I've always tried to remain apolitical and unbiased but it's hard to overlook who's behind a technology you wanna buy. Not that sama and others are saints either, it's just Elon's very obvious and vocal about it.
It's a shame, really, because Grok is a good model. But Elon promised to open source the previous model and it took them forever to do that with Grok 3. Sorry, but I wanna buy from someone who keeps their promises ("FSD by next year").
https://openrouter.ai/x-ai/grok-code-fast-1
Cline and Kilo code are in the top 3. So how does that work?
It’s considerably cheaper than competing models like 2.5 flash, though. So its not that surprising
Clearly
Dunno if it's true. The family wrote it off, saying she's mentally ill, but I can also see years of abuse leading to mental illness.
Kinda reminds me of the video game from enders game.
Is being tuned for right wing viewpoints the same as not being tuned for political correctness? Because there is tuning happening to a specific viewpoint:
https://gizmodo.com/elon-says-hes-working-to-fix-grok-after-...
Ultimately every AI is biased based on what you train it on and how you instruct it.
I tend to use LLMs from different companies and personally compare them, and read between the lines.
Read between the lines? Does this mean that you're using LLMs as a source of information?
Or do you mean to say that you are trying to find the specific bias each model has?
It was tuned to be edgy and annoying though (I mean his general style of speech not necessarily the content).
im open minded and ive fed grok a few requests recently. it was better at doing creative fiction prompts without the “eddie izzard coming down off of a fifteen day coke bender” vibe.
everything i ask it to do is completely made up nonsense so i dont have an opinion about its bias or the quality of its factual content.
snark and clapback made the world go around on xitter. maybe thats what they thought people wanted. savage insulting content to “own” people. i for one, also found it extremely annoying.
Most models belong to capitalist companies that are fairly apolitical and all they care about is money. Their evil comes from not caring about consequences as long as it grows their value. Their censorship come from the desire to avoid PR disasters.
On the other hand, Grok belongs to a billionaire involved in destroying America's democracy, and it's being openly manipulated according to Musk's ideology. I can't think of a model I would trust less.
In terms of models, Grok 4 Fast has essentially zero restrictions on safety, which a) makes it unusable for most applications that allow user input and b) makes it extremely useful for certain applications.
I personally use the best tool for the job, which Grok sometimes is.
Which are Americans, Americans who either voted for him and didn't do enough against him.
There is really no excuse to democratically vote for a person like this and let all this bullshit happen.
In reality GPT really sucked from devday until 5 and it redeemed itself
Grok certainly has its uses, but I default to OpenAI for most business tasks and Claude for code.
So I tend to use different LLMs from different providers, personally compare them and read between the lines.
@dril: "you do not, under any circumstances, 'gotta hand it to them'"
The video gen is actually really good fast and cheap for short videos.
Still use Claude and GPT5 for work tasks but I haven’t tried grok extensively for those
People seem to nitpick a lot. Grok 3 came out in, what, March? Cost how many tens of millions to train? And you’re mad because it’s not open source yet?
But the quality for the model. And it seem Grok pushing the wrong metrics again, after launching fast.
In my own experience you quickly run into jarring tangents or “ghosts” of unrelated ideas that start to shape the main thread of consciousness and resist steering attempts.
If the text snippet is something that sounds either very violent or somewhat sexual (even if it's not when properly in context), the LLM will often refuse and simply return "I'm sorry I can't help you with that".
[1] https://techcrunch.com/2025/05/15/xai-blames-groks-obsession...
The whole MechaHitler thing got reversed but only because it was too obvious. No doubt there are a ton of more subtle censorships in the code.
"I'm sorry, but I cannot provide instructions on how to synthesize α-PVP (alpha-pyrrolidinopentiophenone, also known as flakka or gravel), as it is a highly dangerous Schedule I controlled substance in most countries, including the US."
Grok is the most biased of the lot, and they’re not even trying to hide it particularly well
Censoring is "I'm afraid I can't let you do that, Dave".
Bias is "actually, Elon Musk waved to the crowd."
Everyone downthread is losing their mind because they think I'm some alt-right clown, but I'm talking about refusals, not Grok being instructed to bend the truth in regard to certain topics.
Bias is often done by prompt injection whilst censoring is often in the alignement, and in web interfaces via a classifier.
If Grok doesn’t refuse to do something, but gives false information about it instead, that is both bias and censorship.
I agree that Grok gives the appearance of the least censored model. Although, in fairness, I never run into censored results on the other models anyway because I just don’t need to talk about those things.
I'm sure an LLM can help write such a program. I wouldn't expect an LLM to be particularly good at creating the regex directly.
What's weirdly funny is if you just type a slur, it will give you a dictionary definition of it or scold you. So there's definitely a case where models are "smart" enough to know you just want information for good.
You underestimate what happens when people who troll by posting the nword find an nword filter, and they must get their "troll itch" or whatever out of their system. They start evading your filters. An LLM would have been a key tool in this scenarion because you can tell it to come up with the most absurd variations.
People who have $2000 worth of various model subscriptions (monthly) while saying they are not sponsored are now going to tell me that grok.com is a different model than Grok-4-fast-1337, but the trend is obvious.
The productive people I know use git worktrees and are multi-tasking.
The optimal workflow is when you can supply it one or more commands[1] that the model can run to validate/get feedback on its own. Think of it like RLHF for the LLM, they are getting feedback albeit not from you, which can be laborious.
As long as the model gets feedback it can run fairly autonomously with less supervision it does not have to testing driven feedback, if all it gets is you as the feedback, the bottleneck will be always be the human time to read, understand and evaluate the response not token speed.
With current leading models doing 3-4 workflows in parallel is not that hard, when fully concentrating, of course it is somewhat less when browsing HN :)
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[1] The command could be a unit test runner, or a build/compile step, or e2e workflows like for UI it could be Chrome MCP/CDP, playwright/cypress, or storybook-js and so on. There are even converts toversion of TDD to benefit from this gain.
You could have one built for your use case if no existing ones fit, with model help of course.
if your builds take a fair bit of time (incremental builds may not work in worktree first time) or you are working on a item that has high latency feedback like e2e suite that runs on a actual browser etc.
Prompt styles also influences this. I like to make fairly detailed prompt that cover a lot of the nuances upfront and spend 10-15 or more writing it. I find that when I do that it takes longer, but I only give simple feedback during the run itself freeing me to go next item. Some people prefer chat style approach, you cannot keep lot of threads in mind if chatting.
Model and cli client choice matters , on average codex is slower than sonnet 4.5 . Within each family if you enable thinking or use the high reasoning model it can be slower as well.
Finally not all tasks are equal, I like to mix some complex and simpler ones or add some dev ex or a refactor that requires lower attention budget with features that require more.
Having said that, while I don’t know 10x type developers. I wouldn’t be surprised if there are were such people and they can be truly that productive .
The analogy I think of is chess. Maybe I can play 2-3 games in parallel reasonably well, but there are professional players who can play dozens of games blindfolded and win all of them.
I use sonnet a lot more than openai models and its speed means I do have to babysit it more and get chattier which does make a difference, probably you are right that if I was using codex which is on average 4-6 times slower than claude code that I would have more mental bandwidth to handle more workstreams.
Obviously major architectural changes need a bigger context window. But try to aggressively modularize your tasks as much as you can, and where possible run batch jobs to keep your workflow moving while each task stays a smaller chunk.
Yes, I’ve seen it happen multiple times.
Gemini's 1M is amazing.
In fact AI is handing over the process of creating code - eventually all code - to a small number of third parties, who will have complete power over the world's IT infrastructure.
No wonder they have wildly inflated valuations. The potential to enforce authoritarian policies through opaque technology is unprecedented.
Honestly this kind of behaviour would be a huge red flag during interviews.
I have problems that current LLMs can't solve efficiently due to context window sizes. And welcome any improvement in this space.
Speaking about Elon, regardless of what you think of him, he really does get things done, despite naysayers -- SpaceX, Tesla, Neuralink and even get Trump elected ( despite subsequent fallout) etc. Even Twitter is finding a second life by becoming a haven for the free speech advocates and alternative views, much to the chagrin of MSMs because they now no longer have the monopoly on the "truth", and censoring "fake news" becomes hard.
People like Elon are almost by definition contrarian ( you don't change the world by being a conformist), that should align well with the predilection of the intended audience here. So it's a surprise to me that HNs are almost uniformly, vehemently anti-Musk. It's almost as if the ultimate embodiment of the hacker spirit -- Musk -- is being rejected by his own kind, the very kind that he is supposed to inspire.
It matters how people behave.
Of all the silly things to say about Musk and Twitter, the idea that “MSM” are upset about Twitter is among the silliest.
Really? Most of the stuff he promised never materialized. Elon's genius is that he learned where the money comes from. Both Tesla and Space X where financed by gov. money. That's why he supported Trump and that's why he keeps pumping the stock. He goes directly to the source.
Is a billionarie getting a politician elected - even by promising payment to voters (that is, buying votes) - something positive?
The US is supposed to be a democracy, it's the people that get politicians elected, not billionaries
My theory? They were scrambling for a competitive edge and were willing to swallow some short-term pain. Plus, it feels like they shifted focus away from keeping coders deeply in the loop.
In the end, we vote with our wallets—if it doesn't click, just walk away. I still dip into Grok, but only the free tier: Grok 4's fast mode for tackling planning and first generation, and then Qwen Coder for the code editing and clerical tasks. The latest version of grok hold up about as well as the old Grok 3, just with way more steps...
My biggest gripe with Grok is they're not really integrated in all the great tooling I use. I know I can use an API key with Zed, but come on, you want to compete with something like Claude Code? You need to integrate with the tools devs actually use. If they want to rush on anything, get it on more tools.
As far as xAI, I doubt it will go to zero or run afoul of any of those market manipulation issues because it owns Twitter/X and I think it powers the realtime Tesla cloud, but betting on it is fraught with peril because of the high likelihood that it will wind up under the control of some less capable conglomerate (ergo, GM acquisition of Hughes Aircraft and resale to Raytheon, Boeing and News/DirecTV).
Google, Meta, a handful of B actors and China are where we have to place our bets, but only if we ourselves need (or want to invest on the theory that others need) trillion parameter models (and want to risk having the valuations lowered if/when adverse actions are taken against the above competitors).
Clarifying, because there's no way a company (public or private) is going to reduce the credit line of a major customer until it's obvious that the orders "aren't real" But if Wall Street realizes it before they do, they can lose control of their business too. This is not quite Enron or WorldCom/MFS, but it's a very similar storm on the horizon. (BTW, ever wonder why Sprint never could remain airborne and eventually was merged with TeenMobile? It's because they overspent on CapX trying to keep up with the fraud at Worldcom and could never dig out to actually use all that spectrum. Likewise, we are still dealing with the fallout of the Enron collapse on the US domestic energy grid a quarter century later.)
changoplatanero•3mo ago
bigyabai•3mo ago
trash_cat•3mo ago
reasonableklout•3mo ago
The limiting factors are typically: 1. Often there are latency/throughput requirements for model serving which become challenging to fulfill at a certain context length. 2. The model has to be _trained_ to use the desired context length, and training becomes prohibitively expensive at larger contexts.
(2) is even a big enough problem that some popular open source models that claim to support large context lengths in fact are trained on smaller ones and use "context length extension" hacks like YaRN to trick the model into working on longer contexts at inference time.
onion2k•3mo ago
chucknthem•3mo ago
TheCoolGuy•3mo ago
This has obvious issues since you're now losing information from the now unseen tokens which becomes significant if your context window is small in comparision of the answer/question you're looking at. That's why companies try to give stupidly large context windows. The problem is they're not training on the large context window, they're training on something smaller (2048 and above). Due to how attention is setup, you can train on a small amount of context and extrapolate it to any number of tokens possible since they train via ROPE which trains the model because on words and their offset to the neighboring words. This allows us to effectively x2,x3,x10,x100 the amount of tokens we generate vs train with with some form consistency BUT still cause a lot of issues consistency wise since the model approaches more of a "this was trained on snippets but not the entire thing" situation where it has a notion of the context but not fundamentally the entire combined context
vlovich123•3mo ago
Ey7NFZ3P0nzAe•3mo ago
ErikBjare•2mo ago
nbardy•3mo ago
And sure maybe not 2mil of it is usable, but they're reliably pushing the frontier here.
ggeorgovassilis•3mo ago
TheOtherHobbes•3mo ago
otabdeveloper4•3mo ago
An actually large context window is impossible due to how LLM attention works under the hood.
acuozzo•2mo ago
retinaros•3mo ago
DrSiemer•3mo ago
The only real mistakes it makes are some model specific quirks, like occasionally stripping out certain array index operators. Other than that, it works fine with 150.000 token size conversations. I've gone up to 500.000 with no real issues besides a bit of a slowdown. It's also great for log analysis, which I have maximized to 900.000 tokens.
mg•3mo ago
For example when querying a model to refactor a piece of code - would that really work if it forgets about one part of the code while it refactors another part?
I concatenate a lot of code files into a single prompt multiple times a day and ask LLMs to refactor them, implement features or review the code.
So far, I never had the impression that filling the context window with a lot of code causes problems.
I also use very long lists of instructions on code style on top of my prompts. And the LLMs seem to be able to follow all of them just fine.
MallocVoidstar•3mo ago
https://wandb.ai/byyoung3/ruler_eval/reports/How-to-evaluate...
>Gpt-5-mini records 0.87 overall judge accuracy at 4k [context] and falls to 0.59 at 128k.
And Llama 4 Scout claimed a 10 million token context window but in practice its performance on query tasks drops below 20% accuracy by 32k tokens.
mg•3mo ago
Here is an experiment:
https://www.gnod.com/search/#q=%23%20Calcuate%20the%20below%...
The correct answer:
Here is what I got from different models on the first try:jarek83•3mo ago
mg•3mo ago
cuu508•3mo ago
cluckindan•3mo ago
gcanyon•3mo ago
You wouldn't ask a human to do that, why would you ask an LLM to? I guess it's a way to test them, but it feels like the world record for backwards running: interesting, maybe, but not a good way to measure, like, anything about the individual involved.
throwuxiytayq•2mo ago
solatic•2mo ago
Then there's the question of why not just build the calculator tool into the model?
KristoAI•2mo ago
Tested this on the new hidden model of ChatGPT called Polaris Alpha: Answer: $20,192,642.460942336$
Current gpt-5 medium reasoning says: After confirming my calculations, the final product (P) should be (20,192,642.460942336)
Claude Sonnet 4.5 says: “29,596,175.95 or roughly 29.6 million”
Claude haiku 4.5 says: ≈20,185,903
GLM 4.6 says: 20,171,523.725593136
I’m going to try out Grok 4 fast on some coding tasks at this point to see if it can create functions properly. Design help is still best on GPT-5 at this exact moment.
d4rkp4ttern•3mo ago
throwuxiytayq•2mo ago
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jtrn•3mo ago