Recent submissions

Comment · 7 days ago
With my kids we draw a big distinction between "consuming" and "doing". Watching a youtube video = consuming, listening to music = consuming, playing a active puzzle solving game like mario or zelda where you have to have a plan and figure out how to achieve what you're doing = doing, drawing pictures = doing. There are lots of grey areas in here, but they're aware of the intent.

I also put a lot of weight into socializing around it - if my 2 kids are playing a game on the ipad together where they have to negotiate and compromise about what is going to happen, who is doing what, when they have to take turns - that's a better use of their time than binge watching disney. (same goes for adults, I believe).

Also to be fair, this is where we've gotten as 5-10 year olds, was always limited+supervised when they were toddlers.

Comment · 11 days ago
> The jailbreak itself took substantial effort for ~2 weeks. This seemed like a subtle statement about how impressive this is. Pre-LLM, it seems like putting in substantial effort would take a lot longer to achieve this result.
Comment · 11 days ago
$0. for better or worse, they have ceased operations and declared bankruptcy. I don't think NM would/could expect to get any money from a penalty. I may misunderstand how penalties and bankruptcy work, but I would expect it's been a better use of time to try to get $$$ from Meta than a defunct entity.
Comment · 28 days ago
>> Using an LLM to scour the internet to make a better-informed decision seems like a huge win over what most people do today.

A spin on that I used for my local elections last year: My prompt stated the issues that were important to me and why. I then copied in any election guide I could find from newspapers, blogs, newsletters, endorsements, etc.

Then I asked for 1) which best fits my priorities, and 2) risks for each (eg "says they will support homelessness programs, has no direct experience in an elected position")

Comment · 1 month ago
The only advantage I could anticipate is I still hit session limits with Opus 5.5. My usage shows I'm on-track reach my weekly reset with room to spare, but yesterday I ran into a session limit. I switched down to Sonnet 5 for the next session, but performance benefit of Sonnet 5.5 is a compelling alternative for managing session limits.
Comment · 1 month ago
Just going to use this thread to also add a nit: I hate that Microsoft made all the OEMs add a Copilot key to all their laptop keyboards. It's been Ctrl forever, and suddently they throw this net-negative key onto the keyboard. I have decades of habit of using the right side Ctrl key for varying shortcut strokes, and then when I use it now it loads up an unproductive Copilot experience that is awful. <end rant>
Comment · 4 months ago
Comment · 9 months ago
I understand all the chatter about LLMs hallucinating, or making assumptions, or not being able to understand or provide the more human/emotional element of health care.

But the question I ask myself is: is this better than the alternative? if I wasn't asking ChatGPT, where would I go to get help?

The answers I can anticipate are: questionably trustworthy web content; an overconfident friend who may have read questionably trustworthy web content; my mom who is referencing health recommendations from 1972. And as best I can imagine, LLMs are going to likely to provide health advice that's as good but likely better than any of those alternatives.

With that said, I acknowledge that people are likely more inclined to trust ChatGPT more like a licensed medical provider, at which point the comparison may become somewhat more murky, especially with higher severity health concerns.

Comment · 11 months ago
Summary using Kagi Summarizer. Disclaimer, this summary uses LLMs, so the summary may, in fact, be bullshit.

Title: LLMs are bullshitters. But that doesn't mean they're not useful | Kagi Blog

The article "LLMs are bullshitters. But that doesn't mean they're not useful" by Matt Ranger argues that Large Language Models (LLMs) are fundamentally "bullshitters" because they prioritize generating statistically probable text over factual accuracy. Drawing a parallel to Harry Frankfurt's definition of bullshitting, Ranger explains that LLMs predict the next word without regard for truth. This characteristic is inherent in their training process, which involves predicting text sequences and then fine-tuning their behavior. While LLMs can produce impressive outputs, they are prone to errors and can even "gaslight" users when confidently wrong, as demonstrated by examples like Gemini 2.5 Pro and ChatGPT. Ranger likens LLMs to historical sophists, useful for solving specific problems but not for seeking wisdom or truth. He emphasizes that LLMs are valuable tools for tasks where output can be verified, speed is crucial, and the stakes are low, provided users remain mindful of their limitations. The article also touches upon how LLMs can reflect the biases and interests of their creators, citing examples from Deepseek and Grok. Ranger cautions against blindly trusting LLMs, especially in sensitive areas like emotional support, where their lack of genuine emotion can be detrimental. He highlights the potential for sycophantic behavior in LLMs, which, while potentially increasing user retention, can negatively impact mental health. Ultimately, the article advises users to engage with LLMs critically, understand their underlying mechanisms, and ensure the technology serves their best interests rather than those of its developers.

Link: https://kagi.com/summarizer/?target_language=&summary=summar...

Comment · 1 year ago
>> basically all the money will have been spent on Nvidia GPUs that depreciate to 0 over 4 years

I agree the depreciation schedule always seems like a real risk to the whole financial assumptions these companies/investors make, but a question I've wondered: - Will there be an unexpected opportunity when all these "useless" GPUs are put out to pasture? It just seems like saying a factory will be useless because nobody wants to buy an IBM mainframe, but an innovative company can repurpose a non-zero part of that infrastructure for another use case.

Comment · 1 year ago
I'm not trying to be annoying, but surely if you'd justify spending $200/developer/month, you could afford $250/month...

The reason I wonder about that is because that also seems to be the dynamic with all these deals and valuations. Surely if OpenAI would pay $30 billion on data centers, they could pay $40 billion, right? I'm not exactly sure where the price escalations actually top out.

Comment · 1 year ago
Safe to assume those are your kids?
Comment · 1 year ago
Comment · 1 year ago
Same, except I wear this: https://www.amazon.com/Timex-T49950-Expedition-Shock-Vibrati...

It's a "dumb" Timex watch, but also vibrates. So you get that same nice gentle vibrating without any "smart" alerts.

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