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<title>Nicholas Orlowsky</title>
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<h1>Side Project Log 8/15/2023</h1>
<p>This side project log covers work done from 8/8/2023 - 8/15/2023</p>
<h2 id="olney">Olney</h2>
<p>
I added a frontend to Olney and added a feature where it can automatically keep track of your job applications
by monitoring your email.
</p>
<h3>Frontend</h3>
<p>
The frontend was made with Svelte. I chose not to use any UI/CSS libraries as I wanted to keep the number of
dependencies low. This was another good opportunity to learn about Svelte.
</p>
<h3>Automatic Tracking via E-Mail</h3>
<p>
This is the killer feature that I initially set out to build Olney for. This works by having the user forward their
E-Mail to an instance of Olney. To receive E-Mail, Olney uses <a href="https://inbucket.org">Inbucket</a>, a mailserver
easily hostable within Docker. It listens on a websocket for incoming mail. Whenever a new mail message is received,
Olney uses the OpenAI API to get a summary of the email in the following format:
</p>
<pre><code class="language-json">
{
isRecruiting: bool, // is the message about recruiting?
recruitingInfo: null | {
location: string, // Location in City, Providence/State, Country format
company: string, // Casual name of company e.g: Google, Cisco, Apple
position: string, // Name of job position
type: "assessment" | "interview" | "offer" | "rejection" | "applied" // What the message is discussing
dateTime: string, // DateTime communication rec'd OR DateTime that is being discussed (i.e. interview date confirmation)
name: string // Name of event, giving more detail to type
} // null if message is not about recruiting, fill with values if it is
}
</code></pre>
<p>
Olney then takes some details from this data, namely: company, position, and location and then uses the OpenAI API to generate
an <a href="https://www.pinecone.io/learn/vector-embeddings/">embedding</a>. We then query the closest match out of the job applications
in the database (with <a href="https://github.com/pgvector/pgvector">pgvector</a>). Once we have the job application, we add
the event to the database, using the job application's id as a fkey.
</p>
<p>
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I chose embedding as the lookup method so that we don't have to worry about the data parsed out of the email being an exact
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match for what the user inputted. This also allows the lookup to work even when certain things such as location are missing from the
email.
</p>
<p>
Olney should be open-sourced/released within the next week or two.
</p>
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<p><strong>These projects had minimal/no work done on them:</strong> NWS, RingGold, SQUIRREL</p>
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