Add the demo GIF

README.md

@@ -15,7 +15,11 @@
   <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="license: MIT"></a>
 </p>
 
-<!-- Demo GIF goes here once recorded: `brew install vhs && vhs docs/demo.tape` -->
+<p align="center">
+  <img src="https://raw.githubusercontent.com/devcodes9/agsearch/main/docs/demo.gif"
+       alt="Typing a misspelled half-remembered phrase; the session you meant is row one"
+       width="100%">
+</p>
 
 ## Why not just `/resume`?
 
@@ -168,7 +172,7 @@ opened the session *asking for*, so it's weighted hardest; **length normalizatio
 sprawling transcript that mentions your term once no longer outranks a short, on-point session.
 Near-ties are then nudged by **recency** and by how often you've actually resumed that session
 (read off agsearch's own resume log). Badge `N/T` = query terms matched. A word that barely exists
-anywhere (a typo like `alibrry`) falls back to subsequence matching. No modes or flags.
+anywhere (a typo like `conection`) falls back to subsequence matching. No modes or flags.
 
 **Honest limit:** this is lexical, not semantic. Ranking beats an unordered `rg` dump, but it
 is not a guarantee that row one is the session you meant. Because transcripts are large,

agsearch

@@ -1041,7 +1041,7 @@ def build_sessions(lines):
         fh.write("\n".join(out))
 
 
-# Common words that add noise, not signal, to a search ("migration OF alibrary").
+# Common words that add noise, not signal, to a search ("migration OF the database").
 _STOP = {"of", "the", "a", "an", "to", "for", "in", "on", "and", "or", "is", "it", "this",
          "that", "with", "from", "by", "at", "as", "be", "are", "was", "were", "how", "do",
          "i", "my", "me", "we", "you", "about", "into", "using", "use", "some", "any"}
@@ -1219,7 +1219,7 @@ def rank_sessions(rows, qterms, usage=None, now=None):
     length normalization, so a sprawling session no longer outranks a short exact match, and
     what a session was *opened to do* outweighs a passing mention buried in it. The result is
     then nudged by recency and how often you've resumed that session. A term that exists almost
-    nowhere as text (a typo like 'alibrry') falls back to subsequence matching.
+    nowhere as text (a typo like 'conection') falls back to subsequence matching.
     """
     usage = usage or {}
     n = len(rows) or 1