diff --git a/2025/10/p2.py b/2025/10/p2.py
new file mode 100644
index 0000000..e142191
--- /dev/null
+++ b/2025/10/p2.py
@@ -0,0 +1,75 @@
+import pulp
+import numpy as np
+from scipy.optimize import minimize
+
+
+# pretty chatgpt'd
+def solve_linear_program(A, B):
+    """
+    Solve the integer linear programming problem to minimize the sum of variables
+    given a matrix A (coefficients) and a vector B (right-hand side).
+    """
+    # Number of variables (columns in A)
+    num_variables = A.shape[1]
+
+    # Define the problem as a minimization problem
+    prob = pulp.LpProblem("Minimize_Sum_of_Variables", pulp.LpMinimize)
+
+    # Define variables (all non-negative integers)
+    variables = [pulp.LpVariable(f'x{i}', lowBound=0, cat='Integer') for i in range(num_variables)]
+
+    # Objective function: Minimize the sum of the variables
+    prob += pulp.lpSum(variables), "Minimize sum"
+
+    # Add the constraints based on A * X = B
+    for i in range(A.shape[0]):  # Iterate over each equation (row of A)
+        prob += pulp.lpSum(A[i, j] * variables[j] for j in range(num_variables)) == B[i]
+
+    # Solve the problem
+    prob.solve(pulp.PULP_CBC_CMD(msg=False))
+
+    # Get the results
+    if pulp.LpStatus[prob.status] == 'Optimal':
+        solution = [v.varValue for v in variables]
+        return solution, sum(solution)
+    else:
+        return None, None
+
+
+
+def parse_buttons(bts):
+    return list(map(lambda bt: list(map(int, bt[1:-1].split(","))), bts))
+
+def make_btn(len, bt):
+    btn = [0] * len
+    for idx in bt:
+        btn[idx] = 1
+    return btn
+
+def parse_ln(ln):
+    ln = ln.strip()
+    ln = ln.split(" ")
+
+    coefs = list(map(int, ln[-1][1:-1].split(",")))
+    return list(map(lambda bt:
+        make_btn(len(coefs), bt)
+    , parse_buttons(ln[1:-1]))), coefs
+
+
+total = 0
+with open("input.txt", "r") as file:
+    for line in file:
+        coefs, terms = parse_ln(line)
+        A = np.transpose(np.array(coefs))
+        b = np.array(terms)
+
+        solution, min_sum = solve_linear_program(A, b)
+
+        if solution is not None:
+            total += min_sum
+            # print(f"Optimal solution: {solution}")
+            # print(f"Minimum sum: {min_sum}")
+        else:
+            print("No solution found.")
+
+print(f"total is {total}")