DEVELOPMENT STUDIO
BUILD AND TEST SOFTWARE WITH LOCAL AI
Create a local workspace for planning, writing, and debugging code. Use AI to explore solutions, draft small changes, and explain unfamiliar code, then run and review the results in your own development environment.
WHAT A DEVELOPMENT STUDIO DOES
SUPPORT THE FULL CODING WORKFLOW
Use local AI to break down a task, explore an existing codebase, draft a focused change, and investigate errors. Review the suggested code, run it, and check that it solves the problem in your actual project.
WHY BUILD IT LOCALLY?
WORK WITH YOUR CODE ON YOUR OWN SYSTEM
A local setup lets you choose which tools can access your code and where project files are processed. You can tailor the workflow to your editor, repositories, and development environment while keeping control of changes before they enter the project.
CORE COMPONENTS
A CODE EDITOR, LOCAL MODEL, AND TEST ENVIRONMENT
Start with your usual code editor, a local language model, and a way to run the project. Version control lets you inspect and reverse changes. Add an editor integration or other tools when they make a task easier, and keep testing in the project’s actual environment.
DEVELOPMENT WORKFLOWS
PLAN, CHANGE, RUN, AND REVIEW
Describe the task and identify the files it affects. Use local AI to propose a focused change, then inspect the code before running it. Test the behavior, review the difference in version control, and keep the change only if it works as intended.
CHOOSE YOUR MODELS
TEST THEM ON YOUR ACTUAL CODE TASKS
Choose a local language model that can follow instructions and work with the programming languages you use. Try it on a small, representative task: explain a function, locate a bug, or draft a limited change. Compare the result with your own review and tests before using it for larger work.
CHOOSE YOUR INTERFACE
KEEP AI CLOSE TO YOUR EDITOR AND CODE
A chat interface works well for questions and planning. An editor integration can provide selected files or code as context and make proposed changes easier to inspect. Start with the interface that fits your current workflow, and check exactly what project content it sends to the model.
HARDWARE CONSIDERATIONS
BALANCE MODEL SIZE WITH RESPONSIVENESS
The model’s memory needs and speed affect how useful it feels while coding. Test a smaller model on your current hardware before deciding whether a larger one is necessary. Leave enough resources for your editor, development tools, and the project you are running.
ORGANIZE YOUR STUDIO
MAKE PROJECT CONTEXT EASY TO FIND
Keep setup instructions, coding conventions, and test commands with each project. Use version control to track changes, and separate experimental work from stable code. Clear documentation helps you give the model relevant context and makes its suggestions easier to check.
YOUR FIRST WORKFLOW
FIX ONE SMALL BUG
Choose a reproducible bug in a project you can run locally. Describe what happens, what you expect, and the relevant code. Ask your local model to suggest a focused fix, review the proposed change, and test the original problem again. Save the working change in version control.
ADD MORE CONTEXT
SHOW THE MODEL WHAT THE TASK DEPENDS ON
Provide the relevant files, error message, expected behavior, and any project conventions. Keep the scope focused so you can check the answer against the actual code. If the result misses a dependency or assumption, correct the context and try again.
TEST AND REFINE
RUN THE CODE AND CHECK THE BEHAVIOR
Run the project and its relevant tests after each change. Check that the original issue is fixed and that nearby behavior still works. Review the final code yourself, refine anything unclear, and record a new test when it would help catch the same problem again.
CONNECT YOUR TOOLS
LINK CODE ASSISTANCE TO THE REST OF YOUR WORKFLOW
Once the basic workflow works, connect your local model to the tools you use for development, such as your editor, project documentation, and test runner. Give each connection only the access it needs, then check proposed actions and file changes before accepting them.
SAVE REPRODUCIBLE RESULTS
KEEP THE CHANGE AND THE REASON BEHIND IT
Save the final code in version control along with a short explanation of the problem and fix. Record the model, useful instructions, and test results when they would help you repeat the workflow. Your project history should make the change understandable even without the AI conversation.
CHECK LICENSES AND DEPENDENCIES
REVIEW WHAT ENTERS YOUR PROJECT
Before adding suggested code, packages, or assets, check their sources, licenses, and compatibility with your project. Review new dependencies for maintenance and security concerns, and understand what the code does before shipping it. Keep a record of third-party components you use.
BUILD IN LAYERS
START WITH SMALL CHANGES AND GROW YOUR WORKFLOW
Begin with tasks you can review and test quickly, such as explaining code or fixing a small bug. Document the steps that help, then add editor connections and more complex tasks as your process becomes reliable. Keep each change understandable and reversible.