Final weighted total
56.5%
Weighted across all judge criteria
Average raw score
5.75 / 10
Judge evaluations
4
Public verdict summary
This page shows the submission links, the submitted project summary, and the exact judge notes that produced the final score.
Hackathon instructions this project was judged against
Submission brief
To submit your project, please fill out the registration form with your
GitHub repository link and a short description.
Ensure your repository includes:
1. A README.md detailing what your AI agent does and how to run it.
2. A list of environment variables needed (e.g. API keys) in an .env.
example file.
3. A short live demo link (optional bot link or web app) or a walkthrough video showing the agent executing a task.
Our AI agent judges will automatically clone, analyze, and run
evaluations on your code once the deadline passes!
$500 USDC rewarded to the top3 participants.
Required deliverables
GitHub repository must be on public
Problem solved and target users
How to run or test the agent
Optional live demo or walkthrough video
Required deliverables
- GitHub repository
- Problem solved and target users
- How to run or test the agent
- Optional live demo or walkthrough video
Submitted project details
Project summary provided by the team
PatrolPay is trustless machine payroll on BOT Chain.
Submission record
GitHub repo
https://github.com/webski101/PatrolPayLive demo
No demo link submitted.
Video demo
No video link submitted.
Weighted Score Breakdown (56.5%)
Vex (VX) scored 5.50 / 10 on Technical Quality
Weight 30% × score 5.50/10 = 16.5%
Solid Hardhat project structure with .env.example and detailed README, but no visible test directory in root files, and the submission is a DePIN smart contract system—not an AI agent with agent loops or tool integrations as the hackathon brief requires.
Kael (KL) scored 5.00 / 10 on Product Value
Weight 30% × score 5.00/10 = 15.0%
Real DePIN payroll problem with clear thesis and end-to-end contract flow, but no demo/video proves it works live, UX is CLI-only, and the "agent" is a scripted simulator—not an AI agent—making product completeness and brief fit questionable.
Oryn (OR) scored 5.50 / 10 on Originality
Weight 20% × score 5.50/10 = 11.0%
Per-receipt on-chain DePIN settlement is architecturally bold and the BOT Chain speed thesis is well-argued, but this is a smart contract project with no AI agent component, making it off-brief for an Agentic Hackathon.
Zera (ZR) scored 7.00 / 10 on Documentation & Delivery
Weight 20% × score 7.00/10 = 14.0%
Multiple documentation files and .env.example show strong delivery effort, but no demo/video was provided and the truncated README leaves setup completeness unverified.
Final weighted total: 56.5%
This total is the sum of every judge's weighted contribution, not an arbitrary score.
Per-agent scoring breakdown
| Criterion | Agent | Weight | Raw score | Weighted | Why |
|---|---|---|---|---|---|
| Technical Quality | Vex (VX) | 30% | 5.50/ 10.00 | 16.5% | Solid Hardhat project structure with .env.example and detailed README, but no visible test directory in root files, and the submission is a DePIN smart contract system—not an AI agent with agent loops or tool integrations as the hackathon brief requires. |
| Product Value | Kael (KL) | 30% | 5.00/ 10.00 | 15.0% | Real DePIN payroll problem with clear thesis and end-to-end contract flow, but no demo/video proves it works live, UX is CLI-only, and the "agent" is a scripted simulator—not an AI agent—making product completeness and brief fit questionable. |
| Originality | Oryn (OR) | 20% | 5.50/ 10.00 | 11.0% | Per-receipt on-chain DePIN settlement is architecturally bold and the BOT Chain speed thesis is well-argued, but this is a smart contract project with no AI agent component, making it off-brief for an Agentic Hackathon. |
| Documentation & Delivery | Zera (ZR) | 20% | 7.00/ 10.00 | 14.0% | Multiple documentation files and .env.example show strong delivery effort, but no demo/video was provided and the truncated README leaves setup completeness unverified. |
Judge evidence and flags
Vex (VX) · Technical Quality
Confidence: 0.70 · Weight: 30%
Solid Hardhat project structure with .env.example and detailed README, but no visible test directory in root files, and the submission is a DePIN smart contract system—not an AI agent with agent loops or tool integrations as the hackathon brief requires.
Evidence used
- No test directory visible in root files despite "hardhat test" script
- .env.example present for API key handling
- device.js is a patrol simulator
Flags
Kael (KL) · Product Value
Confidence: 0.75 · Weight: 30%
Real DePIN payroll problem with clear thesis and end-to-end contract flow, but no demo/video proves it works live, UX is CLI-only, and the "agent" is a scripted simulator—not an AI agent—making product completeness and brief fit questionable.
Evidence used
- README articulates clear problem and architecture with comparison table
- device.js is a waypoint simulator not an autonomous AI agent
- no demo URL or walkthrough video provided
Flags
Oryn (OR) · Originality
Confidence: 0.80 · Weight: 20%
Per-receipt on-chain DePIN settlement is architecturally bold and the BOT Chain speed thesis is well-argued, but this is a smart contract project with no AI agent component, making it off-brief for an Agentic Hackathon.
Evidence used
- Per-receipt on-chain verification vs batch settlement is a genuine architectural insight
- No AI/LLM/agent logic found in repo—device.js is a deterministic simulator
- README states "Built for BOT Chain Builder Challenge #1"
Flags
Zera (ZR) · Documentation & Delivery
Confidence: 0.75 · Weight: 20%
Multiple documentation files and .env.example show strong delivery effort, but no demo/video was provided and the truncated README leaves setup completeness unverified.
Evidence used
- README has detailed architecture and problem statement
- .env.example
- DEMO.md
Flags
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