Thank You
After years of helping people build resumes, Resumake has closed its doors. I want to take a moment to thank every single person who used this little tool over the years.
I built Resumake out of a pure love of coding and open source. As a student, I was frustrated by how much time and effort it took to put together a decent resume. I figured if I needed something simpler, others probably did too. So I built it. A free, open-source resume generator that had no ads, required no user accounts, and collected no personal data. It was the kind of software I wished more of the world had. And being open source meant I got to collaborate with other developers who cared about the same things. It was awesome working with others and building something together just because we wanted to make it better.
I never made money from Resumake. But what I got in return was something far more meaningful. Over the years, I watched this small project grow in ways I never could have imagined. It was featured in a Mashable article and shared across the world. Tens of thousands of people used it to build their resumes, and many of them took the time to send thank-you emails, share kind words, and some even donated. Every single one of those messages meant the world to me.
It was beautiful to see how many lives it helped. People landing their first jobs, students applying to internships, career changers putting themselves out there. That was always the point. Not profit, not growth metrics, just making something useful and putting it out there for free.
Thank you and farewell. Please feel free to leave a message at the bottom because I'd really like to hear from you.
With love,
Saad Q.
What's Next for Resume Building
Times have changed, and AI can now do a lot of what Resumake used to do, and honestly, much more. Here are some ways you can build a great resume today.
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Use Claude or ChatGPT to draft your resume
Start a conversation and tell the AI about your experience, skills, and the type of role you're targeting. Ask it to format your resume in a clean, professional layout. You can iterate on the wording, tailor it for specific job descriptions, and get suggestions you might not have thought of. These tools go beyond what Resumake ever could. They can help you write better bullet points, optimize for ATS systems, and adapt your resume for different industries.
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Ask AI for LaTeX output
If you loved Resumake's LaTeX templates, you can ask Claude or ChatGPT to generate LaTeX code for your resume. Just describe the layout you want or share a template you like, and the AI will produce clean, compilable LaTeX. You can then compile it using Overleaf or any local LaTeX setup.
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Get feedback on your existing resume
Upload your current resume (as text or a PDF) and ask the AI to critique it. It can point out weak bullet points, suggest stronger action verbs, identify missing sections, and help you tighten up the language.
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Tailor your resume for specific jobs
Paste a job description into the chat along with your resume and ask the AI to help you align your experience with what the employer is looking for. This kind of targeted customization used to take hours. Now it takes minutes.
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Run your resume through an ATS scanner
Most companies use Applicant Tracking Systems to filter resumes before a human ever sees them. Tools like Jobscan and Resume Worded can scan your resume against a job posting and tell you how well it matches. They'll flag missing keywords, formatting issues that confuse parsers, and sections that could be stronger. It's worth running your resume through one of these before you hit submit.
A word on privacy. Be thoughtful about the personal information you share with AI tools. Your resume contains sensitive data like your full name, address, phone number, and employment history. Consider anonymizing or redacting personal details before pasting your resume into any AI chat. You could use placeholder names and remove contact info, then add the real details back into the final document yourself. It's a small extra step that keeps your personal data out of training datasets.