TetraMesa

Archives for August 2026

The Interface Taxonomy Beyond APIs (2 of 5)

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In this part 2 of 5 for the series, we’re going to look at the “What & When”

The fastest way to make this whole topic area a confusing mess is to just list out technology definitions, though at some point we need to visit REST, GraphQL, gRPC, webhooks, Kafka, WebSockets, MCP and A2A. They don’t all sit at the same conceptual level, and they’re not interchangeable answers to one problem. What we’re going to do is look at some use cases. And later on some actual examples and things you can try yourself if you want to dive in a bit. Again, not trying to be coders. We won’t be experts. But sometimes just seeing something actually work can be those Aha moments we can keep with us over time vs. just spewing buzzwords. We’ll get there.

In the meantime, a useful product taxonomy can perhaps start by asking ”What interaction are you trying to create?” Then look at technologies that can do it. Or ask your dev or architect counterparts.

Things like OpenAPI and AsyncAPI can describe interfaces. OAuth 2.0 handles delegated authorization. JSON and Protocol Buffers represent data. SDKs package interfaces for developers. HTTP, WebSockets and MQTT move information. Those things matter, but they are not all separate product interaction categories.

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Product Leader Mental Models of Connectivity Beyond APIs (1 of 5)

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In this part 1 of 5 for the series, we’re going to look at the “Why”

Why APIs and Interfaces need a fresh high-level consideration.

We know the role “Digital Product Manager” has always been ambiguous, at best. PM, Tech PM, Marketing PM, now AI PM, etc. Ideally, anything with “Product Manager” in it has something to do with market awareness, looking around corners, at higher levels and so on. A lot of us may have come from some kind of coding background, but certainly not always. In any case, even if we don’t, (or shouldn’t), need to be expert developers, at every level we should understand how our products are stitched together.

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Poor PMs Guide to AI Image Generation Workflow With n8n

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A practical setup for kicking out a lot of decent hero images fast, plus the decisions that mattered more than the tools.

Note: Files for this project are available here: GitHub: n8n-blog-image-pipeline

Here’s one from the “Practical Product Quick Build Toolkit.”

I needed bunch of graphics for content. Here’s how I got solid results quickly and inexpensively.

My focus is on product, not production. But time and money were factors (as always) so this was the fastest way to get things done while leaving a reusable workflow for future needs. There’s a variety of tools for this sort of thing, but I wanted specific direction with general rules. Not just “generate an image,” but a repeatable system that produced a consistent series.

Using a workflow agent tool like n8n coupled with an image-generation model did the trick. I could set images to get generated and then do something else. A few caveats up front: you still have to check the images. Every one. And you won’t own the copyright as you might with commissioned work. But you can kick out what you need fast. Even if you’re only using them as placeholders for a prototype or something, you can replace with better stock or custom work later. For a prototype test that just needs a quick hero image on every page, that trade is worth it. And this will work for a production site as well. But for highest level of quality, professional stock and commissioned work can replace these placeholders. If you’re using a full site AI generator, those will usually be able to make your graphics. But if building with a coding assistant, perhaps not. Here’s a solution for that. Create the graphics in bulk, stage them somewhere with know filenames, (ideally based on direction directly from a coding tool), then load them into your site’s image folder.

Here’s the setup, what it cost, and the rules I’d carry to any similar project. The tooling is the least interesting part. The decisions about what to automate, what to keep human, and where quality actually comes from are the ones worth consideration, whether you’re building this pipeline for one time bulk use or deciding how this AI tool flow fits into your product.

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