I was bored to tweak my python code. So, I created my own Python-Tweaker

Raghavulu — Sun, 25 Oct 2026 05:06:55 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

What I Built

I created Sloth-Compile V2 to make fixing Python errors less frustrating. Instead of repeatedly reading tracebacks, copying code into a chatbot, and comparing changes manually, I can drop a Python file into the sloth_dropzone folder. I can also include a screenshot of an error message. The application routes text-only requests to Gemma 4 and
screenshot-assisted requests to Phi-3 Vision, according to my current configuration. It saves proposed fixes separately in sloth_processed,
leaving the original file untouched. SQLite stores processing history and code differences, while a Streamlit dashboard lets me review files and inspect unified diffs. Fixes still need human review and testing.

Demo

https://drive.google.com/file/d/1CmJeZMCe3TO1WyIjufuntbyYPTAx0vYM/view?usp=sharing

Code

Bharathkoya15 / Sloth-Compile-V2

Sloth-Compile V2: Multimodal Drop-Box Edition

An intelligent local Python debugger and code-tweaking agent powered by Ollama.

Features

Background drop-box watcher for automated code repair
Multimodal error traceback detection using local vision models
In-memory code tweaker and Q&A workspace via Streamlit dashboard
Sandboxed execution verification

View on GitHub

How I Built It

I built Sloth-Compile with Python, Ollama, Watchdog, SQLite, Streamlit, and difflib. Gemma 4 (gemma4: e2b) handles text-based debugging, while Phi-3 Vision (phi3: vision) handles requests that include screenshots. Both models are called through Ollama’s local /api/chat endpoint. Watchdog monitors the drop-box folder and waits briefly for a matching screenshot. Separate modules manage configuration, model requests, folder monitoring, database history, and code comparisons. SQLite stores original code, proposed repairs, diffs, timestamps, status, and model details. Streamlit provides a local dashboard to review results. Generated fixes are proposals, not proof that code works.

Why Does Open Innovation Matter?

Open innovation let me experiment with an open-weight model running locally instead of relying entirely on a hosted AI API. Using Gemma 4
through Ollama gave me a way to connect model responses to a folder watcher, database, file-processing workflow, and dashboard. This made
the project more than a chat interface. The original code remains separate from the proposed repair, and users can inspect the differences before deciding whether to use them. Local inference also means source code does not need to be sent to a third-party AI API as part of this workflow, although other services and configurations can affect privacy.
I can change models, prompts, and components as the project develops.

The Lazy Payoff

Sloth-Compile aims to reduce repetitive steps in Python debugging. Instead of manually copying code and tracebacks into an AI chat, copying the answer back, and comparing files, I can drop a file into a folder and review the suggested fix in the dashboard. Adding an error
screenshot can provide extra context without manually transcribing the message. The history also lets me revisit earlier attempts, see which model was used, and inspect the changes. Because the original source is preserved, I can review the proposal before adopting it. The actual time saved depends on the bug, model speed, and correctness of the response. I have not measured time savings yet; testing known bugs and recording pass rates would help evaluate the benefit.

Prize Categories

Sloth-Compile fits several project areas: open-weight models, local inference, AI agents and developer tools, multimodal AI, and open-source
tooling. It uses Gemma 4 for text-based debugging, Ollama for local model access, and a separate vision model for screenshot-assisted
debugging. Python, Watchdog, SQLite, Streamlit, and difflib connect the model to a practical workflow. These are descriptive areas, not verified official DevRelay prize categories. Before submitting, I should compare them with the challenge’s official rules and use only categories that actually exist and apply. If a Gemma or Google AI category is available,
I can consider it if the rules permit it. The project’s central idea is turning open-weight AI into a reviewable local debugging tool.

Plants Journal

Raghavulu — Mon, 12 Oct 2026 05:00:00 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

It will make my friend to get off the screen to increase their journal and compete with my friends.

Demo

https://drive.google.com/file/d/1R0OwRl6q26YRVAUU_uiEDxYslLIy7HqT/view?usp=drivesdk

Code

https://github.com/Bharathkoya15/Leaf-Lens-Jornal.git

How I Built It

I used open-weights model of gemma4:12b to build my app for my friend.

Why Does Open Innovation Matter?

It has changed my opinion to just take pictures of plant I like and save my friend much time writing their journal on plants they like.

DEV Community: Raghavulu

I was bored to tweak my python code. So, I created my own Python-Tweaker

What I Built

Demo

Code

Bharathkoya15 / Sloth-Compile-V2

Sloth-Compile V2: Multimodal Drop-Box Edition

Features

How I Built It

Why Does Open Innovation Matter?

The Lazy Payoff

Prize Categories

Plants Journal

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?