This post was created with the assistance of AI, based on a transcript of the conversation.
AI tools have started to remember their users, but each tool remembers a different version of the same person. Duncan Davidson, an independent consultant and longtime software engineer, has been trying to connect those fragments. He joined Ben Lorica to discuss what personal AI could do if people could carry their context between services and decide what each service gets to see.
They also talked about how AI coding changed web development during Duncan’s time at Webflow, what new developers may lose as entry-level work changes, and why open source projects need new ways to handle AI-assisted contributions.
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Takeaways
06.31 The developer’s job is moving toward outcomes. Duncan compared AI coding to desktop publishing, which removed the physical work of assembling pages but left designers responsible for what the page should look like. At Webflow, he saw people use AI tools to make websites faster than they could with earlier tools. For developers, he said, the question has shifted from how quickly they can type code to “What outcome do you want? What are you trying to do with this code, and how can you make it better?” People still need to judge whether an implementation fits the rest of the system.
10.41 New developers still need a way to become experts. Computer science fundamentals still matter when an agent offers several plausible fixes and a person must choose the one that fits a larger system. Ben asked where future engineers will gain that judgment if AI absorbs entry-level tasks. Duncan compared early career work to an apprenticeship in the arts, where even menial assignments expose newcomers to how the whole operation works. Companies that measure only throughput, he warned, may leave too little room for people to learn.
23.07 AI-assisted changes are straining open source review. Duncan described a library he uses in one of his apps. His fixes, along with performance problems Claude noticed, left his version roughly 25 pull requests ahead of the project’s main branch. The maintainers were using AI too, he said. The difficulty was getting useful changes back to the project without overwhelming its review process. His conclusion was that older open source collaboration practices need a “serious retrofit.”
28.27 Personal memory is fragmented across tools. Duncan first had his coding agents save useful information into a collection of Markdown files. Once commercial agents added memory, he found that “Claude knew me one way and ChatGPT knows me another way.” He could push context back to each tool with instructions and occasional reminders, but that solution demands more tinkering than most people want to do. What he wants is a small set of shared mechanisms that let a person carry context across services.
34.01 Portable memory needs boundaries. A prompt’s context, an agent’s memory of recent interactions, and a person’s longer-term knowledge should be able to travel, but Duncan also wants people to control access by scope. He keeps general notes alongside information about his family and finances. His test for a useful system is whether he can say, “Give Claude access to these scopes of my knowledge, but not everything.” That becomes harder when a personal agent encounters knowledge gained at work. Duncan left open where an employee’s own expertise ends and an employer’s information begins.
36.44 Trust and security shape what people will share. Ben’s not yet comfortable letting a consumer agent reach into his finances. Duncan noted that trust would have to grow through experience, as it would with a human agent. We’re already seeing how AI agents can find vulnerabilities faster than established disclosure practices can handle. Duncan expects software maintainers to face more pressure as agents uncover latent bugs, while Ben questioned how quickly organizations that rely on vulnerable code would learn about the findings. People need control over what an agent can access and confidence in the systems around it.
49.52 A personal memory could answer requests with permission. Duncan imagined keeping his memory on his phone and giving a new service a way to request specific information. A car dealership’s agent, for example, could ask for details, but the phone would seek his approval before sharing them. He put it this way:
I could provide it a handle to my memory. And whatever agents they’re working with can come in and ping me and say, “Hey, we’d like to know these things about you.” And it wouldn’t necessarily do it automatically.