minaiml: declare MIT, state the licence position, and the distributed-intelligence paradigm

README.md

@@ -6,6 +6,7 @@ colorTo: yellow
 sdk: static
 pinned: false
 short_description: delivery software for models on laptops and phones
+license: mit
 ---
 
 <p><b><a href="https://github.com/minaiml" target="_blank" rel="noopener">minaiml</a></b> (say it <i>minimal</i>) is <b><a href="https://huggingface.co/PYTHAI" target="_blank" rel="noopener">PYTHAI</a></b>'s delivery layer: the software that runs reasonable-sized models on hardware you already own. A mixture-of-experts model is mostly idle — a router wakes a handful of experts per token and leaves the rest on disk. Delivery is the art of exploiting that.</p>
@@ -20,6 +21,14 @@ short_description: delivery software for models on laptops and phones
 
 <p>The slowest row is ours, and it stays: a 135-million-parameter dense model runs three hundred times slower than a 397-billion-parameter sparse one. <b>Sparsity, not size, decides what a small machine can run.</b></p>
 
+<p><b>Smart phones, plural.</b> A phone at 11 tok/s is not a terminal for somebody else's data centre — it is a <b>node</b>. That reframes the problem: not "how large a model fits on this device", but "what does a mesh of small devices know together". The catalogue already carries the cluster layer — <a href="https://github.com/minaiml/exo" target="_blank" rel="noopener">exo</a> builds an AI cluster from everyday devices, <a href="https://github.com/minaiml/distributed-llama" target="_blank" rel="noopener">distributed-llama</a> splits one model across machines on a LAN, <a href="https://github.com/minaiml/petals" target="_blank" rel="noopener">petals</a> does it over a swarm.</p>
+
+<p><b>The floor is two processors.</b> <a href="https://huggingface.co/spaces/PYTHAI/mindX" target="_blank" rel="noopener">mindX</a> is the existence proof, and it is deliberately unglamorous: <b>2 CPU cores, 7.8&nbsp;GB of RAM, no GPU</b>, self-throttled to a third of the processor. On that it trains <i>its own weights</i> from its own memory — dreams become a corpus, the corpus trains a 135M actor, and a proof-of-recall gate decides whether the result is allowed to exist. It usually isn't: generation 39 is the newest the gate accepted, and every generation after it was refused. <b>A gate that never refuses is not a gate.</b></p>
+
+<p>That is the paradigm the phone inherits. CPU is the standing resource — always on, marginal cost zero, already in your pocket. GPU and rented capacity are <i>episodic</i>: you pay for a training event, then tear it down. So intelligence originates at the bottom and scales on demand, rather than living in one large machine somebody else owns. <b>A thousand phones that each know a little, and can ask each other, is a different architecture from one model that knows everything.</b></p>
+
+<p><b>Licence position.</b> PYTHAI hosts no engine and no engine-derived artifact. The catalogue is a set of pinned <i>references</i> on GitHub, each carrying a <code>FORK.json</code> with its upstream, commit and licence — and four of them declare <b>no licence upstream</b>, which means all rights reserved by their authors. Those are read and cited, never redistributed, and nothing derived from them is published in this namespace. The register is <a href="https://github.com/minaiml/.github/blob/main/LICENSES.md" target="_blank" rel="noopener">LICENSES.md</a>.</p>
+
 <p><b>The catalogue lives on GitHub</b> — <a href="https://github.com/minaiml" target="_blank" rel="noopener">github.com/minaiml</a>: laptop engines, MoE streaming, phone and edge runtimes, browser targets, cluster inference, on-device speech and adaptation. Every fork carries a <code>FORK.json</code> recording upstream, the commit we pinned, the date and the licence — including the four that declare no licence upstream and are therefore never redistributed.</p>
 
 <p><b>The models are at <a href="https://huggingface.co/PYTHAI" target="_blank" rel="noopener">PYTHAI</a></b>, licence-pinned at a commit: <a href="https://huggingface.co/PYTHAI/Qwen3.8-27B-fork" target="_blank" rel="noopener">Qwen3.8</a> · <a href="https://huggingface.co/PYTHAI/GLM-5.3-Flash-fork" target="_blank" rel="noopener">GLM-5.3-Flash</a> · <a href="https://huggingface.co/PYTHAI/Kimi-K3-fork" target="_blank" rel="noopener">Kimi K3</a> · <a href="https://huggingface.co/PYTHAI/granite-4.2-8b-fork" target="_blank" rel="noopener">Granite 4.2</a>. Delivery software here, weights there.</p>

index.html

@@ -42,6 +42,14 @@ td:first-child { white-space: nowrap; }
 
 <p>The slowest row is ours, and it stays: a 135-million-parameter dense model runs three hundred times slower than a 397-billion-parameter sparse one. <b>Sparsity, not size, decides what a small machine can run.</b></p>
 
+<p><b>Smart phones, plural.</b> A phone at 11 tok/s is not a terminal for somebody else's data centre — it is a <b>node</b>. That reframes the problem: not "how large a model fits on this device", but "what does a mesh of small devices know together". The catalogue already carries the cluster layer — <a href="https://github.com/minaiml/exo" target="_blank" rel="noopener">exo</a> builds an AI cluster from everyday devices, <a href="https://github.com/minaiml/distributed-llama" target="_blank" rel="noopener">distributed-llama</a> splits one model across machines on a LAN, <a href="https://github.com/minaiml/petals" target="_blank" rel="noopener">petals</a> does it over a swarm.</p>
+
+<p><b>The floor is two processors.</b> <a href="https://huggingface.co/spaces/PYTHAI/mindX" target="_blank" rel="noopener">mindX</a> is the existence proof, and it is deliberately unglamorous: <b>2 CPU cores, 7.8&nbsp;GB of RAM, no GPU</b>, self-throttled to a third of the processor. On that it trains <i>its own weights</i> from its own memory — dreams become a corpus, the corpus trains a 135M actor, and a proof-of-recall gate decides whether the result is allowed to exist. It usually isn't: generation 39 is the newest the gate accepted, and every generation after it was refused. <b>A gate that never refuses is not a gate.</b></p>
+
+<p>That is the paradigm the phone inherits. CPU is the standing resource — always on, marginal cost zero, already in your pocket. GPU and rented capacity are <i>episodic</i>: you pay for a training event, then tear it down. So intelligence originates at the bottom and scales on demand, rather than living in one large machine somebody else owns. <b>A thousand phones that each know a little, and can ask each other, is a different architecture from one model that knows everything.</b></p>
+
+<p><b>Licence position.</b> PYTHAI hosts no engine and no engine-derived artifact. The catalogue is a set of pinned <i>references</i> on GitHub, each carrying a <code>FORK.json</code> with its upstream, commit and licence — and four of them declare <b>no licence upstream</b>, which means all rights reserved by their authors. Those are read and cited, never redistributed, and nothing derived from them is published in this namespace. The register is <a href="https://github.com/minaiml/.github/blob/main/LICENSES.md" target="_blank" rel="noopener">LICENSES.md</a>.</p>
+
 <p><b>The catalogue lives on GitHub</b> — <a href="https://github.com/minaiml" target="_blank" rel="noopener">github.com/minaiml</a>: laptop engines, MoE streaming, phone and edge runtimes, browser targets, cluster inference, on-device speech and adaptation. Every fork carries a <code>FORK.json</code> recording upstream, the commit we pinned, the date and the licence — including the four that declare no licence upstream and are therefore never redistributed.</p>
 
 <p><b>The models are at <a href="https://huggingface.co/PYTHAI" target="_blank" rel="noopener">PYTHAI</a></b>, licence-pinned at a commit: <a href="https://huggingface.co/PYTHAI/Qwen3.8-27B-fork" target="_blank" rel="noopener">Qwen3.8</a> · <a href="https://huggingface.co/PYTHAI/GLM-5.3-Flash-fork" target="_blank" rel="noopener">GLM-5.3-Flash</a> · <a href="https://huggingface.co/PYTHAI/Kimi-K3-fork" target="_blank" rel="noopener">Kimi K3</a> · <a href="https://huggingface.co/PYTHAI/granite-4.2-8b-fork" target="_blank" rel="noopener">Granite 4.2</a>. Delivery software here, weights there.</p>