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AI · January 15, 2024

S4

Spent 450hrs to bring my CV down to 1 page (ML, AI)

10 second version: Get 10000feet view of job descriptions - https://be-distinguished.com 2 minute version : Hi HN, Long time lurker, big fan, and first time poster inspired by how this community elaborates on ideas and new products. Recently was given feedback that my CV was too long at 2 pages, I was at loss as to how to update it without having a high-level view of the requirements of the type of jobs I would be interested in. So I built https://be-distinguished.com to help me study relevant job requirements categorized by seniority, salary and keywords. I then used my…

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In plain words

Be Distinguished analyzes job descriptions to help job seekers understand what employers want at a glance. The tool aggregates and categorizes listings by seniority level, salary range, and keywords, then uses natural language processing to extract key requirements. It's designed for professionals in machine learning and AI who need to tailor their applications and resumes to match role expectations without reading through lengthy job postings individually.

written from the facts on this page · September 2026

From the sources

In the maker’s words, at launch

10 second version: Get 10000feet view of job descriptions - https://be-distinguished.com 2 minute version : Hi HN, Long time lurker, big fan, and first time poster inspired by how this community elaborates on ideas and new products. Recently was given feedback that my CV was too long at 2 pages, I was at loss as to how to update it without having a high-level view of the requirements of the type of jobs I would be interested in. So I built https://be-distinguished.com to help me study relevant job requirements categorized by seniority, salary and keywords. I then used my site to update my own CV! Overall the whole process was far simpler than I thought it'd be and the work looked like below: [0]. study corpus (70hrs), [1]. gather job descriptions(requests - 50hrs), [2]. apply NLP to this text (nltk - 120hrs), [3]. have a custom spacy model to decide if a sentence is requirement (40hrs) [4]. return the results in a harmonized format (pandas - 50hrs), [5]. present findings through a website (flask/postgres/heroku/bootstrap - 120hrs). Have a look and let me know what you think. HN Special: I don't want to hoard this data and let it sit on some database. If it inspires, send across queries (sql or otherwise) you would like to run against this database. I would love to add them to a future version of BDDB. you can assume these columns for your mock queries: requirements, location, seniority, title, date, salary, keywords. relay email account for queries 8fi1pj5fb_at_mozmail_dot_com upwards and onwards!

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