ScrollCraft vs Qdrant Viewer

ScrollCraft is a native iPhone, iPad, and Mac app for SwiftUI developers who want to design scrollTransition() effects visually, then copy or export the generated SwiftUI code. Use live sliders to tune opacity, scale, offset, blur, saturation, colors, and corner radius while the preview updates immediately. When the transition feels right, open the code view and take the generated SwiftUI into your own project. Highlights: Live preview for scrollTransition() experiments A focused Apple-platform workflow for exploring SwiftUI scroll effects Phase-specific controls for topLeading, identity, and bottomTrailing Readable SwiftUI code generated from your exact settings One free code copy, with a one-time Pro unlock for unlimited copying Pro export to .swift files and saved local presets No account, no tracking, no ads, and no subscription ScrollCraft is intentionally focused: it is not a code editor, compiler, or general animation suite. It is a small, native tool for learning, tuning, and reusing SwiftUI scroll transitions on iPhone, iPad, and Mac.

Qdrant Viewer is a client app for connecting directly from your iPhone to vector databases hosted either self-hosted or on Qdrant Cloud, allowing you to inspect collections and perform semantic searches. Key Features: - Built-in Demo Environment: Immediately try vector search with approximately 30,000 MeSH (Medical Subject Headings) vectors, with no configuration required. - Flexible Connection Settings: Supports connection via REST API, including authentication with api-key, Bearer tokens, and custom headers. - Collection Information Display: View the number of points, segments, vector dimensions, and distance function. Alias names are prioritized if set. - Semantic Search: Perform vector searches using natural language. Adjust the number of results and score threshold for each search. - Flexible Result Display: Select and list any field within the payload (supports nested structures). Detailed inspection in JSON format is also available. - 3D Visualization: Project search results into a three-dimensional space to visually explore the distance relationships between vectors. - Multiple Embedding Engines: Supports Qdrant Built-in, OpenAI, Google, Hugging Face, Cohere, and custom APIs. - Data Source: Uses MeSH (U.S. National Library of Medicine, public domain) as demo data. The embedding model is intfloat/multilingual-e5-small, licensed under the MIT License.