शैडोइंग अभ्यास: Why Coding Agents Keep Making Your Codebase Worse - वीडियो के साथ अंग्रेजी बोलना सीखें

Oh my god, I ran out six Codex subscriptions in a week.

Isn't that amazing?

What did you ship, dude?

A slop code bench?

Oh, that's incredible.

Listen, have you seen this?

Do not mortgage your code base in the hopes that in a year, the model will come out that's smart enough to fix all your problems.

I'm never letting anyone merge anything with this pattern in it again.

Am I 99th percentile?

Am I being left behind?

Do less things in parallel.

The same thing

that was always true about engineering is still true in all

work today i'm joined by dex horthy ceo of human layer

the man who coined the term context engineering we talk about

most of what you see online for agents is completely fake

why token maxing doesn't work in the real world how benchmarks work

and which one you should look at how much faster you should actually aim to be with agents

and it's not 100x

and at the end he shares his agentic engineering workflow what is automated what is a software factory

and where does he still spend this time i saw a bunch of people commenting as well

that like astra got much better at computer use it got much better at like blenders

and like game slop and this kind of stuff um

but it didn't actually get that much better at like writing maintainable code

or like controlling complexity or like you know removing things

or like changing abstractions or whatever it is uh

and uh i think i think i think

that was my feeling a lot and like a lot of people had the vibe and

so one of the things we're talking about tomorrow is sort

of this uh benchmark i discovered uh called slop code bench

it was made by uh the lab of um gabe orlansky

he's like a researcher at the university of wisconsin have you

seen this no a slop code bench oh it's incredible uh it's an entire benchmark designed around proving

that like models make code bases worth worse over time really yeah okay

so up until

that point there have been like i would say like three

flavors of benchmarks there's like the core like sweet bench benchmark

which is like here's a tiny pr from a real open

source repo uh we'll rewind the repo to before the pr

was made we'll like pull the tests out of the pr basically say hey model here's the issue

that was filed yeah 10 years ago on this random ruby project

or whatever it is uh go like fix the issue

and then we would take basically any changes it made to the test we would throw them out

and we would drop new test like the test in from the benchmark case

and then we would run those tests against the models code

and then if it passed it would get a reward and

if it failed then it also has to like do it without breaking any other tests uh

but it's very very like small targeted like can you get

the test to pass yeah um obviously these are benchmarks they're different from verifiers

but it's like all the benchmark oh this model score is 95 on sweet bench coding is solved

and it's like that's not what real coding looks like

so you have this other chapter of things where it's like

okay cool instead of solving this one little pr it's like

rebuild all of it microsoft excel from scratch with every single feature

or rebuild all of redis from the specs with every single feature um

so have these like marathon benchmarks um those two are from

one called uh sweet marathon actually okay it was like four to 400 hour tasks uh

and then cognition introduced this idea of like yes okay we're gonna use much bigger tasks

and also we are going to have a judge model run afterwards

and give like contribute to the score on on the exercise

of like did this thing break any of our code base rules

or conventions or patterns or whatever it is

so it's sort of like telling it like hey look in

c++ we can't do logging like this it has to be like this

uh but none of those really map to how real software is built

which is they all give the model the entire problem up front yeah here's the thing solve this thing

and what slop code bench does is really interesting is it it gives the model a thing to build greenfield

and then it says cool now take that code base

and add this feature now take that code base

and add this feature now take that and

so they have these like challenges each has like three to ten checkpoints in it yeah and

so the problem is like what it's trying to demonstrate is

like as models continue to work unattended in the same code

base without knowing the whole problem up front it's like

if you know the whole problem up front you can architect for it

but without knowing the whole problem up front you have this

you have this problem where like the code gets worse

and worse over time and it kind of shows

that like the number of failing tests just like trends up for all the models

and like i think gpt 5 .5 codex

when they ran it in may got like a 15 on this oh yeah

and i just ran it i've been doing some like independent research

because they're they're a lot they're not super funded

so we've been kind of sponsoring some research there

and we did it for astra

and astra got like 16 .5 okay one point higher the ability to maintain like complex code bases over time

so anyway we're gonna talk about that because it used to be a vibe

but now there's like okay this i mean it's you know

it's one benchmark out of one lab it hasn't really been

adopted by a lot of model cards I think partially because it's really slow and expensive to run

but I think it's a fascinating sort of

you know proof

that models are still not great at maintaining like a code base over time do you expect

that to change that models will get better at code bases over time

or will we solve that with regards to harness engineering well

so here's the crazy thing is like we used to do

this thing in the like claude 3 .5 3 .7 maybe

like opus 4 days where it's like cool we're just gonna yes i know the code is sloppy

but by the time that matters that'll be gpt7's problem we're on gpt6

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and we haven't made a lot of progress there

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so it's like you can't guess when

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that will happen it will probably eventually happen

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but it seems like the labs are much more interested in

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like hill climbing on other sorts of things like computer use

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and other like frontiers

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that like the last 10 percent of software maintainability may be like as hard

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or harder than the whole first 90 percent

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that we spent the last three

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or four years on yeah interesting what are some of the

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problems where you think the industry is headed like i heard

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a lot of conversations yesterday about remote agent execution a lot

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about multiplayer i still don't know exactly how that's going to

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play out in practice like the theory makes a lot of sense yeah i get

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that get it running off my machine get it running somewhere locally

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or centrally where other colleagues can also look at the context

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and contribute in essence but i haven't seen a company kind of succeed with

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that i feel that's quite frontier stuff yeah yeah

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and i mean a lot of the cloud agent vendors like you look at cursor cloud agents

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and cognition and devon and things like this they've made good progress there um

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but i think uh the the challenge we we work with

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a lot of like larger enterprises our whole thing is like okay

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if you have a hundred

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or more engineers that's where like we have plenty of five person teams

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that love human layer but

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if you have a hundred plus engineers that's where human layer has like the biggest impact

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because it is all driven around this collaboration

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and those teams tend to hit a lot of friction

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when building with cloud agents because they have 10 years of history

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and hundreds of repos and lots of like opinions and asterisks

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and special things about how dev environments are run

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and how can the agent test its work and things like this

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that become quite complex

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so I do think the future is kind of remote background agents both for the collaboration angle

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and also for the like hey I want to be able to close my laptop

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and do this or it's like for small stuff

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if i can kick off a copy change from slack

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and i don't have to bother a developer with it like i

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but i think i think setting up a like ticket to pr

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or like slack to pr workflow is pretty straightforward for the small stuff right like ramp

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and brex and all these companies they've all like published papers i'm like hey we built this background agent

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and it solves 50 of our prs

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or whatever um i actually think that's like not that exciting

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because i've talk to people who've built that in you know a weekend

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or a hack week or something the the bigger question

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that i think um i'm especially excited about trying to figure

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out a human layer is like how do we enable people to collaborate meaningfully on the bigger pieces of work

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um where it's like yeah i could tell the agent to do this thing

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and then i would get a pr out

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and then it would probably be somewhat right

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but it would have a bunch of issues then i'm talking back

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and forth with the agent on pr comments or back in slack thread

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or wherever wherever it is but it's like okay you went

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and wrote the code and it's not good

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and it's a lot harder to like basically like fix what

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was already built agents kind of already committed to a thing there's a lot of context to think through um

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and so our whole thing is like what teams have been doing for 30

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or 40 years back when building a feature took hours or days not five minutes

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is they would like get together

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and they would align on what's being built either with the agent

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or with each other

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or both i mean the metaphorical whoever's actually going to build

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the pr is going to talk to the senior engineer staff engineer

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and just like have a conversation

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and like okay here's the approaches we're thinking about this usually happens in like google docs

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or confluence or whatever it is

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but this whole shift left idea of like can we

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and like you're never going to one shot it right there's

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no there's no amount of effort where it's like okay we're

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going to spend like five hours in a meeting building a perfect plan

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and then we're going to give that to the agent

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and the thing

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that comes out is going to be perfect it's like no

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there's still going to be issues with it like you can't capture everything uh

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and the source of truth should be the code not some specification or plan

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but can you in this planning process eliminate as many failure

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modes as possible can you just you know from a 25

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000 foot view of the problem like a one pager can i eliminate like 50 of the bad things

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that the agent might have done not necessarily going to do

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but could have done and then can we zoom into you know the 10 000 foot view

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and eliminate another like 80 of bad solutions to this problem um

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so that by the end

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when the code comes out it's more likely to be you

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know zero to ten percent rework required rather than you know

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25 to 50 to 75 percent rework which is common with like

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if you try to one -shot most larger agent prs like

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people don't just say hey claude go do this thing they're sitting there working back

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and forth if it's large which ingredients make it a successful plan in the end

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because you mentioned okay what we've already been doing

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when features are kind of more expensive or more time intensive to build we're doing that now more and more

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I feel like people are leaning towards or experimenting with spec -driven frameworks, which I think is good as well.

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But when we're talking about, okay, is this problem actually worth solving, is that then part of the plan or how we solve it from an interface perspective?

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Is that part of the plan or more part of the spec?

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Where are the lines?

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Yeah, I mean, I think the words plan and spec are like kind of mean different things to everybody.

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So I won't, I don't want to get up

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and be like the spec is this and here's what it looks like and here's what it's for.

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And the plan is this and here's how it should be formatted

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and here's how it should be used like i i i

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don't think i think every team's gonna kind of come to

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their own kind of taste on this i think we have

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some opinions on like what a good starting point would be

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but we always

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when we work with customers we always end up like okay

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cool like let's add a little bit of customization to your

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we call it a design doc rather than a plan

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because it's like we separate out like okay cool where are

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we going what's the end state versus like how do we

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actually get there what are the changes we're gonna make um

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but our design doc often is like we'll put some customization in there for for users

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when we're kind of forward deploying with them

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and just say like okay cool your design doc also needs

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these four questions answered at the top before we get into like everything else

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that it kind of comes by default yeah um i don't know

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if that really answers your question i think personally it does as long as it's fine right

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if if it's fine that teams will eventually define what is the plan

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and what is the spec

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and what works for our environment rather than following some standard

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yeah in essence they have to be effective as a team

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and i think it's very context dependent also organizationally

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so in the end i think that's fine you mentioned humiliator is good

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when the engineers are like in the hundreds

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or even beyond at a bigger scale and for me i am wondering

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when agentic engineering kind of breaks or at what scale so

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if we're talking about 100 engineers

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or my context i have a thousand engineers we're trying to

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figure out how to kind of enable people in terms of

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agentic engineering yeah one of the pain points i see is that we make models available.

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We make onboarding as seamless as possible, give people access to internal services through their agentic tools.

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And what I still have a challenge with, and I don't know how to solve yet, is how do I prevent everyone from using the biggest, most expensive model for everything?

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Not just for where it's actually necessary, but for everything.

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Yeah, we work with plenty of teams where Fable has just been banned.

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Figure out how to make it work with Opus, and you can submit a special request for like temporary access to fable for like a specific thing

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but you kind of have to sell it it's straight to

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jail fable yeah um yeah i mean i think the reason

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why we care about this space is one like i've been

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building software factories my whole career basically uh for my first

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job i was like i was just a back -end platform engineer

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and i immediately identified like oh

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that guy over there like one of the first engineers of the company

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that maintains the deployment system and the rollout system

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and the staging environment

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and how like engineers create preview environments of like hey here's

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my feature work in progress like hey pm here's a url

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on the internal network like you can go look at what i'm working on kind of thing um

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that seemed to be the most like impactful person in the company

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and so that was kind of the work i was always drawn towards um

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and what we're finding is like a lot of tools work great in this space uh

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that if you're just building like an xjs app

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or you have a mono repo with everything in it like there's a lot less friction

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but i think like there's a lot more like flexibility

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and thought called for in the case where it's a larger team with a lot of baggage

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and opinions and dev environments are complicated and hundreds of repos

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and like i don't know we we had a we had a i won't call it a complaint

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but like a pain point

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that some people were having is like well we really want

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to get our our pms using ai as well product managers to build better prds

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and of course like someone who doesn't have a lot of skill with ai they say go write this thing

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and they get a 10 page document out

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and they're like amazing here let me go give it to

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the team the whole team is like oh my god what is this like ai psychosis slop

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that you're making me try to read through yeah um

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and part of that is just like giving the model better steering

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and skills of like hey here's how to keep it concise

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make sure you're writing for humans not for whatever uh and

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so make it clear and make it visual

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but it's also uh it would find

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that a lot of the things proposed in these prds were

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so detached from reality in terms of like how does the code base work

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and what are the systems and so like a weird like tactical thing we found

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that works really really well is like give a product person

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who's technical enough to like check out the repos on their box

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and things like this or like set up github mcp to go get repo contents um

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and have them you know drop in a two sentence prompt

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and then let let like a kind of pipeline take over

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like cool here's the thing we want to build go go

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research everything go find across all 200 repos find the seven that matter to this product feature, pull all that context together in one thing.

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And then like interrogate the user, the product person in this case of like, cool, what problem are we solving?

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Because all the things that go into a good product requirements, what problem are we solving?

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How are we going to measure success?

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Uh, like, how are we going to know that it's working or if we should throw it out and abandon this thing?

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Cause everything in these bigger companies, everything kind of is an experiment.

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Right.

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Uh, and then going through like, cool, how should it work?

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Should it work like this?

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Or should it work like this?

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Sort of the like grill me style thing.

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Um, and so like, this is just one example of like.

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that's not really built in and like some teams will build

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that internally and they'll come up with these things slowly over time

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and what we're trying to help people do is like speed run

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that process it's just like

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if you don't have any opinions about how to build good product specs with ai here's a thing

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that will work for you know 80 of teams no matter

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how many repos you have no matter how old your code base is um and

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so it's it's basically like it's as much as anything it's

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about raising the floor that's why like staff principal engineers they come to human layer and and say like, oh, like, I like this.

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Like, I can kind of see this maps onto kind of how I was working, but I had no idea how I would ever be able to take how I was working

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and give it to everybody else on the team.

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Yeah, yeah.

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I like that you highlight that.

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The decision -making in the end, that's still what a human does, right?

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You bring all the context and it's baked in the harness or people are self -solutioning kind of something similar.

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And in the end, it tries to provide whoever is the user with all the context necessary to try and make decisions.

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And likely, if they don't know the decision, they can still kind of spar or ask questions or kind of, in a grill me way, get to an answer in the end.

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For me, when I heard that dark factories is kind of in my bubble, a lot of organizations are striving for dark factories.

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How do we get there?

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How do we do that?

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And I still don't know if that's like a realistic outcome.

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Because especially when someone gives me a magical answer and it's not what I want, I'm just going to say, well, it's not what I wanted.

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But if I created this magical answer based on my decision, like I'm building my own conviction that this is the answer yeah

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so i have way the buy -in is very different i

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was sitting in a meeting yesterday it's all hype it's everyone

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wants to impress each other everyone wants to claim they've figured

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out the dark factory thing i i think most of it

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is is bs okay i was sitting a meeting yesterday um

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with like the head of a department for a very old company you know thousands

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and thousands of engineers um

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and talking with kind of principal engineers on the ground who

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are like drowning in slop prs from people who you know

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either just don't care don't have a strong software engineering background

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and uh he's like so you're telling me that the code

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that is written by these people using ai is bad

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and the two principal engineers are like oh yeah

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and he pulls up a pr it's like look at this 30 000 lines of slop

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or whatever it is like all right well i gotta go talk to my friends at like really big company

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and other really big company who have claimed

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that like 90 of this org's code is written by ai now

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and i got to figure out what what they're talking about

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because you're telling me that that's like completely infeasible

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and like i have to figure out what they're doing that's special

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or i have to like figure out why what is the

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agenda anyways the point is like there's this whole like i

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don't know uh it's like a posturing game among all the top executives at all the top companies

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and everyone in in big enterprises everyone's like am i 90th percentile am i 99th percentile am i being left behind

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and this is like a thing that this conversation at every executive meeting

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that happens is people are just like everyone writes down their

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number of like where do you think we are from zero

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to 100 among all other companies in our kind of zone

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or size yeah um i talked to andre brestloff today actually yeah

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and he coined something which i'm gonna steal he said this is kind of the instagram

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phase yeah right now everything is fake god i love it everything is bells

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and whistles and if you say well actually for me it doesn't work people say skill issue

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because how dare you kind of try

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and disprove my accuracy of what i'm portraying online as i disagree that's incredible i love

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that i mean a lot of it happens on twitter

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but yeah it's the same it's the same thing it's like

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everyone is running around talking about how cool their factory is

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and how many things they have

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and how much adversarial review they're doing posting videos of the cool thing they built

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and I don't know for a

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while there was a thing of like how many tokens there

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was people have gone off on this a little bit

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but it was like the token maxing thing of like oh

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my god I ran out six codex subscriptions in a week yeah isn't that amazing

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and it's like what did you ship dude it's like oh I made like a bunch of games for myself

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that no one will ever play

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and I'm like yeah that's what I thought I've also done

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that I mean it's fine it's

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and it's trying to be like hey I made this cool

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thing check it out I don't think it's okay to be like

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if you're not maxing out six codex subs then you're a

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bad engineer i think that's like hurtful to the industry

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and it's like it's already there's already a lot of noise

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and hype and confusion

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and my my sort of crusade has always been like okay what actually kind of works what is practical

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and like what can you what understanding can you build up

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from like first principles rather than like working backwards from what you see on the internet yeah

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if we give productivity a multiplier player.

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Is 10x then realistic, or when should people be happy?

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Is it like 2x, 5x, 3x?

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Do you have a number?

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I mean, if you think about it, like in terms of like economic impact in the world, I think every engineer going 2 to 4x faster would be transformational to human society.

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The amount of value that we would get, the extent to which our cities would improve, the value of things would improve,

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like just life would get better would be insane.

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But everyone's going to miss out on that because they're too busy trying to get to 100x.

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And I think 10x is possible.

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Sorry, what were you going to say?

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I was saying people are trying to go infinite.

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We don't need engineers anymore.

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Yeah, people are trying to go infinite or 100x or 10x or whatever it is.

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And it's like, figure out how to go two to three times faster.

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And then you can go do two to three times on top of that and two to three times up on that.

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But if you try to throw out everything we've done, I mean, there's something to be said for like, don't get stuck and shackled to your traditions and like some things

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that you used to do may not be necessary anymore but

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if you go and throw out everything that we've been doing

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and try to like rebuild all of engineering from scratch it's not going to work it's just like

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and when an engineer sets to do a project

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and they're like ah yes i'll architect it to be able to do this this this this

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and this when all you needed was this one small thing but

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and what is it the jagney you ain't gonna need it

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it's the same thing is it's just the same story over

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again is like people want to have this perfect match

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and there is a time

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when you're like cool we've kind of like taped this together enough

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but now we understand the problem now we can go build the next thing

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and too many people are trying to skip

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that step trying to skip the like okay let's just get faster

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and faster and faster incrementally over time because that compounds exponentially

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but um I was talking to Dario this morning

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and he said from his perspective what makes his team at

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AMP really productive has nothing to do with AI is they

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don't do pull requests anymore yeah I heard about this yeah everyone just pushes domain exactly

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so no pull request fatigue no no one wants to do

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code reviews anymore still do code reviews i'm like huh maybe

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it's time for me to run an experiment to see

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if it's actually now's the time to do something like

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that i think it's worth trying i mean the thing about

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pull requests is like every team's pull request culture is different

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like on my first job everyone had merged on main

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and there was no pull request requirement

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that was enforced it was a cultural thing it was like

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you don't merge without getting a review from somebody else on the team

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and then culture beats process every single time and so

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if you have a team where like okay we have a

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cultural thing of like you don't push slop to main

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and it's like you don't you don't shirk responsibly well the reviewer reviewed it too

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so we're both guilty

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if it's bad it's like no you own it you are the only person who owns it

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and if someone finds it and like realizes that it's bad

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or you realize

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that it's bad later like it's still on you it's just

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uh we you know we're changing we're changing the process of like how do we guarantee quality

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and what are the outcomes and how do we manage risks

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and things like this i feel like to get to

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that culture that could be a fun experiment right you remove the pull request

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and see if because you're doing

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that you can get to a culture of we own what we push to production

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or to main yeah we don't push any slop especially with

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agents how easy it is nowadays you take ownership see if

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that kind of is a cycle

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that then starts to roll do you did he mention like is is it

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that pull requests aren't required

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or like pull requests are not just not a thing it's is killed

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because i could i could see a world where like okay look i don't require review

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but i want to review on this yeah we have this

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for doc review right like i don't recommend anybody say like

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oh design doc reviews are required before you write the code

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if you didn't get your design doc review the pr is not relevant

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and it's like no i'm incentivized to send my co -founder

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who's the cto is like the kind of slop the slop gatekeeper

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and make sure that like the code base stays maintainable over time

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and things like this i send him my design docs

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because i know it's a lot faster for him to be like no no no on it's easier for him

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and easier for me for us to have

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that discussion on a one page or a two pager

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and then when we get to pr time like after i've polished it

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and i've got the test working and i've maybe like done some stuff

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that wasn't in the plan of like okay let's just change this ui

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and like riff a little bit over here um

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and then he says oh this entire architecture is wrong

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or like i don't like that this was a separate table

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and it should be this instead like

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so I think there are incentives to get feedback from people as early in the process as possible

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and like it's easier to fix things before you merge it

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than after yeah once it's in there then it's like okay

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cool now we're like fixing bugs in production yeah 100 percent yeah how's your thinking evolved

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when people said we're no longer looking at the code

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or doing code reviews specifically we have so many deterministic tools

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that look at code conventions

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and now we even have built -in verification layers where the agent just sends me screenshots

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or sends me videos about the functionality that it's being built

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so i don't necessarily look at the code anymore well there's

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two reasons to look at the code one is to make sure it's correct

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and one is to make sure it's maintainable i mean there's

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a bunch of other reasons there's like alignment with your team

466

and things like this but like in that world like the the video

467

and the screenshots tell you whether it's correct in terms of

468

like the features there doesn't tell me anything about whether the code is maintainable

469

and i have seen many many deterministic litters we use a

470

bunch of I'm going to talk about some of them actually tomorrow during my talk.

471

that help like block large categories of slop um

472

and you can do things like cyclomatic complexity

473

that is like tries to like keep you from creating functions

474

that are too chaotic and hard to grok for a human

475

but i have never seen a prompt or a deterministic tool that can like drive code -based

476

maintainability and fight the model's tendency to write unmaintainable code in any meaningful way without a human in the loop.

477

Do you think we can close that gap with deterministic tooling?

478

That would be, you would make a lot of money if you could figure that out.

479

And too, this is an interesting company in that space, right?

480

Because they're doing deterministic tooling for correctness, but not for maintainability.

481

No. But you want maintainability.

482

You want maintainability uh i use uh matt pokok has a skill called improve code base architecture yeah

483

which i use quite a bit and it does it does help um

484

and there is there are times where i'll just like run this skill

485

and then like cool i'm not even going to read the

486

the discussion just go make the first three fixes there

487

because i'm sure it's probably better than what was there in the first place

488

but i think in general like that the thing that makes

489

that skill useful is is having a human in the loop

490

the ability to be like no i don't like i don't want to refactor it

491

that way i want to refactor it this way and Like I think humans are good at this,

492

uh, skilled experience software engineers are good at this because they have like been bitten by bad architecture.

493

So many, they've been up at three in the morning trying to fix an issue and been like, Oh my God, I hate this pattern.

494

I'm never letting anyone merge anything with this pattern in it again.

495

And you do that hundreds and hundreds of times over the course of, you know, five, 10 year career, you build up this taste.

496

Uh, and we haven't figured out how to put that taste into models. And

497

so for now humans have to do it we might try

498

to figure it out uh literally the last model came out the astro came out

499

and my buddy ian who ran a built a coding agent startup

500

that got acquired by expo uh he posted he's like i

501

am begging the rl researchers at the lab please start including tasks

502

that have like deletion of code as the goal

503

or like hey let's take these three things

504

and refactor out a good abstraction like it's just so obvious

505

that like either we don't know how to do it

506

or we don't care to do it in the in the rl like layer uh

507

so i it could get cracked i mean anything can happen uh

508

but again also is like cool there's all this hype

509

and what is the future and no one knows

510

when it's going to happen

511

and my advice is always like do not mortgage your code base in the hopes

512

that in a year or six months

513

or 18 months a model will come out that's smart enough

514

to fix all your problems yeah someone listening might think i fully agree with the taste aspect

515

but i don't have 10 years under my belt I don't have 15 years under my belt pre -agentic tools

516

or pre -agentic era how do people still build taste from your perspective nowadays and how can they do that

517

I mean I think I think it comes from from burning yourself

518

and stubbing your toe I think it can also come through mentorship

519

and this is a thing I think about a lot

520

and I don't have a good answer for

521

which is like how do we bring on the next generation

522

of software engineers it used to be through pull request review right it's like

523

and like you think about like the goal of a the

524

worst pull request comment is like this is wrong i rewrote

525

it for you here you go like the person learns nothing uh

526

and they learn that

527

if they write bad code someone will just come fix it for them yeah

528

so a good pull request for a good senior engineer

529

or manager will be like hey i don't think this is

530

quite right can you like look at how we do this over here yeah

531

and like do that pattern or why did you do this yeah like try

532

and probe exactly what's the logic behind this why why why

533

not you know whatever whether you give them the answer

534

or not it's like okay why do we do this can

535

you is there any other places in the code base where and

536

that forces you to learn right you have to go read

537

other code oh that's okay cool that's cool i like that

538

and it like you reinforce uh and now if you give comments like

539

that that used to be really helpful they're not

540

because i i mean i talk to people all the time who are like yeah i'm commenting on this pr

541

and i know that the person is not they don't understand the code they wrote

542

and they're not really going to understand the comment

543

and they're just going to paste the comments to their ai

544

and it's going to make the fixes and at

545

that point like i'm doing every part of your job

546

that matters you didn't think about or understand the code before you committed it

547

and shipped it you just love the agent do it

548

and then like you're not even thinking

549

and under you you have the option some people will

550

but like you have the option to sometimes it just feels like i'm just talking to your agent yeah at

551

which point like i might as well just have my agent do it zero zero value on top yeah um

552

so i don't know i've seen mitchell hashimoto do this actually

553

a couple times on on ghosty prs where he doesn't even say hey this needs to be like

554

that over there he says ask your clod why this is wrong that's it he gives no hints

555

or anything yeah he's just like I will tell you

556

that this is wrong I won't tell you why I'm forcing you to whether by understanding the software

557

and building taste in software engineering or by understanding agentic coding

558

and being good at using agents to understand the code base

559

whatever it is like you need to go you need to go do some more homework yeah So I think, I think that's directionally correct.

560

Um, I also think there's going to be a lot more the same way we collaborate on code

561

and pull requests right now as the, as, as design docs and plans become more standardized and formalized, or at least their purpose,

562

parts of them become more universal.

563

There will be a lot of training and coaching that happens during design because design doc is basically a prompt.

564

And so two people, five people commenting on a design doc, you're teaching the author how to prompt better it's like okay

565

cool like I don't like this section like you know you need to go find other patterns

566

or whatever it is and that

567

that there's still a flavor of this where it's like okay I'm just gonna give it to my agent

568

and we're gonna like I'm not gonna learn anything

569

but like I think like learning how to build a really good design doc

570

which again learning how to build a really good prompt for the whatever session is gonna be building

571

or in that session um is uh is going to emerge as well as like just as important

572

if not more important than code review i feel like i

573

learned a lot through kind of defining solutions in a team early in my career

574

because i came from not a traditional computer science education i got rolled into a team

575

because i said i want to do engineering not just operations

576

because i was responsible for operations and then i learned the process of refinement

577

and in that team specifically we had the convention of we also define the solution direction not always

578

but most of the time and sometimes as part of executing on the work

579

that needs to be done the person executing would define the solution direction

580

and align on the on the way but for the ones where we did

581

that in advance i love just listening and learning

582

and asking questions on y x y and z

583

and thinking back now likely whatever we captured back then in

584

jira would have been great food for an agent to then execute on

585

and even the person picking up that task of the work

586

that needed to be done didn't have to do too much thinking anymore of exactly kind of how to execute

587

or what to execute maybe how still because you always have new findings

588

but mainly why and what was answered which i loved

589

and i learned a lot from now it's kind of up to the individual i feel like

590

and my fear is that because it's

591

so easy to be on an island with your agent

592

and go into execution mode create slop

593

or create something valuable it's really up to the user that you lose that that kind of collaborative effort and also interaction,

594

I feel like we'll miss that if we don't put extra effort in that.

595

Yeah, I mean we're talking about building a feature in the

596

human layer where you can call someone on your team

597

and do a voice call and then the transcript becomes an artifact attached to the task because that's the last piece, right?

598

I think the AI native forge is much more than GitHub that manages code.

599

It's going to be the code, it's going to be the artifacts

600

and it's going to be the sessions themselves all kind of linked together at like the top level

601

and the last part is like the human conversation and

602

so like maybe that's part number four is like can you reify those things for the agent

603

but yeah I totally agree it's like a lot of the learning happens

604

in those human conversations with the people who have that learning

605

and like if we can't rely on the models to absorb that taste that

606

things then we have to find other ways to give them

607

to people yeah yeah as a last thought i wanted to

608

zoom in on kind of what your agentic engineering workflow looks like nowadays yeah

609

because i feel like people listening they might see something online all instagramified i'm going to keep using

610

that and keep echoing that because i think it's quite accurate and

611

if they ask a question someone might say skill issue i

612

have no clue what's real what's not real what works for you nowadays um yeah

613

so we do

614

and i i did a pretty fairly deep breakdown on this

615

on um i'll give you a link to put in the show notes

616

or whatever but i basically have a page

617

that is all the podcasts we've ever done all the all the written content

618

and video content we've done

619

and i've done a couple deep dives on our process with screen share

620

and stuff awesome i would say probably at least 50 to

621

60 percent of our work is like basically one -shotted uh not one -shotted like to perfection

622

but like we don't do any planning up front

623

and it's literally just like

624

if it's going to be under a couple hundred lines then um then we just do

625

that about 10 percent of our prs are merged by you know call them like janitor bots

626

or whatever that run overnight my co -founder gave a talk on like control loops

627

and how we build all of this but we basically have like things

628

that we want to be true about the code base like desired end state

629

and then we have like a process to progress the code base

630

that direction it's kind of like a thermostat right okay i want it to be 71

631

or whatever the european version of 71 is uh

632

and it's 69 cool we're going to turn on the heat um and

633

so it's like we wake up every morning to a handful

634

of prs along different dimensions uh of cleaning up something in

635

the code refactoring fixing a react doctor rule migrating an endpoint

636

from the old framework to the new one everything we do automatically in the factory is like very small

637

because we want it to be digestible

638

if the pr is too big it never gets merged um

639

that's probably like 10 of our prs again about 50 are

640

just like hey i want to make this small fix go do it

641

and then i'll test it and maybe like send a couple more prompts on it um Um...

642

Does your day then also start?

643

Wake up, look at the PRs that ran overnight were created then?

644

Yep.

645

We don't merge them every day.

646

It has like a Kanban thing where like if it doesn't get merged, the thing doesn't run the next day.

647

There's always only for each type of PR of which there are like three or four or five, maybe six now.

648

There's only a max one open at any time.

649

And then we use labels to basically say like, okay, if there's already open PR with that label, don't try to fix that thing

650

because you might just try to fix the same thing again yeah um

651

and then probably about 30 of our 30 of our like code shipped it's probably fewer prs

652

but about 30 of what we ship is done doing through a like specific process

653

that we use called rpi

654

or like the new version of it is like crispy although

655

like now it's like qr do i cordoy i don't know anyways we've lost uh i stopped using acronyms

656

but it's all i would count it all in like the rpi family

657

which is like go do a bunch of code based research

658

um take what the user asked for go build a query plan then do another session

659

that doesn't know what the ticket was have that do the research

660

and then like one or two or three sets of like planning sessions uh

661

if it's a very ambiguous like large thing we'll do like

662

a product requirements first otherwise we'll get straight into the technical design

663

and then if it's fairly small

664

if it's like it's it's too big to one shot

665

because it just needs a lot of context from a lot of places

666

but it's like fairly small we might skip the actual like

667

here's the steps it's like cool just go implement this design uh

668

or if it's quite large

669

and i want to check it along the way i'll work with a model in a third type of document

670

that is like breaking down what order are we going to do this in

671

and like how am i going to check it along the way

672

because like it's a lot easier to ship 10 000 lines of code

673

if you check it every thousand lines uh

674

and like before it gets out because

675

if you get to 10 000 lines of code

676

and it's broken uh then you have a massive surface area to debug across

677

but if you can get it you can get a 90 in the first thousand

678

and like fix that

679

and then do the next thousand okay that's okay the next

680

thousand fix ten percent it's like much less overall work

681

and i think much less mental effort as you go you're

682

like checkpointing along the way to get to the final solution

683

sometimes we even do them as individual prs like well what's the plan for stacked prs on this

684

and like what's the first mergeable bit that we can do yeah uh

685

and then we go implement

686

and like use we use sub agents we sometimes we'll use like you know fable input orchestrating soul

687

or we use soul orchestrating fable

688

or we use just opus as the as the main thing

689

and opus as the sub agent like kind of depends on

690

the task i use soul for almost everything now for user

691

choosers um yeah the user chooses what the parent model is

692

and then there's a couple there's a couple like less obvious

693

hacks you can do to do like cross provider uh like

694

if you want fabled orchestrate sole sub agents you have to

695

tell it to use the codex cli right now um we do have a custom harness

696

that will support that soon uh because we just think it's a cool pattern

697

that works well um but uh yeah

698

and then from implementation you know it's review the code as you go

699

or review the code at the end uh make sure it's

700

good we have a kind of an internal cultural rule is like do not send anyone code for review

701

that you yourself have not read like do a pass on

702

it don't just make me read your agent slop sometimes i read the code

703

and i have to change 50 percent of it sometimes i have to read the code

704

and i actually throw it all out and go back to the design

705

because i'm like oh kyle's never going to approve this okay what's a simpler way to do this yeah

706

and sometimes i read the code and it's

707

and it's perfect yeah i think people underestimate

708

that you can just throw out the output

709

and try again well that's why also i love this like incremental like zooming in planning process like product tech

710

and then like the plan of the steps that we're going to do

711

because at any point you're you're getting more

712

and more specific at any point you can be like oh

713

i don't like this okay throw this out we're gonna go back to design we're gonna change some stuff

714

because if we have to do that then then i don't want to do it

715

do you ever see a world where

716

because this is also kind of in my bubble what people

717

that are in leadership teams are envisioning yeah

718

that a lot of the day is working on more of the planning

719

or specification how that is defined within the org or the team and then overnight the work executes

720

yeah i mean i just i i don't want to wake

721

up to 10 000 lines of coke it's like not like

722

if i'm if i'm

723

if i'm planning on reading the code i think the work

724

is a lot more human in the loop than than

725

that um they're very specific things that lend themselves to hill climbing overnight um

726

but i think a lot of the work you do you're

727

just gonna you're going to enjoy it more you're going to

728

get more done you're actually going to have more through because

729

that that also requires you to do a ton of paralyzation of course it's like plan 10 things

730

and then kick them all off overnight and then

731

when i'm gonna spend the first four hours of my day

732

reviewing code like no i'm gonna plan two things i'm going

733

to start them now i'm going to like watch them as they go

734

and i'm going to get changes landed in production today it's

735

much better to do two things two things two things than like okay i have 10 things in flight

736

and they're all kind of going and oh i forgot about two of them

737

and by four days later i did ship two of them

738

but the other three are still in flight it's just the same thing

739

that was always true about engineering is still true and all work is like

740

do less things in parallel it feels more efficient to like uh

741

I don't know how to say like to like try to like multitask really hard uh

742

but your end -to -end throughput of like value delivered to the customer will be faster

743

if you do less things like a little bit slower

744

and just like take a little bit more care with them interesting I wonder

745

if the way we get access to models like

746

if I look at myself my own license with cloud code

747

yeah I spent 200 bucks a month yep at least uh

748

I have weekly limits for my models yep able has a little bar

749

that I want full

750

or close otherwise i don't feel like i squeezed enough juice out of my license yep

751

so when it's friday and i know it's saturday

752

and it's reset time um

753

and i still have a lot of work to do that's really

754

when i kick out these night processes where sometimes i don't

755

really care about the code i want to parallelize i want to maximize my usage yeah um i wonder

756

if that way of thinking or that way of execution has also primed then

757

and kind of catapulted us into maybe this is what the

758

future of engineering is going to look like the only way we got here is

759

because of these limitations on how we can access models in the first place well

760

and this is like also like the biggest disconnect of like

761

everyone who's posting their like maxed out all six subs on

762

twitter versus like the real world is like no enterprise has subscription plans they're all paying per token

763

and so there's this whole cultural thing of like

764

if you can get all three thousand dollars of spend out

765

of your two hundred dollar thing then you're winning i don't know what you built

766

and like what what value it is and who's going to use it

767

but like that's how you win

768

and it's like no the skill issue is not like couldn't use enough tokens like uh

769

because that doesn't apply to most people who are doing serious work we have companies like customers with 15 engineers

770

and they are all they're all on non -subscription plans

771

because they need security requirements they need data retention requirements they need all this stuff

772

that you can only get on enterprise plan which means you

773

and get paying per token uh

774

so yeah i i i think the we're going to look

775

back on the era of subscription maxing i don't know

776

if it's like you know the intelligence is going to get too cheap to meter it's going to get

777

so cheap that they won't need subscriptions you'll just pay for it

778

and it'll be good value

779

or the subscriptions are going to go away at some point uh

780

because all it is is a loss leader

781

and like a way for them to get data on you

782

and like train the next model of course um

783

so i i think we'll probably look back

784

and be like oh yeah all that token maxing stuff was just

785

because of how the model i think you're right it's like

786

because of how we access the models

787

because how of how they're distributed to like especially like independent hackers

788

and builders that it became this thing that like seemed much more important

789

and got a lot of attention

790

and then one day it was just like nobody cared yeah

791

everyone like it feels like there's a lot of fomo

792

if you don't max out whatever plan you have yeah

793

and the best way to do so

794

if i work during the day is going to be

795

that i do my thinking and i let it run overnight yep

796

but i don't think that's how actually i want my day

797

to also be i do think there's there's value in like

798

blocks of thinking and blocks of coding right like my thought is like you know

799

when you run human layer it does this research step it's

800

slow it can in a large code base it can take 10 15 20 minutes

801

and so i might kick off two or three of those things

802

and let it run and go get a cup of coffee

803

or something but it's like rather than me going back

804

and forth with the model

805

and telling okay now go look over here okay now go look

806

and then i noticed oh you didn't understand

807

that thing yeah we also have to go look over there

808

and see how that works

809

and like you're constantly like catching the model's lack of understanding

810

versus like i'm willing to like async for 15 20 minutes

811

and just guarantee that it's like gonna have 90

812

or 99 of the context that it needs to do this task almost certainly

813

and anything that it missed is like very easy to go find

814

that is worth a little bit of async um and then

815

while those are running i'm like reading another plan and going back and forth on that.

816

Um, but I try not to paralyze too hard.

817

And when I do paralyze, I have one most important thing and it's not like, okay, launch three tasks.

818

Okay.

819

Get this one to design.

820

Now get this one to design.

821

Now get this one to design.

822

Okay.

823

Now move this one forward.

824

Now move this.

825

It's like when the top, when the most important thing is unblocked, we drop whatever we're doing.

826

Even if it's like, I'm in the middle of writing a prompt, I will go back to the most important one because the goal should be like land one thing.

827

You should always have one most important thing.

828

And your top goal is to get that thing landed.

829

And the other stuff you're working on is just like stuff to fill the time

830

while you're waiting for

831

that thing to be unblocked yeah you should always know what

832

the most important thing is as has always been true in engineering

833

and everything really the feeling of parallelization is very cool though

834

and i haven't done any i haven't done any measurements

835

but overall if you look at the most important thing

836

that needs to go out and you parallelize along the way i wonder

837

if that thing would go faster if you just wait

838

and just focus on

839

that one thing i want like traditionally i want to see

840

a stud it would be very zen yeah i want to

841

see a study of like the you know they hook up like the brain scans

842

and like watch someone's brain as it's working i want to

843

see someone running six clods in parallel i want to see one person running one coding agent at a time

844

and then i want to see someone sitting in a casino cranking the j the thing

845

and i want to slot machine yeah exactly

846

uh and then maybe maybe the person doing slot machine

847

and then maybe someone else at like the poker table

848

or it's like a poker you play one hand at a time

849

and you focus yeah good stuff thanks

850

so much for coming on dex this is great yeah man

851

this is super fun i'm really excited for the show i think you've had some really dope people on here

852

so hopefully hopefully i don't uh i don't tank your tank

853

your rep man should be good thanks thanks again patrick that's it

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वीडियो के 15 कम प्रचलित शब्द, उच्चारण और अर्थ के साथ:

layer /ˈleɪ̯ɚ/ (संज्ञा) — परत, तह. A single thickness of some material covering a surface.
skill /skɪl/ (संज्ञा) — कुशलता, कौशल. A capacity to do something well; a technique, an ability, usually acquired or learned, as opposed to abilities that are regarded as innate.
solve /sɒlv/ (क्रिया) — सुलझाना, हल करना. To find an answer or solution to a problem or question; to work out.
bench /ˈbɛnt͡ʃ/ (संज्ञा) — बेंच. A long seat with or without a back, found for example in parks and schools.
overnight /əʊvə(ɹ)ˈnaɪt/ (क्रिया विशेषण) — रात भर. During or throughout the night, especially during the evening or night just past.
benchmark /ˈbɛn(t)ʃmɑːk/ (संज्ञा) — मानक, मापदंड. A standard by which something is evaluated or measured.
cloud /ˈklaʊ̯d/ (संज्ञा) — मेघ, बादल. A visible mass of water droplets suspended in the air.
enterprise /ˈɛntəˌpɹaɪz/ (संज्ञा) — उद्यम, उपक्रम. A company, business, organization, or other purposeful endeavor.
nowadays /ˈnaʊ.ə.deɪz/ (क्रिया विशेषण) — आज कल. At the present time; in the current era.
architecture /ˈɑː.kɪˌtɛk.t͡ʃə/ (संज्ञा) — वास्तुकला, स्थापत्य. The art and science of designing and managing the construction of buildings and other structures, particularly if they are well proportioned and decorated.
experiment /ɪkˈspɛɹ.ə.mənt/ (संज्ञा) — प्रयोग, तजुर्बा. A test under controlled conditions made to either demonstrate a known truth, examine the validity of a hypothesis, or determine the efficacy or likelihood of…
execution /ˌɛk.sɪˈkjuː.ʃən/ (संज्ञा) — निष्पादन, अमल. The act, manner or style of executing (actions, maneuvers, performances).
loop /luːp/ (संज्ञा) — कुंडली. A length of thread, line or rope that is doubled over to make an opening.
parallel /ˈpæ.ɹəˌlɛl/ (विशेषण) — समानान्तर. Equally distant from one another at all points.
subscription /səbˈskɹɪpʃən/ (संज्ञा) — सदस्यता. Access to a resource for a period of time, generally for payment.

वीडियो में आने वाली फ़्रेज़ल वर्ब

look at (क्रिया) — हेरना. To observe or watch.
think about /ˈθɪŋk əˌbaʊt/ (क्रिया) — चिंतन करना. To ponder.
wake up /ˌweɪk ˈʌp/ (क्रिया) — जागना. To stop sleeping; to awake.

ध्यान देने योग्य उच्चारण

वक्ता 259 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे don't, I'm, we're। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“th” की ध्वनियाँ: marathon /ˈmæɹəθən/, thread /ˈθɹɛd/, bother /ˈbɑðɚ/
“sh” और “zh” की ध्वनियाँ: benchmark /ˈbɛn(t)ʃmɑːk/, speculation /ˌspɛk.jəˈleɪ.ʃən/, execution /ˌɛk.sɪˈkjuː.ʃən/, subscription /səbˈskɹɪpʃən/, capture /ˈkæp.(t)ʃɚ/
लंबे शब्द — बलाघात सही रखें: speculation /ˌspɛk.jəˈleɪ.ʃən/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, experiment /ɪkˈspɛɹ.ə.mənt/, execution /ˌɛk.sɪˈkjuː.ʃən/, eliminate /ɪˈlɪm.ɪ.neɪt/

इस वीडियो से अभ्यास कैसे करें

पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर layer, skill, solve जैसे शब्दों पर ध्यान दें।

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचना	वीडियो में
शर्त वाले वाक्य if + उपवाक्य, will/would + क्रिया — शर्त और उसका नतीजा	if it passed it would get · if if it's fine that teams will eventually · if they write bad code someone will just
“used to” used to + क्रिया — बीती आदत या स्थिति जो अब नहीं है	used to be · used to do
Present perfect continuous have/has been + -ing — पहले शुरू हुआ काम जो अब भी जारी है	i've been doing · have been doing · we've already been doing
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	have been · we've been · hasn't really been

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

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इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो की मुख्य शब्दावली

वीडियो में आने वाली फ़्रेज़ल वर्ब

ध्यान देने योग्य उच्चारण

इस वीडियो से अभ्यास कैसे करें

इस वीडियो का व्याकरण

शैडोइंग तकनीक क्या है?