Apparently Grok 4.7 has 40% more weights than Grok 4.6, but the price ($6 output token, $2 input) is the same.
Given that the decrease in their margin and the fact they delayed the release of Grok 4.7 almost two weeks past the original date, XAI must not have been happy with the results for 4.7. And XAI also waited the day before Opus 5.5 is rumored to launch. I imagine Opus 5.5 will blow Grok 4.7 out of the water benchmark wise.
However, I have become skeptical of benchmarks. Grok 4.5 solved some issues setting up a buildroot system that Fable 5 couldn't do. I find the post cursor groks are phenomenal at frontend web development, though Claude is much better at backend ruby.
My favorite part of the new Groks has been how they speak in plain english. I simply cannot stand Claudish. Or even GPT, which doesn't have Claude's ticks but definitely likes to handwave explaining technical concepts. Still, nothing beats Claude 3.5 and 4 with explaining since it seems all models have regressed. I wonder if Grok 4.7 will also regress with English because of all the RL.
> My favorite part of the new Groks has been how they speak in plain english. I simply cannot stand Claudish.
Grok has its own feel too. It's not as bad as Claude, but one of the things that bugs me is that it is far too terse.
It regularly seems to come up with terms and descriptions for things in its chain of reasoning and then uses these terms in its output assuming you understand what it's talking about.
I find I often have to ask it to re-explain what it means.
> It regularly seems to come up with terms and descriptions for things in its chain of reasoning and then uses these terms in its output assuming you understand what it's talking about.
GPT does this all the time, too (both Sol and Astra). I constantly have to tell it to not use terms that were not part of the initial prompt.
I don't know how people are managing hands-off with Sol/Astra over long horizon tasks. It will come up with 100 new concepts/terms and then use them as if you've been following every Edit call for 24 hours.
It’s probably an assumption baked into the training pipeline from that ancient time six months ago when we mostly were manually following each edit tool call.
> I constantly have to tell it to not use terms that were not part of the initial prompt.
Hah, yeah even when you put it in AGENTS.md or a skill.. constantly having to remind it.. "what does AGENTS.md" say about doing that?".. Thinking.. Thinking.. "Oh, it says I should never do that, I'll remember that next time.."
I’m pretty sure the big bois don’t do it because it would undermine “confidence”.
Seeing a model output “Oh I should just delete blah. Wait blah is a production service, I shouldn’t touch that. Maybe I can gain access to blah? Oh the aws cli isn’t signed in to blah. I see kubectl has access to blah though! Wait, I should ask user permission first.”
Idk I feel like the more likely answer is to prevent distillation. Having the thinking is definitely better UX (oftentimes, I don’t know if Codex is just hanging, which it often does, or working in silence).
I stay much more hands-on when I'm using models that display full reasoning traces. And I tend to get more things done as a result, because I know exactly when it thought of a good solution that it talked itself out of because of some invalid assumption.
Could be something very stupid like - "I don't have ffmpeg available here. Should I install it? No, I can't. I'll proceed doing something that will take me 100x more tokens and wall clock just to avoid adding a dependency." I can then just stop and say - you've got nix flake there, just add it.
That's impossible with western models. The only way is to ask why it did something stupid when it already spent 50% of your weekly quota and produced millions lines of slop.
Ah, but you CAN see the thinking if you are willing to risk your account being banned. You just have to expose a "tool" with a specially crafted definition.
You can double click on the 'thinking' text and it will expand and you can read it. The problem is that it will often have multiple thinking/tool call sections and it can be a needle/haystack problem to find the one with the thinking you are interested in.
SpaceX is a defense contractor (I don't mean this in a bad way). When the whole DoD/Anthropic thing flared up, I can guarantee you that SpaceX.ai was the first company invited to take their place as DoD AI provider.
I suspect that they specifically train Grok to be able to work well with military personnel -- speaking the way they speak: brief, to the point, efficient communication. Personally I really like this. Claude sounds like some demented clown from the marketing department.
> It regularly seems to come up with terms and descriptions for things in its chain of reasoning and then uses these terms in its output assuming you understand what it's talking about.
FYI a quick fix for claudish is to ask for the response to be in ASD-STE100 (Simple Technical English). Then it is far more readable. But I would agree that this is an annoyance and shouldn't require user workaround to get something readable.
I think this is more a meme than anything else, for a couple reasons:
First, after a while it's just as grating as Claudeish.
Second, my hunch is that it constricts the actual thinking of the LLM, like the same way that Newspeak does in 1984. It shrinks the range of thought that can be expressed if used as an input.
I think the real way to do it is to have another Claude entirely deal with the user as a liaison, but to keep the thinking in whatever format it came in.
Latent space reasoning, if you think about it, is exactly this to a crazy degree: why even formulate a thought as words if you can just keep it as matmuls until the user needs it? And then, if the user needs it, have it always specifically formulated for the user by another LLM rather than constrict its range of thought? Anyway, that's my take.
The fact that it might affect quality is something that is on my mind. In fact, I have mainly been using Astra after being primarily on Claude for several years exactly because of this annoyance. Ultimately, the model (at least for coding purposes) should just... not have this writing style.
I do think an infrastructure where another Claude retranslates the output would be better. Oftentimes I forget to put it in the actual prompt and when I receive back 8 paragraphs of Claudeish I ask for it then.
I would have to disagree that it gets as grating as Claudeish though. Its just direct and professional instead of ring-around-the-rosy clickbait.
The Claudeish drove me back to OpenAI and Codex as well. I find the Claudeish can be improved but it’s impossible to fully mitigate. Opus is unusable for anything I’m going to read.
“I would have to disagree that it gets as grating as Claudeish though.”
It’s hard to imagine anything more grating than Claudeish. To quote Rainer Wolfcastle, "My eyes! The goggles do nothing!"
> my hunch is that it constricts the actual thinking of the LLM
I've found that prompting any constraint on output (length, style, vocab, even simple formatting) not only places additional cognitive load on the model, which burns some of whatever cognitive budget is available, it will also often skew the output in other subtle and completely unrelated ways.
Since I found this artifact interesting, I did some pretty extensive experiments a couple months ago. The increased load is real, although it may not be apparent if you're not near any cognitive boundaries. The subtle skew, however, seems nearly ever-present regardless of load.
When I noticed web chat LLMs wouldn't number section headings correctly and consistently, I began experimenting with modifying the user prompt over a period of weeks. My usage at that time was research and learning not coding. In long, detailed sessions with branching sub-topics and deep follow-ups, I found it helpful if the LLM would number each major section and letter each sub-section in replies. Simple markdown formatting that most web chat LLMs do sometimes, but not consistently nor uniformly.
While extensive, my tests were just following my curiousity, not controlled, exhaustive or well-documented. I identified about a dozen prior sessions of varying length and complexity to test and downloaded them with a browser add-on. I then removed all other user prompt instructions except for the formatting instruction. A test would typically involve changing the wording of the formatting instruction ranging from brutally simple to detailed and complete, then starting a new session, seeding one of the test sessions and continuing it. To get a feel for baseline inter-session variation, I also tried running the exact same prompt/session multiple times back-to-back, at different times and on different days of the week.
Once I identified a promising prompt candidate, I'd make it the formatting instruction in my regular, daily-use prompt for a few days. I quickly got a feel for how seemingly minor user prompt variations impact response quality, compliance and tone across fresh sessions as well as those in various states of context rot, drift, decay and cliff (<--my nicknames for the distinct flavors of session degradation, not technical terms).
My overall conclusion was that every instruction, no matter how minor or unrelated it seems, has some, real impact on the model's cog load, attentional focus and/or attentional weight budget. Both how these impacts manifest and what causes more or less impact is often extremely counteriintuitive. To more fully understand this, I eventually, got to the point of testing null case variants, such as the entire user prompt being one sentence completely unrelated to text formatting or the session topic, like: "Don't reference the cartoon character SnagglePuss" (in a deep dive on ancient Sumerian clay tokens). Similarly, a simple one sentence prompt requesting something the model already always does naturally also has a cost (eg "Capitalize proper nouns"). As others have observed, heavy emphasis, absolute prohibitions or emotional weight in prompts also tend to have outsized impact in both skew (impacting unrelated output tone/style) and in accelerating session degradation. "Avoid referencing SnagglePuss when you can" would have equal compliance but fewer downside impacts than "NEVER reference the cartoon character SnagglePuss" in sessions starting to degrade.
There were also surprises, such as when I was scanning transcripts of an older, longer session and noticed the LLM was doing number formatting almost perfectly. On looking at the active user prompt at the time (I keep a log of every user prompt change I make for every model), it didn't even reference formatting at all. More experimentation showed it a result of the LLM gradually mirroring my consistent use of formatting structure in my prompts over a long session (in which I never mentioned anything about formatting). Unfortunately, that mirrored trait doesn't persist to new sessions and reaching that point requires a substantial number of rounds burning quite a bit of context window.
After spending time surfacing the impacts of just changing lightweight user prompts so they could be observed (which are the lowest priority prompts a model gets), I now wonder just how much more 'brilliant' the models we use daily would be if they didn't have dozens of pages high-priority manufacturer prohibition prompts we never even see weighing them down. We've only ever seen these frontier 'racehorses' when they're already pulling a heavy invisible wagon.
This is the same reason why I am a bit skeptical of LLM superintelligence. LLMs in the end have to operate in natural language concepts and the complexity of natural language is bounded by limits of human cognition. I'm sure super advanced AI could use concepts that humans not only have no words for, but might not be able to understand alltogether. As such if my thesis is correct, the only way forward for true superintelligence may be getting rid of natural language COTs.
> LLMs in the end have to operate in natural language concepts and the complexity of natural language is bounded by limits of human cognition.
I don't think this is true.
They have to express themselves as tokens. The meaning of those tokens doesn't have to be text. See any model that can handle images/video. Also, I don't think math, svg, etc, are "natural" language.
And, only the final expression is tokens. The intermediate layers, with the encoded concepts, aren't "natural language".
But, to address your concern (which nobody can disagree with, since even humans can't fully express through text/pictures), potentially: https://news.ycombinator.com/item?id=49758615
> The model isn't limited to concepts that can be expressed in natural language.
How do we know for sure? We don't even know how the emergent properties we see actually emerged?
For humans we know for sure that people sometimes have concepts that they have no word for (the reason the phrase "It's on the tip of my tongue" is a phrase, after all).
We don't know this for LLMs. When it makes new phrases, it's always a mixup of two existing words hyphenated (aside, that also seems to be the limits of SOTA models creativity - join two unrelated words together with a hyphen).
LLMs never respond with "It's on the tip of my tongue" type responses, indicating it has a concept but cannot remember (or does not have) a word for that concept. Every human, pre-speech-age, has managed to express or convey concepts that they had no word for.
So, no. I'd need a citation, preferably multiple, that did the trials and found that a model can generate concepts for which it does not have any words for.
1. Is natural language holding LLMs back by some %?
2. Is natural language serving as a hard gate that will prevent LLM intelligent progressing past some specific point?
The answer to 1 seems like an obvious yes to me.
Your thesis says the answer to 2 is "yes." That doesn't feel right to me. Think about all of the humans who have pushed various fields forward: Einstein, Newtown, Bach, whoever. If natural language doesn't prevent an entity from surpassing humans in one intellectual field, why would it prevent an entity from surpassing humans in all intellectual fields?
(To be clear, I'm not claiming superintelligence will or won't be achieved; I'm considering your specific thesis about whether or not natural language will be a hard gate)
Each letter has it's own token. LLMs can invent new words if they want to, just like humans do. All the fancy words we use today in science did not exist even 100 years ago. And that didn't stop us.
A few weeks ago two "Claude wrapper" projects showed up here within 24 hours of each other. Both seemed to just sand the details away--useful if Claude is too wordy, I suppose, but seemed to also lose real substance.
I agree there is probably some impact on output quality when doing this (Claude seems to embedded key signals all over the writing, which is much or what makes it horrible to read). However this does work well as a "second pass" over technical docs or designs along the way
Ask for output to the user in "simple technical English", let it think how it wants. It is hard to believe all the flowery prose is actually helpful to the model.
I don’t think system prompts are particularly reliable way to do much at all. It’s better to put it as a hook after each response, or a skill at least so you can trigger it at will if you don’t want it everytime.
Anthropic has probably RL’d the system prompt into nothing because of their fear of the user being able to control the model. If it listened to you about the slop language, it might listen to you if you asked it to help you with no-no tasks.
Your best bet is to use hooks and inject it after every file edit / response by first running the content through haiku and asking if it is asd 100 ste.
It burns more tokens but is the only way to get tolerable text.
The hook sends the text to another agent/context with a request to validate and return a good or bad + reason response. Every request is a fresh context.
I hadn’t heard of ASD-STE100 until last month when the TinyGo AGENTS.md added it as a requirement, along with some other tidbits to improve life for maintainers.
It’s been really productive and I’ve been asking my agents to communicate using it more and more. I believe it’s relieved my cognitive load a bit while working with them.
I created a custom output style based on this (borrowing some from github.com/AminBlg/SimpleEnglish) and I've found it to be better than the default or concise output styles, but still not as good for me as current GPT or Gemini models when it comes to communicating.
I tried this a while back and I felt like the result was the same weird shoehorning of ideas into language, just with a different vocabulary. I’d really like for it to work, though.
Do tell why the downvotes? It actually works. When you tell it to adopt a certain style, just be sure to also tell it not to change the core truth of the output, it must still convey the original intent. It is surprisingly good at outputting Warhammer styles, which is very enjoyable in a dev environments or system admin sessions. Also put on some warhammer soundtrack in the background and you can easily zone in.
I was going to say the reverse - claude has been the less satisfying normalized by benchmark for me in the last year. Both astra and fable have their quirks, but I am 90% codex this year up from 10% last year.
I've been getting a ton done with Fable as the supervisor and astra as the implementer, with opus for adversarial reviews of the astra PRs. You can use terminal multiplexers with custom harnesses to allow Fable to start codex sessions and send instructions / read instructions / allow/deny actions. It's pretty cool!
codex has an option to expose itself as an MCP. You can also use something like OpenCodex to bring Anthropic models into Codex as any other selectable model.
The reason I did it the way I did was so that I can still use the codex and claude code subscriptions vs paying the API cost. Can you do that via OpenCode?
It's not just about benchmaxxing. Sincerely targeting those long-autonomy benchmarks is questionable in the first place, because naturally it drives the model to assume more and more about what you want.
The target market for frontier models is CEOs who want to lay off entire departments of their company. So the long autonomy benchmarks would seem to be sending exactly the right signal.
I totally agree, it’s like that as models become more intelligent, they are less understandable by most of people... but aren’t we humans doing the same?
Agreed. The more knowledge you amass on a subject, the more important it becomes to be extremely specific and nuanced - or your communications end up being incorrect. You become better at expressing your thoughts, but harder to understand.
The weird thing is, that's not what AI models seem to be doing. The prose is just weird.
> You become better at expressing your thoughts, but harder to understand.
This happens most though when the speaker doesn't (or care to) understand their audience.
Eg i find effective communication requires expertise in both the subject matter domain but also the reference of the listener. Eg in ELI5 framing, if you don't know what information 5yr olds are expected to know you'll do a poor job at an ELI5.
It often feels like Claude does poorly at both framing the response relative to what it "thinks" the listener knows, but also the prose is... sideways, just weird as you said.
I find people tend to lack nuance in their communication more often - leading to problems and misunderstandings.
If I don't grok an elaborate explanation, I can ask for clarification. If it's explained to me in an overly simplistic or unnuanced way, I'll walk away with a false sense of understanding.
That said, I'm sure we all have very different concentrations of these types of people and problems around us. I've definitely met some engineers who seem to actively try to make their language incomprehensible
They’re training the models on generated slop. I’ve done a comparison on samples from the different versions of Claude and they’re worlds apart. The ancient, supposedly obsolete models speak normal English.
What I notice about Claudish is that it has its preferred cliche’s and overstretched methaphores, it packs too many ideas in a sentence, and to achieve the latter it makes up adjectives.
I should try adding these tips to my system prompt. Is there a shorthand to describe such language use? I am not a native English speaker.
Look up the output-style setting, which is a bit stronger than putting it in the system prompt. The new "concise" setting is better than the default but in practice, Claude is a very stubborn model when it comes to these patterns and they're really hard to eliminate, mostly you can only hope to mitigate.
As for the wording of the prompt, you're pretty on point, I created a custom output style targeting mostly the first two you have there. Some people have wording that demands a certain technical standard or uses fancy words to describe what to avoid, but I haven't seen evidence those work better than asking plainly and I suspect the opposite: LLMs mimic the user to a degree so talking to it in terms of technical specifications and fancy words is an invitation to get them back.
That's half true. A very smart model should be able make good explanations, which include simple understandable prose. That can should be possible even as its thought process gets more alien.
The best ideas are usually the simplest to elaborate. If someone comes up with a convoluted scheme that are hard to understand or be adequately explained, it's usually fraud.
When claude speak in convoluted mess, they are often going off on tangents in real work that you asked it to do, too.
That believes that the world can be simplified into dichotomies, or at least, simplified. Sometimes problems are complex, and the solutions to them necessarily so. For example, cancer. I order to begin to understand that problem, you have to understand the utter complex scheme it has devised in order to exist. A 20 minute YouTube video isn't going to be able to begin to cover the basics of the subject, although there are some good ones, with clever analogies.
Just because something is difficult to understand doesn't mean it's fraud, although if someone is trying to dazzle you with clever words and names of institutions you recognize because they are selling you something, there's a good chance they're lying to you in order to get some money from you.
No but almost all good ideas can be reduced down to a few sentences if you're good at explaining things. It's a different kind of intelligence than what's commonly called IQ but it's something like that regardless.
Sure the explanation will oversimplify a lot but then you can expand it recursively if needed, you gotta start somewhere.
I agree with you in that there exist such a class of problems, but Occam's razor apply in far more circumstances than it does not. When confronted with novel information under pressure of quick decisions, you have to take the most likely correct path.
> That believes that the world can be simplified into dichotomies, or at least, simplified. Sometimes problems are complex, and the solutions to them necessarily so. For example, cancer
You just simplified most of the problems people work on down to cancer complexity. Ironic, isn't it?
That's also simply not the case, most people are building CRUD apps with some frontend code and some accessory stuff like build systems etc., which while complex, can still be expressed in very plain, easy to understand language for anyone who's a bit technical.
> Part of intelligence is knowing your audience and communicating efficiently.
Bingo! And on this axis many SOTA models fail miserably. These things are acting on my behalf under my direction. All the supposed intelligence in the world means fuck-all if nobody can understand it.
And like somebody else said… when meat-based humans talk like Claude does, it almost always means they either don’t understand what they are talking about, or are actively trying to conceal something and are a fraud. Not always, but almost always.
Yeah just today it told me in a snarky way that my CPU (7940HX) doesn't exist and that I must have misread it and it's either 7945HX or 7940HS. Yes, AMD (re-)branding CPU models makes things difficult but I thought we are past AI models making such egregious mistakes
Perhaps you haven't had the chance to use it, but 3.8 flash is the best model for talking too. Even routing Claudes output through 3.8 to have it explain whats going on is a breath of fresh air
I'll have to try Gemini Flash for coding. The reason I haven't I used Gemini for coding is last time I tried it couldn't call tools very well.
I am a huge fan of Gemini Pro for chat... gemini somehow just knows the most obscure stuff. I'll double check something Gemini said and find the source is deep inside a hard to access scientific paper. Google just has the best index of the internet.
I remember the Gemini 2.5 Pro series was the best at text, but failed half of it's tool calls. They really redeemed themselves with the 3.5 Flash series, it's great. I love how fast it is.
Now if only they could offer a subscription service for it...
Makes sense. When Google Jules was new, I experimented with it for a while and it was embarrassing how often it flubbed tool calls, often repeatedly. It tended to forget the working directory so almost every command needed two tries.
Even if you aren't coding, you really need to double check its answers. Flash 3.8 hallucinated a Keyence camera's max operating temperature for me, last week, and backed it up with "references".
It's still my favorite model for most non-coding stuff, though.
> My favorite part of the new Groks has been how they speak in plain english.
I don't know if it's the plain english or what, but I really like Grok for legal research (as opposed to code). It's got a noticeable edge in getting to the point compared to Opus 5.
I do wonder why a frontier model does this to be honest. It still does good coding wise, but it seems strange to me. r/Claude is full of "load bearing" jokes in every thread.
My favorite part of the new Groks has been how they speak in plain english
And the fact that Grok is the ultimate grandmaster of parallel tool-calls, routinely kicking off four or five at once. Overlapping the latencies makes a huge difference in responsiveness.
I also like how Grok is trained to print a short one-sentence descriptions of what it's doing before each step. Like an airline pilot calling out observations for the black-box recorder to hear.
I've noticed Chatgpt 5.6 Sol High, on the chat interface, inventing words that are a mixture of Portuguese and English. Like "hardcodar" a mix of "hardcode" and the most common verb ending in Portuguese "-ar". Some don't have a single google hit
Do you have any connection to Portugal? I imagine if you have Portuguese in any of your prompts that might bleed into your user profile which becomes a part of every prompt. Alternatively it might use browser language settings
they are bugs with the model. Sol regularly glitches out and will type out random stuff even in tool calls. I'm guessing this is when they have capacity problems
> I find the post cursor groks are phenomenal at frontend web development, though Claude is much better at backend ruby.
Wonder if we'd benefit from a much more specialized + task-specific benchmarks to paint a clearer picture like this. A benchmark solely for frontend, ruby, hardware, etc.
Interesting to note that mimo v2.6 pro has been released at I think 1 trillion parameters and does mostly better or equal than grok 4.7 which is a 2 trillion parameter. Both of which got released on the same day.
That’s a factor of half the parameters. I would be curious to see more on the focus of smaller parameters model and pushing its frontiers
Grok and Zai have both been excellent as adjunct code-reviews, on their cheapest plans, for me. Fable plans, Opus writes, Codex as primary reviewer, but Grok and Zai usually find something worth fixing that the others have missed. Both are well worth whatever the $20 or so I'm paying for them
Same. The issue with Anthropics models is that (speaking regarding code generation) they REFUSE any kind of comment override instructions. I've tried everything and no matter what, after a few turns, they resort to generating the same overtly verbose junk. Bun's codebase is littered with them
See
// `HANDLE` is an opaque kernel handle (kernel32 validates and returns 0/FALSE
// on a non-console handle); every out-param is `&mut T` to a `#[repr(C)]` POD,
// ABI-identical to the Win32 `LP*` pointer (thin non-null). The reference type
// encodes the only pointer-validity precondition, so `safe fn` discharges the
// link-time proof. (`bun_windows_sys::kernel32` declares these with `*mut`;
// redeclared locally so the legacy-conhost cursor path below is plain calls.)
or
// Progress's terminal handle is the canonical `output::File` (vtable-backed
// stderr/File from `OutputSinkVTable`). The duplicate `ProgressTerminalVTable`
// from B-0 round 1 is removed; tty/ansi/winsize route through the new
// `OutputSinkVTable` slots so `bun_core` stays T0 (no `bun_sys` dep).
I actually love fable 5.1 prose. Before I was always on GPT models and I despised the writing. Actually I despised the writing they would do in the markdown docs, the writing in the responses was good. But there was always this weird thing that in the responses the model would write very clear explanations. Then if you ask it to document it what it ends up writing in the .md files is way too verbose, robotically lists facts and omits reasoning and gotchas. Too much unnecessary detail and too little detail it actually needed to capture. I absolutely hated GPT writing. Fable 5.1 though has the same concise, dense but logical and clear prose in both the responses and the documentation and the in-line code comments (oh yea Sol never wrote in-line code comments; and most of the time it deleted existing comments of code it didn’t even touch).
So yea, I find fable 5.1 writing to be excellent everywhere. I still use Sol daily though, but for things like config, quick research, fixes, code review etc. Feature work and writing is for fable 5.1.
Really? I’ve found that when working on my game I can’t let Grok write any text over about 8 words (tooltips for instance) or it will write the most horrendous incomprehensible slop - totally full of invented jargon, Claude-style weird grammar
Another great thing about Groks is sometimes they just simply do what you want to do. There's no fuss about refusals or it doing random stuff you didn't ask for or it speaking in slop or anything like that. They probably should pay a bit more attention to safety/alignment as they get better but they're great models for many use cases
You don't have to go into conspiracy mode about this. Musk clearly stated on X the other day that they delayed the release because they got some thing wrong with the RL behavior.
Flag (comments like the above extremely clearly break the guidelines which is what flagging is for), downvote, and contact the mods if there's a pattern of behavior. HN is partially moderated by its users!
I do not understand how anyone can seriously use a tool that has "Be funny and irreverent when appropriate" baked into the system prompt.
I don't want to waste money because my calculator is cracking jokes. They don't deserve their paltry 5% marketshare or whatever it is they have currently. I'm not even getting into Musk as a person or the horrid things we've seen Grok spit out on twitter. I just don't trust his companies with my data and I have seen very little evidence that it's ever the best tool for the job. I'm sure those cases exist but I can't imagine it's worth it.
I am on the exact same page as you, but there is definitely a market for LLMs which speak more conversationally and less like Claude! Non-programming use cases abound and most users don't like the rigid, exact tone that engineering demands.
Omarchy has me questioning liking Rails because it just… straight up sucks?
It comes preloaded with friggen ZOOM. I don’t think Windows bloat is that bad.
If it makes people happy it makes people happy I guess. These guys trying it would be even more amazed at Fedora Workstation (“you can press windows and it shows all your open windows? That’s so much better”)
Isn't Rails highly opinionated with a focus on being batteries-included? I'm not defending the choice to include Zoom but a batteries-included (for better or for worse) distro is exactly what I would expect from the creator of Rails.
A lot of the "pre-install bloat" are just web apps, including Zoom. Zoom is just an 8 line file so it appears in the menu and to point it to a 22 line bash script to launch the app.zoom.us website at the right address.
Easy to remove. Can even open your favourite AI assistant and ask it to remove it, since it comes with an Omarchy skill and knows how to change everything.
Yeah, GNOME was my previous preference, but I don't see myself going back now
Learning the hotkeys in Omarchy is a bit of a process, but there is a hotkey to show the hotkeys lol (super + K) - and once you get used to them, its super productive, at least to me.
i don't understand why DHH is shipping so much bloat in omarchy. The better solution would be to ask if user wants to install bloatware during installation.
Thats pretty good, then he should have made a toolkit for building and rolling your own badass distro and not a "opinionated" "omakase" system that claims to solve all of deskop linux while personally mocking a lot of people that actually have made real contributions as "clowns".
His tone is horrible. Because he implies untrue things while being 100% certain. He will paint entire communities as against omarchy when it’s just a couple left wing members with a stupid take. It doesn’t help the distribution is just … not that good.
Yeah, he also doesn't seem to separate those of us that hate omarchy because its a stupid distro from those of us that hate omarchy because of the authors annoying political loudness. It's a convenient tool though, label all the detractors as hating him for political reasons when the distro is objectively kinda pointless.
dhh understands what a good user experience is. Installing in a couple minutes and getting right into it is an amazing start compared to most operating systems that take ages to setup.
Plus, many pre-installs (like Zoom) are web-apps that take no space (30 lines of text) and are easy to remove if you don't want them in the menu.
Have you… used any other Linux distributions at all? For longer than a month? Respectfully. The thing DHH killed was demoing Omarchy most stuff is in other distros out of the box. And like installing in a couple minutes is table stakes.
For people who are already addicted to sugar, yes, coffee tastes more aggressive than the drinks full of insane amounts of sugar or artificial sweeteners.
Meanwhile in Asia beans are considered sweet bc their food isn't overloaded with sugar.
It sounds like you're equating "aggressive" with "sweet".
Coffee has a very strong taste (even without sugar). Stronger than anything on the list. I'd wager that the reason some people add a lot of sugar is to make up for that strong taste.
5 hour energy drinks have almost no sugar. Much milder taste than coffee.
> Meanwhile in Asia beans are considered sweet bc their food isn't overloaded with sugar.
Yes, my inference was "people consume caffeine, and if one group is getting it from coffee, the other group is getting it from non-coffee, mostly (highly-caloric) soda water, which explains why the coffee side looks healthier."
I feel like I’m im a different universe than you… 4.5 is one of my favorite models of all time.
Fast, speaks normally. Was able to figure out many issues Claude couldn’t. I thought code readability was a worse than Claude but I could just tell it how I wanted stuff written anyway.
What do you use it for? I’m genuinely curious. I’m also using it in cursor
Some stories/long narratives; and a bunch of coding related stuff in Rust and different LUAs as well as some C; working with graphics and generating effects in DDS images; a bunch of Python stuff. The conversational and story stuff sure ChatGPT is fine but for coding Claude is an order of magnitude better at writing stuff that works right the first time. Using Opus 5.
I have been translating Motorola 6809 assembly into C. It is not that bad. I don’t find AI saves me much time writing the code because understanding it is still the bottleneck, but man it is good at explaining stuff.
Did you even watch the video? Purpose fit algorithms outperform is quite literally the OPPOSITE of Suttons argument. This is the man who wrote this:
“The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin.”
His problem with LLMs are they aren’t general enough… In fact his essay the bitter lesson was what inspired the current LLM scaling.
If you watched the video, you can even see the part where he talks about how AI will fully replace humans as the next stage of life. How wholesome!
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