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Jordan Li
Jordan Li

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Build a PR Commenter on a Free Server with MonkeyCode's 10M Tokens

#ai #opensource #devops #workflow

A pull request landed at 2:14 AM. The diff touched 14 files. CI passed in six minutes. The logic was still wrong. A human caught it at 9 AM. The team lost a deployment window.

That story repeats weekly in engineering chats. The fix is not better CI. The fix is a faster first-pass review. This article builds one. It runs on a free server. It uses free model tokens. It posts structured comments. A human still makes the final call.

This week, DEV threads asked what developers should do while AI codes. Another thread asked who tests the reviewer. Both questions share one answer. Run the reviewer where you can measure it.

Disclosure: This article was prepared as part of MonkeyCode's product outreach.

MonkeyCode is an open-source project. It offers free model access with 10 million free tokens. It also includes a free server option. That combination removes the cost barrier for experiments. This article uses both. The result is a reproducible PR commenter.

What this setup does

The artifact is a review loop. It pulls a PR. It extracts the diff. It sends the diff to the free model. It parses the response. It posts structured comments. The loop enforces a token budget locally.

This is a triage layer, not a replacement reviewer. It catches missing error handling. It flags dead code. It spots risky patterns. It never merges anything. It never approves a PR.

Step 1: Provision the free server

Start with the free server option. SSH into the machine. Confirm the runtime.

ssh user@your-free-server
node --version
git --version
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Then clone the target repository. Keep the clone shallow. A full history wastes disk and time.

git clone --depth 1 https://github.com/your-org/your-repo.git
cd your-repo
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Create a working directory for the loop.

mkdir -p ~/review-bot && cd ~/review-bot
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Step 2: Define the review contract

A model needs a fixed output shape. Free-form comments are hard to parse. Define a contract first.

{
  "file": "src/auth.ts",
  "line": 42,
  "severity": "warning",
  "rule": "missing-error-handling",
  "evidence": "await fetch() without try/catch",
  "suggestion": "wrap in try/catch and log the status"
}
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The contract keeps comments actionable. Short evidence fields cost fewer tokens. This matters on a fixed budget.

Step 3: Build the prompt template

The prompt is part of the artifact. Version it like code. Keep it stable across runs.

You are a first-pass code reviewer.
Analyze the diff below.
Return JSON matching this contract:
{file, line, severity, rule, evidence, suggestion}.
Severity is one of: info, warning, critical.
Only report issues with direct evidence.
If the diff is clean, return an empty list.
Diff:
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The last line is important. It prevents hallucinated nitpicks. An empty diff should produce an empty review.

Step 4: Run the review loop

The loop below is a reference implementation. It is pseudocode, not a shipped product. Adapt it to your runtime.

PR_BUDGET = 4000

def token_cost(text):
    return len(text) // 4  # rough estimate

for pr in pending_prs():
    diff = fetch_diff(pr)
    if token_cost(diff) > PR_BUDGET:
        diff = truncate_diff(diff, PR_BUDGET)
    prompt = build_prompt(diff, TEMPLATE)
    response = call_free_model(prompt)
    comments = parse_json(response)
    for comment in comments:
        if comment["severity"] != "info":
            post_comment(pr, comment)
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Three details matter. Truncate the diff before the prompt. Validate the JSON response. Post only warning and critical comments.

Step 5: Add guardrails

Free tiers have limits. The loop must respect them. Use a decision table.

Condition Action
Diff exceeds token budget Truncate or skip
Rate limit reached Backoff and retry
Response is invalid JSON Retry once, then skip
Empty response Skip silently
PR touches auth or payments Add human-review label

The last row is critical. Free models are useful. They are not auditors. Sensitive paths always get a human label.

Verification checklist

Run these checks before trusting the loop.

# Confirm the model endpoint is reachable
curl -s -o /dev/null -w "%{http_code}" https://api.example.com/health

# Dry-run against a fixture PR
python review_loop.py --pr 123 --dry-run

# Inspect the token log
cat token_budget.log
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The dry-run flag should print comments without posting them. The token log should show the budget per PR. Both checks take five minutes.

What to trust, what to revert

Trust comments with line numbers and evidence. Revert suggestions that rewrite working tests. Test anything that touches authentication. The earlier agent PR review article covers this checklist in depth. This setup feeds that checklist. It does not replace it.

Limitations

Free server resources are shared. Long builds may be slow. The 10 million token figure is a starting budget. It is not a guarantee of uptime. Model behavior changes without notice. Pin your prompts. Version your contract.

Token counting here is approximate. Real tokenizers differ. Leave headroom in the budget. A 4,000-token cap is safer than a 4,096-token cap.

Do not use this approach for regulated data. Do not use it when a missed review causes direct financial risk. Do not use it as a substitute for a senior reviewer.

Who should try this

Teams with small repos and tight budgets. Solo developers drowning in dependency bumps. Anyone who wants a measurable first-pass review. The free server and 10 million tokens make the experiment cheap. Start with one small PR. Measure the false-positive rate. Then decide.

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