AI app for it and development · no coding needed
Cross-device AI coding agent control plane
Run and manage AI coding agents across devices with shared context and team controls.
Made for: Engineering teams running AI coding agents across several devices and providers
What it does for you
The problem
Agent sessions, contexts and approvals are scattered across tools and devices, so teams cannot see or control what agents do.
What it gives you
A source-linked assistant and administrator console with recorded approvals
What you give it
Provider subscriptionsrepository accessdevice sessionsteam rules
Build your own version of ADE, PearAI and more
One app with what these 7 AI tools do, yours to keep and change: ADE, PearAI, Google Antigravity, scritty, KarmaBox, Grass, Intrascope.app.
Everything these tools do, in one app
- AI coding assistance Helps users write, complete, and debug code with AI.Found in PearAI
- Multi-provider agent support Lets users work with multiple AI coding subscriptions or models in one interface.Found in ADE, KarmaBox
- Multi-model routing Automatically sends tasks to the most suitable AI model.Found in KarmaBox
- Agent orchestration Creates and manages autonomous agents that plan and execute development tasks.Found in Google Antigravity
- Parallel agent management Runs multiple long-lived agents in parallel and manages branches and models from one place.Found in Google Antigravity
- Git worktrees for agents Lets agents from different providers work on separate worktrees in the same repository without interfering.Found in ADE
- Cross-device session sync Keeps chat sessions, agent contexts, or terminal sessions synchronized across devices.Found in ADE, scritty
- Mobile monitoring and control Allows users to monitor progress, approve actions, and push changes from a phone.Found in Grass
- Background long-running tasks Queues and checkpoints work so tasks continue when devices go offline and notifies when complete.Found in KarmaBox
- Persistent agent VM Provides an always-ready virtual machine for agent sessions that stays available even if the user's device is offline.Found in Grass
- Pre-configured agent environments Offers ready sandboxes so agents can run without local environment setup.Found in Grass
- Terminal output capture Records conversations from command-line AI coding agents directly from the running process.Found in scritty
- Local vector indexing Indexes captured text into a local searchable vector store, with options for external databases.Found in scritty
- Agent memory querying Allows agents to query their own past turns and the past turns of other agents.Found in scritty
- Custom prompt injection Injects custom rules into every message before it reaches the active agent via a configuration file.Found in scritty
- Shared team workspace Centralizes team AI interactions in a unified chat with shared context.Found in Intrascope.app
- Admin usage controls Lets admins select providers, set usage limits, and enforce rules.Found in Intrascope.app
- Reusable prompt manifests Standardizes prompts and workflows for repeated tasks.Found in Intrascope.app
- Cost management Provides controls to choose providers and manage spending.Found in Intrascope.app, KarmaBox
- Unified credentials Connects models and apps once and reuses them across agents.Found in KarmaBox
- BYOK security Keeps API keys on the user device rather than storing them on servers.Found in Grass
- Execution approvals Gates commands that change state with contextual approvals and scoped auto-approvals for lower-risk actions.Found in Grass, Google Antigravity
- Built-in PR management Reviews and merges pull requests without leaving the application.Found in ADE
- Extendable architecture Allows users to add or customize AI tools.Found in PearAI
- Open source self-hosting Provides open-source code and the option to run on own infrastructure.Found in ADE, PearAI
- Async collaboration Shares agents, progress, and results with teammates without synchronous sessions.Found in Google Antigravity
- Device compute pooling Turns personal devices into a coordinated private compute pool with fallback rules.Found in KarmaBox
How it works, step by step
- Assist with writing, completion and debugging
- Connect multiple providers and models in one interface
- Route tasks to the most suitable model
- Create and manage autonomous planning agents
- Run parallel long-lived agents with branch and model control
- Give each provider agent its own git worktree
- Sync sessions and contexts across devices
- Monitor, approve and push from a phone
- Queue and checkpoint long-running background tasks
- Keep a persistent agent VM available
- Start agents in pre-configured sandboxes
- Capture terminal output from running agents
- Index captured text into a local vector store
- Let agents query their own and other agents' past turns
- Inject custom rules into every message
- Centralize team interactions in a shared workspace
- Set provider, usage and rule controls for admins
- Reuse prompt manifests for repeated tasks
- Track and limit provider spending
- Connect credentials once and reuse them
- Keep API keys on the user device
- Gate state-changing commands with approvals
- Review and merge pull requests in the app
- Add or customize AI tools
- Self-host the open-source code
- Share agents, progress and results asynchronously
- Pool personal devices as private compute with fallback rules
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Cross-device AI coding agent control plane with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Cross-device AI coding agent control plane with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links5 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data196 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Run and manage AI coding agents across devices with shared context and team controls. For engineering teams running AI coding agents across several devices and providers, convert provider subscriptions, repository access, device sessions and team rules into a source-linked assistant and administrator console with recorded approvals. The benefit is a testable hypothesis, measured through accepted agent tasks per engineering hour and unapproved state-changing actions; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect provider subscriptions, repository access, device sessions and team rules, then follow this sequence: 1. Connect providers and credentials. 2. Start or resume agents in sandboxes or worktrees. 3. Capture terminal output and index context. 4. Route and run tasks with approvals. 5. Review diffs and merge pull requests. Resolve uncertain cases with qualified reviewers, approve a source-linked assistant and administrator console, and measure accepted agent tasks per engineering hour and unapproved state-changing actions against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for routing rules, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One repository set and approved provider list; final code review and merge decisions remain with engineers. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve code ownership, source attribution, license compliance and usage permissions. Engineers approve substantive changes and deployment scope. One repository set and approved provider list; final code review and merge decisions remain with engineers. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One repository set and approved provider list; final code review and merge decisions remain with engineers. Implement one approved input format, a bounded representative case set and the first two task modules: assist with writing, completion and debugging; connect multiple providers and models in one interface. Support the third module with operator review: route tasks to the most suitable model. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Team-owned repositories, authorized provider accounts and permitted device sessions. Cloud or self-hosted storage, git hosting, CI and issue trackers. Start with file exchange and validate destination specifications before promising direct deployment. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Agent console, Shared team workspace, Admin controls. Use a device and agent list for sessions, a large central task view with terminal and diff output, and a right-hand panel for context, approvals and cost. Let users compare agent branches side by side. Display running, waiting for approval, blocked and merged states. Provide a mobile view for monitoring, approvals and pushes. Make the task-specific outcome a source-linked assistant and administrator console visible beside its evidence, review state and value baseline.
