Gayatri Intern Workbench
Multi-Tenant, Dependency-Aware Web IDE Orchestrating AI-Assisted Software Development
Architecture & Core Mechanics
Built under the DBERT Internship Program, the Intern Workbench solves the chaos of onboarding junior developers. Instead of messy local clones and git merge nightmares, each intern works in an isolated Git worktree. An in-browser AI engineering assistant provides Monaco diff previews, while a topological DAG dependency graph prevents interns from coding tasks whose prerequisites haven't merged.
Engineering Highlights & Capabilities
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Isolated Git Worktrees: Interns code in isolated file workspaces without branch collisions; features in-app merge conflict resolution.
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Monaco Side-by-Side Diff Editor: Visual review modal comparing original code with streaming AI suggestions before one-click patch application.
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In-Workspace Subprocess Test Runner: Collapsible terminal executing pytest and unittest directly in the worktree with 1-click 'Fix with AI' error piping.
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Sprint Kanban & Topological DAG Graph: Interactive SVG visualizer displaying task prerequisites to prevent work on unmerged dependencies.
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Admin Telemetry Dashboard: Real-time telemetry cards for active worktrees, sprint velocity, commit counts, and AI token consumption.
Verification & Test Telemetry
● Full Playwright E2E test suite · Subprocess isolation · Zero branch conflicts
Technology Blueprint
Repository Access
Access source code, architectural test harnesses, and documentation under the Gayatri-Education organization.
Inspect on GitHub →Explore Other Systems
Interested in deploying or contributing?
Our products are deployed across universities and enterprise pilots. Get in touch with our engineering team.