Want to Contribute to Open Source? Join Me in Building 8DoTs

Dharmendra Kushwaha — Sat, 03 Oct 2026 11:24:06 +0000

Did you know that the software we write has a real-world carbon footprint?

As developers, we constantly profile our code for execution time and memory usage. But energy consumption remains largely invisible. That’s exactly what I’m trying to change with my open-source project, 8DoTs.

What is 8DoTs?

8DoTs (internally structured as the dol8 Python package) is a command-line profiling tool designed to make energy consumption visible during software development.

It reads hardware energy counters (RAPL - Running Average Power Limit) and thermal sensors directly from the Linux kernel to provide repeatable, high-resolution estimates of:

How much energy a specific workload consumes
How much heat it generates
Whether it triggers thermal throttling

It’s built for bare-metal Linux environments (Intel/AMD CPUs) using Python 3.9+.

Why Contribute?

Make a Real Impact: Green software engineering is the future. Your contribution will help developers worldwide write more energy-efficient code.
Beginner-Friendly: Whether you’re a seasoned Python developer or looking for your very first open-source PR, there’s a place for you here.
Learn Low-Level Linux Concepts: Get hands-on experience with hardware counters, thermal sensors, and system-level profiling.

Where You Can Help Right Now

I’m actively looking for contributors to help tackle the following areas:

Expand Hardware Support: Help add support for additional thermal sensor families or battery APIs.
Improve Data Export: Enhance the granularity and usability of the CSV timeline exports.
Enhance Real-Time Profiling: Improve the watch mode for better real-time process estimation.
Documentation & Tutorials: Write guides, blog posts, or case studies demonstrating how to optimize energy in real-world applications. (Perfect for beginners!)

Check out the open issues here: github.com/uyg7x/8DoTs/issues

How to Get Started

Star the repo to show your support: github.com/uyg7x/8DoTs ⭐
Fork and Clone the repository to your local machine.
Check the README.md for setup instructions and hardware requirements.
Pick an issue that interests you, leave a comment saying you’re working on it, and submit a PR!

Note: Since 8DoTs relies on physical hardware counters, actual profiling requires bare-metal Linux. However, you can still run the test suite and contribute to the codebase/docs from Windows/WSL or macOS!

Let’s Connect!

Have a question? Want to brainstorm a new feature? Drop a comment below, open a discussion on GitHub, or reach out to me directly.

Let’s build more sustainable software, together! 🌍💚

If you found this interesting, please **like* and repost to help me reach more potential contributors!*

I Built a Self-Healing Web Scraper That Refuses to Break

Dharmendra Kushwaha — Wed, 12 Aug 2026 13:59:40 +0000

Every developer who has worked with web scraping knows the pain: a website updates its UI, a CSS class name changes, and suddenly your entire data pipeline breaks.
To solve this, I’ve been building a Self-Healing Web Scraper in Python. Instead of failing silently when a website redesigns, it automatically adapts, heals itself, and continues extracting data.
--> The Problem
"Normal scrapers are incredibly fragile. They rely on rigid HTML structures and CSS selectors. The moment a website changes its layout, your scraper dies".
The Solution: A 5-Layer Fallback Pipeline
I designed an extraction engine that tries five different strategies in sequence. If one fails, it automatically falls back to the next:
1) JSON-LD Parsing: Looks for structured data (the most resilient method).
2) CSS Selectors: Traditional fast extraction if classes haven't changed.
3) Regex Fallback: Pattern matching on raw HTML when CSS fails.
4) Fuzzy Self-Healing: Scores nearby text blocks to find price symbols and product data as a last resort.
5) LLM / AI Self-Healing: The newest addition! If all standard methods fail, it uses an LLM (Google Gemini or offline Ollama) to "read" the broken HTML and extract the products anyway.

Self-Learning Memory
The scraper doesn't just survive; it learns. It remembers which extraction strategy worked best for a specific website and automatically prioritizes that method on the next run, saving execution time and API calls.
|--| Key Features

Multi-Site Price Comparison: Tested across Flipkart, Amazon, Robu.in, and more.
AI-Powered Resilience: Uses Google Gemini or 100% offline Ollama for intelligent data extraction.
Smart Garbage Filtering: Rejects junk data like menu items, ads, and accessories.
Auto Currency Conversion: Automatically normalizes prices to ₹ INR.
Dual Fetching & Stealth: Uses Playwright with stealth mode to bypass basic anti-bot detection.
Persistent Storage: Saves data to an SQLite database and exports clean, timestamped CSV files.

-->Watch it in Action
I recorded a quick demo showing how the scraper handles failures and triggers the AI fallback:
-->Watch the Demo Video Here
--> Check out the Code
I am open-sourcing this project and would love to get feedback from the community on architecture, edge cases, or feature requests!
🔗 GitHub Repository: https://github.com/uyg7x/Self-Healing-Web-Scraper
If you find this useful or interesting, I would highly appreciate a ⭐ on the repo and your thoughts in the comments below! How do you currently handle website redesigns in your scraping pipelines?