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authorllzmb <46303940+llzmb@users.noreply.github.com>2021-08-20 14:47:15 +0200
committerGitHub <noreply@github.com>2021-08-20 14:47:15 +0200
commitb9b497241b5432dac6662d6c063ab08a7de9b117 (patch)
tree33f29ee1d397bb70c9961c9e798811a78e274a92
parentab43289c1a6232a308eeba9ed9b1a8fe8168f72d (diff)
downloadafl++-b9b497241b5432dac6662d6c063ab08a7de9b117.tar.gz
Update docs/rpc_statsd.md
Co-authored-by: Edouard SCHWEISGUTH <edznux@gmail.com>
-rw-r--r--docs/rpc_statsd.md2
1 files changed, 1 insertions, 1 deletions
diff --git a/docs/rpc_statsd.md b/docs/rpc_statsd.md
index e7144c1d..63037bb0 100644
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@@ -4,7 +4,7 @@ With StatsD, Prometheus, and Grafana you can set up a tool chain for remote moni
 
 This enables you to create nice and readable dashboards containing all the information you need on your fuzzer instances. There is no need to write your own statistics parsing system, deploy and maintain it to all your instances, and sync with your graph rendering system.
 
-Compared to the default integrated UI of AFL++, this can help you to visualize trends and the fuzzing state over time. You might be able to see when the fuzzing process has reached a state of no progress and visualize what are the "best strategies" for your targets (according to your own criteria). You can do so without logging into each instance manually.
+Compared to the default integrated UI of AFL++, this can help you to visualize trends and the fuzzing state over time. You might be able to see when the fuzzing process has reached a state of no progress and visualize what are the "best strategies" for your targets (according to your own criteria). You can do so without logging into each instance individually.
 
 ![example visualization with Grafana](resources/statsd-grafana.png)