I don't think this is a relevant comparison. Snap guns break pins, which isn't the case with this robot.

> Snap guns break pins

They are a kinetic attack on the pins, but they don't shear or shatter them.

Is this from 2024? It mentions "With global data center demand at 60 GW in 2024"

Also, there is no mention of the latest-gen NVDA chips: 5 RNGD servers generate tokens at 3.5x the rate of a single H100 SXM at 15 kW. This is reduced to 1.5x if you instead use 3 H100 PCIe servers as the benchmark.

Work around from comments:

  rm -rf ~/.claude/cache
  mkdir -p ~/.claude/cache
  echo "# Changelog" > ~/.claude/cache/changelog.md
  chmod 444 ~/.claude/cache/changelog.md

Have you experimented with all of these things on the latest models (e.g. Opus 4.5) since Nov 2025? They are significantly better at coding than earlier models.

Yes, December 2025 and January 2026.

There are no watermarks in the arena.

There are no visible watermarks, but model makers can use steganographic codes to identify outputs from their own models.

Text-to-Image Models Leave Identifiable Signatures: Implications for Leaderboard Security

https://arxiv.org/pdf/2510.06525

This is true, however LMArena does employ some methods to mitigate attempts to manipulate the leaderboard, see https://openreview.net/forum?id=zf9zwCRKyP

They also control for style https://news.lmarena.ai/sentiment-control/

Not rock, but silver-plated and EM-shielding: https://vollebak.com/products/shielding-pants

From Semrush’s website: “ Why You Need Keywords You need keywords because they are the exact words and phrases people type into Google. When you know the keywords people use to search for things related to your website or business, you can create pages and content that answer real searches and attract interested visitors.”

$1.9B isn’t a huge sum but a business focused on keywords for SEO? Isn’t this like buying a pet dinosaur right before the K-Pg extinction event?

You seem to be suggesting that current frontier models are only trained on text and not "sensor data". Multi-modal models are trained on the entire internet + vast amounts of synthetic data. Images and videos are key inputs. Camera sensors are capable of capturing much more "sensor data" than the human eye. Neural networks are the worst way to model intelligence, except all other models.

You may find this talk enlightening: https://simons.berkeley.edu/talks/ilya-sutskever-openai-2023...

> You seem to be suggesting

As soon as you start a response like that you should just stop. After all, this is written communication, and what I wrote is plain to see right there.

When you need to start a response that way you should become self-aware that you are not responding to what the person you respond to wrote, but to your own ideas.

There is no need to "interpret" what other people wrote.

Relevant: https://i.imgur.com/Izrqp7d.jpeg

I ordered one that arrived last week. It seems like a great idea with horrible execution. The UI shows strange glitchy/artifacts occasionally as if there's a hardware failure.

To get a sense for use cases, see the playbooks on this website: https://build.nvidia.com/spark.

Regarding limited memory bandwidth: my impression is that this is part of the onramp for the DGX Cloud. Heavy lifting/production workloads will still need to be run in the cloud.

The graphics company has given up on graphics.

"When it comes to software engineering at scale, nothing beats Google"

I agree with many of your statements, but this one simply isn't true. They've really struggled to integrate AI into products in a useful way. Do you remember glue on pizza? Founding Fathers reimagined for DEI? The Brain/DeepMind merger was largely an acknowledgement of their many misses from a product perspective.

The pizza glue and DEI stuff were temporary glitches on new products. Their basic engineering on search, gmail etc is pretty solid.