171–180 of 236 posts
I think what the CountThings app is doing is great. There are too many industries where tasks are being performed inefficiently due to a lack of applying readily available technology. They had the foresight to develop a product for a very common problem and make a business out of it. I’m a bit surprised that this is making the rounds of HN though. OpenCV and other computer vision libraries have trivialized such count…
Wow, this is such a simple application of ML and it is incredibly valuable. On their website, they charge $100/month per device or $1000/year per device [0] and I bet they're making a killing. [0] https://countthings.com/en/
I wish I could get a bead on how useful ML is. Hackernews makes me think it’s all hype.
Right now machine learning seems like a useful tool for certain tasks but not as revolutionary as e.g. the invention of the car.
Nobody cares that this is just an ad? It's got too many paragraphs of bullshit filler for something that's basically "they're taking pictures and counting the pearls using machine learning".
Seems like a pretty poor ad in that case -- they are advertising a profitable business sub-niche to a huge audience of engineers, many of whom have the knowledge, execution, and money to build a clone service and drive down margins.
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Value pricing at it's finest. If the customer saves $1000/month per device, they'll happily pay the $100/month. As it still saves them $900. They don't care how complex/simple the ML implementation is.
But is there any barrier to entry though? By the looks of it, none, the next app will charges $5 per month and it could even be generic ('round objects counter')
I think what the CountThings app is doing is great. There are too many industries where tasks are being performed inefficiently due to a lack of applying readily available technology. They had the foresight to develop a product for a very common problem and make a business out of it. I’m a bit surprised that this is making the rounds of HN though. OpenCV and other computer vision libraries have trivialized such count…
LOL you're not getting this done trivially with OpenCV. https://countthings.com/en/counting-templates
Wow, this is such a simple application of ML and it is incredibly valuable. On their website, they charge $100/month per device or $1000/year per device [0] and I bet they're making a killing. [0] https://countthings.com/en/
I wish I could get a bead on how useful ML is. Hackernews makes me think it’s all hype.
The latest trendy forms of machine learning, which are all some form of deep learning neural network, are pushing beyond the boundaries of human capability, but for a fairly narrow set of usecases. Some people get excited cause they can exceed human capabilities for some object recognition type of task, and then end up thinking that SkyNet is around the corner and they rightfully get called out for it.
You should know though that it is rarely the experts that are guilty of overhyping. It's usually VCs, or product managers, or marketers, or regular software engineers that took an intro class on Coursera where they were told exactly how to solve a problem but haven't yet been exposed to how hard it is to generalize.
With deep neural networks in particular, all of the new innovations have come from novel neural connection topologies. Most of the successful new topologies are the result of attempts to model biological function of some sorts, but that is just the tip of the iceberg. With neuron counts technically unbounded, and the topological search space essentially being the factorial of the neuron count, we will never fully explore the capabilities of neural networks, and only an infinitessimally tiny fraction of those would ever be useful in any circumstance. So deep learning is still extremely exciting because the opportunities are so boundless, yet still extremely disappointing because of how hard it is to find anything useful.
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Mac fans being rabid Mac apologists isn't something leaving HN is going to change.
This doesn't come from fanboys, this comes from dictators.
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Depends on your baseline excitement. If you're so hyped up that you think it can classify bad and good prospective employees from a single photo, you should tone it down because that's nonsense. If you think it's all fluff, then you are also wrong. There are many great ML applications for constrained scenarios. But this pearl counting does not require modern ML at all. It can be done with decades old image processing…
There’s also a technique called “weighing”. It’s a transformation which, by assuming the items are identical, turns quantity from a discrete value to a continuous one with some loss of precision. In many cases measurement times can be reduced my more than 99%.
Wow, this is such a simple application of ML and it is incredibly valuable. On their website, they charge $100/month per device or $1000/year per device [0] and I bet they're making a killing. [0] https://countthings.com/en/
There's no ML here. This is a decades old software technology being packaged as a phone app.