LapWise – AI Laptop Advisor for Finding the Best Laptop

Bhavna Makode — Sun, 04 Oct 2026 13:18:23 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

Buying a laptop can be overwhelming—between comparing specs across dozens of models, checking prices across multiple online retailers, and decoding technical jargon, my friend was struggling to figure out which laptop offered the absolute best value for their budget and specific needs.

To help them out, I built LapWise—an AI-powered laptop advisor.

LaptopWise automatically:

Scrapes and aggregates laptop hardware specifications and pricing from multiple tech storefronts.
Cross-references real-time market prices to surface the best available deals.
Analyzes my friend's exact budget, performance needs, and usage preferences using an open-source AI model.
Generates personalized recommendations with clear, plain-language explanations of why a specific model is the perfect fit.

Code

https://github.com/Bhavna2003/lapwise

How I Built It

LaptopWise was built using a modern full-stack setup focused around open-source AI and automated data collection:

Open-Source AI Model: Powered by Ollama running Llama 3 / Mistral locally (or deployed via Hugging Face Inference API). The model acts as the decision engine, taking structured user requirements (e.g., "programming & light gaming, under $1000, good battery life") and matching them against normalized spec sheets.
Data Aggregation & Price Comparison: Built a scraping/ingestion service in Python using BeautifulSoup and Playwright to extract price and spec updates across different e-commerce stores.
Framework & Orchestration: Developed with LangChain / LlamaIndex to handle context retrieval (RAG) over the aggregated specs database, combined with a Next.js frontend for an intuitive conversational UI.

Why Does Open Innovation Matter?

Building LaptopWise around open-source AI and open tooling made a massive difference:

Complete Data Transparency: Closed commercial AI models can be biased or influenced by sponsored results. Using an open-weight model lets us audit the reasoning logic to ensure recommendations are 100% unbiased and truly best for the user.
Custom Logic & Fine-Tuning: Open weights allow us to customize system prompts, fine-tune context windows, and run fast local inference without worrying about strict API rate limits or recurring API fees.
Community Control: Hardware specs and deals change daily. Open innovation enables the community to contribute new store parsers, comparison metrics, and prompt strategies freely.

Introduction to Git and Github for beginners

Bhavna Makode — Thu, 15 Feb 2024 15:09:16 +0000

Many students who are new to coading might have heard about the terms Git and Github. But they dont't know what is git and github? Or how to use it? Or why to use it?
This article is the answer to all this questions.
Before moving to learning git and github we need to know about Version control system and some important terminologies.

What is version control system?
Version control also known as source control is the practice of tracking and managing changes in the source code.

Here are some important terminologies we need to know before learning git and github.

repository/repo- The databese/folder storing the files.
push- Push changes to the github repo.
pull- Pull changes from the github repo.
clone- Making copy of the repo on your machine.
fork- Making copy of the repo that allows you to make your own changes without impacting the original project.

What is Git and Github?
Git and Github are two separate things. Github is a developer plateform that allows developers to create, store, manage and share their code. Git is a software or a version control system used to track changes in the source code, enabling multiple developers to work together.

Let's understand the use of git and github with the help of a problem statement.

Problem Statement-
Suppose we have a team of 10 members and they are working on a website. The problem is how they will keep track of small as well as the larger changes made by different peoples of this team?

Solution-
The one and only solution to this is github. A head of the team made a repository and kept the code on this github repo. Now, other members will fork and clone that repo and will start working. They will know push and pull the new changes timely. While pushing the changes we create a pull request which is seen and merged by the repo owner or the head of the team.

Let me know in the comments how this example was??