跟讀練習: Why Software Factories Fail - 通過影片學習英語口語

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What's up everybody, how are we doing?

Guys, give it up for all the great speakers today so far.

All right, this is harness engineering is not enough and why software factories fail.

And we're gonna click, maybe.

Oh, that's way too many slides, hold on guys.

Okay, so we're all racing to put AI coding into production.

and there's been lots been said about loop engineering and we should probably write more loops and yeah, I don't know, I guess we're doing loops now.

StrongDM built a Lightsoft software factory where nobody even reads the code

and the prevailing narrative is we should just spend more tokens, you are the bottleneck, the models are good enough,

code is free, just ship more stuff.

But at the same time, we are starting to see the cracks.

Our friend Mario at AI Engineer Europe begged us to slow down

because companies that should not be having outages because of coding agents are having outages due to coding agent mishaps.

Code bases are falling apart faster than they ever have before.

And our friends at Pharos AI actually even did a report

since we all picked up all these AI coding tools in January, maybe February.

Pull request, code review quality is way down we're having more comments, longer comments, and tons of PRs being merged without any review at all.

Incidents are way up, bugs per developer are way up, and many people will tell you that you're holding it wrong.

That's the only reason.

You're not.

Well, maybe you are, but that's not the point.

I've spoken a lot about how to hold it better when it comes to working with AI.

Probably a million views on YouTube at this point across a bunch of different talks.

And the basic thing is, as engineers, we've been told that if token maxing isn't working, then it's a skill issue.

You just need to spend more tokens.

Let go of reading the code.

That with enough harness engineering, if we maybe sprinkle some magic words, adversarial review, on enough of our PR bots, that we can get the best of both worlds.

10 to 100x faster, high quality, and nobody has to do that thing we all hate called code review you.

I'm here to convince you today that this is in fact not a skill issue,

that no amount of harness engineering or loops maxing can solve what is fundamentally a model training issue.

That's why we say the harness is not enough.

And to understand this, we kind of have to grapple and dig into how coding models are trained.

I'm going to talk about what I think the shortcomings are with some of the current benchmarks

and what better ones might look like, and we'll talk about how to move faster safely in the meantime.

It's going to sound like a rant, but there is hope here.

I'm going to talk about our journey and a bunch of the landmines we've hit building in this world, a bunch of exciting new techniques that we've been working with a lot of our users and customers to develop,

and I think how we all as a community get to

the next chapter of agentic engineering after whatever this thing that we're in.

So we use a lot of words here.

I'm going to zoom out a little bit.

I want to give you kind of like a brief history of the software factory factory, and it's actually, I just learned this last week,

the term software factory was defined at a NATO conference in 1968.

We're going to start around 2022, right before AI started coming around.

And basically, in a typical 2022 software factory, you will have some people building stuff.

You'll have engineers, you'll have PMs, maybe you have some sort of leadership team that is driving the vision here, and they all decide that stuff needs to get done.

And so you put it in a tracker, a linear, a JIRA, a beads, some sort of state machine that tracks what needs to be done.

And then someone goes and grabs something off of there and they build the thing.

And there may be some automated testing in that process, maybe some manual testing in that process.

At a certain point, we make this pull request thing.

It says, okay, cool, we've got to run a bunch of checks, automated stuff, a human's going to review the change and review the code, and perhaps we might even have a human pull it down and test it somehow.

And if anything goes wrong here, we loop back to someone builds the thing, and eventually we're ready for prod.

And so we ship it to production, and once it's in prod, it makes contact with our users.

And users do a thing that we all love.

Users love to complain.

I love our users, but yeah, they're gonna ask for things, they're gonna find bugs, they're gonna file feature requests, and that goes back to your team.

You might also add monitoring.

And so, you know, what do we want more than anything else?

We want to wake up engineers at three in the morning when something breaks, so they can get dragged out of bud to try to go fix it.

And we go on and on in this loop, and we ship a bunch of code.

And one thing that we noticed here is that Teams figured this out decades ago,

is that this someone -builds -the -thing step is usually going to take hours or days in most cases.

And the review part will also take hours or days for large things.

And so teams started doing these upfront planning, architecture proposals, sprint planning, and they would collaborate on these things as a team with the hopes

that we might decrease the percent chance that something would need to be reworked, that we would be able to reduce the time spent in reviewing every line of code

because we aligned on everything ahead of time.

This brings us to the agentic software factory.

Every company and their mother is talking about how they built a coding agent factory

that shifts 75 % of their code now.

Literally everybody.

And so if we look at the software factory from 2022, we just replace someone builds the thing with an agent builds the thing.

And we have an orchestration and a harness and a sandbox and a model and computer use, and I'm not gonna get into the details of that.

You can watch 100 talks about that this week, I'm sure.

But now the building part takes minutes or hours, but this human part still takes hours or days if you're gonna review the code and you're gonna test the changes.

And so we bring an agentic code review.

And we bring in agentic regression testing and it makes this part faster, but it's probably still the bottleneck.

But we can do more loops here.

Why not?

Let's do some more loops.

So we can route all incidents straight into the factory.

Why does someone need to get woken up and try to fix it

when they could just wake up to a pull request?

And maybe that fixes the issue for you.

You can take all the user feedback

and just stick it straight into the factory so

that people ask for stuff and it gets built

and now your only job is how much things can you stuff into the queue of stuff to do

and how fast can you review and test the changes

which brings us of course to I'm sure you know the

lights off software factory where basically Dan Shapiro coined this is

we no longer read the code we say you know what this is going great

that code review thing no thanks we're just not going to do

that anymore and we invest into all these other parts of the system you're testing you're monitoring your rollout, everything else.

We just write more code and build those systems better.

And now our job really is just how much stuff can we ask the agent to build.

I am going to posit that this does not work.

And this is why software factories fail.

As an aside, what I'm going to say has nothing to do with vibe coding.

So Addy had this great post, I'm just going to literally take his quote verbatim, a developer vibe coding a side project a dozen people will ever run

and a team keeping a 10 year old enterprise system alive for another quarter share almost no constraints worth naming.

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And most of what you hear on the internet is one

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of these groups of people telling the other group of people how to live their lives.

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So if you love vibe coding, please go on.

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At HumanLayer what we care about is how do we help people solve hard problems in complex code bases.

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We use the word brownfield a lot, which historically has meant like some 10 -year -old Java thing.

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I actually think agents really start to struggle after maybe three to six months, especially with the pace at which we can ship now.

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You can ask me how I know this, and I will tell you that it is because in July 2025, we tried this.

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We went full lights off, and if you have tried this seriously for a number of months, you probably found at least one issue that the agent couldn't solve.

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Even with your most advanced prompting, you do research, you do reproductions, you just, you have to go and dig into that code base

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that you stopped reading three months ago to try to figure out what's broken.

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And in the meantime, your site was down, your users were pissed, and if you were like me, you were probably miserable reading all this slop code that you let slip into your system.

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And what I want to get to is basically models have a shortcoming.

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They can't maintain and improve code base quality over time, not without a decent amount of human steering.

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And when When I say maintainability, I'm basically talking about issues like it becomes really, really hard to make a change in one part of the code base without breaking other parts of the code base.

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This is Martin Fowler's Shotgun Surgery textbook code smell.

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I'm not going to say much more about maintainability, there's a bunch of books that you can go read about it.

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In fact, John Osterhut is actually here speaking this week, so you can go ask him in person about the philosophy of software design if you want to.

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But it brings us to this question of like, why can't models do software maintainability? quality.

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And you may also be saying, but Dex, you know, surely the models have gotten much better since then.

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They've gotten better in some ways, but they're still about the same in others.

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If you want to solve one -off problems or Vibe code a new marketing site, yes, they got way better since 2025 and 2024.

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But as far as improving code -based quality, I think they have not gotten much better.

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Now, I cannot prove this because there are no good benchmarks for a model's ability to maintain code -based quality, and I'll get into, like, where we're going with that,

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but if you've worked with coding agents for a while, a lot of people are posting about this, it's just like.

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You probably have this vibe that they generally make things worse over time and make the code base harder to work.

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And to figure out why this happens, I want to zoom out to the first great coding agent.

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Why did Cloud Code go from nothing to $4 billion, and I think now they're at $9 billion in revenue in under a year?

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Because there were great CLI agents before Cloud Code.

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You had Ader, you had CodeBuff.

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There was a bunch of tools in this category.

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They had all the same tools, read, write, edit, grab, bash.

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So what was the difference?

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The difference was that this was the first time

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that a model lab trained a model against the harness that they were going to distribute it to users in.

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And it got really, really good, and this is just some of the tools, but it got really, really good at calling these sorts of tools in an agentic loop.

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In fact, the OpenAI team did a talk in November about basically

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if you are a harness builder and you don't own the model weights and you can't RL the model in your harness,

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you will always be at a disadvantage compared to somebody who owns both the model and the harm.

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And I'm gonna slide a couple slides from my buddy Calvin

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French -Owen who was a MCS on Codex during the initial launch, but LMs are just next token predictors.

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This is a slide from over a year ago where basically as you're doing your agentic loop, context window goes in, next step comes out.

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And we're gonna try to do this, I haven't actually timed this, but we're gonna see if we can do coding agent reinforcement learning in 60 seconds.

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So So what we're going to do is we want to train a model to get better at tool calling, better at solving software problems, we're going to give it a problem and we're going to generate a bunch of traits, try to solve the problem a bunch of different times,

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we're going to score them all on correctness and the test tasks and all this stuff, and then we're going to reinforce, we're going to make the bad behavior less likely, and we're going to update the weights to make the good behavior more likely.

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One of the classic ones here is Sweebench Multilingual, they're about 15 minute tasks, they're from open source repos like Redis, JQ, and Django and all this stuff, and they have binary one

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or zero rewards on did you fix the problem you were trying to fix

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and did you do it without breaking anything else

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and we look at actually a real problem for one of these benchmarks this is fast lane

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which is a Ruby project basically there was some issue where we weren't checking for nil

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and we have a stack trace blow up

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because you have a null point

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and in this in this benchmark you have a base commit

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that we're gonna check out before the issue was solved by

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a human in the past we're gonna give it a test patch

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that says here's what the behavior should be afterwards we have

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a golden patch both these are hidden from the model and

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so we have the agent go try to solve the problem

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we store its patch we undo all the changes it made to any test files

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so I'm sure you've seen models comment out tests to get things working

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and then we're going to apply our golden test patch

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and then we're gonna run the test old tests

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and did the new test pass and

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if they both pass then then we get the reward otherwise we don't

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and so models are trying to get the test to pass there's There's no way in this system

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that we can penalize it for poor program design or for eroding the maintainability of our system.

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That's why we get things like this.

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Try catches around things that probably don't need a try catch.

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Or things like this.

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I think Vaibov gave us this example earlier of casting things to other things just

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so the model just wants to get the test to pass.

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And so if you can't verify the maintainability of the code, it gets way harder to train on this.

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If you remember this picture, verifying code quality and maintainability is orders of magnitude harder than the code runs because the cost function of bad architects

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is measured in months and years if you have a coding episode

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and then you only find out months later

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that like oh somebody vibed this a little bit too hard it's really hard to propagate

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that reward signal back across the gap

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and now the frontier is getting better slowly

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and since I know someone's gonna be in the YouTube comments about this yes I know benchmarks

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and verifiers are different and they actually have to be separate data sets

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but they're shaped the same and the the structure of these benchmarks is directionally correct

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when we look at these as like what is the future of evaluating code maintainability

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there's a really cool one called three marathon from abundant ai where they do like 400 hour tasks

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that like clone all of microsoft excel every single feature

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and they have some sophisticated reward channel stuff deep sweep from

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data curve is also like large tasks on oss repos

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that are not actually in the training set because they were never actually built in the real world

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And then you have Frontier Code from Cognition, which is multi -PR tasks.

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They do interesting things like, hey, if the model writes tests that don't fail on the pre -patch code, then it gets penalized.

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And then we have a judge model that says, okay, did this follow all of our code quality rules?

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So we're getting better.

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But I think models judging quality can only go so far, because if the new model, if the model knew what good code looks like, it would probably write it in the first place.

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And review agents and throwing more tokens at the problem, it can raise the floor, but we're still constrained by what we can teach during RL.

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And so I will posit that for now we're stuck reading the code, but we can still move pretty fast.

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And of course, there's a world where this should solve in the future, and if you want to just keep YOLOing prompts until you get to GPP7, you don't have to think about this, by all means, please.

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But bitter lesson be damned, we've got some problems to solve, so let's engineer our way out of it.

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So, turning the lights back on, we're gonna put the code review back.

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We're gonna embrace this approach of like, how do we plan up front to reduce the chance that we have a long or difficult review process.

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We're gonna find leverage, we're gonna use AI to help with this.

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The first thing we're gonna do is we're gonna do some sort of product review, understanding what problem we're solving, what's the desired behavior, maybe looking at mockups.

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Here's a product review I was working on yesterday with a mockup of a new feature.

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Once we have our product review, we're gonna, by the way, small stuff still just goes straight straight to the agent, but once we have the product review, we're gonna also do architecture, system architecture.

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A lot of people have been doing this for a while, component contracts, data models, constraints.

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This is an example of a doc

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that we build to understand how these systems are gonna fit together and what's like the high level picture of it.

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From there we do something that I think is really under emphasized in agent encoding these days, which is program design.

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I think people assume that once you get the architecture right, the model can just cook.

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But we often look into the types and the method signatures, the program layout and the call stacks.

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So here's some examples, I don't think you'll be able to read this one, but this is like the level of abstraction we're at, is how are we actually gonna lay this stuff out and how are these systems gonna interact?

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Dylan Mulroy from Cloudflare talks a lot about how he's using these call graphs as part of his planning process, I think this is exactly right.

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And then once we've done the program design, we can do this thing called vertical slices, which is the order of implementation, multi -repo coordination, how are we gonna build this across our entire system

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and how are we gonna check it along the way.

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I've talked a little bit about how models have horizontal plans.

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I won't go too deep into it.

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If you want to learn more about this, you can go watch our talk from AI Engineer Miami.

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Couple shots of a doc like this, going through the tests and the steps in between each phase.

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The main idea here is 30 minutes over here in pre -planning and alignment can save you hours in review.

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And so it's actually feasible to still read every line of code.

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Let me skip this part.

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Basically, the summary here is like you don't have too many PRs.

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If you're drowning in PRs, you actually have too many bad PRs.

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Because a good PR is a joy to review.

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You're just reading through it like, yep, this is great.

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This is what we discussed.

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This is what we talked about.

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But even if a PR needs 20 % rework, which is generous for a lot of AI vibe -coded slop,

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It's an emotional and intellectual burden on both the reviewer and the submitter.

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And so if you use model -assisted planning and alignment, your alignment is shorter because you used AI to get all the information at once, your code review is faster because you aligned up front,

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and your coding is faster because AI did it, and so now you're actually really moving faster, but you're still reading everything and you're still owning the code.

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So closing advice, it's easy to hear all this and be a little bummed out.

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I really like the world where we just YOLO everything and we can just not have to ever read code ever again, but we're engineers and these are just constraints

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and models are good at certain things and they're not good at other things

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and so go figure out how to solve problems given a set of constraints.

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Use loops, they're great.

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Go solve hard problems, seek leverage.

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If you wanna help with this, we're building a human layer.

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Human layer is an AI IDE and collaboration platform.

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It's building blocks for your software factory and soon to be better verifiers for software quality.

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We've got sort of a Figma for cloud code and codex style collaborative workspace.

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It walks you through the workflows for doing this sort of work.

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And we are talking to design partners.

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We are hiring founding engineers here in San Francisco.

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And these slides are live.

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You can go get them right now.

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You can try human layer at humanlayer .com.

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It's free for small teams.

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Go solve hard problems in complex code bases.

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Thank you all for your energy.

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FOR YOU

本課的詞彙與口語要點

這段影片共有 246 個句子、3834 個單詞可供跟讀。講話部分時長為 18:29。說話人語速較快，每分鐘約 208 個詞，會出現較多連讀和弱讀。 85% 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
solve 動詞	/sɒlv/	解決 /解決
loop 名詞	/luːp/	圈, 環 /環
vibe 名詞	/vaɪb/	氛圍 /氛圍
slide 名詞	/ˈslaɪ̯d/	滑梯
token 名詞	/ˈtoʊkən/	代幣 /代幣, 籌碼 /籌碼
architecture 名詞	/ˈɑː.kɪˌtɛk.t͡ʃə/	建築 /建築, 建築學 /建築學
reward 名詞	/ɹɪˈwɔɹd/	報酬 /報酬, 獎勵 /獎勵
cloud 名詞	/ˈklaʊ̯d/	йүн, 雲 /雲
layer 名詞	/ˈleɪ̯ɚ/	層 /層
alignment 名詞	/əˈlaɪn.mənt/	對齊 /對齊
skill 名詞	/skɪl/	技巧, 技能
monitor 名詞	/ˈmɑ.nɪ.tɚ/	顯示器 /顯示器
bug 名詞	/bʌɡ/	臭蟲 /臭蟲
frontier 名詞	/ˈfɹʌntɪə/	邊界 /邊界, 邊疆 /邊疆
leverage 名詞	/ˈlɛv.(ə.)ɹɪd͡ʒ/	槓桿 /槓桿

影片中出現的短語動詞

單詞	發音	釋義
figure out 動詞		弄清楚
wake up 動詞	/ˌweɪk ˈʌp/	醒, 醒來 /醒來
blow up 動詞		炸掉, 爆破
check out 動詞		退房
come out 動詞	/ˌkʌm ˈaʊt/	出, 出來 /出來
find out 動詞		找出, 查明
go through 動詞		通過 /通過
pick up 動詞		拾取, 撿 /撿

需要注意的發音

說話人用了 134 次縮略和弱讀形式，例如 we're, gonna, I'm。請按聽到的簡短形式來說。

“sh” 和 “zh” 音: benchmark /ˈbɛn(t)ʃmɑːk/, implementation /ˌɪmplɪmənˈteɪʃən/, collaboration /kəˌlæbəˈɹeɪʃən/, coordination /kəʊˌɔːdɪˈneɪʃən/, bash /bæʃ/
長單詞——注意重音位置: architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, developer /dɪˈvɛləpɚ/, implementation /ˌɪmplɪmənˈteɪʃən/, intellectual /ˌɪntəˈlɛkt͡ʃʊəl/, collaboration /kəˌlæbəˈɹeɪʃən/

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 solve, loop, vibe 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
現在完成進行時 have/has been + -ing — 以前開始、現在仍在繼續的動作	we've been working · have been doing
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	there's been · I've spoken · we've been told
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	been said · being merged · we've been told
定語從句 who / which + 從句 — 補充說明人或事物	somebody who owns · Cognition, which is · days, which is

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: Why Software Factories Fail - 通過影片學習英語口語

選擇練習模式

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同頻道

The Multi-Agent Architecture That Actually Ships — Luke Alvoeiro, Factory

Anthropic's CCA Exam as a Field-Guide for Agentic Engineering — Frank Coyle, UC Berkeley

How I deleted 95% of my agent skills and got better results — Nick Nisi, WorkOS

"Software Fundamentals Matter More Than Ever" — Matt Pocock

Beyond Code Coverage: Functionality Testing with Playwright — Marlene Mhangami, Microsoft

Don't Build Agents, Build Skills Instead – Barry Zhang & Mahesh Murag, Anthropic

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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

需要注意的發音

如何用這段影片練習

影片中的語法

什麼是跟讀法？