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  1. artefacts.aiArtefacts.AI

    Artefacts is a 3D AI toolkit that enables users to effortlessly transform text or 2D images into 3D assets. Unleash your creativity with Artefacts - the future of 3D content creation.

  2. Images

    • Ancient Artifacts - AI Generated Artwork - NightCafe Creator
    • Beyond Shovels and Brushes: The AI-driven Evolution of Archaeological ...
    • Free Artifacts. AI-generated digital art. | Figma
    • Artifact.AI | ora.ai

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  3. app.artefacts.aiArtefacts.AI

    Artefacts.AI. Log in to Artefacts.AI to continue. Forgot password? Continue. Need an account? Sign Up. Other Log-in Methods. Having trouble logging in? Contact us!

  4. prototypr.io › post › claude-artifacts-ai-app-generatorClaude Artifacts: From Conversational AI to App Workspace (a no...

    Jul 18, 2024 · AI generated apps with Claude Artifacts. This overview explores Artifacts' capabilities in generating web tools, games, and visualizations, comparing it to traditional coding and tools like ChatGPT.

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  6. artifacts.aiARTiFACTS - Protecting the Global Drug Supply Chain from...

    Artifacts VERIFY is a post-market surveillance system using chemical analysis, AI, and blockchain to detect and report counterfeit medicines.

  7. support.anthropic.com › en › articlesWhat are Artifacts and how do I use them? | Anthropic Help Center

    Artifacts are self-contained pieces of content that Claude can share with you in a separate window. Learn how to use Artifacts to edit, iterate, and reuse them in your conversations with Claude.

  8. h2o.ai › wiki › artifactsWhat is an Artifact in Machine Learning? - H2O

    Learn what artifacts are in machine learning and how to export them using H2O Driverless AI. Artifacts are the output, input, or intermediate results of the training process or the software development process.

  9. censius.ai › wiki › ai-artifactsWhat is AI Artifacts - Machine Learning | MLOps Wiki - Censius

    AI artifacts are the output generated by the training process, such as models, datasets, metrics, and code. Learn how to manage AI artifacts across the ML lifecycle stages and why it is important for reproducibility and traceability.