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Deploy Auralynq RAG (Llama-3.3-70B via HF Inference Providers)
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const STATS = [
{ value: "$0", label: "default cost" },
{ value: "100%", label: "local-capable" },
{ value: "7", label: "MCP tools" },
{ value: "<1s", label: "fast-route answers" },
];
const TECH = [
"FastAPI",
"PathRAG",
"Qdrant",
"Hybrid retrieval",
"Cross-encoder rerank",
"LangGraph agent",
"Whisper ASR",
"Kokoro TTS",
"Cohere · OpenAI · Anthropic",
"MCP server",
"Caddy TLS",
"Podman",
];
export function Stack() {
return (
<section id="stack" className="relative mx-auto max-w-7xl px-4 py-14 md:px-6">
<div className="glass overflow-hidden p-8 md:p-10">
{/* stats */}
<div className="grid grid-cols-2 gap-6 border-b border-edge pb-8 md:grid-cols-4">
{STATS.map((s) => (
<div key={s.label} className="text-center">
<div className="text-3xl font-bold gradient-text sm:text-4xl">{s.value}</div>
<div className="mt-1 text-xs uppercase tracking-wider text-fg3">{s.label}</div>
</div>
))}
</div>
{/* tech credibility */}
<div className="pt-8">
<p className="mb-4 text-center text-sm text-fg2">
Production-shaped, provider-agnostic, swappable at every layer
</p>
<div className="flex flex-wrap justify-center gap-2">
{TECH.map((t) => (
<span key={t} className="chip">
{t}
</span>
))}
</div>
</div>
</div>
</section>
);
}