Git commits are not sufficient to train the next generation of frontier coding models. They are noisy, the majority of problems they capture are already trivial for state-of-the-art agents, and stratifying them by difficulty is highly nontrivial. What is required are curated corpora of verifiably difficult problem–solution pairs.
At Parsewave we work directly with AI labs to design and build custom datasets of coding and command-line challenges that cutting-edge agents cannot yet solve. These datasets are authored from the ground up by senior engineers across Central Europe, Australia, and the United States. Every problem, solution trace, and reasoning annotation is created with research objectives in mind and aligned to real-world engineering standards.
How We Work
- Define data specifications jointly with research teams
- Generate problem sets, solution traces, and reasoning annotations to match research requirements
- Refine iteratively until research-grade quality is achieved, with unlimited revisions
- Provide immediate access to a global pool of senior engineers for rapid turnaround
- Share 5–10 representative samples for verification prior to scale-up
Why It Matters
Recent advances and the availability of large but noisy datasets, such as algorithmic exercises and git commit logs, have not solved the challenge of training high-performing coding agents. The most effective approach is to use curated, real-world engineering tasks that represent a reliable ground truth. Parsewave datasets are designed for use in supervised fine-tuning, reinforcement learning, or as benchmarks to assess model performance.
We will provide a tiny sample corpus for evaluation to any AI lab researchers