Amor

Product Introduction

1. Amor is a recruitment platform that identifies top software engineers through automated analysis of GitHub commit activity and contribution patterns. It helps hiring teams bypass traditional networking platforms to directly access developers with proven coding track records.
2. The core value lies in reducing engineering hiring timelines by 10x through data-driven candidate matching, focusing on technical capabilities rather than resume-based selection.

Main Features

1. Contribution Pattern Analysis tracks coding frequency, weekend activity, and consistency across 100,000+ engineers with 500+ annual commits to assess work ethic alignment.
2. Smart Location Filtering cleans noisy GitHub location data to enable precise searches by city/region while respecting privacy regulations.
3. Repository Insights automatically summarizes developers' technical interests through project types, starred repos, and primary programming languages used.

Problems Solved

1. Eliminates manual screening of unqualified candidates by pre-filtering engineers based on verifiable GitHub activity metrics.
2. Serves recruiters, engineering managers, and agencies needing to source senior developers who prioritize coding over professional networking.
3. Ideal for scaling tech teams rapidly, finding niche skill sets, or accessing passive candidates not visible on LinkedIn.

Unique Advantages

1. GitHub-first approach bypasses the 87% of top engineers who don't actively maintain LinkedIn profiles according to platform data.
2. Culture fit indicators analyze coding hours/weekend activity to predict alignment with team work styles.
3. Proprietary algorithms score candidates based on contribution quality rather than just quantity, filtering out automated/low-value commits.

Frequently Asked Questions (FAQ)

1. How does Amor ensure candidate quality? All profiles must have 500+ annual GitHub contributions across non-forked repositories with meaningful code changes.
2. Can I search for specific tech stack expertise? Yes, filters include primary languages, framework usage from repo analysis, and project type categorizations.
3. Do candidates know they're being sourced? Recruiters contact engineers directly through GitHub, maintaining privacy until mutual interest is established.
4. How does location filtering work? We parse location signals from GitHub events/geo-IP data, then cluster results into standardized city/region names.
5. Is there team collaboration support? Yes, shared candidate lists with comments and profile sharing streamline hiring committee workflows.