Mini-Hackathon Kit: Build a Warehouse Automation Microapp in 24 Hours

Ship a working warehouse automation microapp in 24 hours: a pragmatic hackathon kit

Hook: You have one day, a small team, and a real warehouse problem to prove. Recruiting, time, and integration worries slow you down. This kit compresses decision-making and engineering into a practical 24-hour plan so teams can prototype a single warehouse automation feature that demonstrates value fast — think picker optimization or a live KPI dashboard delivered from edge devices to the cloud.

Why this matters in 2026

Warehouse automation in 2026 moves beyond siloed robots and conveyor belts. Leaders expect integrated, data-driven systems that increase throughput without breaking labor workflows. Recent hardware advances like AI accelerators for small form-factor devices have made on-site inference practical. Cloud and edge integration patterns are now mainstream, enabling rapid prototyping and safe pilot rollouts. This kit reflects those trends and gives teams a reproducible brief to show measurable uplift in a day.

Project snapshot: scope, outcomes, and success metrics

• Objective: Build a demo microapp that improves one warehouse capability in 24 hours. Choose either picker optimization or a live KPI dashboard with edge-collected telemetry.
• Deliverable: A deployed prototype with an interactive dashboard and a short demo script of a 3-minute scenario showing before vs after.
• Success metrics: Clear, measurable KPIs such as percent reduction in picker travel time, percent error reduction, or a dashboard with sub-5s latency for live KPIs.
• Constraints: Use one edge device per simulated zone, a minimal cloud stack, and a 24-hour timebox.

Recommended team and roles (ideal for small hackathon teams)

• Product lead / Facilitator: Keeps scope tight and runs demo script.
• Edge engineer: Hands-on with Raspberry Pi 5, NVIDIA Jetson, or similar. Deploys lightweight agents and models.
• Backend dev: API, message bus, cloud ingestion, and persistence.
• Frontend / UX: Builds the dashboard and demo UI, integrates real-time feeds.
• Data / ML engineer (optional): Prepares simulation data, runs local inference, and optimizes model latency/size.

Two validated project paths

Path A: Picker optimization microapp

Goal: Reduce picker travel distance and increase picks per hour for a small zone by recommending optimal pick sequences and locations in real time.

• Inputs: Simulated order stream, SKU locations, zone heatmap, pick times.
• Edge role: Collect beacon or simulated location updates, run local route optimizer or demand heat calculation.
• Cloud role: Aggregate metrics, run batch re-optimization, persist historical trends.
• UX: Map view with suggested pick path and alternative routes, simple KPI tiles for picks/hour and picker travel distance.

Path B: Live KPI dashboard microapp

Goal: Deliver a low-latency dashboard that surfaces real-time KPIs across a few zones with alerting and live trend lines.

• Inputs: Telemetry from edge devices (picking counts, conveyor status, temperature, worker check-ins).
• Edge role: Publish MQTT or HTTP event stream and run simple pre-aggregation to reduce cloud egress.
• Cloud role: Ingest events into a time-series store and provide WebSocket feeds to the UI.
• UX: Dashboard focused on latency, anomalies, and a single predictive KPI widget such as predicted throughput next 15 minutes.

Core architecture patterns and tech stack (24-hour friendly)

Keep architecture simple. The following pattern is proven for quick prototyping while remaining production-relevant.

• Edge device: Raspberry Pi 5 with AI HAT+ 2 for small-scale inference, or NVIDIA Jetson Nano for heavier models. Both are 2025/2026 viable choices for on-device ML acceleration.
• Edge software: Lightweight agent in Python or Node, using MQTT or HTTP to publish events. Use local SQLite or Redis for transient state.
• Message backbone: MQTT for simplicity, or a managed Cloud Pub/Sub service for scale. MQTT brokers like Mosquitto are easy to run locally.
• Cloud ingestion: Serverless endpoints (FastAPI, AWS Lambda, Azure Functions) to accept events and forward to a time-series DB or simple JSON store.
• Data store: InfluxDB, Timescale, or a managed time-series store. For an all-in-one hack, use InfluxDB OSS or a hosted free tier.
• Dashboard: Grafana for rapid results, or a React/Next app for custom visuals with WebSockets for live updates.
• Model runtime: Deploy small models in ONNX format or quantized PyTorch/torchscript. For Pi 5, leverage the AI HAT+ inference runtime; for Jetson use TensorRT or ONNX Runtime.

24-hour timeline with timeboxed milestones

Structure the day into clear sprints. Assign someone to the clock and the facilitator to reprioritize scope if needed.

1. Hour 0-1: Kickoff and scope lock
   • Choose path A or B
   • Set clear MVP acceptance criteria
   • Sketch demo script: what the audience will see and metric delta to show
2. Hour 1-3: Infra & skeletons
   • Provision edge device and install agent runtime
   • Stand up broker (local Mosquitto) and cloud ingestion endpoint
   • Skeleton UI with live placeholder metrics
3. Hour 3-8: Core integration
   • Wire edge agent to send real telemetry or simulation
   • Persist events into a simple time-series DB or in-memory store
   • Implement one optimization routine or aggregation on edge
4. Hour 8-14: UX and model tuning
   • Polish map, charts, and KPI tiles
   • Optimize model for size/latency and deploy to the edge
   • Prepare demo scenario data variations
5. Hour 14-20: Testing and instrumentation
   • Run end-to-end tests using simulated orders or replay data
   • Add logging, trace IDs, and simple monitoring endpoints
   • Measure latency and resource usage on edge
6. Hour 20-24: Polish, slides, and demo
   • Create a 1-slide problem + 1-slide solution + 1-slide metrics deck
   • Rehearse the demo script and timing
   • Prepare next steps for handoff and pilot recommendations

Practical engineering tips and code snippets

Keep code minimal and observable. Below are quick examples used in 2026 hack sessions.

Edge agent: publish a telemetry message to MQTT

import time
import json
import paho.mqtt.client as mqtt

client = mqtt.Client()
client.connect('broker.local', 1883)

while True:
    msg = {'device': 'edge-1', 'picks': 5, 'timestamp': int(time.time())}
    client.publish('warehouse/zone1/telemetry', json.dumps(msg))
    time.sleep(2)

Simple cloud ingest endpoint (Python FastAPI)

from fastapi import FastAPI, Request
import asyncio

app = FastAPI()

@app.post('/ingest')
async def ingest(request: Request):
    payload = await request.json()
    # forward to time-series DB or enqueue
    return {'status': 'ok'}

For edge ML, convert models to ONNX and use ONNX Runtime or vendor runtimes on Pi 5 AI HAT+ 2. Quantize aggressively to reduce memory and CPU usage while keeping acceptable accuracy.

Data and simulation — quick datasets and how to generate them

Real warehouses are messy. For a one-day prototyping sprint, simulate realistic patterns:

• Replay order files as simple JSON lines with SKU, quantity, and timestamp
• Create a small warehouse map grid and assign locations to SKUs
• Simulate picker location traces using random walk within a zone
• Include noise such as delayed picks or blocked aisles to test robustness

Scoring rubric for judges or stakeholders

Use a lightweight scorecard so teams aim for outcomes, not flashy UI.

• Impact (30%): Clarity of KPI improvement and business value
• Functionality (25%): End-to-end data flow works and is demonstrable
• Latency & Efficiency (15%): Edge inference under target latency and reasonable resource usage
• Usability (15%): Clear demo and dashboard that stakeholders can interpret
• Extensibility & Documentation (15%): Readme, runbook, and next-step recommendations

Security, privacy, and operational notes

Even in a hack, model responsible practices. Avoid shipping PII. Use TLS for cloud endpoints. For edge devices, enable secure boot or disk encryption where supported. Prepare a short threat assessment that includes data flows and who can access the demo environment.

Common pitfalls and recovery tactics

• Overambitious scope: If a model fails, fall back to rule-based heuristics. Demonstrate business value with simple logic first.
• Network flakiness: Use local buffering on edge and replay for the demo.
• Hardware variance: Test your chosen edge device early; swaps cost time late in the sprint.

Real-world example: microapp prototype that cut picker travel in a pilot

In a late 2025 internal pilot, a small team used Raspberry Pi 5 devices with an AI HAT accelerator to run a local demand heat predictor and route reorderer. They integrated with cloud-synced historical data and used Grafana for a fast dashboard. The MVP reduced walking distance by 12% in a controlled test and showed a clear pathway to place higher-demand SKUs into faster-to-reach slots. This prototype turned into a 6-week pilot with phased rollouts and human-in-the-loop validation.

Advanced strategies and future predictions for 2026 and beyond

Expect these trends to matter when moving a prototype to pilot:

• Edge-first ML inference: More teams will move lightweight predictive logic to badge-sized devices to reduce latency and cloud costs. Device accelerators announced in late 2025 make this shift practical.
• Human+AI workflows: Solutions that augment pickers and supervisors rather than replace them see faster adoption. Design for feedback and override paths.
• Composable automation: Microapps that expose APIs and events will be stitched into broader orchestration platforms. Build with clear interfaces from day one.
• Observability as a product: Real-time dashboards with anomaly detection will be expected across pilots, not optional extras.

Handoff and next steps after the 24-hour demo

• Deliver a short technical runbook with architecture diagram, install steps, and resource estimates for a 30-day pilot.
• List the experimentation plan and success criteria for scaling beyond the demo zone.
• Assign owners for cost, security review, and operations.

Actionable takeaways — what to do first

1. Decide your microapp goal: picker optimization or KPI dashboard and lock the demo script.
2. Provision one edge device, a message broker, and a minimal cloud ingest endpoint before the hack day.
3. Prepare a small set of realistic simulation data and a fallback rule-based method if the model latency becomes an issue.

Start small, show measurable impact, and build trust with operators. A 24-hour microapp that demonstrates value is the fastest route to pilot funding.

Resources and templates

• Edge quickstart: minimal MQTT agent, model inference example, and packaging notes for Raspberry Pi 5 with AI HAT+ 2.
• Dashboard template: Grafana dashboard JSON and React WebSocket UI scaffold.
• Data generator: Python script to emit simulated order and picker trace events.

Wrap-up and call to action

If your team wants a ready-to-run kit for an upcoming hackathon, download the starter repo, simulation data, and slide templates we use in internal pilots. Use this brief as the single source of truth during your 24-hour sprint and focus the team on measurable impact, not perfect tech. Reserve time for human validation and deployment risk planning if the prototype proves promising.

Call to action: Ready to run this at your next hackathon or meetup? Request the starter repo, sample data, and a curated hardware list and we will email a downloadable kit and a 15-minute onboarding call to walk your team through the plan. Ship a microapp that matters in 24 hours and turn a weekend hack into a real pilot.

Related Reading

• Consolidation RFP: How to write an RFP to replace five underused tools with one platform
• Rehab Storylines and Character Arcs: How Medical Dramas Can Shift Audience Engagement
• Where to Watch the Next Comet Near Tokyo: Skywatching Spots and Tips
• How to calculate the ROI of consolidating CRM, marketing automation and booking tools
• Secure Citizen‑Dev Deployments: CI/CD and Policy Guards for Micro Apps