Opinion: Cheap Tokens Are for Disagreement, Not Generation
Free model capacity will not make your code better. It can make your review sharper. That is the whole argument. Cheap tokens have little value where you generate code. They have real value where you disagree with a first answer. Most teams spend a free allowance the wrong way. They point it at generation. Then they compare one cheap output against one expensive output. The cheap one loses, and…
Cheap model capacity does not improve your code. It can refine your review process. That is the crux of the argument. Cheap tokens offer little when generating code. Their true worth lies in challenging a first answer. Most teams misallocate their free allowance. They focus it on generation. Then they compare a cheap result with an expensive one.
The cheap one falls short, leaving the allowance unused. Turn the target. Allocate volume to review. Invest quality in writing. Why generation penalizes volume A single mistake in production equals a mistake from any author. Ten cheap drafts do not become one excellent draft. Accountability and readability remain essential. Generation favors one strong attempt.
Review values many flawed ones. This disparity presents an opportunity. If you trust a model for code generation, adding another cheap model only adds noise. The place where noise is undesirable is the artifact itself. Maintain a consistent writer. Amplify the critics instead. Review benefits from volume. A first reviewer may overlook something.
A second reviewer adds a different perspective. Correctness, security, and reversibility each need their own lens. You do not require the best reviewer. You need enough disagreement to scrutinize a line twice. Volume accomplishes this. Quality alone does not. The hidden cost teams often overlook Volunteer reviewers make errors. They cite non-existent locations, wasting human effort.
Do not pay a model to guess line numbers. Calculate them. The artifact should be a vote gate with a deterministic anchor. Six steps, no framework, one script: Take a bounded diff with no context lines. Send the same diff through three fixed lenses. Require strict JSON output with file and line information. Discard any finding outside the diff.
Group survivors by file and line. Escalate if two or more reviewers agree; otherwise, consider it a candidate, not a task. This simple implementation runs against any OpenAI-compatible endpoint, so customize it as needed. It is a non-executed example: adapt the endpoint and test it on a small diff first.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
