I built a tool that writes your entire job application from one paste, would love feedback!
Hello IHs,
For months I was stuck in the same loop. Open a job posting, rewrite my CV, write a new cover letter, tweak the LinkedIn message, send it off. Hear nothing. Repeat.
At some point I noticed something. The applications that actually got replies weren't the ones I sent the most of , they were the ones I spent three hours tailoring. Reading the posting properly. Mirroring the language. Telling my story the way that specific role needed to hear it.
That's what eventually landed me the job.
But three hours per application isn't realistic. Not when you're applying to twenty roles a week. Not when you're burned out from rejection. Most people give up and go back to sending the same generic CV everywhere , and then wonder why nothing's working.
I kept thinking: this whole process could be done in a minute if someone built it right.
So I built it. It's called Wrendit.
You paste your CV and a job description. Under a minute later you get the full package — CV feedback, a cover letter, a recruiter email, a LinkedIn message, a follow-up, and a match score that tells you what to fix before you send. All tailored to that exact role. No templates, no invented experience.
It's live, has some early users, and I'm mostly here for honest feedback. If you've ever felt the job hunt grind, I'd love for you to try it and tell me what's broken.
Link: https://wrendit.com/
Happy to talk about anything, the build, the stack, what's working, what isn't.
One thing I'd be watching closely is whether users are succeeding because applications are more tailored or because they're applying to better-fit roles.
Those can look identical in early feedback.
A lot of job seekers assume the problem is application quality when the bigger variable may be role selection. The validation signals can get mixed very quickly.
That's exactly why I found the thread interesting.
My concern isn't that the signals are mixed.
It's that the same improvement in callback rate could end up being used as evidence for two very different conclusions.
One about application quality.
One about role selection.
And the next product decisions can look very different depending on which conclusion gets reinforced.
Happy to share the fuller thought if useful. What's the best email to reach you on?
That distinction is exactly what I keep circling back to — because the two conclusions point at completely different roadmaps. If it's application quality, you double down on the tailoring engine. If it's role selection, the match score becomes the core product, not a feature. Same metric, opposite bets.
I'd genuinely value the fuller thought and especially how you'd go about isolating the two signals. Myemail is za.zakariaeallali@gmail. com Thanks for taking this seriously, it's a sharper lens than I'd had on it.
Sent over the fuller thought by email.
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