diff --git a/README.md b/README.md
index 77477601..c2ae1f8d 100644
--- a/README.md
+++ b/README.md
@@ -1,84 +1,5 @@
 # motleycrew
-A lightweight agent interaction framework.
 
-Minimal example with maximum automation (might take a while to build ;) ):
+Multi-agent systems as they should be.
 
-```python
-from motleycrew import Task, MotleyCrew
-
-task = Task("""Research arxiv on the latest trends in machine learning
-and produce an engaging blog post on the topic""",
-            documents=["paper1.pdf", "paper2.pdf"])
-crew = MotleyCrew(tasks=[task])
-crew.run()
-```
-Come to think of it, might it make sense to make it 2 libraries, one with the interaction
-primitives, and the other on top of it with the automation?
-
-Here the MotleyCrew auto-spawns agents to complete the task, and picks additional tools for them.
-
-Short term, more crewAI-style (here some is copy-pasted from crewAI,
-will need to edit before going public)
-```python
-from langchain import hub
-from langchain_community.tools import DuckDuckGoSearchRun
-from langchain_openai import ChatOpenAI
-from langchain.agents import AgentExecutor, create_openai_tools_agent
-
-from motley_crew import Task, MotleyCrew
-from motley_crew.agents import LangchainAgent, MotleyAgent
-
-tools=[DuckDuckGoSearchRun()]
-researcher_prompt = hub.pull("hwchase17/openai-tools-agent")
-llm = ChatOpenAI(model="gpt-4-0125-preview", temperature=0)
-
-researcher_agent = AgentExecutor(
-    agent=create_openai_tools_agent(llm, tools, researcher_prompt),
-    tools=tools,
-    verbose=True,
-)
-
-researcher = LangchainAgent(
-    agent=researcher_agent,
-    goal="Research the web and any documents they are given, and summarize the results",
-    allow_delegation=False
-)
-
-writer = MotleyAgent(
-    goal="Craft compelling content on tech advancements",
-    description="""You are a renowned Content Strategist, known for your insightful and engaging articles.
-  You transform complex concepts into compelling narratives.""",
-    verbose=True,
-    kind = "crewai",
-    delegation=True,
-)
-
-# Create tasks for your agents
-task1 = Task(
-    description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
-  Identify key trends, breakthrough technologies, and potential industry impacts.
-  Your final answer MUST be a full analysis report""",
-    agent=researcher,
-    documents = ["paper1.pdf", "paper2.pdf"],
-)
-
-task2 = Task(
-    description="""Using the insights provided, develop an engaging blog
-  post that highlights the most significant AI advancements.
-  Your post should be informative yet accessible, catering to a tech-savvy audience.
-  Make it sound cool, avoid complex words so it doesn't sound like AI.
-  Your final answer MUST be the full blog post of at least 4 paragraphs.
-  """,
-    agent=writer,
-    depends_on=task1,
-)
-
-# Instantiate your crew with a sequential process
-crew = MotleyCrew(
-    agents=[researcher, writer],
-    tasks=[task1, task2],
-    verbose=2,  # You can set it to 1 or 2 to different logging levels
-)
-
-# Get your crew to work!
-result = crew.run()
+Stay tuned at https://www.motleycrew.ai/