My team and I have worked on an agentic benchmark to see how well agents can perform at reverse engineering threats. The results are pretty interesting, we would love the communities feedback.
Over the last year I’ve been building RacoGrad, a deep learning library written in Racket (a Lisp). It started as an experiment in understanding autodiff and neural nets from first principles, but it’s grown into something surprisingly usable.
Recently, with help of opencalw I added transformer support and finished a working GPT-2 implementation. The model loads pretrained GPT-2 weights and runs end to end inference entirely in Racket.
Large language models (LLMs) demonstrate impressive capabilities across numerous domains, from programming and technical support to cooking instructions and research enhancement. However, they continue to struggle with precise symbol manipulation in tasks requiring rigorous logical reasoning, mathematical proofs, and complex algebraic operations. As a student of philosophy and computer science, I find this intersection especially fascinating because of the connections between logical precision and conceptual clarity.
Where did you come up with this ? This is just not true, that Pythagoras had little interest in math. He had a love of numbers and thought that math was a way to the divine or at least understanding the divine. His philosophy, not religion , but philosophy was a way of life that entangled mathematics profusely.
Synapticyte is my startup dedicated to stopping school and workplace shootings using advanced AI technology. We’ve developed a highly accurate model for identifying firearms and are working on building a robust alert system. This system will enable schools or organizations to notify police, faculty, and staff when weapons are detected. Our model leverages a combination of Convolutional Neural Networks (CNN) and transformers. Although it’s still in the early stages, we believe this is a powerful and important application of AI technology.
Synapticyte is my startup dedicated to stopping school and workplace shootings using advanced AI technology. We’ve developed a highly accurate model for identifying firearms and are working on building a robust alert system. This system will enable schools or organizations to notify police, faculty, and staff when weapons are detected. Our model leverages a combination of Convolutional Neural Networks (CNN) and transformers. Although it’s still in the early stages, we believe this is a powerful and important application of AI technology.
MIND is a simple, educational framework for building feedforward neural networks. Includes tensor operations, activation functions, and backpropagation. Ideal for learning core deep learning concepts and exploring Racket. Future updates will be released!