**The Dark Side of Natural Language Interfaces**

As we dive headfirst into the world of large language models (LLMs), it's easy to get caught up in their promise of seamless communication and intelligent assistance. However, there's a problem lurking beneath the surface: natural language interfaces are slow and expensive.

LLM inference is typically a sluggish process, taking tens of seconds to complete. This can lead to frustratingly long response times, making it difficult for users to interact with LLM-powered systems. But is this truly unavoidable? The answer lies in how we design our interfaces around these models.

**A Glimpse into the World of Latency**

Consider the classic CS diagram that illustrates latency numbers for various compute operations: nanoseconds to lock a mutex, microseconds to reference memory, milliseconds to read 1 MB from disk. LLM inference, on the other hand, typically takes 10s of seconds to complete. This is an order of magnitude slower than traditional graphic user interfaces (GUIs), which respond in mere milliseconds.

But GUIs have their own limitations. They're inflexible and don't take advantage of semantic understanding. Can we find a middle ground that combines the best of both worlds? Enter popup-mcp, a tool I've developed to bridge this gap.

**Popup-MCP: The Solution**

popup-mcp is an LLM interface modality that generates dynamic GUI elements on the fly, using arbitrary collections of GUI elements. This allows for fast, responsive interactions with LLMs, without sacrificing semantic understanding or flexibility.

The tool supports a range of GUI elements, including multiple choice checkboxes, drop-down menus, sliders, and text boxes. Conditional visibility enables context-specific follow-up questions, allowing LLMs to construct complex dialogue trees that capture the nuances of conversation.

**A Deeper Look into Popup-MCP**

One of the key benefits of popup-mcp is its ability to reduce amortized interaction latency. By generating GUI elements locally on the user's machine, popup-mcp can speed up interactions by 25-75%, depending on how well the LLM anticipates where the conversation will go.

The tool also includes an "escape hatch" feature, which automatically adds an 'Other' option to multiselects and drop-down menus. This allows users to intervene when the LLM has incorrect assumptions about the conversation's trajectory.

**The Future of LLM Interfaces**

popup-mcp is not just a tool for improving interaction latency; it's also a blueprint for the future of LLM interfaces. By combining the strengths of GUIs and natural language interfaces, we can create systems that are both flexible and fast.

I'm excited to share my library and design principles with the community, in the hopes that others will build upon this work and push the boundaries of what's possible. Whether you're working on OS-native popups, terminal user interfaces, or web UIs, popup-mcp offers a compelling solution for reducing amortized interaction latency.

**Stay Tuned**

This is just the beginning of our exploration into LLM interfaces. I'll be publishing more about my work and its implications in the coming months. If you're interested in learning more or would like to discuss further, please don't hesitate to reach out. Together, let's create a future where LLMs are faster, more responsive, and more powerful than ever before.