update readme for micro-opts and fixed benchmarks

Author: indirect

README.md

@@ -10,21 +10,24 @@ For an 80MB gzipped log file containing 915,427 JSON event objects (which is 1.0
 - 5.0 seconds total to also parse every JSON object into a Rust struct
 - 7.8 seconds total to further parse every User Agent field for Bundler, RubyGems, and Ruby versions and other metrics
 
-This is... very good. For comparison, a Python script that used AWS Glue to do something similar took about _30 minutes_. My first approach of writing a `nom` parser-combinator to parse the User Agent field, instead of using a regex, took 18.7 seconds. Processing a gigabyte of almost a million JSON objects into useful histograms in less than 8 seconds just blows my mind. But then I figured out how to use Rayon, and now if you give it 8 gzipped log files on an 8-core MacBook Pro, it can parse 399,300 JSON objects per second.
+This is... very good. For comparison, a Python script that used AWS Glue to do something similar took about _30 minutes_. My first approach of writing a `nom` parser-combinator to parse the User Agent field, instead of using a regex, took 18.7 seconds. Processing a gigabyte of almost a million JSON objects into useful histograms in less than 8 seconds just blows my mind. But then I figured out how to use Rayon, and now it can parse 8 gzipped log files in parallel on an 8-core MacBook Pro, and that's super fast.
 
 ### Wait, _how_ fast?
 
        ~525 records/second/cpu in Python on AWS Glue
-     50,534 records/second/cpu in Rust with nom
-    121,153 records/second/cpu in Rust with regex
+   ~300,000 records/second/cpu in Rust with regex
 
 ### Are you kidding me?
 
-No. It gets even better if you have multiple cores.
+No. The latest version (which I am now benchmarking without also running `cargo build` 🤦🏻‍♂️) can parse records really, really fast.
 
      ~4,200 records/second in Python with 8 worker instances on AWS Glue
-    399,300 records/second in Rust with 8 cores and rayon on a MacBook Pro
+ ~1,085,000 records/second in Rust with 8 cores and rayon on a MacBook Pro
 
 ### What does it calculate?
 
-It counts Bundler, RubyGems, and Ruby versions, in hourly buckets, and prints those out as nested JSON to stdout.
+It counts Bundler, RubyGems, and Ruby versions, in hourly buckets, and prints those out as nested JSON to stdout.
+
+### Tell me more about how this happened.
+
+Okay, I wrote [a blog post with details about creating this library](https://andre.arko.net/2018/10/25/parsing-logs-230x-faster-with-rust/).