I really found the story in chapter 14 (recursive self-improvement) about the guy who got so addicted to self-improvement that he ended up in his own meta-reality unable to understand even himself because he was getting so much better and hacking his learning. A completely fabricated story with no basis in reality that I'm aware of but man there are a lot of bullet points to make it seem factual. What are we going to do about the worrying trend of 10X hackers self-improving so much that they aren't able to exist in the real world.
Here's an excerpt
"The Addiction to Acceleration
The fourth uncomfortable truth is how recursive improvement becomes compulsive. Kenji can’t stop because each day of not improving his improvement feels like stagnation. When you’re accelerating, constant velocity feels like moving backward.
This addiction manifests as:
• Inability to accept plateau phases
• Anxiety when not optimizing optimization
• Devaluing of steady-state excellence
• Compulsion to add meta-levels
• Fear of falling behind yourself
Recursive improvement can become its own trap."
I find that this criticism is far less applicable to say individuals but perhaps it could be levied against the way companies are currently treating AI. Which of course is where this comes from.
Honestly there are some interesting concepts and broad overviews of them but this is hardly a "book" but just a verbose LLM document that briefly lists a lot of concepts without sufficiently or consistently fleshing them out into actual meaningful chapters. Not to say that this sort of thing isn't potentially useful but it seems more like the starting point of an outline of a book rather than anything resembling a finished published book.
I have a horrible time editing my own work. Decision paralysis and what not, but I did have the idea that a good way to practice would be editing the content of LLM generated fictional narratives. I think the point that many are making that LLMs are useful as cognitive aids that augment thinking rather than replacements for thinking. They can be used to train your mind by inspiring thoughts you wouldn't have came up with on your own.
Nice. I leverage the strengths of AI in a way that affirms the human element in the collaboration. AI as it exists in LLMs is a powerful source of potentially meaningful language but at this point LLMs don't have a consistent conscious mind that exists over time like humans do. So it's more like summoning a djinn to perform some task and then it disappears back into the ether. We of course can interweave these disparate tasks into a meaningful structure and it sounds like you have some good strategies for how to do this.
I have found that using an LLM to critique your writing is a helpful way of getting free generic but specific feedback. I find this route more interesting than the copy pasta AI voiced stuff. Suggesting that AI embodys a specific type of character such as a pirate can make the answers more interesting than just finding the median answer, add some flavor to the white bread.
One of the things I found helpful about getting out of the specific / formulaic feedback was asking the LLM to ask me questions. At one point I asked a fresh LLM to read the book and then ask me questions. It showed me where there were narrative gaps / confusing elements that a reader would run into, but didn't realy on the specific "answer" from the LLM itself.
I also had a bunch of personal stories interwoven in and it told me I was being "indulgent" which was harsh but ultimately accurate.
That's a great approach. I find LLMs work really well as Socratic sounding boards and can lead you as the writer to explore avenues you might have otherwise not even noticed.
In the end there are plenty of stories, but they're ones that are relevant. The story that the LLM gave feedback on was about flipping a raft on the Grand Canyon, the LLM's advice was that it felt unrelated to the point I was trying to make. That made me realize I had it in there more because I wanted to talk about the rafting Grand Canyon, vs. it being useful and entertaining to readers.
And now just think of all of the people who will be getting their knowledge from LLMs which are literally making up stuff through statistical linguistic inference on a grand scale from hearsay.
> LLMs are literally making up stuff through statistical linguistic inference on a grand scale from hearsay.
Hearsay being some personal "truth" it may be useful to know what the statistical average of that "truth" is. If we do it right perhaps we can get the various personal errors to cancel out.
That was my read. They can now identify species of very fragmentary bone remains via collagen protein matching. They didn't say what if anything clues this would/could lead to.
One party rule is almost never a good thing over the long run as politicians tend to become more self-serving and corrupt the longer they don't have to worry about being held accountable to voters. Instead they worry more about being accountable to their party leaders and funders who try to maintain the status-quo. Not sure the duopoly we have in the USA is preferable compared to say a robust democracy with smaller parties forming coalitions.
Take 30-60 minutes and read up on ranked choice voting, if you aren't familiar. I talk to a lot of people about it and the idea that we can vote any other way seems foreign to most.
It won't instantly change the world, but it will allow people to vote their true conscience vs. strategic voting, and allows smaller parties a chance at real power.
Yeah there was no mention of functional programming during my formal CS degree. I actually had started trying to learn more academic programming by taking a Coursera MOOC that I only realized was entirely based on functional programming after being a self-taught ad-hoc user developer. It was fascinating and intellectually stimulating but also challenging beyond my free time while at the time lacking more of the CS fundamentals. It looks like it was wisely broken up into multiple 3 week sections but I suspect this is what the course evolved into - https://www.coursera.org/learn/programming-languages
Of course when I took in person university courses we learned Java as the fundamental programming language.
I followed the instructions to disable the recovery partition as I don't think I had allocated any space to it and upon reboot I was able to upgrade to Windows 11 and hopefully I didn't create any further problems down the road. I really didn't want to mess around with resizing my system partition as the instructions alluded to as the next step. I guess YMMV but I find it amusing that this random bug I ran into is on the front page or Hacker news.
They built up their user base many years ago by providing some level of resources and coordination of desktop usability built on top of Debian's unstable release. Thus becoming something of the default target for application developers who wanted to support Linux. Debian never had the polish that Ubuntu did with its catchy animal based nicknames and 6 month release cycle. They built everything on top of the Debian community and just provided a little bit more testing and marketing. Ubuntu released it's first release almost 20 years ago with Warty Warthog. I switched to Ubuntu around that time as my primary machine although I switched to POP! os by System76 which is a desktop distribution built on top of Ubuntu that focuses on the desktop when System76 engineers decided Ubuntu was straying from supporting desktop Linux.
Also something like this happens in the Greg Egan book Zendegi. It starts off with a anecdote about someone ripping their music files and the issues that arise from the copy (or something along those lines). But definitely an interesting read and one that tackles the possible issues that could arise in a different way than other novels.
"The Addiction to Acceleration The fourth uncomfortable truth is how recursive improvement becomes compulsive. Kenji can’t stop because each day of not improving his improvement feels like stagnation. When you’re accelerating, constant velocity feels like moving backward.
This addiction manifests as: • Inability to accept plateau phases • Anxiety when not optimizing optimization • Devaluing of steady-state excellence • Compulsion to add meta-levels • Fear of falling behind yourself Recursive improvement can become its own trap."
I find that this criticism is far less applicable to say individuals but perhaps it could be levied against the way companies are currently treating AI. Which of course is where this comes from.