How (and why) to build a personal API
The conversation explores the concept of personal APIs that store individual preferences like favorite restaurants and coffee orders, enabling others to make thoughtful gestures without asking. The discussion evolves to envision AI agents autonomously accessing these APIs to perform personalized tasks like making reservations.
Summary
The speaker introduces the concept of a personal API as a solution to a common social problem: wanting to do something nice for someone (like making a restaurant reservation) but lacking specific knowledge about their preferences. Rather than asking directly and spoiling the surprise, a personal API would store this information in an accessible format.
The conversation then shifts to a more forward-looking perspective on how this concept would actually be used in practice. While the initial idea assumes humans would manually query the API, the respondent argues that the more realistic future involves AI agents performing this work autonomously. They paint a scenario where an agent, given a simple verbal instruction after a podcast episode, would independently access the personal API, retrieve relevant information like travel dates and dining preferences, and execute actions like restaurant reservations without human intervention.
The discussion touches on how this concept integrates with broader trends in agentic commerce, suggesting that personal APIs could become a key component in a larger ecosystem where AI agents handle increasingly complex, personalized transactions on behalf of users. The exchange indicates growing interest in exploring what kinds of APIs and data structures could enable this future of agent-driven personalized services.
Key Insights
- The original motivation for personal APIs stems from wanting to perform thoughtful gestures for others without directly asking about their preferences, preserving the element of surprise
- The respondent challenges the assumption that humans would be the primary consumers of personal APIs, arguing that AI agents will actually be the ones accessing and utilizing this data
- The practical vision for personal APIs involves agents receiving high-level natural language instructions and autonomously executing multi-step tasks like API access, preference retrieval, and reservation booking
- Personal APIs are positioned as part of a larger ecosystem combining agentic AI with commercial transaction capabilities
- The concept bridges personal data infrastructure with emerging agent-based commerce systems, creating new opportunities for automated, personalized interactions
Topics
Transcript
[0:00] I had this idea about personal APIs. And that stemmed from this problem that you have where you want to do something nice, take them to their favorite restaurant, and make a reservation, but you don't really remember what their coffee order is or what their favorite restaurant is. And you want to do something nice, but you don't want to bug them about it. So, I was like, "Well, what if there was a way where you could just hit an API and you get all of this information about them?" >> Do you know what I love about this idea of the API? You're very adorable in that you think that humans are going to hit this…
Full transcript available for MurmurCast members
Sign Up to AccessMore from How I AI
Google Places + Grok Bot auto-updates this website
A designer created an automated workflow using Grok Bot and Google Places API to effortlessly update their personal website with high-quality, processed images of locations. The system identifies places from photos, generates 3D miniature visual styles in light and dark versions, and automatically publishes them to the site, solving the problem of website staleness.
How SpaceXAI designers use Grok Bot and Figma MCP to ship faster
SpaceX AI designers John and Pong demonstrate practical AI workflows using Grok Bot and Figma MCP to accelerate design and personal projects. They showcase how AI agents handle repetitive tasks—from building a location-based check-in website to managing design production work—enabling designers to focus on higher-level creative decisions rather than manual execution.
Enterprise AI fails on governance, not the model
The speaker discusses the importance of skill management and governance in enterprise AI platforms, emphasizing that enabling people to build skills is insufficient without proper monitoring, maintenance, and telemetry. The focus is on providing automatic platform-driven suggestions to keep skills current and using data insights to promote high-value skills while deprecating low-value ones.
Onboard your employees to Cowork in 15 minutes with this system
The transcript describes a workstation operating system that streamlines employee onboarding through an intuitive chat-based interface. The system automates tool connections, role confirmation, colleague mapping, calendar integration, and personalization to get new employees productive from day one.
Your prompt is a spec, and a good spec is the whole game
A prompt acts as a specification that defines quality criteria for AI outputs. By clearly articulating 5-10 defining characteristics of what makes something good (whether a garment, photo, or illustration), you provide the AI with the same level of detail a human creator would need to produce excellent results.