In a few years we'll be able to look back and compare the technical debt in code written since 2026 vs code written in the 40 years prior. We'll then have another metric to quantify the impact and usefulness of AI.

If AI's code is sloppy but bug-free, is it better than the alternative?

I'm not sure if I missed something, but the benchmarks [0] look outdated since they use Claude Opus 5 (instead of Opus 5.5), GPT 5.6 Sol (instead of 6.1 Sol), and GLM 5.2 (instead of DeepSeek V4.1 Flash 1017, which is the most popular model on OpenRouter currently [1]). I also cannot find any information regarding thinking efforts for each.

Since these benchmarks were done in July, they were probably using older versions of Claude Code, Codex, and OpenCode as well.

Would appreciate if the benchmarks are updated, and the thinking efforts and the versions of Claude Code, Codex, and OpenCode are specified.

[0] https://github.com/Muvon/octomind#benchmarks

[1] https://openrouter.ai/rankings

I remember when Opus 5.5 came out, we had a discussion about whether it was nerfed a week later [1]. It's not possible to do so with Gemini 4 models since none has been made available the day it is announced.

Without us being able to access Carbon when it is announced, there's no way to know the Carbon we get a few weeks later is the same thing as what has been benchmarked here. I think benchmarks should verify what is benchmarked is exactly the same as what's been released.

  [1] https://news.ycombinator.com/item?id=49901736

I used to land on SO questions and answers through Google. Nowadays I ask ChatGPT or Claude instead of Google, and then go to the links they give me to verify. The links are either to the official docs or some blog posts. Never have I seen LLMs recommend any SO links.

True. And if Carbon (6) is better than Argon (18), wouldn't that make Hydrogen (1) the ultimate Gemini model, or will the next model jump to another random element?

  O, B, Sc, U, Re?
  Ra, Nd?

It’s a missed opportunity that they didn’t go with atomic numbers or mass for model sizes. Both OpenAI and Anthropic does that.

Helium < Neon < Argon < Krypton < Xenon in increasing order, all very cool names.

Google went with the alphabetical order instead, which is less meaningful than the version numbers for the average user.

The problem is that these names are random and never-ending. They are not categorized like Claude and GPT, both of which have four classes of models each. That's eight classes that I can remember.

There is no way I will be able to (nor do I want to) remember more than four names just for Google or any other single provider, and there's no value in doing so for me. There's nothing unique or distinguished that Gemini does that neither Claude nor GPT can.

I agree. Announcing a new model without immediate availability or a definite release date seems confusing than just doing it later. When Carbon is made GA, OpenAI or Anthropic might just release their new model, and we'd be like: Carbon what?

Having said that, I don't see people excited about using Argon so far, so that might be why Google is announcing this to hype Gemini up a bit.

Today was their earnings day. They could have been trying to justify their AI Capex. Their earnings were not good otherwise.

Despite that move, the gravitational pull was quite strong on their stock today after the earnings call.

I created a proxy using the 6 compromised eMudhra CA keys (2 of the 8 are code-signing and not for TLS). I initially tried just one (the one with d=4) like in the commit, but it did not seem to work for anything. Once I added all 6 keys, I was able to intercept some.

I was mainly interested in testing which IoT devices are affected and protecting them, not trying to sniff their traffic since it's been a long day. My proxy thus just intercepted and forwarded the traffic upstream using a fresh TLS connection that does not use the compromised keys.

After my shock and anger subside, I can think more clearly now. I see many have mentioned using this to perform MITM on the IoT devices.

On the other hand, can we not utilize that to also protect the IoT devices? Can we not create a MITM proxy on our own network that proxies all of our IoT devices's traffic and then forwards them to the trusted HTTPS, MQTTS, ... endpoints upstream? Since the proxy already revokes these bad certificates, the upstream traffic should be protected. We should be able to revoke any other bad certs in the the future as well.

Yeah, this would work.

Thank you! Working on the proxy now ...

After my shock and anger subside, I can think more clearly now. I see many have mentioned using this to perform MITM on the IoT devices.

On the other hand, can we not utilize that to also protect the IoT devices? Can we not create a MITM proxy on our own network that proxies all of our IoT devices' traffic and then forwards them to the trusted HTTPS, MQTTS, ... endpoints upstream? Since the proxy already revokes these bad certificates, the upstream traffic should be protected. We should be able to revoke any other bad certs in the the future as well.

Do we call this human's slop? Should we be so hard on AI when humans do something like this? I'm pretty sure LLMs did not and would not allow such things to happen.

On the bright side, hopefully with AI we won't see silly slops like this.