AI app for it and development · no coding needed
Source-linked AI coding and review console
Reduce tool sprawl while keeping code changes under team review.
Made for: Software teams that write, review and manage code with AI assistance
What it does for you
The problem
Coding, review, pull request and agent work are split across several rented AI tools, so context and approvals are scattered.
What it gives you
Reviewer-approved code changes and release records linked to source
What you give it
Repository codeissue trackerspull requestsworkspace documentsagent logs
Build your own version of Terramind Nucleus, Windsurf 2.0 and more
One app with what these 4 AI tools do, yours to keep and change: Terramind Nucleus, Windsurf 2.0, Zerve AI, Athena.
Everything these tools do, in one app
- AI-assisted code generation Generates code suggestions and completions to speed up writing code.Found in Terramind Nucleus
- In-editor code reviews Provides AI-powered code review feedback directly inside the editor.Found in Terramind Nucleus
- Pull request management Handles or integrates with pull requests for code collaboration and review.Found in Terramind Nucleus, Windsurf 2.0, Athena
- Multiple access surfaces Lets users work through an editor extension, command-line interface, cloud agents, and version-control integration.Found in Terramind Nucleus
- External model subscriptions Connects to external AI model subscriptions for additional capabilities.Found in Terramind Nucleus
- In-platform local models Offers built-in models for local AI assistance without external subscriptions.Found in Terramind Nucleus
- Workspace app integration Links with notes, calendar, projects, search, and drive so project context informs coding tasks.Found in Terramind Nucleus
- Agent management dashboard Provides a Kanban-style view to monitor and manage AI agents across local and cloud environments.Found in Windsurf 2.0
- Project context grouping Groups agent sessions, pull requests, files, and related context by project for easy resumption.Found in Windsurf 2.0
- Autonomous cloud agent Runs tasks on its own cloud VM, continuing work even when the local machine is off and setting up environments from repo files.Found in Windsurf 2.0
- Per-agent controls Allows pausing, resuming, and reviewing agent progress and results from one interface.Found in Windsurf 2.0
- Stable data exploration Enables simultaneous data exploration and writing of production-ready code in a stable environment.Found in Zerve AI
- Collaborative coding environment Supports team collaboration with engineering controls similar to VSCode for safe and efficient teamwork.Found in Zerve AI
- Parallel code execution Runs multiple code blocks in parallel to reduce wait times and improve productivity.Found in Zerve AI
- Multi-language support Allows using Python, R, SQL, and Markdown interchangeably on the same canvas.Found in Zerve AI
- Flexible deployment options Deploys directly from the platform or exports code in formats suitable for other teams.Found in Zerve AI
- Teams integration Embeds development workflow management directly into Microsoft Teams.Found in Athena
- Automated task and release management Automates handling of pull requests, tasks, and release management.Found in Athena
- AI virtual teammate Acts as an AI assistant that supports developers like a virtual teammate.Found in Athena
- Open-source agent template Provides the Dex template for creating custom AI agent workflows.Found in Athena
- Centralized communication Keeps development conversations and project tracking in one platform.Found in Athena
How it works, step by step
- Generate code suggestions and completions
- Provide in-editor AI code review feedback
- Manage pull requests for collaboration and review
- Support editor extension, command-line interface, cloud agents and version-control integration
- Connect external AI model subscriptions
- Offer built-in local models without external subscriptions
- Link notes, calendar, projects, search and drive context to coding tasks
- Show a Kanban-style agent management dashboard across local and cloud environments
- Group agent sessions, pull requests, files and context by project
- Run autonomous cloud agents on their own VM from repo files
- Pause, resume and review agent progress from one interface
- Support simultaneous data exploration and production-ready code in a stable environment
- Provide a collaborative coding environment with engineering controls
- Run multiple code blocks in parallel
- Allow Python, R, SQL and Markdown interchangeably on one canvas
- Deploy directly or export code for other teams
- Embed workflow management in Microsoft Teams
- Automate pull requests, tasks and release management
- Act as an AI virtual teammate for developers
- Provide an open-source agent template for custom workflows
- Keep development conversations and project tracking in one platform
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewer-approved code changes and release records linked to source with source references and unresolved questions
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 Source-linked AI coding and review console 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 Source-linked AI coding and review console 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 links4 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 data195 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
Reduce tool sprawl while keeping code changes under team review. For software teams that write, review and manage code with AI assistance, convert repository code, issue trackers, pull requests, workspace documents and agent logs into reviewer-approved code changes and release records linked to source. The benefit is a testable hypothesis, measured through accepted changes per review hour and post-merge corrections; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect repository code, issue trackers, pull requests, workspace documents and agent logs, then follow this sequence: 1. Generate code suggestions and completions. 2. Provide in-editor AI code review feedback. 3. Manage pull requests for collaboration and review. Resolve uncertain cases with qualified reviewers, approve reviewer-approved code changes and release records linked to source, and measure accepted changes per review hour and post-merge corrections 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 arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One repository host and one supported language set; final merge and release checks remain with the engineering team. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve code ownership, source attribution, license accuracy and usage permissions. Engineering leads approve substantive changes and release scope. One repository host and one supported language set; final merge and release checks remain with the engineering team. 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 host and one supported language set; final merge and release checks remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: generate code suggestions and completions; provide in-editor AI code review feedback. Support the third module with operator review: manage pull requests for collaboration and review. 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 issue trackers and permitted workspace documents. Cloud code storage, version-control import/export and deployment destinations. 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: Repository and task intake, Editable code and agent workspace, Review and release console. Use a project list for repositories, a large central editing and diff canvas, and a right-hand panel for agent sessions, pull requests, files and comments. Let users compare agent branches side by side. Display draft, changes requested and approved states. Provide a client or stakeholder preview link with comments anchored to the relevant change. Make the task-specific outcome reviewer-approved code changes and release records linked to source visible beside its evidence, review state and value baseline.
