Pragmatic systems. Built for real operations.

1# In-memory parsing and vector scoring pipeline2async def process_document_pipeline(file_bytes: bytes) -> DocumentScorecard:3    extracted = await pdf_extractor.stream_extract(file_bytes)4    embeddings = local_embedder.encode_transient(extracted.text)5    scores = pgvector_session.cosine_similarity(embeddings, TARGET_VECTORS)6    return DocumentScorecard(relevance=scores.max(), scored_at=time.perf_counter())

1// Typed edge route with tenant guardrails2export async function GET(req: Request) {3  const telemetry = await edgeTelemetry.probe({ session: req.headers.get('x-session') });4  return NextResponse.json({5    status: 'OPTIMAL',6    cache: 'HIT',7    tenant_guardrails: 'ENFORCED'8  }, { headers: { 'Cache-Control': 'no-store' } });9}

1// WebGPU compute shader for spatial telemetry2const shaderModule = device.createShaderModule({3  code: `4    @compute @workgroup_size(64)5    fn main(@builtin(global_invocation_id) id: vec3<u32>) {6      particles[id.x].position += particles[id.x].velocity * 0.016;7    }8  `9});

1# Local vLLM quantised regional serving2from vllm import LLM, SamplingParams3llm = LLM(model="Qwen/Qwen2.5-7B-Instruct-AWQ", quantization="awq")4output = llm.generate(["Regional agricultural market dispatch..."],5                      SamplingParams(temperature=0.1, max_tokens=150))6print(output[0].outputs[0].text)