CodePilot

Your local AI coder that builds and fixes apps end to end.

CodePilot is a desktop-first AI coding agent for solo builders, freelancers, and small teams who want an assistant that can generate website and app code, run terminal commands, fix errors, and preview builds locally. Users can start from a folder or zip upload, connect multiple AI providers, and manage separate project memories so each workspace stays organized and context-aware.

Business Goals

  • Reach 1,000 active workspaces within 90 days of launch.
  • Convert at least 8 percent of free users to paid plans within 60 days of activation.
  • Keep weekly active workspace retention above 35 percent after 8 weeks.
  • Reduce average time-to-first-working-preview to under 10 minutes for new users.
  • Achieve 99.5 percent platform uptime and fewer than 2 percent failed agent runs by month 3.

User Goals

  • Generate a working project scaffold from a zip file or empty folder in a few clicks.
  • Let the AI inspect errors, edit code, and rerun builds without manual copy-paste.
  • Run terminal commands safely from inside the app with visible output.
  • Preview the website locally on localhost before exporting or deploying.
  • Keep separate memory per project and reset or delete it when needed.

Non-Goals

  • Not a full cloud IDE with multi-user live collaboration in v1.
  • Not a general-purpose file manager for arbitrary non-code archives.
  • Not a replacement for CI/CD pipelines or production deployment hosting.
  • Not a model training or fine-tuning platform.

Solo Founder Ayaan, 29 - Ayaan ships MVPs fast and often works alone. He wants to move from idea to preview without setting up a full dev environment or debugging every error himself.

Solo Founder Ayaan, 29

  • As a solo founder, I want to upload a zip of my starter project so that the agent can inspect the codebase and continue from there.
  • As a solo founder, I want the agent to fix build errors automatically so that I can keep momentum without getting stuck.
  • As a solo founder, I want project memory isolated per app so that one client's context does not leak into another project.

Agency Developer Sana, 34 - Sana builds small client websites and needs speed, repeatability, and safe controls. She wants a reliable assistant that can handle routine code changes while she reviews the result.

Agency Developer Sana, 34

  • As an agency developer, I want to choose between Grok, Gemini, and OpenRouter so that I can optimize cost and output quality per project.
  • As an agency developer, I want terminal commands to be visible and editable so that I can approve risky actions before execution.
  • As an agency developer, I want a localhost preview link for each run so that I can validate UI changes quickly.

Beginner Maker Imran, 22 - Imran can describe what he wants but does not yet know how to structure a project. He needs guided steps and clear feedback when the AI is unsure or hits an error.

Beginner Maker Imran, 22

  • As a beginner maker, I want step-by-step guidance from folder import to preview so that I can finish my first project without confusion.
  • As a beginner maker, I want errors explained in plain language so that I understand what changed and why.
  • As a beginner maker, I want a new chat button per project so that I can start fresh without deleting the whole workspace.

Project Import and Setup · High priority

  • Users can create a project from an existing folder or uploaded zip file, and the system indexes the contents for the agent before generation starts.
  • Accept drag-and-drop folder import in desktop or browser-supported environments, plus zip upload fallback.
  • Validate archive size, file types, and nested folder structure before import.
  • Detect common web app frameworks such as Next.js, Vite, React, and Express when present.
  • Show import status, file counts, and index completion before allowing the first agent run.
  • Preserve original files in a project snapshot so rollback is possible.

AI Agent Coding and Repair Loop · High priority

  • The agent can generate code, read errors, propose fixes, edit files, rerun commands, and repeat until the app builds or a user stops the loop.
  • Support task-based prompts such as build app, fix error, add page, and refactor component.
  • Run a controlled edit-build-debug loop with explicit user approval for destructive actions.
  • Capture terminal output, stack traces, and exit codes for model context.
  • Stop after configurable retry limits, then surface a summary of attempted fixes.
  • Store a changelog of file edits with before/after diffs.

Terminal and Local Preview · High priority

  • Users get an in-app terminal panel and a localhost preview so they can inspect runtime behavior without leaving the product.
  • Provide a shell session per project with copyable output and clear command history.
  • Start and restart preview servers automatically when build commands succeed.
  • Detect the active port and surface a clickable localhost preview link.
  • Warn when a command may affect system files or require elevated permissions.
  • Allow users to pause the agent while manually running commands.

Project Memory and Chat Management · Medium priority

  • Each project keeps its own memory, conversation history, and instructions, with controls for new chat and memory deletion.
  • Separate memory scopes by project, not by user-wide global context.
  • Provide a New Chat action that starts a fresh conversation within the same project.
  • Include a Delete Memory action with confirmation and irreversible warning.
  • Allow short project notes such as stack, goals, and constraints to be pinned.
  • Sync memory state with the selected AI provider session context.

AI Provider Settings · Medium priority

  • Users can connect and switch between supported AI providers from settings to balance quality, cost, and availability.
  • Support Google AI Studio via Gemini API as the default onboarding path.
  • Allow provider selection for Grok, Gemini, and OpenRouter with per-project defaults.
  • Store API keys securely and never expose them in logs or UI.
  • Show provider-specific model options and token limits.
  • Fallback gracefully when a provider rate-limits or fails authentication.

First-Time Setup and Project Import

  • Sign in and choose a workspace name.
  • Select AI provider and add API key through Settings.
  • Import a folder or upload a zip file.
  • Review detected framework, package manager, and run target.
  • Click Start Build to let the agent create the first preview.
  • Reach a working localhost preview in under 10 minutes for a typical starter project.

1. Create or Import Project

  • The user starts with either a blank project or an existing codebase. The app validates the import and prepares it for the agent.
  • Reject unsupported archive types and show a clear error message.
  • If framework detection fails, ask the user to specify the stack manually.
  • Create a project snapshot before any edits happen.

2. Choose AI Brain and Goal

  • The user picks the provider and tells the agent what to do in one short prompt.
  • Support preset prompts like build website, fix build, and add login page.
  • Show estimated complexity and possible command execution needs before starting.
  • Require confirmation for high-impact operations such as file deletion.

3. Agent Works in the Background

  • The agent edits code, runs terminal commands, and reads errors until the task succeeds or hits a limit.
  • Stream intermediate progress and terminal logs in real time.
  • Automatically include recent diffs and errors in the next model call.
  • Stop after configurable retries and summarize remaining issues.

4. Preview and Review Locally

  • When the build passes, the app launches a local preview and opens the browser frame or local URL.
  • Surface the active localhost port and a one-click open action.
  • Reload the preview after each successful fix loop.
  • If the app crashes, capture the crash and return to the fix loop.

5. Organize Memory and Continue

  • The user can save context, start a fresh chat, or delete memory for the current project.
  • Keep project memory visible as short notes and recent decisions.
  • New chat resets conversation while preserving files and snapshots.
  • Delete memory requires double confirmation and removes only that project scope.

Power User Controls

  • Model routing by task type, such as fast model for code edits and stronger model for debugging.
  • Command allowlist and denylist for shell actions.
  • File diff review mode with accept, reject, and restore options.
  • Session snapshots so users can revert to a known good state.
  • Multi-project dashboard with separate memory, logs, and previews per workspace.
  • Provider failover when Gemini, Grok, or OpenRouter becomes unavailable.

Design and Interaction Principles

  • Use a three-panel layout with files, chat/agent, and terminal preview for fast scanning.
  • Keep status visible with clear build, run, and fix states.
  • Use high-contrast text, keyboard shortcuts, and accessible focus handling throughout.
  • Optimize for speed with streamed logs and lazy-loaded file trees.
  • Show risk badges for destructive commands and memory deletion actions.

Ayaan has a React starter project in a zip file and wants to turn it into a polished landing page. He uploads the archive into CodePilot, selects Gemini from Google AI Studio, and writes a short prompt asking for a modern marketing site with a pricing section and contact form.

CodePilot inspects the codebase, generates the missing pages, runs the build, catches a TypeScript error, and fixes it automatically. The terminal shows the exact command output while the localhost preview updates in real time, so Ayaan can review the result without switching tools.

By the end of the session, Ayaan has a working app preview, a clean project-specific memory trail, and a repeatable workflow for future projects. For CodePilot, that means faster activation, more retained workspaces, and a clearer path from free usage to paid power-user features.

User-Centric Metrics

  • At least 70 percent of new users reach a working localhost preview in under 10 minutes.
  • At least 60 percent of first-time projects complete one successful build-fix-preview loop.
  • At least 80 percent of users rate the error explanations as clear or very clear.
  • Average manual intervention per successful build stays below 2 actions per session.
  • Project memory reset or delete is used successfully in under 30 seconds for 95 percent of cases.

Business Metrics

  • Free-to-paid conversion reaches 8 percent within 60 days.
  • Weekly retention of active projects reaches 35 percent by week 8.
  • Average sessions per retained workspace reaches 4 or more per month.
  • At least 25 percent of new signups connect an AI provider within the first session.
  • Refund or churn complaints related to failed builds stay below 3 percent of paid users.

Technical Metrics

  • Platform uptime remains at or above 99.5 percent monthly.
  • Median command execution latency stays below 2 seconds for local shell start.
  • Median time from build failure to first proposed fix stays below 45 seconds.
  • No exposed API keys or secrets in logs across production telemetry.

Tracking Plan

  • track_project_created
  • track_zip_uploaded_or_folder_imported
  • track_provider_connected
  • track_agent_run_started
  • track_build_failed
  • track_fix_applied
  • track_localhost_preview_opened

Technical Needs

  • Frontend built with Next.js 14 or React for the app shell and dashboard.
  • Backend orchestration with Node.js and a job queue such as BullMQ or Cloud Tasks.
  • File system access layer with isolated per-project sandboxes.
  • Terminal execution using containerized workers or secure subprocess runners.
  • Real-time streaming over WebSockets or Server-Sent Events for logs and progress.
  • Auth and billing via Clerk or Auth.js plus Stripe for subscriptions.
  • Optional vector memory store such as PostgreSQL with pgvector for project context retrieval.

Integration Points

  • Google AI Studio Gemini API for the default model path.
  • OpenRouter API for model aggregation and fallback.
  • xAI Grok API for alternate model routing.
  • Stripe for subscriptions and usage-based billing.
  • Auth provider such as Clerk, Auth0, or Google sign-in.

Data Storage & Privacy

  • Store API keys encrypted at rest and never in plain text.
  • Keep project memory scoped to the project and deletable by the user.
  • Retain file snapshots only as long as needed for rollback and debugging.
  • Provide export and deletion flows aligned with GDPR and CCPA expectations.
  • Log only necessary metadata and redact secrets from terminal output and prompts.

Scalability & Performance

  • Use per-project isolation so parallel agent runs do not block each other.
  • Stream logs incrementally to reduce perceived wait time.
  • Cache framework detection and package metadata to avoid repeated scans.
  • Limit retry loops and command execution timeouts to protect worker capacity.

Potential Challenges

  • Models may generate broken code or hallucinate fixes; mitigate with build verification, diff review, and bounded retries.
  • Terminal commands can be risky; mitigate with command allowlists, user confirmations, and sandboxed execution.
  • Provider costs may spike; mitigate with per-project budgets, token caps, and model routing rules.
  • Imported codebases may contain secrets or malware; mitigate with file scanning, secret redaction, and archive validation.
  • Memory can pollute future responses; mitigate with explicit project scoping, reset controls, and retrieval filters.

Team & resourcing - Small team - 2 full-stack engineers, 1 product designer, part-time PM, part-time DevOps/security support

Phase 1: MVP Import and Chat · Weeks 1-4

  • Project creation from folder or zip upload
  • Single project chat with Gemini via Google AI Studio
  • Basic project memory scoped per workspace
  • File tree viewer and prompt composer
  • Initial analytics and auth setup

Phase 2: Agent Edit Loop and Terminal · Weeks 5-8

  • Secure terminal execution panel
  • Read-edit-run-debug agent loop
  • Build error capture and retry logic
  • Diff viewer with accept/reject changes
  • localhost preview launch and port detection

Phase 3: Multi-Provider Settings and Memory Controls · Weeks 9-11

  • Provider settings for Grok, Gemini, and OpenRouter
  • Per-project new chat and delete memory actions
  • Command safety controls and retry limits
  • Project snapshots and rollback
  • Billing integration with Stripe

Phase 4: Hardening and Launch · Weeks 12-14

  • Sandbox hardening and secret redaction
  • Performance tuning and queue scaling
  • Onboarding polish and empty-state guidance
  • Monitoring dashboards and failure alerts
  • Beta launch to first 100 users

Paste this into Cursor, Bolt, Lovable, or v0 to start building.

Build a desktop-first AI coder agent called CodePilot that helps users import an existing website/app project, chat with an AI coding assistant, run terminal commands, automatically fix build errors, and preview the app on localhost. Use a modern web stack such as Next.js 14, TypeScript, Tailwind CSS, and PostgreSQL with Prisma. Support project-scoped memory, New Chat, and Delete Memory actions per project. Support AI providers from Google AI Studio Gemini as default, plus optional Grok and OpenRouter in Settings with secure API key storage.

Core screens and flows:
1. Auth and onboarding
2. Workspace dashboard with project cards and recent activity
3. Project import flow for folder or zip upload
4. Project detail screen with three panes: file tree, AI chat, terminal/output, plus localhost preview
5. Settings page for model provider selection, API keys, safety controls, and defaults
6. Memory management UI with new chat and delete memory confirmation

Functional requirements:
Project import must validate zip/folder uploads, detect common web frameworks, and create a project snapshot before edits.
The agent must be able to read files, generate code changes, run terminal commands, inspect build errors, propose fixes, and rerun until success or retry limit.
Show streamed terminal logs, diffs, build status, and a localhost preview link after successful start.
Keep memory scoped to each project only. New Chat clears conversation context for that project without deleting files. Delete Memory removes project memory after explicit confirmation.
Settings must allow switching between Gemini, Grok, and OpenRouter, with secure secret storage and graceful fallback if a provider fails.
Add command safety controls, retry limits, and per-project snapshots for rollback.

Data model suggestion:
User, Workspace, Project, ProjectSnapshot, ChatThread, Message, MemoryItem, FileChange, TerminalSession, BuildRun, ProviderSetting, and UsageEvent.

Implementation details:
Use server actions or API routes for project operations, WebSockets or SSE for live logs, a job queue for background agent runs, and containerized or sandboxed workers for terminal execution. Add analytics events for project created, import completed, provider connected, agent run started, build failed, fix applied, and localhost preview opened.

Build the MVP first with clean UX, sensible empty states, and production-ready validation. Make the interface fast, accessible, and optimized for solo builders who want a reliable local AI coding assistant.

Business Idea

Mujhe Khud ka AI coder agent banana hai jo website app ka code Khud likhe aur Build kare error aane par khud usko fix kare Tarminal bhi hona Chahiye Aor Localhost pe preview dikhaye Aor Zip file Folder accept kare Build kare Project wise memory Delete Batan New chat Icon ... Seting ke under Free Modals Ai 🧠 Brains Integration "Grok AI" "Gemini AI" "Open router" Google Ai Studio se Banana hai Step by step process Batao

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