Nicely done Jon. I really like the UI - I wanted to have buttons as well but didn't find how to do it in Streamlit.

I also built Resume Chatbot but using slightly different stack: Python, Langchain, Faiss as vector store, MongoDB to store chat logs and Streamlit for UI. Here is a link: https://www.artkreimer.com/resume/ or you can try it on streamlit https://art-career-bot.streamlit.app/. Code is available here https://github.com/kredar/data_analytics/tree/master/career_.... Great thread and I got some ideas for my next project. Thanks a lot everyone.

Hey Jon - I'm Jon too - working on an AI startup in the recruiting space and will be hiring remotely. I like your resume ap and can definitely see utility in it. I'd be happy to connect and maybe see if there is a way we could work together! I'll find you on LinkedIn and send you an invite request.

Resume AI is cool, really nicely done, mate !

Thanks. This is the first real test apart from a couple dozen test users. I've received hundreds of prompts in the past 24 hours from Hacker News users, mainly from my suggested questions buttons.

The actual questions I got did not provide a response that is to my liking. Most of that is due in part because I'm using gpt3.5 since gpt4-turbo is a lot more expensive, and I can learn a lot more by using an inferior LLM.

For example, using an llm router to analyze the query and route to a specific helper function with a specific prompt would be helpful. Sometimes a user starts with a greeting but the response is a pre-written "Sorry an answer cannot be found". Questions are typically grouped into a category such as skills, experience, project, personal (ie: where are you located), preferences (ie: favorite language), and general interview questions (ie: why should I hire you). Questions in categories can be better answered by using a different prompt and/or RAG technique.

I’m sorry it’s been tough. The job market for seniors and leads is still quite strong in Australia if you can move here

Thanks. I'm starting to realize that part of the problem is job search and matching.

I was contacted by a company recruiter for a small healthcare SaaS in California and had 3 interviews recently. When I looked up the job, only 7 people had applied in 2 weeks on LinkedIn. They are a very real company with very real people, but their job post is not getting seen (it's not a promoted post).

My next AI project will be to scrape LinkedIn jobs, analyze it for repost/promoted behavior, group it by consulting/headhunters vs company job post, eliminate duplicates, and filter based on my skillset and hard-no qualities (such as can't work if I live in California, must be in EST but I'm in PST timezone, requires Java experience, etc).

... btw. are you sure that only 7 people applied for that job? Because there are a lot of job announcements on LinkedIn which just won't show the number of applicants correctly in case there's an application link outside of LinkedIn for applying, meaning the application doesn't take place within LinkedIn. In that case, you'll get the question by LinkedIn if you have applied for the job, which most people just won't click. I'm seeing this all the time.

But still good point that there might be promoted jobs and non-promoted ones, maybe it's worth creating an own job scraper.

That's a good point about the applicant number. I don't think anyone knows exactly how it works, but it was the first time I saw a job posting older than a few hours with <10 applicants with such straightforward skills such as Python.

Just played with your app, I think is super cool! I especially liked the way that you can just click the next question within an answer, makes it super convenient and fun to use.

I'm currently also looking for a dev job. So you have 15 years of experience, live in California and struggle to find something? That sounds a bit demotivating to me lol, because I'm kinda half of all of that or a bit less.

I also like your LinkedIn analysis idea, should try that maybe, too.

I'm looking for remote dev job, which is much harder, since I am in Sacramento.

I had a similar idea. Scraping LinkedIn and some other job boards and analyzing and highlighting jobs that best fit specific criterias.