QingyangZhang/TEMPO

TEMPO addresses one of the most competitive frontiers in AI: scaling laws for inference-time compute (similar to the logic behind OpenAI's o1 and DeepSeek-R1). While the project targets a high-value niche (Test-Time Training for reasoning), it currently lacks any quantitative signals of adoption (0 stars, 0 forks, 0 days old). The defensibility is extremely low as the code appears to be a fresh research release or a placeholder. From a competitive standpoint, frontier labs (OpenAI, Anthropic, Google, and DeepSeek) are the primary actors in this space; they are actively developing proprietary TTT and 'search-based' reasoning methods that would likely supersede or absorb any open-source scaling technique that isn't backed by massive compute or a unique, hard-to-replicate dataset. The displacement horizon is very short (6 months) because the field of 'reasoning scaling' is currently the highest-velocity area in AI research. Any breakthrough here is likely to be integrated into major model providers' APIs or system prompts almost immediately. Platform domination risk is high because these techniques require deep integration with model weights and inference engines (like vLLM or TensorRT-LLM) to be performant at scale.