191–200 of 268 posts
Earlier quoted context omitted.
The standard way to use Claude Code is with a constant-cost subscription; one of their standard website accounts. It’s rate-limited but still generous. You can also use API tokens, yes, but that’s 5-10x more expensive. So I wouldn’t.
If API tokens are 10x more expensive doesn’t that imply that the constant-cost subscription is massively subsidized?
Earlier quoted context omitted.
Subscription?
I have one, and upgrades don't have unlimited access as far as I can tell. Correct me if I'm wrong. This cost scaling will be an issue for this whole AI employee thing, especially because I imagine these providers are heavily discounting.
One thing I'll add that isn't touched on here is about context windows. While not "infinite", humans have a very large context window for problems they're specialized in solving. Models can often overcome their context window limitations by having larger and more diverse training sets, but that still isn't really a solution to context windows. Yes, I get the context window increases over time and that for many purpos…
Do they? I certainly don't. I don't know if it's my memory deficiency, but I frequently hit my "context window" when solving problems of sufficient complexity.
Can you provide some examples of problems where humans have such large context windows?
Earlier quoted context omitted.
I think you're crossing reliability and availability. Reliability means 99.9% of the time when I hand something off to someone else it's what they want. Availability means I'm at my desk and not at the coffee machine. Humans very much are 99.9% accurate, and my deliverable even comes with a list of things I'm not confident about
> Humans very much are 99.9% accurate This is an extraordinary claim, which would require extraordinary evidence to prove. Meanwhile, anyone who spends a few hours with colleagues in a predominantly typing/data entry/data manipulation service (accounting, invoicing, presales, etc.) KNOWS the rate of minor errors is humongous.
The claim is pretty clearly 'can' achieve (humans) vs 'do' achieve (LLM). Therefore one example of a human building a system at 99.9% reliability is sufficient to support the claim. That we can compute and prove reliability is really the point.
For example, the function "return 3" 100% reliably counts the Rs in strawberry. We can see the answer never changes, if it is correct once therefore, it will always be correct because the answer is always the same correct answer. A LLM can't do that, and infamously gave inaccurate results to that problem, not even reaching 80% accuracy.
For the sake of discussion, I'll define reliability to be the product of availability and accuracy and will assume accuracy (the right answer) and availability (able to get any answer) to be independent variables. In my example I held availability at a fixed 100% to illustrate why being able to achieve high accuracy is required for high reliability.
So, two points: humans can achieve 100% accuracy in the systems they build because we can prove correctness and do error checking. Because LLM cannot do 100%, frankly, there is going to be a problem that shows a distinction between max capabilities. While difficult, humans can build highly reliable complex systems. The computer is an example, that all the hardware interfaces together so well and works so often is remarkable.
Second, if every step along a pipeline is 99% reliable, then after 20 steps we are no longer talking about a system that usually works, but one that _rarely_ works. For a 20 step system to work above 50%, it really needs some steps that are effectively at 100%
> Production systems need 99.9%+ reliability This is not remotely true. Think of any business process around your company. 99.9% availability would mean only 1min26 per day allowed for instability/errors/downtime. Surely your human collaborators aren't hitting this SLA. A single coffee break immediately breaks this (per collaborator!). Business Process Automation via AI doesn't need to be perfect. It simply needs to…
I think you're crossing reliability and availability. Reliability means 99.9% of the time when I hand something off to someone else it's what they want. Availability means I'm at my desk and not at the coffee machine. Humans very much are 99.9% accurate, and my deliverable even comes with a list of things I'm not confident about
(Your point remains largely the same, just more precise with the updated definition replacing 'reliable' with 'accurate'.)
Earlier quoted context omitted.
The standard way to use Claude Code is with a constant-cost subscription; one of their standard website accounts. It’s rate-limited but still generous. You can also use API tokens, yes, but that’s 5-10x more expensive. So I wouldn’t.
If API tokens are 10x more expensive doesn’t that imply that the constant-cost subscription is massively subsidized?