Original Reddit post

Hi all, Note this is a technical post however I will try to keep it high-level. For those who are working in and deploying artificial intelligence solutions, we operate in a highly fragmented space. Many of you will know that typically AI solutions are coded in Python , but to get these out into the real world often means large teams in startups that can help provide complementary skill-sets to rewrite the ‘prototype’ and/or AI models into a “more serious” programming language, often C/C++ , and sometimes Rust . Additionally, for embedded AI we have Prolog and a number of other dedicated options. However, a blocker in the speed of deployment is the highly fragmented data ecosystem and their tools. For e.g., when working in Python you might be using ‘PyTorch’, ‘NdArray’, ‘Jax’, some object store, some embedded devices that do or don’t support your NVIDIA hardware, and basically the library/tooling list goes on until you have a 100 person startup with roughly 30-50% of peoples’ time spent on this kind of integration. Today though, we now have AI agents which, means eliminating that fragmentation and can help close the loop on how fast we can get Artificial Intelligence technology to market quicker, by closing gradually closing the cycle. I strongly believe the answer to this is less tools not more , and helping to get more leverage, so that it is way less difficult to innovate and build more quickly. As a result, I have built an Apache 2.0 (open source) licensed project that I believe can greatly assist with this. It is built in Rust , but provides AI compatible data structures, and lets people bridge from Rust to Python in ~220 nanoseconds on a consumer laptop, and back in about ~3 microseconds. It operates in a manner whereby both the computer science experts deploying AI either on physical hardware or in the cloud can very easily interface with the required underlying shared data structures that AI scientists need for training and fine tuning their AI model systems. This can help reduce friction, by providing a shared set of data abstractions for software development in the field of AI. I am sharing this because I believe this is an important and valuable problem to solve which meaningfully advances AI technology by directly addressing actual underlying bottlenecks. If you work and/or operate in that space, or otherwise have further questions for someone who has worked deploying AI in defense and high-performance trading contexts, please feel free to post in the comments or shoot me a DM. The project is called Minarrow and you can access it via the attached link. Thanks submitted by /u/peterxsyd

Originally posted by u/peterxsyd on r/ArtificialInteligence