open-source ats hiring-technology developer-tools

HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88

HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74/100. No — 88/100. Actually 83/100.

danunparsed.com

June 29, 2026

5 min read

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75/100

Summary

HackerRank has open-sourced its Applicant Tracking System (ATS), available on GitHub. A user tested the ATS and received varying resume scores, ranging from 74 to 90 out of 100.

Key Takeaways

HackerRank has open-sourced its Applicant Tracking System (ATS), which has gained significant attention on platforms like LinkedIn and Reddit.
The scoring system for resumes ranges from 66 to 99, with variations in scores for the same resume due to factors like LLM output randomness.
Technical skills scoring is consistent, while project evaluations show significant variability, indicating a fundamental design flaw in the LLM's judgment capabilities.
The experience scoring lacks detailed criteria, leading to inconsistencies where candidates with vastly different backgrounds can receive the same maximum score.

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Coverage & discussions

Hacker News · danunparsed.com

HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88 ↗

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Analysis ready

Community Sentiment

Mixed

Positives

The ability to self-evaluate resumes using automated scoring systems can empower candidates to better understand their market position and improve their applications.
Automated resume filtering can significantly speed up the hiring process, allowing recruiters to manage large volumes of applicants more efficiently.

Concerns

The reliance on AI-driven resume filtering may lead to qualified candidates being overlooked, as the system can prioritize less experienced applicants based on arbitrary scoring.
Many users express frustration that AI has exacerbated existing hiring challenges rather than resolving them, indicating a lack of trust in these automated systems.
There is a significant misunderstanding about how LLMs operate, which raises concerns about transparency and fairness in automated hiring processes.