Sure, but the deal is that at very small numbers, a massive change in % difference doesn’t translate in to a massive difference. It’s _entirely_ dependent on context. The raw stats are meaningless.
The author is muddying the water for effect, that's why his explanation is confusing. To understand what he means, you should ignore the results expressed as percentages and instead focus on 1:19 vs 1:99.
Now, imagine a grid, where the unit on the x-axis is "No", vs "Yes" on the y-axis.
Place both studies in terms of their respective No/Yes relationship.
I’m interested in this as well. I’m work for different clients frequently and it seems like there is always so much time wasted on knowledge transfer when the client already has a system for recording these types of things.
Interestingly it seems some teams from within a company use the tools more effectively. I think it’s largely about building a culture around sharing information. Confluence is the tool I’ve seen most commonly used. I don’t have strong opinions one way or the other about it, but would like to see what tools effective teams are using
None. We sign NDA's for specific clients. Other than that, we don't have a non-compete or similar clause prohibiting side work. In fact, it's encouraged. We talk about our side projects openly. Also, if you want to have some company backing on your project, you can get paid 50% of your effective hourly rate for every hour you work on your side project if you agree to split potential profits with the company. Kind of an in-house funding.
You should put an example of a use case for some of the task bars in your website photos. "Task Progress" doesn't really convey any type of value to me. Maybe a specific example like "Gym sessions attended" would help show new users what the app can be used for.
If you're looking for example use cases, first thought was that it would be fun to see a countdown until my wedding, which I suppose would take the form of "% of engagement completed."
You cant make something like this in 8.2 mins considering minimum wage is 7.5$ in USA. Why make things for free anyway, people rarely appreciate things they get for free.
>In contrast, take music recommendations. If I give you a series of suggested songs, can you detect a pattern?
It seems to me that there has been a lot of success in recommendation engines (i.e. Spotify, the "Netflix Algorithm", etc). Maybe not so much on a smaller scale, but large companies are using recommendation engines all the time to keep user's engaged.
Other than that, I agree with the rest of the post. The eagerness to throw ML at any and everything can be kind of exhausting. When used right, it can be super powerful but sometimes a simpler method that achieves 80% of the results may be enough for certain use cases.
> It seems to me that there has been a lot of success in recommendation engines
Not as much as you might think:
https://apenwarr.ca/log/20190201
"Mainstream movies are specially designed to be inoffensive to just about everyone. My Netflix recommendations screen is no longer "Recommended for you," it's "New Releases," and then "Trending Now," and "Watch it again." "
"As promised, Netflix paid out their $1 million prize to buy the winning recommendation algorithm, which was even better than their old one. But they didn't use it, they threw it away."
I definitely got a lot of songs from it that were spot-on, but I also got a ton that were not-as-enjoyable covers of songs I already had. I don't get why they keep doing that. It turned me off.
Yeah same here! The daily mixes that Spotify makes and groups based on my different listening habits has been pretty great. I get a ton of value out of it.
