thebooglebooski

I think primeagen sums it up well. At the end of the day, no one cares about the internals of a the product. They care that it actually works.

I'd caveat the statement that the only people who care about the internals, are the maintainers.

Just because the factory came about, didn't mean Bentley, Rolls Royce, and Bugatti stopped existing. Out of everyone who appreciates a car beyond making it go forward, there's still a slice of the population who will go out and look underneath the hood, admiring the intent and time spent handbuilding without any automation.

Is this not just an alternate form of DDOS? Imagine if you're operating emergency services, and your phone rings non-stop.

When someone actually has a legitimate emergency, the emergency service provider is essentially overwhelmed by the volume.

Seriously. As an avid NYtimes loyalist, I'd prefer they exhaustively enumerate through EVERY company where contractors outnumber FTEs. And then I want an equal amount of coverage distributed over all companies.

I fourth Heroku.

I think the author missed an opportunity at the end, when threading the role of AI/decision-making with respect to our lives...

Our behavior in society is fluid and dynamic. And so the extent to which data gets used to influence our future can, and should also reflect our changing values and beliefs.

The caveat is: what's the lag behind to which automated systems accurately reflect our beliefs of what's right and wrong?

Things like the semantics of words change regularly, at least in English.

And so is it safe to assume and believe that systems using "big data" accurately project and reflect society within a given moment of time?

I'm confused, where does it say the sample of people with ASD is generalizable to people without ASD?

There are a lot of comments in here saying instructors spoon-feed content.

I attended a bootcamp, and maybe other bootcamp grads can back up my claim here but...

Instructors don't spoon-feed material.

Curriculums are set up to be project or goal-based, kind of like being given specs or requirements in the working world.

What instructors do is teach you HOW to effectively learn. They also explain some of the more complicated nuances of a language...spots where a learners mental model typically struggle, that aren't well explained in Stack Overflow or MDN. And that's probably where learning gets expedited.

I don't refute self-learning as an effective means of finding an engineering job, but if you're starting with zero programming background, your only feedback loop is the compiler or interpreter giving you a result you didn't expect.

Sure, there's definitely some curated content. But it's not the job of an instructor to diffuse every API or system design needed to stand up an app into a students brain.

There's a difference between being told what to do, versus being coached how to do something effectively. And I think the two are conflated because of the perception or marketing bootcamps offer.

Any other bootcamp grads with the same experience? Or am I alone here?

This seems like the most likely reason for domestic companies to deny everything, and for their supply chain distributors to play along.

1) Big company partners with distributor. 2) Distributor has security issues. 3) Gov is already aware of security issues, says nothing. 4) Big company becomes aware of issues. 5)Gov steps in and pitches a deal: i)Both big company and distributor must deny. ii)In return, gov gets to: iii)Preserve any existing contracts iv)Protect the big company and distributor, with any legal, trade, or commercial benefits

I don't really understand how you can measure popularity without doing a census-like survey, reaching out to every developer possible in a coordinated effort.

Awhile back, I took a statistics class that explained how polling everyone by calling everyone with a phone landline resulted in a misrepresented sample.

The reason: not everyone has a phone landline. And those with phone landlines had a tendency to belong to specific demographics.

So...if you survey everyone using Github, and not every developer uses Github, are you not prone to the same fallacy?

"The trust must be restored."

Playing devil's advocate, what happens when the trust doesn't become restored?

The company loses ad revenue?

Developers flee in mass droves to...Yahoo and Microsoft?

Look what happened when people "lost trust" in Facebook. Shares are down 4% relative to a year ago. Is the company permanently crippled? Are people revolting and moving to alternate forms of social media? Fewer of the people immediately connected to your local graph are probably using Facebook. But that local loss of users gets completely offset by the new users they grow internationally.

Ultimately, these companies are beholden to their boards and shareholders.

Unless Inbox users clicked enough ads to make a dent in revenue, the organization has every reason to deprioritze competing products, and continue down the path of creating free A/B tests, and then merging the winner into a hybrid/better product.

Take a look at Fuchsia.

Or Dart and Flutter.

Or Hangouts and Talk.

An easy heuristic here might be, "Lose trust in the few in order to benefit the many." It sounds really sad, but it is the reality numerous companies live by.

I think the biggest blocker with using publicly available datasets is stale data.

If you, or anyone else who aggregates these datasets could make it EASY to find the FREQUENCY of updates, rather than just the LAST UPDATED timestamp, it'd incentivize people to consume APIs more.

I realize having a snapshot from 2014 is better than what was publicly available before. But I feel no one's really talked about why they would or wouldn't use particular data.