EverythingMe is Closing Down | Hacker News Reader

EverythingMe is Closing Down

blog.everything.me

30 pointsdvirsky19 comments

EverythingMe was a great product. Having been a long time Android user and a AOSP developer, the platform holds a tremendous promise, in my eyes.

EverythingMe tried solving the right problem. Launcher has so much potential to be smarter, yet it wasn't (then). Also, the amount of data one can get hands on must be monteziable somehow...think how valuable it is to be at the homepage of a browser... A launcher is similar, only that it absolutely dwarfs browser hompages in usage time.

I'm sure EverythingMe tried everything, but like Aviator (acquired by Yahoo!), they found out that there's no competing with Google products.

If Google has a competing app on Android, what choices have you got of beating them? They control all the keys parts on the platform (rightly or wrongly), they rule the web, they have got endless source of resources... It's a tough world out there being an Android developer. Even Facebook, with all its might, couldn't make much of its Launcher, 'Home.'

Google Now is great and all. And with custom APIs for contextual cards, in-app searching and so on, Google is making it more difficult for the competition. It's web-search type dominance all over again, but on a much much bigger scale.

Looking at the picture they put on the post... Can someone please explain why a launcher needs so many employees?

Yeah, I mean, sorry they closed down, but an Android only niche product doesn't scream "sustainable business model" to me.

Here is the answer -> They have received $35 millions in funding and... the story now looks "familiar". https://www.crunchbase.com/organization/everythingme#/entity

Wow. How does something like this even get $35m in funding and just burn through that over 5 years, then shutdown because they couldn't monetise?

Were the investors hoping for a Google acquisition?

In Australia I'm struggling to raise $50k for a global product with a clear monetisation and cashflow strategy.

Please do not get caught on that hook. Try to earn that 50k yourself.

Yeah I've given up on seed/accelerator/incubator over here. There's a reason why only the boring cashflow +ve enterprise startups come out of Australia. Just means I need to be juggling various different projects to bootstrap. Currently monetising a separate startup in order to fund the one I actually want to work on.

And, as for the question "how", the answer is " irrational exuberance".

My first thought as well. I imagine a good portion of them focused on getting through a work day with nothing to do.

From reading the post I understand that they're taking down the launcher, or that it otherwise won't work anymore. And I have to ask - why? Why just not leave it there? Is there something in it that requires Internet connection to access their servers? It's a launcher, even predictive smart whatever features should not require Internet access.

The prediction is likely entirely centralized machine learning; similar to how Netflix predictions work. Tell the server <x apps installed, y location, z time of day>, get a response showing <show apps a, b, q>.

> <x apps installed, y location, z time of day>

This is exactly the kind of data that should not fly over the wire unless it's absolutely necessary, which in this case I believe it isn't.

I can't imagine what kind of machine learning they'd have to be using to make it not work on a phone. It doesn't take much computing power to do a decent predictor. I'm going to assume process laziness here - being used to the idea that if everything is running on your server, you can tweak stuff there and have it immediately working on everyone's (Internet-connected) endpoints. It makes sense for websites, but IMO it's a wrong approach for devices.

Big data learning. They're (probably) inferring how you do stuff based also on how other people do. In fact, there's no way to recommend apps not already installed on your phone without consulting a server.

I think I misunderstood their app's description. I thought it was about recommending things to launch out of the things you have installed.

EDIT: And I'd pay for a launcher that learns from my interactions with it off-line, and recommends me apps based on context such as location, time of day, previously launched apps, etc. Such a thing does not need "big data learning". It's an undergrad-level machine learning exercise.

You should just build it yourself as a passion project. You can probably use the launcher in the AOSP as a base.

It might be faster to just hard-code manually arranged home screens based on time of day rather than do machine learning.

> It doesn't take much computing power to do a decent predictor

Collaborative filtering, a standard recommendation method, requires a great deal of computing power. Depending on the feature engineering, this could result in "big data" (whatever that means) even considering only one users' activity in isolation.

The prediction was mostly done on the client itself, but other stuff required servers: app recommendations, cards content, folder classification.

I hope this doesn't mean that re:dash will stop getting love from the developer. It's one of the best browser-based query front-ends I've used!

http://redash.io/

Edit: Looks like the Github repo has changed from the EverythingMe owned one to a Redash-specific one, huzzah!

https://github.com/getredash/redash

Discovery for anything is always overrated

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