9
points
LeftHandPath
Joined 1,017 karma
The name is based on a Gilfoyle quote from Silicon Valley.
- LeftHandPath parentMoved to NYC. Have a good team at my new job. Satisfied with my income. Have enough free time. Made a lot of good friends really fast, and now I see a rotating cast of them 3-7 days a week. Happy with my apartment. Have an east facing window so I don't have to set an alarm to wake up in the morning, I wake with the sunrise. Getting plenty of exercise and walking a 8-12k steps a day.
- For the first time in a long time, I can look at a title like this and not feel like it necessarily relates to my current situation. The past few months have been the happiest and most satisfied with life I have been in many years. Grateful.
- This tracks very well with my experience... Too much time in a cubicle away from windows -- say, just for a week -- and my vision gets noticeably worse. A few days away from the screen, like a week on the lake, and it gets much sharper.
I'd be inclined to agree with him that it can be prevented and maybe even reversed.
- If the books are set in 55 BC, how would the characters know it was 55 BC?
- Agreed.
- Wow, never saw 2D programming languages before. That's neat!
Reminds me of a lighthearted meme video about esolang enthusiasts: https://www.youtube.com/watch?v=ieqsL5NkS6I
- Hiking is my go-to when I need to figure something out. I think walking also helps chew on complex problems, more so than more intense exercise like strength training and running (though that's also beneficial).
- "Networking" makes it sound more active than it really is. It's just who they are meeting over those four years. The friends they make, the professors they meet and perhaps work with, the guest lecturers they're able to talk to, the parents of friends that they meet on the holidays who make calls to help land internships. The companies that show up to the career fair.
Over four years, there's a big difference in your future prospects if you were meeting people with ties to Google, Berkshire, Goldman Sachs, Amazon, Meta, et al., vs meeting people from Garmin (<$100k/year for fresh CS grads, when I graduated) and small local midwestern companies. Even if you don't get direct referrals to those big-name places, you're talking to people who know what a resume that can get in there looks like, rather than having to blindly follow whatever advice you can find online.
Harvard has a page about it: https://careerservices.fas.harvard.edu/blog/2025/04/04/how-t...
- This is a flawed idea. It presumes, in the abstract and conclusion, that changing admissions preferences to artificially increase diversity would result in a more diverse group of leaders in the country, because elite colleges tend to produce leaders and high-income earners.
Fundamentally altering the admissions process will also fundamentally alter the institution and what graduates of it look like. If we alter selection for Navy SEALs -- because SEAL school graduates are known to be athletic, motivated, and team-oriented, and we want more people like that -- I have a feeling that we'd simply destroy the image of the SEALs rather than increasing the number of people with those personality traits.
Back to my point: It always starts with the notion of focusing on merit, but devolves into checkboxes and quotas and arbitrary preferences, because those are easier to measure than actual merit. The end result is an institution that still fails to reward merit, and no longer creates the generous benefits it did at the start of the exercise. It also creates a group of bitter, resentful people who feel wronged by the changes that will significantly reduce any prior public goodwill towards the institution.
To be even more explicit, a lot of the reason that these colleges are so strongly correlated with high incomes and high achievement is that they allow attendees to network with people whose parents and family members have already reached the upper echelon. Refactoring admissions to replace that will shred that benefit altogether. Colleges are not a totally isolated system; the value of a degree does not derive solely from the lectures the individual receives there, nor perhaps from the name-prestige of the university, but largely from the networking people are able to do there and the companies that visit and recruit from the school because of that network.
- Great list. I remember being called in to look at software my company was thinking of buying, once... Only our finance/accounting people had looked at it so far. The thing barely worked. The desktop window kept flashing. Special characters were accepted and then broke the output. Extreme lack of features needed for the supposed goal.
I was able to find us an open-source / self-hosted solution that worked far better (bookstack). But I was amazed at how far the company selling the software got with us -- seems like most never get pushback, they just make sales by labeling themselves as fit-for-purpose regardless of whether or not they generally work.
- The posts I see most often on LinkedIn are ones that try to capture a trope of "flipping expectations" that people associate with great business people. Silly, inane conclusions are made about everyday events so that people who are startlingly mediocre can cling to them as a differentiating factor.
Basic politeness is sold as the secret hack to become the next Steve Jobs. Boasts of frugality are made and used to explain why the poster will inevitably become ultra-rich (no avocado toast, no lattes!). HR people explaining the mostly arbitrary reasons they passed over anonymous candidates, seeking to be seen as oracles of career success. Tech people saying "Ten things that separate junior developers from seniors" and then citing meaningless things like the modulo and ternary operators, or the poster's personal favorite whitespace style.
Realistic advice is hard to find, probably because it's so general in its best form that material would run out quickly. I think of Rob Dahm's old video where he suggested, Lamborghini in the background, to "Find something that you're so good at it feels like you're cheating." Or a quote from Kurt Vonnegut's player piano, "Nobody's so damn well educated that you can't learn ninety per cent of what he knows in six weeks. The other ten per cent is decoration... Almost nobody's competent, Paul. It's enough to make you cry to see how bad most people are at their jobs. If you can do a half-assed job of anything, you're a one-eyed man in a kingdom of the blind."
- > Pricing is good, but profits may continue to be elusive; still no clear technical moat.
It's quite ironic that the technology that is displacing so many people from so many industries, has yet to make a profit. I fear the "creative" part of their destruction will take longer to achieve than they advertise.
- Really, really love the interface and the way you have the calendar, FIA docs, etc available too. Shout out for the delay feature -- I was able to sync it perfectly with my TV stream!
The big problem I had was desync. Every 5-10 minutes, random parts of the interface would stop updating. Sometimes the whole page would blank out. I was able to fix it once by loading one of the other panels (like the FIA docs) and then going back to the live data. Eventually it broke and I wasn't able to fix it (around lap 18 or so). Thinking maybe sockets aren't reconnecting when their connection drops from the client side? And it seemed like there was some issue that prevented it from fixing with a simple refresh.
- What a lovely essay. Reminds me of the way I loved the liberal arts growing up. I missed having classes like that in college (AP'd and ACT'd my way out of most requirements).
English teachers seem especially prone to that friendly and sporting demeanor the author has. Professors from the engineering schools are far more prescriptive, probably due to the nature of the material.
- This is really slick. I remember when I used to have the F1TV version pulled up on my laptop during sessions - eventually I decided it wasn't that helpful. The radio and race control panels on this are awesome.
Definitely giving it a test run during the race tomorrow!
- Or taking up any white collar job! There's a reason I spend a lot of my free time swimming and hiking.
- The best way to make a happy, healthy person into an unhappy, unhealthy person is to keep them lonely and keep them still. It should come as no surprise that the inverse also tends to hold true.
On a tangent, I think that's part of why volunteering can be so rewarding.
- The discussion of how the medium affects what you build reminds me a bit of The Beginner's Guide (https://en.wikipedia.org/wiki/The_Beginner's_Guide). If I recall, it had a bit of dialogue about how the game-creation tool made it easy to build straight, square corridors... And so the map had a lot of straight, squared corridors.
I don't really remember much of that game, but for some reason that part stuck with me. It's a good bite-sized insight into creativity (to be aware that constraints, while limiting, will also guide you).
- Describes the growing use of surveillance and AI for things like predicting crime, detecting a migrant's region of origin using their dialect, etc. The video in the article has a lot of detail and discusses the potential benefits and drawbacks.
Seems somewhat relevant to the ChatControl discussion on here the other day.
- I really miss some of the companies from that era... Red Storm Entertainment, Tom Clancy's vision, comes to mind. The early Ghost Recon and Rainbow Six games had a dedication to immersion that their modern counterparts totally lack.
Ghost Recon (2001) runs perfectly through proton on my linux desktop. I still fire it up from time to time.
- Jeez, that arxiv paper invalidates my assumption that it can't model the game. Great read. Thank you for sharing.
Insane that the model actually does seem to internalize a representation of the state of the board -- rather than just hitting training data with similar move sequences.
...Makes me wish I could get back into a research lab. Been a while since I've stuck to reading a whole paper out of legitimate interest.
(Edit) At the same time, it's still worth noting the accuracy errors and the potential for illegal moves. That's still enough to prevent LLMs from being applied to problem domains with severe consequences, like banking, security, medicine, law, etc.
- Yes, really!
Smaller models may hallucinate less: https://www.intel.com/content/www/us/en/developer/articles/t...
The RAG technique uses a smaller model and an external knowledge base that's queried based on the prompt. The technique allows small models to outperform far larger ones in terms of hallucinations, at the cost of performance. That is, to eliminate hallucinations, we should alter how the model works, not increase its scale: https://highlearningrate.substack.com/p/solving-hallucinatio....
Pruned models, with fewer parameters, generally have a lower hallucination risk: https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00695.... "Our analysis suggests that pruned models tend to generate summaries that have a greater lexical overlap with the source document, offering a possible explanation for the lower hallucination risk."
At the same time, all of this should be contrasted with the "Bitter Lesson" (https://www.cs.utexas.edu/~eunsol/courses/data/bitter_lesson...). IMO, making a larger LLMs does indeed produce a generally superior LLM. It produces more trained responses to a wider set of inputs. However, it does not change that it's an LLM, so fundamental traits of LLMs - like hallucinations - remain.
- I was worried about that a couple of years ago, when there was a lot of hope that deeper reasoning skills and hallucination avoidance would simply arrive as emergent properties of a large enough model.
More recently, it seems like that's not the case. Larger models sometimes even hallucinate more [0]. I think the entire sector is suffering from a Dunning Kruger effect -- making an LLM is difficult, and they managed to get something incredible working in a much shorter timeframe than anyone really expected back in the early 2010s. But that led to overconfidence and hype, and I think there will be a much longer tail in terms of future improvements than the industry would like to admit.
Even the more advanced reasoning models will struggle to play a valid game of chess, much less win one, despite having plenty of chess games in their training data [1]. I think that, combined with the trouble of hallucinations, hints at where the limitations of the technology really are.
Hopefully LLMs will scare society into planning how to handle mass automation of thinking and logic, before a more powerful technology that can really do it arrives.
[0]: https://techcrunch.com/2025/04/18/openais-new-reasoning-ai-m...
[1]: https://dev.to/maximsaplin/can-llms-play-chess-ive-tested-13...
- There are some things that you still can't do with LLMs. For example, if you tried to learn chess by having the LLM play against you, you'd quickly find that it isn't able to track a series of moves for very long (usually 5-10 turns; the longest I've seen it last was 18) before it starts making illegal choices. It also generally accepts invalid moves from your side, so you'll never be corrected if you're wrong about how to use a certain piece.
Because it can't actually model these complex problems, it really requires awareness from the user regarding what questions should and shouldn't be asked. An LLM can probably tell you how a knight moves, or how to respond to the London System. It probably can't play a full game of chess with you, and will virtually never be able to advise you on the best move given the state of the board. It probably can give you information about big companies that are well-covered in its training data. It probably can't give you good information about most sub-$1b public companies. But, if you ask, it will give a confident answer.
They're a minefield for most people and use cases, because people aren't aware of how wrong they can be, and the errors take effort and knowledge to notice. It's like walking on a glacier and hoping your next step doesn't plunge through the snow and into a deep, hidden crevasse.
- Interesting. I don’t use GPT for code but I have been using it to grade answers to behavioral and system design interview questions, lately. Sometimes it hallucinates, but the gists are usually correct.
I would not use it if it was for something with a strictly correct answer.
- I picked up Player Piano, which had been collecting dust on my shelf, yesterday, and quickly found myself having blitzed through it in its entirety by the evening.
The book has many elements and predictions that feel dated, and a lot more that feel like they might've been made yesterday. Highly relevant to the cultural effects of automation. It puts its finger on the nose of the obvious questions that have somehow gone un-asked in current dialogues -- importantly, as the linked article quotes, "What are people for?"
- 3 points
- I wonder if the sunk cost fallacy - that usually refers to an abstract cost, like time or money - would truly be the same effect as an aversion to retracing a path in 3D space.
Possible, or even likely, but interesting nonetheless. Towards the end of the article, they describe an interesting other direction of their research that's not so directly correlated with sunk cost:
> More recently, we’ve been examining a related form of hesitation. This time, it’s not in switching paths, but in committing to one at all.
> “While it might seem that having enticing options (e.g., a great apartment one could rent, a fun event one could sign up for) would make commitment easier, we’ve found that it’s often the loss of a great option that finally pushes people to choose. People often hold out for something even better, but the disappearance of a pretty good option inspires some pessimism that encourages people to grab onto what is as good as they can get for now.”