421–430 of 498 posts
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If I explicitly turn this off in Claude’s settings isn’t it the same thing?
Can they get 5000 people to do the same correctly on every reinstall and enforce it ?. Small individual tasks become complex at scale , and that is why these enterprise contracts sell
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The thing I keep coming back to is - does it matter? Really does it matter if a company produces something that breaks constantly or gets worse or slower.(See github) Megacorps have a wide moat and have forced out all competition or they just buy them with low interest loans. The quality of products keeps getting worse and we can do nothing but live with it. So if that's the state of the world, why wouldn't you just…
Probably, at some point. People used to say "So Windows is bad, but does it matter?" And it seems that this does matter, so much that Microsoft (appears to) want to improve Windows' user experience.
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You can always say "it's not because of LLMs", that's nearly unfalsifiable. But if all of your peers are saying LLMs are more productive, if you're building things faster than ever before, the macro picture speaks for itself.
It sounds like this has a pretty falsifiable claim here - is the revenue attributed to a tax thing? Then it's clearly not attributable to code. I agree that the macro picture would speak for itself. Can you point to any macro level detail that is indeed cleanly showing benefits from increased productivity from LLMs?
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> If it was actually productive, then the revenue would increase and affordability wouldn't be a question. Revenue has increased. Have you seen Meta's latest earnings? +33% revenue - in this economy. Affordability is not a question. There is a reason companies like Meta have no issue with their engineers spending $1k/day on tokens. It's just not that much compared to how much they make per employee.
That means absolutely nothing in the context of this conversation. It says right in their release ad impressions are up almost 20% and cost per add is up 12%. Those two metrics alone account for most of the increase in their revenue. Absolutely no conclusion can be drawn regarding the impact of AI on those numbers one way or the other. It's not like they used AI to crank out some new revenue generating piece of softw…
Have you wondered why this is the case? How do you think they increased impressions so much at their scale? How they did this despite losing 20M users?
To put it clearly, AI at every part of the pipeline: writing software, product features/experiments, A/B testing them, and pushing them out to users. Even before you get to something like LLM driven recommendations, you can virtually entirely automate the process of finding more "engagement alpha" with AI.
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Strange pricing model for a company selling the idea of having fewer employees.
Don't the incentives align? If you have fewer employees, then you pay less...
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> I just can't figure how _how_ to burn that much money a month responsibly. I always have a few agents (2-5) doing research and working on plans in parallel. A plan is a thorough and unambiguous document describing the process to implement some feature. It contains goals, non-goals, data models, access patterns, explicit semantics, migrations, phasing, requirements, acceptance criteria, phased and final. Plans often…
I dunno I've seen agents make boneheaded mistakes even a junior engineer wouldn't make. Treating them as strictly better than junior engineers is a problem, not just for that reason but because you're effectively killing the pipline for senior engineers. Then what?
Yes, of course.
> you're effectively killing the pipline for senior engineers. Then what?
I honestly don't know _what_. Its a prisoner's dilemma.
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That means absolutely nothing in the context of this conversation. It says right in their release ad impressions are up almost 20% and cost per add is up 12%. Those two metrics alone account for most of the increase in their revenue. Absolutely no conclusion can be drawn regarding the impact of AI on those numbers one way or the other. It's not like they used AI to crank out some new revenue generating piece of softw…
> It says right in their release ad impressions are up almost 20% and cost per add is up 12% Have you wondered why this is the case? How do you think they increased impressions so much at their scale? How they did this despite losing 20M users? To put it clearly, AI at every part of the pipeline: writing software, product features/experiments, A/B testing them, and pushing them out to users. Even before you get to so…
Edit: Also, historically Meta has been growing revenue by 30 to 50 percent for the last decade. With the only exception being 2022 and 2023. So it's not like recent performance is an outlier.
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> I just can't figure how _how_ to burn that much money a month responsibly. I always have a few agents (2-5) doing research and working on plans in parallel. A plan is a thorough and unambiguous document describing the process to implement some feature. It contains goals, non-goals, data models, access patterns, explicit semantics, migrations, phasing, requirements, acceptance criteria, phased and final. Plans often…
Just out of curiosity, what type of systems are you working on? What type of features did you implement on your 100k LOC week?
I work on 3rd party API integrations, of which, we have hundreds, each in its own repo. We need to build thousands more at a fraction of the cost. Any given integration historically takes a human a few days up to a few months to build and is subject to ongoing maintenance. We frequently do not have access to the API and we mostly never have a representative data set if we do. Complex APIs tend to expose multiple, entwined data models. Documentation may be wrong or in a foreign language.
I've been building a new framework to do it better. Ideally, we can get an agent to spit them out in a few minutes to hours with a much reduced ops burden for managing the fleet, all with very high confidence. The later requires pushing as much into the type system as possible and leveraging static analysis. Much of the work has been embarrassingly parallelizable. Consider categorizing access patterns across the entire set or ensuring byte for byte parity (over the input space of third party API responses).
This is absolutely not a problem that a human or 2 could tackle prior to AI.
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I think that you send the entire conversation with every request.
As long as you stay under the 1-hour caching TTL for your open threads, I guess your marginal cost is linear. This is me on a weekday flicking between Ghostty tabs to enter “stand by” every ~45 mins.