Smoke Tests Should Run Themselves: A Free Model and Free Server Workflow

The worst deploy is the one you catch at 3 AM, after a user sends a screenshot of an error page. You verified everything manually. You clicked every button. You still missed something.

Manual smoke tests are not a strategy. They are a memory test. You forget a step, or you skip it because you are tired, or you just assume the last change didn't break anything.

You do not need a full CI system to fix that. You need two things: a free model to write the boring test script, and a free server to run it on a schedule. MonkeyCode provides both a free model tier and a free server option, which makes this workflow possible without opening your wallet. Disclosure: This article was prepared as part of MonkeyCode's product outreach.

The 3 AM Problem

You have three services: a product catalog, a cart, and a checkout. Each one has an HTTP endpoint. After every deploy, you check them by hand.

The first check is fine. The second check is fine. The third check happens in your head while you are already thinking about the next task. That is where the bug hides.

A scheduled smoke test removes the memory part. It runs the same checks every few minutes, writes results to a log, and fails when something is off. You only look at it when the log says something went wrong.

The hard part was always the same: writing the script and finding a place to run it. That is where the free model and the free server come in.

What You Are Actually Building

Think of this as a tiny robots.txt for your API. A robot visits each endpoint, checks the status code, and moves on. If the response does not match the expectation, you want a loud failure.

The script itself is simple. Here is a minimal version in Node.js, using the built-in fetch:

// smoke.js
const endpoints = [
  { name: 'products', url: 'https://api.example.com/products', expect: 200 },
  { name: 'cart', url: 'https://api.example.com/cart', expect: 200 },
  { name: 'checkout', url: 'https://api.example.com/checkout', expect: 302 },
];

const results = [];

for (const ep of endpoints) {
  try {
    const res = await fetch(ep.url, { redirect: 'manual' });
    results.push({ ...ep, ok: res.status === ep.expect });
  } catch (err) {
    results.push({ ...ep, ok: false, error: err.message });
  }
}

console.table(results);
process.exit(results.every(r => r.ok) ? 0 : 1);

That is the core. You can hand this script to a free model and ask it to add more checks. Or you can start with the script and let the model generate the test cases for your specific endpoints.

The Prompt That Turns a Model Into a Test Writer

A free model is not magic. It is a faster way to turn a description into code. Describe your endpoint, the shape of the response, and what counts as healthy.

For example, you might write:

Write a Node.js script that calls GET /products and verifies the response is an array. Each item must have a string id, a string name, and a number price. Log the first failure and exit with code 1.

A half-decent model will produce something close to this:

async function checkProducts() {
  const res = await fetch('https://api.example.com/products');
  if (!res.ok) throw new Error(`HTTP ${res.status}`);
  const data = await res.json();
  if (!Array.isArray(data)) throw new Error('Not an array');
  for (const item of data) {
    if (typeof item.id !== 'string') throw new Error(`Bad id: ${item.id}`);
    if (typeof item.name !== 'string') throw new Error(`Bad name: ${item.name}`);
    if (typeof item.price !== 'number') throw new Error(`Bad price: ${item.price}`);
  }
  console.log('products OK');
}

You should review the generated code. Free models can hallucinate field names or miss error handling. But the first draft saves you ten minutes of typing, and the review is a two-minute read.

The Free Server's Job

A script on your laptop is not a scheduled smoke test. It is a manual test with extra steps. You still have to remember to run it.

A free server solves that. You push the script to the server, add a cron line, and walk away. The server becomes a dedicated health checker.

MonkeyCode's free server option is one way to get that. Upload or clone your script, install Node, and set a cron job. The exact deployment commands depend on the server image, but the pattern is standard:

# On the server
mkdir -p ~/smoke
# copy smoke.js there
crontab -e

Then add a line:

*/5 * * * * node ~/smoke/smoke.js >> ~/smoke/smoke.log 2>&1

That runs every five minutes. If a check fails, process.exit(1) bubbles up, and the log captures the output.

You can go further. Redirect the output to a dedicated log file and use a simple watcher to alert you. Or wrap the script in a tiny HTTP endpoint and poll it from a monitoring service. But the cron version is already a massive upgrade over manual clicking.

A Decision Table for Real Teams

Situation	Use this workflow?	Why
Side project with no CI	Yes	Low effort, catches regressions
Startup with a few APIs	Yes	Fast setup, no infra cost
Regulated environment with strict SLA	No	You need real observability and on-call
Team that already has GitHub Actions	Maybe	Use CI instead if the repo is already there

The line is simple. If you just need a canary that says "the deploy didn't instantly explode," a free model and a free server are enough. If you need paging, dashboards, and audit trails, build something heavier.

Where This Workflow Breaks

Free servers can sleep after inactivity. Your cron job may not fire at the exact second you want. The script might not survive a server restart unless you configure it properly.

Also, free models are not deterministic. The code they generate today may be different tomorrow. Always keep a reviewed copy in the server, and treat the model output as a suggestion, not a final artifact.

The biggest limitation is coverage. A status code check will not catch a wrong price or a missing field in a large payload. You need deeper assertions for that. The free model can write those, but you have to tell it what "wrong" looks like.

Who Should Not Use This

If you are building a payment system, a medical device, or anything where a silent failure costs real money, do not rely on a cron job on a free server. You need proper distributed tracing and alerting.

If your team already has a CI pipeline, do not add another tool. Use your existing scheduler.

And if you do not trust code written by a free model, that is healthy. Review it, run it locally, then deploy.

The Ritual

The workflow takes less than an hour to set up. You write or generate one script, deploy it to a free server, and schedule it. The next time you deploy at 4 PM on a Friday, you get a clean log instead of a nervous feeling.

Your future self will thank you when the smoke test catches the exact regression you would have missed.

If you try this with your own endpoints, the only real work is deciding what to check. The model writes the rough draft. The server runs it while you sleep. Deploys stop being a leap of faith.

And when the log turns red, you finally have a reason to say: "I didn't miss it. The test caught it."