Grammar pass

Signed-off-by: Danila Fedorin <danila.fedorin@gmail.com>

content/blog/pdf_flashcards_llm/index.md

@@ -26,14 +26,14 @@ the latter was unpleasant, making me constantly break from the prose
 
 In the end, I decided to underline the words, and come back to them later.
 However, even then, the task is fairly arduous. For one, words I don't recognize
-aren't always in their canonical form (they can conjugated, plural, compound,
+aren't always in their canonical form (they can be conjugated, plural, compound,
 and more): I have to spend some time deciphering what I should add to a
 flashcard. For another, I had to bounce between a PDF of my book
 (from where, fortunately, I can copy-paste) and my computer. Often, a word
 confused the translation software out of context, so I had to copy more of the
 surrounding text. Finally, I learned that given these limitations, the pace of
 my reading far exceeds the rate of my translation. This led me to underline
-less words.
+fewer words.
 
 I thought,
 
@@ -60,10 +60,10 @@ interleaved with the technical details.
 ### The Core Solution
 The core idea has always been:
 
-1. Find thing that look like underlines
+1. Find things that look like underlines
 2. See which words they correspond to
 3. Perform {{< sidenote "right" "lemmatization-node" "lemmatization" >}}
-Lemmatization (<a href="https://en.wikipedia.org/wiki/Lemmatization">wikipedia</a>) is the
+Lemmatization (<a href="https://en.wikipedia.org/wiki/Lemmatization">Wikipedia</a>) is the
 process of turning non-canonical forms of words (like <code>am</code> (eng) /
 <code>suis</code> (fr)) into their canonical form which might be found in the
 dictionary (<code>to be</code> / <code>être</code>).
@@ -182,7 +182,7 @@ plenty of Flask applications in Codex's training dataset. In one shot,
 it generated a little web application that enabled me to tweak the source word
 and final translation. It also enabled me to throw away certain underlines.
 This was useful when, across different sessions, I forgot and underlined
-the same word, or when I underlined a word but later decided it not worth
+the same word, or when I underlined a word but later decided it was not worth
 including in my studying. This application produced an Anki deck, using
 the Python library [`genanki`](https://github.com/kerrickstaley/genanki).
 Anki has a nice mechanism to de-duplicate decks, which meant that every