83 lines
3.5 KiB
HTML
83 lines
3.5 KiB
HTML
<head>
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<title>Nicholas Orlowsky</title>
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<link rel="stylesheet" href="/style.css">
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<link rel="icon" type="image/x-icon" href="/favicon.ico">
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</head>
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<body>
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<nav>
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<a href="/">[ Home ]</a>
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<a href="/blog.html">[ Blog ]</a>
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<a href="/projects.html">[ Projects ]</a>
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<a href="/extra.html">[ Extra ]</a>
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<hr/>
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</nav>
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<h1>Side Project Log 8/15/2023</h1>
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<p>This side project log covers work done from 8/8/2023 - 8/15/2023</p>
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<h2 id="olney">Olney</h2>
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<p>
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I added a frontend to Olney and added a feature where it can automatically keep track of your job applications
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by monitoring your email.
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</p>
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<h3>Frontend</h3>
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<p>
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The frontend was made with Svelte. I chose not to use any UI/CSS libraries as I wanted to keep the number of
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dependencies low. This was another good opportunity to learn about Svelte.
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</p>
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<h3>Automatic Tracking via E-Mail</h3>
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<p>
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This is the killer feature that I initially set out to build Olney for. This works by having the user forward their
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E-Mail to an instance of Olney. To receive E-Mail, Olney uses <a href="https://inbucket.org">Inbucket</a>, a mailserver
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easily hostable within Docker. It listens on a websocket for incoming mail. Whenever a new mail message is received,
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Olney uses the OpenAI API to get a summary of the email in the following format:
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</p>
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<pre><code class="language-json">
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{
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isRecruiting: bool, // is the message about recruiting?
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recruitingInfo: null | {
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location: string, // Location in City, Providence/State, Country format
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company: string, // Casual name of company e.g: Google, Cisco, Apple
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position: string, // Name of job position
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type: "assessment" | "interview" | "offer" | "rejection" | "applied" // What the message is discussing
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dateTime: string, // DateTime communication rec'd OR DateTime that is being discussed (i.e. interview date confirmation)
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name: string // Name of event, giving more detail to type
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} // null if message is not about recruiting, fill with values if it is
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}
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</code></pre>
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<p>
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Olney then takes some details from this data, namely: company, position, and location and then uses the OpenAI API to generate
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an <a href="https://www.pinecone.io/learn/vector-embeddings/">embedding</a>. We then query the closest match out of the job applications
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in the database (with <a href="https://github.com/pgvector/pgvector">pgvector</a>). Once we have the job application, we add
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the event to the database, using the job application's id as a fkey.
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</p>
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<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
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email.
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</p>
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<p>
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Olney should be open-sourced/released within the next week or two.
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</p>
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<hr>
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<p><strong>These projects had minimal/no work done on them:</strong> NWS, RingGold, SQUIRREL</p>
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<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/styles/dark.min.css">
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<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min.js"></script>
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<script>hljs.highlightAll();</script>
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<footer>
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<hr />
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<p style="margin-bottom: 0px;">Copyright © Nicholas Orlowsky 2025</p>
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<p style="margin-top: 0px; margin-bottom: 0px;">Hosting provided by <a href="https://nws.nickorlow.com">NWS</a></p>
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<p style="margin-top: 0px;">Powered by <a href="https://github.com/nickorlow/anthracite">Anthracite Web Server</a></p>
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</footer>
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</body>
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