AI app for it and development · no coding needed
Source-linked open model coding and reasoning console
Reduce dependence on rented assistants while keeping source-linked answers inside the buyer's own infrastructure.
Made for: Engineering teams and IT administrators who need a self-hosted open model for coding, reasoning and long-context work
What it does for you
The problem
Teams rent several closed coding and reasoning assistants, cannot inspect or self-host the model, and cannot link answers to their own code, documents and tool calls.
What it gives you
Source-linked assistant answers and administrator-reviewed run records
What you give it
Permitted codedocumentsfilestool definitions
Build your own version of DeepSeek-R1-0528, Grok 2.5 (OSS Ver.) and more
One app with what these 6 AI tools do, yours to keep and change: DeepSeek-R1-0528, Grok 2.5 (OSS Ver.), QwQ-32B, Mistral Medium 3.5, Kimi K2 Thinking, Command A Reasoning.
Everything these tools do, in one app
- Open-source availability The model's source code and weights are publicly available for use, modification, and self-hosting.Found in DeepSeek-R1-0528, Grok 2.5 (OSS Ver.), Mistral Medium 3.5 and 2 more
- Large language model A model with a large number of parameters capable of generating coherent and detailed text.Found in DeepSeek-R1-0528, Grok 2.5 (OSS Ver.), QwQ-32B and 3 more
- Coding and reasoning The model can perform programming tasks and logical reasoning.Found in DeepSeek-R1-0528, Mistral Medium 3.5, Kimi K2 Thinking and 1 more
- Long context window The model can process very long inputs such as large documents or codebases.Found in DeepSeek-R1-0528, Mistral Medium 3.5, Kimi K2 Thinking
- API availability The model can be accessed and integrated via an application programming interface.Found in DeepSeek-R1-0528, QwQ-32B
- Reduced hallucinations The model generates fewer incorrect or fabricated outputs.Found in DeepSeek-R1-0528
- Multilingual support The model can understand and generate text in multiple languages.Found in QwQ-32B
- Customizable output settings Users can adjust parameters to control the style and length of generated responses.Found in QwQ-32B
- Configurable reasoning effort Users can adjust the amount of computation per request to balance speed and depth.Found in Mistral Medium 3.5
- Self-hostable The model can be deployed and run on the user's own hardware.Found in Mistral Medium 3.5, Kimi K2 Thinking, Command A Reasoning
- Agentic tool support The model can execute sequences of tool calls with step-by-step reasoning and self-correction.Found in Kimi K2 Thinking
- Quantized inference The model supports low-bit quantization to reduce latency and resource usage.Found in Kimi K2 Thinking
- Web search integration The model can perform real-time web searches to gather information.Found in Kimi K2 Thinking
- File analysis The model can analyze multiple files to extract information.Found in Kimi K2 Thinking
- Slide and website generation The model can generate slide presentations and websites.Found in Kimi K2 Thinking
- Transparent chain-of-thought The model provides detailed explanations of its reasoning steps for auditability.Found in Command A Reasoning
- User-controlled token budget Users can set a limit on the number of tokens generated to manage cost and speed.Found in Command A Reasoning
- Runs on single GPU The model can operate efficiently on a single high-end GPU.Found in Command A Reasoning
How it works, step by step
- Load and run an open-source model from published weights
- Answer coding and reasoning prompts with source-linked citations
- Process long codebases and documents within the context window
- Expose an API for integration into internal tools
- Flag low-confidence or unsupported statements for review
- Answer in multiple languages
- Adjust output style, length and token budget
- Set reasoning effort per request
- Deploy on buyer-controlled hardware, including a single GPU
- Run quantized inference for lower latency and memory
- Execute agentic tool calls with step-by-step reasoning and self-correction
- Perform permitted web searches and record the sources used
- Analyze multiple uploaded files and extract structured information
- Generate slide outlines and website drafts from reviewed inputs
- Show transparent chain-of-thought for audit
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned source-linked assistant answers and administrator-reviewed run records 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 open model coding and reasoning 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 open model coding and reasoning 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 Cloudflare28 KB
- prompt-vps.mdThe same build on your own server (Docker)28 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria15 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 dependence on rented assistants while keeping source-linked answers inside the buyer's own infrastructure. For engineering teams and IT administrators who need a self-hosted open model for coding, reasoning and long-context work, convert permitted code, documents, files and tool definitions into source-linked assistant answers and administrator-reviewed run records. The benefit is a testable hypothesis, measured through accepted answers per reviewer hour and corrections after review; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect permitted code, documents, files and tool definitions, then follow this sequence: 1. Load and run an open-source model from published weights. 2. Answer coding and reasoning prompts with source-linked citations. 3. Process long codebases and documents within the context window. Resolve uncertain cases with qualified reviewers, approve source-linked assistant answers and administrator-reviewed run records, and measure accepted answers per reviewer hour and corrections after review 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 approved model build and hardware profile; final code, security and compliance decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, license terms and usage permissions. Named owners approve substantive changes and deployment scope. One approved model build and hardware profile; final code, security and compliance 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 approved model build and hardware profile; final code, security and compliance decisions remain human. Implement one approved input format, a bounded representative case set and the first three task modules: load and run an open-source model from published weights; answer coding and reasoning prompts with source-linked citations; process long codebases and documents within the context window. 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
Buyer-owned repositories, document stores and internal tools. Cloud or on-premise storage, code hosting 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: Model and deployment setup, Assistant workspace, Administrator console. Use a project list for repositories and document sets, a large central chat and code canvas, and a right-hand panel for sources, tool calls and parameters. Let users compare model versions and reasoning settings side by side. Display draft, changes requested and approved states. Provide a source-linked answer view with citations anchored to the relevant file or line. Make the task-specific outcome source-linked assistant answers and administrator-reviewed run records visible beside its evidence, review state and value baseline.
