It's been interesting seeing several comments like this in the comments, since Laravel's docs may be one of the most highly-praised aspects of the framework. I suspect the divide may be that newer developers get everything they need explained in the docs in clear language, but the more advanced stuff requires some digging.
I use Laravel personally, and I've definitely seen both sides of this myself. For basic "happy-path" API reference, the docs are great. If I really need to understand how the framework is doing something, I pretty much always end up diving into the code. Unfortunately the heavy use of Facades can sometimes make it annoying to find the underlying code.
I disagree, at least in this specific context. With Laravel Cloud you build a standard Laravel application which can be deployed anywhere, then use the service to handle DevOps for you. Because it doesn't include any unique services of its own, you can always decide to hire a DevOps team and run all the required services yourself.
You're totally correct in scenarios where you need to build your project around the service, but this service is specifically a DevOps shortcut with no lock-in.
Should your Fortune 500 company use this? Maybe not. Should a one-man dev shop use this? Quite possibly - you pay for the convenience, but it frees up more time to improve your application.
This is true, if you're billing your hypothesis as a hypothesis. The problem is that prominent Republicans billed their "election was stolen" hypothesis as a fact, claimed to have boatloads of evidence in order to convince the public, and then never published that evidence.
In the aftermath of this clearly deceptive behavior, they've maintained the support of Republican voters who still believe the lie despite none of the evidence ever being released.
It's one thing to claim something is true and that you have evidence, then release the evidence and find out that it's insufficient to win in court. It's another thing entirely to make a claim, say you have overwhelming evidence to support it, and never release any evidence at all. In the former case, maybe you got overzealous or maybe you were dealing with an unsympathetic judge. In the latter, the only rational way to interpret the situation is that you were intentionally misleading your audience.
It's well established that adults who read incorrect information frequently don't find out it was wrong and become more skeptical of the source. Some people operate that way, but it's a small minority unfortunately.
In particular, it's been shown that people with dogmatic beliefs strengthen those beliefs when shown evidence to the contrary rather than questioning them.
Moderated media leans left. At least some of the reason it ends up that way is that many of the people who violate incredibly reasonable rules are conservative. Certain groups of hard-right people will say some incredibly bigoted shit that's absolutely out of line and makes it impossible to have a civilized conversation, then they complain about getting banned and drag a bunch of moderately-more-reasonable people with them when they leave. Once those people leave, normal everyday non-asshole conservatives realize the platform has less conservative content and leave in search of spaces that they feel respect their viewpoints more. In some cases entire topic-groups get banned (/r/the_donald is a good example) for legitimate reasons that frequently involve a small extremely-active group of members, and the rest of the members will also leave the platform because all they see is that a group they were part of got banned.
People who lean to the left tend to believe that it's bad to do some of the things that get you justifiably banned (such as intentionally using language that demeans people based on immutable traits). Because of this, it's much easier for them to avoid being deplatformed.
Assuming that random factors like "it rained" or "voters got in car accidents and couldn't make it to the polls" aren't a significant factor, there's always a 100% probability of one specific candidate winning since everyone has made up their minds before the day of the election. What polls do is not telling you the real-world probability, it's telling you the likelihood of a given outcome given known data.
Polls always need to be skewed in some way to be accurate, since not everybody will vote. You can't just get a random distribution of the population's preference and assume the more-preferred candidate will win. Polls can never be truly accurate because people will lie about which candidate they're voting for and whether they're planning to vote, and sometimes people who genuinely intended to vote never make it to the polls. There are a huge number of variables to consider when trying to predict the outcome of the election, but it's important enough that it's still worth trying.
Serious question - what do you think will happen to AI that currently relies on human reporters if everyone switches to getting their news from AI and the reporters lose their jobs?
Morals aside, AI will run into serious problems in 10-20 years when the world has rearranged itself around AI content. With less non-AI content available and no reliable way to differentiate AI vs non-AI content, there will no longer be a dataset to train against.
Individual humans summarizing the news can reduce revenue for news organizations slightly, but AI summarizing every news article is a problem on a whole different scale. Basically the same as the difference between getting a mosquito bite and being stabbed in your carotid artery - both are just blood loss, but one is a minor annoyance and the other is fatal.
This no reporter argument is so false but gets repeated often. If I only read internet articles from internet media companies you might have a little bit of a point, but actual newspapers have actual journalists. Some might be good and some might be bad, but they do employ people that do more than build an article around a tweet.
> Serious question - what do you think will happen to AI that currently relies on human reporters if everyone switches to getting their news from AI and the reporters lose their jobs?
It will evolve towards consuming more raw data and more information that people self publish to produce news. Newslike narration constructed on actual factual information is so bland and repeatable that there is no need for more training material. News is so uncreative and predictable that I can pick up a newspaper in language I don't know and still guess with high probability what most articles are about from photos, common names and few words of that language that I do know and general tone.
> Individual humans summarizing the news can reduce revenue for news organizations slightly,
There are so many humans doing that that the effect is not negligible. I skip reading all paywalled articles and read just their comments instead.
> Serious question - what do you think will happen to AI that currently relies on human reporters if everyone switches to getting their news from AI and the reporters lose their jobs?
Then the AI will go to the primary sources that the human journalist currently go to.
Will it be flawed? Yes.
Are humans already? Also yes.
Is there a huge risk that "the algorithm" will be politically biased? Totally.
Can you name one press organisation, larger than a local one-city-only paper, that hasn't been accused of that?
I'll confess - I have a project that uses Heroku's managed Postgres and my preferred upgrade method is to set the maintenance window to the middle of the night, create a backup, and be awake at 1am to make sure that nothing is broken after they force the upgrade. Their auto-upgrade process hasn't failed me so far, but there's no way to manually trigger it.
Proposal: Apply a ceiling function to your pizza-counting algorithm and always leave the last slice in the freezer. Then simply throw out that slice when you want a new pizza!
Depends how you define "a lot" - rural areas make up 20% of the population of the US, so you might need some rural voters but not necessarily a lot (percentage of actual voters may vary, but I don't have those numbers handy).
I'm not totally sure that your numbers are correct, at least for presidential elections. Philadelphia's split in 2020 was 81% for Biden and 18% for Trump.
I use Laravel personally, and I've definitely seen both sides of this myself. For basic "happy-path" API reference, the docs are great. If I really need to understand how the framework is doing something, I pretty much always end up diving into the code. Unfortunately the heavy use of Facades can sometimes make it annoying to find the underlying code.