Seamless User Interaction Models

You've heard me say that UX should be invisible, that the user should use the design seamlessly, without drawing attention to itself. It should enable users to interact with the system naturally, without unnecessary interruptions or confusion. Here's how UX could be invisible: - Align with User Mental Models: The design should match how users think and expect things to work. This means understanding users deeply—how they approach tasks, their mental shortcuts, and their expectations. When the design aligns with these mental models, users don’t have to pause and learn; they just act, and the interface works as anticipated. - Streamline Tasks and Remove Clutter: An invisible UX simplifies tasks by removing unnecessary steps and presenting only what is essential at each stage. Every element on the interface has a purpose directly tied to the user's goal. By stripping away anything extraneous, users can complete their tasks without distraction. - Guide Users Subtly, Not Forcefully: Instead of overt instructions or heavy-handed guidance, the interface should provide subtle cues that guide users gently. This could be through visual hierarchy, natural language, or affordances that hint at what actions are possible. Users should feel in control and empowered rather than managed or restricted by the design. - Error Prevention and Recovery: The design should anticipate potential user errors and prevent them before they occur. If errors do happen, the system should offer simple, immediate ways to correct them without penalty or frustration. - Consistency in Interaction Patterns: Consistent design patterns help users build a reliable mental map of how to interact with the system. Use familiar conventions so users feel comfortable and confident. Consistency reduces the learning curve and makes the interaction feel second nature, contributing to the sense of an invisible UX. - Proactive Support Without Interference: Interfaces could offer proactive help—like suggestions, auto-completions, or predictive inputs—exactly when needed, but without overwhelming the user. The support should feel like an enhancement rather than an interruption. - Design for Flow: Design for flow, where users are fully engaged and can move through tasks without disruption. Remove points of friction and create smooth transitions between different parts of the task, allowing users to maintain their momentum and focus. - Functional Simplicity: Invisible UX focuses on the core functions that directly contribute to user goals, avoiding unnecessary features or complexities that might confuse or slow down the user. Good UX is not about showcasing every possible feature but about prioritizing what’s truly necessary for the user’s success. In summary, create an experience that is so aligned with the user's needs, expectations, and behaviors that it becomes an almost subconscious interaction. The user should achieve what they set out to do with minimal thought about the interface.

I just taught Claude to directly query my CRM. Complex workflows became single prompts: A month ago my network kept talking about something called Model Context Protocol (MCP). Initially abstract, I understood it simply as: MCP lets AI models directly access your existing tools and databases. Think of it like the invention of USB: → Before USB: Multiple incompatible ports → After USB: One universal connection → Before MCP: Custom data integrations → After MCP: Universal plug-and-play AI connectivity Then a week ago I got an email from my personal CRM provider Clay that they had support for MCP. Historically, CRMs have acted as passive databases, requiring manual interactions to deliver insights. Here is what I used to do when I wanted to know who within my network had changed roles recently: OLD PROCESS: → Log into Clay CRM, export contacts as CSV → Clean and format data in a spreadsheet → Copy-paste formatted data into Claude → Manually instruct Claude to analyze job changes → Copy Claude’s insights back to Clay → Update contact records individually → Manually set follow-up tasks for each contact NEW PROCESS: → Simply instruct Claude: “Identify contacts in my network who recently changed jobs, showing their old and new positions and when I last interacted with them.” → Claude directly accesses Clay via MCP → Finds contacts who’ve recently changed jobs → Instantly provides a detailed, actionable list The results aren't perfect, but they turned a previously tedious process into an effortless query. The technical setup took 5 minutes: → Generated a Clay API key → Connected through Clay’s Smithery page → Installed Node.js locally → Ran one terminal command → Restarted Claude, confirming integration MCP's power comes from three shifts: → From isolated silos to interconnected intelligence → From sequential tasks to seamless orchestration → From human middleware to direct and automated interactions While it is early days, I believe we are scratching the surface with what is possible. I'm now working with several of our portfolio companies to explore how we can do deeper AI integrations. In an age where everyone has access to similar AI tools, the real competitive advantage isn't the tool itself. It's how deeply you embed it into your workflows.

Exciting research from Snap Inc.'s engineering team! Just came across their paper on Universal User Modeling (UUM) that's revolutionizing how they handle cross-domain user representations. The team at Snap has developed a framework that learns general-purpose user representations by leveraging behaviors across multiple in-app surfaces simultaneously. Rather than building separate user models for each surface (Content, Ads, Lens, etc.) and combining them post-hoc, UUM directly captures collaborative filtering signals across domains. Their approach formulates this as a cross-domain sequential recommendation problem, processing user interaction sequences of up to 5,000 events and using sliding windows of 800-length subsequences to balance computational efficiency with capturing long-range dependencies. The architecture leverages transformer-based self-attention mechanisms to model these sequences, with a clever design that projects feature vectors from different domains into a shared latent space before applying multi-head attention layers. The results are impressive! After successful A/B testing, UUM has been deployed in production with significant gains: - 2.78% increase in Long-form Video Open Rate - 19.2% increase in Long-form Video View Time - 1.76% increase in Lens play time - 0.87% increase in Notification Open Rate They're also exploring advanced modeling techniques like domain-specific encoders and self-attention with information bottlenecks to address the challenges of imbalanced cross-domain data. This work demonstrates how sophisticated user modeling can drive substantial engagement improvements across multiple recommendation surfaces within a large-scale social platform.

How proactive AI will change UX - 📆 schedule ChatGPT requests! OpenAI has introduced a new task scheduling feature for ChatGPT. This means you can now ask ChatGPT to handle tasks at a future time — like sending you a weekly global news update, recommending a daily personalized workout, or setting reminders for important events. 💡 Why is this interesting from a UX perspective? This shift is a step toward proactive AI — moving from reactive systems (waiting for user input) to anticipatory, context-aware experiences that help users save mental energy and stay on top of their routines. Let’s break it down from a real-life use case - creating daily recipes: I currently eat sugar-free, gluten-free (because I am celiac), and generally low-carb and like to let ChatGPT create recipes for me. I don’t want a fixed meal plan, but I do need flexible, personalized recipe suggestions that fit my nutrition goals. Ideally, I’d want ChatGPT to  → suggest automatically 3-4 recipes daily around 3 PM → send them to me  → and based on my choice adjust future suggestions for the next days based on what I’ve already eaten that week (for balanced nutrients). With the new task feature, this kind of personalized experience could become much much more seamless. I wouldn't need to ask repeatedly — the assistant would learn my preferences over time and adapt its suggestions accordingly. 🎯 What can we learn from this in AI-UX design? 1️⃣ From static interactions to dynamic experiences: We often design AI tools that rely on users asking for something. But this update shows the value of continuous, evolving interactions. Users shouldn’t need to start from scratch every time — systems can proactively adjust to their needs and context. 2️⃣ Mental models of AI assistants: For users to trust AI routines, they need to understand what the assistant will do and when. It’s about designing predictability and transparency in a way that still allows for flexibility and spontaneity. 3️⃣ Proactive ≠ intrusive: There’s a fine balance between helpful and annoying. The best AI interactions feel like a supportive partner — offering assistance at the right time, based on context and past behavior, without overwhelming users with irrelevant notifications. In AI-UX, we’re increasingly designing for systems that adapt and evolve with the user.  This new feature is a great example of how AI can shift might be able rom a passive tool to an active assistant — can’t wait to try it. How do you see proactive AI changing the way we design user experiences? Would love to hear your thoughts! 👀

What if an interface could adapt to your world in real time? Imagine your car’s dashboard subtly shifting to shades of green as you drive through a forest, or an app adjusting to your personal accessibility needs without breaking. For the past few months, I've spoken with many of you and I’ve realized we’re all working toward the same ambitious goal: creating interfaces that offer a seamless blend of brand personalization, true adaptability, and accessibility. This is about building an experience that is not only true to a brand's perception but is also tailored to our individual needs as consumers. My exploration so far has revealed three foundational concepts that I feel are important to make this a reality. In the upcoming months, I’ll be sharing our journey as we explore these concepts. Some ideas will work, some will fail. I don’t know where this path will lead, but I want to bring you along in the process. 1.⁠ ⁠Contextual Awareness This is the idea that an element understands its environment. A button, for example, knows what surface it’s sitting on and adapts accordingly. While tools like Figma use variable collections to simulate this, the approach is often fragile because it lacks a scalable underlying logic. This very challenge was a driving force behind developing the graph engine. I’m excited to share that a solution for this is now possible directly in modern browsers with pure CSS, laying a powerful and scalable foundation for the future. 2.⁠ ⁠Content Awareness Imagine an interface that reflects the content it displays. We see a version of this in Spotify’s UI, which adapts to album art to create a more immersive experience. This principle allows the UI to react dynamically, personalizing the experience in real-time based on its content. 3.⁠ ⁠User Awareness This pillar brings it all together by focusing on the user’s specific needs. It means designing systems that can respond to a user with Parkinson’s who may need more forgiving interaction areas, or accommodating the universal reality that as we get older, we need larger fonts. The key is to make these adjustments without breaking the interface or compromising the brand experience. These three pillars form the blueprint for the next generation of user interfaces. By understanding where an element is, what it contains, and who is using it, we can create experiences that feel truly alive. I think there’s more to discover beyond our current methods. Let's explore what it means to build something truly adaptive, together.

USER JOURNEYS vs. USER FLOWS – The Blueprint for Seamless Experience   Ever launched a product only to hear: “This flow is confusing” or “It doesn’t solve my actual problem”? 😧 As product managers, we often juggle multiple priorities, but one distinction we can’t afford to blur is user journeys vs. user flows. Misunderstanding these can result in friction-filled experiences or worse, users abandoning your product entirely. 🔍 Let’s break it down USER JOURNEYS – The Big Picture ❓ Why: Focuses on your user’s end-to-end experience, tracking emotions, motivations, and pain points across touchpoints. ⚙️ How: Map your journey with tools like empathy maps or storyboards to capture the "why" behind user actions. 📌 Example: Think of a user exploring a new fitness app. They’re seeking motivation, tracking progress, and celebrating wins. What emotions drive them at each stage? USER FLOWS – The Step-by-Step Guide ❓ Why: Details specific steps users take to complete a task. It’s about clarity and efficiency. ⚙️ How: Use flowcharts to identify bottlenecks and streamline actions like account creation or checkout processes. 📌 Example: Onboarding for the same fitness app - how quickly and easily can a user create a profile and start their first workout? 🔗 WHY YOU NEED BOTH Ignoring user journeys risks missing the “why” behind user actions. Ignoring user flows can frustrate users with clunky processes. Together, they create a seamless user experience. 🔑 TAKEAWAY: Start with the journey to empathize with your users. Refine with flows to make tasks intuitive and frictionless. 💬 Your Turn: How do you balance user journeys and flows in your product design process? Do you start with one or tackle both simultaneously? Let’s share tips in the comments! #ProductManagement #UserExperience #UserJourneys #UserFlows #PMTips