Yeah, the zwave/zigbee options tend to be more barebones on the local display, so I very carfeully looked at the wifi options to find the best of both worlds.
Of course my opengarage is wifi based too, but that’s fine.
Nothing against wifi, just it’s so difficult to tell a ‘local wifi’ from a ‘cloud wifi’ device.
Yeah, I got some z wave thermostats for home.
I got an Emersonl “homekit” thermostat for my in-law and managed to get it on wifi without “cloud”. Unfortunately you have to be careful because the follow on model requires their cloud service for online control.
It’s a real shame that most every house is well equipped to do standalone hosting for remote access, but most of the investment has gone toward cloud connected to force the recurring revenue opportunity.
Yeah, I read that. New leadership felt that the eternal sales stuff was bad and changed to “everyday low prices” sort of thing thinking the customers would appreciate the transparency. Nope, the fake “on sale” works.
It’s all over the place in sales across every industry. I think it is dumb but I am surprised someone actually got a lawsuit against it.
Note that this in theory speaks to performance of a non volatile memory. It does not speak to cost.
We already have a faster than NAND non volatile storage in phase change memory . It failed due to expense.
If this thing is significantly more expensive even than RAM, then it may fail even if it is everything it says it is. If it is at least as cheap as ram, it’ll be huge since it is faster than RAM and non volatile.
Swap is indicated by cost, not by non volatile characteristics.
How silly, it’s obvious that would be an odormeter. An odometer is about something else entirely.
I had been planning to, but being lazy about trying to enable my IDE setup but was giving it the benefit of the doubt. Your feedback resonates with how much I end up fighting auto-complete/auto-correct in normal language and seeing it potentially ruin current code completion (which sometimes I have to fight, but on balance it helps more than it annoys). I suppose I’ll still give it a shot, but with even more skepticism. I suppose maybe it can at least provide an OK draft of API documentation… Maybe sometimes…
On the ‘vibe coding’, on the cases I’ve seen detailed, it seems they do something that, to them, is a magical outcome from technologies that intimidated them. However, it’s generally pretty entry level stuff for those familiar with the tools of the trade, things you can find already done dozens of time on github almost verbatim, with very light bespoke customization. Of course there is a market for this, think of all the ‘no code’/‘low code’ things striving to make approachable very basic apps that just end up worse than learning to code. As a project manager struggles to make a dashboard out of that sort of sensibility, a dashboard that really has no business being custom but tooling has fostered the concept that everyone has a snowflake dashboard, it’s a pain. But maybe AI can help them generate their dashboard. Of course, to be a human subjected to the workflows those PMs dream up is a nightmare. Bad enough already at my work there are hundreds of custom issue fields, a dozen issue types, and 50 issue states with maddening project to project unique workflows to connect the meaning of all this, don’t like AI emboldening people to customize further.
The thing about ‘vibe coding’ is when they get stuck and they get confused/frustrated about why the LLM stopped getting them what they want. One story was someone vibe coding up a racing game. He likely marveled as his vision materialized. From typing prose without understanding how to code he got some sort of 3D game with cars and tracks and controls. This struck him as incredibly difficult otherwise, but reachable through ‘vibe coding’. Then he wanted to add tire marks when the player did something, maybe on a hard turn) and it utterly couldn’t do it. After all the super hard stuff, why could the LLM not do this conceptually much simpler thing? Ultimately spitting out that the person needed to develop the logic himself (claiming it was refraining to do it because it would be better for him to learn, but I’m wagering that’s the generated text after repeated attempts to generate code that the LLM just could not do).
else: # not my list, it is ourlist
In context, one can consider it a bool.
Besides, I see c code all the time that treats pointers as bool for the purposes of an if statement. !pointer is very common and no one thinks that means pointer it’s exclusively a Boolean concept.
I occasionally check what various code generators will do if I don’t immediately know the answer is almost always wrong, but recently it might have been correct, but surprisingly convoluted. It had it broken down into about 6 functions iterating through many steps to walk it through various intermediate forms. It seemed odd to me that such a likely operation was quite so involved, so I did a quick Internet search, ignored the AI generated result and saw the core language built-in designed to handle my use case directly. There was one detail that was not clear in the documentation, so I went back to the LLM to ask that question and it gave the exact wrong answer.
I am willing to buy that with IDE integration or can probably have much richer function completion for small easy stuff I know to do and save some time, but I just haven’t gotten used to the idea of asking for help on things I already know how to do.
That’s been one of the things that has really stumped a team that wanted to go all in on some AI offering. They go to customer evaluations and really there’s just nothing they can do about the problems reported. They can try to train and hope for the best, but that likely won’t work and could also make other things worse.
That was the job of reader’s digest.
In practice, I don’t see how I would even know someone is a pedophile if they didn’t act on their inclinations. I guess they could publicly declare it but that seems unwise.
I would be concerned if the Internet vigilantes ran with unsubstantiated rumors, like if say Elon musk just called someone a pedophile or of the blue.
For the scope of WebEx and Zoom, it’s… fine… mostly. I mean I hate that I can’t really full screen a remote screen share, so it could be better, but broadly speaking, video, audio, and screen sharing is fine. Not coincidentally, this is pretty much the only standalone stuff Teams bothered to uniquely implement, most everything else is built upon sharepoint…
It starts getting annoying for chat platform. You want to scroll back, it’s going to be painfully slow. You participate in cross-company conversations, oh boy you get to deal with the worst implementation of instancing to keep your activity segregated I have seen. Broadly speaking it just scales poorly at managing the sorts of conversations you have at a larger company. If your conversations are largely “forget it after a few hours”, you may be fine.
Then you get into what these platforms have been doing for ages, Lotus Notes and Sharepoint suggesting companies build workflows on top of their platform. Now the real pain and suffering begins.
Hell, put any two people on a “knowledge” task and even if both were capable, there’s going to be a person that pretty much does the work and another that largely just sits there. Unless the task has a clear delineation, but management almost never assigns a two person team a task that’s actually delineated enough for the two person team to competently work.
If the people earnestly try, they’ll just be slower as they step on each other, stall on coordination, and so on.
It really can’t. It can take your original prompt and fluff it out to obnoxiously long text. It can take your visual concept and sometimes render roughly the concept you describe (unless you hit an odd gap in the training data, there’s a video of image generation being incapable of generating a full wine glass of wine).
A pattern I’ve seen is some quick joke that might have been funny as a quick comment, but the poster asks an LLM to make a “skit” of it and posts a long text that just utterly wears out the concept. The LLM is mixing text content in a way consistent with the prompt, but it’s not mixing in any creatively constructed comment, only able to drag bits represented in the training data.
Now for image generation, this can be fine. The picture can be nice enough in a way analogous to meme text on well known pictures is adequate. Your concept can only ever generate a picture, and a picture doesn’t waste the readers time like a wall of text does. However if you come at an LLM with specific artistic intent, then it will frustrate as it won’t do precisely what you want, and it’s easier to just do it yourself at some point
They went to great lengths to explain that and why a trailer load may transiently exceed it and used a 20 year old wrecked truck as a reference.
The other concern they mentioned was aluminum characteristics over time. Brand new strength will not equal strength over time. So 10k pounds is the trucks strength at its absolute best, but it will degrade over time. Also the mix of metals may cause a galvanic reaction to degrade it over time. No one else in the industry will use aluminum for the frame, for good reason
They even admit it fared better than they thought, but it’s another example of Tesla ignoring engineering principles and the predicted consequences being demonstrated.
I assume there’s a large amount of people who do nothing but write pretty boilerplate projects that have already been done a thousand times, maybe with some very milquetoast variations like branding or styling. Like a web form doing one to one manipulations of some database from user input.
And/or a large number of people who think they need to be seen as “with it” and claim success because they see everyone else claim success. This is super common with any hype phase, where there’s a desperate need for people to claim affinity with the “hot thing”.
And because a friend insisted that it writes code just fine.
It’s so weird, I feel like I’m being gaslit from all over the place. People talking about “vibe coding” to generate thousands of lines of code without ever having to actually read any of it and swearing it can work fine.
I’ve repeatedly given LLMs a shot and always the experience is very similar. If I don’t know how to do it, neither does it, but it will spit out code confidently, hallucinating function names or REST urls as needed to fit the narrative that would have been convenient. If I can’t spot the logic issue with some code that isn’t acting correct, it will also fail to generate useful text that would describe the problem.
If the query is within reach of copy/paste of the top stack overflow answer, then it can generate the code. The nature of LLM integration with IDEs makes the workflow easier to pull in than stack overflow answers, but you need to be vigilant as it’s impossible to tell a viable result from junk, as both are presented with equal confidence and certainty. It can also do a better job of spotting issues within things like key values that are strings with typo than traditional code analysis, and by extension errors in less structured languages like Javascript and Python (where ‘everything is a hash/dictionary’ design prevails).
So far I can’t say I’ve seen improvements, I see how it could be seen as valuable, but the resulting babysitting carries a cost that has been more annoying than the theoretical time saves. Maybe for more boilerplate tasks, but generally speaking those are highly wrapped by libraries already, and when I have to create significant volume of code, it’s because there’s no library and if there’s no library, it’s niche enough that the LLMs can’t generate either.
I think the most credible time save was a report of refreshing an old codebase that used a lot of deprecated function and changing most of the calls to the new method without explicit human intervention. Better than tools like ‘2to3’ for python, but still not magical either.
Yeah, it does some tricks, some of them even useful, but the investment is not for the demonstrated capability or realistic extrapolation of that, it is for the sort of product like OpenAI is promising equivalent to a full time research assistant for 20k a month. Which is way more expensive than an actual research assistant, but that’s not stopping them from making the pitch.
That video was pretty janky, I thought for sure it wouldn’t be direct from Kawasaki but yeah…
Someone fed the concept into an AI video generator, proclaimed a 2050 target date, so absolutely far enough away this will be forgotten by the time everyone realizes that this was zero actual effort towards anything.
So now AI lets marketing folks make their completely going nowhere idea into a substantial concept video casually…