Onno (VK6FLAB)

Anything and everything Amateur Radio and beyond. Heavily into Open Source and SDR, working on a multi band monitor and transmitter.

#geek #nerd #hamradio VK6FLAB #podcaster #australia #ITProfessional #voiceover #opentowork

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  • 35 Comments
Joined 8 months ago
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Cake day: March 4th, 2024

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  • In my understanding, deleting (and moderation) are pretty much unsolved mysteries on this platform.

    For example, last I heard, an administrator has to drop into a command line to delete media from removed posts, otherwise they’d still be accessible if the URL was known. (Think illegal material.)

    Filtering is similarly done at the client end, so that’s fun.

    Note that I’m not associated with the source code, only as a user and am repeating things I’ve observed, read, or have been told. YMMV.




  • I think that this reflects “real life”.

    You can go to a pub, spout some nonsense and cross the road and spout it again. Each time you are likely to have a different audience, but there’s no guarantee that someone else crossed the road with you.

    In the fediverse the same is true. Each community is distinct, even if they’re called the same thing and have shared participation.

    You can choose to participate in whichever one you want to, or all of them. There’s no karma associated with voting, it’s about visibility in that community.

    If you think it warrants a vote or comment, then do. If you don’t, then don’t.

    There’s no right answer here. Do what you think will improve the community you are part of.





  • The underlying issue with an LLM is that there is no “learning”. The model itself doesn’t dynamically change whilst it’s being used.

    This article sets out a process that gives the ability to alter the model, by “dialling up” (or down) concepts. In other words, it’s changing the balance of the weight of concepts across the whole model.

    Altering one concept is hardly “learning”, especially since it’s being done externally by researchers, but it’s a start.

    A much larger problem is that the energy consumption is several orders of magnitude larger than that of our brain. I’m not convinced that we have enough energy to make a standalone “AI”.

    What machine learning actually gave us is the ability to automatically improve a digital model of things, like weather prediction, something that took hours on a supercomputer to give you a week of forecast, now can be achieved on a laptop in minutes with a much longer range and accuracy. Machine learning made that possible.

    An LLM is attempting the same thing with human language. It’s tantalising, but ultimately I think the idea applied to language to create “AI” is doomed.



  • You can change how long a phone rings for. Talk to her telco for both landline and mobile.

    In my experience, if someone doesn’t want to answer the phone, strapping it to their arm is unlikely to make any difference and in my experience they’re more likely than not to leave it on the charger.

    Long battery life and tiny battery are on opposite ends of physics. Pick your poison.

    Health monitoring is unlikely to be transmitted to emergency services, except iOS fall detection.

    iOS and Android are both tracking as much as they can get away with.

    Remote management is likely only with devices used in corporate settings.