tobiadefami

I got tired of spending hours digging through Reddit threads and YouTube comments when trying to figure out if a product was worth buying. So I built a thing.

It's basically an AI-powered scraper that pulls reviews from Reddit and YouTube, then structures all that messy data into something actually useful - pros, cons, sentiment analysis, and an overall "should you buy this?" recommendation. Built it with FastAPI, Next.js and PostgreSQL, all wrapped up in Docker.

Looking back, I probably should've just made it a simple CLI tool instead of a whole web app. Would've saved me a lot of time...

Still rough around the edges, but the core works. Code’s on GitHub if you want to tinker

Prompt library has also been implemented and can be opened with ctrl+shift+L; or cmd+shift+L on mac

Great question,

Unlike Jupyter or Deepnote where you write code directly, Probly is primarily prompt-based - you describe what analysis you want in natural language, and the AI generates and executes the appropriate Python code behind the scenes using Pyodide.

The key difference has to be that Probly runs python entirely in your browser using WASM, while jupyter/Deepnote run code on servers.

Docker config is already implemented—you can check out the repo now. Adding support for other LLM providers like Ollama is something to consider for future development. Thanks for the suggestion!

Thanks for taking the time to try it out and share your thoughts. I really appreciate the detailed feedback from a real-world use case.

Glad to hear the setup was smooth and that the chat box + import/export features worked well for you. Noted on the UI tweaks, I'll look into making them more intuitive.

On the categorization issue, yeah, LLMs can struggle with nuanced transaction labeling, especially without proper context or examples. Structured prompting could help, which ties into your second point -- having a library of refined prompts that can be reused for repetitive tasks would be really valuable.

I love your feedback -- it's exactly what helps improve the tool. And again, thanks for testing it out!

I'm actually a fan of what the team at Quadratic is building. It's definitely the more mature product with a robust Python implementation and a well-designed interface that bridges spreadsheets and code. Their stack appears to be built on Rust, which likely gives them performance advantages.

Probly is earlier stage and more minimalist, but we're tackling the same fundamental problem. Our specific focus is on making data analysis a fully autonomous process powered by AI - where you can describe what you want to learn from your data and have the system figure out the rest.

Probly doesn't have built-in test assertion functionality yet, but since it runs Python (via Pyodide) directly in the browser, you can write test functions with assertions in your Python code. The execute_python_code tool in our system can run any valid Python code, including test functions.

This is something we're considering for future development, so this is a great shout!

haha! i'll consider it

posted one about a week or so ago on my LinkedIn

https://www.linkedin.com/posts/oluwatobiadefami_vibe-coding-...

Or the AI is a man united fan and is hopeful for a top 4 finish this season :D

Glad you like it!

Thanks you!

Right now, you can clone the repo and follow the instructions to run it locally with your own OpenAI key. I'm working on a hosted demo that will let you try it out directly without any setup. stay tuned :)

Hi HN,

I'm excited to share my project, Degrees of Torvalds. Inspired by the "The oracle of Kevin Bacon" concept, this tool maps the open-source community by tracing connections to Linus Torvalds through GitHub commit histories.

What is Degrees of Torvalds?

Degrees of Torvalds lets you visualize how any GitHub user is connected to Linus Torvalds. By analyzing commit graphs, it reveals the collaborative network within the open-source ecosystem. This first iteration features a straightforward user interface that shows a given user's connection history to Torvalds.

Why Did I Create This?

The project celebrates the interconnected nature of open-source development, illustrating the collaborative spirit that drives it. Whether you're a seasoned developer or new to open-source, Degrees of Torvalds aims to inspire by showing how close you might be to one of the community's most influential figures.

Technical Details:

Backend: Built with FastAPI for efficient request handling. Frontend: Developed using React for a smooth user experience. Deployment: Hosted on Google Cloud Platform for scalability and reliability. Try It Out! Explore Degrees of Torvalds at sixdegreesofgithub.com. Your feedback and suggestions are welcome!

Happy coding!

- Oluwatobi Adefami

This blog post suggests approaching LLM prompt management by drawing parallels with RAM optimization and memory management techniques in programming, offering a new perspective on enhancing LLM efficiency.

Datasynth is a pipeline for synthetic data generation and normalization operations using LangChain and LLM APIs. Using Datasynth, you can generate absolutely synthetic datasets to train a task-specific model you can run on your own GPU.