AI app for it and development · no coding needed
Integrated AI development and delivery workspace
Reduce tool sprawl and repeated context setup while keeping code review and release decisions with the team.
Made for: Software teams and solo developers building and shipping applications
What it does for you
The problem
Development work is split across several AI coding subscriptions, so context, reviews and deployment steps are fragmented and hard to govern.
What it gives you
Reviewed, merged and deployable code changes linked to a tracked task
What you give it
Repository codeproject filestask descriptionsapproved model endpoints
Build your own version of Trae, Trae AI and more
One app with what these 10 AI tools do, yours to keep and change: Trae, Trae AI, Trae 2.0, Kiro, Oppla AI IDE, Windsurf Editor, Prompter IDE, Dev, CodeRide (Beta), AI Operator by BLACKBOX AI.
Everything these tools do, in one app
- Real-time code suggestions Provides context-aware code completions and suggestions as you type.Found in Trae, Trae AI, Dev
- Multi-language support Supports programming in multiple languages and frameworks.Found in Trae AI, Kiro, Dev
- AI pair programming Acts as an AI collaborator that understands your codebase and assists in real time.Found in Trae, Windsurf Editor
- Task decomposition Automatically breaks down complex tasks into manageable steps.Found in Trae, Trae AI
- Automated task planning Organizes and prioritizes development tasks automatically.Found in Trae, Trae 2.0
- Code generation Generates code snippets to reduce repetitive coding.Found in Trae AI
- Debugging assistance Helps identify and fix errors in code.Found in Trae AI, Kiro, Dev
- Multimodal input Interprets visual content like images within projects.Found in Trae
- Autonomous development AI can plan, code, test, and deploy features autonomously.Found in Trae 2.0
- Real-time previews Provides instant feedback on code changes.Found in Trae 2.0
- Automated environment setup Automatically configures development environments.Found in Trae 2.0
- Prototype to production Supports transition from prototyping to production within the environment.Found in Kiro
- Collaboration features Facilitates teamwork and collaboration among developers.Found in Kiro, Trae
- Customizable workflows Allows customization of workflows to suit project needs.Found in Kiro, Dev
- Unlimited context windows Retains comprehensive context from user needs to product goals.Found in Oppla AI IDE
- Strategic insights Provides actionable signals on what features to build next.Found in Oppla AI IDE
- High performance Delivers fast and responsive experience.Found in Oppla AI IDE
- GitHub integration Connects with GitHub for automated pull requests, code reviews, and bug triaging.Found in Oppla AI IDE
- Open source foundation Built on open-source editor with proprietary enhancements.Found in Oppla AI IDE
- Multi-file editing Enables editing across multiple files with deep contextual awareness.Found in Windsurf Editor
- Terminal command suggestions Provides suggestions for terminal commands.Found in Windsurf Editor
- LLM-based search Uses LLM for codebase search and navigation.Found in Windsurf Editor
- File system integration Integrates project files as context within LLM chat sessions.Found in Prompter IDE
- Seamless merging Saves and merges AI-generated changes back into the local file system.Found in Prompter IDE
- Online LLM support Supports any online LLM service, including popular chat models.Found in Prompter IDE
- Iterative review cycles Facilitates assigning complex development tasks and iterative review cycles.Found in Prompter IDE
- IDE integration Integrates with widely used IDEs and code editors.Found in Dev
- Persistent context Maintains project context across sessions to avoid repeated explanations.Found in CodeRide (Beta)
- AI agent task optimization Optimizes tasks specifically for AI coding agents.Found in CodeRide (Beta)
- Multi-tool compatibility Works with multiple AI coding tools like GitHub Copilot, Cursor, Windsurf, and Claude.Found in CodeRide (Beta)
- Token usage reduction Reduces token consumption to improve efficiency and result quality.Found in CodeRide (Beta)
- Browser screen analysis Analyzes on-screen content in real time within the browser.Found in AI Operator by BLACKBOX AI
- Voice and text interaction Allows communication via voice and text.Found in AI Operator by BLACKBOX AI
- Privacy controls Gives users full control over what information is shared.Found in AI Operator by BLACKBOX AI
- Browser-based setup Instantly activates in the browser with no complex installation.Found in AI Operator by BLACKBOX AI
How it works, step by step
- Suggest context-aware code completions while typing
- Support multiple languages and frameworks
- Run an AI pair-programming session over the open codebase
- Decompose complex tasks into ordered steps
- Plan and prioritize development tasks automatically
- Generate code snippets for repetitive work
- Assist debugging with error explanations and fixes
- Interpret images and screenshots supplied in a task
- Run bounded autonomous plan-code-test-deploy cycles
- Show real-time previews of code changes
- Configure development environments automatically
- Move a prototype toward production in the same workspace
- Support team collaboration and shared review
- Allow customizable per-project workflows
- Retain long project context across sessions
- Surface signals on what to build next
- Keep the editor fast and responsive
- Connect to GitHub for pull requests, reviews and bug triage
- Build on an open-source editor foundation
- Edit across multiple files with contextual awareness
- Suggest terminal commands
- Search and navigate the codebase with LLM assistance
- Include project files as chat context
- Merge AI-generated changes back into the local file system
- Support any approved online LLM endpoint
- Run iterative review cycles on assigned tasks
- Integrate with widely used IDEs and editors
- Maintain persistent project context across sessions
- Optimize tasks for AI coding agents
- Work alongside multiple AI coding tools
- Reduce token consumption on repeated context
- Analyze on-screen browser content in real time
- Accept voice and text interaction
- Provide privacy controls over shared information
- Activate in the browser without complex installation
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 Integrated AI development and delivery workspace 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 Integrated AI development and delivery workspace 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 links6 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data200 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 and repeated context setup while keeping code review and release decisions with the team. For software teams and solo developers building and shipping applications, convert repository code, project files, task descriptions and approved model endpoints into reviewed, merged and deployable code changes linked to a tracked task. The benefit is a testable hypothesis, measured through accepted merged changes per developer hour and rework after review; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect repository code, project files, task descriptions and approved model endpoints, then follow this sequence: 1. Suggest context-aware code completions while typing. 2. Run an AI pair-programming session over the open codebase. 3. Decompose complex tasks into ordered steps. 4. Generate code snippets for repetitive work. 5. Assist debugging with error explanations and fixes. 6. Edit across multiple files with contextual awareness. 7. Merge AI-generated changes back into the local file system. 8. Run iterative review cycles on assigned tasks. 9. Connect to GitHub for pull requests, reviews and bug triage. 10. Show real-time previews of code changes. Resolve uncertain cases with qualified reviewers, approve reviewed, merged and deployable code changes linked to a tracked task, and measure accepted merged changes per developer hour and rework after review against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Final code review, security checks and release approval remain with the development 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. Development teams approve substantive changes and release scope. One repository type and one approved model endpoint; final code review, security checks and release approval remain with the development 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 type and one approved model endpoint; final code review, security checks and release approval remain with the development team. Implement one approved input format, a bounded representative case set and the first two task modules: suggest context-aware code completions while typing; run an AI pair-programming session over the open codebase. Support the remaining modules with operator review: decompose complex tasks into ordered steps; generate code snippets for repetitive work; assist debugging with error explanations and fixes; edit across multiple files with contextual awareness; merge AI-generated changes back into the local file system; run iterative review cycles on assigned tasks; connect to GitHub for pull requests, reviews and bug triage; show real-time previews of code changes. 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
Customer-owned repositories, approved model endpoints and permitted project sources. Cloud code storage, Git hosting, CI/CD destinations 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: Repository and task intake, Editable code and agent workspace, Review and release. Use a project list for repositories, a large central editor with multi-file tabs, and a right-hand panel for agent chat, task steps, terminal and preview. Let users compare generated diffs side by side. Display draft, changes requested and approved states. Provide a reviewer link with comments anchored to the relevant file and line. Make the task-specific outcome reviewed, merged and deployable code changes linked to a tracked task visible beside its evidence, review state and value baseline.
