Nextdoor的工程师如何使用Codex无限制地构建
How engineers at Nextdoor use Codex to build without limits
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Nextdoor的工程师如何使用Codex和GPT-5.5来研究难以重现的问题，跨平台构建，并专注于产品成果。
How engineers at Nextdoor use Codex with GPT-5.5 to investigate hard-to-reproduce issues, build across platforms, and focus on product outcomes.
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June 9, 2026
How engineers at Nextdoor use Codex to build without limits
On Nextdoor’s core platform team, Codex investigates issues and puts product engineers in the driver’s seat.
A product like Nextdoor, which serves over 110 million users across 11 countries, puts many demands on a platform team. For Cory Dolphin, Head of Engineering at, Codex represents an essential shift: “away from iteratively prompting an agent, and towards outcome engineering, where engineers start to think about the result they want to see and work with an agent to engineer that result.”
This means that individual engineers move up the stack—no longer locked up as specialists in a certain system or framework, they’re able to own the product experience more or less end-to-end, even across multiple platforms. Productivity has accelerated so much that the bottleneck is no longer engineering, but rather the hard strategic questions about what to build next.
Product engineers can focus on the product
With Codex, “engineers get to spend a lot less time thinking about exactly how they build, and more time thinking about the outcome,” Dolphin explains. That outcome might take the form of screenshots or video that the agent can build towards, a certain performance or test result, or a brand new feature idea.
Nextdoor recently released Opportunity Alerts, which let people find service providers near them; with Codex, engineers are driving the product experience and roadmap. As an example, one engineer working on the alerts realized it would be helpful to show service providers on a map. Historically, that kind of feature would have required collaboration between three teams—mobile, frontend, and backend engineering—and might have never made it out of the backlog.
But with Codex, “we were able to have one engineer build it end to end,” Dolphin explains, “which means not only are they able to drive the product faster, but they’re able to better understand the actual product experience and what the right thing to ship is.”
Compressing software engineering time
Working with embedded Rust databases and systems with tight race conditions, Nextdoor turns to Codex for help debugging the most hard-to-reproduce issues. The team provides the agent with a clean environment and harness for investigation, then uses it for everything from figuring out why Kubernetes pods won’t start, to finding the right trend line in a data analysis.
“With GPT‑5.4 and 5.5, it’s been a really impressive upgrade. We see Codex excel at being extremely persistent and trying to figure out the right solution, diving deep into some seemingly esoteric technical details to arrive at the root cause,” Dolphin explains.
About Fast Mode with Codex and GPT‑5.5, Dolphin says, “I’ve got to be honest, a lot of the team are addicted to it. When you have a quick feedback loop with the problem that you’re working on, the feeling is exhilarating as an engineer.”
Engineering work has gotten so much faster that Dolphin has seen a shift in the pressures on different parts of the organization. “We’re moving so much faster that the bottlenecks are no longer in engineering. It’s really now a question of, how can we identify the right things to build and the right strategy—and less about how we actually build it.”
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