diff --git a/.github/workflows/cicd.yaml b/.github/workflows/cicd.yaml
index b97926d2..67073f58 100644
--- a/.github/workflows/cicd.yaml
+++ b/.github/workflows/cicd.yaml
@@ -19,10 +19,20 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ env.PYTHON_VERSION }}
+      - name: Install compatible virtualenv and pip
+        run: |
+          pip install --upgrade pip
+          pip install virtualenv==20.30.0
+      - name: Remove Poetry cache to avoid stale virtualenv
+        run: |
+          rm -rf ~/.cache/pypoetry
       - name: install poetry
         uses: snok/install-poetry@v1
         with:
           version: ${{ env.POETRY_VERSION }}
+      - name: Configure Poetry to disable virtualenv creation
+        run: |
+          poetry config virtualenvs.create false
       - name: show python version ${{ env.PYTHON_VERSION }}
         run: |
           poetry run python --version
diff --git a/Makefile b/Makefile
index 816bfcdc..244764bc 100644
--- a/Makefile
+++ b/Makefile
@@ -11,7 +11,7 @@ install-extras: install  ## Install the package from source with extra dependenc
 	poetry run pip install numpy framegrab[youtube]
 
 install-lint:  ## Only install the linter dependencies
-	poetry install --only lint
+	poetry install -vvv --only lint
 
 install-dev: ## Only install the dev dependencies
 	poetry install --only dev
diff --git a/README.md b/README.md
index 4dfc0e58..0d5a3d82 100644
--- a/README.md
+++ b/README.md
@@ -24,6 +24,10 @@ print(f"The answer is {image_query.result}")
 
 Your images are first analyzed by machine learning (ML) models which are automatically trained on your data. If those models have high enough confidence, that's your answer. But if the models are unsure, then the images are progressively escalated to more resource-intensive analysis methods up to real-time human review. So what you get is a computer vision system that starts working right away without even needing to first gather and label a dataset. At first it will operate with high latency, because people need to review the image queries. But over time, the ML systems will learn and improve so queries come back faster with higher confidence.
 
+### Extra session
+
+Hello, please trigger a github action.
+
 ## Learn more
 
 Some more resources you might like:
diff --git a/pyproject.toml b/pyproject.toml
index 56f944b5..76f548d7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,6 +22,7 @@ python-dateutil = "^2.9.0"
 requests = "^2.28.2"
 typer = "^0.12.3"
 urllib3 = "^1.26.9"
+virtualenv = ">=20.30,<20.31" # TODO: unpin when fixed?
 
 [tool.poetry.group.dev.dependencies]
 datamodel-code-generator = "^0.22.1"