diff --git a/website/src/widgets/landing.js b/website/src/widgets/landing.js
index 1f67b5e5b..6905d46d0 100644
--- a/website/src/widgets/landing.js
+++ b/website/src/widgets/landing.js
@@ -23,32 +23,29 @@ import Link from '../components/link'
 import BenchmarksChoi from 'usage/_benchmarks-choi.md'
 
 const CODE_EXAMPLE = `# pip install spacy
-# python -m spacy download en_core_web_md
+# python -m spacy download en_core_web_sm
 
 import spacy
 
 # Load English tokenizer, tagger, parser, NER and word vectors
-nlp = spacy.load("en_core_web_md")
+nlp = spacy.load("en_core_web_sm")
 
 # Process whole documents
-text = (u"When Sebastian Thrun started working on self-driving cars at "
-        u"Google in 2007, few people outside of the company took him "
-        u"seriously. “I can tell you very senior CEOs of major American "
-        u"car companies would shake my hand and turn away because I wasn’t "
-        u"worth talking to,” said Thrun, now the co-founder and CEO of "
-        u"online higher education startup Udacity, in an interview with "
-        u"Recode earlier this week.")
+text = ("When Sebastian Thrun started working on self-driving cars at "
+        "Google in 2007, few people outside of the company took him "
+        "seriously. “I can tell you very senior CEOs of major American "
+        "car companies would shake my hand and turn away because I wasn’t "
+        "worth talking to,” said Thrun, in an interview with Recode earlier "
+        "this week.")
 doc = nlp(text)
 
+# Analyze syntax
+print("Noun phrases:", [chunk.text for chunk in doc.noun_chunks])
+print("Verbs:", [token.lemma_ for token in doc if token.pos_ == "VERB"])
+
 # Find named entities, phrases and concepts
 for entity in doc.ents:
     print(entity.text, entity.label_)
-
-# Determine semantic similarities
-doc1 = nlp(u"my fries were super gross")
-doc2 = nlp(u"such disgusting fries")
-similarity = doc1.similarity(doc2)
-print(doc1.text, doc2.text, similarity)
 `
 
 /**