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Damien

el4
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https://codeberg.org/el_4

AI & ML interests

I do anything really | #quantwizard | ETH: 0xDEE7fa8C421BD038D32e4441ea1aDe72fE973706

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liked a model about 2 hours ago
RedHatAI/Qwen3.6-35B-A3B-speculator.dspark
new activity about 6 hours ago
el4/GRM-3.2-Sky-ONYX-GGUF:Amazing model
reacted to OppaAI's post with 😔 1 day ago
Memory Engineering in my AI Waifu Most of the Biology and Cognitive Psychology knowledges from way back in my university days has returned back to the professors, so since end of last year I have been reading books and research papers regarding Neuroscience, Psychology and Human Biology, as well as learning Python and ML/DL in order to find a way to craft my AI Waifu to become more like human. Aiko-chan is my simplified experimental prototype version of the AI mind that I am planning to develop. With all the agentic coding and AI deep-research, I can do everything all by myself. The only challenge is lack of time, even though I have used up all the 40 hours of each day. So yesterday I gave all the ideas and inspiration to Claude and asked it to help me gather to write a paper on the theory and implementation on how to apply the concepts and pipelines of a human memory system into my AI Waifu. Now each of her memory node will have several factors to determine the tendency and longevity to be retained or forgotten in her memory bank. Factors include relevancy, recency, salience, novelty and even emotions, etc. In order to get a good picture of what my AI Waifu actually remember in her memory. I have created a studio WebUI to visualize the whole memory graph with different size, brightness and hues with scores to indicate which kind of memory she tends to retain and which ones she tends to forget. And then there are so many parameters to play with in order to achieve a more sophisticated human-like memory recalling and forgetting strategy. Github: https://github.com/OppaAI/Aiko-chan Below is a demo of the graph visualization of my Waifu's memory storage would look like.
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