diff --git a/src/activations.zig b/src/activations.zig
index a15fe86..384e08f 100644
--- a/src/activations.zig
+++ b/src/activations.zig
@@ -6,7 +6,6 @@ pub const Sigmoid = struct {
     }
 
     pub fn derivative(y: f32) f32 {
-        // Nota: qui assumiamo che 'y' sia già il risultato della sigmoide
         return y * (1.0 - y);
     }
 };
diff --git a/src/main.zig b/src/main.zig
index 9c3b555..36a7f42 100644
--- a/src/main.zig
+++ b/src/main.zig
@@ -1,18 +1,16 @@
 const std = @import("std");
 const World = @import("env.zig").World;
-const env = @import("env.zig"); // Per accedere a costanti come GRID_SIZE
+const env = @import("env.zig");
 const Network = @import("modular_network.zig").Network;
 
-// --- IPERPARAMETRI ---
 const GAMMA: f32 = 0.9;
-const LR: f32 = 0.005; // Basso perché output lineare
+const LR: f32 = 0.005;
 const EPSILON_START: f32 = 1.0;
 const EPSILON_END: f32 = 0.05;
-const DECAY_RATE: f32 = 0.0001; // Decadimento più lento dato che abbiamo tanti step
+const DECAY_RATE: f32 = 0.0001;
 
-// Helper per trovare max e argmax
 fn maxVal(slice: []const f32) f32 {
-    var m: f32 = -1.0e20; // Numero molto basso
+    var m: f32 = -1.0e20;
     for (slice) |v| if (v > m) {
         m = v;
     };
@@ -31,17 +29,14 @@ fn argmax(slice: []const f32) usize {
     return idx;
 }
 
-// Export aggiornato per Multi-Formica
 fn exportAntJSON(world: *World, file_path: []const u8, episode: usize, epsilon: f32) !void {
     const file = try std.fs.cwd().createFile(file_path, .{});
     defer file.close();
 
-    // Buffer grande per contenere le coordinate di 20 formiche
     var buffer: [65536]u8 = undefined;
     var fba = std.heap.FixedBufferAllocator.init(&buffer);
     const allocator = fba.allocator();
 
-    // Costruiamo la lista delle formiche in JSON: [[x,y], [x,y], ...]
     var ants_json = std.ArrayList(u8){};
     defer ants_json.deinit(allocator);
     try ants_json.appendSlice(allocator, "[");
@@ -52,8 +47,6 @@ fn exportAntJSON(world: *World, file_path: []const u8, episode: usize, epsilon:
     }
     try ants_json.appendSlice(allocator, "]");
 
-    // Scriviamo il JSON completo
-    // QUI RISOLVIAMO L'ERRORE: Usiamo 'epsilon' nella stringa
     const json = try std.fmt.allocPrint(allocator, "{{\n \"grid_size\": {d},\n \"food\": [{d}, {d}],\n \"ants\": {s},\n \"episode\": {d},\n \"epsilon\": {d:.3}\n}}", .{ env.GRID_SIZE, world.food_x, world.food_y, ants_json.items, episode, epsilon });
 
     try file.writeAll(json);
diff --git a/src/mnist.zig b/src/mnist.zig
index 86b74a3..93370d9 100644
--- a/src/mnist.zig
+++ b/src/mnist.zig
@@ -7,17 +7,14 @@ pub const MnistData = struct {
     allocator: Allocator,
 
     pub fn init(allocator: Allocator, img_path: []const u8, lbl_path: []const u8, max_items: usize) !MnistData {
-        // --- 1. CARICAMENTO IMMAGINI ---
         const img_file = try std.fs.cwd().openFile(img_path, .{});
         defer img_file.close();
         try img_file.seekTo(16);
 
-        // --- 2. CARICAMENTO ETICHETTE ---
         const lbl_file = try std.fs.cwd().openFile(lbl_path, .{});
         defer lbl_file.close();
         try lbl_file.seekTo(8);
 
-        // --- 3. ALLOCAZIONE ---
         var images: std.ArrayList([]f32) = .{};
         defer images.deinit(allocator);
         var labels: std.ArrayList([]f32) = .{};
@@ -29,11 +26,9 @@ pub const MnistData = struct {
         var i: usize = 0;
 
         while (i < max_items) : (i += 1) {
-            // Leggi direttamente dal file
             const bytes_read = try img_file.read(&buffer);
             if (bytes_read < img_size) break;
 
-            // Leggi 1 byte per l'etichetta
             const lbl_read = try lbl_file.read(&label_byte);
             if (lbl_read < 1) break;
 
diff --git a/src/modular_network.zig b/src/modular_network.zig
index 0aae241..851a991 100644
--- a/src/modular_network.zig
+++ b/src/modular_network.zig
@@ -33,7 +33,6 @@ pub const Network = struct {
         return current_input;
     }
 
-    // --- QUESTA ERA LA FUNZIONE MANCANTE ---
     pub fn printTopology(self: *Network) void {
         std.debug.print("Architettura Rete: [Input]", .{});
         for (self.layers.items) |layer| {
@@ -41,7 +40,6 @@ pub const Network = struct {
         }
         std.debug.print("\n", .{});
     }
-    // ---------------------------------------
 
     pub fn train(self: *Network, input: []const f32, target: []const f32, lr: f32) !f32 {
         _ = self.forward(input);
@@ -49,9 +47,8 @@ pub const Network = struct {
         const last_layer_idx = self.layers.items.len - 1;
         const last_layer = &self.layers.items[last_layer_idx];
 
-        // Questo è il buffer iniziale degli errori
         var output_errors = try self.allocator.alloc(f32, last_layer.neurons_count);
-        defer self.allocator.free(output_errors); // Zig pulirà questo automaticamente alla fine
+        defer self.allocator.free(output_errors);
 
         var total_loss: f32 = 0.0;
         for (0..last_layer.neurons_count) |i| {
@@ -68,23 +65,15 @@ pub const Network = struct {
             var layer = &self.layers.items[i];
             const prev_input = if (i == 0) input else self.layers.items[i - 1].output;
 
-            // 1. Calcoliamo i nuovi gradienti (nuova allocazione in memoria)
             const new_gradients = layer.backward(next_gradients, prev_input, lr);
 
-            // 2. CHECK ANTI-LEAK:
-            // Se il vecchio next_gradients NON è output_errors (che è protetto dal defer),
-            // allora è un buffer intermedio creato dal layer precedente. Dobbiamo distruggerlo.
             if (next_gradients.ptr != output_errors.ptr) {
                 self.allocator.free(next_gradients);
             }
 
-            // 3. Aggiorniamo il puntatore per il prossimo giro
             next_gradients = new_gradients;
         }
 
-        // 4. Pulizia Finale:
-        // L'ultimo buffer creato dal primo layer (input layer) è rimasto in mano nostra.
-        // Dobbiamo liberare anche quello.
         if (next_gradients.ptr != output_errors.ptr) {
             self.allocator.free(next_gradients);
         }
@@ -92,27 +81,23 @@ pub const Network = struct {
         return total_loss;
     }
 
-    // --- SALVATAGGIO BINARIO ---
     pub fn save(self: *Network, file_path: []const u8) !void {
         const file = try std.fs.cwd().createFile(file_path, .{});
         defer file.close();
 
         const MagicNumber: u64 = 0xDEADBEEF;
 
-        // Scriviamo Header
         try file.writeAll(std.mem.asBytes(&MagicNumber));
 
         const layer_count = @as(u64, self.layers.items.len);
         try file.writeAll(std.mem.asBytes(&layer_count));
 
-        // Scriviamo Layers
         for (self.layers.items) |layer| {
             const inputs = @as(u64, layer.inputs_count);
             const neurons = @as(u64, layer.neurons_count);
             try file.writeAll(std.mem.asBytes(&inputs));
             try file.writeAll(std.mem.asBytes(&neurons));
 
-            // Scriviamo Pesi e Bias
             const weights_bytes = std.mem.sliceAsBytes(layer.weights);
             try file.writeAll(weights_bytes);
 
@@ -121,7 +106,6 @@ pub const Network = struct {
         }
     }
 
-    // --- CARICAMENTO BINARIO ---
     pub fn load(allocator: Allocator, file_path: []const u8) !Network {
         const file = try std.fs.cwd().openFile(file_path, .{});
         defer file.close();
@@ -131,7 +115,6 @@ pub const Network = struct {
 
         defer if (!success) net.deinit();
 
-        // Header Check
         var magic: u64 = 0;
         _ = try file.readAll(std.mem.asBytes(&magic));
         if (magic != 0xDEADBEEF) return error.InvalidNetworkFile;
@@ -139,7 +122,6 @@ pub const Network = struct {
         var layer_count: u64 = 0;
         _ = try file.readAll(std.mem.asBytes(&layer_count));
 
-        // Layers Loop
         var i: u64 = 0;
         while (i < layer_count) : (i += 1) {
             var inputs: u64 = 0;
@@ -148,7 +130,6 @@ pub const Network = struct {
             _ = try file.readAll(std.mem.asBytes(&inputs));
             _ = try file.readAll(std.mem.asBytes(&neurons));
 
-            // SE è l'ultimo layer, niente sigmoide!
             const is_last = (i == layer_count - 1);
             try net.addLayer(@intCast(inputs), @intCast(neurons), 0, !is_last);
 
@@ -165,8 +146,6 @@ pub const Network = struct {
         return net;
     }
 
-    // --- EXPORT JSON CON IMMAGINE INPUT ---
-    // Aggiungiamo il parametro 'input_snapshot' (può essere null se non vogliamo stampare nulla)
     pub fn exportJSON(self: *Network, file_path: []const u8, epoch: usize, loss: f32, input_snapshot: ?[]const f32) !void {
         const file = try std.fs.cwd().createFile(file_path, .{});
         defer file.close();
@@ -181,11 +160,9 @@ pub const Network = struct {
 
         try Utils.print(file, "{{\n  \"epoch\": {d},\n  \"loss\": {d:.6},\n", .{ epoch, loss });
 
-        // SEZIONE NUOVA: Stampiamo l'array dei pixel se presente
         if (input_snapshot) |pixels| {
             try file.writeAll("  \"input_pixels\": [");
             for (pixels, 0..) |p, idx| {
-                // Arrotondiamo a 2 decimali per risparmiare spazio
                 try Utils.print(file, "{d:.2}", .{p});
                 if (idx < pixels.len - 1) try file.writeAll(",");
             }
@@ -197,7 +174,6 @@ pub const Network = struct {
         for (self.layers.items, 0..) |layer, i| {
             try Utils.print(file, "    {{\n      \"layer_index\": {d},\n      \"neurons\": {d},\n      \"inputs\": {d},\n      \"weights\": [", .{ i, layer.neurons_count, layer.inputs_count });
 
-            // Salviamo solo un sottoinsieme dei pesi per non intasare il file
             const max_w = if (layer.weights.len > 1000) 100 else layer.weights.len;
 
             for (layer.weights[0..max_w], 0..) |w, w_idx| {