AI app for it and development · no coding needed
Source-linked autonomous code repair console
Reduce manual repair cycles while keeping every change reviewable and attributable.
Made for: Engineering teams maintaining multi-repository codebases
What it does for you
The problem
Bugs, style drift and design debt accumulate faster than developers can find, fix and verify them across repositories.
What it gives you
Reviewer-approved code changes linked to their source evidence
What you give it
Repository accessissue trackerstest suitesteam conventions
Build your own version of Polarity, SWE-agent and more
One app with what these 4 AI tools do, yours to keep and change: Polarity, SWE-agent, BobCA, Hy4 preview.
Everything these tools do, in one app
- Autonomous bug fixing Automatically identifies and fixes bugs in code without human intervention.Found in Polarity, SWE-agent, BobCA and 1 more
- Codebase-wide changes Makes coordinated changes across multiple files rather than isolated single-file edits.Found in Polarity, SWE-agent, Hy4 preview
- Context engine Maintains cross-file consistency and reduces regressions by understanding the entire codebase.Found in Polarity, BobCA, Hy4 preview
- CLI integration Integrates into existing developer pipelines via command-line interface.Found in Polarity
- Team coordination Supports team-level coordination and workflows.Found in Polarity
- Documentation polishing Automatically improves and polishes code documentation.Found in Polarity
- Style enforcement Enforces code style and quality standards automatically.Found in Polarity
- Function optimization Optimizes individual functions for better performance or readability.Found in Polarity
- System-design refinements Suggests and applies improvements to system design.Found in Polarity
- Agent-Computer Interface Enables AI to browse, edit, and execute files in a repository through a specialized interface.Found in SWE-agent
- Versatility to other tasks Adapts to tasks beyond debugging, such as cybersecurity challenges and competitive coding.Found in SWE-agent
- User preference learning Learns and adapts to individual technical and workflow preferences over time.Found in BobCA
- Linked conversations Visual tracing of idea branches and context forks to carry forward or fork logic without rebuilding context.Found in BobCA
- Sandbox testing Provides an isolated environment to test changes before merging.Found in BobCA
- Negative constraint logging Records failed approaches to avoid repeating them in future sessions.Found in BobCA
- Autonomy HUD Real-time dashboard reporting bugs fixed and features shipped, with tools to set grading targets and monitor agent activity.Found in BobCA
- Background agents Specialized agents that handle repetitive, granular tasks in the background.Found in BobCA
- Large context window Accommodates entire code repositories or long technical documents without truncation.Found in Hy4 preview
- Open-source distribution Allows modification and commercial use under an open-source license.Found in Hy4 preview
How it works, step by step
- Detect bugs and failing tests across repositories
- Propose and apply coordinated multi-file fixes
- Maintain cross-file context to reduce regressions
- Run changes through a command-line interface in existing pipelines
- Support team-level coordination and review workflows
- Polish and update code documentation
- Enforce code style and quality standards
- Optimize individual functions for performance or readability
- Suggest and apply system-design refinements
- Browse, edit and execute files through an agent-computer interface
- Adapt to tasks beyond debugging, such as security checks and competitive coding
- Learn individual technical and workflow preferences over time
- Trace linked conversations and context forks without rebuilding context
- Test changes in an isolated sandbox before merging
- Log failed approaches as negative constraints for future sessions
- Report bugs fixed and features shipped on an autonomy dashboard with grading targets
- Run background agents for repetitive granular tasks
- Hold entire repositories or long technical documents in context without truncation
- Distribute under an open-source license for modification and commercial use
- 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 linked to their source evidence 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 autonomous code repair 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 autonomous code repair 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 criteria11 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 manual repair cycles while keeping every change reviewable and attributable. For engineering teams maintaining multi-repository codebases, convert repository access, issue trackers, test suites and team conventions into reviewer-approved code changes linked to their source evidence. The benefit is a testable hypothesis, measured through accepted changes per engineering hour and regressions after merge; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect repository access, issue trackers, test suites and team conventions, then follow this sequence: 1. Detect bugs and failing tests across repositories. 2. Propose and apply coordinated multi-file fixes. 3. Maintain cross-file context to reduce regressions. 4. Run changes through a command-line interface in existing pipelines. 5. Support team-level coordination and review workflows. 6. Polish and update code documentation. 7. Enforce code style and quality standards. 8. Optimize individual functions for performance or readability. 9. Suggest and apply system-design refinements. 10. Browse, edit and execute files through an agent-computer interface. 11. Adapt to tasks beyond debugging, such as security checks and competitive coding. 12. Learn individual technical and workflow preferences over time. 13. Trace linked conversations and context forks without rebuilding context. 14. Test changes in an isolated sandbox before merging. 15. Log failed approaches as negative constraints for future sessions. 16. Report bugs fixed and features shipped on an autonomy dashboard with grading targets. 17. Run background agents for repetitive granular tasks. 18. Hold entire repositories or long technical documents in context without truncation. 19. Distribute under an open-source license for modification and commercial use. Resolve uncertain cases with qualified reviewers, approve reviewer-approved code changes linked to their source evidence, and measure accepted changes per engineering hour and regressions 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 repository set and approved toolchain; final merge and security 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 teams approve substantive changes and deployment scope. One repository set and approved toolchain; final merge and security 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 repository set and approved toolchain; final merge and security decisions remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: detect bugs and failing tests across repositories; propose and apply coordinated multi-file fixes. Support the remaining modules with operator review: maintain cross-file context to reduce regressions; run changes through a command-line interface in existing pipelines. 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, authorized issue trackers and permitted test suites. Cloud code storage, CI/CD 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 agent setup, Change review console, Autonomy and value dashboard. Use a repository list with agent status, a central diff and evidence canvas, and a right-hand panel for linked conversations, constraints and approvals. Let users compare branches and agent runs side by side. Display proposed, tested, changes requested and merged states. Provide a reviewer link with comments anchored to the relevant file and line. Make the task-specific outcome reviewer-approved code changes linked to their source evidence visible beside its evidence, review state and value baseline.
