Show IH: CV Compiler - A Tool for Improving Tech Resumes
Hey Guys!
We've just launched CV Compiler (https://cvcompiler.com/) - a Python-based tool for making you more competitive by improving your tech resume.
Why?
Many qualified applicants for tech jobs get rejected without even an interview, because of the resumes.
I believe that tech professionals don’t need to be resume experts. It makes much more sense to invest time in self-development.
All that inspired our team to build the online resume review technology that scans weak points and offers immediate suggestions on how to make it better.
The current version of CV Compiler works well for Software Engineers. For Product Managers, Designers and Interns, the service will be available in the next few months.
We are using NLTK and spaCy for tokenization, lemmatization, and POS-tagging. The keyword analysis tool for large datasets (resumes, job descriptions) is built upon a seq2seq model in TensorFlow.
It’s a freemium product. You can check the app here - https://cvcompiler.com/
Your feedback and ideas on how we can improve CV Compiler are much appreciated!
I have not looked too thoroughly at the tool yet but it looks cool. However, I noticed some awkward phrasing.
In the section, "How does CV Compiler compare with other services on the market?" one of the rows, "Focus on the contents" sounds pretty strange. You might want to rephrase it as "Focused on content."
Duly noted. Thanks a lot for your feedback!
Can you clarify weak points? E.g. it may depends on the job requirements, right? Facebook would want someone who knows php, so php is a strong point but for other ‘cool’ kids company it is actually could be a weak point. Is your scanning the ‘generic’ weak point?
Thanks for your question. We're mostly scanning for the 'generic' weak points relative to the best practices in the industry.
We also analyze datasets (job descriptions) from companies like Google, Facebook and Amazon. We provide you with the most popular technologies are used there, as well as match your resume to their most frequently repeated keywords.