461–470 of 620 posts
It looks like you don't allow testing of anything beyond a certain token size.
Which makes your test kind of pointless, because if you are chatting about AI with something that's only a few hundred tokens, the data your collecting is pretty minimal and specific, not something that's generally applicable or relevant to wider user outside of that specific context.
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From what I understand you shouldn't wait more than 5min between prompts without compacting or clearing or you'll pay for reinitializing the cache. With compaction you still pay but it's less input tokens. (Is compaction itself free?)
>pay for reinitializing the cache Why can't they save the kv cache to disk then later reload it to memory?
Typically it’s cached for about 5 minutes, you can pay extra for longer caches.
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It thinks less and produces less output tokens because it has forced adaptive thinking that even API users can't disable. Same adaptive thinking that was causing quality issues in Opus 4.6 not even two weeks ago. The one bcherny recommended that people disable because it'd sometimes allocate zero thinking tokens to the model. https://news.ycombinator.com/item?id=47668520 People are already complaining about low quali…
> Why are you handwaving things away though? I've got you on max effort. I even patched the system prompts to reduce this. Do you think it knows what max effort or patched system prompts are? It feels really weird to talk to an LLM like it’s a person that understands.
As someone who's been programming alone for over a decade, I absolutely do want to enjoy my coding buddy experience. I want to trust it. I feel pretty bad when I have to treat Claude like a dumb machine. It's especially bad when it starts making mistakes due to lack of reasoning. When I start explaining obvious stuff it's because I've lost the respect I had for it and have started treating it like a moron I have to babysit instead of a fellow programmer. It's definitely capable of understanding and reasoning, it's just not doing it because of adaptive thinking or bad system prompts or whatever else.
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You have to build a computer to learn about computers!
I would argue that if you've just watched videos about building computers and haven't sat down and done one yourself, then yeah I don't see any evidence that you've learned how to build a computer.
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I was worried about skill atrophy. I recently started a new job, and from day 1 I've been using Claude. 90+% of the code I've written has been with Claude. One of the earlier tickets I was given was to update the documentation for one of our pipelines. I used Claude entirely, starting with having it generate a very long and thorough document, then opening up new contexts and getting it to fact check until it stopped…
I have also found LLMs are a great tool for understanding a new code base, but it's not clear to me what your comment has to do with skill atrophy.
My syntax writing skills may well be atrophying, but I'll just do a leetcode by hand once in a while.
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https://github.blog/changelog/2026-04-16-claude-opus-4-7-is-... > Over the coming weeks, Opus 4.7 will replace Opus 4.5 and Opus 4.6 in the model picker for Copilot Pro+[...] > This model is launching with a 7.5× premium request multiplier as part of promotional pricing until April 30th.
Promotional pricing? Are they saying that after the promotion, it will cost more than 7.5x??
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Mirrors my sentiment. Those tools seem mostly useful for a Google alternative, scaffolding tedious things, code reviewing, and acting as a fancy search. It seems that they got a grip on the "coding LLM" market and now they're starting to seek actual profit. I predict we'll keep seeing 40%+ more expensive models for a marginal performance gain from now on.
I just don’t see how they’ll be able to make a profit. Open models have the same performance on coding tasks now. The incentives are all wrong. Why pay more for a model that’s no better and also isn’t open? It’s nonsense
So far, Opus 4.7 seems a bit smarter than Opus 4.6 for my use case. That's my only concern. Is an $80 bottle of wine a better value than a $20 or $40 bottle of wine? Pretty much never. If there are those of us willing to buy $80 bottles of wine, of course the market will facilitate this.
People can use whatever model they want. I'm too worried about worms crawling through my dead body to waste time on any but the smartest model any moment can offer.
The bump from 4.6 to 4.7 is not very noticeable to me in improved capabilities so far, but the faster consumption of limits is very noticeable. I hit my 5 hour limit within 2 hours yesterday, initially I was trying the batched mode for a refactor but cancelled after seeing it take 30% of the limit within 5 minutes. Had to cancel and try a serial approach, consumed less (took ~50 minutes, xhigh effort, ~60% of the rem…
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> ... but it seems like Anthropic is going for the Tinder/casino intermittent reinforcement strategy: optimized to keep you spending tokens instead of achieving results. This part of the above comment strikes me as uncharitable and overconfident. And, to be blunt, presumptuous. To claim to know a company's strategy as an outsider is messy stuff. My prior: it is 10X to 20X more likely Anthropic has done something othe…
> To claim to know a company's strategy as an outsider is messy stuff. I said "it seems like". Obviously, I have no idea whether this is an intentional strategy or not and it could as well be a side effect of those things that you mentioned. Models being "worse" is the perceived effect for the end user (subjectively, it seems like the price to achieve the same results on similar tasks with Opus has been steadily incr…
>> This part of the above comment strikes me as uncharitable and overconfident. And, to be blunt, presumptuous. To claim to know a company's strategy as an outsider is messy stuff.
> I said "it seems like".
Sorry. I take back the "presumptuous" part. But part of my concern remains: of all the things you chose to wrote, you only mentioned "the Tinder/casino intermittent reinforcement strategy". That phrase is going to draw eyeballs, and you got mine at least. As a reader, it conveys you think it is the most likely explanation. I'm trying to see if there is something there that I'm missing. How likely do you think is? Do you think it is more likely than the other three I mentioned? If so, it seems like your thinking hinges on this:
> I am claiming that there is no incentive for Anthropic to address this issue because of their business model (maximize the amount of tokens spent and price per token).
No incentive? Hardly. First, Anthropic is not a typical profit-maximizing entity, it a Public Benefit Corporation [1] [2]. Yes, profits matter still, but there are other factors to consider if we want to accurately predict their actions.
Second, even if profit maximization is the only incentive in play, profit-maximizing entities can plan across different time horizons. Like I mentioned in my above comment, it would be rather myopic to damage their reputation with a strategy that I summarize as a short-term customer-squeeze strategy.
Third, like many people here on HN, I've lived in the Bay Area, and I have first-degree connections that give me high confidence (P>80%) that key leaders at Anthropic have motivations that go much beyond mere profit maximization.
A\'s AI safety mission is a huge factor and not the PR veneer that pessimists tend to claim. Most people who know me would view me as somewhat pessimistic and anti-corporate and P(doomy). I say this to emphasize I'm not just casting stones at people for "being negative". IMO, failing to recognize and account for Anthropic's AI safety stance isn't "informed hard-hitting pessimism" so much as "limited awareness and/or poor analysis".
I'm not naive. That safety mission collides in a complicated way with FU money potential. Still, I'm confident (P>60%) that a significant number (>20%) of people at Anthropic have recently "cerebrated bad times" [3] i.e. cogitated futures where most humans die or lose control due to AI within ~10 to ~20 years. Being filthy rich doesn't matter much when dead or dehumanized.
[1]: https://law.justia.com/codes/delaware/title-8/chapter-1/subc...
[2]: https://time.com/6983420/anthropic-structure-openai-incentiv...
[3]: Weird Al: please make "Cerebration" for us.