diff --git a/README.md b/README.md
index 4bd2be3a..b7c6c14a 100644
--- a/README.md
+++ b/README.md
@@ -185,7 +185,7 @@ python -m cellpose
 
 The first time cellpose runs it downloads the latest available trained model weights from the website.
 
-You can now **drag and drop** any images (*.tif, *.png, *.jpg, *.gif) into the GUI and run Cellpose, and/or manually segment them. When the GUI is processing, you will see the progress bar fill up and during this time you cannot click on anything in the GUI. For more information about what the GUI is doing you can look at the terminal/prompt you opened the GUI with. For example data, see [website](http://www.cellpose.org) or this [zip file](https://www.cellpose.org/static/images/demo_images.zip). For best accuracy and runtime performance, resize images so cells are less than 100 pixels across. 
+You can now **drag and drop** any images (*.tif, *.png, *.jpg, *.gif) into the GUI and run Cellpose, and/or manually segment them. When the GUI is processing, you will see the progress bar fill up and during this time you cannot click on anything in the GUI. For more information about what the GUI is doing you can look at the terminal/prompt you opened the GUI with. For example data, see [website](https://www.cellpose.org) or this [zip file](https://www.cellpose.org/static/images/demo_images.zip). For best accuracy and runtime performance, resize images so cells are less than 100 pixels across. 
 
 For 3D data, with multi-Z, please use the 3D version of the GUI with:
 ~~~~
@@ -195,7 +195,7 @@ python -m cellpose --Zstack
 
 ## Step-by-step demo
 
-1. Download this [folder](http://cellpose.org/static/images/demo_images.zip) of images and unzip it. These are a subset of the test images from the paper.
+1. Download this [zip file](https://www.cellpose.org/static/images/demo_images.zip) of images and unzip it. These are a subset of the test images from the paper.
 2. Start the GUI with `python -m cellpose`.
 3. Drag an image from the folder into the GUI.
 4. Set the model (in demo all are `cyto`) and the channel you want to segment (in demo all are `green`). Optionally set the second channel if you are segmenting `cyto` and have an available nucleus channel.