new blogs

src/blog.filler.html

@@ -2,6 +2,8 @@
   <h1 style="margin-bottom: 0px;">Blog</h1>
   <p style="margin-top: 0px;">A collection of my thoughts, some of them may be interesting</p>
 
+  <p><a href="./blogs/side-project-8-15-23.html">[ Side Project Log 8/15/23 ]</a> - August 15th, 2023</p>
+  <p><a href="./blogs/side-project-8-8-23.html">[ Side Project Log 8/08/23 ]</a> - August 8th, 2023</p>
   <p><a href="./blogs/side-project-7-12-23.html">[ Side Project Log 7/12/23 ]</a> - July 12th, 2023</p>
   <p><a href="./blogs/side-project-4-29-23.html">[ Side Project Log 4/29/23 ]</a> - April 29th, 2023</p>
   <p><a href="./blogs/side-project-3-27-23.html">[ Side Project Log 3/27/23 ]</a> - March 27th, 2023</p>

src/blogs/side-project-8-15-23.filler.html

@@ -0,0 +1,56 @@
+<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>
+
+<code>
+{
+  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>
+
+<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>
+  Embeddings was chosen as the lookup method that way we don't have to worry about data being parsed out of the email being an exact
+  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>
+
+<hr>
+<p><strong>These projects had minimal/no work done on them:</strong> NWS, RingGold, SQUIRREL</p>

src/blogs/side-project-8-8-23.filler copy.html

@@ -0,0 +1,65 @@
+<h1>Side Project Log 8/8/2023</h1>
+<p>This side project log covers work done from 7/12/2023 - 8/8/2023</p>
+
+<h2 id="squirrel">SQUIRREL</h2>
+<p>
+  SQUIRREL has been updated to work with INSERT INTO and SELECT queries. I also refactored much of the codebase to do error handling more elegantly and to make the parser
+  more extensible. Here's a screenshot of table creation, data insertion, and data selection: 
+</p>
+
+<p>
+  The biggest challenge of this part was working on the parser which has now been written three times. The approaches to the parsing were:
+</p>
+
+<ol>
+  <li>
+    <b>Stepping through whitespace:</b> <p>This was my initial and naive approach to the problem. I split the input string by its whitespace
+    and then queried values by referencing their indexes in the split string. </p>
+  </li>
+  <li>
+    <b>Regex:</b> <p>This approach was cleaner than the first and led to a small parser, however it required an external dependency (which I'm 
+    trying to minimize), and would make it hard to add additional features to commands later down the line.</p>
+  </li>
+  <li>
+    <b>Finite state machine:</b> <p>This solution was more verbose than the others, however it allows for easier development. This method works 
+    by splitting the query string into tokens. Tokens are the smallest piece of data that a parser recognizes. SQUIRREL gets them by splitting
+    the input by delimiters and using the split list as tokens (excluding whitespace) SQUIRREL recognizes the following characters as delimiters: 
+    </p>
+
+    <code>
+      ' ', ',', ';', '(', ')'
+    </code>
+    
+    <p>
+      This means that the string "INSERT INTO test (id) VALUES (12);" would be parsed into the list: "INSERT", "INTO", "test", "(", "id", etc..
+    </p>
+
+    <p>
+      Once we have our list of tokens, we iterate through them starting at a default state and perform a certain task for the given state, which 
+      usually includes switching to another state. We do this until we reach the end state. 
+    </p>
+
+    <p>
+      For example, with the above insert statement, we would start in the IntoKeyword state which would ensure that "INTO" is the current token. 
+      We would then transition to the TableName state which would read the table name and store it in the ParsedCommand struct we're returning. We 
+      would then move to the ColumnListBegin state which would look for an opening parenthesis, and switch the state to ColumnName. This process
+      continues with the other parts of the query until the Semicolon state is reached which checks that the statement ends with a semicolon, then 
+      returns the ParsedCommand struct. 
+    </p>
+  </li>
+</ol>
+
+<p>
+  Next steps for this are to add column selection to SELECT statements and add WHERE clauses to SELECT statements.
+</p>
+
+<h2 id="olney">Olney</h2>
+<p>
+  I added a feature to the Olney API which scans the <a href="https://github.com/SimplifyJobs/Summer2024-Internships">pittcsc (now Simplify) summer internships Github repo</a> 
+  and parses the data into JSON format. I parsed the markdown file they have uisng regex which was relatively simple. There were some issues during development due to the 
+  changing structure of the markdown file. These issues are being fixed on a rolling basis. I expect the changes to slowdown now that the transition from pittcsc to Simplify
+  is complete. You can access the JSON at <a href="https://olney.nickorlow.com/jobs">olney.nickorlow.com/jobs</a>.
+</p>
+
+<hr>
+<p><strong>These projects had minimal/no work done on them:</strong> NWS, RingGold</p>

src/extra.filler.html

@@ -36,21 +36,21 @@
     </tr>
     <tr>
       <td>Bench Press</td>
-      <td>275 Lbs</td>
-      <td>2023</td>
-      <td>Gregory Gym</td>
+      <td>280 Lbs</td>
+      <td>August 10th, 2023</td>
+      <td>FUTO HQ</td>
     </tr>
     <tr>
       <td>Squat</td>
-      <td>405 Lbs</td>
-      <td>2023</td>
-      <td>Gregory Gym</td>
+      <td>415 Lbs</td>
+      <td>August 10th, 2023</td>
+      <td>FUTO HQ</td>
     </tr>
     <tr>
       <td>Deadlift</td>
-      <td>405 Lbs</td>
-      <td>2023</td>
-      <td>Gregory Gym</td>
+      <td>415 Lbs</td>
+      <td>Augist 10th, 2023</td>
+      <td>FUTO HQ</td>
     </tr>
   </table>
 </div>

src/projects.filler.html

@@ -39,6 +39,7 @@
 
   <div>
     <h2>SQUIRREL</h2>
+    <a href="https://github.com/nickorlow/squirrel">[ GitHub Repo ]</a>
     <p>
       SQUIRREL stands for SQL Query Util-Izing Rust's Reliable and Efficient Logic. It is a SQL database 
       that I am writing in Rust. Currently, it can parse CREATE TABLE commands, and works with the