101–110 of 620 posts
Subsidies don't last forever.
Tell that to oil and defense companies. If tech companies convince Congress that AI is an existential issue (in defense or even just productivity), then these companies will get subsidies forever.
And shafting your customers too hard is bad for business, so I expect only moderate shafting. (Kind of surprised at what I've been seeing lately.)
Earlier quoted context omitted.
qwen3.5/3.6 (30B) works well,locally, with opencode
I want to bump this more than just a +1 by recommending everyone try out OpenCode. It can still run on a Codex subscription so you aren’t in fully unfamiliar territory but unlocks a lot of options.
Earlier quoted context omitted.
You can argu that you will have skill atrophy by not using LLMs. We have gone multi cloud disaster recovery on our infrastructure. Something I would not have done yet, had we not had LLMs. I am learning at an incredible rate with LLMs.
Yes, you certainly can argue that, but you'd be wrong. The primary selling point of LLMs is that they solve the problem of needing skill to get things done.
You very much decide how you employ LLMs.
Nobody are keeping a gun to your head to use them. In a certain way.
Sonif you use them in a way that increase you inherent risk, then you are incredibly wrong.
Earlier quoted context omitted.
You can argu that you will have skill atrophy by not using LLMs. We have gone multi cloud disaster recovery on our infrastructure. Something I would not have done yet, had we not had LLMs. I am learning at an incredible rate with LLMs.
>I am learning at an incredible rate with LLMs. I don't believe it. Having something else do the work for you is not learning, no matter how much you tell yourself it is.
Open your eyes, and you might become a believer.
Subsidies don't last forever.
Running an open like Kimi constantly for an entire month will cost around 100-200$, being roughly equal to a pro-tier subscription. This is not my estimate so I’m more than open to hearing refutations. Kimi isn’t at all Opus-level intelligent but the models are roughly evenly sized from the guesses I’ve seen. So I don’t think it’s the infra being subsidized as much as it’s the training.
Conspiracy time: they released a new version just so hey could increase the price so that people wouldn't complain so much along the lines of "see this is a new version model, so we NEED to increase the price") similar to how SaaS companies tack on some shit to the product so that they can increase prices
Subsidies don't last forever.
I've been assuming this for a while. If I have a complex feature, I use Opus 4.6 in copilot to plan (3 units of my monthly limit). Then have Grok or Gemini (.25-.33) of my monthly units to implement and verify the work. 80% of the time it works every time. Leave me plenty of usage over the month.
If I can have Claude write up the plan, and the other models actually execute it, I'd get the best of both worlds.
(Amusingly, I think Codex tolerates being invoked by Claude (de facto tolerated ToS violation), but not the other way around.)
AFAICT this uses a token-counting API so that it counts how many tokens are in the prompt, in two ways, so it's measuring the tokenizer change in isolation. Smarter models also sometimes produce shorter outputs and therefore fewer output tokens. That doesn't mean Opus 4.7 necessarily nets out cheaper, it might still be more expensive, but this comparison isn't really very useful.
I’ve noticed 4.7 cycling a lot more on basic tasks. Though, it also seems a bit better at holding long running context.