Init.dev - Your AI-powered Career Assistant that enables you to prepare for jobs and interviews

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

I built init.dev, an offline-first, agentic career co-pilot designed to run locally on low-resource machines without a GPU.

Navigating a tech career can be overwhelming. Developers need to figure out what skills to learn, how to structure their studies, and whether their resumes will actually pass Applicant Tracking Systems (ATS). Existing tools are often fragmented, expensive, or require sending personal data (like resumes and study habits) to closed cloud models.

init.dev solves this by providing a unified, local tool that:

1. Parses your career intent and current skills.
2. Generates a structured, modular learning roadmap.
3. Scores your resume locally using the TabPFN-3.5 foundation model (with a local Gemma fallback) to predict ATS pass probability.
4. Acts as a persistent, chat assistant to help you study.
5. Conducts realistic, role-specific mock technical interviews right in the terminal.

Demo

Code

nillohitroy / init-dev-cli

How I Built It

init.dev is a terminal-first application built with Node.js, combined with a Next.js web interface for visualizing roadmaps and chatting with the agent.

The core of the project relies on open-weight models and agentic workflows:

• Local Inference: I used Ollama running the gemma:2b model locally. Gemma handles natural language intent parsing, formats the learning roadmaps, conducts mock interviews, and acts as the conversational engine for the Next.js chat interface.
• ATS Scoring: I integrated TabPFN-3.5 (via Prior Labs' API). TabPFN handles the structured tabular data extracted from resumes (years of experience, skill match, keywords). If TabPFN rejects the unstructured text, the system seamlessly falls back to gemma:2b to analyze the raw text.
• Agentic Data Fetching: The system dynamically switches between a "Simple Search" (using free GitHub/OpenLibrary APIs) and a "Professional Search" (SerpApi) to scrape real-time market data and resources to inform the roadmap generation.

Why Does Open Innovation Matter?

Open innovation is the entire reason init.dev can exist as an accessible tool for students and developers worldwide.

1. Privacy & Cost: By running gemma:2b locally via Ollama, users don't have to pay for API credits to chat with their study buddy or parse their career goals. It also means they aren't forced to upload their personal resumes or learning histories to a closed-source cloud provider.
2. Resilience: The application was specifically designed to run on low-resource machines without a GPU. By utilizing small, open-weight models, init.dev democratizes access to AI-powered career coaching.
3. Flexibility: The open-source ecosystem allowed me to build a seamless fallback mechanism. If the TabPFN API is unavailable or the data doesn't fit its tabular expectations, the local Gemma model steps in to ensure the user still gets a valuable ATS evaluation.

Prize Categories

• Best Use of Gemma: gemma:2b is the core intelligence engine for init.dev, running locally via Ollama to handle intent parsing, mock interviews, and the chat interface.
• Best Use of TabPFN: Used TabPFN-3.5 as the primary engine to predict ATS pass probabilities based on tabular features extracted from user resumes.
• Best Use of ElevenLabs: Used ElevenLabs to create the audio for the product demo which explains how the product works and how you can use it for your day-to-day tasks.