How to Fine-Tune Refact.ai on Your Own Codebase
Improve Refact.ai completions by training on your repos — what data to use, how long it takes, and how to measure the gain.
Improve Refact.ai completions by training on your repos — what data to use, how long it takes, and how to measure the gain.
Install Refact.ai self-hosted, connect your own model, and keep your code entirely on your own infrastructure.
Reconcile AI-generated mockups with your brand system: prompt patterns, review checklists, and where to hand off to code.
Use Sculptor to turn a written description into a working UI mockup with real components, then hand it off to code.
Go past single-prompt apps: chain model calls, branch on conditions, and handle failures in Google Opal workflows.
Create a working AI mini-app in Google Opal without code — from a blank canvas to a shareable tool in about 20 minutes.
Extend Rovo Dev beyond one-off coding tasks: batch ticket triage, automated PR descriptions, and guardrails for agent work.
Install Atlassian Rovo Dev, connect it to your Jira and Bitbucket work, and ship your first ticket with an AI agent in the loop.
Use the Daytona SDK to let coding agents create, use, and destroy their own isolated sandboxes — with cleanup and cost guards.
Create reproducible dev environments with Daytona in seconds, from install to your first sandbox and workspace.
Wire E2B into an LLM loop so the model writes, runs, and fixes code against a CSV — with error feedback and chart output.
Set up E2B sandboxes to execute LLM-generated code in isolation, from API key to your first Python run in under 10 minutes.