rsanek

Depends alot on the values inputted into such a calculator. See [0] for a solid discussion of what his future could have been (search for "That brings up the important question of how much time Swartz was actually facing"). TLDR, it's certainly not the case that only probation was guaranteed or even particularly likely.

[0] https://volokh.com/2013/01/16/the-criminal-charges-against-a...

There are of course other trade-offs with these modes of transport, but they are usually just as fast (and sometimes faster) at single-digit-mile distances. My hope is that as we improve biking infrastructure, biking will become an increasingly-viable primary mode of transit.

[0] https://nhts.ornl.gov/assets/2022/pub/2022_NHTS_Summary_Trav...

https://fred.stlouisfed.org/series/PRS85006173

https://fred.stlouisfed.org/series/W270RE1A156NBEA

  Month       Tonnes
  July 2006   6,606
  July 2011   6,197
  July 2016   5,807
  July 2021   5,742
  July 2026   5,755

  Month       Book value ($M)  Tonnes
  July 2006   8,967            6,606
  July 2011   8,412            6,197
  July 2016   7,883            5,807
  July 2021   7,794            5,742
  July 2026   7,812            5,755

I haven't seen any evidence that Anthropic is delaying its IPO; they're slated to unveil the public IPO prospectus in a week and start trading sometime in October.

https://ourworldindata.org/grapher/energy-mix?tab=stacked-di...

This hasn't been my experience. Especially when it comes to enterprise software, eventually everyone that was involved with the design of and iteration on a system will have left the company. Yet, often those systems need to continue running because they support "legacy" customers (who sometimes actually tend to be more economically valuable to the company than the customers on the shiny new system).

Before AI, in these cases you just had to dive into the code and start trying to understand it yourself from zero. Usually, you would come to a very limited understanding of the system, just enough to fix the latest bug or keep it running until it hit another issue.

I'm not sure why it's necessarily worse to rely on an agent to do this for you these days.

Seems weird to me to not take advantage of the great deal the frontier labs are currently giving for subscription pricing when there's such an easy fallback in the worst case.

https://ourworldindata.org/grapher/energy-mix?tab=stacked-di...

https://ourworldindata.org/grapher/energy-mix?stackMode=rela...

There's plenty of other issues with the study, one of the primary ones being that they chose a relatively 'dumb' AI (Step 3.5 Flash), but also, specifically hand-selected wrong answers. People that are used to using competent AIs that are mostly correct would be operating off of experience that suggests they should trust AI. In this case, by design, they shouldn't have, but it's hard to fault the user for that.

The marketing around this 'study' has been impressive. They published the HBR article [0] and other promotional material [1] over 5 months ago, but as far as I can tell, the actual study itself (even a preprint version) still isn't available anywhere. Hard to evaluate how believable the results are without being able to actually see the details of how the study was done. For now, all we know is that it's based on ~40 qualitative interviews from a single company in a single industry.

[0] https://hbr.org/2026/02/ai-doesnt-reduce-work-it-intensifies...

[1] https://newsroom.haas.berkeley.edu/ai-promised-to-free-up-wo...

> customers will flock to the cheapest inference

https://artificialanalysis.ai/models/kimi-k3#price-cost

I'm not really sure the problem is AI here. I've had plenty of bad experiences with human customer service agents that were poorly-trained and hard to understand. An AI can be implemented in a shitty way, where it's at best a regurgitation of the help docs. Or, it can be implemented well, where it can autonomously take action without any human involved.

I have had a couple of experiences with the latter. Once you do, you realize that you actually don't need to talk to a person per se -- you just want your problem solved efficiently. That can be done with a human or an AI; it isn't really the most significant bit to be filtering on IMO.

> I won’t say LLMs are useless for this. But from my trials, I get about 1 card that’s useful to me out of 10, and even that 1 card still needs rewriting.

I don't know the specifics of how the author tried to do so, but from what I've seen the majority of attempts are, let me drop a chapter of a textbook and say "make flashcards." If that is what we are talking about, then yes, LLMs are useless.

In my mind, though, this is sort of like looking at the very first GitHub Copilot LLM autocomplete from a couple of years ago and concluding, yeah it's nice for one-liners, but it cannot write an app.

If you create a framework around your card-creation AI so that it can use tools, and verify its work to ensure common card-creation pitfalls don't happen, you can get pretty high-quality cards. In my experience, you go from a 10~20% hit rate to a ~90% hit rate, which in my mind is good enough. I got to ~75% quality just from a two extra LLM calls that would assess a potential card against a standard set of rules (adapted from [0]). There are huge Pareto gains to be had here.

I've generated thousdands of cards over the last few months this way. I let the AI add it directly to Anki via AnkiConnect. Then, if when I go to review I find a card that my AI created and I don't like it, I just delete it.

Removing the limitation of card creation is really quite compelling, and I think the area is still highly under-invested in. Would be cool to see a generic framework evolve that one could use. For now, I've been using a personal fork of clanki [1].

[0] https://supermemo.guru/wiki/20_rules_of_knowledge_formulatio...

[1] https://github.com/jasperket/clanki