[100% Off] Meta Muse Wonder: Build Apps And Agents With Muse Code
Build a real Python tool with Muse Code, call Muse Wonder from the API, and measure effort, caching and cost
What you’ll learn
- Choose between Muse Wonder
- Muse Wonder Full and Muse Spark
- and know where each one is available and what it costs,Install and configure Muse Code safely: model
- Contributor mode
- imported rules
- permissions and the sandbox,Plan
- build and test a real Python tool with Muse Code
- its sub-agents and an AGENTS rules file,Make a small Muse Code plan last,Call Muse Wonder from Python with the OpenAI SDK and read what each call really cost,Tune reasoning effort and prompt caching
- and compare models with an automatic quality test,Use structured output and screenshots with Muse Wonder
- and know what each one guarantees
Requirements
- Basic Python: you can read a short script and run it from a terminal,A Muse Code plan (the five-dollar Everyday plan is enough) and a Meta Model API key with a payment method; the API examples cost a few cents,Windows
- macOS or Linux
Description
This course contains the use of artificial intelligence.
On October 12, 2026 Meta released Muse Wonder, a model built for coding, long-running agents and computer use. You can use it in two places: inside Muse Code, Meta’s coding agent for the terminal, and through the Meta Model API, which works with the OpenAI SDK.
In this course you build ReleaseNotes Bot, a small Python tool that reads the git commits of a project since its last version and turns them into release notes that normal users can read. You plan it and build it with Muse Code, and then you call Muse Wonder from your own code and measure what every choice costs.
Everything was recorded with Muse Code 1.4.7 and the October 2026 release, on the five-dollar Everyday plan, with real sessions and real API calls. Some of what you will see:
What Muse Wonder, Muse Wonder Full and Muse Spark are, where each one is available, and what Meta’s own benchmarks really say.
The settings to check before your first task: the default model is not Wonder, Contributor mode is on, and Muse Code imports your Claude Code rules.
How Muse Code plans before it codes, asks questions, splits work between sub-agents, and runs every command through a policy and a sandbox.
How fast the cheapest plan runs out, and how to make it last.
Project rules in an AGENTS file that Muse Code followed without being reminded.
Reasoning effort measured: minimal effort wrote the same notes as high, four times cheaper.
Prompt caching measured, and why output, not input, is most of the bill.
Muse Wonder against Muse Spark with a small automatic test: complete notes three times out of three, against one out of three.
Structured output that always returned valid JSON, and still got the content wrong.
A user’s screenshot turned into a bug report and a release-note line for under one cent.
How this course is made: I designed the project, ran every session and every API call myself, and reviewed every lesson. The narration uses an AI voice and the animations are generated from my scripts. The complete project is included as a download.
