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).
But still good point that there might be promoted jobs and non-promoted ones, maybe it's worth creating an own job scraper.
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.
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 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.
The backend is a Python FastAPI that uses ChromaDB to store my resume and Q&A pairs, OpenAI, and Airtable to log requests and responses. The UI is Sveltekit.
I'm currently building a different tool and will apply some learnings to my Interactive Resume AI. Instead of Airtable, I am going to use LangSmith for observability.
I started writing and my Substack articles are also linked to via my website. I'm currently working on applying sentence window retrieval and that article will be out shortly. This is part of a #buildinpublic effort to help build my brand as well.
I've been unemployed since Sept as a Senior Software Engineer. The market is tough so I'm focusing on the above to help get employment or a contract.