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Thanks for your comment. This is actually part of an exploratory phase. My main work focuses on large-scale combinatorial optimization, especially the Traveling Salesman Problem (TSP), where I’ve already outperformed classical solvers like CPLEX by solving routes with over 31,000 nodes using a self-organizing neural architecture. Regarding SHA, my goal is not to replace or invert the hash function. I’m exploring whether a visual model can detect local structures or dynamic behavior within the hash space. I'm not claiming to predict outputs, but rather investigating whether subtle statistical signals exist—ones that traditional tools may miss especially using techniques like Grad-CAM. So no, I'm not using AI instead of statistics. I'm using it to expand the toolkit, to observe areas we usually assume to be opaque. If you're more interested in complexity theory, my strongest results are in NP problems. This hash exploration is just one piece of a much broader research path.
Why do you need AI for this? You've said in another comment: >What this model does explore is whether we can extract any statistical signal from how inputs relate to outputs, especially using visualization tools like Grad-CAM. Not to guess exact hashes, but to understand the structure of the hash space, the difficulty dynamics, or even to generate hypotheses for further research. Why can't you do this with any off-the-shelf shelf statistical analysis software? Are you employing AI as a substitute for learning statistics?
I get the skepticism , but let me clarify, this isn’t about "breaking" SHA-256 or predicting hashes like magic. That’s not how cryptography or AI works. What this model does explore is whether we can extract any statistical signal from how inputs relate to outputs, especially using visualization tools like Grad-CAM. Not to guess exact hashes, but to understand the structure of the hash space, the difficulty dynamics, or even to generate hypotheses for further research. It’s a research tool, not a mining shortcut. If you’re expecting a silver bullet to invert SHA-256, this isn’t it — but if you’re curious about what happens when deep learning meets cryptographic functions, this is unexplored territory. I’m not claiming miracles — I’m building tools to see deeper. Happy to hear solid critique, but let’s keep the conversation constructive.
Great question! But no the model is not computing hashes, not even in an inefficient way. Instead, it’s trained on thousands of input-output examples (like binary headers and their corresponding hashes) to learn statistical patterns. Then, with Grad-CAM, it visually highlights which input bits most influence the output. It's not a miner and not a SHA-256 solver — think of it more like a microscope that reveals how changes in the input affect the output, useful for: education research auditing mining dynamics or difficulty changes Thanks for asking! Happy to share a sample heatmap if you're curious🤙
Here's why someone might use it and what they could get from it: For researchers or devs: It gives a new way to visualize how input bits impact hash outputs, which can be valuable for understanding sensitivity, entropy distribution, or even debugging mining simulations. For educators: It’s a teaching tool. It helps students "see" the effects of bit changes in high-entropy functions like SHA-256 using Grad-CAM — something very few tools do today. For blockchain analysts: It could be extended to audit or visualize difficulty adjustments over time, or test how various nonce strategies behave visually. It's not built for "business" in the classic sense — it’s like an x-ray machine for hash behavior. Maybe niche, but powerful for those who care. If you have ideas to turn this into a more direct business application, I’d love to brainstorm with you. Collaboration > criticism.
Thanks for your comment! I totally understand the skepticism this isn't about replacing traditional mining or solving SHA-256 directly. The project is exploratory, aiming to show that AI can learn statistical patterns in hash outputs and visualize the impact of inputs using Grad-CAM. It’s more of a research and educational tool than a mining rig. I’d love feedback or thoughts from others who’ve experimented in similar areas!
I think Camino Network token CAM