101–110 of 175 posts
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Yes, "street". Typing from my phone, sorry. And search engines are narrow tools that can only output copies of its dataset. An LLM is capable of surprisingly novel output, even if the exact level of creativity is heavily debated.
Remixes aren't novel.
Controversial opinion from a casual user, but state-of-art LLMs now feel to me more intelligent then the average person on the steet. Also explains why training on more average-quality data (if there's any left) is not making improvements. But LLMs are hamstrung by their harnesses. They are doing the equivalent of providing technical support via phone call: little to no context, and limited to a bidirectional stream…
entirely so. i think anthropic updated something about the compact algorithm recently, and its gone from working well over long times to basically garbage whenever a compact happens
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Yes, I think this is basically an instance of the "emergent abilities mirage." https://arxiv.org/abs/2304.15004 If you measure completion rate on a task where a single mistake can cause a failure, you won't see noticeable improvements on that metric until all potential sources of error are close to being eliminated, and then if they do get eliminated it causes a sudden large jump in performance. That's fine if you ju…
That's how the public perceive it though. It's useless and never gets better until it suddenly, unexpecty got good enough.
There is a decent case for this thesis to hold true especially if we look at the shift in training regimes and benchmarking over the last 1-2 years. Frontier labs don't seem to really push pure size/capability anymore, it's an all in focus on agentic AI which is mainly complex post-training regimes. There are good reasons why they don't or can't do simple param upscaling anymore, but still, it makes me bearish on AGI…
> In practice this still doesn't mean 50 % of white collar can't be automated though. Let me ask you this, though: if we wanted to, what percentage of white collar jobs could have been automated or eliminated prior to LLMs? Meta has nearly 80k employees to basically run two websites and three mobile apps. There were 18k people working at LinkedIn! Many big tech companies are massive job programs with some product on…
There was a long flat line before the step, models improve, but PR pass rate without human intervention is inherently a staircase function
I am pretty convinced that for most types of day to day work, any perceived improvements from the latest Claude models for example were total placebo. In blind tests and with normal tasks, people would probably have no idea if they're using Opus 4.5 or 4.6.
It's because they are getting so good it's impossible to recognize them. Haiku 4.5 is already so good it's ok for 80% (95%?) of dev tasks.
I am pretty convinced that for most types of day to day work, any perceived improvements from the latest Claude models for example were total placebo. In blind tests and with normal tasks, people would probably have no idea if they're using Opus 4.5 or 4.6.
It's because they are getting so good it's impossible to recognize them. Haiku 4.5 is already so good it's ok for 80% (95%?) of dev tasks.
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> In practice this still doesn't mean 50 % of white collar can't be automated though. Let me ask you this, though: if we wanted to, what percentage of white collar jobs could have been automated or eliminated prior to LLMs? Meta has nearly 80k employees to basically run two websites and three mobile apps. There were 18k people working at LinkedIn! Many big tech companies are massive job programs with some product on…
This is unfair and dismissive of many roles. Coordination in a massive, technically complex company that has to adhere to laws and regulations is a critical role. I don't get why people shit on certain roles (I'm a SWE). Our PgMs reduce friction and help us be more productive and focused. Technical writers produce customer-facing content and code, and have nothing to do with supporting internal bureaucracy. There are…
The reality is that you could run LinkedIn with far, far fewer people. You probably need fewer than 100 for core engineering, and likely less than 1,000 overall if you include compliance, sales, and so on - especially since a lot of overseas compliance stuff is outsourced to consulting firms, it's not like you have a team of lawyers in every country in the world.
Before there was so much money in the system, we used to run companies that way. Two decades ago, I worked for a company that had tens of millions of users, maintained its own complex nationwide infra (no AWS back then), and had 400 full-time employees. That made coordination problems a lot easier too. We didn't need ten layers of people and project management because there just wasn't that many of us.
There is a decent case for this thesis to hold true especially if we look at the shift in training regimes and benchmarking over the last 1-2 years. Frontier labs don't seem to really push pure size/capability anymore, it's an all in focus on agentic AI which is mainly complex post-training regimes. There are good reasons why they don't or can't do simple param upscaling anymore, but still, it makes me bearish on AGI…
> In practice this still doesn't mean 50 % of white collar can't be automated though. Let me ask you this, though: if we wanted to, what percentage of white collar jobs could have been automated or eliminated prior to LLMs? Meta has nearly 80k employees to basically run two websites and three mobile apps. There were 18k people working at LinkedIn! Many big tech companies are massive job programs with some product on…
They build generative AI tools so people can make ads more easily.
They have some of the most sophisticated tracking out there. They have shadow profiles on nearly everyone. Have you visited a website? You have a shadow profile even if you don't have a Facebook account. They know who your friends are based on who you are near. They know what stores you visit when.
Large fractions of their staff are making imperceptible changes to ads tracking and feed ranking that are making billions of dollars of marginal revenue.
What draws you in as a consumer is a tiny tip of the iceberg of what they actually do.