Job seekers are increasingly turning to AI-powered tools to automate and streamline the application process, with some reporting significant success. Guilherme, a 28-year-old software engineer from Brazil, used AIHawk to submit over 1,300 job applications in just one month after being laid off in April 2024. The AI tool ultimately helped him land his desired role by November.
AIHawk, created by Federico Elia and published on GitHub in August 2024, automates the application process for LinkedIn’s easy-apply jobs by pulling information from user profiles to fill in applications. The open-source tool has been bookmarked by more than 22,000 people globally on GitHub, with over 6,300 members in its Telegram community where users share tips and job search updates.
While Guilherme’s final job offer came from a recruiter who found him on LinkedIn rather than through an AIHawk application, he credits the tool with significantly boosting his visibility. After starting to use AIHawk in mid-October, he began receiving multiple LinkedIn InMails daily from recruiters, hiring managers, and C-suite executives — something that had never happened before. Guilherme believes that applying to approximately 50 jobs per day through AIHawk boosted his profile in LinkedIn’s algorithm, making him more discoverable to recruiters.
However, LinkedIn disputes this theory. A company spokesperson told Business Insider that applying to more roles does not make a profile more visible to recruiters, and that keeping profiles up to date is what matters most. The spokesperson also clarified that LinkedIn does not permit the use of third-party software, bots, or tools that scrape or automate activity on the platform.
The use of AI in job applications comes with notable risks and concerns. These include potential résumé errors, possible rejection by HR departments that frown upon AI-generated applications, and unclear guidelines about how employers and job platforms view such tools. AIHawk can be used for free, but requires some familiarity with the Python programming language, which may limit accessibility for non-technical job seekers.
Guilherme recommends that users carefully filter job titles to ensure good fits and use interviews as practice opportunities to improve communication skills. His biggest takeaway: the time savings were invaluable. “Imagine if I had to do this manually?” he said. “I’d probably go insane.” AIHawk represents just one of many AI job application tools emerging in the market as job seekers navigate an increasingly competitive and time-consuming application landscape.
Key Quotes
This the type of job I was looking for. It was certainly a byproduct of AIHawk.
Guilherme, a 28-year-old software engineer from Brazil, credited the AI tool with helping him land his desired role after submitting over 1,300 applications in one month. His success story demonstrates the potential effectiveness of AI-powered job application automation.
I got several LinkedIn InMails a day, every single day, since mid-October, from recruiters, hiring managers, and C-suites of companies. This was something that never happened to me before.
Guilherme described the dramatic increase in recruiter outreach after he began using AIHawk, suggesting that high-volume applications may have increased his profile visibility, though LinkedIn disputes this mechanism works as he believes.
With my account’s activity being through the roof, my profile was boosted up in searches, which led to my new boss finding me.
Guilherme’s theory about how AIHawk helped him get hired, though a LinkedIn spokesperson contradicted this, stating that applying to more roles does not make profiles more visible to recruiters.
Imagine if I had to do this manually? I’d probably go insane.
Guilherme emphasized the primary benefit of AIHawk — time savings — highlighting the exhausting nature of modern job searches and why automation tools are becoming increasingly attractive to job seekers.
Our Take
This case study reveals a fundamental tension in modern hiring: the asymmetry between employer and candidate use of AI. Companies have long used applicant tracking systems and AI screening tools to process thousands of applications, yet platforms like LinkedIn prohibit candidates from using similar automation. This creates an uneven playing field that tools like AIHawk attempt to correct.
The real story here isn’t just about one person’s success, but about the emergence of a parallel job search ecosystem operating in violation of platform terms of service. With 22,000 GitHub stars and thousands of active users, AIHawk represents a grassroots movement of frustrated job seekers taking matters into their own hands. The fact that it requires Python knowledge also highlights a new digital divide: technical workers can automate their way to better opportunities while non-technical workers cannot. As AI tools become more accessible, expect platforms and employers to respond with stricter detection methods, potentially escalating this automation arms race.
Why This Matters
This story highlights a significant shift in how AI is transforming the job search process, revealing both opportunities and tensions in the employment ecosystem. As job seekers face increasingly competitive markets and time-consuming application processes, AI automation tools like AIHawk are democratizing access to high-volume job applications — something previously only possible through manual effort or expensive services.
The story also exposes emerging conflicts between AI tool users and platform policies. LinkedIn’s explicit prohibition of third-party automation tools creates a gray area where job seekers may be violating terms of service while trying to level the playing field. This raises important questions about fairness, as companies increasingly use AI to screen candidates while platforms restrict candidates from using AI to apply.
More broadly, this represents the “AI arms race” in recruitment: employers use AI to filter thousands of applications, prompting candidates to use AI to submit thousands of applications, potentially creating an inefficient cycle. The lack of clear industry standards around AI use in hiring suggests this tension will intensify, requiring new frameworks that balance automation benefits with authenticity and platform integrity concerns.
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