Santiago, now Head of Applied AI, built Career-Ops after being laid off and used the AI-powered job search automation platform to evaluate 740+ job listings, generate over 100 personalized CVs, and ultimately land his current role. Since open-sourcing the entire system on April 4, 2026, the JavaScript-based project has accumulated 26,710 GitHub stars and 4,900 forks.
From Personal Tool to Community Resource
Career-Ops emerged from months of manual application work. As Santiago explains: "Companies use AI to filter candidates. I gave candidates AI to choose companies." The system essentially reverse-engineers the recruitment process to give candidates computational advantage, automating the tedious parts of job searching while maintaining human decision-making.
The platform evaluates job offers using a structured A-F scoring system across 10 weighted dimensions, generates tailored ATS-optimized CVs for each position, scans 45+ pre-configured company portals automatically, and processes multiple offers in parallel using Claude Code workers.
Technical Architecture and Features
Career-Ops implements a 6-block evaluation framework for each job opportunity:
- Role summary and company overview
- CV alignment analysis measuring skill match
- Level strategy assessing seniority fit
- Compensation research with market data
- Personalization assessment for cover letters
- Interview preparation using STAR+Reflection methodology
The system includes interview story accumulation that builds a bank of 5-10 master behavioral stories across evaluations, making interview prep more efficient. Portal scanning supports Ashby, Greenhouse, Lever, Wellfound, and company career pages.
A terminal UI dashboard built in Go with Bubble Tea enables browsing and filtering the pipeline. PDF generation uses Playwright-based ATS-optimized resume creation with Space Grotesk typography. The system operates across 14 skill modes for different job search scenarios.
Philosophy: Quality Over Quantity
Unlike spray-and-pray application tools, Career-Ops emphasizes human oversight: "AI evaluates and recommends, you decide and act. The system never submits an application." It's positioned as a filter for quality opportunities, with the creator discouraging applications below a 4.0/5 score.
This design philosophy reflects Santiago's own experience: rather than maximizing application volume, the tool helps candidates identify the best-fit opportunities and present themselves optimally for those specific roles.
Viral Reception and Market Context
The project gained significant traction on social media, with one post receiving 4,068 likes, 386 retweets, and 892,538 impressions. Community members noted the contrast with traditional job search services: "Resume writers charge $500. Career coaches charge $200/hour. Job search platforms charge $30/month. Someone automated the entire job search pipeline with AI. For free."
The system demonstrates how AI coding assistants like Claude Code can be used to build sophisticated automation for personal use cases, then scaled to help entire communities facing similar challenges.
Key Takeaways
- Career-Ops has accumulated 26,710 GitHub stars and 4,900 forks since launching April 4, 2026
- Creator Santiago used the system to evaluate 740+ job listings and land a Head of Applied AI role before open-sourcing it
- The platform supports 45+ job portals including Ashby, Greenhouse, and Lever with automated scanning
- System generates ATS-optimized CVs using Playwright and evaluates opportunities across 10 weighted dimensions with A-F scoring
- Philosophy emphasizes quality over quantity: AI recommends but never auto-submits applications, with human decision-making retained