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AyodeleFikayomi

4 karma · joined May 31, 2025

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AyodeleFikayomi··on Show HN: A tool that audits healthcare ML models for safety and trust
Thank you for your advice, I will make it less technical and easier to understand. I appreciate your time taken to check it out.
AyodeleFikayomi··on Show HN: Lossless Semantic Matrix Analysis (99.999% accuracy, no training)
and definitely i think it is great to explore other means to Ai without the pain of DL
AyodeleFikayomi··on Show HN: Lossless Semantic Matrix Analysis (99.999% accuracy, no training)
Hi thank you for your feedback, and your project is really impressive... it's good to find like minds like yourself however to clarify the system i built is a dynamic and adaptive system focused on high-dimensional connection discovery and coherent transformations, not a static matrix pipeline. It's designed to find and adapt to structure over time, not to apply fixed transformations.
AyodeleFikayomi··on QuantumAccel: A High Performance Quantum-Inspired Logic Library in Rust+Python
Thank you!
AyodeleFikayomi··on QuantumAccel: A High Performance Quantum-Inspired Logic Library in Rust+Python
Hi everyone, I've released an open-source project called QuantumAccel which is built around a symbolic logic engine that transforms traditional logic gates like AND, XOR, and Toffoli into optimised quantum-inspired operations, all within a constrained mathematical space.

Features:

Ultra-fast logic compression using sparse attention

Evolving symbolic gates that simulate Hadamard, CNOT, XNOR

Memory-efficient operation (as low as 4 KB for massive input)

Reversible logic operations for feature extraction, pattern recognition, and error detection

Use Cases:

Quantum simulation

Edge AI with kilobytes of RAM

Memory compression & logic acceleration

NLP/vision feature extraction without neural nets

This is part of a larger symbolic AI framework I'm building. Would love your feedback or contributions! Let me know if you're interested in symbolic computation, quantum logic, or memory-efficient learning.

Demo benchmarks and documentation are available in the repo. Apache Licensed.