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Joined 1 year ago
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Cake day: July 8th, 2023

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  • This is hopeful, and we need more nuclear, but I have very serious questions about the methodology to this survey.

    The prior marks on the line graph indicate not all categories of response are represented, as the don’t add up to 100%. Then there is a sudden change over the last 4 years where the % supporting jumps to the mid 70s and all four periods add up to exactly 100%.

    This, to me, feels like a question change on or around 2021, or a methodology change that’s not clearly labeled, and casts doubt on the integrity of the research, especially given the generally modest level of knowledge about nuclear, which, according to my read of the article and survey details, doesn’t appear to have changed at any point.


  • Just piling on at this point, but we made 2 changes last spring that made summer so much more tolerable in our house.

    1. More insulation. I bought a cheap thermal camera on Amazon and found entire closets and a bathroom with no insulation. Those rooms are a solid 10+ degrees cooler now.
    2. More ventilation. Half my house didn’t have any soffit vents, but had attic vents. Adding soffit vents made that half the house 5 degrees cooler all on its own.

    And we haven’t found ourselves needing it, but a mini split has popped up a lot here already and is a great idea.



  • coolkicks@lemmy.worldtoPrivacy@lemmy.mlGithub vs. Email Aliases
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    4 months ago

    Sure, self-hosting is a great option for very large projects, but a random python library to help with an analytics workflow isn’t going to self-host. Those projects, along with 27,999,990 others have chosen GitHub, often times explicitly to reduce the barrier to contribution.

    Also, all of those examples are built on thousands of other FOSS projects, 99% of which aren’t self-hosting. This is the same as arguing only Amazon is a bookseller and ignoring the thousands of independent book publishers creating the books Amazon is selling.




  • Lots of boring applications that are beneficial in focused use cases.

    Computer vision is great for optical character recognition, think scanning documents to digitize them, depositing checks from your phone, etc. Also some good computer vision use cases for scanning plants to see what they are, facial recognition for labeling the photos in your phone etc…

    Also some decent opportunities in medical research with protein analysis for development of medicine, and (again) computer vision to detect cancerous cells, read X-rays and MRIs.

    Today all the hype is about generative AI with content creation which is enabled with Transformer technology, but it’s basically just version 2 (or maybe more) of Recurrent Neural Networks, or RNNs. Back in 2015 I remember this essay, The Unreasonable Effectiveness of RNNs being just as novel and exciting as ChatGPT.

    We’re still burdened with this comment from the first paragraph, though.

    Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense.

    This will likely be a very difficult chasm to cross, because there is a lot more to human knowledge than thinking of the next letter in a word or the next word in a sentence. We have knowledge domains where, as an individual we may be brilliant, and others where we may be ignorant. Generative AI is trying to become a genius in all areas at once, and finds itself borrowing “knowledge” from Shakespearean literature to answer questions about modern philosophy because the order of the words in the sentences is roughly similar given a noun it used 200 words ago.

    Enter Tiny Language Models. Using the technology from large language models, but hyper focused to write children’s stories appears to have progress with specialization, and could allow generative AI to stay focused and stop sounding incoherent when the details matter.

    This is relatively full circle in my opinion, RNNs were designed to solve one problem well, then they unexpectedly generalized well, and the hunt was on for the premier generalized model. That hunt advanced the technology by enormous amounts, and now that technology is being used in Tiny Models, which is again looking to solve specific use cases extraordinarily well.

    Still very TBD to see what use cases can be identified that add value, but recent advancements to seem ripe to transition gen AI from a novelty to something truly game changing.


  • I thought the millennial aspirations were a bit extreme, but as a millennial I get it. We had the Great Recession, outrageous prices for college, home prices are out of control.

    And I say this as a millennial doing well. We don’t even think about money day to day or paycheck to paycheck, and are saving enough to largely minimize or potentially mitigate our kids needing student loans. But I am still strategically thinking about money and what will happen when the next recession or financial calamity hits, or hyper-inflation wiping us out.

    The cost to live has been trying to outrun our income our entire adult lives, so sure, fuck it, double our income then maybe we have a chance to sleep at night even when it’s going well.


  • The study forbes referenced appears to be essentially “how to design offices for gen z”, presuming they really want to use an office.

    The tips to drive virtual engagement are pretty standard management material at this point.

    Would have liked to see some real evidence to “boomerang” being philosophical, that felt like a cheap misuse of the term to seem more relevant than “what kind of games should be in the break room”