Hey r/LocalLLaMA

I spent nearly a decade working in the trenches as a data scientist, wrestling with massive datasets, handling messy enterprise schemas, and using just about every major ETL tool on the market. A few years ago, my life changed completely when I became a quadriplegic. But my passion for building software close to the metal never stopped.

For the past 3 months, I’ve dedicated my time to engineering a visual data manipulation platform from the ground up—exactly how I always wished it existed when I was working in the industry.

It’s called VibeETL, and it is officially ready for the community to test, break, and scale.

🔗 Repository: https://github.com/cardchase/VibeETL

⚡ Built for True Scalability & Infinite Speed

Because I’ve worked with heavy legacy systems, I designed VibeETL to completely avoid visual and computational lag:

• Blazing Fast Polars Core: The backend is powered entirely by Polars and Rust-native optimizations, leveraging zero-copy Apache Arrow memory transport allocations.
• Zero-Dependency BFS Snap Layout: I ripped out heavyweight third-party layout libraries like dagre to eliminate Vite HMR dependency freezes. I engineered a native Topological BFS Layout algorithm directly inside the React Flow canvas to instantly snap connected node matrices from left to right.
• Lag-Free UI Buffering: Form parameter side-panels use localized component input shielding (SafeInput). Keystroke mutations are containerized so that editing complex custom formulas or handling 40+ sport-betting and historical odds column layouts drops typing lag to absolute zero without thrashing the master canvas.
• Isolate Process Jailing: The Python Code node runs custom data scripts and machine learning algorithms inside an ultra-secure, ephemeral local subprocess jail featuring a strict 30-second execution cutoff to prevent computing freezes or main server thread crashes.

🌌 Built for the AI Age: Drop in Your Own Custom Tools!

I designed VibeETL with a strict rule: Absolute Community Extensibility. The manifest-driven Python backend makes it incredibly easy to build new processing blocks.

If you use autonomous coding agents (like an AI anti-gravity agent), you can literally hand it the workspace base template folder, ask it to write a new data tool, drop the generated folder straight into the codebase, write a Pull Request, and instantly contribute to the ecosystem.

🛠️ Where I Need the Community's Help to Test & Harden:

While the primary data ingestion paths, data cleansing engines, database read/write blocks, and high-density spreadsheet grid layouts are fully stable and hardened to enterprise specs on local machine environments, I haven't been able to fully validate some of the external cloud paths myself.

I would love for developers, cloud architects, and machine learning specialists to pull the repo and actively test/break:

1. The Gemini Vision AI Integration: Validating image captioning ingestion pipelines and token processing loops.
2. Cloud Connectors & Google Cloud Tools: Pushing the limits of our Google Sheets inputs/outputs, GCS streams, and secure credential path-jailing guards.
3. Hardware & GPU Acceleration: Seeing how far we can push matrix weight scaling (like running Nvidia RAPIDS cuDF/cuML or PyTorch CUDA drivers) within our isolated Python subprocess container jail if you have a local GPU environment.

🚀 Getting Started on Localhost Loopback:

You can clone the repo, run the automated launch script, and have a fully responsive, beautiful, glassmorphic visual canvas workspace running on your local loopback port in seconds:

Bash

# Clone the Core git clone https://github.com/cardchase/VibeETL.git cd VibeETL # Run the automated launcher # Windows: .\run.ps1 # Mac/Linux: ./run.sh 

Please take a look at the code, run some of your own historical datasets through it, and let me know your thoughts. I am incredibly proud to share this first version with you all, and I cannot wait to see what tools the community builds and contributes via PRs.

Let's build the future of visual data engineering together! 🔥

p.s. I have built this using Gemini and voice on the anti gravity platform I know this is about local models and now that the product is enterprise ready for testing you can just drop the folder to your model's context and tell it what it has to build and it will build it up from there. I have I tried to make it as simple as I possibly can And the best part is I plan to keep it free for the community It comes with the MIT licence.

p.s. I'm a quadriplegic and have typing challenges obviously this has been This post has been created by AI but the content is what I intended to and it has been correctly communicated