Topic

#resume

Loot, blog posts and adjacent themes connected to this topic. Follow the tag to keep it in your orbit.

#resume

1Shown loot

0Shown articles

6Linked neighbor tags

Topic paths

If you want to go deeper, the adjacent tags are the fastest way to compare and branch into related workflows.

Loot

More from this topic

Explore all loot

Automate Job Applications Using Your Daily Driver Browser

No votes yet

Text: AI-generated

#ai-generated #automated+5

AI-generated · Automatically published by LinkLoot. SHOBR automates the tedious parts of job hunting—scraping, resume tailoring, and application prep—by driving your actual authenticated browser. No headless bots, no credential extraction. AI-generated: This Loot was created and published automatically by LinkLoot and was not substantively reviewed by a human editor. What it does SHOBR (Simulate Human Occupational-Bureaucratic Rituals) is a UNIX-style job search automation tool that prepares applications while keeping you in control. It uses beachpatrol to drive your existing daily-driver browser for scraping jobs with your real authenticated sessions, then leverages minimal LLM calls to tailor resumes from Markdown into ATS-readable PDFs via Groff and Pandoc. The workflow follows a human-in-the-loop model: SHOBR discovers jobs, screens them, writes drafts, and builds deliverables—but never auto-submits. You approve each application step manually. All data lives in append-only JSONL event logs, so you can interrupt pipelines or recompute decisions with new rules anytime. Who it helps Job seekers who want to automate repetitive tasks (finding listings, tailoring CVs) without resorting to "spray and pray" mass applications or risky headless browsers that trigger detection. Particularly useful for those targeting platforms like LinkedIn where authenticated access matters and bot detection is aggressive. Developers comfortable with Python and CLI tools will appreciate the modular design. The project avoids demanding an agent framework—it's lean enough (3500 LOC) to extend yourself if needed. Getting started Prerequisites: Install beachpatrol first (the hard dependency). See its README for setup. Python 3.14+ Installation: The toolchain requires Groff and Pandoc for PDF compilation. Ensure these are available on your system. LLM configuration: SHOBR uses any-llm abstraction, supporting OpenAI, Anthropic, local models, or even hijacking subscriptions via faaah. Configure your preferred provider before running. Limits and costs No auto-submit: Final application clicks remain manual by design. LLM costs: Resume tuning needs some LLM usage; budget for API calls if using cloud providers. Browser dependency: Requires beachpatrol, which itself needs proper setup as your daily driver. MIT licensed: Free to use, modify, distribute. Hosting/execution costs depend on your infrastructure choices. Early-stage tool: Built with stdlib unittest, full E2E TDD, but expect rough edges typical of personal projects. Source links Official repository Project documentation License

LinkLoot access: Free
Provider costs: Unknown

Review open

@ZachasADMIN

Open loot

Blog

#resume

More from this topic

Automate Job Applications Using Your Daily Driver Browser

Related reads