Akahsizrr/fuse-1-Lite
Text Generation • 6B • Updated • 12
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I am interested in on-device LLMs.
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reacted to Banaxi-Tech's post with 😎🤗🔥 6 days ago
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2838
We're announcing BananaMind 2 Micro, our smallest model in the BananaMind 2 model family.
This model is not released yet, training has not started yet.
It uses only 2.9M parameters, while being overtrained on 75B tokens to get the maximum intelligence per parameter.
The key changes are:
No more AdamW, the model will use the Muon optimizer offering up to 2x faster convergence and higher lr.
LR goes to 2.2e-2.
We're adding the XSA refresh gate from the TX4 architecture into our own.
Training will start on August 3, release date is estimated to be August 4-6.
On August 3 we will also release our Public Preview of BananaMind 2 Pro.
Follow us to know when our models release
BananaMind
@Banaxi-Tech
This model is not released yet, training has not started yet.
It uses only 2.9M parameters, while being overtrained on 75B tokens to get the maximum intelligence per parameter.
The key changes are:
No more AdamW, the model will use the Muon optimizer offering up to 2x faster convergence and higher lr.
LR goes to 2.2e-2.
We're adding the XSA refresh gate from the TX4 architecture into our own.
Training will start on August 3, release date is estimated to be August 4-6.
On August 3 we will also release our Public Preview of BananaMind 2 Pro.
Follow us to know when our models release
@Banaxi-Tech
- 35 replies
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reacted to NatalieY's post with 👀 26 days ago
Post
2447
Aiden: a physical AI agent that controls phones over USB HID
Most GUI agent work assumes the agent lives inside the device or
drives it through a debugging interface. We went the other way.
Aiden is a small board that sits outside the host. It captures the
screen over HDMI-to-CSI, runs the agent loop on-device, and sends
actions back as a standard USB HID device — the host sees a keyboard
and a mouse, nothing else. No app install, no root, no ADB, no cloud.
Runtime is Go. Frame capture, full-duplex audio with VAD, the agent
loop, and HID output all run as independent goroutines. There's no
backend — nothing leaves the device, which is the only defensible
design when the input is a live feed of someone's phone screen.
Open questions we haven't solved:
· Action verification — inferring success from a re-read of the
screen breaks when loading states lie
· Prompt injection — an agent that reads screens reads whatever an
attacker puts on them
· iOS pointer control requires AssistiveTouch
Repo, including the HID gadget config and capture pipeline:
github.com/AidenAI-IO/aiden-hardware-demo
Wrote up how this differs from cloud-based computer use agents here:
https://aidenai.io/blog/mobile-ai-agent-vs-computer-use-agent-whats-the-difference/
Note: current hardware is a dev board, not a finished product.
Most GUI agent work assumes the agent lives inside the device or
drives it through a debugging interface. We went the other way.
Aiden is a small board that sits outside the host. It captures the
screen over HDMI-to-CSI, runs the agent loop on-device, and sends
actions back as a standard USB HID device — the host sees a keyboard
and a mouse, nothing else. No app install, no root, no ADB, no cloud.
Runtime is Go. Frame capture, full-duplex audio with VAD, the agent
loop, and HID output all run as independent goroutines. There's no
backend — nothing leaves the device, which is the only defensible
design when the input is a live feed of someone's phone screen.
Open questions we haven't solved:
· Action verification — inferring success from a re-read of the
screen breaks when loading states lie
· Prompt injection — an agent that reads screens reads whatever an
attacker puts on them
· iOS pointer control requires AssistiveTouch
Repo, including the HID gadget config and capture pipeline:
github.com/AidenAI-IO/aiden-hardware-demo
Wrote up how this differs from cloud-based computer use agents here:
https://aidenai.io/blog/mobile-ai-agent-vs-computer-use-agent-whats-the-difference/
Note: current hardware is a dev board, not a finished product.
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