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.
Neural Object Detector was designed to be used by both developers and people who are enthusiastic about Machine Learning, Computer Vision, and Object Detection / Image Classification using the combination of both
$2.99 · In-app purchases
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 Neural Object Detector: 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: Limited object recognition for complex images
Validate with people who use this workflow before building.
Hypothesis 2
Assumption to check: Slow processing times for large images
Validate with people who use this workflow before building.
Hypothesis 3
Assumption to check: Poor user guidance for first-time users
Validate with people who use this workflow before building.
Hypothesis 4
Assumption to check: Inconsistent results across different lighting conditions
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.
Neural Object Detector was designed to be used by both developers and people who are enthusiastic about Machine Learning, Computer Vision, and Object Detection / Image Classification using the combination of both. Neural Object Detector, by default is bundled with YOLOv3 model, which is a neural network for fast object detection that detects 80 different classes of objects. In addition to that, the app allows the users to import any custom machine learning model designed for object detection or image classification, with a single tap, the downloaded model can be imported via the Files app import window which is available within the app by simply pressing the plus icon on the models view so users do not have to leave the app. The app, based on the model selected, draws a rounded rectangle over the detected objects, the annotated image can be rendered and saved to photos or shared if the user chooses to share it directly from the app. To meet every users needs a handful of settings for computer vision algorithm and camera resolution can be changed. Users have the option to enable CPU only mode, which helps test their models under that specific condition. In addition to Object Detection, Neural also supports Image Classification, by default, Neural is bundled with Resnet50 Image Classification Model. Core ML* models which are a type of "Pipeline" in the format of *.mlmodel is supported by this app for Object Detection. No other model format is supported as of now. Core ML Supported Tools, Services, and Converters: • Turi Create* - https://github.com/apple/turicreate • IBM Watson Services* - https://developer.apple.com/ibm/ • Core ML Tools* - https://pypi.org/project/coremltools/ • Apache MXNet* - https://github.com/apache/incubator-mxnet/tree/master/tools/coreml • TensorFlow* - https://github.com/tf-coreml/tf-coreml • ONNX* - https://github.com/onnx/onnx-coreml * Turi Create, IBM Watson Services, Core ML, Apache MXNet, TensorFlow, ONNX might be registered trademarks of their respected owners / proprietors. Neural Object Detector nor the developer is not affiliated with any of the above services or companies. YOLOv3 Model bundled with the is app is free, open source model. More info: https://github.com/pjreddie/darknet Resnet50 Model bundled with this app for Image Classification is free, and open source model. More info: https://github.com/fchollet/deep-learning-models/blob/master/LICENSE Visit https://hariharanm.com/neural/acknowledgements/ for Acknowledgments. The machine learning aspects are all proceed on device so nothing leaves the device. No cloud services are involved. No private analytics services. If, a user decides to contact app support a handful of data will be complied into a log file and attached to the mail composer, these data include app version, app configuration, device model, battery info, device software version, CPU utilised my the app. If the user decided not share, they can simply delete it and continue with the composing the support mail. No data is that is individually identifiable is collected. https://hariharanm.com/
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.