AI app for it and development · no coding needed
Source-linked code assistant and admin console
Reduce tool sprawl while keeping code and review evidence inside the buyer's own environment.
Made for: Engineering teams and platform owners who need AI coding help inside their own infrastructure
What it does for you
The problem
Coding AI is rented across several tools, so code, prompts and review evidence sit outside the buyer's control and workflow.
What it gives you
Source-linked code suggestions with named-owner approval
What you give it
Repositoryapproved model checkpointspolicy rulesreview requirements
Build your own version of Qwen3-Coder, Mistral Code and more
One app with what these 3 AI tools do, yours to keep and change: Qwen3-Coder, Mistral Code, MiMo-V2-Flash.
Everything these tools do, in one app
- AI coding assistance Provides AI-powered help for programming and code-related tasks.Found in Qwen3-Coder, Mistral Code, MiMo-V2-Flash
- Mixture-of-Experts architecture Uses a sparse model design with many parameters but only a subset active at runtime, improving efficiency.Found in Qwen3-Coder, MiMo-V2-Flash
- Large context window Allows processing of very large codebases in a single session.Found in Qwen3-Coder
- Strong coding benchmark performance Achieves high results on established coding benchmarks.Found in Qwen3-Coder, MiMo-V2-Flash
- Open-source availability Released under an open-source license, allowing free use and modification.Found in Qwen3-Coder, MiMo-V2-Flash
- CLI tool Includes a command-line interface for easy usage and customization.Found in Qwen3-Coder
- API access Can be accessed through an API for integration into various environments.Found in Qwen3-Coder, MiMo-V2-Flash
- Local deployment option Supports running the model locally on your own infrastructure.Found in Qwen3-Coder, Mistral Code
- IDE integration Integrates with popular integrated development environments like VS Code and JetBrains.Found in Mistral Code
- Model fine-tuning Allows customizing the AI model on your own codebase to improve relevance.Found in Mistral Code
- On-premises deployment Supports deployment within your own data center for security and compliance.Found in Mistral Code
- Enterprise security controls Provides full-stack control to ensure security and compliance with enterprise policies.Found in Mistral Code
- Multiple checkpoint types Offers base, SFT, and RL-tuned versions for different use cases.Found in MiMo-V2-Flash
- Agent workflow tuning Explicitly optimized for agent-style workflows and reasoning tasks.Found in MiMo-V2-Flash
- Public testing studio Provides a public environment to test the model before integration.Found in MiMo-V2-Flash
How it works, step by step
- Index the repository and retrieve relevant files
- Generate code suggestions with source links
- Run agent-style multi-step coding tasks
- Support a large context window over the codebase
- Offer base, SFT and RL-tuned checkpoint choices
- Serve suggestions through CLI and API
- Integrate with VS Code and JetBrains
- Run locally or on-premises
- Apply enterprise security and policy controls
- Fine-tune on the buyer's own codebase
- Provide a public testing studio for evaluation
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before merge
- Export a versioned source-linked code suggestions with named-owner approval record 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 assistant and admin 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 assistant and admin 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 Cloudflare24 KB
- prompt-vps.mdThe same build on your own server (Docker)24 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 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 while keeping code and review evidence inside the buyer's own environment. For engineering teams and platform owners who need AI coding help inside their own infrastructure, convert the repository, approved model checkpoints, policy rules and review requirements into source-linked code suggestions with named-owner approval. The benefit is a testable hypothesis, measured through accepted suggestions per developer hour and corrections after merge; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect the repository, approved model checkpoints, policy rules and review requirements, then follow this sequence: 1. Index the repository and retrieve relevant files. 2. Generate code suggestions with source links. 3. Run agent-style multi-step coding tasks. Resolve uncertain cases with qualified reviewers, approve source-linked code suggestions with named-owner approval, and measure accepted suggestions per developer hour and corrections 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 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 approved model checkpoint set and one repository scope; final code review and merge 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 owners approve substantive changes and deployment scope. One approved model checkpoint set and one repository scope; final code review and merge 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 approved model checkpoint set and one repository scope; final code review and merge decisions remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: index the repository and retrieve relevant files; generate code suggestions with source links. Support the third module with operator review: run agent-style multi-step coding tasks. 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
Buyer-owned repositories, approved model checkpoints and permitted policy sources. Cloud or on-premises storage, IDE import/export 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 policy setup, Editable code suggestion workspace, Admin console and audit. Use a project list, a large central diff canvas, and a right-hand panel for sources, model checkpoint, policy constraints and comments. Let users compare suggested and current code side by side. Display draft, changes requested and approved states. Provide a reviewer queue with comments anchored to the relevant file and line. Make the task-specific outcome source-linked code suggestions with named-owner approval visible beside its evidence, review state and value baseline.
