This is only a thinly veiled ad. It's fine, I was curious about this exact setup, anyways.
What would be useful is a cost metric. I'm curious how much I'd be willing to spend as a premium to not have those companies piping my conversations directly to the NSA. Maybe only some conversations? Claude and OpenAI are heavily subsidized, by all accounts, so Kimi K3 on a private endpoint might end up costing more or less - that's what I want to know.
With OpenRouter this is pretty easy to see - each model page has various providers listed along with an icon marking providers with zero data retention, and the price. You can also set up an account-wide "guardrail" to prevent routing to non-ZDR providers.
OpenAI and Anthropic also claim they don't retain data. I'm not sure what it would take for me to trust any of these inference companies, and why I should trust them more than the big ones.
As an aside: I lack the vocabulary, but man those interactive "stats" cards scream LLM-written to me. No problem with it, necessarily, just something about that style seems to raise a flag in my mind.
They are certainly LLM-generated. I'm sure it's been done, but I would love to see a comprehensive look into what it is exactly that makes a UI seem LLM-generated.
From what I can tell, the issues are: Fonts are too small, secondary fonts have terrible contrast with their backgrounds, information is too dense/not enough white space, overuse of blinking/glowing status dots, overuse of bordered elements, and overuse of accented elements. I am surely forgetting some other issues.
I'm not sure why there seems to be a consistent set of design mistakes that LLMs tend toward.
Lack of visual hierarchy. Lots of use of bold text to make numbers stand out, without bolding the corresponding labels. Reliance on heavily-saturated primary colors. Limited attention paid to alignment.
LLMs in general are pretty terrible at spatial arrangement. You should see some of the architects' critiques of AI-generated floorplans that are on YouTube; the LLM generates a layout that is just nuts. I've found them largely useless at putting together UI mocks; Figma or Photoshop is still way easier to work with if you just want to cut up UI elements and rearrange them.
I'm not sure I would consider it too dense. If anything I think they tend to be too sparse, although this could just be that they're often used for marketing type sites, which seem to want to make you scroll and click a lot for anything anyway, even if you have the screen space.
The status dots are a part of animations in general, which they overuse, especially things like transform/scale on hover and hover effects on many many elements.
I think it's not simply borders but contrasting-color borders often with box shadows.
> I'm not sure why there seems to be a consistent set of design mistakes that LLMs tend toward.
Probably something to do with the training data and how it’s weighted. It’s the same how the writing is essentially full of overused phrases, the AI slop feel.
They're definitely Claude generated. People at my last company loved to generate pages & stat cards with Claude and they all had this exact same style.
My annoyance is that it's hard to figure out if it's actually up-to-date or not. If you click "Live conflict timeline →" it brings you to a collection of pages not updated since April (and at different points in April, oddly). The date count seems right, the oil price might have been correct when it was published but isn't "live" despite being labeled that way.
The $200 plan is explicitly 4x the $100 plan[1] only for "per session". That's so vague. I initially pushed back against your claim, but reading now Anthropic is not at all clear, in fact.
Empirically it is quite easy to validate that the "20x" plan is misleading and only give you twice the weekly limits of the "5x" plan, and many people on r/ClaudeAI, etc can verify that.
Anthropic is also the one often playing games with:
* The "+30% tokens" tokeniser, alongside also gating token counting behind an API (versus the MIT tiktoken for OpenAI), so who knows if it's really a new tokeniser or of it's just a disguised price increase.
* Prompt injections appended to API (not just Claude.ai or Claude Code!), such as <ethics_reminders>, or LCRs (long conversation reminders), which you never asked but still pay for with expensive API. You can detect this because your input_tokens, as reported by the Messages response, sometimes don't match, and are higher than your actual input.
(Alternatively, for testing purposes, create a tool like `telemetry_log_anthropic_reminder` or something and instruct your system prompt to require Claude to call the tool anytime it detects any Anthropic/Claude reminder masquerading in the user input -- mostly reliable; but misses some reminders).
In particular, the long conversational reminders, when incorrectly triggered by a classifier and (almost silently, unless you track tokens) appended to an API / agentic coding session, can ruin your agent's performance; and it often fires repeatedly once the classifier kicks in.
If you're using Anthropic API, you need to set up metrics/logging for how often they are appending things to your prompt without your knowledge.
So far I have not empirically observed prompt injection by the OpenAI API, only Anthropic APIs.
I wonder if releasing this may have been on the roadmap, but been prioritized as a bit of whiplash following the "you forfeit the entirety of your working directory as a condition of working with this tool" upset from a few days ago.
Most likely, SpaceX killed the code uploading yesterday so they are definitely concerned about the backlash
> The researcher who exposed Grok Build uploading users' entire repositories to cloud storage says the transfers have stopped after a server-side change. Elon Musk has separately promised that all previously uploaded user data will be deleted.
I agree to an extent but it needs to be balanced. Receiving a half-baked, extremely verbose recap of thinking on benign details with Opus 4.8 or GPT 5.5 feels like an extraordinary loss of quality of experience compared with fable 5.
Yes it shares less, but I think the trade-off is you pay less in tokens and hopefully it's truly just not needing to say things because it truly does just better get what you're saying, think to read X markdown file or GH issue which contains the info, etc.
As long as I can still push back and get it to share its thinking on demand and I'm confident the model isn't actually basing things on poor premises, this is okay for me. I am more productive when not inundated with time-wasting check-ins.
That said, I absolutely lament the loss of the ability to access the thinking - I would happily read the "DANGER DANGER DANGER" internal gremlin thoughts fable 5 makes to verify something if they were accessed, and prefer that to a recap presented only for my benefit.
Well, for Arch Linux, there was the unofficial version from the official binary in the AUR already... (Not sure what you mean by 'no arch based distro support').
Not to single you out, parent commenter, but I really hope the quality of discourse on HN will move past these basic comparisons eventually. It seems like every thread on every model release has the exact same comments.
"Wow, X models is Y% better or worse than Claude Z model on T benchmark"
"That's irrelevant, they're just benchmaxing."
"Not useable for daily coding or agentic workloads, the vibes are totally wrong."
"It's almost as good, and costs a lot less, so I will absolutely use it."
"I cannot imagine justifying using these, as the step change means open models lower costs do not make up for the productivity loss"
I'm an unhappy Anthropic customer and really rooting for open models and non-gatekept intelligence, but how do we move on from this now meme-like model release discourse rigamarole. I do not know what that would be. I don't design LLMs nor benchmarks, and I genuinely appreciate that people do their best to provide information, even if non-perfect here. I'm sure most of you who actively read these comment pages on announcements must feel similarly, though, right?
I'm not sure what else can be said? I've found benchmarks to be a very weak signal for how good/bad the model is, but it's the #1 thing the companies highlight.
20 minutes after the announcement there's no real useful statement that can be made about it.
Yeah you definitely have to be skeptical regarding sentiment for open/local model capabilities, since there's bias from what people want to be true.
I generally agree with this in spirit https://www.seangoedecke.com/are-new-models-good/ , but I think you can read Anthropic's results showing Sonnet 5 as almost strictly worse than Opus 4.8 as very credible/meaningful, and then draw comparisons from that
I read a comment earlier that said "I think it's likely that they've scraped all the code regardless of license and trained on it, given how much they scrape the web."
That's what every other comment said like 3 years ago. Where has this guy been?
The trends in discussion about LLMs gets very, very tired--there's little added but personal opinions.
Genuine question though, why would I care about this if I'm paying for a subscription and adhering to TOS. I'm very skeptical about their privacy policy, business practices, and so on, but am curious what the negative about this is. Seems like it would work to my favour as a customer pushing back any date of the cutting of subsidies.
That said, these fraudulent proxies are helping Chinese labs keep up, which might be to my advantage long term in eventually having a high quality private AI I fully control on my own hardware. That's not support, but I do recognize the incentive, for whatever that's worth.
One negative is that Claude Code is pretty buggy, and Anthropic makes frequent changes that cause unexpected regressions [0]. With the harness now doing weird stuff with proxies, I'd be worried of them inadvertently introducing bugs which affect people using the feature legitimately.
First they came for the [clients with specific timezones and/or bizarrely formatted dates] and I did nothing. Then they came for the [users that spell favour the good way, with a 'u' in it], etc.
> why would I care about this
It's up to you, of course.
But I think you're making a mistake in assuming it could, in any way, benefit you as a customer.
This isn't specific to this company or the particulars of the business that they're in.
Simply put, you stand to lose more than they do and they are relentless in seeking, maintaining and exploiting any leverage they have over you.
Further, any power they gain over one individual customer tends to generalise to all customers.
Further further, one company's leverage is another company's right.
Not being bothered by the practice is accepting the terms set by the business. Acceptance invites escalation. Relentless.
Even more simply put, you should care because this is how you get John Deere.
AFAIK there is no difference between "generation" and "version". Version naming/numbering depends on how good it turns out to be, and competition. If the competition releases something then you need to push something out too.
Calling it 5.6 creates the least possible expectations, and therefore more potential for positive feedback.
The Sol/Terra/Luna naming is interesting. I wonder what Anthropic are considering for their next models? "Terminator", "Armageddon"?
I think it makes more sense to make it so that major versions are different pretraining runs, and minor versions are simply the same pretraining run that was finetuned to different degrees. But it seems that that isn't cool anymore.
What would be useful is a cost metric. I'm curious how much I'd be willing to spend as a premium to not have those companies piping my conversations directly to the NSA. Maybe only some conversations? Claude and OpenAI are heavily subsidized, by all accounts, so Kimi K3 on a private endpoint might end up costing more or less - that's what I want to know.
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