Tired of copy-pasting prompts? Prompter Dock gives you a private, offline prompt organizer with a global Spotlight-style HUD. Access your prompts instantly inside any app, chain prompts to automate workflows, and improve them using a bundled local LLM.
Hi Product Hunt! 👋
I’m the creator of Prompter Dock, and I’m incredibly excited to share it with you all today!
Like many of you, I found myself interacting with AI tools (ChatGPT, Claude, local models) dozens of times a day. But my workflow was broken. My prompts were scattered across messy text files, Google Docs, and random Apple Notes. Every time I needed to prompt, I had to stop what I was doing, search through these files, copy, modify, and paste. Plus, I felt increasingly uneasy about copy-pasting proprietary source code and client docs into cloud-based prompt organizers.
I built Prompter Dock to solve exactly this. It's a native desktop app for macOS & Windows that acts as your prompt nerve center:
Key Features:
• Global HUD: Summon your prompts instantly from inside VS Code, Slack, or any browser tab using Ctrl+Opt+Z (macOS) or Ctrl+Alt+Z (Windows) (or simply by shaking your cursor!). Copy and auto-hide in a split second.
• 100% Local & Private: A bundled local LLM handles prompt refinement (6+ specialized personas) completely offline. Your prompt data never leaves your device.
• Prompt Chains: Automate complex workflows by linking multiple prompts together sequentially.
• Active Ranking & History: Your most-used prompts bubble to the top automatically, and every version is saved so you never lose an iteration.
Launch Day Special:
Anyone can download Prompter Dock and enjoy a 10-day, fully unrestricted free trial. For the Product Hunt community, we will also be offering a special lifetime discount very soon! We are working on it as you read on this. We will be offering 10% off for all Product Hunt community members.
I'd love to hear your feedback:
1. What does your current prompt organization look like?
2. What kinds of workflows or chains would make your daily tasks easier?
I’ll be here all day answering questions and chatting. Thank you for the support! 🙏
Report
How does the bundled local LLM actually perform for refining complex prompts, like compared to running something through GPT-4 in the cloud?
Report
Maker
@anlcengi7zjn Fantastic question! I tried giving this local LLM several different raw prompts to generate improved prompts during testing. I gave it prompts for the same task at three different levels: 1. No context e.g. Build a fully featured calculator app. 2. Low context e.g. Build a fully featured calculator app. Implement a clean, responsive UI with standard arithmetic (+, −, ×, ÷), decimal support, parentheses, percentage, sign toggle (±), memory functions (MC, MR, M+, M−), clear (C/AC), backspace, keyboard shortcuts, calculation history, error handling, and dark/light mode. Structure the project with maintainable, modular code, include tests where appropriate, and ensure the app is production-ready with good UX and accessibility. 3. High context e.g. Build a production-ready calculator app using React + TypeScript + Vite and Tailwind CSS. Use Zustand for state management and Vitest for testing. Create a responsive, accessible UI with dark/light mode, keyboard shortcuts, standard arithmetic, parentheses, percentage, ±, memory (MC/MR/M+/M−), backspace, AC/C, calculation history (persisted in localStorage), configurable precision, and robust error handling. Organize the code with feature-based architecture, reusable components, custom hooks, and utility modules. Follow clean code principles, strict TypeScript, ESLint + Prettier, and include unit tests, README, and complete project structure.
I can assure you that the local LLM was surprisingly good for its size. The system prompt is highly optimized and the model creates fantastic results even with low context. Also, it is worth noting that very long prompts and too much context degrades any model's performance. The trick is to give the right amount of information.
Report
The offline Spotlight-style HUD actually pops up faster than the cloud alternatives I have tried, and chaining prompts together for repetitive tasks is genuinely useful. Nice to see a local LLM included too, no data leaving the machine.
Report
Spotlight-style HUD for prompts is a great idea, especially the offline angle. The chaining feature feels like it could seriously speed up repetitive writing tasks I do all day.
Report
Maker
We have an ongoing promotion for all the Product Hunt community members. Get 10% off on lifetime licenses. Use promo code PRODUCTHUNT10% to avail this fantastic offer!
Report
No reviews yetBe the first to leave a review for Prompter Dock
How does the bundled local LLM actually perform for refining complex prompts, like compared to running something through GPT-4 in the cloud?
@anlcengi7zjn Fantastic question! I tried giving this local LLM several different raw prompts to generate improved prompts during testing. I gave it prompts for the same task at three different levels:
1. No context
e.g. Build a fully featured calculator app.
2. Low context
e.g. Build a fully featured calculator app. Implement a clean, responsive UI with standard arithmetic (+, −, ×, ÷), decimal support, parentheses, percentage, sign toggle (±), memory functions (MC, MR, M+, M−), clear (C/AC), backspace, keyboard shortcuts, calculation history, error handling, and dark/light mode. Structure the project with maintainable, modular code, include tests where appropriate, and ensure the app is production-ready with good UX and accessibility.
3. High context
e.g. Build a production-ready calculator app using React + TypeScript + Vite and Tailwind CSS. Use Zustand for state management and Vitest for testing. Create a responsive, accessible UI with dark/light mode, keyboard shortcuts, standard arithmetic, parentheses, percentage, ±, memory (MC/MR/M+/M−), backspace, AC/C, calculation history (persisted in localStorage), configurable precision, and robust error handling. Organize the code with feature-based architecture, reusable components, custom hooks, and utility modules. Follow clean code principles, strict TypeScript, ESLint + Prettier, and include unit tests, README, and complete project structure.
I can assure you that the local LLM was surprisingly good for its size. The system prompt is highly optimized and the model creates fantastic results even with low context. Also, it is worth noting that very long prompts and too much context degrades any model's performance. The trick is to give the right amount of information.
The offline Spotlight-style HUD actually pops up faster than the cloud alternatives I have tried, and chaining prompts together for repetitive tasks is genuinely useful. Nice to see a local LLM included too, no data leaving the machine.
Spotlight-style HUD for prompts is a great idea, especially the offline angle. The chaining feature feels like it could seriously speed up repetitive writing tasks I do all day.
We have an ongoing promotion for all the Product Hunt community members. Get 10% off on lifetime licenses. Use promo code PRODUCTHUNT10% to avail this fantastic offer!