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Developer builds offline AI career advisor using Gemma 4

A computer science instructor developed an offline AI career advisor named GuidanceOS, designed to run entirely on a local GPU without internet access. The system utilizes Google's Gemma 4 model, specifically the `gemma-4-e4b-it` variant, which was loaded using 4-bit quantization to fit within 15GB of VRAM. For matching user skills to jobs and courses, the advisor employs a TF-IDF index built from over 130,000 LinkedIn job postings and Coursera course records, ensuring fast and reproducible results. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Demonstrates practical application of smaller LLMs for specialized, offline tools.

RANK_REASON The article describes a personal project using an existing model for a specific application, not a new model release or significant industry event.

Read on dev.to — LLM tag →

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 · soohan abbasi ·

    I Built an Offline AI Career Advisor Using Gemma 4 — Here's Exactly How It Works

    <h1> I Built an Offline AI Career Advisor Using Gemma 4 — Here's Exactly How It Works </h1> <p><em>A technical walkthrough of GuidanceOS: from model loading to multi-agent orchestration, running entirely on a Kaggle T4 GPU with no internet at inference time.</em></p> <p>I teach C…