TEC: A Collection of Human Trial-and-error Trajectories for Problem Solving

TEC (Trial-and-Error Collection) enters the market at the peak of the 'Reasoning Model' trend popularized by OpenAI's o1 and DeepSeek-R1. The project's core thesis—that AI needs to learn from the messy process of human failure and correction rather than just final answers—is theoretically sound but faces massive competition from synthetic data scaling. With 0 stars and only 4 forks in its first week, the project currently lacks the community momentum or data scale to be a standalone moat. Frontier labs are already generating millions of 'synthetic' trial-and-error trajectories using Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS), which scales much faster than collecting human data. The primary value of TEC is as a qualitative benchmark for how human reasoning differs from LLM search, but as a training resource, it is likely to be overshadowed by proprietary datasets and massive-scale synthetic reasoning chains within 6 months. Its defensibility is limited to the uniqueness of the human data, which is expensive to replicate but difficult to scale to the levels required by modern frontier models.