Learn how to use OpenCode with an open-weight model to make a small, practical change in a sample application—from defining the task through reviewing and testing the result before you accept it.
Together, we’ll choose the most suitable model provider—GLM, Kimi, or DeepSeek—based on reliable access and the group’s setup. You’ll see how to work with an AI coding agent in a controlled, repeatable way: set clear acceptance criteria, keep the scope manageable, inspect every proposed change, and validate it with tests.
What we’ll cover
- Set up the challenge: Review the sample repository, define a focused task, and agree on clear acceptance criteria.
- Configure your tools: Connect OpenCode to the selected model provider and understand credentials, access requirements, and potential usage costs.
- Build with an agent: Use OpenCode to plan and implement a small, well-bounded feature.
- Review before accepting: Inspect the generated diff, run the test suite, and learn how to diagnose or recover from an unsuccessful attempt.
- Make the process reusable: Turn the workflow into a practical checklist you can apply to future coding tasks.
- Ask questions: Bring your setup, workflow, and agent-development questions to the final Q&A.
By the end of the session, you’ll have:
- A reviewed and tested change in a sample app
- Hands-on experience guiding an AI coding agent through a scoped task
- A reusable checklist for planning, reviewing, and validating agent-generated code
This workshop has been presented at AI Coding Summit NYC, check out the latest edition of this Tech Conference.
