Addy Osmani

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every time that we've made it easier to create software, we've exponentially created more of it.

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But they're the person that's on the hook for understanding, for gating, for making sure that someone is deciding what ships, what's blocked, what do we defer. And so, I think that that is something that engineers are going to continue to be valuable for.

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what what is the current thing that models are not very good at doing? Alpha is going to decay in some way with every model release or every series of model releases. So, it's going to change over time.

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An agent can tell you if a thing looks correct, if it's matching a spec. It doesn't necessarily mean it can tell you what's good.

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And it's effectively the next step of, you know, every phase of of software evolution is just like a rising tide of abstractions. This is the next abstraction.

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But, simply just having your loops build everything without having some guardrails around the blast radius without having guardrails around how you think about quality, I think is a recipe for disaster.

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I'm a really big fan of this idea of mutual amplification. If you are working with an agent, a coding agent, there are a lot of things that you can do to make sure that the agent is getting better every day and you as an engineer are getting better every day.

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The first thing I do is I try to make sure that if there is a summary at the very end, here are all the decisions that were made, I will read through that end to end. If there hasn't been, I will prompt for that decision process.

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I think that we still need to understand enough about how things work so that if something does go wrong, we're actually able to fix it and not just hope and pray that the agent is able to figure things out.

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the first is cognitive debt. So the more that you use AI, it's it's sort of the erosion of your ability to have good memory and have good understanding of the problems that you're working on. And the natural follow-up to that is cognitive surrender, which is where you, you know, you blindly give in to whatever the AI says as your answer.

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One of the things I found most exciting in the last couple of years was seeing as model qualities gotten better and and harnesses and tools have gotten better, how many people um that were directors or VPs or SVPs or any of these levels were actually rolling up their sleeves and trying things out.

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you want to get to a point where you know, your your machine, your org is self-sufficient enough that you know, you just need to occasionally tap the blimp, make sure that things are working. You can course correct if it's not, but that frees you up to then focus on the next important sets of problems that the org needs to, you know, tackle heads on.

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And that embrace of people are going to use whatever tech they want to use. You can't tell people what to use very often. They're going to use whatever they want, and your job is to help them be successful on your platform, and to help your users have a great time.

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even if you set aside, you know, maybe there's some validity around a business needs to monetize, those things shouldn't cause a really bad experience.

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Humans are humans are are shockingly simple, you know, uh, if you think about the experience you have with somebody that's just trying to cross the street, if, uh, you know, if the light doesn't turn, you know, doesn't say they can walk fast enough, they'll just keep hitting that button. That's the same experience they have on the internet.

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I think for a very long time we had this this almost nebulous way of thinking about page load times. And the team felt like it was finally time to come up with a more nuanced perspective around how we reason about performance.

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I remember anytime I would work with a big site on their performance problems, uh you could easily spend half a day, um you know, just looking at traces before you've even written any fixes at all. And now that we have LLMs, it's very quick to like reason through massive stack traces and actually be able to get down to fixes you can make.

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I would say, sometimes, I don't know if it's controversial, that very few developers understand memory management. And that makes it even harder to debug memory problems. And so the state of the art around memory debugging hasn't evolved all that much over the years.

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if you treat computing as this onion where you just keep peeling back the layers, there's always something interesting behind the scenes. And the more of those layers that you can peel back and understand, I think in some ways the better you can optimize for that world.