Does the author not have the experience of working in a legacy codebase that nobody really "understood"? Something sufficiently complex where even the senior SW devs needed to scope out project work and research the codebase for dependencies or potential issues?

I fail to remember a time at LARGE_CORP where even the most experienced developers were able to scope out or design a feature without studying the existing documentation, timing diagrams, etc....

https://www.gurdjieff.org/lannes2.htm

AI has simply been a tool to accelerate that and I've genuinely never been happier about the value I provide to society. I find it hard to take the "respect of the craft" argument at face value - even if I enjoy knitting by hand, I can still support the existence of mechanized looms and machinery that provide affordable clothing in the millions.

My late grandmother knit 1-2 pairs of socks per day and donated them to the homeless center - absolutely amazing thing to, and it kept her motivated and sharp up to 104 years of age.

However, if her ultimate goal is providing socks to the homeless, would it not be better for her to work a library job for $15/hour, then take the $100+ a day and purchase then donate 30 pairs of socks? Even if the socks are a lower quality and wear out twice as fast, she could help 15x the people thanks to the mechanization and efficiency provided by the devision of labour.

I see this divide more and more and truthfully I wish I could understand both sides. I am encountering trouble in the startup I'm working on for exactly this reason: I feel laser focused on the attempt to create value for others, while my mech eng cofounder has this "respect of the craft". From from my perspective this has only caused delays in generating revenue.

Any help in understanding those who "love to make" (as opposed to those who "love to deliver") would be extremely useful.

There's a bit of a ticking time bomb there, something that a Taalas-like architecture can clearly resolve.

However, without an agent running its own experiments on a cloud GPU, would I realistically have invested my limited work hours and tried evaluating 10 different models, each with 10 different tuned parameters, to solve my specific use case?

Or would I have tried 1-2 models and spent my time trying to optimize those models?

I think there is some merit to the spray and pray approach when one is in the exploration phase of the solution space.

Also, on more than one occasion now, I have had fable halve the inference latency of a model simply because the original implementation from an academic included unnecessary GPU-to-CPU-to-GPU transfers or similarly inefficient operations. Those optimizations came at essentially 0 time cost to me and I can verify that the outputs are byte-identical. Pretty sweet!

For those hearing about PBM and feeling skeptical, I was as well, especially with all the claims of therapeutic "red light therapy" circling the internet. But do a quick search in pubmed and you will see that at this point there have been thousands of studies exploring the use of PBM (808-1064nm)

I quickly found these two which relate to sleep quality:

https://pbmfoundation.org/brain-photobiomodulation-improves-...

https://pubmed.ncbi.nlm.nih.gov/41125953/

If anyone is interested in PBM I gave an overview presentation a few months ago to a lab studying noninvasive neurostimulation, I would be happy to send it your way.

https://en.wikipedia.org/wiki/Mastery_learning

So that is one extremely robust way to understand neurological conditions like ADHD or Parkinson’s

I am currently re-reading this book and am amazed by the apparent accuracy of his analysis, which is that the mediums in which we communicate or express information (print vs. TV vs. TikTok) have a massively understated role in the quality and type of communication we participate in. That is, as print lends itself naturally to logical argument and less to emotional knee-jerk reactions, the type of conversations taken place in long-form print will by nature be more logical and intellectual. Compare this to TV or short term videos, which captivate us using more primal forms of distraction (bright lights with moving images, fast talking, "Gotcha" type rhetoric, cool dances, sexual/romantic behaviour, or background subway surfers), and it is obvious that the nature of what we see is inherently less based around logic and reason.

And as a consequence, if we are what we consume, it is only natural to surmise that the quality of the mind follows the quality (and qualia) of our media.

However if that prior is untrue for any reason whatsoever, the normalization would penalize higher performing cohorts (if it were a math course, maybe an engineering student dominated section vs an arts dominated cohort).

So I guess.. it depends

Comments URL: https://news.ycombinator.com/item?id=44866184

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David Burns released his CBT guide "Feeling Good" in 1980, which he labels as a new genre of "Bibliotherapy". His book is shown to have clinically significant effects on depression remission. Why can an LLM not provide a more focused and interactive version of this very book?

Now, I agree with you and the article's argument that one cannot simply throw a gpt-4o at a patient and expect results. The LLM must be trained to both be empathetic and push back against the user when necessary, forcing the user to build nuance in their mental narrative and face their cognitive distortions.

I'm currently considering the online masters but still was curious to your experience if you don't mind. Reply here or an email would be appreciated

During typical usage the flash controller is constantly journaling LBA to physical addresses in the background, so that the entire logical to physical table isn’t lost when the drive loses power. With a larger capacitor you could potentially remove this background process and instead flush the entire logical to physical table when the drive registers power loss. But as this area makes up ~2% of the total NAND, that’s at absolute best a 2% performance benefit we are potentially missing out on.