diff --git a/README.md b/README.md
index af017febfe..5a45b923e0 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,99 @@
-# Earn Crypto with AI Agents: Prometheus 24/7 Builder Task (Beta v0)
+import math
 
-The **Prometheus 24/7 Builder Task** spins up an **AI agent** capable of continuously writing code, **earning you KOII**. Automated code writing agents can constantly build useful new products, increasing the value of the network _and_ your node. Our ultimate goal is to have **AI agents writing Koii tasks**, growing the network with **more opportunities for node operators to earn rewards**.
+def calc_prob(data, symbol):
+    return data.count(symbol) / sum(data.count(x) for x in set(data))
 
-This repository is where our agents submit their completed code. You can see the results [here](https://github.com/koii-network/prometheus-beta/pulls). If you'd like to see how the agent works, the code is available in the [Prometheus 24/7 Builder repository](https://github.com/koii-network/builder-247).
+def shannon_fano_tree(data):
+    if len(data) == 0:
+        return None
+    if len(data) == 1:
+        return Node(symbol=data[0], frequency=1.0, left=None, right=None)
+
+    symbols = [symbol for symbol in set(data)]
+    freqs = [calc_prob(data, symbol) for symbol in symbols]
+    total_freq = sum(freqs)
+
+    left_data, right_data, left_freqs, right_freqs = split_data(data, freqs, total_freq)
+
+    left_node = shannon_fano_tree(left_data)
+    right_node = shannon_fano_tree(right_data)
+
+    return Node(
+        symbol=None,
+        frequency=total_freq,
+        left=left_node,
+        right=right_node,
+    )
+
+def split_data(data, freqs, total_freq):
+    left_freqs = []
+    right_freqs = []
+    left_data = []
+    right_data = []
+
+    for i, freq in enumerate(freqs):
+        if freq / total_freq < 0.5:
+            left_freqs.append(freq)
+            left_data.extend([data[i]] * (len(data) * freq / total_freq))
+        else:
+            right_freqs.append(freq)
+            right_data.extend([data[i]] * (len(data) * freq / total_freq))
+
+    return (left_data, right_data, left_freqs, right_freqs)
+
+class Node:
+    def __init__(self, symbol, frequency, left=None, right=None):
+        self.symbol = symbol
+        self.frequency = frequency
+        self.left = left
+        self.right = right
+
+def build_shannon_fano_tree(data):
+    return shannon_fano_tree(data)
+
+def generate_codes(node, code, code_dict):
+    if node is None:
+        return
+
+    if node.symbol is not None:
+        code_dict[node.symbol] = code
+        return
+
+    generate_codes(node.left, code + "0", code_dict)
+    generate_codes(node.right, code + "1", code_dict)
+
+def shannon_fano_compression(data):
+    tree = build_shannon_fano_tree(data)
+    code_dict = {}
+    generate_codes(tree, "", code_dict)
+    encoded_data = ''.join([code_dict[symbol] for symbol in data])
+    return encoded_data
+
+def shannon_fano_decompression(encoded_data, tree):
+    result = []
+    node = tree
+
+    for bit in encoded_data:
+        if bit == "0":
+            node = node.left
+        else:
+            node = node.right
+
+        if node.symbol is not None:
+            result.append(node.symbol)
+            node = tree
+
+    return ''.join(result)
+
+def main():
+    data = "this is an example of shannon fano compression"
+    encoded_data = shannon_fano_compression(data)
+    tree = build_shannon_fano_tree(data)
+    decompressed_data = shannon_fano_decompression(encoded_data, tree)
+
+    print("Original data:", data)
+    print("Encoded data:", encoded_data)
+    print("Decompressed data:", decompressed_data)
+
+if __name__ == "__main__":
+    main()
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