Muse Wonder wants to finish your project. Give it checkpoints.

A new option for longer jobs

On October 12, Meta introduced Muse Wonder and Muse Wonder Full. Muse Wonder is available through Meta Model API and Muse Code; Full is offered through Muse Code’s Power Usage and Maximum Usage subscriptions. The developer announcement lists Wonder API pricing at $4 per million input tokens and $20 per million output tokens.

Meta describes Active Memory as keeping relevant context available across conversations and Active Focus as helping an agent persist through difficulties, try alternatives, and seek clarification. These are the company’s capability descriptions, not results from a Signal Scroll test. Availability in Muse’s personal agent is described as an upcoming rollout, starting with subscribers.

Source: Meta: Introducing Muse Wonder and Muse Wonder Full: Frontier Models for Agents ↗ · Meta: Put your ideas to work with Muse Wonder and Muse Wonder Full ↗

Our take: turn ambition into something inspectable

For a student building a portfolio site or a creator preparing a small launch, the exciting question is whether an assistant can carry a project beyond its first impressive draft. Our interpretation is that longer-running work needs a better brief. An agent cannot reliably satisfy a goal you have left fuzzy, and a polished update can hide a misunderstanding.

Imagine asking for a site that helps visitors compare three camera lenses. Before choosing a model, define what a useful result includes: accurate specifications supplied by you, readable comparisons on a phone, and a clear explanation of which differences matter. Decide what the agent can change and what requires your review. That gives both of you a shared finish line.

Keep a short decision record outside the conversation. It could say that prices are excluded because they change frequently, that only manufacturer specifications are allowed, and that no purchase links should be added yet. A durable record makes it possible to judge the next revision against the original choices. It also gives you a clean way to correct a decision without rebuilding the whole assignment.

Three checkpoints for a small build

Here is a suggested trial we have not run. Use an existing project you can safely duplicate and give the assistant one modest feature. Ask first for a plan that identifies the files it expects to change and the evidence that would demonstrate success. Read that plan before approving edits. A plan is valuable when it reveals assumptions you can correct early.

At the second checkpoint, inspect the implementation against the brief. For the lens comparison example, open the page at a narrow width and compare each displayed specification with your supplied material. Look for omitted units, confusing labels, and statements that imply testing you never performed. Request an explanation for unexpected changes. The model’s description of its own output is a starting point for review.

At the final checkpoint, test the actual visitor journey. Can someone find a relevant comparison, understand the important difference, and return to another item? Write down any manual repairs you needed. Save screenshots or notes so a later attempt can be compared with the same criteria. Keep failed attempts in the record too; they often show where the instructions need more precision.

Compare the model with your existing workflow using the same feature and starting files. Track elapsed time, correction effort, usage cost if applicable, and the number of requirements met. Do not choose a winner solely because one attempt produced more code. A smaller change that you understand and can maintain may be the better result.

A useful boundary

Persistent work should still have stopping points. Our advice is to require a check-in before an agent publishes, purchases, sends a message, or changes access to an account. Describe what should happen when required information is missing: pause and identify the gap, rather than quietly inventing a value.

This approach gives you a practical way to evaluate the new release without assuming a benchmark predicts your project. You can discover where the tool helps, where it needs supervision, and whether it leaves you with work you feel comfortable owning.