diff --git a/www/bend.html b/www/bend.html
index 70e84c4..d7d0784 100644
--- a/www/bend.html
+++ b/www/bend.html
@@ -87,7 +87,7 @@ make gpu-worker LUMBDA=python PORT=8321   # second worker, off-BEND port</code><
     <tr><td><code>cammy.foxhop.net</code></td><td>Tesla P40 (24 GB)</td><td>sm_61</td><td>8320</td><td><strong>active</strong> &mdash; 3-node mesh (Pascal)</td></tr>
   </tbody>
 </table>
-<p>3-node mesh active. 3090 + 4090 + P40 all serve bend workloads on port 8320 (BEND mnemonic). Round-robin or explicit selection via <code>(bend-set-workers! '((&quot;3090-ai.foxhop.net&quot; . 8320) (&quot;ai.foxhop.net&quot; . 8320) (&quot;cammy.foxhop.net&quot; . 8320)))</code>. A cost-routed coordinator pattern lives at <a href="https://www.foxhop.net/gpu-mesh">foxhop.net/gpu-mesh</a> — greedy bin-pack by predicted cost per host (factor 1.00 / 0.51 / 1.84 for 3090 / 4090 / P40); one worker thread per node fires concurrently against our wire protocol.</p>
+<p>3-node mesh active. 3090 + 4090 + P40 all serve bend workloads on port 8320 (BEND mnemonic). Round-robin or explicit selection via <code>(bend-set-workers! '((&quot;3090-ai.foxhop.net&quot; . 8320) (&quot;ai.foxhop.net&quot; . 8320) (&quot;cammy.foxhop.net&quot; . 8320)))</code>. Cost-routed coordinator pattern: greedy bin-pack candidates by predicted cost; per-host wall factor 1.00 / 0.51 / 1.84 (3090 / 4090 / P40 — Pascal lags but contributes a third concurrent stream); one worker thread per node fires concurrently against our wire protocol. Each node serializes its own queue (workers do not multiplex requests cleanly inside one CUDA context).</p>
 </section>
 
 <section id="health">
@@ -143,7 +143,7 @@ make gpu-worker LUMBDA=python PORT=8321   # second worker, off-BEND port</code><
     <tr>
       <td><code>cuda-sim-ops-bin</code></td>
       <td>RTX 3090</td>
-      <td>1.27&times; at 141 batches (9024 shots); crossover ~115 batches; <strong>SHAKE-RNG mode</strong> matches upstream Pareto bit-for-bit (&Sigma; Toffoli = 15,999,651,264, avg Toffoli = 1,773,011.000 on stock <code>ops.bin</code>). <strong>Kernel wall scales linearly with &Sigma; Toffoli across circuit variants</strong> &mdash; <a href="https://www.foxhop.net/ecdsa">foxhop ecdsa lever sweep</a> at p=251 (n+1=9 secp256k1-toy) over 6 lumbda-emitted ops.bin variants ran 9.83&times; wall reduction Fermat-schoolbook &rarr; refined-Solinas, 261.4 ms &rarr; 26.6 ms / batch at 1024 shots; matches 6.95&times; &Sigma; Toffoli ratio + parallel Clifford drop. No scheduler surprise: pick any QECCOPS1 circuit, predict GPU wall from a cheap CPU op-counter.</td>
+      <td>1.27&times; at 141 batches (9024 shots); crossover ~115 batches; <strong>SHAKE-RNG mode</strong> matches upstream Pareto bit-for-bit (&Sigma; Toffoli = 15,999,651,264, avg Toffoli = 1,773,011.000 on stock <code>ops.bin</code>). <strong>Kernel wall scales linearly with &Sigma; Toffoli across circuit variants</strong> &mdash; <a href="https://www.foxhop.net/5243e3fe-6146-11f1-8ce9-040140774501/secp256k1-point-addition-challenge-with-lumbda-attack">foxhop secp256k1 lever sweep</a> at p=251 (n+1=9 secp256k1-toy) over 6 lumbda-emitted ops.bin variants ran 9.83&times; wall reduction Fermat-schoolbook &rarr; refined-Solinas, 261.4 ms &rarr; 26.6 ms / batch at 1024 shots; matches 6.95&times; &Sigma; Toffoli ratio + parallel Clifford drop. No scheduler surprise: pick any QECCOPS1 circuit, predict GPU wall from a cheap CPU op-counter.</td>
       <td><code>(cuda-sim-ops-bin "path/to/ops.bin" n-batches)</code> &mdash; <code>demo_ops</code> defaults <code>--rng-mode shake</code> (Fiat-Shamir over op stream); <code>--rng-mode lfsr</code> keeps legacy xorshift for debug parity</td>
     </tr>
     <tr>