AI app for it and development · no coding needed
Coding practice and lab workshop platform
Run one owned practice environment instead of renting several tools.
Made for: Instructors and team leads running coding practice sessions and lab workshops
What it does for you
The problem
Coding practice, AI tutoring, code review and lab equipment sit in separate tools, so learners lose context and instructors cannot see progress in one place.
What it gives you
Instructor-approved practice records linked to learner progress
What you give it
Learner codeexercise definitionslab schedulesreview rules
Build your own version of Codebay.ai, Study with GPT and more
One app with what these 4 AI tools do, yours to keep and change: Codebay.ai, Study with GPT, LabEx.io, Code Companion.
Everything these tools do, in one app
- AI code completion Provides intelligent suggestions to complete code as you type.Found in Codebay.ai
- Automated code review Automatically checks code for bugs and style issues.Found in Codebay.ai
- Code optimization and refactoring Suggests improvements to make code faster and easier to read.Found in Codebay.ai
- Development environment integration Works within popular coding tools and version control systems.Found in Codebay.ai
- Customizable recommendations Adapts suggestions to your preferences and project needs.Found in Codebay.ai
- Personalized tutorials Generates custom learning content based on your interests.Found in Study with GPT
- Full-stack topic coverage Covers backend, DevOps, and other full-stack development areas.Found in Study with GPT
- Continuous updates Improves content and features based on user feedback.Found in Study with GPT
- Text-based learning interface Provides a simple, text-focused environment for conceptual learning.Found in Study with GPT
- Remote lab equipment access Lets you control real laboratory instruments over the internet.Found in LabEx.io
- Diverse experiments Offers a wide range of experiments across scientific disciplines.Found in LabEx.io
- Lab scheduling and booking Allows you to reserve lab time efficiently.Found in LabEx.io
- Real-time data collection Collects and analyzes experiment data as it happens.Found in LabEx.io
- Support resources and tutorials Provides guidance to help you conduct experiments.Found in LabEx.io
- Online code editor Lets you write and run code directly in the browser.Found in Code Companion
- AI tutor feedback Offers hints, guidance, and solution reviews from an AI tutor.Found in Code Companion
- Broad programming topic support Answers questions on a wide range of programming topics.Found in Code Companion
- General learning assistance Helps with both coding problems and general learning inquiries.Found in Code Companion
How it works, step by step
- Suggest code completions as the learner types
- Check submitted code for bugs and style issues
- Suggest optimizations and refactoring steps
- Connect to version control and common editors
- Adapt suggestions to learner preferences and project settings
- Generate personalized tutorials from stated interests
- Cover backend, DevOps and full-stack topics
- Update content from recorded feedback
- Provide a text-first conceptual learning view
- Control remote lab instruments over the internet
- Offer experiments across scientific disciplines
- Book and schedule lab time
- Collect and chart experiment data as it arrives
- Supply lab guides and support resources
- Run code in a browser editor
- Give AI tutor hints and solution reviews
- Answer broad programming questions
- Assist with general learning inquiries
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned instructor-approved practice record linked to learner progress 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 Coding practice and lab workshop platform 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 Coding practice and lab workshop platform 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 Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 KB
- demo/index.htmlThe working demo on sample data218 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
Run one owned practice environment instead of renting several tools. For instructors and team leads running coding practice sessions and lab workshops, convert learner code, exercise definitions, lab schedules and review rules into instructor-approved practice records linked to learner progress. The benefit is a testable hypothesis, measured through completed exercises per learner hour and instructor review time per cohort; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect learner code, exercise definitions, lab schedules and review rules, then follow this sequence: 1. Suggest code completions as the learner types. 2. Check submitted code for bugs and style issues. 3. Suggest optimizations and refactoring steps. 4. Connect to version control and common editors. 5. Adapt suggestions to learner preferences and project settings. 6. Generate personalized tutorials from stated interests. 7. Cover backend, DevOps and full-stack topics. 8. Update content from recorded feedback. 9. Provide a text-first conceptual learning view. 10. Control remote lab instruments over the internet. 11. Offer experiments across scientific disciplines. 12. Book and schedule lab time. 13. Collect and chart experiment data as it arrives. 14. Supply lab guides and support resources. 15. Run code in a browser editor. 16. Give AI tutor hints and solution reviews. 17. Answer broad programming questions. 18. Assist with general learning inquiries. Resolve uncertain cases with qualified reviewers, approve instructor-approved practice records linked to learner progress, and measure completed exercises per learner hour and instructor review time per cohort 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 fixed exercise format and approved lab set; final grading and safety checks remain instructor-led. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve learner voice, source attribution, code accuracy and usage permissions. Instructors approve substantive changes and publication scope. One fixed exercise format and approved lab set; final grading and safety checks remain instructor-led. 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 fixed exercise format and approved lab set; final grading and safety checks remain instructor-led. Implement one approved input format, a bounded representative case set and the first two task modules: suggest code completions as the learner types; check submitted code for bugs and style issues. Support the remaining modules with operator review. 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
Instructor-owned exercise banks, authorized lab equipment and permitted learning sources. Cloud code storage, version control import/export and learning management destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Exercise and lab setup, Live practice workspace, Instructor review and cohort report. Use a thumbnail gallery for cohorts and exercises, a large central editor with a run panel, and a right-hand panel for AI hints, lab controls and comments. Let users compare attempts side by side. Display draft, changes requested and approved states. Provide a learner preview link with comments anchored to the relevant line or lab step. Make the task-specific outcome instructor-approved practice records linked to learner progress visible beside its evidence, review state and value baseline.
