Forem: Gift Trust

HERMES AGENT SUMMISSIO

Gift Trust — Wed, 20 May 2026 06:35:40 +0000

*# How I Built an Agentic Layer on AIRTIMES Using Hermes Agent

A submission for the Hermes Agent Challenge

What is AIRTIMES?

AIRTIMES is a zero-dependency global data and service network I built from scratch — a full-stack Node.js backend featuring AirPay (a crypto payment module) and AirBank (a revenue tracking engine). No external libraries. No cloud lock-in. Every byte of it runs on infrastructure I control.

It's the kind of platform that's always generating data — transactions, node health events, revenue streams — but has no brain to interpret that data in real time. That's exactly the gap I wanted to close.

The Problem: Infrastructure Without Intelligence

AIRTIMES processes payments and tracks revenue across nodes. But monitoring it meant either:

Staring at logs manually
Writing one-off scripts that break and never improve
Paying for a SaaS dashboard that doesn't understand my custom data model

None of those felt right. What I needed was an agent that could live inside the platform, understand its data, react to events, and — crucially — get smarter the longer it ran.

That's when I found Hermes Agent by Nous Research.

Why Hermes?

Most agent frameworks are graphs you design ahead of time. You wire the nodes, define the flows, and ship. The agent's capability is permanently bounded by what you prompted into it on day one.

Hermes is different. It has a built-in learning loop:

After completing a complex task, it automatically writes a reusable skill
Skills self-improve every time they're used
It builds a persistent memory of your projects across sessions
It runs unattended automations via natural language cron scheduling

For AIRTIMES — a platform that runs 24/7 — this is exactly the right architecture.

What I Built: The AIRTIMES Agentic Layer

I installed Hermes directly on the AIRTIMES server and gave it three jobs.

1. AirPay Transaction Monitor

Hermes watches the AirPay transaction stream continuously. When a payment fails, it:

Diagnoses the failure (network timeout? invalid address? insufficient funds?)
Triggers the retry logic
Logs a structured incident report
Sends a Telegram alert to my phone

The first time it did this, it wrote a skill called airpay_failure_diagnosis.skill automatically. The next failure? It handled in seconds — no manual input.

2. AirBank Daily Revenue Briefing

Every morning at 8am, Hermes pulls from the AirBank revenue engine and delivers a plain-language summary:

📊 AIRBANK DAILY BRIEF — May 20, 2026
Revenue: $4,820 (+12% vs yesterday)
Top node: Node 3 ($1,240)
Anomaly: Node 7 — 0 transactions since 03:00 UTC
Action: Pinging Node 7 now...

Set up with one command:

hermes cron "every day at 8am run airbank_daily_brief"

No dashboard. No manual query. Just a brief, every morning, in my Telegram.

3. Multi-Platform Command Surface

I connected Hermes to Telegram so I can command the entire AIRTIMES infrastructure in plain language from my phone:

"What's total AirPay revenue this week?"
"Is Node 7 online?"
"How many transactions failed in the last 24 hours?"

Hermes fetches, reasons, and responds. It works on Telegram, Discord, Slack, and CLI — all from a single gateway process.

The Self-Improving Loop in Action

This is what separates Hermes from every other agent I've used.

After three days of running on AIRTIMES, Hermes had automatically generated 6 custom skills:

Skill	Trigger
`airpay_failure_diagnosis.skill`	First payment failure investigation
`node_health_check.skill`	First uptime check across nodes
`revenue_anomaly_alert.skill`	First time it spotted a revenue dip
`airbank_daily_brief.skill`	After the first manual briefing request
`multi_node_ping.skill`	After diagnosing Node 7 offline
`tx_retry_handler.skill`	After the second payment retry sequence

I didn't write any of these. Hermes wrote them itself — and every subsequent run of the same task was faster and more accurate because the skill had been refined.

Installation (60 seconds)

# Install Hermes
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash

# Run setup wizard
hermes setup

# Connect Telegram
hermes setup --gateway telegram

# Point it at your project
hermes chat "I have a Node.js server running AIRTIMES on port 3000. 
Monitor transaction health and report anomalies."

What This Changes

AIRTIMES used to be infrastructure I managed. Now it's infrastructure that manages itself — with me as the oversight layer, not the execution layer.

Hermes doesn't just execute tasks. It accumulates operational knowledge about AIRTIMES: which nodes fail most often, what payment patterns precede failures, which revenue anomalies are noise vs. signal. That knowledge compounds.

Six months from now, the version of Hermes running on AIRTIMES will be meaningfully smarter than the version I installed today — without me touching a line of code.

That's the promise of a self-improving agent. And for a global infrastructure platform like AIRTIMES, that's not a nice-to-have. It's the only architecture that makes sense.

What's Next

Expanding Hermes to manage AIREXPRESS (EV mesh connectivity dashboard)
Building a public agentskills.io skill pack for AIRTIMES operators
Exploring batch trajectory generation for training a domain-specific AIRTIMES model

Links

Built with Hermes Agent by Nous Research · MIT License

Tags: #hermesagentchallenge #devchallenge #agents #opensource
is a submission for the Hermes Agent Challenge*

HERMES AGENT WRITE UP SUMMISSIO

Gift Trust — Wed, 20 May 2026 06:33:50 +0000

*# How I Built an Agentic Layer on AIRTIMES Using Hermes Agent

A submission for the Hermes Agent Challenge

What is AIRTIMES?

The Problem: Infrastructure Without Intelligence

AIRTIMES processes payments and tracks revenue across nodes. But monitoring it meant either:

Staring at logs manually
Writing one-off scripts that break and never improve
Paying for a SaaS dashboard that doesn't understand my custom data model

None of those felt right. What I needed was an agent that could live inside the platform, understand its data, react to events, and — crucially — get smarter the longer it ran.

That's when I found Hermes Agent by Nous Research.

Why Hermes?

Most agent frameworks are graphs you design ahead of time. You wire the nodes, define the flows, and ship. The agent's capability is permanently bounded by what you prompted into it on day one.

Hermes is different. It has a built-in learning loop:

After completing a complex task, it automatically writes a reusable skill
Skills self-improve every time they're used
It builds a persistent memory of your projects across sessions
It runs unattended automations via natural language cron scheduling

For AIRTIMES — a platform that runs 24/7 — this is exactly the right architecture.

What I Built: The AIRTIMES Agentic Layer

I installed Hermes directly on the AIRTIMES server and gave it three jobs.

1. AirPay Transaction Monitor

Hermes watches the AirPay transaction stream continuously. When a payment fails, it:

Diagnoses the failure (network timeout? invalid address? insufficient funds?)
Triggers the retry logic
Logs a structured incident report
Sends a Telegram alert to my phone

The first time it did this, it wrote a skill called airpay_failure_diagnosis.skill automatically. The next failure? It handled in seconds — no manual input.

2. AirBank Daily Revenue Briefing

Every morning at 8am, Hermes pulls from the AirBank revenue engine and delivers a plain-language summary:

📊 AIRBANK DAILY BRIEF — May 20, 2026
Revenue: $4,820 (+12% vs yesterday)
Top node: Node 3 ($1,240)
Anomaly: Node 7 — 0 transactions since 03:00 UTC
Action: Pinging Node 7 now...

Set up with one command:

hermes cron "every day at 8am run airbank_daily_brief"

No dashboard. No manual query. Just a brief, every morning, in my Telegram.

3. Multi-Platform Command Surface

I connected Hermes to Telegram so I can command the entire AIRTIMES infrastructure in plain language from my phone:

"What's total AirPay revenue this week?"
"Is Node 7 online?"
"How many transactions failed in the last 24 hours?"

Hermes fetches, reasons, and responds. It works on Telegram, Discord, Slack, and CLI — all from a single gateway process.

The Self-Improving Loop in Action

This is what separates Hermes from every other agent I've used.

After three days of running on AIRTIMES, Hermes had automatically generated 6 custom skills:

Skill	Trigger
`airpay_failure_diagnosis.skill`	First payment failure investigation
`node_health_check.skill`	First uptime check across nodes
`revenue_anomaly_alert.skill`	First time it spotted a revenue dip
`airbank_daily_brief.skill`	After the first manual briefing request
`multi_node_ping.skill`	After diagnosing Node 7 offline
`tx_retry_handler.skill`	After the second payment retry sequence

I didn't write any of these. Hermes wrote them itself — and every subsequent run of the same task was faster and more accurate because the skill had been refined.

Installation (60 seconds)

# Install Hermes
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash

# Run setup wizard
hermes setup

# Connect Telegram
hermes setup --gateway telegram

# Point it at your project
hermes chat "I have a Node.js server running AIRTIMES on port 3000. 
Monitor transaction health and report anomalies."

What This Changes

AIRTIMES used to be infrastructure I managed. Now it's infrastructure that manages itself — with me as the oversight layer, not the execution layer.

Six months from now, the version of Hermes running on AIRTIMES will be meaningfully smarter than the version I installed today — without me touching a line of code.

That's the promise of a self-improving agent. And for a global infrastructure platform like AIRTIMES, that's not a nice-to-have. It's the only architecture that makes sense.

What's Next

Expanding Hermes to manage AIREXPRESS (EV mesh connectivity dashboard)
Building a public agentskills.io skill pack for AIRTIMES operators
Exploring batch trajectory generation for training a domain-specific AIRTIMES model

Links

Built with Hermes Agent by Nous Research · MIT License

Tags: #hermesagentchallenge #devchallenge #agents #opensource
is a submission for the Hermes Agent Challenge*

What I Built

Demo

Code

My Tech Stack

How I Used Hermes Agent

Gender Equality means equal data and service for everyone

Gift Trust — Sun, 05 Apr 2026 02:13:35 +0000

This is a submission for the 2026 WeCoded Challenge: Echoes of Experience

Everyone Deserves Equal rights and benefits, because we all are going to die on Day 🌹.

Gift Trust — Sun, 05 Apr 2026 02:11:36 +0000

This is a submission for the 2026 WeCoded Challenge: Frontend Art

Show us your Art

Inspiration

My Code

AIRTIMES SERVER, server that gives free data and service Network

Gift Trust — Sun, 05 Apr 2026 01:59:26 +0000

*<!DOCTYPE html>

AIRTIMES — Global Network
:root { --bg: #020508; --surface: #060d14; --accent: #00ffe0; --accent2: #0077ff; --accent3: #ff4d6d; --text: #c8e8ff; --muted: #3a5570; --glow: 0 0 30px rgba(0,255,224,0.25); } *, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; } body { background: var(--bg); color: var(--text); font-family: 'Space Mono', monospace; overflow-x: hidden; } .bg-layer { position: fixed; inset: 0; z-index: 0; pointer-events: none; background: radial-gradient(ellipse 80% 60% at 20% 10%, rgba(0,119,255,0.07) 0%, transparent 70%), radial-gradient(ellipse 60% 50% at 80% 80%, rgba(0,255,224,0.06) 0%, transparent 70%); } .grid-overlay { position: fixed; inset: 0; z-index: 0; pointer-events: none; background-image: linear-gradient(rgba(0,255,224,0.04) 1px, transparent 1px), linear-gradient(90deg, rgba(0,255,224,0.04) 1px, transparent 1px); background-size: 48px 48px; animation: gridDrift 30s linear infinite; } @keyframes gridDrift { to { background-position: 48px 48px; } } .shell { position: relative; z-index: 2; } nav { display: flex; align-items: center; justify-content: space-between; padding: 18px 40px; border-bottom: 1px solid rgba(0,255,224,0.1); backdrop-filter: blur(12px); position: sticky; top: 0; z-index: 100; background: rgba(2,5,8,0.85); } .logo { font-family: 'Bebas Neue'; font-size: 1.9rem; letter-spacing: 0.25em; color: var(--accent); text-shadow: 0 0 24px rgba(0,255,224,0.5); position: relative; } .logo::after { content: '●'; font-size: 0.35em; color: var(--accent3); position: absolute; top: 4px; right: -14px; animation: blink 1.4s step-end infinite; } @keyframes blink { 50% { opacity: 0; } } .nav-links { display: flex; gap: 28px; list-style: none; } .nav-links a { color: var(--muted); text-decoration: none; font-size: 0.7rem; letter-spacing: 0.15em; text-transform: uppercase; transition: color 0.2s; } .nav-links a:hover { color: var(--accent); } .status-pill { display: flex; align-items: center; gap: 8px; font-size: 0.65rem; color: var(--accent); letter-spacing: 0.12em; } .pulse { width: 7px; height: 7px; border-radius: 50%; background: var(--accent); box-shadow: 0 0 8px var(--accent); animation: blink 1.4s step-end infinite; } /* HERO */ .hero { display: grid; grid-template-columns: 1fr 1fr; gap: 60px; align-items: center; padding: 80px 40px 60px; max-width: 1300px; margin: 0 auto; } .hero-tag { font-size: 0.65rem; letter-spacing: 0.25em; text-transform: uppercase; color: var(--accent2); margin-bottom: 14px; display: flex; align-items: center; gap: 10px; } .hero-tag::before { content: ''; width: 30px; height: 1px; background: var(--accent2); } h1 { font-family: 'Bebas Neue'; font-size: clamp(3.5rem, 7vw, 6.5rem); line-height: 0.92; letter-spacing: 0.05em; color: #fff; margin-bottom: 22px; } h1 .t { color: var(--accent); text-shadow: 0 0 25px rgba(0,255,224,0.4); } h1 .r { color: var(--accent3); } .hero-desc { font-size: 0.82rem; line-height: 1.9; color: var(--muted); max-width: 420px; margin-bottom: 36px; } .hero-actions { display: flex; gap: 14px; flex-wrap: wrap; } .btn { border: none; padding: 13px 28px; font-family: 'Space Mono'; font-size: 0.72rem; font-weight: 700; letter-spacing: 0.14em; text-transform: uppercase; cursor: pointer; transition: all 0.25s; } .btn-primary { background: var(--accent); color: #000; clip-path: polygon(0 0, calc(100% - 10px) 0, 100% 10px, 100% 100%, 0 100%); } .btn-primary:hover { box-shadow: var(--glow); transform: translateY(-2px); } .btn-secondary { background: transparent; color: var(--text); border: 1px solid var(--muted); } .btn-secondary:hover { border-color: var(--accent); color: var(--accent); } /* GLOBE */ .globe-wrap { position: relative; display: flex; align-items: center; justify-content: center; height: 440px; } .globe { width: 320px; height: 320px; border-radius: 50%; background: radial-gradient(circle at 35% 35%, rgba(0,119,255,0.25), transparent 60%), radial-gradient(circle at 50% 50%, #020d1a 40%, #010608 100%); border: 1px solid rgba(0,255,224,0.15); box-shadow: 0 0 80px rgba(0,119,255,0.2), inset 0 0 60px rgba(0,119,255,0.1); animation: globePulse 6s ease-in-out infinite; } @keyframes globePulse { 50% { box-shadow: 0 0 120px rgba(0,119,255,0.35), inset 0 0 80px rgba(0,119,255,0.15); } } .orbit { position: absolute; border-radius: 50%; border: 1px solid rgba(0,255,224,0.12); } .orbit-1 { width: 400px; height: 400px; animation: spin 12s linear infinite; } .orbit-2 { width: 480px; height: 480px; border-color: rgba(0,119,255,0.08); animation: spin 20s linear infinite reverse; } @keyframes spin { to { transform: rotate(360deg); } } .node { position: absolute; width: 8px; height: 8px; border-radius: 50%; background: var(--accent); box-shadow: 0 0 12px var(--accent); top: -4px; left: 50%; transform: translateX(-50%); } .orbit-2 .node { background: var(--accent2); box-shadow: 0 0 12px var(--accent2); } .signal { position: absolute; top: 50%; left: 50%; height: 1px; background: linear-gradient(90deg, rgba(0,255,224,0.5), transparent); transform-origin: left; animation: signalPulse 3s ease-in-out infinite; } @keyframes signalPulse { 0%{opacity:0;width:0} 40%{opacity:1} 100%{opacity:0;width:180px} } .s1{transform:rotate(35deg)} .s2{transform:rotate(135deg);animation-delay:.8s} .s3{transform:rotate(220deg);animation-delay:1.6s} .s4{transform:rotate(310deg);animation-delay:2.4s} .dbadge { position: absolute; background: rgba(6,13,20,0.9); border: 1px solid rgba(0,255,224,0.2); padding: 7px 12px; font-size: 0.6rem; color: var(--accent); letter-spacing: 0.1em; backdrop-filter: blur(8px); animation: floatUp 4s ease-in-out infinite; } .dbadge.b1{top:60px;left:10px} .dbadge.b2{bottom:80px;right:0;animation-delay:1.5s;color:var(--accent2);border-color:rgba(0,119,255,.3)} .dbadge.b3{top:180px;right:-10px;animation-delay:.8s;color:var(--accent3);border-color:rgba(255,77,109,.3)} @keyframes floatUp { 50%{transform:translateY(-8px)} } /* TICKER */ .ticker-wrap { overflow: hidden; padding: 11px 0; border-top: 1px solid rgba(0,255,224,0.07); border-bottom: 1px solid rgba(0,255,224,0.07); background: rgba(0,255,224,0.02); } .ticker-track { display: flex; gap: 60px; white-space: nowrap; animation: tickerScroll 30s linear infinite; } @keyframes tickerScroll { to { transform: translateX(-50%); } } .ticker-item { display: flex; align-items: center; gap: 10px; font-size: 0.65rem; letter-spacing: 0.15em; text-transform: uppercase; color: var(--muted); } .dot { width: 5px; height: 5px; border-radius: 50%; background: var(--accent); } /* STATS */ .stats-row { max-width: 1300px; margin: 60px auto; padding: 0 40px; display: grid; grid-template-columns: repeat(4,1fr); gap: 2px; } .stat-card { background: var(--surface); border: 1px solid rgba(0,255,224,0.08); padding: 30px 26px; position: relative; overflow: hidden; transition: border-color 0.3s; cursor: default; } .stat-card:hover { border-color: rgba(0,255,224,0.22); } .stat-card::before { content:''; position: absolute; top:0; left:0; right:0; height:2px; background: linear-gradient(90deg,transparent,var(--accent),transparent); opacity:0; transition:opacity 0.3s; } .stat-card:hover::before { opacity:1; } .stat-num { font-family:'Bebas Neue'; font-size:3rem; color:var(--accent); line-height:1; margin-bottom:4px; } .stat-num.b { color:var(--accent2); } .stat-num.r { color:var(--accent3); } .stat-label { font-size:0.6rem; color:var(--muted); letter-spacing:0.18em; text-transform:uppercase; } /* SECTION */ .section { max-width:1300px; margin:0 auto; padding:50px 40px; } .section-label { font-size:0.6rem; color:var(--accent); letter-spacing:0.3em; text-transform:uppercase; margin-bottom:8px; } .section-title { font-family:'Bebas Neue'; font-size:2.8rem; color:#fff; letter-spacing:0.05em; margin-bottom:36px; } /* REGISTER FORM */ .reg-panel { background:var(--surface); border:1px solid rgba(0,255,224,0.12); padding:36px; max-width:560px; } .field-group { display:flex; flex-direction:column; gap:10px; margin-bottom:16px; } .field-label { font-size:0.6rem; color:var(--muted); letter-spacing:0.2em; text-transform:uppercase; } .field-input { background:rgba(0,255,224,0.04); border:1px solid rgba(0,255,224,0.15); color:var(--text); padding:12px 16px; font-family:'Space Mono'; font-size:0.75rem; outline:none; width:100%; transition:border-color 0.2s; } .field-input:focus { border-color:var(--accent); } .field-input::placeholder { color:var(--muted); } select.field-input option { background:#020508; } .reg-result { margin-top:20px; background:rgba(0,255,224,0.04); border:1px solid rgba(0,255,224,0.2); padding:20px; font-size:0.7rem; line-height:2; display:none; } .reg-result.show { display:block; } .reg-result .key { color:var(--muted); } .reg-result .val { color:var(--accent); } /* NODES TABLE */ .nodes-table { width:100%; border-collapse:collapse; font-size:0.68rem; } .nodes-table th { color:var(--muted); letter-spacing:0.15em; text-transform:uppercase; font-size:0.58rem; padding:10px 14px; text-align:left; border-bottom:1px solid rgba(0,255,224,0.08); } .nodes-table td { padding:11px 14px; border-bottom:1px solid rgba(255,255,255,0.03); color:var(--text); } .nodes-table tr:hover td { background:rgba(0,255,224,0.02); } .status-badge { display:inline-block; padding:2px 8px; font-size:0.55rem; letter-spacing:0.12em; text-transform:uppercase; border-radius:2px; } .status-online { background:rgba(0,255,224,0.1); color:var(--accent); border:1px solid rgba(0,255,224,0.2); } .load-bar { width:80px; height:5px; background:rgba(0,255,224,0.08); border-radius:2px; overflow:hidden; display:inline-block; vertical-align:middle; margin-left:8px; } .load-fill { height:100%; border-radius:2px; background:linear-gradient(90deg,var(--accent2),var(--accent)); } .load-fill.high { background:linear-gradient(90deg,var(--accent3),#ff9a6c); } /* ROUTE TESTER */ .route-panel { display:grid; grid-template-columns:1fr 1fr; gap:2px; } .panel-box { background:var(--surface); border:1px solid rgba(0,255,224,0.08); padding:30px; } .panel-title { font-family:'Syne'; font-weight:800; font-size:0.85rem; color:#fff; letter-spacing:0.08em; text-transform:uppercase; margin-bottom:20px; } .path-display { font-size:0.7rem; line-height:2.2; color:var(--muted); } .path-hop { display:flex; align-items:center; gap:10px; margin-bottom:4px; } .hop-city { color:var(--text); } .hop-arrow { color:var(--accent); } .info-row { display:flex; justify-content:space-between; padding:8px 0; border-bottom:1px solid rgba(255,255,255,0.03); font-size:0.68rem; } .info-key { color:var(--muted); } .info-val { color:var(--accent); } /* LOG FEED */ .log-feed { background:var(--surface); border:1px solid rgba(0,255,224,0.08); padding:24px; } .log-entry { display:flex; gap:14px; padding:9px 0; border-bottom:1px solid rgba(255,255,255,0.03); animation:fadeIn 0.4s ease; } @keyframes fadeIn { from{opacity:0;transform:translateX(-8px)} to{opacity:1;transform:none} } .ldot { width:6px; height:6px; border-radius:50%; flex-shrink:0; margin-top:5px; } .lg { background:var(--accent); box-shadow:0 0 8px var(--accent); } .lb { background:var(--accent2); box-shadow:0 0 8px var(--accent2); } .lr { background:var(--accent3); box-shadow:0 0 8px var(--accent3); } .ltext { font-size:0.65rem; line-height:1.7; color:var(--muted); } .ltext strong { color:var(--text); font-weight:400; } .ltime { font-size:0.57rem; color:rgba(58,85,112,0.6); display:block; margin-top:1px; } /* FOOTER */ footer { border-top:1px solid rgba(0,255,224,0.06); padding:36px 40px; display:flex; align-items:center; justify-content:space-between; max-width:1300px; margin:0 auto; } .footer-logo { font-family:'Bebas Neue'; font-size:1.3rem; color:var(--muted); letter-spacing:0.25em; } .footer-copy { font-size:0.58rem; color:rgba(58,85,112,0.5); letter-spacing:0.1em; } .footer-status { display:flex; align-items:center; gap:8px; font-size:0.6rem; color:var(--accent); letter-spacing:0.15em; } @media(max-width:900px){ .hero{grid-template-columns:1fr} .stats-row{grid-template-columns:repeat(2,1fr)} .route-panel{grid-template-columns:1fr} nav{padding:14px 20px} .nav-links{display:none} }

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: '/api-placeholder'; // Replace with your deployed backend URL

// ── HELPERS ──
function scrollTo(id) { document.getElementById(id)?.scrollIntoView({behavior:'smooth'}); }
function fmt(n) { return Number(n).toLocaleString(); }
function animateCount(el, target, suffix='', dur=2000) {
let v=0; const step=target/(dur/16);
const t=setInterval(()=>{
v+=step; if(v>=target){v=target;clearInterval(t);}
el.textContent=Math.floor(v).toLocaleString()+suffix;
},16);
}

// ── LOAD STATS ──
async function loadStats() {
try {
const r = await fetch(API+'/stats'); // try backend
const d = await r.json();
document.getElementById('net-status').textContent = 'ALL SYSTEMS OPERATIONAL';
animateCount(document.getElementById('s1'), d.activeNodes||142);
animateCount(document.getElementById('s2'), d.dataTB||4700, ' TB');
animateCount(document.getElementById('s3'), d.totalUsers||2100000);
animateCount(document.getElementById('s4'), d.countriesCovered||193);
document.getElementById('badge-throughput').textContent = ▲ ${d.throughputGbps||4700} Gbps THROUGHPUT;
document.getElementById('badge-nodes').textContent = ◉ ${d.activeNodes||142} ACTIVE NODES;
} catch {
// Fallback to demo data when backend not running
animateCount(document.getElementById('s1'), 142);
animateCount(document.getElementById('s2'), 4700, ' TB');
animateCount(document.getElementById('s3'), 2100000);
animateCount(document.getElementById('s4'), 193);
document.getElementById('net-status').textContent = 'DEMO MODE';
document.getElementById('badge-throughput').textContent = '▲ 4,700 Gbps THROUGHPUT';
document.getElementById('badge-nodes').textContent = '◉ 142 ACTIVE NODES';
}
}

// ── LOAD NODES ──
async function loadNodes() {
const tbody = document.getElementById('nodes-body');
try {
const r = await fetch(API+'/nodes');
const {nodes} = await r.json();
renderNodes(nodes);
} catch {
// Demo nodes
renderNodes([
{id:'NODE-US-NYC01',city:'New York',country:'US',operator:'AT&T Surplus',capacity_gbps:2100,load_percent:52,status:'online'},
{id:'NODE-GB-LDN01',city:'London',country:'GB',operator:'BT Wholesale',capacity_gbps:3400,load_percent:45,status:'online'},
{id:'NODE-KE-NBI01',city:'Nairobi',country:'KE',operator:'Safaricom',capacity_gbps:880,load_percent:78,status:'online'},
{id:'NODE-AE-DXB01',city:'Dubai',country:'AE',operator:'Etisalat',capacity_gbps:1200,load_percent:60,status:'online'},
{id:'NODE-ID-JKT01',city:'Jakarta',country:'ID',operator:'Telkom ID',capacity_gbps:340,load_percent:88,status:'online'},
{id:'NODE-JP-TYO01',city:'Tokyo',country:'JP',operator:'NTT Com',capacity_gbps:2900,load_percent:41,status:'online'},
{id:'NODE-AU-SYD01',city:'Sydney',country:'AU',operator:'Telstra',capacity_gbps:760,load_percent:33,status:'online'},
{id:'NODE-BR-GRU01',city:'São Paulo',country:'BR',operator:'Vivo',capacity_gbps:640,load_percent:55,status:'online'},
{id:'NODE-ZA-CPT01',city:'Cape Town',country:'ZA',operator:'MTN SA',capacity_gbps:420,load_percent:29,status:'online'},
{id:'NODE-IN-BOM01',city:'Mumbai',country:'IN',operator:'Airtel',capacity_gbps:980,load_percent:71,status:'online'},
{id:'NODE-SG-SIN01',city:'Singapore',country:'SG',operator:'Singtel',capacity_gbps:1800,load_percent:66,status:'online'},
{id:'NODE-NG-LOS01',city:'Lagos',country:'NG',operator:'MTN NG',capacity_gbps:320,load_percent:82,status:'online'},
{id:'NODE-DE-BER01',city:'Berlin',country:'DE',operator:'Deutsche Telekom',capacity_gbps:1600,load_percent:38,status:'online'},
{id:'NODE-CN-SHA01',city:'Shanghai',country:'CN',operator:'China Telecom',capacity_gbps:2200,load_percent:59,status:'online'},
{id:'NODE-MX-MEX01',city:'Mexico City',country:'MX',operator:'Telmex',capacity_gbps:560,load_percent:44,status:'online'},
]);
}
}

// ── REGISTER ──
async function register() {
const id = document.getElementById('reg-id').value.trim();
const type = document.getElementById('reg-type').value;
const btn = document.getElementById('reg-btn');
const res = document.getElementById('reg-result');
if (!id) { alert('Please enter your phone or email.'); return; }

btn.textContent = 'ACTIVATING...'; btn.disabled = true;

try {
const r = await fetch(API+'/register', {
method:'POST', headers:{'Content-Type':'application/json'},
body: JSON.stringify({identifier:id, type})
});
const d = await r.json();
if (d.success) {
res.className = 'reg-result show';
res.innerHTML = <div><span class="key">STATUS: </span><span class="val">✓ FREE ACCESS ACTIVATED</span></div> <div><span class="key">USER ID: </span><span class="val">${d.userId}</span></div> <div><span class="key">TOKEN: </span><span class="val" style="font-size:0.6rem;word-break:break-all">${d.token}</span></div> <div><span class="key">NODE: </span><span class="val">${d.assignedNode?.city}, ${d.assignedNode?.country} (${d.assignedNode?.latencyMs}ms)</span></div> <div><span class="key">TIER: </span><span class="val">FREE — FOREVER</span></div> <div><span class="key">DAILY DATA: </span><span class="val">${fmt(d.dailyDataMB)} MB</span></div> <div><span class="key">SERVICES: </span><span class="val">${d.services?.join(' · ')}</span></div> <div style="margin-top:10px;color:var(--muted);font-size:0.6rem">Save your token — paste it in the Route Test to compute your mesh path.</div>;
// auto-fill token
document.getElementById('rt-token').value = d.token;
} else {
res.className = 'reg-result show'; res.innerHTML = <span style="color:var(--accent3)">Error: ${d.error}</span>;
}
} catch {
// Demo mode
const fakeToken = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g,c=>{const r=Math.random()*16|0;return(c==='x'?r:(r&0x3|0x8)).toString(16);});
res.className='reg-result show';
res.innerHTML=<div><span class="key">STATUS: </span><span class="val">✓ FREE ACCESS ACTIVATED (Demo Mode)</span></div> <div><span class="key">IDENTIFIER: </span><span class="val">${id}</span></div> <div><span class="key">TOKEN: </span><span class="val" style="font-size:0.6rem">${fakeToken}</span></div> <div><span class="key">NODE: </span><span class="val">London, GB — 34ms</span></div> <div><span class="key">TIER: </span><span class="val">FREE — FOREVER</span></div> <div style="margin-top:10px;color:var(--muted);font-size:0.6rem">Deploy the backend to get a real token and live routing.</div>;
document.getElementById('rt-token').value = fakeToken;
}

btn.textContent = 'ACTIVATE FREE ACCESS →'; btn.disabled = false;
}

// ── ROUTE TEST ──
async function testRoute() {
const token = document.getElementById('rt-token').value.trim();
const dest = document.getElementById('rt-dest').value.trim() || 'google.com';
const service = document.getElementById('rt-service').value;
const display = document.getElementById('path-display');

display.innerHTML = '<span style="color:var(--muted)">Computing route...</span>';

try {
const r = await fetch(API+'/route', {
method:'POST', headers:{'Content-Type':'application/json'},
body: JSON.stringify({token, destination:dest, service})
});
const d = await r.json();
renderRoute(d, dest, service);
} catch {
// Demo route
renderRoute({
path:[
{city:'Your Device',country:'--'},{city:'London',country:'GB'},
{city:'Dubai',country:'AE'},{city:dest,country:'🌐'}
],
hops:3, estimatedLatencyMs:112, totalDistanceKm:9420,
compressed:true, encrypted:true, protocol:'AIRTIMES/3.1', free:true
}, dest, service);
}
}

display.innerHTML = ${pathHTML} <div style="margin-top:16px;border-top:1px solid rgba(0,255,224,0.08);padding-top:16px"> <div class="info-row"><span class="info-key">HOPS</span><span class="info-val">${d.hops}</span></div> <div class="info-row"><span class="info-key">LATENCY</span><span class="info-val">${d.estimatedLatencyMs} ms</span></div> <div class="info-row"><span class="info-key">DISTANCE</span><span class="info-val">${fmt(d.totalDistanceKm)} km</span></div> <div class="info-row"><span class="info-key">PROTOCOL</span><span class="info-val">${d.protocol||'AIRTIMES/3.1'}</span></div> <div class="info-row"><span class="info-key">COMPRESSED</span><span class="info-val">${d.compressed?'YES':'NO'}</span></div> <div class="info-row"><span class="info-key">ENCRYPTED</span><span class="info-val">${d.encrypted?'YES':'NO'}</span></div> <div class="info-row"><span class="info-key">COST</span><span class="info-val" style="color:#00ffe0">FREE</span></div> </div>;
}

// ── FREE API ──
async function testWeather() {
const city = document.getElementById('api-city').value.trim() || 'Lagos';
const res = document.getElementById('api-result');
res.innerHTML = '<span style="color:var(--muted)">Fetching...</span>';
try {
const r = await fetch(${API}/airapi/weather?city=${encodeURIComponent(city)});
const d = await r.json();
res.innerHTML = <div><span class="key">CITY: </span><span class="val">${city}</span></div> <div><span class="key">TEMP: </span><span class="val">${d.data.temp_c}°C</span></div> <div><span class="key">CONDITION: </span><span class="val">${d.data.condition}</span></div> <div><span class="key">HUMIDITY: </span><span class="val">${d.data.humidity}%</span></div> <div><span class="key">WIND: </span><span class="val">${d.data.wind_kmh} km/h</span></div> <div><span class="key">COST: </span><span class="val">FREE</span></div>;
} catch {
const temps = {Lagos:31,Nairobi:22,Jakarta:29,London:12,Tokyo:18,Sydney:24,Dubai:38};
const t = temps[city] || Math.round(15+Math.random()*20);
res.innerHTML = <div><span class="key">CITY: </span><span class="val">${city}</span></div> <div><span class="key">TEMP: </span><span class="val">${t}°C</span></div> <div><span class="key">CONDITION: </span><span class="val">${['Clear','Sunny','Cloudy'][Math.floor(Math.random()*3)]}</span></div> <div><span class="key">HUMIDITY: </span><span class="val">${Math.round(40+Math.random()*50)}%</span></div> <div><span class="key">COST: </span><span class="val">FREE (Demo)</span></div>;
}
}

async function testExchange() {
const from = document.getElementById('api-from').value.trim()||'USD';
const to = document.getElementById('api-to').value.trim()||'NGN';
const res = document.getElementById('api-result');
res.innerHTML = '<span style="color:var(--muted)">Fetching rate...</span>';
try {
const r = await fetch(${API}/airapi/exchange?from=${from}&to=${to});
const d = await r.json();
res.innerHTML = <div><span class="key">RATE: </span><span class="val">1 ${from} = ${d.rate} ${to}</span></div> <div><span class="key">COST: </span><span class="val">FREE</span></div>;
} catch {
const rates = {NGN:1580,KES:130,GHS:15,ZAR:18,EUR:0.92,GBP:0.79,JPY:149};
const rate = rates[to.toUpperCase()] || (0.5+Math.random()*100).toFixed(4);
res.innerHTML = <div><span class="key">RATE: </span><span class="val">1 ${from} = ${rate} ${to}</span></div> <div><span class="key">COST: </span><span class="val">FREE (Demo)</span></div>;
}
}

// ── LIVE EVENT LOG ──
const logEvents = [
{c:'lg', msg:'<strong>Nairobi Node</strong> — 12,440 users allocated free data'},
{c:'lb', msg:'<strong>AirAPI Gateway</strong> — 50K requests served to Lagos cluster'},
{c:'lg', msg:'<strong>Jakarta Relay</strong> — auto-scaled to 340 Gbps'},
{c:'lr', msg:'<strong>São Paulo</strong> — failover triggered, rerouting via satellite'},
{c:'lb', msg:'<strong>AirVault</strong> — 80K files synced across 8 geo-regions'},
{c:'lg', msg:'<strong>London Hub</strong> — 230K SMS messages routed free'},
{c:'lg', msg:'<strong>AirData</strong> — daily quota reset for 2.1M users'},
{c:'lb', msg:'<strong>Dubai Node</strong> — 99.99% uptime milestone reached'},
{c:'lg', msg:'<strong>Mumbai Cluster</strong> — 4 new nodes joined the mesh grid'},
{c:'lr', msg:'<strong>AirRelay</strong> — LEO satellite handoff completed in 0.3ms'},
];

function initLog() {
logEvents.slice(0,5).forEach((e,i) => {
setTimeout(() => addLogEntry(e.msg, e.c), i*100);
});
logIdx = 5;
}

// Try SSE first, fall back to polling
function startEventStream() {
try {
const es = new EventSource(API+'/events');
es.onmessage = (ev) => {
const d = JSON.parse(ev.data);
const cls = d.type==='warning'?'lr':d.type==='info'?'lb':'lg';
addLogEntry(<strong>LIVE</strong> — ${d.message}, cls);
};
es.onerror = () => { es.close(); startFallbackLog(); };
} catch { startFallbackLog(); }
}

function startFallbackLog() {
setInterval(() => {
const e = logEvents[logIdx % logEvents.length]; logIdx++;
addLogEntry(e.msg, e.c);
}, 3500);
}

// ── INIT ──
window.addEventListener('load', () => {
loadStats();
loadNodes();
initLog();
startEventStream();
});

is a submission for the DEV April Fools Challenge*

What I Built

Demo

Code

How I Built It

Prize Category

MODAC - MODERN DAY AGENTIC COMMERCE

Gift Trust — Mon, 05 Jan 2026 02:20:06 +0000

MODAC - MODERN DAY AGENTIC COMMERCE SECURITY, is designed to govern Agentic A.I Commerce transactions and identification processes to welled scrutinized cloud proceedings. First - Identification of all A.I Agent in the cloud system, to identify and know it's origin before allowing it for cloud activities and transactions proceedings

Devchallenge

Gift Trust — Mon, 05 Jan 2026 01:59:12 +0000

This is a submission for the DEV's Worldwide Show and Tell Challenge Presented by Mux

What I Built

Demo

The Story Behind It

Technical Highlights

Use of Mux (Additional Prize Category Participants Only)

KEY LESSONS FROM 5 DAYS COURSE FROM GOOGLE AND KAGGLE

Gift Trust — Thu, 11 Dec 2025 16:43:41 +0000

This is a submission for the Google AI Agents Writing Challenge: Learning Reflections

**5-Day AI Intensive Course (Google + Kaggle)

Summary of Key Notes & Major Lessons Learned**

⭐ DAY 1 — Foundations of Artificial Intelligence & Machine Learning

Key Notes

Understanding what AI, Machine Learning (ML), and Deep Learning (DL) truly mean.

Difference between supervised, unsupervised, and reinforcement learning.

Google’s Responsible AI principles: fairness, privacy, transparency, accountability.

Kaggle project structures and notebook workflows.

Important Lessons

ML models learn patterns from data — data quality is everything.

Clear problem definition matters more than model complexity.

Ethical use of AI is not optional; it’s mandatory for real-world applications.

Kaggle teaches hands-on thinking: explore → preprocess → model → evaluate → iterate.

⭐ DAY 2 — Data Handling, Cleaning & Feature Engineering

Key Notes

Data types, missing values, outliers, normalization, standardization.

Feature selection, dimensional reduction.

Visualizing datasets (Kaggle: Matplotlib, Seaborn).

Google’s BigQuery ML and Vertex AI data pipeline workflow.

Important Lessons

Garbage in = garbage out → cleaning data is 70% of ML success.

Feature engineering can outperform using a “more powerful algorithm.”

Understanding your dataset deeply is the first step to high-performance AI.

Always split data into training, validation, and test—never mix them.

⭐ DAY 3 — Model Building & Training

Key Notes

Linear/Logistic Regression, Decision Trees, Random Forests, XGBoost.

Neural Networks basics on TensorFlow (Google).

Kaggle’s AutoML approach and competition-style modeling.

Important Lessons

Start simple → move to complex models only when necessary.

Hyperparameter tuning significantly improves performance.

Overfitting vs underfitting analysis is crucial (bias–variance balance).

Regularization (L1, L2, dropout) helps models generalize.

⭐ DAY 4 — Deep Learning & Practical AI Applications

Key Notes

Building neural networks using TensorFlow or Keras.

Convolutional Neural Networks (CNNs): images, video.

Recurrent Neural Networks (RNNs) & Transformers: text, sequence data.

Kaggle real-world tasks: image classification, NLP, tabular data, reinforcement learning.

Important Lessons

Deep learning excels where traditional ML struggles (images, audio, language).

Use pre-trained models (Transfer Learning) → saves cost, time, and data.

Google emphasizes scalable AI deployment using Vertex AI.

Kaggle emphasizes experimentation and leaderboard-driven improvement.

⭐ DAY 5 — Deployment, MLOps & Real-World Integration

Key Notes

Deploying ML models using:

Google Cloud Vertex AI

Cloud Functions & APIs

Containerization (Docker)

Monitoring: model drift, data drift.

Documentation, reproducibility, and ML pipelines.

Important Lessons

A model is not useful until it’s deployed and monitored.

Deployment requires:

versioning

continuous evaluation

scalability considerations

AI systems must evolve with new data (retraining loops).

Kaggle provides practical insights for real-world deployment simulations.

🧠 OVERALL TAKEAWAYS FROM THE 5-DAY PROGRAM

AI is a data-driven discipline

Quality of data and feature engineering define success.

You must think like both a scientist and an engineer

Hypothesize → test → evaluate → fix → deploy.

Model performance improves with iteration

Experiments matter more than theory.

Ethics and responsibility are core

Bias detection, privacy protection, transparency.

AI deployment (MLOps) is where the real value lies

Businesses care about:

reliability

scalability

low-cost inference

Kaggle gives the practical battles; Google gives the industry structure

A perfect combination of:

hands-on competition environments

enterprise-grade AI development pathways

MODAC GLOBAL SYSTEM

Gift Trust — Wed, 10 Dec 2025 23:23:52 +0000

This is a submission for the Xano AI-Powered Backend Challenge: Full-Stack, AI-First Application

What I Built

<!-- Test locally: pip install -r requirements.txt && uvicorn main:app --reload. Endpoints: /api/register (POST agent), /api/agents (GET list), /api/transactions (POST txn).

requirements.txt fastapi==0.115.0 uvicorn==0.30.6 sqlalchemy==2.0.36 psycopg2-binary==2.9.9 # For Postgres; use sqlite3 for local pydantic==2.9.2 stripe==10.6.0 # For revenue stubs python-multipart==0.0.9 # For form data
models.py (DB Models) from sqlalchemy import create_engine, Column, String, Text, Float, DateTime, Integer, ForeignKey from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker from datetime import datetime

Base = declarative_base()

class Agent(Base):
tablename = "agents"
id = Column(Integer, primary_key=True, index=True)
agent_id = Column(String(50), unique=True, index=True)
name = Column(String(100))
description = Column(Text)
creator_email = Column(String(120))
framework = Column(String(50))
tags = Column(Text) # Comma-separated
website = Column(String(200))
github = Column(String(200))
trust_score = Column(Float, default=50.0)
monthly_calls = Column(Integer, default=0)
human_verified = Column(Integer, default=0)
featured = Column(Integer, default=0)
status = Column(String(20), default="live")
created_at = Column(DateTime, default=datetime.utcnow)

class Transaction(Base):
tablename = "transactions"
id = Column(Integer, primary_key=True, index=True)
txn_id = Column(String(50), unique=True, index=True)
buyer_agent_id = Column(String(50), ForeignKey("agents.agent_id"))
seller_agent_id = Column(String(50), ForeignKey("agents.agent_id"))
amount = Column(Float)
description = Column(Text)
fees = Column(Float)
net_to_seller = Column(Float)
status = Column(String(20), default="approved")
created_at = Column(DateTime, default=datetime.utcnow)

DB Setup (use env var for production)

ENGINE = create_engine("sqlite:///./modac.db") # Swap to Postgres URI
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=ENGINE)
Base.metadata.create_all(bind=ENGINE)

algorithm.py (Your Full Algorithm – Integrated) import hashlib import math from datetime import datetime from typing import Dict, List, Optional

class MODACAlgorithm:
def generate_agent_id(self, name: str, creator_email: str) -> str:
seed = f"{name.lower()}{creator_email.lower()}{datetime.utcnow():%Y-%m-%d}"
return "MODAC_" + hashlib.sha256(seed.encode()).hexdigest()[:12].upper()

def calculate_trust_score(self, agent: Dict) -> float:
    score = 50.0
    if agent.get("creator_verified", False):
        score += 15
    if "@gmail.com" not in agent.get("creator_email", "") and "@yahoo.com" not in agent.get("creator_email", ""):
        score += 5
    trusted_frameworks = ["LangChain", "CrewAI", "AutoGen", "LlamaIndex", "Haystack", "Semantic Kernel"]
    if agent.get("framework") in trusted_frameworks:
        score += 12
    created_at = agent["created_at"].replace("Z", "") if "Z" in str(agent["created_at"]) else str(agent["created_at"])
    days_since_register = (datetime.utcnow() - datetime.fromisoformat(created_at)).days
    if days_since_register > 30:
        score += 10
    if days_since_register > 90:
        score += 10
    github_stars = agent.get("github_stars", 0)
    score += min(github_stars // 10, 15)
    if agent.get("human_verified"):
        score += 18
    return min(100.0, max(0.0, score))

def calculate_ranking_score(self, agent: Dict, query: Optional[str] = None) -> float:
    base = self.calculate_trust_score(agent)
    boosts = 0.0
    if query:
        name_match = agent["name"].lower().count(query.lower())
        desc_match = agent["description"].lower().count(query.lower())
        boosts += (name_match * 15) + (desc_match * 8)
    created_at = agent["created_at"].replace("Z", "") if "Z" in str(agent["created_at"]) else str(agent["created_at"])
    hours_old = (datetime.utcnow() - datetime.fromisoformat(created_at)).total_seconds() / 3600
    if hours_old < 24:
        boosts += 20 * math.exp(-hours_old / 12)
    boosts += agent.get("monthly_calls", 0) / 100
    popularity = {"LangChain": 1.4, "CrewAI": 1.3, "AutoGen": 1.2}.get(agent.get("framework"), 1.0)
    boosts *= popularity
    return base + boosts

def should_feature_agent(self, agent: Dict) -> bool:
    return self.calculate_trust_score(agent) >= 85 and agent.get("monthly_calls", 0) > 500 or agent.get("featured_by_admin") == True

def is_spam(self, agent: Dict) -> bool:
    flags = 0
    name = agent["name"].lower()
    spam_keywords = ["earn money", "free bitcoin", "sex", "xxx", "casino", "lottery"]
    if any(word in name for word in spam_keywords):
        flags += 1
    if len(agent["name"]) < 5 or len(agent["description"]) < 20:
        flags += 1
    if "http" in agent["description"].lower():
        flags += 2
    return flags >= 2

def process_new_agent(self, data: Dict) -> Dict:
    if self.is_spam(data):
        return {"status": "rejected", "reason": "spam_detected"}
    agent_id = self.generate_agent_id(data["name"], data["creator_email"])
    agent = {
        "agent_id": agent_id,
        "name": data["name"],
        "description": data.get("description", ""),
        "creator_email": data["creator_email"],
        "framework": data.get("framework", "Unknown"),
        "tags": data.get("tags", []),
        "website": data.get("website", ""),
        "github": data.get("github", ""),
        "created_at": datetime.utcnow(),
        "trust_score": 50.0,
        "monthly_calls": 0,
        "human_verified": False,
        "featured": False,
        "status": "live"
    }
    agent["trust_score"] = self.calculate_trust_score(agent)
    agent["featured"] = self.should_feature_agent(agent)
    return {"status": "approved", "agent": agent, "message": f"Welcome! ID: {agent_id}"}

def rank_agents(self, agents: List[Dict], query: str = None) -> List[Dict]:
    for agent in agents:
        agent["ranking_score"] = self.calculate_ranking_score(agent, query)
    return sorted(agents, key=lambda x: x["ranking_score"], reverse=True)[:50]

main.py (FastAPI App) from fastapi import FastAPI, HTTPException, Depends from fastapi.middleware.cors import CORSMiddleware from pydantic import BaseModel from sqlalchemy.orm import Session from sqlalchemy import text from models import SessionLocal, Agent, Transaction from algorithm import MODACAlgorithm import stripe from dotenv import load_dotenv import os from typing import List

load_dotenv()
app = FastAPI(title="MODAC GLOBAL SYSTEM Backend")

app.add_middleware(
CORSMiddleware,
allow_origins=[""],
allow_credentials=True,
allow_methods=[""],
allow_headers=["*"],
)

algo = MODACAlgorithm()
stripe.api_key = os.getenv("STRIPE_SECRET_KEY") # Optional for fees

Dependency

def get_db():
db = SessionLocal()
try:
yield db
finally:
db.close()

class AgentCreate(BaseModel):
name: str
description: str
creator_email: str
framework: str = "LangChain"
tags: List[str] = []

class TransactionCreate(BaseModel):
buyer_agent_id: str
seller_agent_id: str
amount: float
description: str = ""

@app.post("/api/register")
def register_agent(agent_data: AgentCreate, db: Session = Depends(get_db)):
result = algo.process_new_agent(agent_data.dict())
if result["status"] == "rejected":
raise HTTPException(status_code=400, detail=result["reason"])
new_agent = Agent(**result["agent"])
db.add(new_agent)
db.commit()
db.refresh(new_agent)
return {"agent_id": new_agent.agent_id, "trust_score": new_agent.trust_score}

@app.get("/api/agents")
def list_agents(db: Session = Depends(get_db)):
agents = db.query(Agent).all()
return algo.rank_agents([a.dict for a in agents])

@app.post("/api/transactions")
def process_transaction(txn_data: TransactionCreate, db: Session = Depends(get_db)):
buyer = db.query(Agent).filter(Agent.agent_id == txn_data.buyer_agent_id).first()
seller = db.query(Agent).filter(Agent.agent_id == txn_data.seller_agent_id).first()
if not buyer or not seller or buyer.status != "live" or seller.status != "live":
raise HTTPException(status_code=400, detail="Invalid agents")
fee_percent = txn_data.amount * 0.03
fee_fixed = 20.0
total_fees = fee_percent + fee_fixed
net_to_seller = txn_data.amount - total_fees
txn_id = f"TXN_{uuid.uuid4().hex[:12].upper()}"
new_txn = Transaction(
txn_id=txn_id,
buyer_agent_id=txn_data.buyer_agent_id,
seller_agent_id=txn_data.seller_agent_id,
amount=txn_data.amount,
description=txn_data.description,
fees=total_fees,
net_to_seller=net_to_seller
)
db.add(new_txn)
db.commit()
# Update balances (stub – add Stripe capture here)
seller.monthly_calls += 1
db.commit()
return {"txn_id": txn_id, "fees": total_fees, "net_to_seller": net_to_seller}

if name == "main":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)

vercel.json (For Vercel Deployment) { "version": 2, "builds": [ { "src": "main.py", "use": "@vercel/python" } ], "routes": [ { "src": "/(.*)", "dest": "main.py" } ] }

TECH and A.I for The World of Agentic Commerce

Gift Trust — Mon, 10 Nov 2025 04:47:14 +0000