Amor

Amor is an innovative, GitHub-first sourcing platform engineered to continuously refine and revolutionize the hunt for elite software engineering talent. It is designed for engineering managers, technical recruiters, and sourcers who are committed to an iterative, data-driven approach to hiring, moving beyond the saturated networks like LinkedIn. By leveraging sophisticated algorithms on a massive dataset of over 8 million developer profiles and 145 billion repository stars, Amor pinpoints the top 1% of engineers based on real coding activity, contribution patterns, and cleaned location data. The platform's core value proposition is its cyclical improvement of the sourcing workflow: it saves countless hours otherwise spent on manual scripts or profile sifting, provides deep, actionable insights into a candidate's technical habits and interests, and enables faster, more informed hiring decisions. This commitment to continuous enhancement ensures teams can consistently identify and engage with high-quality, often "hidden" engineers who prioritize building over networking, thereby raising the hiring bar with every search cycle.

Top 1% Engineer Identification

Amor's foundational feature is its systematic, data-backed method for isolating the most active and impactful engineers on GitHub. The platform tracks over 100,000 developers with more than 500 contributions in the last year, applying advanced filters to surface individuals based on commit frequency, project impact, and repository stars. This continuous sifting of massive data ensures you are not just finding engineers, but are consistently presented with the proven top performers, refining your candidate pool with each use to focus only on exceptional talent.

Smart Location Data & Advanced Filtering

GitHub location data is notoriously noisy. Amor applies intelligent cleaning algorithms to parse and standardize location information, allowing for precise searches by city, region, or country. This is part of a broader suite of advanced filters that let you drill down by programming languages, contribution frequency, and specific repository contributions (e.g., Next.js, React). This feature embodies iterative refinement, saving hours of manual screening by letting you build and perfect your search criteria to instantly generate qualified, geographically relevant candidate lists.

Easy-to-Skim Profiles & Repository Insights

Amor transforms raw GitHub activity into immediately digestible intelligence. Each profile features an automatic summary of a developer's technical interests, skills, and activity patterns. You can quickly understand the types of projects they build and star, gaining a clear snapshot of their passions and expertise. This continuous distillation of complex data into simple insights allows for faster, more accurate profile evaluation, enabling you to iterate through candidates efficiently without missing key details.

Seamless Ashby Export & Collaboration Tools

Amor integrates directly into the hiring workflow with one-click export to Ashby's CSV import format. Profiles are auto-enriched with emails from commits, LinkedIn URLs, and smart tags. Furthermore, built-in collaboration tools like shared Candidate Lists and profile comments allow teams to iterate on feedback and insights collectively. This creates a cycle of continuous improvement in the hiring process, reducing time-to-hire by streamlining handoffs and enabling faster, unified decision-making.

For In-House Recruiting Teams

In-house recruiters at scaling startups and tech companies use Amor to build a robust, high-quality engineering pipeline efficiently. The platform provides the data to assess both technical chops and work ethic indicators like coding consistency, helping to reduce time-to-hire without compromising on quality. It enables a continuous cycle of sourcing, evaluating, and shortlisting candidates who are proven coders, often uncovering talent invisible on traditional platforms.

For Technical Recruiting Agencies

Recruiting agencies leverage Amor to gain a competitive edge by accessing an exclusive, untapped pool of passive, high-caliber candidates. By sourcing from GitHub, they find expert senior engineers that their LinkedIn-only competitors cannot, leading to faster placements. The tool allows for rapid, iterative searching and list-building, enabling agencies to refine their sourcing strategies and deliver exceptional talent to clients more reliably.

For Engineering Managers & Tech Leads

Engineering managers use Amor to take a hands-on role in sourcing, ensuring candidates have the genuine technical expertise and project interests that align with their team's needs. The platform's insights into contribution patterns and technical summaries help managers quickly evaluate culture and skill fit, empowering them to raise their team's bar through a more informed and iterative hiring process.

For Building Diverse & Underrepresented Talent Pools

Amor facilitates proactive sourcing from underrepresented communities in tech by enabling searches based purely on verifiable contribution history and technical merit, reducing unconscious bias from traditional resume screening. Teams committed to DEI initiatives can use the platform to continuously discover and engage with highly skilled engineers from diverse backgrounds who demonstrate their abilities through their public work.

How does Amor find engineer contact information?

Amor auto-enriches candidate profiles by programmatically extracting email addresses from their public Git commit history. This provides a direct, verified contact method that is often more reliable than searching for contact info elsewhere. The platform also surfaces associated LinkedIn and social profiles when available, giving you multiple channels for outreach.

What makes Amor better than searching on LinkedIn or GitHub directly?

Amor provides structured, filtered access to GitHub's vast data that manual searches cannot match. Instead of spending hours writing custom scripts or sifting through irrelevant profiles, Amor applies sophisticated filters (contribution frequency, location, languages) instantly across 8+ million profiles. It delivers cleaned, actionable insights and identifies consistently active "builders" who may not maintain a polished LinkedIn presence.

How current is the data on Amor?

Amor's data is continuously updated to reflect the most recent activity. The platform tracks real-time commits and contributions, ensuring that metrics like "contributions in the last year" and coding frequency are accurate and current. This commitment to fresh data means you are always evaluating candidates based on their latest work.

Can I try Amor before committing to a paid plan?

Yes, Amor offers a free tier that allows you to experience the platform with limited search results. This lets you test the advanced filtering, explore profile insights, and understand the workflow without a credit card. You can upgrade to a paid plan for unlimited access and full feature functionality when ready.

Amor offers simple, transparent pricing designed to scale with your team's size. The platform emphasizes no usage fees or surprises, with plans structured to support everything from individual recruiters to large enterprises. You can explore the different tiers and their specific features directly on their website at amor.dev. A free tier with limited search results is available to start, and all plans require no credit card for the trial and can be canceled at any time.