This is something I kept bumping into when building my own tracker (Simple Wallet - https://simplewallet.app).
You're right that "$100 at a supermarket" is useless but I found even knowing "I spent $400 on groceries" wasn't that useful either. I kept asking myself "okay, but on what?"
So I leaned hard into making categories the starting point instead of the endpoint. Groceries breaks down into what I'm actually buying. Turns out I was spending way more on coffee than I realized.
Did you ever consider going deeper into categories, or do you find users just want the high level view? I've been torn on how much detail is actually helpful vs. overwhelming.
This is the same idea I had building Simple Wallet (https://simplewallet.app). I didn’t want a budgeting app telling how and where to spend. I simply wanted an app to track and organize my money.
I’ve been thinking about how to visualize money. I don’t think pie charts are the way to go. I’m thinking something like RuleMark on SwiftUI.
Notes usually go too far in the other direction, for instance you will rarely see notes on mainstream politicians even when they are speaking objective falsehoods. This is because that system requires an agreement from people who usually disagree for a note to show up.
The founder mentions the dust proofing as a nice side effect of the high back pressure, but I'm wondering if that might actually be necessary to prevent the mechanical membranes from getting jammed with gunk and failing over time.
Asked ChatGPT to explain like I’m 5. This is what it produced.
“ Okay! Imagine that you have a big robot in your head that knows a lot about lots of different things. Sometimes, the robot might make mistakes or say things that aren't true. The proposed method is like a way to ask the robot questions and figure out what it knows, even if it says something that isn't true. We do this by looking inside the robot's head and finding patterns that make sense, like if we ask the robot if something is true and then ask if the opposite of that thing is true, the robot should say "yes" and then "no." Using this method, we can find out what the robot knows, even if it sometimes makes mistakes.”
That just show that the robot is consistent, not that it actually makes sense. So this explanation is bullshit even though it sounds convincing at first. That also the issue with most of ChatGPT, it is hard to know when it sounds convincing and is false.
The essence of bullshit is that it is different from a lie, for a liar respects the fact that their is a truth and knows what the truth is well enough to purposefully misrepresent it, whereas a bullshitter neither knows nor cares if what they are saying corresponds to anything in reality just so long as it makes the right impression.
>The point that troubles Wittgenstein is manifestly not that
Pascal has made a mistake in her description of how she feels.
Nor is it even that she has made a careless mistake. Her laxity, or
her lack of care, is not a matter of having permitted an error to
slip into her speech on account of some inadvertent or
momentarily negligent lapse in the attention she was devoting to
getting things right. The point is rather that, so far as
Wittgenstein can see, Pascal offers a description of a certain state
of affairs without genuinely submitting to the constraints which
the endeavor to provide an accurate representation of reality
imposes. Her fault is not that she fails to get things right, but that
she is not even trying.
Some days ago, it told me "well, Boost has a function for that". I was surprised that I haven't found that myself.
I took me 10 minutes and opening the Git Log of Boost ("maybe they removed it?") until I realized "well, it just made that up". The whole answer was consistent and convicing enough, that I started searching, but it was just nonsense. It even provided a convincing amount of example code for it's made up function.
That experience was... insightful.
While we often say "If you need something in C++, Boost probably has it" and it's not untrue, ChatGPT seems to exercise that idea a little too much.
And a lot of highly linked forum questions and answers tend to be of the form “how do you do X in library Y?”, “Use the Z function!” - so naturally chatGPT loves to reproduce this popular pattern of communication.
> ChatGPT just matches the most statistically-likely reply based on a huge corpus of internet discussions, it doesn't actually have any ideas
Presumably you think humans have ideas, but you don't really have any evidence that humans aren't also producing the most statistically likely replies. Maybe we're just better at this game.
I'm astonished on how much worth people seem to give this bot. It's a bullshit generator, based on other people's bullshit. The bot does not know right or wrong. The bot does not know what command line utilities are. It just predicts what answer you want. Based on answers already given before. Nothing more, nothing less.
Because people want to believe in the magical AI - they want something for nothing and have yet to grasp not only are they unable to change the immutable laws of the universe (something will not come for nothing), but they are willfully blind to the very real price they are about to pay...
I guess the point is that it generates convincing and consistent texts. That's new and it's a building block for any futuristic AI that actually knows stuff: it also has to generate good text to communicate the knowledge.
Likewise, I spent 40 minutes looking for fictional command line arguments it recommended for Docker. When told the command line options did not exist, it directed me down a rabbit hole of prior versions that was a dead end. It really felt like a arrogant 8-year old with it's continued evasions of being flat out wrong.
The other day I saw someone, who by asking ChatGPT a series of questions, had it carefully explain why abacus-based computing was more efficient than GPU-based computing. It's not your google replacement yet...
If you read the abstract it appears that ChatGPTs explanation is on point. You're right that the paper is relying on consistency, which doesn't guarantee accuracy, but it is what the paper is proposing (and they claim it does lead to increased accuracy).
An accurate answer has to be consistent so it's not all bullshit. I'm guessing you can at least filter out inaccuracies by finding inconsistencies. Or in more plain English if you find somewhere it gives inconsistent answers you know those are wrong.
I'm not sure if that's a good path forward. You really want to find when it's good, not filtering out bad cases.
Your comment had less value than the parent. Humanity is doomed.
If you call bullshit, you have to say what was wrong or even what you think is wrong. Otherwise you are just insulting our new robot overlords.
Now, it seems you claim that consistency isn’t the same as making sense. But having more logically consistent robots seems like a big win! Otherwise I could criticize math papers for not making sense, even as I don’t doubt their consistency.
I did, I said this is just proving consistency and not anything more. I also in another comment said that I'm not sure filtering out bad or inconsistent answers is a good way to give us the truth or just filtering out the worst takes making it more convincing.
It looks to me like ChatGPT explained accurately what the abstract says. And indeed, the abstract sounds like this research is largely bullshit. But it's not ChatGPT that is at fault here.
But that is actually a fairly accurate description of the paper you asked it to summarize for you. It's not the models fault that you don't like the argument of the paper.
This is one thing apple got right. I don’t have to deal with individual apps process of cancelling a subscription. If I don’t like it, I can go to the App Store and cancel it there
The difference is ultimately money. A while ago (early 90s?) Switzerland voted to spend lots of money on the railway network to make it work the way it does. It was discussed on HN before. The video you linked notes single tickets are expensive and most commuters get half price or free travel paid by their employers. It's still a marvel, and I'd love to have the Swiss train network in my country even if it costs the same, but definitely worth pointing out when comparing to countries who don't have the political will to spend more on their railways.
Currently I’m working on a app that helps you keep track of your spending including subscriptions. I myself ran into this problem of what/where my money is going.
Keeping track of spending habits is hard to see just from looking at your bank transactions.
You're right that "$100 at a supermarket" is useless but I found even knowing "I spent $400 on groceries" wasn't that useful either. I kept asking myself "okay, but on what?"
So I leaned hard into making categories the starting point instead of the endpoint. Groceries breaks down into what I'm actually buying. Turns out I was spending way more on coffee than I realized.
Did you ever consider going deeper into categories, or do you find users just want the high level view? I've been torn on how much detail is actually helpful vs. overwhelming.