Amor

Amor is an innovative, data-driven platform designed to revolutionize technical recruitment by sourcing the world's top engineering talent directly from GitHub. It serves engineering managers, in-house recruiters, talent sourcers, and recruiting agencies who need to identify and hire highly skilled developers efficiently. The platform's core value proposition is its ability to uncover exceptional, "cracked" engineers who prioritize building and coding over professional networking, a talent pool often missed by traditional LinkedIn-focused searches. Amor analyzes an immense dataset of over 8 million developer profiles, 66 million repositories, and 145 billion stars to help users find the top 1% of engineers based on real, verifiable contribution activity. By offering advanced filtering on commit frequency, programming languages, project types, and cleaned location data, Amor eliminates the need for manual scripting and endless profile scraping. It streamlines the entire sourcing workflow, from discovery and collaboration to exporting enriched candidate data directly into Applicant Tracking Systems (ATS) like Ashby, enabling teams to hire better engineers significantly faster.

Top 1% Engineer Discovery

Amor's algorithm identifies over 100,000 engineers who have made more than 500 contributions in the last year, representing the most active and prolific developers on GitHub. This metric serves as a powerful initial filter to surface individuals with proven, consistent output, allowing recruiters to start their search with a pre-vetted pool of highly engaged technical talent.

Contribution Pattern Analysis

The platform provides deep insights into a developer's work ethic and coding habits by analyzing their commit frequency, consistency, and even weekend activity. This goes beyond a simple contribution count, helping hiring managers assess cultural fit, dedication, and the natural working rhythms of potential candidates before making contact.

Smart Location Filtering

Amor solves the common problem of noisy or ambiguous location data on GitHub profiles by cleaning and standardizing it. Users can search for engineers by specific city, region, or country with high accuracy, enabling precise geo-targeting for roles with location-specific requirements.

One-Click Export to Ashby

This feature seamlessly integrates the sourcing and hiring pipeline. Users can export curated candidate lists directly into an Ashby-compatible CSV format. The export is auto-enriched with data like emails parsed from GitHub commits and LinkedIn URLs, and profiles are tagged for easy organization within the ATS.

In-House Recruiting Teams Scaling Quickly

Internal talent acquisition teams at fast-growing startups and tech companies use Amor to build a high-volume pipeline of qualified engineers without compromising on quality. The platform's filters for technical skills and contribution patterns help reduce time-to-hire while ensuring candidates have the demonstrated technical chops required.

Recruiting Agencies Gaining a Competitive Edge

Agencies leverage Amor to access an exclusive, untapped talent pool of engineers not actively found on LinkedIn. This allows them to present unique, high-caliber candidates to clients faster than competitors, improving placement speed and success rates for specialized or senior engineering roles.

Engineering Managers Hiring for Team Fit

Hiring managers utilize Amor to find engineers who will thrive within their specific team culture and project needs. By reviewing repository insights and contribution consistency, they can gauge a candidate's technical passions and work ethic, leading to more informed and successful hiring decisions.

Sourcing for Niche Technical Stacks

Recruiters and sourcers answer highly specific queries efficiently, such as finding front-end engineers in New York City proficient in Next.js, or backend developers in Europe with extensive experience in Rust. Amor's granular filters turn these complex searches into simple, quick processes.

How does Amor find candidate contact information?

Amor auto-enriches profiles by programmatically parsing email addresses from developers' public Git commit histories on GitHub. This provides a direct, work-related contact method. The platform also searches for and includes links to publicly available LinkedIn and social media profiles when possible.

What makes Amor better than searching on LinkedIn?

Amor focuses exclusively on verifiable coding activity and technical output from GitHub, the primary platform for developer work. This surfaces candidates who are actively building and may not maintain a polished LinkedIn profile. It provides objective data on skills and consistency, unlike the self-reported information often found on professional networking sites.

Can I collaborate with my team on the platform?

Yes, Amor includes built-in collaboration tools designed for hiring teams. Users can create and share candidate lists, add internal notes and comments directly to profiles for team visibility, and soon will be able to share profiles directly with hiring managers without requiring them to log in.

How does the export to Ashby work?

After building a list of candidates, you can click "Export to Ashby." Amor compiles the list into a formatted CSV file that matches Ashby's import requirements. The file includes enriched data like emails, LinkedIn URLs, and tags indicating the source repository and list name, making it ready for immediate import into your Ashby ATS.