Laya the open source version of Jev
A developer recounts their experience building a non-autoregressive AI model for decision-making, only to see a well-funded startup, TypeSafe AI, launch a similar product later. The author is now releasing their own open-source version, Laya, to address limitations in existing architectures.
Why it matters
It highlights the tension between independent open-source researchers and well-funded frontier labs in the rapidly evolving AI landscape.
Everyone in AI right now is talking about a new kind of model: an architecture that is not autoregressive, does not generate text, and gives lightning-fast probability predictions over structured schemas.
Seeing the hype online feels both validating and deeply frustrating.
I worked on this literally one year back in March 2025. I spent months of hard work, sweat, and sleepless nights building it, published an arXiv paper ( arXiv:2503.23303 ), released the model weights on Hugging Face ( sales-conversion-model-reinf-learning ), published the open dataset ( saas-sales-conversations ), built a PyPI package, and posted the whole approach on Reddit ( r/LocalLLaMA discussion ).
Then in September 2025, I published a second paper ( arXiv:2510.01237 ), formalizing the framework for schema-based decisions guided by reinforcement learning. The guiding brain in my system was always reinforcement learning, not just an embedding model or an autoregressive LLM.
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