Feature spec
Goal: Send a clear invoice after finishing a job.
- Add a client, line items, a due date, and payment instructions.
- Preview the total before exporting a PDF.
- Keep an editable draft if export fails.
Core ML Models is a curated, on-device AI playground
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Evidence scope: No current overall US chart position is available. Category-chart positions shown in discovery are a separate scope. Revenue is a directional model estimate, not verified earnings.
A product spec, screen plans, and build steps for your coding agent.
First kit free with a verified email
Combine features and design from your favorite apps.
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Generating… usually about a minute.
What should your app do differently? Your note travels with the kit so your coding agent builds around your idea.
A product spec, phased build plan, differentiation notes, and screen references.
Illustrative example: an invoice app for independent contractors. Your kit is tailored to the app you choose; this is not its generated content.
Goal: Send a clear invoice after finishing a job.
Invoice editor: Client at the top, editable line items in the middle, total and preview action at the bottom.
States: Empty draft, validation errors beside each field, exporting, and a retry action that preserves the draft.
Hypothesis: Contractors need faster repeat invoices more than more templates.
First experiment: Test duplicating a previous job with five contractors. Watch where they hesitate before expanding the feature set.
The full kit adds a phased build plan, evidence notes, tool prompts, and implementation guidance. It is a plan for your coding agent, not a finished app.
Usage: Mechanics are fair game; never reuse the original name, branding, assets, or verbatim copy.
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Good fit for mobile-first iOS and Android prototypes with managed app scaffolding.
Rork plan preview for Models Zoo: map the core Developer Tools workflow, choose the smallest differentiated feature set, define the data and monetization boundaries, then prototype the riskiest user journey first.
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These are generated suggestions, not verified review quotations. Check current App Store reviews and speak with users before treating a possible gap as a requirement.
Hypothesis 1
Assumption to check: Lack of user reviews and ratings can deter new users.
Validate with people who use this workflow before building.
Hypothesis 2
Assumption to check: Limited model search and filtering options.
Validate with people who use this workflow before building.
Hypothesis 3
Assumption to check: In-app purchase clarity is often confused by users.
Validate with people who use this workflow before building.
Hypothesis 4
Assumption to check: No community features to engage users.
Validate with people who use this workflow before building.
Original App Store material. Ratings and screenshots describe the existing app, not proof of demand for your version.
Core ML Models is a curated, on-device AI playground. Download any model from the built-in catalog and try it out instantly — no server, no account, nothing leaves your device. Every model is an open-source release converted from PyTorch to Apple's Core ML format and tuned to run efficiently on the Neural Engine, GPU, or CPU. WHAT YOU CAN DO • Chat with small language models (Gemma, etc.) including multimodal vision+text • Generate images from text with Hyper-SD • Remove backgrounds, matte video subjects, and colorize old photos • Estimate depth and reconstruct 3D faces from a single photo • Detect and classify with YOLO, SigLIP, and zero-shot open-vocabulary models • Separate music stems with Demucs and transcribe audio to MIDI with Basic Pitch • Clone voices and synthesize speech with Kokoro and OpenVoice • Super-resolve images with SinSR and run anomaly detection with EfficientAD DESIGNED FOR THE NEURAL ENGINE Each model ships with compute-unit settings tuned per-architecture (Neural Engine, GPU, CPU) so you get the best inference speed Apple Silicon can deliver. OPEN AND TRANSPARENT Every model in the catalog links back to its original paper, repository, and license. Conversion scripts are public on GitHub so you can see exactly how the .mlpackage files were produced. PRIVACY FIRST All inference runs locally on your device. The app only fetches model files from public storage over HTTPS — there are no analytics, no ads, and no account to create. REQUIREMENTS • iPhone or iPad with Apple Silicon recommended for best performance • Some large models require several GB of free storage and ≥ 6 GB RAM
Written user reviews are not shown here. Check current App Store reviews >
Plan a focused first version with your coding agent. These are planning assumptions, not a delivery guarantee.
Choose one audience and one core workflow. Use the kit to agree on its screens, data, and acceptance criteria before building.
Decide which secondary features, integrations, and platform support can wait. Your version does not need to reproduce everything in the original.
Validate external services, specialist technology, data access, and ongoing costs for your chosen scope.
From the Build Kit assessment.
A reliable timeline needs an agreed scope and a technical check. Ask your agent to estimate the phases in BUILD_PLAN.md after that review.