who have worked at companies like Amazon and AMD.
In general, they get great engineers who 1) already have good careers, and 2) already enjoy doing programming challenges outside of work. Datacurve's gamified platform pays them for solving problems, which they already do for fun.
Data for AI dev-tool startups to train use-case specific models:
- UI design to React components generation
- Framework-specific optimized code generation
- Repository-wide automatic PRs from GitHub issues
- Intelligent coding copilot integrated IDEs. Data for code completion and debugging
For foundation model labs, the kinds of data their platform creates are:
- Refactoring code for readability
- Improving code for performance
- Code generation for difficult problems or new features
- Debugging runtime errors
- Code walkthrough and explanation
