Flurry provides an analytics library to app makers that gets compiled into their applications and then reports back. Flurry compiles data from all users and apps and gets a global portrait.
The title is misleading. This is an article about steel, not heavy metal as most people here expect. The subtitle is more informative: "The creation of stainless steel took equal parts metallurgy and perseverance". The father of modern metal is Iommi U+1F918
for 1) you generally don't want to advertise that you are looking for work while still employed. Showing-up in results is the equivalent of painting a target on your back.
We used Papertrail for about 1 year. It works for fairly well for basic reporting, but it gets pretty expensive once you have enough volume. We moved to Logstash, Elasticsearch and Kibana. The power to aggregate, digest, and search the logs from various apps is quite awesome.
The answer can depend on some of the architectural choices you made when you prototyped your product. If the original use-case has changed significantly, and you find yourself fighting the codebase to get it to do what you want today, the technical debt is something to worry about. The same thing applies if you cannot scale to serve clients requiring a lot more volume than what you anticipated originally. It's possible that some clients have modified the original use-case they wanted when they signed-up with you, so your product must evolve to keep serving them. This may require refactoring, whether p/m fit was achieved or not.
> Salesmen aren't a substantial part of the workforce in a dealership. Mechanics, secretaries, service coordinators are
You're describing a regular garage here. We have those on most corners. Some also sell used cars btw. The argument is centered around the sales activities, hence the comment.
> Most of the personnel retained at a dealership do useful work. That same work will be required regardless of who runs the show.
Some people may benefit from this "service", but not me, nor any of the people I know. The only benefit I could see from my previous buying experiences is the act of handing out the key for a test drive. Everything else was simply designed to extract as much money from my pocket without giving me back anything in return. I had all the information I needed to make a purchase decision, the financing doesn't need to be done at the dealership, the car can be bought from a website, and delivered to the curb. A bunch of grown men sitting around all day and swarming people that enter the floor with the goal of "giving them a good deal" is not a value proposition. It's a complete misallocation of resources.
Clearly not context aware: typing "Wonder Woman and Superman" gives:
"Woman" may be insensitive, use Person, Friend, Pal, Folk, Individual instead
"Superman" may be insensitive, use Titan instead
There is a lot of work done in the area of linguistic analysis and word disambiguation, and none of it it's trivial. So it will be a big leap moving from a toy project to something of actual use. Integrating with an existing ML api for disambiguation could vastly decrease the amount of false positives. Idilia, BabelNet/BabelFly and LingPipe come to mind.
Accessories should be part of the options. You need cables for example to connect the HDs to the MB, DVI adapters for older monitors, etc. Some people may have a spare one, while others will have to make an additional trip to the store.
I always wondered how you do paperwork for something like this. It must be a nightmare from an accountant perspective. What is the bill code for "blackmail" when you file the income tax and you write a 6 figure expense. In the end your cash has to balance out, you cannot not declare it. Anybody with experience in something like this?
Datacratic has been doing what you ask for a while now (disclaimer, I work for them). We do ML, audience optimization, segmentation, multivariate and nonlinear models for small clients or for some of the largest shops in the industry. But the algorithms are indeed a small problem compared to building the on-demand infrastructure, managing the dataset, and all the rest of the challenges related to managing other people's data. Trust to handle data that doesn't belong to you is earned with great difficulty, unless it comes from shady sources.