AI app for it and development · no coding needed
Source-linked coding assistant and admin console
Reduce tool sprawl while keeping code, prompts and review evidence inside the team's own environment.
Made for: Engineering teams and platform owners who need a code assistant they can run and govern themselves
What it does for you
The problem
Developers rent several coding assistants that each cover part of the job, and none of them give the team one source-linked record of suggestions, reviews and approvals.
What it gives you
Reviewed, source-linked code changes and documentation
What you give it
Repository codeeditor contextissue trackersCI signals
Build your own version of Gemini Code Assist, Amazon CodeWhisperer and more
One app with what these 10 AI tools do, yours to keep and change: Gemini Code Assist, Amazon CodeWhisperer, Github Copilot, Zencoder, DevPromptAi, Refact.ai, Safurai, Sweep AI, Qodo Gen, Augment Agent.
Everything these tools do, in one app
- Real-time code suggestions Provides instant code completions and suggestions as you type.Found in Gemini Code Assist, Amazon CodeWhisperer, Github Copilot and 1 more
- Multi-language support Works with many programming languages.Found in Gemini Code Assist, Amazon CodeWhisperer, Zencoder and 1 more
- IDE integration Integrates directly into popular code editors and IDEs.Found in Gemini Code Assist, Amazon CodeWhisperer, Github Copilot and 3 more
- Debugging assistance Helps identify and fix coding errors.Found in Gemini Code Assist, Amazon CodeWhisperer, DevPromptAi and 2 more
- Code generation Generates code snippets or full functions based on context.Found in Amazon CodeWhisperer, Github Copilot, DevPromptAi and 1 more
- Code optimization Suggests improvements to code performance and readability.Found in Gemini Code Assist, DevPromptAi
- Code refactoring Restructures existing code to improve maintainability.Found in Refact.ai, Safurai
- Documentation creation Generates technical documentation for code.Found in DevPromptAi, Safurai
- Customizable settings Allows tailoring the assistant to user preferences.Found in Gemini Code Assist
- Agent mode Analyzes code, suggests edits across files, runs tests, and validates results.Found in Github Copilot
- Next edit suggestions Shows the ripple effects of code changes across a project.Found in Github Copilot
- Code review capability Scans code to uncover hidden bugs before human review.Found in Github Copilot
- In-app chat Provides chat for quick access to logs, feature toggles, and app deployment.Found in Github Copilot, Refact.ai
- Repo Grokking Understands large codebases to offer context-aware suggestions and fixes.Found in Zencoder
- DevOps integrations Connects with DevOps tools like JIRA, Sentry, GitHub, and GitLab.Found in Zencoder
- Multi-model support Allows choosing from different AI models for coding tasks.Found in Refact.ai
- Self-hosted deployment Offers option to deploy on your own infrastructure for privacy.Found in Refact.ai
- Per-user statistics Tracks individual developer progress and coding metrics.Found in Refact.ai
How it works, step by step
- Suggest code completions as the developer types
- Support many programming languages
- Integrate into popular editors and IDEs
- Help identify and fix coding errors
- Generate code snippets and full functions from context
- Suggest performance and readability improvements
- Restructure existing code for maintainability
- Generate technical documentation for code
- Allow per-user and per-team settings
- Run agent mode across files, tests and validation
- Show next-edit ripple effects across the project
- Scan code for hidden bugs before human review
- Provide in-app chat for logs, feature toggles and deployment
- Understand large codebases for context-aware fixes
- Connect to JIRA, Sentry, GitHub and GitLab
- Allow choosing among approved AI models
- Offer self-hosted deployment for privacy
- Track per-user coding statistics
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed, source-linked code changes and documentation set 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 coding assistant and admin 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 coding assistant and admin 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 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 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, prompts and review evidence inside the team's own environment. For engineering teams and platform owners who need a code assistant they can run and govern themselves, convert repository code, editor context, issue trackers and CI signals into reviewed, source-linked code changes and documentation. The benefit is a testable hypothesis, measured through accepted changes per developer 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, editor context, issue trackers and CI signals, then follow this sequence: 1. Suggest code completions as the developer types. 2. Support many programming languages. 3. Integrate into popular editors and IDEs. 4. Help identify and fix coding errors. 5. Generate code snippets and full functions from context. 6. Suggest performance and readability improvements. 7. Restructure existing code for maintainability. 8. Generate technical documentation for code. 9. Allow per-user and per-team settings. 10. Run agent mode across files, tests and validation. 11. Show next-edit ripple effects across the project. 12. Scan code for hidden bugs before human review. 13. Provide in-app chat for logs, feature toggles and deployment. 14. Understand large codebases for context-aware fixes. 15. Connect to JIRA, Sentry, GitHub and GitLab. 16. Allow choosing among approved AI models. 17. Offer self-hosted deployment for privacy. 18. Track per-user coding statistics. Resolve uncertain cases with qualified reviewers, approve reviewed, source-linked code changes and documentation, and measure accepted changes per developer 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 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 approved repository set and model list; final merge, security and architecture decisions remain human. 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 owners approve substantive changes and deployment scope. One approved repository set and model list; final merge, security and architecture decisions remain human. 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 approved repository set and model list; final merge, security and architecture decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: suggest code completions as the developer types; support many programming languages. Support the remaining modules with operator review: integrate into popular editors and IDEs; help identify and fix coding errors; generate code snippets and full functions from context; suggest performance and readability improvements; restructure existing code for maintainability; generate technical documentation for code; allow per-user and per-team settings; run agent mode across files, tests and validation; show next-edit ripple effects across the project; scan code for hidden bugs before human review; provide in-app chat for logs, feature toggles and deployment; understand large codebases for context-aware fixes; connect to JIRA, Sentry, GitHub and GitLab; allow choosing among approved AI models; offer self-hosted deployment for privacy; track per-user coding statistics. 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 CI signals. Cloud code storage, editor 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 context setup, Editable code and review console, Admin and usage console. Use a project list for repositories, a large central editor and diff canvas, and a right-hand panel for suggestions, sources, tests and comments. Let users compare suggested and accepted versions 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, source-linked code changes and documentation visible beside its evidence, review state and value baseline.
