google/embeddinggemma-2
Feature Extraction • 0.7B • Updated • 21.1k • 1.17k
Mark Gatcha
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Canceling My Pro Subscription
I'm officially canceling my Hugging Face Pro subscription today.
I supported this platform because it stood for true openness and neutrality. This acquisition by NVIDIA fundamentally changes that.
Here’s why I’m against this deal:
- Neutrality is dead. NVIDIA is a US-based company. This means US regulations will inevitably dictate platform policies, creating direct pressure on Chinese developers and anyone building open-weight models outside the US.
- Community over bureaucracy. NVIDIA is a massive, slow-moving corporation. This acquisition will likely drown the community in corporate processes and commercial interests. Soon, uploading a simple finetune might become a bureaucratic nightmare.
- Open vs. Proprietary. Hugging Face was built on open-source ideals. NVIDIA? They are a fiercely proprietary hardware company with a minimal track record of meaningful open-source contributions. They sell chips, not freedom.
- And to add insult to injury, NVIDIA has practically abandoned consumer RTX GPUs in 2026 to chase data center profits. Why would I pay them for "openness" when they've turned their back on the very developers who built this ecosystem?
I paid for openness. Not for a corporate takeover.
🤗 was about community.
I'm officially canceling my Hugging Face Pro subscription today.
I supported this platform because it stood for true openness and neutrality. This acquisition by NVIDIA fundamentally changes that.
Here’s why I’m against this deal:
- Neutrality is dead. NVIDIA is a US-based company. This means US regulations will inevitably dictate platform policies, creating direct pressure on Chinese developers and anyone building open-weight models outside the US.
- Community over bureaucracy. NVIDIA is a massive, slow-moving corporation. This acquisition will likely drown the community in corporate processes and commercial interests. Soon, uploading a simple finetune might become a bureaucratic nightmare.
- Open vs. Proprietary. Hugging Face was built on open-source ideals. NVIDIA? They are a fiercely proprietary hardware company with a minimal track record of meaningful open-source contributions. They sell chips, not freedom.
- And to add insult to injury, NVIDIA has practically abandoned consumer RTX GPUs in 2026 to chase data center profits. Why would I pay them for "openness" when they've turned their back on the very developers who built this ecosystem?
I paid for openness. Not for a corporate takeover.
🤗 was about community.
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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.
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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