跟讀練習: I Spent 1.5 Billion AI Tokens on Absolute Slop - 通過影片學習英語口語

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I spent 1.5 billion tokens on this project and put it through GPT-56-SOL on high.

I did have it spawn extra workers on high to try and knock this out in a couple days.

Overall, about 24 hours of consistent compute runs.

There were some times where it stopped because of usage errors or a reset needed to happen.

But I did use, I think, four resets of the $100 plan and just a ton.

I got lucky in opening.

I had a reset as well.

So I was able to use a ton of inference.

I don't know what that 1.5 billion tokens will equate to in dollars, but I would assume probably around $10,000, maybe more.

And I wanted to say this was terrible.

It made slop.

and I wanted to kind of showcase it because what I had was a .NET project.

Most people know my Windows utility that's written in PowerShell

and my .NET basis I've built over the last year or two off and on just kind of hacking away at it.

It had roughly, I think 5,000 lines of code and I wanted to expand it out so it has feature parity.

An idea behind Win OneShot was just to have something kind of on deck in case something happened with PowerShell, I could easily translate everything to .NET.

So all the functions I built was like changing registry edits.

Instead of doing a set item property, I could actually just force that through the .NET libraries instead of using that section of the code.

So I could always fix windows essentially.

So it's always been kind of like my fail safe or a possibility of maybe doing a standalone.

But I wanted to see what AI could do with it.

So I already had the scaffolding.

I already had the structure.

I just needed to feed it a roadmap, a spec sheet, and then have it build out of tasks for it to run.

And I thought, hey, let's split this up with workers.

And I got this whole idea because I saw something online about Bun being rewritten and ported from Zig to Rust.

And I was like, well, shit, if it can do that, surely 5.6 Sol High could do this.

This is nothing compared to that kind of port.

And sure, they use Fable 5, but I was like, well, 5, 6 old should be good enough.

And I want to say, I think we're to the spot where it's not really about the models.

It's about you, your ideas, your skill, your software development ability.

And I think this is always going to be a thing

that you just I don't see a way around this where you have to think through the problems, the structure, and AI just can't think for you.

You can tell it and direct it, but my directions was have feature parity.

Here's my parity project with PowerShell script.

Here's the scaffolding where it already has the functions in place.

Just add the missing functionality, whether that was, I think win 11 creator where it creates an ISO or,

uh, the app X removal that was just added in this last update to when you till.

And I was like, I, that should be relatively easy to do maybe five, 10,000 lines of code.

And at the end of the day, we ended up with 169,310 lines of code that was added.

Holy shnikes, Batman.

That is an absolute ton of code.

What does that mean when you have, well, we'll just call it 170,000 lines of code that was changed.

It means the project's no longer years.

You don't know what's up.

You don't know what's down.

When you look at the code base, it is no longer maintainable.

You just have so much stuff, you're better off just starting from scratch again.

In this instance, I'm going to show you what it created, and then I'm going to show you what it had to work off of,

because they're just two vastly different things here.

What it created was this.

Pretty good project on launch.

I did notice right away on launch, it launched considerably slower.

Well, to be expected with over six figures of lines added to the project.

But with a built binary, it's going to be menial to the end user.

Compute these days is so cheap.

And most people, you know, most software developers, even before this, had bloated projects, and it really didn't affect performance that much.

So I looked at it, I was like, yeah, all right, that's good.

Now, when it first spit out, it was all one single column.

So of course, a couple of reiterations needed to be made.

It was like, all right, let me give this an honest try.

Let's split it up.

Let's match this.

Did okay job after all of that.

We still have like a category load fail element not found down here.

Trying to track that down.

I was like, okay, that needs to be reworked a bit, but it does work.

And then for the tweaks page, I was like, okay, Let's see what happens there.

It tries to inspect all the tweaks and then tell you what the current state is.

Sometimes it can do this.

Sometimes it can't.

It just depends on the tweak.

But to try and go through and maintain this, let's say I was to release this on the world.

Well, people are going to go, hey, the acrylic blur is applied, but it says not applied.

And you're like, well, I, okay.

And then there'd just be tons of issues like

that with this iteration and you're relying on an LLM prompt to fix this.

At this point, the code is pretty much unreadable, unmaintainable, and that's a problem, especially if you're charging money for something like this.

And this is where I think a lot of people are gonna get in trouble.

So this is what happened with the tweaks, configuration.

I had to kind of move this around.

Initially, it made this check boxes.

Then I was like, no, those need to be buttons.

That was fixed the updates just didn't work at all i

think they still don't work something about state infection i was like okay well i'll go ahead

and click this hopefully it doesn't blow up my system it kind of works on a lot of things

but it's close but not and i think the fallacy here for someone

that never done programming before i even saw i think it

was a meme online where someone was like the new state of development, you know, idea, first build,

and then the last 10% is this massive chart where the last 10% took up more than 60, 70% of the time.

And honestly, this is kind of how it's always been to try and polish that last 10%.

The problem is, when you have so much tech debt from all this AI and iteration for big projects, it's impossible to read the code.

So that last 10%, if AI can't fix it, you're kind of screwed.

It just doesn't, you're basically relying on it 100% with no safety net.

So you can't go to market with something like this.

It's just really important that people have that or know that up front.

And you might be thinking, Titus, just use a different model.

Models will not save you from just plain bad foundation.

You have to have maintainable code.

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It has to be readable.

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And then you could pass it off.

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I've also heard horror stories of like CEOs

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or someone from the C-suite making something or vibing something up and then handing it off to a development staff.

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That is the opposite of how that should go.

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It should be the development staff makes a mock and then gives it to the C-suite to look at, not the other way around.

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Where we're failing with AI is, or LLMs, whatever the hell you want to call it,

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is normal people still can't make software let's just be real here let's i'm going to say

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that and scream

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that from the mountaintops you have to know the foundations you have to think through the projects

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and understand what you're getting into even when i gave it proper instruction

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and just let it run wild it completely screwed everything up now it's on git

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so it's not a big deal let's get to and rewind to show you what I gave it, where we were at using Git.

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And if you don't understand Git, you should not be doing any of this.

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Git is the very first thing And it's so important to understand.

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I can't say that enough.

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It will save your bacon so much because this is 170 lines, 170,000 lines, thousands of dollars.

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Let's say if it's a company, it would have been probably tens of thousands of dollars it would have spent on.

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And now it has to be thrown away.

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So I'm going to go directly to this private repo because I didn't want this to be public.

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If you're interested in the code, I could make it public so someone can look over it.

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But I'm going to go back to before this all started.

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So here's the commit history for win one shot.

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I'm going to just grab this because I haven't pushed any of these changes.

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And we can just discard pretty much all of it.

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So the commands get reset, dash dash hard, and this.

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It's going to reset everything from here.

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So once we do this, say goodbye to the project.

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And then you're going to see how the project changes.

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Look at the current structure.

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And here we go with the git reset.

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So now we're back to this.

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And if we look at what we have, we have a sources, test tools, all this.

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These are untracked.

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We could actually get rid of these.

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We're going to just do a git clean FD.

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It's going to push out all these tests, everything that was wired up from it.

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So now we're working off of a clean from May.

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I'm going to do a clean and then the build really fast.

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It looks like there was a bunch of artifacts left over.

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So essentially we can use LLMs.

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I'm not saying I'm not anti-LLM.

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We can use it to troubleshoot this.

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Like, why didn't this reset happen?

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And it said there's a bunch of leftover sources in the OBJ directory.

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And then we looked through, it was still looking and referencing some of those sources from the source directory that was created by the LLM,

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which was causing the errors.

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So now that that's done, it probably will wipe out that.

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We're going to clean those generated paths and restore and build.

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And now we should be able to build the full release.

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Now it's successful.

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Now we can do a .NET run.

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And here is the project as it was before the LLM took over.

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Obviously quite a bit simpler, obviously not so much bloat.

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I did kind of like the sidebar.

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So while this seems like a massive waste, it did give me some ideas.

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Like I really did like that sidebar pop out.

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I might actually take that, implement it.

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That's actually a pretty good thing.

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Obviously still have to work on the config page.

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Updates page, I was kind of thinking through a few ideas here for like default security and disabling.

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This all kind of has a few different changes here.

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Obviously it's not reading my Windows version.

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This is actually Windows 11, not Windows 10.

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So this is not without its own bugs, because it was hand coded by me.

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But you got to look at the code at the end of the day, like this is what was there.

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It's so much easier to just kind of right there's my views.

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I don't have five or six different sub projects.

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It's very maintainable in this structure.

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So I look at the spec sheet and all the stuff that the LLM built over that.

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And the problem isn't the models.

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The problem is me, my ideas, how I instructed it, and then just letting it run wild without proper checkpoints.

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Bigger projects need those checkpoints.

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You need to understand before it just completely goes off the reservation

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and then makes 150,000 lines of code when it should be 10,000 lines of code.

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Well, I don't even know how many lines of code is in this entire project.

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Let's find out.

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Here we go.

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Roughly about 10,000 lines of code.

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So you see the difference there.

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And to manipulate this to be better,

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like removing the checkboxes, making those icons and those cards like I did in WinUtil, you can do all of that here.

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And the lines of codes change for that kind of implementation.

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We're talking maybe 100, 200 lines of code.

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Maybe it's in that ballpark.

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And it's so important to understand you can't just let these things run wild.

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So what this should be is go from here and say, hey, let's improve the cards.

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Instead of check boxes, let's add cards with icons.

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And then slowly go through and change the things that you need changed.

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And then go from there.

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That's probably the biggest takeaway from this is I think we're in an era where you have these extremes.

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You have people that don't have any business making software, trying to vibe up something and then handing it off to a professional.

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That should never happen.

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Professionals need to make it using these tools that can make it faster, but they are the ones to commit that structure,

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do those checkpoints, make sure it's doing it properly and making sure it's maintainable in the future. So important.

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And then the second thing is There's also people, the tech bros out there that just say, hey, make me an app, go.

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And then just sit there and reiterate, spending billions upon billions of tokens to make something that kind of works.

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But as soon as they go to market with this, it's going to fall apart.

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And then it's not going to be maintainable.

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And then all of a sudden they sold something that just doesn't work at all.

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And they're just like, oh crap, that was false advertising.

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And it just runs down this entire terrible cycle.

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So out of my last video, 10 projects for AI to do that I've done, I wanted to make a video today saying,

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here's where AI failed miserably.

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And I don't blame AI for this.

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I blame me for it.

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I should have checked in.

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It should have been a phased approach where after each phase, I'm checking the code and going, nope, stop, stop.

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That's just bad.

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No, we don't need to do that.

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We don't need to write 20,000 lines of pester tests for something that should never even be tested.

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We don't need to do all these other things

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that it ended up doing because it was just not the right way of doing it.

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So it ended up implementing it incorrectly.

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Not that it didn't work, but it's not done how I would want it being done.

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So hopefully this all makes sense to somebody.

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Don't one feel like, hey, you're a normal person and then you're just gonna be able to develop software that you never developed before.

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Small software, sure.

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Dashboards, go for it.

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I think normal folks should do that all day long.

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I do it.

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It's great.

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Like just look back.

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Websites, wow.

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Fantastic at websites.

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But when it comes to big projects, I very much caution you.

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You need to think of it as a pair programmer code review.

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It's fantastic at those things, fixing really like nuanced bugs.

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Like why the hell is it doing this weird glitch right here?

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Tell me why.

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And then it'll spit out and go, well, you idiot, you're utilizing it this way improperly.

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And you should be pulling this library.

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And you'd be like, Oh yeah, that, that makes sense.

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That's where it's at with great, like big projects.

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It's fantastic.

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It's a great tool for it, but it will never replace the programmer.

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It will never replace the developer.

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I think it'll cut down or make that developer a lot faster

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and it'll make them be able to do a lot more with less, but a replacement is not.

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And that's what this video was.

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This is just kind of a state of 5.6 Soul.

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You could say, hey, Fable 5 would have done a better job.

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Maybe, probably.

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I mean, I couldn't imagine doing a worse job than this.

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But I don't think it's a model issue.

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I think it's a me issue, an ideas issue, a foundation issue, an instruction issue.

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You have to think through these projects.

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You have to think through these problems before it encounters them.

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So it sets it up with a structure that can be maintainable and usable and verifiable.

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It's so important.

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anyways I hope this helps somebody with

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that let me know your thoughts down in the comment section and I'll see you in the next one

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本課的詞彙與口語要點

這段影片共有 251 個句子、2877 個單詞可供跟讀。講話部分時長為 16:48。說話人語速自然，每分鐘約 171 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
reset 動詞	/ɹiːˈsɛt/	重置
token 名詞	/ˈtoʊkən/	代幣 /代幣, 籌碼 /籌碼
essentially 副詞	/ɪˈsɛnʃəli/	本質上 /本質上, 基本上
sheet 名詞	/ʃit/	篇, 張 /張
rely 動詞	/ɹɪˈlaɪ/	依賴 /依賴, 依靠
bug 名詞	/bʌɡ/	臭蟲 /臭蟲
implement 動詞	/ˈɪmpləmənt/	實施 /實施, 執行 /執行
suite 名詞	/swiːt/	套房
icon 名詞	/ˈaɪ.kɑn/	聖像 /聖像
instruction 名詞	/ɪnˈstɹʌkʃən/	教導 /教導
dash 名詞	/ˈdæʃ/	破折號 /破折號
vibe 名詞	/vaɪb/	氛圍 /氛圍
directory 名詞	/dɪˈɹɛktəɹi/	名錄 /名錄, 簿
spit 動詞	/spɪt/	吐痰, 吐
programmer 名詞	/ˈpɹoʊɡɹæmɚ/	程式設計師 /程式設計師, 程式員 /程式員

影片中出現的短語動詞

單詞	發音	釋義
end up 動詞		到達
go through 動詞		通過 /通過
blow up 動詞		炸掉, 爆破
cut down 動詞		砍倒
find out 動詞		找出, 查明
go ahead 動詞	/ˌɡoʊ əˈhɛd/	請便 /請便
look back 動詞		回顧 /回顧
take over 動詞		接管, 接手

需要注意的發音

說話人用了 78 次縮略和弱讀形式，例如 don't, you're, I'm。請按聽到的簡短形式來說。

“sh” 和 “zh” 音: essentially /ɪˈsɛnʃəli/, sheet /ʃit/, instruction /ɪnˈstɹʌkʃən/, dash /ˈdæʃ/, speculation /ˌspɛk.jəˈleɪ.ʃən/
長單詞——注意重音位置: developer /dɪˈvɛləpɚ/, essentially /ɪˈsɛnʃəli/, speculation /ˌspɛk.jəˈleɪ.ʃən/, directory /dɪˈɹɛktəɹi/, implementation /ˌɪmplɪmənˈteɪʃən/

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 reset, token, essentially 這類單詞。

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
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	being rewritten · was just added · was added
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	I've built · it's always been · I've also heard

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: I Spent 1.5 Billion AI Tokens on Absolute Slop - 通過影片學習英語口語

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