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What is Muse Code?

Muse Code is Meta’s terminal-based AI coding agent built to take on complex software engineering tasks across large repositories. The agent is powered by Muse Spark 1.2 and is designed to plan code changes, write implementation code, validate results, and support end-to-end developer workflows. Muse Code can coordinate multiple persistent subagents for each task, helping solve difficult problems faster and with less manual intervention. Its architecture uses a simple agent loop enhanced by async background agents that remain active for the full session instead of being spawned only for individual steps. These background agents reduce repeated information gathering, carry out next actions, and communicate back to the main agent when useful. Muse Code’s runtime uses a local event log where model calls, tool runs, approvals, and edits are continuously appended. This event log serves as a single source of truth, making the runtime replay-exact and restart-safe if a crash or interruption occurs. The design allows Muse Code to handle long-running development work without losing progress or context. Muse Code includes bundled skills such as /plan for approval-gated task planning, /grill for stress-testing plans, and /goal for working toward successful completion of a defined objective. Example workflows include interpreting a video input, understanding the requested output, and producing a rich software experience such as a vacation home marketing and booking page. By combining terminal execution, autonomous planning, persistent background agents, replay-safe runtime design, bundled skills, and Muse Spark 1.2 model support, Muse Code helps developers complete ambitious coding tasks with greater reliability.

What is MuSES?

MuSES sets a new standard for precision in electro-optic and infrared visualizations by initiating a meticulous procedure that begins with detecting heat sources such as engines, exhaust systems, bearings, and electronic components, followed by an exhaustive in-band diffuse radiosity solution. Once your sensor is positioned at the desired distance, you can produce multi-bounce radiance values that have been spectrally summed, employing DeltaT-RSS contrast metrics for nuanced examination. If a sensor response curve is accessible, you can import it to reveal insights that may have previously gone unnoticed, enhancing your understanding of the thermal landscape. With MuSES, the exploration of reality is taken to an extraordinary level of detail. The software is equipped to fully consider the physics behind heat sources and the effects of environmental factors, allowing for effective management of thermal signature contrasts and evaluation of control kits crucial for low observable design in various geographical settings. You can perform thorough assessments of heat shields, cooling techniques, and camouflage surface treatments for in-band radiance while also factoring in the atmospheric attenuation present along the sensor’s line-of-sight. By focusing on engineering priorities with MuSES at the beginning of your project development cycle, you enable your team to make well-informed decisions that optimize overall design efficacy. This proactive approach not only enhances the efficiency of the development process but also leads to improved outcomes for your projects, ensuring that every detail is accounted for and meticulously analyzed. Ultimately, MuSES empowers users to navigate complex thermal environments with confidence and precision.

Media

Media

Integrations Supported

Meta AI
Muse Spark
Muse Spark 1.1
Muse Spark 1.2

Integrations Supported

Meta AI
Muse Spark
Muse Spark 1.1
Muse Spark 1.2

API Availability

Has API

API Availability

Has API

Pricing Information

$1.25 per 1M tokens (input)
Standard pricing: $1.25 per million input tokens and $4.25 per million output tokens

Discounted "contributor" tier costing $0.10 per million input tokens and $0.20 per million output tokens for users who agree to share feedback to improve the AI.
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

meta.ai

Company Facts

Organization Name

ThermoAnalytics

Date Founded

1996

Company Location

United States

Company Website

www.thermoanalytics.com/muses

Categories and Features

Categories and Features

Simulation

1D Simulation
3D Modeling
3D Simulation
Agent-Based Modeling
Continuous Modeling
Design Analysis
Direct Manipulation
Discrete Event Modeling
Dynamic Modeling
Graphical Modeling
Industry Specific Database
Monte Carlo Simulation
Motion Modeling
Presentation Tools
Stochastic Modeling
Turbulence Modeling

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