## Backstory is three things

1. **An interactive Q&A** -- let potential employers ask questions about an individual's work history (aka "Backstory".) Based on the content the job seeker has provided to the RAG system, that can provide insights into that individual's resume and curriculum vitae that are often left out when people are trying to fit everything onto one page.


2. **A resume builder** -- if you have a job position, and you think this person might be a candidate, paste your job description and have a resume produced based on their data. If it looks interesting, reach out to them. If not, hopefully you've gained some insight into what drives them.

3. **A curated expert about you** -- as a potential job seeker, you can self host this environment and generate resumes for yourself.

  While this project was generally built for self-hosting with open source models, you can use any of the frontier models. The API adapters in this project can be configured to use infrastructure hosted from Anthropic, Google, Grok, and OpenAI (alphabetical.) For information, see [https://github.com/jketreno/backstory/README.md](https://github.com/jketreno/backstory/README.md#Frontier_Models).


## This application was developed to achieve a few goals:

1. See if it is realistic to self-host AI LLMs. Turns out, it is -- with constraints. I don't have the GPU hardware to run models larger than about 8 billion parameters, which puts my local deployment in the realm of a Small Language Model (SLM.) I've been meaning to write a blog post about what to buy to build an AI PC that can run the latest "small" (7B) parameter models.
2. Provide a recent example of my capabilities; many of my projects while working for Intel were internally facing. The source code to this project is available on [GitHub](https://github.com/jketreno/backstory). It doesn't touch on much of my history of work, however it does represent the pace at which I can adapt and develop useful solutions to fill a gap. During this project's development I have had the opportunity to test and use many of the latest frontier models, which has allowed me to develop at a pace that far exceeds what I could have done even a year ago.
3. Explore Stable Diffusion (SD), Reinforced Learning (RL), Large Language Models (LLM), Paramater-Efficient Fine-Tuning (PEFT), Quantized Low-Rank Adapters (QLORA), open source and frontier models, tokenizers, and the vast open-source ecosystem for Machine Learning (ML) and Artificial Intelligence (AI). I wanted to do this to understand the strengths, weakness, and state of the industry in its development and deployment of those technologies.
4. My career at Intel was diverse. Over the years, I have worked on many projects almost everywhere in the computer ecosystem. That results in a resume that is either too long, or too short. This application is intended to provide a quick way for employers to interactively ask about me. You can view my resume in totality, or use the Resume Builder to post your job position to see how I fit. Or go the Backstory and ask questions about the projects mentioned in my resume.

## Some questions I've been asked

Q. <ChatQuery query={
  prompt: "Why aren't you providing this as a Platform As a Service (PaaS) application?", 
  tunables: { "enable_tools": false }
  } />

A. I could; but I don't want to store your data. I also don't want to have to be on the hook for support of this service. I like it, it's fun, but it's not what I want as my day-gig, you know? If it was, I wouldn't be looking for a job...

Q. <ChatQuery query={
  prompt: "Why can't I just ask Backstory these questions?",
  tunables: { "enable_tools": false }
  } />

A. Try it. See what you find out :)
