There's a lot of tires that are poorly made and just bad tires. For example, one tire got 2/5 for rolling resistance, tested to 55,000 miles for tread, and 2/5 and 3/5 ratings for the traction categories like wet braking, snow traction, ice braking.

I'm not arguing that there aren't trade-offs, but tire companies have been working to make better tires that minimize the trade-offs and produce better tires on all three.

> The rule is “designed to ensure that replacement tires sold in the state are at least as energy efficient, on average, as tires sold in the state as original equipment.”

It sounds like the rule isn't exactly that strict. This (combined with the fact that all season tires aren't covered) presents a very low bar. But there are tires that are just garbage - they're bad at all three. It doesn't sound like this rule would impact good tires that have very long tread life and amazing traction. It sounds like it'd impact the tires that are a pile of garbage.

There's a difference between a tire that trades off a small portion of rolling resistance for traction and tire wear and tires that are poorly made and are trading off a huge amount of one or two of those categories because they're just bad tires.

Per-token cost isn't a great metric given that some use way more tokens than others.

They end up wasting their energy on work that won't help them reproduce and needing to waste more energy getting rid of it.

For example, if a data center is illegally running gas turbines, the penalty could be that the data center must be shut down for 3 years and no equipment can be removed from the data center during that time.

With financial penalties, there's always calculus on whether the penalty is high enough to deter the action. But the only reason they'd want to, for example, use illegal power generators at a data center is to run the data center. Losing access to 100% of the value of that data center for 3 years would make the calculus easy: it wouldn't be worth it for the companies. They'd lose the entire reason for doing it and so much of the capital investment in equipment as it depreciated during those 3 years.

With AI companies believing the market is worth trillions, it's hard to imagine a financial penalty high enough to deter illegal actions. It might need to be in the hundreds of billions - a level which they probably can't pay given their current resources and which governments wouldn't levy. Not that they'll essentially confiscate a data center and its hardware for multiple years as a penalty either.

There are parts of states like Grant County Washington that have cheap hydro power, but it's very rare for power to be that cheap in the US. Even if this applies to you, it won't apply to the vast majority of people on here who will have electric rates 2-4x higher.

Average electric rates by region:

    New England            28.1 cents
    Mid Atlantic           25.1 cents
    East North Central     20.8 cents
    West North Central     14.8 cents
    South Atlantic         16.1 cents
    East South Central     15.5 cents
    Mountain               14.6 cents
    Pacific Contiguous     26.1 cents
    Pacific Noncontiguous  42.1 cents

Models that people like the design aesthetic of (Claude, GLM) tend to do better in LMArena than they do on other benchmarks. Design matters, but you look at a model like GPT-5.5 and it's behind Kimi K2.6, Sonnet 4.6, Qwen3.7 Max, and GLM-5.1 on LMArena's code leaderboard. Then you look at benchmarks like DeepSWE and GPT-5.5 blows them out of the water with only Fable and GPT-5.6 beating it.

I'm not saying that the LMArena leaderboard isn't useful, but I'm not sure how much weight I'd give it as a "code" leaderboard. I think often times it's a design comparison of simple front-end React apps rather than a coding comparison. GLM-5.2 is a very good model, but when you look at DeepSWE or Terminal-Bench v2, GPT-5.5 is well ahead.

At this point, I always look at things like Artificial Analysis' total cost to run their tests. It'll take into consideration the cost of tokens, how many tokens it burns through, and how effectively it uses caching (and the price of that caching).

If a model "costs the same" but its reasoning ends up going through a ton more tokens, it doesn't really cost the same in real world usage.

I feel like the "something new" was price. Over time, that price kept creeping up. Yes, it went from being a 95% flagship to being a 100% flagship, but it also went from being half price to full price.

It was also cool that it used Cyanogenmod which meant you got a community OS that actually got updates, but over time other manufacturers started offering updates for their phones (rather than abandoning them soon after manufacturing). And that was something new other than price. But I think the big thing was that it was a half-price phone when it launched. In 2014, it was just such an amazing deal. Today, it's the same price as Samsung phones.

- Telegram had usernames in 2014 before Signal added them a decade later, allowing people to chat without sharing their phone number

- Telegram has unencrypted chats which allow for giant chat rooms of 200,000+ and channels with millions of subscribers. Signal warns about performance issues when you have more than 150 people in a group. Telegram isn't just a messenger - it's often used as a social publishing platform like Instagram.

I don't use Telegram and use Signal a lot, but I also understand why other people use Telegram: the same reason they use Instagram.

Cursor's benchmark finds that Cursor's model (Composer 2.5) is basically as good as Opus 4.8 max and GPT-5.5 xhigh, but at a fraction of the price.

Artificial Analysis' testing shows Composer 2.5 to be pretty far behind: https://artificialanalysis.ai/agents/coding-agents. You look at the DeepSWE benchmark (which is probably the hardest to game at this point) and GPT-5.5 xhigh gets a 64, Opus 4.8 max gets 56, and Cursor 2.5 gets 16.

I don't doubt that Cursor works well for some people. It's beating DeepSeek v4 Pro in the DeepSWE benchmark and that's a very capable model. But I'm skeptical of the claims that it's a competitor for Opus 4.8 and GPT-5.5. It just seems convenient that their model does so well on their own benchmark while third party benchmarks have it far behind. Maybe it's a really great benchmark and a better measure than third party ones - I'd love for a cheap model to do as well as the expensive ones.

It's not simply marketing since the Pro/Max chips of a generation use the same cores as the regular version, just more of them or different combinations of performance and efficiency cores.

They hold the keys for your DID. If they don't allow you to move to another PDS, you can't move. The original theory was that you'd hold the private keys, but that's something that would hugely limit adoption so they decided to hold the keys themselves.

In terms of moving your backlog of posts to a new server, part of the issue is liability (not merely legal liability, but reputational as well). When you have a user on your platform and they're posting stuff, you're moderating them in real time. If they turn out to be a horrible troll, you've get the reports. Let's say a horrible troll has been on EvilServer and EvilServer has been ignoring the reports against them. They now want to move to your GoodServer and bring all their post history with them. As an admin of GoodServer, you can't see that everyone has been reporting this troll for years. They're now moving over lots of horrible, inflammatory, potentially illegal posts to your server.

In the coding index, GPT-5.5 gets 59.1, 58.5, 56.2, and 52.1 for xhigh, high, medium, and low while Muse Spark is behind at 47.5. For agentic, GPT-5.5 gets 74.1, 72.0, 69.4, and 59.7 (xhigh, high, medium, low) while Muse Spark gets 62.0 (beating only GPT-5.5 low).

GPT-5.5 only gets beaten by Opus 4.8 in their general index, is the top spot for coding, and is #3 behind Opus 4.8 and GLM-5.2 for agentic (excluding Fable 5 which takes the top spot, but is unavailable).

For me, the big thing with MiMo-V2.5-Pro and DeepSeek V4-Pro is that cached inputs are practically free. Kimi K2.7 Code is 53x more expensive for cached inputs which is 95% of my costs.

If I use 95M cached input tokens, 4M input tokens, and 1M output tokens, that'd be: $18 for cached input on Kimi K2.7 Code vs $0.34 with MiMo/DS; $3.80 for inputs on Kimi vs $1.74 with MiMo/DS; and $4 for output on Kimi vs $0.87 with MiMo/DS.

Of all the places where I'm accumulating costs by using Kimi, it's the cached inputs. The real savings with MiMo/DS's price cut is the cached inputs.

Qwen-3.6-27b is closer to Claude Opus 4.7 than it is to Haiku 4.5 in a lot of benchmarks - and it's way smaller than Microsoft's new model.

Sure, it competes with Haiku, but it shows how far Microsoft is behind lots of other small models that are available.

Yes, being exploited will leave you in a bad state, but it's also important to learn why other similarly colonized countries have done a lot better over the past 30 years - what are the conditions and policies that improve things