AI app for it and development · no coding needed
Source-linked code generation and review console
Reduce tool sprawl and review effort while keeping generated code traceable to the repository it came from.
Made for: Engineering leads and developers working in private or regulated codebases

What it does for you
The problem
Developers rent several code assistants that suggest code without traceable sources, cannot be trained on private repositories, and leave review, testing and documentation work split across tools.
What it gives you
Reviewer-approved code changes linked to their source files
What you give it
Permitted repositoriescoding standardsticket contexttest suites
Build your own version of Tabnine, Code Spoonfeeder and more
One app with what these 8 AI tools do, yours to keep and change: Tabnine, Code Spoonfeeder, Codespell.ai, Refraction, SourceAI, CodeSquire, Codeium, SmartScripter.
Everything these tools do, in one app
- Code generation Generates code automatically from user input or context.Found in Tabnine, Code Spoonfeeder, Codespell.ai and 5 more
- Code completion Provides real-time suggestions to complete code as you type.Found in Tabnine, Code Spoonfeeder, CodeSquire
- Multi-language support Works with many programming languages.Found in Tabnine, Code Spoonfeeder, Refraction and 3 more
- IDE integration Integrates with popular code editors and development environments.Found in Tabnine, Code Spoonfeeder, Codespell.ai
- Context-aware suggestions Adapts suggestions based on the current project and coding style.Found in Tabnine, Code Spoonfeeder, Codeium
- Debugging assistance Helps identify and fix errors in code.Found in Code Spoonfeeder, Codespell.ai, Codeium
- Documentation generation Automatically creates or updates code documentation.Found in Codespell.ai, Refraction
- Automated testing Generates unit tests and test scripts.Found in Codespell.ai, Refraction, Codeium
- Code refactoring Improves code structure and quality.Found in Codespell.ai, Refraction
- Natural language to code Converts plain language instructions into functional code.Found in CodeSquire, Codeium
- Library recommendations Suggests popular libraries and helps integrate them.Found in CodeSquire
- SQL query generation Translates plain language into SQL queries.Found in CodeSquire
- Customizable AI models Allows training on private codebases for better accuracy.Found in Tabnine
- Deployment options Offers cloud-based and local deployment for privacy and performance.Found in Tabnine
- Infrastructure scripting Generates scripts for infrastructure configuration.Found in Codespell.ai
- Script saving Automatically saves generated scripts for later use.Found in SmartScripter
How it works, step by step
- Generate code from prompts or surrounding context
- Complete code in real time as the developer types
- Support many programming languages
- Integrate with common editors and development environments
- Adapt suggestions to the current project and coding style
- Flag likely errors and propose fixes
- Generate and update code documentation
- Generate unit tests and test scripts
- Refactor code structure and quality
- Convert plain-language instructions into code
- Recommend libraries and show integration steps
- Translate plain language into SQL queries
- Train on permitted private codebases for accuracy
- Run in cloud or local deployment for privacy and performance
- Generate infrastructure configuration scripts
- Save generated scripts for later reuse
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before merge
- Export a versioned reviewer-approved code changes linked to their source files 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 code generation 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 code generation 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 Cloudflare27 KB
- prompt-vps.mdThe same build on your own server (Docker)27 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria14 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
Reduce tool sprawl and review effort while keeping generated code traceable to the repository it came from. For engineering leads and developers working in private or regulated codebases, convert permitted repositories, coding standards, ticket context and test suites into reviewer-approved code changes linked to their source files. The benefit is a testable hypothesis, measured through accepted suggestions per developer hour and rework after merge; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect permitted repositories, coding standards, ticket context and test suites, then follow this sequence: 1. Generate code from prompts or surrounding context. 2. Complete code in real time as the developer types. 3. Adapt suggestions to the current project and coding style. 4. Flag likely errors and propose fixes. 5. Generate unit tests and test scripts. 6. Refactor code structure and quality. Resolve uncertain cases with qualified reviewers, approve reviewer-approved code changes linked to their source files, and measure accepted suggestions per developer hour and rework after merge 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 permitted repository set and one supported editor; final merge, security and architecture decisions 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 merge scope. One permitted repository set and one supported editor; final merge, security and architecture decisions 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 permitted repository set and one supported editor; final merge, security and architecture decisions remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: generate code from prompts or surrounding context; complete code in real time as the developer types. Support the remaining modules with operator review: adapt suggestions to the current project and coding style; flag likely errors and propose fixes; generate unit tests and test scripts; refactor code structure and quality. 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, issue trackers and permitted documentation. Cloud code storage, editor plugins and CI 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 permission setup, Source-linked assistant, Administrator console. Use a project list for repositories, a large central editor with inline suggestions, and a right-hand panel for sources, tests and review comments. Let users compare suggested and original code side by side. Display draft, changes requested and approved states. Provide a review link with comments anchored to the relevant file and line. Make the task-specific outcome reviewer-approved code changes linked to their source files visible beside its evidence, review state and value baseline.





