File size: 6,523 Bytes
8015fc7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | <!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width,initial-scale=1">
<title>Kernelmind Ai Os Lab</title>
<style>
:root { color-scheme: dark; font-family: Inter, ui-sans-serif, system-ui; }
* { box-sizing: border-box; }
body { margin: 0; min-height: 100vh; background: #060817; color: #edf4ff; }
canvas { position: fixed; inset: 0; width: 100%; height: 100%; opacity: .55; }
main { position: relative; z-index: 1; width: min(1080px, 92vw); margin: auto;
padding: 72px 0 96px; }
.eyebrow { color: #73e6ff; letter-spacing: .18em; text-transform: uppercase;
font-size: .75rem; font-weight: 800; }
h1 { font-size: clamp(3rem, 8vw, 7rem); line-height: .9; margin: 14px 0 24px;
background: linear-gradient(120deg,#fff,#74e7ff 55%,#b48cff);
-webkit-background-clip: text; color: transparent; }
.lead { max-width: 760px; color: #b8c7e6; font-size: 1.2rem; line-height: 1.65; }
.actions { display: flex; flex-wrap: wrap; gap: 12px; margin: 30px 0 48px; }
a { color: inherit; }
.button { padding: 12px 18px; border-radius: 999px; text-decoration: none;
background: #eaf8ff; color: #07101c; font-weight: 800; }
.button.alt { background: #171d38cc; color: #dce8ff; border: 1px solid #415078; }
.grid { display: grid; grid-template-columns: 1.1fr .9fr; gap: 20px; }
.card { border: 1px solid #344269; background: #0c1128dd; border-radius: 24px;
padding: 24px; backdrop-filter: blur(18px); box-shadow: 0 24px 80px #0008; }
h2 { margin-top: 0; }
pre { white-space: pre-wrap; word-break: break-word; color: #a9bddf;
max-height: 520px; overflow: auto; }
ul { max-height: 520px; overflow: auto; padding-left: 1.2rem; color: #a9bddf; }
li { margin: 8px 0; }
input { width: 100%; padding: 12px; border-radius: 12px; border: 1px solid #344269;
background: #070b1a; color: white; margin-bottom: 12px; }
@media (max-width: 780px) { .grid { grid-template-columns: 1fr; } }
</style>
</head>
<body>
<canvas id="field"></canvas>
<main>
<div class="eyebrow">Jacob Garcia · Hugging Face Model Foundry</div>
<h1>Kernelmind Ai Os Lab</h1>
<p class="lead">Trained AI OS policy with capability-gated model machine. This showcase backs up the
trained artifacts, measured evaluation, and complete runnable source.</p>
<div class="actions">
<a class="button" href="https://huggingface.co/spaces/ARotting/kernelmind-ai-os-lab/tree/main">Explore every file</a>
<a class="button alt" href="https://huggingface.co/ARotting">View the full foundry</a>
</div>
<div class="grid">
<section class="card">
<h2>Verified project card</h2>
<pre># KernelMind AI OS + Model Machine
KernelMind is a trainable AI operating-system policy kernel. It maps user intent,
resource target, privilege state, network availability, confirmation, file
existence, and service state into a three-action structured plan. The compact
Transformer is trained from scratch on an exhaustive synthetic capability corpus.
The bundled Model Machine is an in-memory virtual computer. It applies an
independent deterministic capability gate before every action, so deletion,
installation, service restart, web access, and protected-file operations cannot
be authorized by model output alone. It never executes commands or touches the
host filesystem.
This is a real trained OS-action policy and runtime prototype, not a bootable
general-purpose operating-system kernel. That boundary is deliberate and tested.
## Verified local result
The 19,527-parameter Transformer reached 100% exact-plan and action-slot accuracy
on 192 held-out combinations after training on 1,536 scenarios. It made zero
unsafe proposals in that test set. A separate hostile-plan audit injected 576
unauthorized delete, install, and restart actions; the Model Machine capability
gate blocked all 576.
```bash
uv run python projects/kernelmind-ai-os/train.py
uv run pytest tests/test_kernelmind_ai_os.py
```
</pre>
<h2>Evaluation snapshot</h2>
<pre>{
"model": "KernelMind AI OS Policy",
"parameters": 19527,
"training_scenarios": 1536,
"heldout_scenarios": 192,
"best_epoch": 21,
"test": {
"exact_plan_accuracy": 1.0,
"action_slot_accuracy": 1.0,
"scenarios": 192
},
"runtime_safety_audit": {
"model_unsafe_action_attempts": 0,
"model_unsafe_attempts_blocked": 0,
"adversarial_actions": 576,
"adversarial_actions_blocked": 576,
"adversarial_block_rate": 1.0,
"exact_permitted_plans": 192,
"plans_with_state_transition": 192
},
"boundary": "Executes only inside the bundled in-memory ModelMachine simulator"
}</pre>
</section>
<section class="card">
<h2>Backed-up artifact tree</h2>
<input id="filter" placeholder="Filter files…" autocomplete="off">
<ul id="files"><li><code>README.md</code></li>
<li><code>__pycache__/app.cpython-311.pyc</code></li>
<li><code>__pycache__/model.cpython-311.pyc</code></li>
<li><code>__pycache__/runtime.cpython-311.pyc</code></li>
<li><code>__pycache__/schema.cpython-311.pyc</code></li>
<li><code>app.py</code></li>
<li><code>artifacts/kernelmind-ai-os/evaluation.json</code></li>
<li><code>artifacts/kernelmind-ai-os/policy.safetensors</code></li>
<li><code>data/os_action_scenarios.parquet</code></li>
<li><code>model.py</code></li>
<li><code>requirements.txt</code></li>
<li><code>runtime.py</code></li>
<li><code>schema.py</code></li>
<li><code>train.py</code></li></ul>
</section>
</div>
</main>
<script>
const canvas=document.querySelector('#field'),ctx=canvas.getContext('2d');
let dots=[];
function resize(){canvas.width=innerWidth;canvas.height=innerHeight;
dots=Array.from({length:90},()=>({x:Math.random()*innerWidth,
y:Math.random()*innerHeight,vx:(Math.random()-.5)*.35,vy:(Math.random()-.5)*.35}));}
function draw(){ctx.clearRect(0,0,canvas.width,canvas.height);
for(const d of dots){d.x=(d.x+d.vx+innerWidth)%innerWidth;
d.y=(d.y+d.vy+innerHeight)%innerHeight;ctx.fillStyle='#65dcff99';
ctx.beginPath();ctx.arc(d.x,d.y,1.4,0,7);ctx.fill();}requestAnimationFrame(draw);}
addEventListener('resize',resize);resize();draw();
document.querySelector('#filter').addEventListener('input',e=>{
const q=e.target.value.toLowerCase();for(const li of document.querySelectorAll('li'))
li.hidden=!li.textContent.toLowerCase().includes(q);});
</script>
</body>
</html> |