prism-ml/Ternary-Bonsai-27B-gguf
Text Generation • 4B • Updated • 23 • • 414
Ibrahim.H
bitsnaps
AI & ML interests
Text mining, sentiment analysis, text summarization
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🌼 DaisyChain-Web: train a language model with friends or by yourself with multiple devices, in the browser, no install
Open a webpage, share a room link, and every device that joins becomes part of the training cluster. Phones, laptops, old PCs: they connect peer-to-peer over WebRTC and train one shared transformer together, entirely in the browser.
What's actually happening under the hood:
🧠 A mini transformer LM trains on FineWeb-Edu, streamed live from the HuggingFace Hub. Each device pulls its own slice (data parallelism), tokenized with our 16.5k-token Spikewhale tokenizer
⚡ Every single multiply runs through verified INT8 neural units, no float fallback. On WebGPU browsers it uses the GPU's DP4A integer dot-product hardware, admitted only after proving bit-identical results against the verified units, with a 3×INT8 fast-accurate scheme (CUTLASS's 3xTF32 trick, ported to 8-bit)
🔒 Devices average gradients every step under a sync guard: a per-step roster protocol plus weight-hash verification keeps every device's model bit-identical. If anything drifts, training stops instead of silently forking
📊 Live logs show exactly what every device contributes, step by step
💾 When you're done: test generations right on the page, download a checkpoint, or grab the inference kit, a single self-contained HTML file with the weights baked in that runs generations offline, anywhere
Works solo too. Every extra device just grows the effective batch.
👉 Try it: Quazim0t0/DaisyChain-Web
🛠 Training framework: DaisyChainAI/DaisyChain-Train
Proof of concept: only train with devices you trust. Feedback welcome!
Open a webpage, share a room link, and every device that joins becomes part of the training cluster. Phones, laptops, old PCs: they connect peer-to-peer over WebRTC and train one shared transformer together, entirely in the browser.
What's actually happening under the hood:
🧠 A mini transformer LM trains on FineWeb-Edu, streamed live from the HuggingFace Hub. Each device pulls its own slice (data parallelism), tokenized with our 16.5k-token Spikewhale tokenizer
⚡ Every single multiply runs through verified INT8 neural units, no float fallback. On WebGPU browsers it uses the GPU's DP4A integer dot-product hardware, admitted only after proving bit-identical results against the verified units, with a 3×INT8 fast-accurate scheme (CUTLASS's 3xTF32 trick, ported to 8-bit)
🔒 Devices average gradients every step under a sync guard: a per-step roster protocol plus weight-hash verification keeps every device's model bit-identical. If anything drifts, training stops instead of silently forking
📊 Live logs show exactly what every device contributes, step by step
💾 When you're done: test generations right on the page, download a checkpoint, or grab the inference kit, a single self-contained HTML file with the weights baked in that runs generations offline, anywhere
Works solo too. Every extra device just grows the effective batch.
👉 Try it: Quazim0t0/DaisyChain-Web
🛠 Training framework: DaisyChainAI/DaisyChain-Train
Proof of concept: only train with devices you trust. Feedback welcome!
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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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Huge news from MiniMax: we’ve secured a $2B funding round, paired with a formal long-term commitment from our CEO IO to allocate 1% of total company equity from his personal holdings to support the global open-source AI community over the next four years.
This capital backs our continuous open model releases, community tooling and transparent frontier AI research. We’re just getting started on our open-source roadmap toward accessible AGI.
If you build with open foundation models and want to push frontier AI together, come join us.
Intelligence with Everyone. 🚀
MiniMaxAI
This capital backs our continuous open model releases, community tooling and transparent frontier AI research. We’re just getting started on our open-source roadmap toward accessible AGI.
If you build with open foundation models and want to push frontier AI together, come join us.
Intelligence with Everyone. 🚀
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