Zero to Verified: A Free Coding Model Tutorial

A developer stares at a red test suite. The failing test is small. The fix is obvious. The clock is not.

The ticket says "fix the pricing bug." The codebase is ten years old. The test suite takes four minutes. Nobody wants to touch it. This is the moment a free model earns its place. Not by rewriting the world. By fixing one line.

The usual options are familiar. A paid API costs money and setup time. A local model costs a GPU and an afternoon. This tutorial walks a third path. It uses a free coding model, a tiny repo, and a verification gate at every step.

The goal is narrow. Fix one failing test. Deploy the result. Prove every claim with a command.

The workflow uses MonkeyCode for model access and hosting. Disclosure: This article was prepared as part of MonkeyCode's product outreach. MonkeyCode is an open source project. It offers free model access and a free server option for small services. This article promises no quotas, benchmarks, or uptime. Those numbers change. The project README is the source of truth.

Stage 0: Seed a failure you can see

Start in an empty directory. Build a minimal Python project.

mkdir fixme && cd fixme
git init
python3 -m venv .venv
source .venv/bin/activate
pip install pytest flask

Create service.py with a planted bug.

def discount(price: float, rate: float) -> float:
    if not 0 <= rate <= 1:
        raise ValueError("rate must be between 0 and 1")
    return price * rate

The function returns the discount. It should return the final price. Now add the test.

from service import discount

def test_discount_returns_final_price():
    assert discount(100, 0.2) == 80

def test_discount_rejects_bad_rate():
    try:
        discount(100, 1.5)
    except ValueError:
        return
    raise AssertionError("expected ValueError")

Run the suite.

pytest -q

One test fails. One passes. Gate 1 is complete. The failure reproduces locally. A model cannot fix what you cannot see.

Stage 1: Constrain the prompt

The prompt matters more than the model. Open MonkeyCode and select the free model access option. Paste this prompt into the chat interface.

Fix the failing test in service.py. Do not modify test_service.py. Do not change the function signature. Keep the ValueError behavior.

The constraint is the contract. It turns a vague request into a checkable task. Without it, the model may rewrite the test or change the API. Both outcomes break the workflow.

Stage 2: Apply the diff, then verify

The model returns a one-line change. Apply it.

-    return price * rate
+    return price * (1 - rate)

Then run the gates.

pytest -q
git diff --name-only
git diff service.py | grep "^[-+]def "

Three checks, in order. Tests pass. Only service.py changed. The function signature is intact. Gate 2 is the diff scope. Gate 3 is the behavior. The grep check is cheap and precise. It catches signature drift that tests miss.

One line. Ten seconds to review. That is the point. Small tasks keep the review honest.

Suppose the model rewrites the test instead. The signature check passes. The diff scope check fails. The workflow stops. That is a feature. A gate that never stops you is decoration.

A green badge says a model helped. It does not say the diff is safe. The diff review is the real signal. Read the diff, not the badge.

Stage 3: Wrap the fix in a service

A fixed function is nice. A deployed endpoint is better. Add app.py.

from flask import Flask, request
from service import discount

app = Flask(__name__)

@app.route("/discount")
def apply_discount():
    try:
        price = float(request.args.get("price", 0))
        rate = float(request.args.get("rate", 0))
        return {"final": discount(price, rate)}
    except ValueError as exc:
        return {"error": str(exc)}, 400

Run it locally.

flask --app app run

Verify the happy path in another terminal.

curl "http://127.0.0.1:5000/discount?price=100&rate=0.2"

The response is {"final":80.0}. Now verify the guard.

curl -i "http://127.0.0.1:5000/discount?price=100&rate=1.5"

The response is a 400 with the error message. Gate 4 is local parity. The service behaves like the test.

Stage 4: Deploy to the free server

MonkeyCode's free server option targets small experiments like this. Push the repo to a remote. Follow the project instructions to deploy app.py to the free server. The server returns a public URL. Send the same request to that URL.

curl "https://<your-app-url>/discount?price=100&rate=0.2"

Same JSON. The pipeline is complete. A failing test became a public endpoint in four gates.

The free server is a sandbox with a public door. Treat it that way. It is not a cloud. It has no autoscaling and no backup promises. It has one job. Run your experiment and answer a curl.

Limitations

This workflow has limits. The free server is for experiments, not production traffic. The free model access is for evaluation, not a guaranteed service level. Token allowances and model names change without notice. Check the README before building anything serious.

Some teams should skip this path. Regulated data needs governed tooling. Strict license review needs a human pipeline. Production uptime needs a signed contract. For those cases, a paid and supported route is the right call.

The model may still produce a wrong fix. Passing tests are not proof. They are evidence. The four gates make the evidence easy to read.

The takeaway is small and practical. A free model can fix a real bug. Verification makes the fix trustworthy. Four gates turn a chat reply into a deployable change. Try the same flow on your own failing test. The model will change. The gates will not.