job-application-bot-by-ollama-ai

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Basic Information

JobHuntr is an end-to-end AI agent that automates job search and application tasks for users who want a hands-free way to apply to roles. The project provides a desktop application with macOS and Windows installers and tutorials and runs AI models locally via Ollama with an option for a faster cloud model. It automates generating ATS-optimized resumes and personalized cover letters, filters and ranks listings with semantic matching, and submits high-quality application answers automatically 24/7. The system supports iterative learning by asking the user to review initial outputs (typically around ten reviews) before free-running. It includes progress tracking, a company blacklist and custom filters, and utilities for skipping optional questions, pausing and resuming activity, and viewing the AI’s reasoning for transparency.

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Features
The README lists concrete features: ATS-optimized resume generation, personalized cover letter generation, automated high-quality application submission, and smart semantic filtering to skip mismatches. It supports a company blacklist and shows which answers derive from your resume or FAQs. The agent can auto-apply continuously, skip optional or non-critical questions, pause and resume operation, and send a personalized direct message to hiring teams after applying. The product emphasizes privacy and speed by running on-device via Ollama, while offering a cloud-based AI option for improved matching. Additional features include progress and history tracking, iterative learning, European country support in the latest release, downloadable desktop clients, a demo and sample outputs, and community channels such as Slack.
Use Cases
JobHuntr saves time and reduces repetitive work by automating the end-to-end application workflow including resume tailoring, cover letters, and form answers. Smart filtering and semantic matching reduce irrelevant submissions and improve match quality, while ATS-optimized resumes increase the chance of passing automated screening. Running models locally via Ollama improves privacy and responsiveness and the optional cloud model offers speed and enhanced matching. Iterative learning lets the agent adapt to user preferences after a small number of reviews, and progress tracking provides visibility into past applications and seen FAQs. Features like company blacklists, skip logic for optional questions, pause/resume control, and seeable AI reasoning increase user control and trust during continuous background operation.

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