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.
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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 LatentChat - Assistant LLM: map the core Productivity 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 task management features compared to competitors
Validate with people who use this workflow before building.
Hypothesis 2
Assumption to check: Inconsistent performance on older devices
Validate with people who use this workflow before building.
Hypothesis 3
Assumption to check: Subscription model not clearly explained
Validate with people who use this workflow before building.
Hypothesis 4
Assumption to check: Lack of customization options
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.
Try different generative language models with LatentChat, a fully offline, on-device private AI chatbot. LatentChat is ready to use right out of the box. Ask questions, brainstorm, reason, transcribe speech, add your own models and create with your customized chat companion from anywhere, even without an internet connection. - Local chat Try models like Llama, Gemma, Qwen and Mistral or use your own local LLMs by selecting your preferred open-source language models stored on your device. Reasoning models are supported, too. - Remote chat LatentChat can be used as a client to connect to any OpenAI-compatible and Anthropic-compatible API endpoint, whether it is running on your own private servers like Ollama, LM Studio or on cloud services OpenRouter (Bring your own API Key). - Retrieval-Augmented Generation (RAG) Create your knowledge center from text or files (PDF, TXT, JSON, HTML) and retrieve your documents while you chat to ask related questions or summarize. A RAG system can also help the model reduce hallucinations by referencing the knowledge you added. - Vision capabilities Try multimodal models like Qwen 3 VL, InternVL and LFM2.5 with vision capabilities, to analyze images or extract text. - Transcription and Voice (STT and TTS) Transcribe recordings or audio files with Whisper and read messages aloud with different voice styles. Multiple languages are supported. - System Prompts Use System Prompts to personalize your conversations by providing custom instructions that can modify the behavior or personality of the AI. Features: == Completely offline and on-device processing == Your prompts remain private and are never shared == Model catalog: a selection of LLMs downloadable from HuggingFace == Bring your AI models: download and choose your favorite LLMs (.gguf) == Connect to servers like Ollama or LM Studio for more powerful models == Local RAG: Integrate your own knowledge from text == Vision capabilities with multimodal models to process images == Text to speech: read messages aloud (English, Spanish, French, Portuguese) == Multilingual transcription (STT) using Whisper == Customize system instructions and parameters == Shortcuts integration == No subscriptions, buy once and get any update Read the terms and privacy policy before using this app, available on latentlake.com/termspolicy/chat-policy-terms
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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.