AI app for it and development · no coding needed
Source-linked engineering agent console
Reduce tool sprawl and review effort while keeping every automated change source-linked and human-approved.
Made for: Engineering leads and platform teams running repositories with pull requests, CI and issue trackers
What it does for you
The problem
Coding, review, CI fixes, triage and project tracking are split across several rented AI tools that do not share repository context or approval records.
What it gives you
Reviewed, source-linked code changes, review comments, triage decisions and project reports
What you give it
Repository accesspull requestsCI logsissuesteam conventions
Build your own version of Palmier, Daemons by Charlie Labs and more
One app with what these 9 AI tools do, yours to keep and change: Palmier, Daemons by Charlie Labs, Replicas, Jules, Hyrax AI, Claude Code auto-fix, Tusk (YC W24), Missio, Maxium AI (V1).
Everything these tools do, in one app
- Autonomous code writing AI writes code to implement features or fix bugs without constant human prompting.Found in Palmier, Jules, Tusk (YC W24)
- Automated pull request review AI reviews pull requests and provides feedback or suggestions.Found in Palmier, Hyrax AI, Claude Code auto-fix
- CI failure auto-fix Automatically fixes continuous integration failures and pushes changes to the pull request.Found in Claude Code auto-fix, Hyrax AI, Tusk (YC W24)
- Issue triaging and assignment Automatically triages new issues and assigns them to appropriate team members.Found in Palmier
- Multi-platform integration Connects with tools like GitHub, Slack, Linear, and Sentry to fit into existing workflows.Found in Palmier, Daemons by Charlie Labs, Replicas and 2 more
- Asynchronous background operation Runs tasks in the background without interrupting the developer's workflow.Found in Palmier, Jules, Replicas
- Context-aware codebase understanding Uses full repository context to generate accurate code and reviews.Found in Palmier, Jules, Hyrax AI
- Event-driven triggers Starts actions based on events like pull requests, CI failures, or issue creation.Found in Palmier, Claude Code auto-fix, Daemons by Charlie Labs
- Isolated cloud environments Runs agents in isolated virtual machines with full development environments.Found in Replicas, Jules
- Bring-your-own model subscriptions Allows teams to use their own AI model subscriptions or API keys.Found in Replicas
- Audio changelogs Provides verbal summaries of code changes for easier review.Found in Jules
- Full repository audit Audits the entire codebase across multiple quality categories.Found in Hyrax AI
- Self-verification loop Writes failing tests and runs repo tests, build, and lint to verify fixes before delivery.Found in Hyrax AI
- MCP server for coding agents Profiles repository architecture and conventions for use by coding agents.Found in Hyrax AI
- UI change automation Automates UI improvements from tickets to pull requests.Found in Tusk (YC W24)
- Learning from past reviews Improves generated changes by learning from previous pull requests and code reviews.Found in Tusk (YC W24)
- Project management automation Automates task scheduling, reminders, and provides analytics for project oversight.Found in Missio
- Engineering metrics dashboard Provides a customizable dashboard with engineering metrics and real-time alerts.Found in Maxium AI (V1)
How it works, step by step
- Write code for features and bug fixes from repository context
- Review pull requests with source-linked comments
- Fix CI failures and push changes to the pull request
- Triage new issues and assign owners
- Connect GitHub, Slack, Linear and Sentry
- Run agents asynchronously in the background
- Read full repository context before generating changes
- Trigger actions on pull requests, CI failures and issue creation
- Run agents in isolated cloud environments
- Accept bring-your-own model keys and subscriptions
- Produce audio changelogs of code changes
- Audit the repository across quality categories
- Write failing tests and run tests, build and lint before delivery
- Expose an MCP server profiling architecture and conventions
- Automate UI changes from tickets to pull requests
- Learn from past reviews and merged pull requests
- Schedule tasks, send reminders and report project analytics
- Show an engineering metrics dashboard with real-time alerts
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 engineering agent 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 engineering agent 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 Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 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 and review effort while keeping every automated change source-linked and human-approved. For engineering leads and platform teams running repositories with pull requests, CI and issue trackers, convert repository context, pull requests, CI logs, issues and team conventions into reviewed, source-linked code changes, review comments, triage decisions and project reports. The benefit is a testable hypothesis, measured through accepted changes per engineering hour and post-merge corrections; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect repository access, pull requests, CI logs, issues and team conventions, then follow this sequence: 1. Write code for features and bug fixes from repository context. 2. Review pull requests with source-linked comments. 3. Fix CI failures and push changes to the pull request. Resolve uncertain cases with qualified reviewers, approve reviewed, source-linked code changes, review comments, triage decisions and project reports, and measure accepted changes per engineering 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 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 connected repository set and one approved model configuration; final merge, release and assignment 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 leads approve substantive changes and merge scope. One connected repository set and one approved model configuration; final merge, release and assignment 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 connected repository set and one approved model configuration; final merge, release and assignment decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: write code for features and bug fixes from repository context; review pull requests with source-linked comments. Support the third module with operator review: fix CI failures and push changes to the pull request. 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
GitHub, Slack, Linear and Sentry, plus authorized repository and CI systems. Cloud environment storage, code host 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, Review queue, Admin console. Use a repository list with connection status, a central review queue showing proposed changes beside their source references and CI results, and a right-hand panel for agent settings, model keys, triggers and permissions. Let users compare proposed and current code side by side. Display draft, changes requested, approved and merged states. Provide an audit view linking each action to its trigger, evidence and named approver. Make the task-specific outcome reviewed, source-linked code changes, review comments, triage decisions and project reports visible beside its evidence, review state and value baseline.
