User Experience

Back in 2007, Nobel Prize-winning psychologist Daniel Kahneman taught a private master class to tech founders including Larry Page and Jeff Bezos. The following year, Elon Musk joined. Among the topics: priming, where subtle cues shape our decisions without us realizing it. In that room, Musk pressed on subliminal versus explicit persuasion: “Does the hidden beat the obvious?” Kahneman's answer: "There are many situations in which subliminal effects are stronger than superliminal effects." Translation: Hidden influences shape behavior more than obvious ones. You can't resist what you don't notice. Later after that session, Bezos connected the dots: “You can choose your choice architect.” You either design the decision environment, or it designs you. Amazon designed theirs. One-click purchasing removes the pause where doubt lives. Every additional step is an exit ramp. They chose zero exits. Google designed theirs. That empty white homepage isn't minimal by accident. No portals, no distractions. Just one thought: search. Most companies let chaos choose. Cluttered onboarding. Buried CTAs. Friction everywhere. They're not architects. They're accidents. So how do you become the architect instead of the accident? 1. Choose your pricing architect: Sell your core product for $99/month. Then offer a bundle with two add-ons for $119. The bundle makes the core feel essential. 2. Choose your onboarding architect: When users first sign up, make their first action create immediate value - a report generated, first customer added, dashboard live. Success in 30 seconds primes confidence in everything that follows. In contrast, when you make the frame obvious, you lose it. Slap "Most Popular!" on everything and watch trust erode. The moment users detect manipulation, they create their own frame - one where you're untrustworthy. Kahneman warned Musk about this directly. Covert cues work precisely because they're not noticed. Priming is architecture, not decoration. By the time logic kicks in, the frame has already decided. Because you’re already an architect. The only question is whether you know what you're building.

Cold digital interactions will destroy your D2C brand. Your beauty product is at risk of failing if you don’t address this. Industry is rooted in enhancing self-image and boosting confidence, goals that are inherently emotional. While online shopping is undeniably convenient, it often comes at the expense of personal interaction. This is especially problematic in the beauty sector, where purchasing decisions are often influenced by sensory experiences, personalized recommendations, and emotional connections. +64% consumers believe brands are losing touch with customer experience. +85% higher sales achieved by brands that emotionally engage. +68% women & 56% men choose beauty products based on how they make them feel. >>Online emotional challenges << →Lack of personalization in online transactions. E-commerce lacks the personal engagement of in-store shopping, such as trying products and consulting with beauty experts. →Absence of sensory experiences. Customers are unable to explore key sensory elements like texture, scent, and application when shopping online. →Overwhelming variety. The sheer number of options online can confuse customers and lead to decision fatigue without proper guidance. >>Strategies to build emotional connections<< →Transform brick and mortar stores into immersive experiences. Redefine your physical stores as experiential hubs where customers can enjoy personalized consultations, interactive product trials, and even beauty treatments. +30% boost in sales is seen in flagship stores that offer immersive experiences. →Retail as entertainment, retailtainment. Host creative pop-up shops, in-store events, and experiential retail activations to engage customers emotionally with unique deco, exclusive product offerings, and hands-on activities. +70% of beauty consumers value in-store experiences over online alternatives. →Leverage influencer trust. Partner with influencers who bring authenticity to your brand by sharing personal stories, reviews, and tutorials. Their relatability and trustworthiness create stronger emotional ties with consumers. +49% customers rely on influencer recommendations for beauty product purchases. →Build community through social media. Use social media to foster continuous engagement through live Q&A sessions, interactive content, user-generated campaigns, and community forums. +72% of beauty consumers discover products through social media. To finish. Despite the challenges of the digital era, the industry is finding ways to close the emotional gap with creative solutions like flagship experiences, influencer collaborations, virtual consultations, and community-driven marketing. Check out my curated collection of visuals to spark your next big idea. Featured brands: Benefit Glossier Kylie Cosmetics Marc Jacobs Molecula Miu Miu Nina Ricci Laneige Rhode #BeautyBusiness #EmotionalConnection #SocialBeauty #ExperientialRetail #InfluencerMarketing

🔬 How To Run UX Research In B2B and Enterprise. Practical techniques of what you can do in strict environments, often without access to users. 🚫 Things you typically can’t do 1. Stakeholder interviews ← unavailable 2. Competitor analysis ← not public 3. Data analysis ← no data collected yet 4. Usability sessions ← no users yet 5. Recruit users for testing ← expensive 6. Interview potential users ← IP concerns 7. Concept testing, prototypes ← NDA 8. Usability testing ← IP concerns 9. Sentiment analysis ← no media presence 10. Surveys ← no users to send to 11. Get support logs ← no security clearance 12. Study help desk tickets ← no clearance 13. Use research tools ← no procurement yet ✅ Things you typically can do 1. Focus on requirements + task analysis 2. Study existing workflows, processes 3. Study job postings to map roles/tasks 4. Scrap frequent pain points, challenges 5. Use Google Trends for related search queries 6. Scrap insights to build a service blueprint 7. Find and study people with similar tasks 8. Shadow people performing similar tasks 9. Interview colleagues closest to business 10. Test with customer success, domain experts 11. Build an internal UX testing lab 12. Build trust and confidence first In B2B, people buying a product are not always the same people who will use it. As B2B designers, we have to design at least 2 different types of experiences: the customer’s UX (of the supplier) and employee’s UX (of end users of the product). In customer’s UX, we typically work within a highly specialized domain, along with legacy-ridden systems and strict compliance and security regulations. You might not speak with the stakeholder, but rather company representatives — who regulate the flow of data they share to manage confidentiality, IP and risk. In employee’s UX, it doesn’t look much brighter. We can rarely speak with users, and if we do, often there is only a handful of them. Due to security clearance limitations, we don’t get access to help desk tickers or support logs — and there are rarely any similar public products we could study. As H Locke rightfully noted, if we shed the light strongly enough from many sources, we might end up getting a glimpse of the truth. Scout everything to see what you can find. Find people who are the closest to your customers and to your users. Map the domain and workflows in service blueprints and . Most importantly: start small and build a strong relationship first. In B2B and Enterprise, most actors are incredibly protective and cautious, often carefully manoeuvring compliance regulations and layers of internal politics. No stones will be moved unless there is a strong mutual trust from both sides. It can be frustrating, but also remarkably impactful. B2B relationships are often long-term relationships for years to come, allowing you to make huge impact for people who can’t choose what they use and desperately need your help to do their work better. [continues in comments ↓] #ux #b2b

Did you know there’s a font designed just for accessibility? Meet Atkinson Hyperlegible, it was created by the Braille Institute of America to help people with low vision read more easily. It’s not a braille font (doesn’t include raised dots), but a print typeface. It even won the Fast Company Innovation Design Award in 2019! Molly Burke recently worked with her publisher to use the font for her memoir, Unseen. What makes it different? ⤵️ Hyperlegible exaggerates letter shapes so you can tell the difference between the letter “o” and the number zero (0), capital “i” vs. lowercase “l”, and the capital letter “b” vs. the number “8”. Other design features include: - Big open shapes - Clear spaces inside letters (known as open counters) - Distinct forms for commonly confused characters But who benefits? People who are blind or low vision, and people with dyslexia or visual processing differences. Clearer text equals easier reading! And the best part? It’s totally free 🎉 You can download it via Google Fonts or from the Braille Institute website. It also happens to be the same font this graphic post is written in. Accessibility isn’t always about doing more. It’s about doing things so that everyone benefits! This font is a small design choice with a big impact. Next time you design something: Try Atkinson Hyperlegible. Because readability is inclusion. Did you know about this font?  Share your thoughts or tag a designer friend in the comments! 👇 Image Description: Document with 9 slides. Each slide has a lime green border. The Blindish Latina logo with bold graphic black outline of an eye is at bottom of all slides. There is a white background behind all of the text on all slides. The text is in black and some emphasized phrases are purple. On the bottom of slides 1 and 7 is an image of Catarina, a light-skinned, Latiné woman with medium length wavy brown hair. She’s wearing a black jumpsuit with a V neck and her hands are on her hips. Slide 1 is the title slide that reads: “Did you know there’s a font designed just for accessibility?” On slide 1 there is clip art of a book with a red cover and a brain inside a light bulb. Slide 2 has clip art of an award ribbon. Slide 3 has a screenshot of advocate & content creator Molly Burke speaking at an event from one of her TikTok videos inside the outline of an iPhone. Slide 5 has a dark purple check mark inside a circle. Slide 6 has clip art of a computer outline in black with a wrench and gear in the center. All text on the slides is in the caption and alt text. #Disability #Accessibility #UniversalDesign

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

In many Chinese schools, students pause class for 1–3 minutes and move together — inside the classroom. Are you taking breaks during your office hours? Not a dance. Not military. System design. It’s called 广播体操 (Radio Calisthenics) and it’s been used nationally for decades to reset posture, circulation, and attention. • Prolonged sitting reduces cognitive performance after 30–40 minutes • Short movement breaks improve focus and working memory by 10–15% • Light physical activity increases blood flow to the brain by up to 20% • Even 2 minutes of movement measurably reduces mental fatigue Now apply this to tech and business. Knowledge workers sit 9–11 hours/day, live in back-to-back video calls, and are expected to make high-quality decisions at speed. That’s not a productivity issue. It’s a human-system mismatch. As AI scales execution, human attention becomes the bottleneck. The next performance upgrade may not be more software — but movement designed into workflows. China implemented it at national scale. Optimize the human. Then optimize the system. #FutureOfWork #AI #Productivity #Leadership #HumanPerformance #Neuroscience #TechLeadership #DigitalTransformation #WorkplaceDesign #CognitivePerformance

Candidates overcomplicate Portfolios. Listen, if you’re a Senior Product Designer Avoid: 🔻 6 case studies about absolutely irrelevant products 🔻 3-5 personas from your user research for each study 🔻 5 iteration cycles are explained and shown in granular details 🔻 10+ visuals of wireframes of all fidelities 🔻 Different structure for each case study  🔻 End without an end. We launched… that's it Instead: 💚 2-3 case studies that are relevant to your future employers  💚 Strong problem statement - can be longer than 1 sentence, just has to be crystal clear 💚 UXR methodology and 2-3 KEY insights. 💚 The process with 1 visual + one key insight of your testing 💚 Final solution - 3 visuals max and tell me what I see 💚 IMPACT of your solution. 💚 Your reflections. 💚 Next steps. No one has 15 minutes to spend trying to dig out relevant information. It's your job to think about the UX of your portfolios. Show what matters. Show a skeleton. Show to create an impression, but leave them wanting more. Focus on this for the first 5 applications. Get feedback. Build from there.

Many are asking me... Should I continue to track "Open Rates" on Cold Emails? It's still no. My answer hasn't changed. I had predicted this about 9 months ago if you want to look back. Why? Analyze the image in the post. Does the position of the "Report as Spam" increase the amount of people who click it by 3 on 1,000 recipients? If you said yes, you agree with me. This is a subtle way Google is asking you for more feedback on the quality of your outbound campaigns. Here are 5 reasons NOT to use Open Tracking for Cold Email: Reason 1: Limits Your Use Of Plain Text Emails Plain Text Emails get superior deliverability. Open Trackers can't be used in Plain Text emails. Reason 2: Inconsistent Tracking Open Trackers identify "opens" differently and ultimately can't prove someone opened the email. Every sequencer has a different way of tracking it. Reason 3: Email Fingerprints Open Trackers provide a fingerprint for your domain reputation. It's shared amongst everyone using the sequencer your company uses. Do you want to be part of this group? Reason 3: Misleading Data Secure Email Gateways open emails for their users to protect their privacy. Budget has increased significantly here and will continue to go up. Most of these systems will put your email in spam because of it. Reason 4: Easy To Block Even simple rules can block emails with open trackers. No AI required. It's simple. Reason 5: Bad Metric Teams and internet gurus are obsessed with open tracking. However, it doesn't mean your email has been opened. It could mean that, but it depends who you emailed. Here are 3 Insider Tips to Improve Deliverability Today: Insider Tip #1: Send to less technical audiences. This isn't my favorite advice to give. However, less technical audiences hit the report as spam button less. Insider Tip #2: Send to companies without Proofpoint, Cisco, and Mimecast MX Records. Prioritize companies invested in email security systems lower than ones who don't. Use LeadMagic to figure out what the company uses in the email finder. Insider Tip #3: Use LeadMagic's New Features on MX Detection & Valid_Catch_All Status to prioritize who to send to first. Prioritize valid (mail server checked) > catch_all. Use valid_catch_all status from LeadMagic which detects if the email has been found other ways. Prioritize Google or Microsoft email servers higher than Proofpoint, Cisco, and Mimecast email servers. This will lead to better delivery & reply rates. p.s. open tracking is not dead for email marketing, but that's not what I am talking about.

STOP asking ChatGPT to "make it better". Here's how to better prompt it instead: ☑ Clearly Identify the Issue Rather than a vague “make it better,” specify the exact element that needs change. For example: "Rewrite the second paragraph so it includes three concrete examples of our product’s benefits. The tone must be formal and persuasive. Remove any informal language or redundant phrases." ☑ Divide the Task into Discrete Steps Break the overall revision into a sequence of manageable tasks. For example: "Go through my instructions, step by step. – Step 1: Summarize it in one sentence. – Step 2: Identify two specific weaknesses. – Step 3: Rewrite the text to address these weaknesses, incorporating specific data or examples." ☑ Specify the Format and Level of Detail Define exactly how the final output should look. For example: "Provide the final revised text as a numbered list where each item contains 2–3 sentences. Each item must include at least one statistical fact or concrete example, and the overall response should not exceed 250 words." ☑ Request a Chain-of-Thought Explanation Ask the model to detail its reasoning process before giving the final output. For example: "Before providing the final revised text, explain your reasoning step-by-step. Identify which parts need improvement and how your changes will enhance clarity and professionalism. Then, present the final revised version." ☑ Conditional Instructions to Enforce Compliance Add if/then conditions to ensure all requirements are met. For example: "If the revised text does not include at least two concrete examples, then add a sentence with a real-world statistic. Otherwise, finalize the response as is." ☑ Consolidate All Instructions into One Prompt Integrate all the detailed instructions into a single, comprehensive prompt. For example: "First, identify the section of the text that needs improvement and explain why it is lacking. Next, summarize the current text in one sentence and list two specific weaknesses. Then, rewrite the text to address these weaknesses, ensuring the revised version includes three concrete examples, uses a formal and persuasive tone, and is structured as a numbered list with each item containing 2–3 sentences. Each list item must include at least one statistical fact or example, and the overall response must be no longer than 250 words. Before providing the final text, explain your reasoning step-by-step. If the revised text does not include at least two concrete examples, add an additional sentence with a real-world statistic." ___ Why This Works People never give enough context. And once ChatGPT answers, they never correct it enough. Think about it like an intern. Deep prompting is all about precision: give clear instructions, context & the right corrections. PS: Don't forget to use the new o3-mini model. It's crushing any other one. Yes – even DeepSeek.

Building an API that empowers developers and fosters a thriving ecosystem around your product takes intentionality. Here are 11 guiding principles to design and create robust APIs: 1. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗨𝘀𝗲𝗿:  Identify your target developers and understand their needs. What tasks will they be using the API for? Design with their experience in mind. 2. 𝗖𝗹𝗲𝗮𝗿 𝗮𝗻𝗱 𝗖𝗼𝗻𝗰𝗶𝘀𝗲 𝗗𝗲𝘀𝗶𝗴𝗻:  Strive for simplicity and consistency in your API's design. Use well-defined resources, intuitive naming conventions, and a consistent HTTP verb usage (GET, POST, PUT, DELETE). 3. 𝗩𝗲𝗿𝘀𝗶𝗼𝗻𝗶𝗻𝗴:  Plan for future changes with a well-defined versioning strategy. This allows developers to adapt to updates smoothly and prevents breaking changes. 4. 𝗗𝗲𝘁𝗮𝗶𝗹𝗲𝗱 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Invest in comprehensive and up-to-date documentation. Include clear explanations of endpoints, request/response formats, error codes, and example usage. 5. 𝗘𝗿𝗿𝗼𝗿 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴:  Implement a robust error handling system. Provide informative error messages with clear explanations and HTTP status codes for easy debugging. 6. 𝗥𝗮𝘁𝗲 𝗟𝗶𝗺𝗶𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆:  Protect your API from abuse and ensure data security. Implement rate limiting to prevent overwhelming your servers and enforce strong authentication and authorization mechanisms. 7. 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗶𝘀 𝗖𝗿𝘂𝗰𝗶𝗮𝗹:  Thoroughly test your API before exposing it to developers. Use unit testing, integration testing, and automated testing tools to ensure functionality and reliability. 8. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻:  Focus on optimizing API performance. Implement caching mechanisms, minimize data transfer sizes, and choose efficient data formats (JSON, XML). 9. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴:  Track API usage and gather insights into developer behavior. Analyze data to identify areas for improvement and potential new features. 10. 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁:  Foster a developer community around your API. Provide forums, discussions, and clear communication channels for feedback and support. 11. 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁:  APIs are not static. Be prepared to iterate and evolve based on developer feedback and changing needs. Continuously improve your API to enhance its usefulness. By following these principles, you can design APIs that are not just functional, but also a joy to use for developers, ultimately leading to a more successful product and ecosystem. Have I overlooked anything? Please share your thoughts—your insights are priceless to me.