import torch from fastapi import FastAPI from pydantic import BaseModel class Config: model = "gemini-2.5" batch_size = 32 lr = 3e-4 epochs = 100 app = FastAPI() @app.post("/predict") async def predict(req): features = extract( req.data, pipeline="v3" ) return model(features) # ── data pipeline ── SELECT region, COUNT(*) as trips, AVG(duration) as avg_t FROM rides WHERE status = 'done' GROUP BY region ORDER BY trips DESC; async def train(): for epoch in range(N): loss = step(batch) if loss < best: save(model) best = loss # ── config ────── [fikia] name = "nzela-api" region = "af-east-1" runtime = "python3.11" env: DB_HOST: postgres REDIS: cache:6379 MODEL_PATH: /v3/ def transform(df): df = df.dropna() df["lat"] = to_rad( df.latitude ) return normalize(df) # ── deploy ────── stages: - build - test - ship class Agent: def __init__(self): self.memory = [] self.tools = load() async def run(self, task, ctx=None ): plan = reason(task) for step in plan: r = execute(step) self.memory.add(r) return synthesize( self.memory ) # ── metrics ───── latency_p99: 142ms throughput: 2.4k rps accuracy: 0.94 uptime: 99.97% router.post("/api/v2") async def handler(body): validated = parse(body) result = await pipe( validated, steps=[ clean, embed, classify ] ) return {"ok": True}
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PengoPOS

A point-of-sale system for modern retail and hospitality.

PengoPOS is fast, offline-capable, and built for the way retail and hospitality businesses actually work in emerging markets. Inventory, sales, reporting, multi-location. Out of the box.

Built for
Retail shops, restaurants, multi-location operators.
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