Career-Ops Fork Adds LinkedIn Job Discovery Using Apify
What This Fork Adds
A developer has forked the open-source career-ops project by u/Beach-Independent and added LinkedIn as a job discovery source using Apify. The original career-ops system evaluates job listings, generates tailored CVs, and tracks application pipelines, but was limited to scanning pre-configured company career pages.
Key Features
- Searches LinkedIn by keywords, not limited to a fixed company list
- Command:
node apify-linkedin.mjs --search "AI Engineer" --location "Remote"finds matching jobs - Caches full job descriptions offline for evaluation
- Deduplicates against existing pipeline to avoid re-evaluations
- If company has direct application page → goes there. If not → applies via LinkedIn
- Plugs into existing career-ops pipeline (scan → evaluate → PDF → track)
Setup and Costs
- Sign up at apify.com (free tier with $5/month credits)
- Run
apify login - Add keywords to
portals.yml - Costs approximately $0.50 per 1,000 jobs scraped
Project Links
- Fork: https://github.com/kovalov/career-ops-linkedin
- Original: https://github.com/santifer/career-ops
📖 Read the full source: r/ClaudeAI
👀 See Also
Claude Workflow Library Now Tracks and Rates Reddit- Sourced Workflows Automatically
A searchable, auto-updated index of Claude and Claude Code workflows from major subreddits, with steps, artifacts, and community ratings.
ANE Optimization Through Phone-Steered AI Experiments Shows Kernel Fusion Benefits
A developer ran 55 experiments on Apple Neural Engine optimization, steering the process from their phone using Claude for brainstorming. Key improvements included fusing 3 ANE kernels into 1 mega-kernel, reducing validation loss from 3.75 to 2.49 and step time from 176ms to 96ms.
Solo developer builds cross-platform desktop AI agent with mobile remote control in 3 weeks, ships to 40+ countries
A solo developer built Skales, a native desktop AI agent with 139+ tools and a mobile companion app for remote control — all in 3 weeks using Claude. The app runs on macOS, Windows, and Linux, is local-first and free, and already has active users in 40+ countries.
Testing MiniMax M2.7 via API on Three Real ML and Coding Workflows
A developer benchmarks MiniMax M2.7 against Claude Opus 4.7 on three real tasks: refactoring a PyTorch project, drafting Obsidian notes, and more. Key findings and setup included.