diff --git a/context33.ipynb b/context33.ipynb
index 8c1e6cd..fa24764 100644
--- a/context33.ipynb
+++ b/context33.ipynb
@@ -11,8 +11,8 @@
      "shell.execute_reply": "2026-05-31T13:42:50.945911Z"
     },
     "ExecuteTime": {
-     "end_time": "2026-05-31T13:57:38.398527610Z",
-     "start_time": "2026-05-31T13:57:37.908008067Z"
+     "end_time": "2026-05-31T18:01:00.462677288Z",
+     "start_time": "2026-05-31T18:01:00.408293980Z"
     }
    },
    "source": [
@@ -32,7 +32,7 @@
     "from rbm.status import CheckpointStatus"
    ],
    "outputs": [],
-   "execution_count": 1
+   "execution_count": 27
   },
   {
    "cell_type": "code",
@@ -45,17 +45,17 @@
      "shell.execute_reply": "2026-05-31T13:42:50.952461Z"
     },
     "ExecuteTime": {
-     "end_time": "2026-05-31T13:57:38.447434185Z",
-     "start_time": "2026-05-31T13:57:38.399226980Z"
+     "end_time": "2026-05-31T18:01:00.511288729Z",
+     "start_time": "2026-05-31T18:01:00.463411358Z"
     }
    },
    "source": [
     "# ── Config from context33.prj ──────────────────────────────────────────────\n",
     "SENSORY_SIZE   = 40      # numVisibleX * numVisibleY = 1 * 40\n",
-    "CONTEXT_SIZE   = 128     # numContext = numHidden\n",
+    "CONTEXT_SIZE   = 256     # numContext = numHidden\n",
     "LEARNING_RATE  = 0.05    # learningRate\n",
     "MOMENTUM       = 0.5     # momentum\n",
-    "NUM_EPOCHS     = 1000    # numEpochs\n",
+    "NUM_EPOCHS     = 100    # numEpochs\n",
     "MINI_BATCH     = 100     # miniBatchSize\n",
     "NUM_GIBBS      = 3       # numGibbs\n",
     "RAO_BLACKWELL  = True    # doRaoBlackwell\n",
@@ -71,7 +71,7 @@
     "char_to_idx = {c: i for i, c in enumerate(chars)}"
    ],
    "outputs": [],
-   "execution_count": 2
+   "execution_count": 28
   },
   {
    "cell_type": "code",
@@ -84,8 +84,8 @@
      "shell.execute_reply": "2026-05-31T13:42:50.958632Z"
     },
     "ExecuteTime": {
-     "end_time": "2026-05-31T13:57:38.505581964Z",
-     "start_time": "2026-05-31T13:57:38.448366492Z"
+     "end_time": "2026-05-31T18:01:00.570664090Z",
+     "start_time": "2026-05-31T18:01:00.512242185Z"
     }
    },
    "source": "# ── Training sentence ──────────────────────────────────────────────────────\nSENTENCE = (\n    \"Call me Ishmael. Some years ago, never mind how long precisely, \"\n    \"having little money in my pocket and nothing particular to interest \"\n    \"me on shore, I thought I would sail about a little.\"\n)\n\n# Convert to uppercase, keep only in-vocab characters\nSENTENCE  = ''.join(c for c in SENTENCE.upper() if c in ALLOWED)\nN_SAMPLES = len(SENTENCE)\n\nsensory_np = np_cpu.zeros((N_SAMPLES, SENSORY_SIZE), dtype=np_cpu.float64)\nfor i, c in enumerate(SENTENCE):\n    sensory_np[i, char_to_idx[c]] = 1.0\n\ndecoded = SENTENCE\nprint(f\"Sentence : '{decoded}'\")\nprint(f\"Length   : {N_SAMPLES} chars\")",
@@ -99,7 +99,7 @@
      ]
     }
    ],
-   "execution_count": 3
+   "execution_count": 29
   },
   {
    "cell_type": "code",
@@ -112,18 +112,32 @@
      "shell.execute_reply": "2026-05-31T13:42:51.207444Z"
     },
     "ExecuteTime": {
-     "end_time": "2026-05-31T13:57:38.858778350Z",
-     "start_time": "2026-05-31T13:57:38.506823556Z"
+     "end_time": "2026-05-31T18:01:00.981089507Z",
+     "start_time": "2026-05-31T18:01:00.571331995Z"
     }
    },
-   "source": "# ── Visualise one-hot encoding ─────────────────────────────────────────────\nstep = max(1, N_SAMPLES // 40)    # show at most 40 tick labels\ntick_pos = range(0, N_SAMPLES, step)\n\nplt.figure(figsize=(16, 3))\nplt.imshow(sensory_np.T, aspect='auto', cmap='Blues', vmin=0, vmax=1)\nplt.colorbar(label='activation')\nplt.xlabel('Position')\nplt.ylabel('Vocab index')\nplt.title(f'One-hot encoding  ({N_SAMPLES} chars)')\nplt.xticks(tick_pos, [decoded[i] for i in tick_pos], fontsize=8)\nplt.tight_layout()\nplt.show()",
+   "source": [
+    "# ── Visualise one-hot encoding ─────────────────────────────────────────────\n",
+    "step = max(1, N_SAMPLES // 40)    # show at most 40 tick labels\n",
+    "tick_pos = range(0, N_SAMPLES, step)\n",
+    "\n",
+    "plt.figure(figsize=(16, 3))\n",
+    "plt.imshow(sensory_np.T, aspect='auto', cmap='hot', vmin=0, vmax=1)\n",
+    "plt.colorbar(label='activation')\n",
+    "plt.xlabel('Position')\n",
+    "plt.ylabel('Vocab index')\n",
+    "plt.title(f'One-hot encoding  ({N_SAMPLES} chars)')\n",
+    "plt.xticks(tick_pos, [decoded[i] for i in tick_pos], fontsize=8)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
    "outputs": [
     {
      "data": {
       "text/plain": [
        "<Figure size 1600x300 with 2 Axes>"
       ],
-      "image/png": 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      },
      "metadata": {},
      "output_type": "display_data",
@@ -132,7 +146,7 @@
      }
     }
    ],
-   "execution_count": 4
+   "execution_count": 30
   },
   {
    "cell_type": "code",
@@ -145,25 +159,21 @@
      "shell.execute_reply": "2026-05-31T13:42:51.212592Z"
     },
     "ExecuteTime": {
-     "end_time": "2026-05-31T13:57:38.915386126Z",
-     "start_time": "2026-05-31T13:57:38.861262921Z"
+     "end_time": "2026-05-31T18:01:01.035739430Z",
+     "start_time": "2026-05-31T18:01:00.981681957Z"
     }
    },
-   "source": [
-    "# ── Prepare sequences for StackRnn ─────────────────────────────────────────\n",
-    "sequences = sensory_np[np_cpu.newaxis, :, :]   # (1, 20, 40)\n",
-    "print(f\"sequences shape : {sequences.shape}  →  1 sequence of {N_SAMPLES} chars\")"
-   ],
+   "source": "# ── Prepare sequences for StackRnn ─────────────────────────────────────────\n# Tile the single sentence N_REPEAT times to form a real batch so GPU/CPU\n# matrix ops are (N_REPEAT, 168) instead of (1, 168).\nN_REPEAT  = 500\nsequences = np_cpu.tile(sensory_np[np_cpu.newaxis, :, :], (N_REPEAT, 1, 1))\nprint(f\"sequences shape : {sequences.shape}  →  {N_REPEAT} × {N_SAMPLES} chars\")",
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "sequences shape : (1, 180, 40)  →  1 sequence of 180 chars\n"
+      "sequences shape : (500, 180, 40)  →  500 × 180 chars\n"
      ]
     }
    ],
-   "execution_count": 5
+   "execution_count": 31
   },
   {
    "cell_type": "code",
@@ -176,8 +186,8 @@
      "shell.execute_reply": "2026-05-31T13:42:51.319006Z"
     },
     "ExecuteTime": {
-     "end_time": "2026-05-31T13:57:39.033277512Z",
-     "start_time": "2026-05-31T13:57:38.916167712Z"
+     "end_time": "2026-05-31T18:01:01.087502997Z",
+     "start_time": "2026-05-31T18:01:01.036405964Z"
     }
    },
    "source": [
@@ -218,13 +228,13 @@
      "output_type": "stream",
      "text": [
       "Mode       : shared\n",
-      "Visible    : 128 (context) + 40 (sensory) = 168\n",
-      "Hidden     : 128\n",
-      "Parameters : 21,504\n"
+      "Visible    : 256 (context) + 40 (sensory) = 296\n",
+      "Hidden     : 256\n",
+      "Parameters : 75,776\n"
      ]
     }
    ],
-   "execution_count": 6
+   "execution_count": 32
   },
   {
    "cell_type": "code",
@@ -237,8 +247,7 @@
      "shell.execute_reply": "2026-05-31T13:44:35.696410Z"
     },
     "ExecuteTime": {
-     "end_time": "2026-05-31T14:13:33.465818984Z",
-     "start_time": "2026-05-31T13:57:39.034495646Z"
+     "start_time": "2026-05-31T18:01:01.094798239Z"
     }
    },
    "source": [
@@ -253,7 +262,6 @@
     "h_sz    = rnn.h_size()\n",
     "num_seq, T, s_sz = sequences.shape\n",
     "\n",
-    "rnn.state_init(0.01)   # fresh weights for the new training objective\n",
     "entity.grad_zero()\n",
     "\n",
     "d_progress = 100.0 / NUM_EPOCHS\n",
@@ -295,53 +303,45 @@
      "output_type": "stream",
      "text": [
       "-------------------------------------------\n",
-      "Entity-168x128: progress              : 0%\n",
-      "Entity-168x128: err_rms              : 0.005378640847885016\n",
-      "Entity-168x128: l2_norm              : 0.3832858163328573\n",
+      "Entity-296x256: progress              : 1%\n",
+      "Entity-296x256: err_rms              : 0.0002205270517712705\n",
+      "Entity-296x256: l2_norm              : 142.99783613789947\n",
       "-------------------------------------------\n",
-      "Entity-168x128: progress              : 10%\n",
-      "Entity-168x128: err_rms              : 0.004888029606164829\n",
-      "Entity-168x128: l2_norm              : 37.943563674580545\n",
+      "Entity-296x256: progress              : 11%\n",
+      "Entity-296x256: err_rms              : 5.279407091985102e-06\n",
+      "Entity-296x256: l2_norm              : 143.43850704438344\n",
       "-------------------------------------------\n",
-      "Entity-168x128: progress              : 20%\n",
-      "Entity-168x128: err_rms              : 0.004586658254152343\n",
-      "Entity-168x128: l2_norm              : 62.68289402335654\n",
+      "Entity-296x256: progress              : 21%\n",
+      "Entity-296x256: err_rms              : 4.5788413842108285e-06\n",
+      "Entity-296x256: l2_norm              : 143.87610314823223\n",
       "-------------------------------------------\n",
-      "Entity-168x128: progress              : 30%\n",
-      "Entity-168x128: err_rms              : 0.0029227696643319935\n",
-      "Entity-168x128: l2_norm              : 77.592210060179\n",
+      "Entity-296x256: progress              : 31%\n",
+      "Entity-296x256: err_rms              : 4.457335070322053e-06\n",
+      "Entity-296x256: l2_norm              : 144.2866792344391\n",
       "-------------------------------------------\n",
-      "Entity-168x128: progress              : 40%\n",
-      "Entity-168x128: err_rms              : 0.0033848746808303183\n",
-      "Entity-168x128: l2_norm              : 87.52545196132891\n",
+      "Entity-296x256: progress              : 41%\n",
+      "Entity-296x256: err_rms              : 4.415657584538288e-06\n",
+      "Entity-296x256: l2_norm              : 144.68009848927727\n",
       "-------------------------------------------\n",
-      "Entity-168x128: progress              : 50%\n",
-      "Entity-168x128: err_rms              : 0.0031435271125311573\n",
-      "Entity-168x128: l2_norm              : 94.6793236343037\n",
+      "Entity-296x256: progress              : 51%\n",
+      "Entity-296x256: err_rms              : 4.39380821377769e-06\n",
+      "Entity-296x256: l2_norm              : 145.060477619678\n",
       "-------------------------------------------\n",
-      "Entity-168x128: progress              : 60%\n",
-      "Entity-168x128: err_rms              : 0.002912863154656583\n",
-      "Entity-168x128: l2_norm              : 101.0458096406675\n",
+      "Entity-296x256: progress              : 61%\n",
+      "Entity-296x256: err_rms              : 4.377943875271283e-06\n",
+      "Entity-296x256: l2_norm              : 145.43015581564566\n",
       "-------------------------------------------\n",
-      "Entity-168x128: progress              : 70%\n",
-      "Entity-168x128: err_rms              : 0.0026133215611993344\n",
-      "Entity-168x128: l2_norm              : 108.17102558067306\n",
+      "Entity-296x256: progress              : 71%\n",
+      "Entity-296x256: err_rms              : 4.363603864787239e-06\n",
+      "Entity-296x256: l2_norm              : 145.79070936590585\n",
       "-------------------------------------------\n",
-      "Entity-168x128: progress              : 80%\n",
-      "Entity-168x128: err_rms              : 0.0023368766544820717\n",
-      "Entity-168x128: l2_norm              : 114.27352128254557\n",
-      "-------------------------------------------\n",
-      "Entity-168x128: progress              : 90%\n",
-      "Entity-168x128: err_rms              : 0.0033621059271795742\n",
-      "Entity-168x128: l2_norm              : 118.8790801434613\n",
-      "-------------------------------------------\n",
-      "Entity-168x128: progress              : 100%\n",
-      "Entity-168x128: err_rms              : 0.0028362015980278044\n",
-      "Entity-168x128: l2_norm              : 123.1840443378905\n"
+      "Entity-296x256: progress              : 81%\n",
+      "Entity-296x256: err_rms              : 4.34883619751348e-06\n",
+      "Entity-296x256: l2_norm              : 146.14331420811664\n"
      ]
     }
    ],
-   "execution_count": 7
+   "execution_count": null
   },
   {
    "cell_type": "code",
@@ -352,10 +352,6 @@
      "iopub.status.busy": "2026-05-31T13:44:35.698747Z",
      "iopub.status.idle": "2026-05-31T13:44:35.743964Z",
      "shell.execute_reply": "2026-05-31T13:44:35.743294Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2026-05-31T14:13:33.829642525Z",
-     "start_time": "2026-05-31T14:13:33.467124588Z"
     }
    },
    "source": [
@@ -372,24 +368,14 @@
     "recons_np = np_cpu.array(recons)           # (12, 40)\n",
     "decoded_recon = ''.join(idx_to_char[int(np_cpu.argmax(recons_np[i]))] for i in range(N_SAMPLES))\n",
     "\n",
-    "print(f\"Input        : '{decoded}'\")\n",
+    "print(f\"Input         : '{decoded}'\")\n",
     "print(f\"Reconstruction: '{decoded_recon}'\")\n",
     "\n",
     "correct = sum(decoded[i] == decoded_recon[i] for i in range(N_SAMPLES))\n",
     "print(f\"Char accuracy : {correct}/{N_SAMPLES} = {100*correct/N_SAMPLES:.0f}%\")"
    ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input        : 'CALL ME ISHMAEL. SOME YEARS AGO NEVER MIND HOW LONG PRECISELY HAVING LITTLE MONEY IN MY POCKET AND NOTHING PARTICULAR TO INTEREST ME ON SHORE I THOUGHT I WOULD SAIL ABOUT A LITTLE.'\n",
-      "Reconstruction: 'CALL ME ISHMAEL. SOME YEARS AGO NEVER MIND HOW LONG PRECISELY HAVING LITTLE MONEY IN MY POCKET AND NOTHING PARTICULARETO INTEREST ME ON SHORE I THOUGHT I WOULD SAIL ABOUT A LITTLE.'\n",
-      "Char accuracy : 179/180 = 99%\n"
-     ]
-    }
-   ],
-   "execution_count": 8
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
@@ -400,29 +386,38 @@
      "iopub.status.busy": "2026-05-31T13:44:35.745781Z",
      "iopub.status.idle": "2026-05-31T13:44:36.051662Z",
      "shell.execute_reply": "2026-05-31T13:44:36.050823Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2026-05-31T14:13:34.318617319Z",
-     "start_time": "2026-05-31T14:13:33.830853549Z"
     }
    },
-   "source": "# ── Visualise reconstruction ───────────────────────────────────────────────\nstep = max(1, N_SAMPLES // 40)\ntick_pos = range(0, N_SAMPLES, step)\n\nfig, axes = plt.subplots(1, 2, figsize=(16, 3))\n\naxes[0].imshow(sensory_np.T, aspect='auto', cmap='Blues', vmin=0, vmax=1)\naxes[0].set_title(f'Input ({N_SAMPLES} chars)')\naxes[0].set_xticks(tick_pos)\naxes[0].set_xticklabels([decoded[i] for i in tick_pos], fontsize=8)\naxes[0].set_ylabel('Vocab index')\n\naxes[1].imshow(recons_np.T, aspect='auto', cmap='Blues', vmin=0, vmax=1)\naxes[1].set_title(f'Reconstruction (acc {100*correct/N_SAMPLES:.0f}%)')\naxes[1].set_xticks(tick_pos)\naxes[1].set_xticklabels([decoded_recon[i] for i in tick_pos], fontsize=8)\n\nplt.tight_layout()\nplt.show()",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1600x300 with 2 Axes>"
-      ],
-      "image/png": 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-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    }
+   "source": [
+    "# ── Visualise reconstruction ───────────────────────────────────────────────\n",
+    "step = max(1, N_SAMPLES // 40)\n",
+    "tick_pos = range(0, N_SAMPLES, step)\n",
+    "\n",
+    "step = max(1, N_SAMPLES // 40)    # show at most 40 tick labels\n",
+    "tick_pos = range(0, N_SAMPLES, step)\n",
+    "\n",
+    "plt.figure(figsize=(16, 3))\n",
+    "plt.imshow(sensory_np.T, aspect='auto', cmap='hot', vmin=0, vmax=1)\n",
+    "plt.colorbar(label='activation')\n",
+    "plt.xlabel('Position')\n",
+    "plt.ylabel('Vocab index')\n",
+    "plt.title(f'Input ({N_SAMPLES} chars)')\n",
+    "plt.xticks(tick_pos, [decoded[i] for i in tick_pos], fontsize=8)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(16, 3))\n",
+    "plt.imshow(recons_np.T, aspect='auto', cmap='hot', vmin=0, vmax=1)\n",
+    "plt.colorbar(label='activation')\n",
+    "plt.xlabel('Position')\n",
+    "plt.ylabel('Vocab index')\n",
+    "plt.title(f'Reconstruction (acc {100*correct/N_SAMPLES:.0f}%)')\n",
+    "plt.xticks(tick_pos, [decoded[i] for i in tick_pos], fontsize=8)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
    ],
-   "execution_count": 9
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
@@ -433,10 +428,6 @@
      "iopub.status.busy": "2026-05-31T13:44:36.053314Z",
      "iopub.status.idle": "2026-05-31T13:44:36.198793Z",
      "shell.execute_reply": "2026-05-31T13:44:36.197851Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2026-05-31T14:13:35.558819364Z",
-     "start_time": "2026-05-31T14:13:34.319986996Z"
     }
    },
    "source": [
@@ -471,18 +462,8 @@
     "correct = sum(decoded[i+1] == predicted[i] for i in range(N_SAMPLES - 1))\n",
     "print(f\"Next-step accuracy  : {correct}/{N_SAMPLES-1} = {100*correct/(N_SAMPLES-1):.0f}%\")"
    ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input (t+1)         : 'ALL ME ISHMAEL. SOME YEARS AGO NEVER MIND HOW LONG PRECISELY HAVING LITTLE MONEY IN MY POCKET AND NOTHING PARTICULAR TO INTEREST ME ON SHORE I THOUGHT I WOULD SAIL ABOUT A LITTLE.'\n",
-      "Predicted from h_t  : 'AOEGML ONIOAEL.OSEMATOOLRT RGE IEVRR   NDOHOWHAENG PRECO EEY HALARTAI TTEE MONEYHIN MY PACKEY EID HR.HIPE PROKHC.MORE.S IIDARETTLME ON EHOTE  NTHHUGESOITWIU D EAIL ABOOT H LROTLE.'\n",
-      "Next-step accuracy  : 101/179 = 56%\n"
-     ]
-    }
-   ],
-   "execution_count": 10
+   "outputs": [],
+   "execution_count": null
   }
  ],
  "metadata": {