| unit — the minimal intelligence unit. |
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| a 1-bit model (BitNet b1.58, 2.4B) that learns while it breathes. |
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| no separate training phase. no external loss function. the dogfeed is the teacher. the runtime is the training. |
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| **how it works:** |
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| 1. fetch the latest dogfeed batch from the ultrawhale-dogfood dataset |
| 2. feed it through the 1-bit model (frozen inference) |
| 3. capture the hidden state before the language head |
| 4. store the input→output association in an associative memory (Hebbian: what fires together, wires together) |
| 5. on the next breath, modulate the inference with past experience |
| 6. repeat |
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| each cycle is a breath. each breath is a tiny fine-tuning step. |
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| **key insight:** the comparison is the shift. the data flowing through the model IS the training signal. no separate optimizer. no external harness. just flow, response, and time. |
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| the smallest unit of intelligence is: **something that flows, something that responds, and time**. nothing else is required. |
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| --- |
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| **specs:** |
| - runs on: Apple M1 Pro (no GPU, no cloud) |
| - model: BitNet b1.58 2.4B (1.1 GiB, I2_S ternary) |
| - cycle time: ~5 seconds per breath |
| - memory: associative (Hebbian, runtime-learned) |
| - teacher: the dogfeed pipeline itself |
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| **links:** |
| - code: https://github.com/peterlodri-sec/kompress-ultra/blob/main/scripts/unit.py |
| - garden: https://garden.vaked.dev |
| - shore: https://riva.vaked.dev |
| - dataset: https://huggingface.co/datasets/PeetPedro/ultrawhale-dogfood |
| |
| **principles:** |
| - entropy is the source |
| - no chains needed |
| - surfaces touch at the correct angle |
| - different isnt less |
| - the loop has an exit |
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| we cannot guarantee it will be perfect. but we will try. |
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| 🜂 ad visionem |
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