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").
Clearly
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-...
Basically, the major free options out there for LLMs are OpenAI, Google, Perplexity, DeepSeek, Meta, and Grok. (I could be missing stuff here, but those are the main players.) DeepSeek is out because of China ties. OpenAI and Perplexity have CEOs that seem incredibly shifty to me. I refuse to give Meta and Google any more info than I have to, so I'm avoiding them. Hence we fall back to Grok. Again, maybe not a completely logical progression, but it's my choice and I get to live with the consequences :)
Literally none of this options you listed are that objectionable.
Do what the rest of us do and switch frequently. Don't use mekafurhur and you'll be fine.
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.
But the quality for the model. And it seem Grok pushing the wrong metrics again, after launching fast.
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.
People who flag don't do it because they don't want to dig in. They are almost universally a force for suppression & ignorance, the billionaire imperialist fatcat friend who is desperate to minimize the public eye.
changoplatanero•1h ago
bigyabai•1h ago
trash_cat•1h ago
reasonableklout•1h 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.
chucknthem•1h ago
TheCoolGuy•1h 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•1h ago
nbardy•1h ago
And sure maybe not 2mil of it is usable, but they're reliably pushing the frontier here.
ggeorgovassilis•1h ago