To discover high-value AI use cases, reframe the problem. Instead of thinking about features, ask, "If my user had a human assistant for this workflow, what tasks would they delegate?" This simple question uncovers powerful opportunities where agents can perform valuable jobs, shifting focus from technology to user value.

Designing for users with motor disabilities who control interfaces with their minds presents a unique challenge. Unlike typical design scenarios, it's impossible for designers to truly imagine or simulate the sensory experience, making direct empathy an unreliable tool for closed-loop interactions.

Text descriptions of physical pain are often vague. To improve an AI coach's helpfulness, use multi-modal inputs. Uploading a photo and circling the exact point of pain or a video showing limited range of motion provides far more precise context than words alone.

The team obsesses over perfecting the BCI cursor, treating it as the key to user agency on a computer. However, the long-term vision is to eliminate the cursor entirely by reading user intent directly. This creates a fascinating tension of building a masterwork destined for obsolescence.

Cues uses 'Visual Context Engineering' to let users communicate intent without complex text prompts. By using a 2D canvas for sketches, graphs, and spatial arrangements of objects, users can express relationships and structure visually, which the AI interprets for more precise outputs.

Open-ended prompts overwhelm new users who don't know what's possible. A better approach is to productize AI into specific features. Use familiar UI like sliders and dropdowns to gather user intent, which then constructs a complex prompt behind the scenes, making powerful AI accessible without requiring prompt engineering skills.

For frontier technologies like BCIs, a Minimum Viable Product can be self-defeating because a "mid" signal from a hacky prototype is uninformative. Neuralink invests significant polish into experiments, ensuring that if an idea fails, it's because the concept is wrong, not because the execution was poor.

Due to latency and model uncertainty, a BCI "click" isn't a discrete event. Neuralink designed a continuous visual ramp-up (color, depth, scale) to make the action predictable. This visual feedback allows the user to subconsciously learn and co-adapt their neural inputs, improving the model's accuracy over time.

Neuralink's initial BCI cursor used color to indicate click probability. As users' control improved, the design evolved to a reticle that uses motion and scale for feedback. This change was more effective because the human eye is more sensitive to motion than color, and it better supported advanced interactions.