跟讀練習: GitHub's AI Problem - How AI Is Damaging the Platform - 通過影片學習英語口語

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GitHub has an AI problem.

You may have heard about GitHub's uptime problem, but the problem is a lot more complex than you might know.

So let's talk about why AI is crushing GitHub and what the solutions might be.

Now, if you don't know me, my name is Tim Corey, and I teach people how to be an expert software developer,

not just an AI code monkey.

In today's world of just prompting for solutions, expert software development skills are more important than ever.

Otherwise, you become just another easily replaceable part with no actual skill.

That's why I provide free training here on YouTube, including multiple free courses and Patreon.

I am Tim Corey dot com.

I just wrapped up work on my game development master course, which covers how to become a game developer using

Unity and C Sharp.

So definitely check it out.

OK, let's look at the GitHub status page.

You probably heard that, you know, GitHub's been down a lot recently.

And yet, if you look at the GitHub status page, you see, well, Git operations, 99 .78%.

Now, that's not great, to be clear.

99 is only two nines of availability.

They should be shooting for four or five.

Before the Microsoft acquisition of GitHub, it was five nines.

Actually, it was zero nines.

It was 100 % uptime, but we could call it at least five nines or six nines or seven nines.

But right now, we're down to two nines in Git operations.

And you look at webhooks, 99 .66%.

That's still not great.

Two nines.

API requests, 99 .95.

That's three, actually three and a half nines.

That's pretty good.

And then issues, 99 .77 and so on.

You look at these and they're all 99 point something.

And you might say, well, that looks pretty good.

I mean, it's not four nines of availability, but it's something, right?

Well, they actually just put these percentages back up because they had taken them down.

And that really kind of hides the problem.

If you look at this and you see a lot of green

and you have some red and some yellows in here and some darker greens.

But, you know, you mostly see, you know, green.

So it looks like things are going OK.

And if you look at this, you might say, well, GitHub's not really having that much of a problem.

But then someone built the missing GitHub status page.

And this shows a whole different story.

Because when you talk about Git, you're talking about the whole, right?

If you're using GitHub, you're talking about the whole of GitHub.

And sure, if the site is down, but actions are up, that might mean that some things are still working.

But that's still a problem, right?

You still have issues.

And so, cumulatively, the GitHub platform should be a accumulation of all of those uptimes or downtimes, actually.

And based upon that, the 90 -day uptime is 87 .93%.

That is abysmal.

That is horrible.

Notice here, 93 incidents in the last 90 days.

That is, quite frankly, unacceptable.

Now, even GitHub will say that's unacceptable.

But notice that this shows a different number, right?

In fact, we come down here to get operations, which again, we go back over here, get operations.

This is this category, right?

This category says 99 .78 % on GitHub's status page.

On the missing status page, it says 98 .898%.

What's the difference?

Well, if we look here, let's come to this incident right here.

March 22nd, 2026.

There's a partial outage for 57 minutes.

We can actually look at this, let's open this up, And we'll see that this outage says disruption with some GitHub services.

Okay, on March 22nd, between 9 .05 and 10 .02, that's just under an hour, users may have experienced intermittent errors and increased latency.

Intermittent errors, if you look down here, high latency and errors.

Basically, people are timing out trying to do Git operations on GitHub.

That's a pretty significant thing because that's what you normally do with GitHub, right?

It's Git operations.

So this is a big deal, but you're saying, well, some people might have, and they use these words, try and dumb it down.

But that was March 22nd, 2026 for GitHub or Git operations, right?

So let's go to Git operations and hover over March 22nd, 2026, no downtime record on this date.

So their own status site says, yeah, we had downtime.

It wasn't for everybody, but we had downtime of some people

that had significant timeouts or significant latency connecting to and doing Git operations on GitHub.

But yet on their own status page, they don't even list it.

So that's a problem.

So when you look at this status page, you realize...

this isn't actually accurate.

This is even by their own standards is inaccurate, which is why this missing status page is a whole lot more accurate.

And even down here, you can look and see 98 .9,

98 .9, 99 .78, they actually have a higher uptime than what's listed on GitHub's status page for API requests.

But you can see 98s and some 99s in here.

So, but all told, you add all this up to this, which is the github platform you're at 87 .93

and this is why again we're talking about building software quick aside here we talk building software

and you say well you know the ai is going to write my software for me

and it's only you know it's 99 right

and that's not a problem right well 99 is right here

and i put 78 98 .9 98 .9 these different parts let's say as parts your application, they're all in the high 90s, right?

They're high 99, 98.

But when they add them all together, when you use this as a whole, the application as a whole,

your uptime is more like 87 % because these things add up, right?

They don't just live in their own little bubble where, yeah, sure, ordering's down, but that doesn't affect the other parts of your application.

Well, yeah, it kind of does because it doesn't matter if the website's up, if you can't order anything, right?

So those, the ordering affects the website.

And so there's, these are interconnected problems.

And so it actually compounds.

So when you're using a system that is mostly accurate and you do that over and over and over again, these issues and errors compound.

Okay.

Off a soapbox, off the rabbit trail here, let's get back to GitHub and let's just understand what's going on at GitHub.

100

And then we'll talk about why it's happening.

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It's important to understand what's happening first.

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What's happening is they're having downtime all over.

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Now, some of us is co -pilot.

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We have code spaces, pages, packages, actions.

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They've got a lot of new stuff.

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In fact, to be clear, when Microsoft took over GitHub, GitHub was basically, now I'm simplifying it,

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but it was basically a place to put your Git repository with issues,

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a place for issues and a place for tracking or commits and those kinds of things.

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But that was basically what GitHub was.

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And then it had a little bit of actions, but not very much.

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Microsoft came in and said, Hey, let's do more with, with actions and with, with code code spaces and pages

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and co -pilot and let's broaden it and open it up to more people in the community for free.

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So there's a lot more that went into Microsoft add a lot more features and a lot more moving parts.

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And when you add a lot more moving parts, what do you get?

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Well, you get more problems, right?

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That's just the nature of the game.

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When you add more of these things to track, that reduces your downtime every time one of the components has a problem.

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So to be fair, they're doing more things, but there's more to it than that.

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And, you know, some people say, well, how do you know this is AI's problem?

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I can tell you for a fact, this is AI problem, but it's actually a couple of AI problems.

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And let's talk about each.

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First, let's talk about this right here.

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This is Kyle Daigle.

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I hope I'm saying that name right.

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He's a chief operating officer over at GitHub.

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By the way, GitHub does not have a CEO.

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Microsoft, when the last CEO left, said, hey, we're not going to add a new CEO.

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That might be adding to things that's been some speculation here that without that clear direction,

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that clear oversight, they may be floundering a bit under Microsoft's leadership.

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That may be true, that's speculation.

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But here's what

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Kyle says about the status issue, the downtimes.

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He said, yep, platform activity is surging.

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There were, listen very carefully to this, 1 billion commits in all of 2025.

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Now, 2025 was an up year because AI is starting to pick up

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and people are starting to use AI more and a lot more

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Vibe code was starting and those kind of things were happening.

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And so there is a lot more code commits being done on GitHub in 2025.

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But it's now 275 million commits per week.

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Okay.

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So 1 billion commits all of 2025 five, that's happening now in about four weeks.

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So they're on track for 14 billion commits this year.

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It was 1 billion.

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Now it's 14 billion now.

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And that's if their growth remains linear, which he said, spoiler, it won't.

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This is because of AI.

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this is because everyone is using ai

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and they're doing a lot more commits ai is a lot more verbose it does a lot more things

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and people are using things like agents and background agents

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and you know their own spun off tasks

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that run in the background all the time they're trying to see how many tokens they can use

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and they're writing a ton of code now unfortunately some that code is for existing production apps.

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I don't love that.

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And we'll see why in the future, I've got a lot more content coming around what AI is doing to our everything, to society.

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But we know that a lot of code is being written by AI in production applications,

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but there also is a lot more sample apps.

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There's a lot more, hey, is it just an app for me?

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And so the amount of code being put on GitHub has exploded.

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So 14 billion commits this year.

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Okay.

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Now, seeing the trickle -down effects, because again, if you look at all these different services, okay, so you're doing Git operations, right?

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And that's going to take a load because you're going to upload the files,

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you're going to download the files when people pull the new versions, et cetera.

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But then there's also more issues because you have more code there's more pull requests

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because you're creating more commits and you're creating more prs

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but then here's the big one actions now it depends on the repository how you set it up

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but for example in my repository whenever

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i commit a new setup changes to one of my branches it will build

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that branch and deploy it to a development server so

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if you create a new pull request on my sites than on my actual code sites.

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Then what it's going to do, it's going to build that.

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It's going to deploy it to a development server, a different one.

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It's its own standalone.

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We are using Azure.

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So I can easily see it running.

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Well, every time I create a pull request, a new one of those gets created.

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Think about if I'm creating 10 times or 100 times in a pull request, what that's doing to the number of actions that are running.

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So it's not just that there's more code or more pull requests.

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It's that there's more processing going on.

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And Microsoft integrated co -pilot into GitHub.

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And quite frankly, it's the best co -pilot Microsoft owns.

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There's so many different ones.

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This is the GitHub co -pilot.

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And it's not perfect.

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It's far from perfect, but it's the best one they have.

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And what it can do is it can do things like review pull requests automatically.

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And it can do code reviews, it can create its own pull request.

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It can do work on code.

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If you submit an issue, it can do initial work and do some initial code commits for fixing that issue.

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All of that is triggered off of issues.

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If you have more issues, you have more code, you have more work being done by Copilot.

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So the load isn't just linear with the number of pull requests is actually probably exponential

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because of all these additional things that have been added.

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So with that in mind, GitHub Actions has grown from 500 million minutes per week in 2023.

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Now that's three years ago to a billion minutes per week in 2025.

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That's last year to now 2 .1 billion minutes so far this week.

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Okay, think about that.

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So it was a billion minutes a week in 2025.

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Now it's twice as many minutes.

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Again, that's CPUs running.

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That's processes running.

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And that's 2 .1 billion.

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And that's at this time in the week.

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I don't think this was the end of the week, right?

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So there's a lot of stuff going on, right?

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Their load has dramatically increased because of AI, okay?

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OK, so we're pushing pretty hard on more CPUs, scaling services and strengthening GitHub's core features.

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OK, so that's the explanation from GitHub.

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Always take an explanation from a person in power with a big grain of salt,

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because that is their perspective of what they want you to know.

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Now, I'm not saying this person is lying.

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I'm not.

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But there's always a bit more to the story, right?

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There's a bit more that can be talked about beyond just what they say, because there's things they don't say.

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So yes, platform activity is surging.

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That's absolutely true.

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And I love the fact that he shared these numbers, because this is really important to understand that, yes.

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There is an incredible load on these servers.

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So it's not unexpected to see downtime as we stress out platforms.

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This is actually one of the big problems right now, even with AIs, where we're seeing more downtime with LLMs

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because of the fact they simply don't have enough GPUs running to support the amount of work people are asking for.

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And so they're raising prices

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and they're doing all these other things to try to figure out how to manage

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that because there isn't enough processing power for all of the demand because the demand is spiking so hard.

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So yes, that is absolutely accurate, but that doesn't mean it's the only part of the story.

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Because first of all, this downtime, again, remember, Rosie view the downtime, actual view based on their actual numbers, right?

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So this downtime didn't start since AI took over.

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It didn't start in the past 90 days.

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Now, it's gotten a lot worse in the last 90 days, but this actually started soon after

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Microsoft took over.

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And remember, Microsoft added Copilot and Codespaces and actions, well, they improved actions, a lot more of actions,

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and did some other things as well that added to the platform and added to the complexity.

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So it's natural to see some downtime from that, but it has not gotten better.

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In fact, it's getting worse.

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Now you add additional things with the additional load.

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You might say, well, that probably is enough.

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I would argue it's not enough.

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I argue it's not the whole story.

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It's a great part of the story.

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And it's, it's a great thing to say because it feels like it's right, but we're ignoring a couple of things here.

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Again, Microsoft is a heavy user of AI.

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Microsoft has been telling us for a while now

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that AI is here and we need to use AI more to build things.

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They've been saying since early 2025 that they are using AI to write more and more of their software.

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When you talk to, you know, you hear from any of the, like the head of OpenAI or the head of Anthropic,

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you'll hear them say things like, we're going to replace developers with just AI

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or AI is going to do most of the work and we should have AI write all of our code.

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These are things they're saying, but we're also seeing that the result of that is a large increase in the number of errors.

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There's a report out recently, and I'll be sharing it in a future video.

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There's a report out recently where it says that for every line of code that AI writes,

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there's three times as likely to have bugs compared to a human writing that code.

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That's stunning, right?

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That's stunning the amount of problems.

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And that's a recent report using things like Opus

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and other late gen models that are supposed to be the best at software development.

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And there's still that significant of an issue.

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So when you put that into perspective

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And you say, well, if AI was great and

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if AI really was able to solve these major problems and solve things at scale,

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then we should at least see a leveling out where,

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yeah, maybe you still have some downtime because that's going to happen under load and under stress.

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But it should be getting better because they should have written more tests and they should have fixed more problems.

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But we're not seeing that.

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We're not seeing the improvement.

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We're seeing a degradation that continues.

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And so AI is not solving this problem, is not stabilizing this environment.

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What we're seeing instead is they're increasing in the number of problems they have and significant ones.

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So there is a problem.

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Let's pull up.

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So this one right here, this happened as I'm recording this a couple of days ago.

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So, on April 23rd, between 4 p .m and 8 p .m.,

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the pull request service experienced a regression affecting merge queue operations.

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Pull requests merged via merge queue using the squash merge method produced incorrect merge commits

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when the merge group contained more than one pull request.

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In affected cases, changes from previously merged pull requests and prior commits were inadvertently reverted by subsequent merges.

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This is a five alarm fire.

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This is a major issue.

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This is not just the site was slow.

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We had to wait for an hour for the site to come back up.

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We couldn't commit because there was no, like it was down.

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This is not that.

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What this is, is the site was working but it destroyed or damaged the Git history of your commits.

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That right there is a massive problem.

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Now, to their credit, they did reach out in theory to all the people affected and said,

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hey, we're going to, here's how to fix this problem.

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We apologize to the problem, et cetera but they told them how to fix it in theory.

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But what this means is that code could have gone to production

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that was the wrong code or missing parts and trying to fix that and revert that and change that and bring it.

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That's a significant downtime for their customers.

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Now, again, of all the pull requests on this day, now this is again where numbers can really be used to manipulate.

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um again this was from uh kyle i know the tweet pulled up here

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but kyle said hey there was four million pull requests in this day

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and only 0 .07 were affected about 2700 i believe um

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which sounds like well it's not a big deal well actually even one is a big deal

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and he did say it later he's like no anything more than zero is a problem

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but if you start looking at this well it's over a few hours, four hours, not all day.

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And so your 4 million pull requests in that day was actually a smaller fraction of that.

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And it wasn't all pull requests.

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It was pull requests that were merged via the merge queue.

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So that's an even smaller number.

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And so also your percentages look a lot bigger because you're actually looking at real numbers, not just, you know, If I said,

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well, they merged 14 billion pull requests this year and only 0 .00004 % had this problem,

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that sounds tiny.

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But the reality is, it's a significant problem for a significant number of customers.

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This right here is not an issue where, oh, they had too many things going on at once.

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This was not an issue where they had, you know, too much load at the time because they had too many people doing too many things.

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This is a bug in the code.

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This is a major bug in the code.

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This is a bug in the system that should never have gotten to production.

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This is just one of the many issues you'll see in this status reports

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that can be attributed to something besides just it was load, right?

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Load is an easy thing to point to because you have all these big numbers.

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If you look at these numbers, this seriously is a huge number.

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And I can't imagine 2 .1 billion minutes of GitHub actions running in a week.

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That's just an insane amount.

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So yes, totally understand that load is a major factor and that's coming from AI usage of everybody else.

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However, that's not the only thing in the picture.

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We can see there are other issues that are going on at GitHub because of poorly written code or not checked correctly.

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And they did say, hey, we're deploying new tests to cover those areas.

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Why were there not tests beforehand?

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That's a great question because you have an AI that could have helped you write those tests in theory.

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But the reality is that when you're dealing with production applications, you can't just say, hey, let the AI do more work.

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Because what happens is you have more issues like this, your uptime looks like this, and your customers start looking elsewhere.

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And if you look online, This is what a lot of people are asking right now is, where do I go if I'm not on GitHub?

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GitHub has a stranglehold on the market.

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They have so much of the market share.

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And yet, their customers are saying, hey, what about GitLab?

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Or what about, you know, this or that?

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And all of a sudden, they can start losing real customers.

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And they might get to the point where they have real competition.

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Not because their competition was better necessarily.

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but because their competition wasn't as bad.

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And that's not a great place to be.

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So it's important that when you look at, if you look at this from your perspective, when you are writing software,

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your software, whatever part of the business it's supposed to help, the reality is your business will rely on that software.

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And if your software can't get the job done, then you're going to cause problems for your business.

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And if you sell that software, software as a service, something else, you know, then your customers will rely on that software.

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And if they can't use that software well, they're going to look somewhere else.

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And so if your job depends on, if your company's...

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Income depends on software, and it almost certainly does to one extent or another, then you need to think about, is this okay?

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Is it okay to lose customers?

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Is it okay to potentially damage our customers?

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Like this issue right here,

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they damaged their customers, and potentially those customers had problems with their own customers because of this.

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That's a big deal.

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Are you okay with that?

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Now, I encourage you to really think about how you use AI

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because of the fact that this type of thing just snowballs.

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It doesn't get better with writing more AI code.

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What happens is it gets worse and worse and worse.

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When I talked about this about a month or so ago, this was in the 90s.

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It was 92 or 3, I think it was.

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So, this has dropped another 3 % to 5 % in uptime in just the past month and a half or so.

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Like this is a major problem and it's getting worse, not better.

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Now, again, load is part of it.

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I don't want to discount that, but I do want you to think about how you write your software, writing software that is performance,

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writing software that is secure, writing software that has been tested and verified is really important.

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And just saying it's faster to let the AI write it isn't going to be good enough

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because it will be for a while.

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And then you'll see the snowball and that snowball is going to keep rolling and keep getting bigger.

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And it's going to bury you under downtime, under lost customers, under lost revenue and major issues.

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So I guess my conclusion here is don't be like GitHub, right?

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Plan for how you write good software, not just provide faster software,

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as in faster to market with new versions and new features.

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It's more important that your features work than that they're faster because getting things out there faster,

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getting more features isn't going to be helpful when they start to break down even more frequently.

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So make sure that you're writing good software.

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Make sure you know how to actually read the code that your AI writes.

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And yes, you have to verify line by line.

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And that's a problem because of the fact that AI writes so many lines of code.

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You have to actually tell it, write less lines of code.

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And that's part of being a senior developer is saying, how can we solve this problem with less lines of code?

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So you have to build up your skills so you can better drive AI.

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Otherwise, you're in danger of your entire business collapsing if you're not careful, okay?

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Thanks for watching.

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I hope that this is helpful.

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I hope that this can give you some new things to think about and new ways of approaching software development.

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But as always, I am Tim Corey.

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We'll be right back.

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本課的詞彙與口語要點

這節口語課以影片“GitHub's AI Problem - How AI Is Damaging the Platform”為素材。影片中反覆出現的詞有：github, code, software, request, pull。這段影片共有 379 個句子、4368 個單詞可供跟讀。講話部分時長為 28:19。說話人語速自然，每分鐘約 154 個詞，接近日常對話。 86% 的單詞屬於英語最常用的 3,000 詞，其餘的詞建議在練習前先學一下。

影片中的重點詞彙

影片中 15 個值得學習的單詞，附發音和釋義：

單詞	發音	釋義
merge 動詞	/mɝd͡ʒ/	合併
solve 動詞	/sɒlv/	解決 /解決
server 名詞	/ˈsɝvɚ/	伺服器 /伺服器, 伺服器
depend 動詞	/dɪˈpɛnd/	信任, 信賴 /信賴
queue 名詞	/kju/	佇列 /佇列
deploy 動詞	/dɪˈplɔɪ/	部署
percentage 名詞	/pə(ɹ)ˈsɛntɪd͡ʒ/	百分率, 百分比
argue 動詞	/ˈɑɹ.ɡju/	辯論 /辯論, 爭辯 /爭辯
explanation 名詞	/ˌɛkspləˈneɪʃən/	解釋 /解釋, 說明 /說明
helpful 形容詞	/ˈhɛlp.fl̩/	有幫助 /有幫助
rely 動詞	/ɹɪˈlaɪ/	依賴 /依賴, 依靠
compound 名詞	/ˈkɑmpaʊnd/	化合物
availability 名詞	/əˌveɪ.ləˈbɪl.ɪ.ti/	可及性
linear 形容詞	/ˈlɪn.i.əɹ/	線性
verify 動詞	/ˈvɛɹɪfaɪ/	校驗 /校驗, 驗證 /驗證

影片中出現的短語動詞

單詞	釋義
take over 動詞	接管, 接手
break down 動詞	壞了 /壞了, 出故障
come down 動詞	下來 /下來, 降
figure out 動詞	弄清楚
pick up 動詞	拾取, 撿 /撿

值得反覆跟讀的句子

影片中簡短而完整的句子，可以直接用在日常對話裡：

First, let's talk about this right here.
I hope I'm saying that name right.
There's a lot more, hey, is it just an app for me?
Is it okay to potentially damage our customers?
Are you okay with that?

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
現在完成進行時 have/has been + -ing — 以前開始、現在仍在繼續的動作	has been telling · They've been saying
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	are interconnected · being done · being written
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	GitHub's been · that's been · has exploded
定語從句 who / which + 從句 — 補充說明人或事物	inaccurate, which is · this, which is

需要注意的發音

說話人用了 94 次縮略和弱讀形式，例如 you're, they're, we're。請按聽到的簡短形式來說。

“sh” 和 “zh” 音: explanation /ˌɛkspləˈneɪʃən/, potentially /pəˈtɛnʃ(ə)li/, speculation /ˌspɛk.jəˈleɪ.ʃən/, conclusion /kənˈkluːʒən/, acquisition /ˌæk.wɪˈzɪʃ.ən/
長單詞——注意重音位置: developer /dɪˈvɛləpɚ/, explanation /ˌɛkspləˈneɪʃən/, potentially /pəˈtɛnʃ(ə)li/, availability /əˌveɪ.ləˈbɪl.ɪ.ti/, speculation /ˌspɛk.jəˈleɪ.ʃən/

中文母語者容易讀錯的音：

/v/ — 不要讀成 /w/，上齒輕觸下唇: solve /sɒlv/, developer /dɪˈvɛləpɚ/, server /ˈsɝvɚ/, availability /əˌveɪ.ləˈbɪl.ɪ.ti/, verify /ˈvɛɹɪfaɪ/
詞尾子音 — 要讀清楚，後面不要加母音: commit /kəˈmɪt/, merge /mɝd͡ʒ/, solve /sɒlv/, depend /dɪˈpɛnd/, percentage /pə(ɹ)ˈsɛntɪd͡ʒ/
/r/ 和 /l/ 的區別: rely /ɹɪˈlaɪ/, linear /ˈlɪn.i.əɹ/, frankly /fɹæŋkli/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/, carefully /ˈkɛɹf(ə)li/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 merge, solve, server 這類單詞。

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: GitHub's AI Problem - How AI Is Damaging the Platform - 通過影片學習英語口語

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本課的詞彙與口語要點

影片中的重點詞彙

影片中出現的短語動詞

值得反覆跟讀的句子

影片中的語法

需要注意的發音

如何用這段影片練習

什麼是跟讀法？