Mac only I'm afraid, but I already did [1]. Packaged it too as I figured it might be useful to others [2] (and I'd want to install it on machine's that I might not have Xcode on)
I spent the best part of a couple weeks making improvements and tidying up the UI, but to actually get something working was essentially only a couple of prompts.
Slightly off-topic: There is just too much content in this world...
How to know what is important and relevant? Imo that's the main question.
I'd rather have access to nothing except a carefully curated stream of relevant news. Hackernews is somewhat working but a bit too restricted and also not perfect.
For me it's like a media research library. I definitely can't "couch potato out" and watch for a long time, but I never do that with any TV. My favorite thing is the variety and access to content that we don't normally get in the US because none of the big streaming providers bother to offer it.
Yes.
You and some random indigenous guy in the Amazon likely share the same intelligence but you are more capable because you have access to writing/reading, computer, car etc.
Intelligence is more than raw intelligence. It's harness, skills, tools, memory etc. If you improve all the latter but keep the raw intelligence (LLM) fixed, you certainly get better results.
Same with us humans.
Of course, I’m not trying to dismiss gains from harness, actually the opposite.
But the narrative that 4.Y is an improvement over 4.X is essential to keep the model training music playing.
If 90+% of the gains come from the harness, how can you continue to justify spending billions of dollars on training and an 80% gross margin on inference on the latest model? (Reportedly what Anthropic commands on the top tier of their frontier model API billing).
So differentiating between the two (what I’m trying to do here) is really consequential!
Except LLMs are simulacra of actual intelligence. Frequently in a single conversation working on a single narrowly scoped task, I am both surprised by a few insights and cursing at how it can miss obvious issues. The "raw intelligence" of LLMs leaves much to be desired.
Agree. You have these tipping points when a model is good enough to do some task. Yes, a better model will further improve your capabilities but the unlock is at a certain intelligence level.
We see this also with humans. People with very low intelligence can't learn to read. Once you cross a certain threshold of intelligence you can learn to read. More intelligence doesn't really help you in the task of reading. A person with an IQ of 160 is not substantially better in reading than someone with an IQ of 85. If your IQ is 50, you might not be able to learn to read at all.
Depends on the task and the writing though doesn't it?
There's not that much depth in a lot of 'everyday' writing. For many tasks that means that you don't need to be hyperintelligent - reading a recipe or a shopping list, reading a newspaper article, etc.
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