Open-Source AI Water Task Automator
Budget: $15 – $25 USD
I want to release a fully open-source AI application that “just works” for everyday water-related operations while remaining flexible enough to take on any other repetitive duties people throw at it—from finance spreadsheets to social-media scheduling and marketing reports. The core objective is automation: once the software is installed, users should be able to hand off a job and trust the AI to carry it through without constant oversight.
Where water is concerned, I’m less interested in a single specialty like leak detection or usage monitoring and more in building a solid AI that simply gets the work done, whatever that work happens to be. To prove the concept, you can start with a small demonstrator focused on a common water task of your choice, then show how the same framework plugs into non-water scenarios (e.g., quick-books-style finance reconciliation or auto-posting campaign content).
Must-haves
• 100 % open-source code in a public Git repository
• Clear instructions for local setup and Docker deployment
• Modular architecture so additional task “skills” can drop in with minimal refactoring
• An example water module plus at least one non-water module (finance, social media, or marketing—your pick)
• Concise user documentation and a short video or GIF that walks through the hand-off / execution / result flow
The simpler the tech stack the better, but I’m fine with Python, Node.js, or a small Rust service if you feel performance demands it. Lean on well-supported libraries such as TensorFlow, PyTorch, scikit-learn, or OpenAI’s function-calling API—whichever speeds you toward a clean, repeatable release.
I’ll sign off once I can:
1. follow your README to spin up the app on my own machine,
2. assign a sample water job plus an unrelated business task, and
3. see both completed autonomously with logs confirming each step.
If that sounds straightforward, tell me how you would architect the first release and what timelines you need.
Where water is concerned, I’m less interested in a single specialty like leak detection or usage monitoring and more in building a solid AI that simply gets the work done, whatever that work happens to be. To prove the concept, you can start with a small demonstrator focused on a common water task of your choice, then show how the same framework plugs into non-water scenarios (e.g., quick-books-style finance reconciliation or auto-posting campaign content).
Must-haves
• 100 % open-source code in a public Git repository
• Clear instructions for local setup and Docker deployment
• Modular architecture so additional task “skills” can drop in with minimal refactoring
• An example water module plus at least one non-water module (finance, social media, or marketing—your pick)
• Concise user documentation and a short video or GIF that walks through the hand-off / execution / result flow
The simpler the tech stack the better, but I’m fine with Python, Node.js, or a small Rust service if you feel performance demands it. Lean on well-supported libraries such as TensorFlow, PyTorch, scikit-learn, or OpenAI’s function-calling API—whichever speeds you toward a clean, repeatable release.
I’ll sign off once I can:
1. follow your README to spin up the app on my own machine,
2. assign a sample water job plus an unrelated business task, and
3. see both completed autonomously with logs confirming each step.
If that sounds straightforward, tell me how you would architect the first release and what timelines you need.