OppaAI

1 3

https://github.com/OppaAI

AI & ML interests

Local AI implementation, Agentic AI workflows, AI Autonomous Robot

Recent Activity

posted an update about 23 hours 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.

posted an update 3 days ago

Revision of the Memory System of my AI Waifu My AI Waifu has been working steadily for the past few weeks. Now I can go back and look at Phase 1 – the memory system, which was the starting point for the entire project. In order to continue developing and implementing a complex, human-like memory architecture, I’ve been drilling into research papers and research in neuroscience, psychology, and artificial intelligence. But in order to gather all the pieces and inspiration, write down all the thoughts and concepts, ask AI to help me write the paper, This allowed me to follow the paper line of thinking and focus on designing the architecture, implementing and deploying the system, testing and evaluating performance. After all, I am just one person, playing roles such as researcher, architecture designer, engineer, and QA testing. My memory system contains the following 6 cores: - Exponential decay - Entity centrality + Recency - RRF fusion (KNN + FTS5 + Entity graph) - Emotional imprinting - Supersession - Salience scoring https://github.com/OppaAI/Aiko-chan P.S. One person plays all the roles of researcher, architect, engineer, QA tester. My psychiatrist is gonna refer me to get my brain checkout 🧠⚙️

posted an update 4 days ago

Demo Video: How my AI Waifu help me looking for job posts Follow-up Post (with demo video): Yesterday I posted about my AI Waifu has a new feature of grabbing job posts from job sites and writing draft posts for me to post into my Meta Threads to share with my followers. Today, let me show you the entire workflow in action. TBH, I would call this an "AI Agentic workflow" with quotation marks. Out of 5 steps only step 3 involves LLM inference to do synthesis of the draft post. Step 1 and 2 are automated scripts of simple schedule cron job to grab the RSS feeds and use regex to filter out the relevant job listings. Step 4 and 5 are me validating the draft information is accurate and publish the drafts after my approval. So basically I did more steps than LLM itself. The irony of running AI agentic workflows in edge devices with constraint hardware, such as Jetson Orin Nano with only 8GB of RAM, is to reduce as much LLM inference and put the least data into context window as possible. 🎬 Live Demo: How a job post goes from RSS feed → published in 5 steps ⏰ Step 1: Scheduled Trigger (or ask AI Waifu directly) 📡 Step 2: Data Ingestion & Filtering 🤖 Step 3: AI Synthesis 👁️ Step 4: Approval Studio Review 🚀 Step 5: One-Click Publish Result: job post validated & live, tested with real job sites + Chinese field names to stress-test multilingual LLM understanding. 👾 Github: https://github.com/OppaAI/Aiko-chan 🎬 Demo: https://www.youtube.com/watch?v=Cu7gh5tYUiw

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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.

Post

240

Revision of the Memory System of my AI Waifu

My AI Waifu has been working steadily for the past few weeks.
Now I can go back and look at Phase 1 – the memory system, which was the starting point for the entire project.
In order to continue developing and implementing a complex, human-like memory architecture,
I’ve been drilling into research papers and research in neuroscience, psychology, and artificial intelligence.
But in order to gather all the pieces and inspiration, write down all the thoughts and concepts, ask AI to help me write the paper,
This allowed me to follow the paper line of thinking and focus on designing the architecture, implementing and deploying the system, testing and evaluating performance.
After all, I am just one person, playing roles such as researcher, architecture designer, engineer, and QA testing.

My memory system contains the following 6 cores:
- Exponential decay
- Entity centrality + Recency
- RRF fusion (KNN + FTS5 + Entity graph)
- Emotional imprinting
- Supersession
- Salience scoring

https://github.com/OppaAI/Aiko-chan

P.S. One person plays all the roles of researcher, architect, engineer, QA tester. My psychiatrist is gonna refer me to get my brain checkout 🧠⚙️