[fix] Add heuristic evaluation, fork detection, and Phase 1 win/block split to AI.

Minimax leaf nodes now return a positional score instead of 0, using
playable-threat detection (±100), non-playable threats (±40), fork
bonus (±200), two-in-a-row (±5), and center control (±3). Phase 1
is split into two passes so the AI never blocks when it can win.
Game sequence is now auto-logged to the browser console on game end.
Applied to all three implementations (C++, JS, Python).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

connect_four.py

@@ -1,4 +1,4 @@
-"""Connect Four terminal game with AI, using Rich for display."""
+"""Connect Four terminal game with AI (minimax + alpha-beta + heuristic), using Rich for display."""
 
 import os
 import queue
@@ -271,6 +271,8 @@ def log_game(games: list[dict], game_menu_mode: int, level: int, winner: int, mo
 
 def evaluate_board(board: list[list[int]], ai_p: int, hu_p: int) -> int:
     score = 0
+    ai_threats = 0
+    hu_threats = 0
 
     # Center column bonus
     for r in range(ROWS):
@@ -281,21 +283,30 @@ def evaluate_board(board: list[list[int]], ai_p: int, hu_p: int) -> int:
 
     # Score a window of 4 cells by piece counts
     def score_window(c: int, r: int, dc: int, dr: int) -> int:
-        ai, hu = 0, 0
+        nonlocal ai_threats, hu_threats
+        ai, hu, empty_c, empty_r = 0, 0, -1, -1
         for i in range(4):
-            v = board[c + i * dc][r + i * dr]
+            cc = c + i * dc
+            rr = r + i * dr
+            v = board[cc][rr]
             if v == ai_p:
                 ai += 1
             elif v == hu_p:
                 hu += 1
+            else:
+                empty_c, empty_r = cc, rr
         if ai > 0 and hu > 0:
             return 0
         if ai == 3:
-            return 50
+            ai_threats += 1
+            playable = empty_r == 0 or board[empty_c][empty_r - 1] != 0
+            return 100 if playable else 40
         if ai == 2:
             return 5
         if hu == 3:
-            return -50
+            hu_threats += 1
+            playable = empty_r == 0 or board[empty_c][empty_r - 1] != 0
+            return -100 if playable else -40
         if hu == 2:
             return -5
         return 0
@@ -317,6 +328,12 @@ def evaluate_board(board: list[list[int]], ai_p: int, hu_p: int) -> int:
         for c in range(COLS - 3):
             score += score_window(c, r, 1, -1)
 
+    # Fork bonus: multiple threats are disproportionately dangerous
+    if ai_threats >= 2:
+        score += 200
+    if hu_threats >= 2:
+        score -= 200
+
     return score