Llama Coder - Turn Your Idea Into an App With AI
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Llama Coder Brief Overview
Llama Coder is a browser-based AI code generator that turns a plain-English idea into a working small web app. You describe what you want (for example, “Build a pomodoro timer with start/pause/reset and a clean UI”), and LlamaCoder produces an app you can interact with right away.
It’s positioned as a fast “prompt-to-app” experience similar in spirit to artifact-style coding tools: you focus on the product behavior and UI, and the model generates the code to match. The public demo highlights that it’s “Powered by Together AI” and is used at large scale (the site lists 1.1M+ users). It also promotes common starter ideas (like a quiz app, landing page, blog app, flashcards, and a timezone dashboard) so you can generate something useful in a few clicks.
Under the hood, LlamaCoder is also available as an open-source project. That matters if you want to inspect how it works, self-host a version, or adapt the workflow for your own internal “prompt-to-app” tooling.
How to Use
- Open the LlamaCoder website in your browser.
- Choose a model option (the app presents quality/speed trade-offs depending on the model).
- In the prompt box, describe the app you want. Include:
- What the app does (features, rules, edge cases)
- The UI layout (screens, buttons, forms, navigation)
- Any data needs (sample data, storage expectations)
- (Optional) Click one of the suggested starter prompts (Quiz app, SaaS landing page, Pomodoro timer, etc.) to pre-seed your idea.
- (Optional) Use Attach to add extra context when the UI supports it (requirements, notes, examples).
- Generate the app, then test it in the built-in preview/sandbox.
- Iterate by refining your prompt (e.g., “Add dark mode,” “Make it mobile-friendly,” “Validate inputs,” “Improve spacing and typography”).
- When satisfied, copy/export the generated code for further development or deployment.
Key Features and Functions
- Prompt-to-app generation: Converts a single prompt into a functional small app (commonly React-based in the open-source implementation).
- Model selection: Supports multiple hosted model choices (for example, options such as GLM 4.6, Kimi K2.x, Qwen 3 Coder, and DeepSeek V3.x may be presented), allowing you to balance quality vs. speed.
- Quick-start templates: One-click idea starters (Quiz app, SaaS landing page, Pomodoro timer, Blog app, Flashcard app, Timezone dashboard).
- In-browser running/testing: Uses a code sandbox approach so you can immediately run and interact with the generated UI without setting up a local environment.
- Streaming-style UX (implementation pattern): The reference build approach emphasizes fast feedback while code is being produced, rather than waiting for a single final output.
- Open-source availability: You can review the implementation, run it locally, and modify the workflow (models, prompts, UI, and sandbox behavior).
Llama Coder Pricing
- Public web demo: The LlamaCoder website is presented as a free-to-try demo (no visible subscription checkout flow on the main interface).
- Self-hosting costs (usage-based): If you run your own version, you typically bring your own API keys and pay for underlying services. In the open-source setup, Together AI inference is usage-based, with pricing shown as price per 1M tokens (input/output vary by model). Example serverless inference prices include:
- GLM-4.6: $0.60 input / $2.20 output per 1M tokens
- Kimi K2 Instruct: $1.00 input / $3.00 output per 1M tokens
- Qwen3-Coder 480B A35B Instruct: $2.00 input / $2.00 output per 1M tokens
- DeepSeek-V3-1: $0.60 input / $1.70 output per 1M tokens
- Other potential costs (self-hosted): Depending on your setup, you may also need a code-sandbox key/service and a database provider (each with its own pricing).
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