AI app for it and development · no coding needed
Source-linked autonomous web app bug testing console
Reduce debugging time while keeping developers in control of every fix.
Made for: Engineering teams shipping web applications who need bugs found and reported before release
What it does for you
The problem
Manual and scripted testing misses bugs, and findings arrive without root cause, fix or reproducible session context.
What it gives you
Developer-reviewed bug reports with root cause, fix suggestions and replayable sessions
What you give it
Authorized app URLtest credentialsrepository access
Build your own version of Replay QA, Jazzberry and more
One app with what these 3 AI tools do, yours to keep and change: Replay QA, Jazzberry, AI QA.
Everything these tools do, in one app
- Autonomous app exploration The tool crawls the app and interacts with elements without pre-written scripts to find bugs.Found in Replay QA, AI QA
- Bug detection Automatically identifies bugs in the application.Found in Replay QA, Jazzberry, AI QA
- GitHub integration Integrates with GitHub to post findings and fixes on pull requests.Found in Replay QA, Jazzberry
- Root cause analysis Identifies the likely cause of each bug found.Found in Replay QA
- Fix suggestions Generates a code change suggestion to fix the bug.Found in Replay QA
- Coding agent integration Passes root cause and fix to coding agents like Cursor or Claude Code.Found in Replay QA
- Session recording Records every session with complete browser state for debugging.Found in Replay QA
- Console and network logs Captures console logs and network requests for debugging.Found in Replay QA
- DOM state capture Captures DOM state to enable debugging without re-running the failure.Found in Replay QA
- Auth handling Allows providing credentials, auto-account creation, or skipping authentication.Found in Replay QA
- Time-travel debugging Enables debugging in familiar DevTools with full session context.Found in Replay QA
- Sandboxed code execution Runs real code in a secure sandbox to identify bugs realistically.Found in Jazzberry
- Automatic repository cloning Clones repositories automatically to analyze code changes contextually.Found in Jazzberry
- Continuous monitoring Provides ongoing feedback throughout the code review process.Found in Jazzberry
- Natural language test creation Converts plain language inputs into automated test cases.Found in AI QA
- Self-healing tests Adapts tests automatically when UI elements change.Found in AI QA
- Scheduled test execution Runs automated test suites on a schedule without supervision.Found in AI QA
- Manual test control Allows manual intervention or adjustment of tests as needed.Found in AI QA
How it works, step by step
- Explore the app autonomously without pre-written scripts
- Interact with elements and detect bugs
- Post findings and fixes on GitHub pull requests
- Identify the likely cause of each bug
- Generate a code change suggestion to fix the bug
- Pass root cause and fix to coding agents
- Record every session with complete browser state
- Capture console logs and network requests
- Capture DOM state for debugging without re-running the failure
- Handle authentication via credentials, auto-account creation or skip
- Support time-travel debugging in familiar DevTools
- Run real code in a secure sandbox
- Clone repositories automatically for contextual analysis
- Monitor continuously through the code review process
- Convert plain language into automated test cases
- Adapt tests automatically when UI elements change
- Run test suites on a schedule without supervision
- Allow manual intervention or adjustment of tests
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 web app bug testing 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 web app bug testing 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 links3 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare23 KB
- prompt-vps.mdThe same build on your own server (Docker)23 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 debugging time while keeping developers in control of every fix. For engineering teams shipping web applications, convert an authorized app URL, test credentials and repository access into developer-reviewed bug reports with root cause, fix suggestions and replayable sessions. The benefit is a testable hypothesis, measured through confirmed bugs per testing hour and time from finding to merged fix; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect an authorized app URL, test credentials and repository access, then follow this sequence: 1. Explore the app autonomously without pre-written scripts. 2. Interact with elements and detect bugs. 3. Post findings and fixes on GitHub pull requests. Resolve uncertain cases with qualified reviewers, approve developer-reviewed bug reports with root cause, fix suggestions and replayable sessions, and measure confirmed bugs per testing hour and time from finding to merged fix 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 authorized target app and repository per pilot; final triage and code changes remain developer decisions. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve developer intent, source attribution, code accuracy and usage permissions. Developers approve substantive changes and deployment scope. One authorized target app and repository per pilot; final triage and code changes remain developer decisions. 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 authorized target app and repository per pilot; final triage and code changes remain developer decisions. Implement one approved input format, a bounded representative case set and the first two task modules: explore the app autonomously without pre-written scripts; interact with elements and detect bugs. Support the third module with operator review: post findings and fixes on GitHub pull requests. 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
Developer-owned repositories, CI pipelines and issue trackers. Cloud code hosting, pull-request APIs and notification destinations. Start with file exchange and validate destination specifications before promising direct posting. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Target and access setup, Live exploration and findings, Report and handoff. Use a run list for targets, a large central findings view with session replay, and a right-hand panel for root cause, fix suggestion and logs. Let users compare runs side by side. Display open, triaged, fixed and dismissed states. Provide a developer handoff link with findings anchored to the relevant pull request. Make the task-specific outcome developer-reviewed bug reports with root cause, fix suggestions and replayable sessions visible beside its evidence, review state and value baseline.
