User Experience

Boss, it's not no-code VS custom code. It's knowing when to switch between them. I've built dozens of AI workflows in n8n. Here's the framework that actually works. ✳️ Start with no-code when you need: Speed → Something running today, not next month Standard patterns → Email routing, data syncing, basic AI responses Team collaboration → Non-technical folks will modify it later n8n's 300+ integrations get you from zero to working in under an hour. ✳️ Switch to custom code when you hit: Complex logic → Nested conditionals taking 10+ visual nodes to build Performance walls → Processing thousands of records where JavaScript runs 10x faster Unique AI behavior → Fine-grained prompt control that built-in nodes can't handle 💡 The hybrid approach wins most often. Use n8n's visual builder for workflow structure. Drop in Code Nodes only where you need custom logic. A good analogy would be LEGO vs clay. Standardized blocks snap together fast. Custom molding gives you precision. Smart builders know when to use each. The mistake isn't picking the wrong tool. It's not knowing when to switch. What's your experience? Do you fight with no-code when code would be faster, or over-engineer with custom scripts when simple integrations would work? Follow me, Bhavishya Pandit, for practical AI automation insights 🔥

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.

🧑🏼 How To Design Better Personas In UX (https://lnkd.in/eGPXmPNZ), a step-by-step guide to reduce decoration and add meaningful data to make personas more helpful and effective. Neatly put together by Slava Shestopalov. ✅ We need to know who users are and what they need to do. ✅ We can use both personas and Jobs-to-Be-Done for that. 🤔 They serve different purposes and focus on different things. ✅ Jobs-to-Be-Done focuses on user needs and outcomes. ✅ Personas focus on users, their behavior and mental model. ✅ Useful personas emerge from profound user research. ✅ They help visualize users, their goals and motivation. 🚫 Don’t focus on demographics to avoid stereotypes. ✅ Include the way of thinking, background, “a day in life”. ✅ Always add at least one persona with a disability. ✅ Add a story, pain points and how they use your product. ✅ List user’s habits/products they use daily, often and rarely. ✅ Finally, add needs, wants and fears mentioned by users. ✅ Then, prioritize key points for each role in your team. We often speak about personas being an outdated tool, successfully replaced by Jobs-to-Be-Done. Yet often in practice they are compatible. Both move the focus to user needs, yet they shed light onto user from different perspectives. Knowing how users think, behave and feel is as important as what they do. As Page Laubheimer noted, personas help remove box-checking mentality. They tell a story of the customer, what their environment is, what their habits are, the tools they use daily — and give product teams a way to think about users in a much more approachable and tangible way. Ultimately, use what works for you and for your team: just make sure that the user details aren’t invented, and root in actual research with actual customers. Useful resources: Personas vs. Jobs-to-Be-Done, by Page Laubheimer https://lnkd.in/eHA2Ft4J A Guide To Building Personas For UX, by Maze https://lnkd.in/ehCzACZW Personas for UX, Product, and Design Teams, by UserInterviews https://lnkd.in/eeE3pVUK A Simple Guide To Personas, by Rikke Friis Dam, Yu Siang Teo https://lnkd.in/eRA52v5m Five-Steps Framework for Building Better Personas, by Nikki Anderson, MA https://lnkd.in/eGWpqkdz Fixing User Personas, by Jordan Bowman https://lnkd.in/eDPCr63Q Personas Make Users Memorable, by Aurora Harley https://lnkd.in/eh-PYMxc A Closer Look At Personas (A Series), by Mo Goltz https://lnkd.in/eGqbr9wy https://lnkd.in/eBDsSsaR #ux #design #research

Big day for our show Marketing Against the Grain Posted our 200th episode. Passed 2 million downloads. Passed 3 million YouTube views. Nice work Kieran Flanagan Some lessons from doing a podcast for 200 episodes: 1. Cross Promotion Drives Growth - doing guest spots or promo swaps with other shows or being part of a network that promotes you on other shows increases growth significantly by 30-40 percent. 2. Audio and Video at VERY different - what works on audio RSS in terms of content and format often does work as well on YouTube and vice versa. You need a playbook that incorporates both. 3. Feedback is how you grow - Listener round tables, YouTube comments, emails, engagement data make you better. Look at it and make adjustments each week and you get 10x better as those adjustments compound. 4. Guest need time to settle in - The first 5-10 min with a guest is going to get cut as they are getting comfortable and in the flow. You have to plan for that. 5. Guest bring listeners - As someone comes on your show they do bring their audience. Our guest episodes have higher views and downloads than non guest episodes. This is why so many shows are guest driven.

𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲. Because most people explain it from the inside out: policies, councils, standards, stewardship. But the business does not buy any of that. The business buys outcomes: → trustworthy KPIs → vendor and partner data you can actually use → faster financial close → fewer reporting escalations → smoother M&A integration → AI you can deploy without creating risk debt Most AI programs fail for boring reasons: nobody owns the data, quality is unknown, access is messy, accountability is missing. 𝗦𝗼 𝗹𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁. 𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗳𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗴𝘀: → ownership → quality → access → accountability 𝗔𝗻𝗱 𝗶𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘃𝗲𝗿𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗶𝗻 𝟰 𝗹𝗮𝘆𝗲𝗿𝘀: 1. Data Products (what the business consumes) → a named dataset with an owner and SLA → clear definitions + metric logic → documented inputs/outputs and intended use → discoverable in a catalog → versioned so changes don’t break reporting 2. Data Management (how products stay reliable) → quality rules + monitoring (freshness, completeness, accuracy) → lineage (where it came from, where it’s used) → master/reference data alignment → metadata management (business + technical) → access controls and retention rules 3. Data Governance (who decides, who is accountable) → data ownership model (domain owners, stewards) → decision rights: who can change KPI definitions, thresholds, and sources → issue management: triage, escalation paths, resolution SLAs → policy enforcement: what’s mandatory vs optional → risk and compliance alignment (auditability, approvals) 4. Data Operating Model (how you scale across the enterprise) → domain-based setup (data mesh or not, but clear domains) → operating cadence: weekly issue review, monthly KPI governance, quarterly standards → stewardship at scale (roles, capacity, incentives) → cross-domain decision-making for shared metrics → enablement: templates, playbooks, tooling support If you want to start fast: Pick the 10 metrics that run the business. Assign an owner. Define decision rights + escalation. Then build the data products around them. ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗮𝘀 𝗔𝗜 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

“[N]o such fee is needed to fund your services,” they argue. “‘Pay or okay’ suggests a false choice between purchasing an ads-free experience or consenting to pervasive tracking of our online lives followed by surveillance-based advertising. There is a third possibility of presenting contextual advertising that does not require personalised tracking and surveillance. Studies suggest that contextual advertising is nearly as profitable as surveillance-based advertising.” “#Meta’s approach fails to seek genuine consent as required by the #GDPR [General Data Protection Regulation], coercing users into acceptance by making privacy unaffordable,” he said in a statement accompanying the letter’s release. “The reason Meta insists in an unlawful consent model is because its business model is dependent on pervasive tracking." "Meta claims the fee is in line with other mainstream digital subscriptions. “As we have previously discussed, our current pricing is firmly in line with similar services offered by our competitors (e.g. YouTube Premium),” said company spokesman Matthew Pollard.However, as we’ve pointed out before, the comparison is bogus given Meta gets the content that fills Facebook and Instagram for free from users. Its ad-free subscription is not also selling access to premium and/or professional content, as is the case with YouTube Premium (which bundles access to music streaming and original movies); or indeed with news publications, which were the first types of sites to push the ‘pay or okay’ tactic, as they employ journalists to carry out reporting and produce professional content." By Natasha Lomas https://lnkd.in/eGj_JkSG

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

This Stanford study examined how six major AI companies (Anthropic, OpenAI, Google, Meta, Microsoft, and Amazon) handle user data from chatbot conversations.  Here are the main privacy concerns. 👀 All six companies use chat data for training by default, though some allow opt-out 👀 Data retention is often indefinite, with personal information stored long-term 👀 Cross-platform data merging occurs at multi-product companies (Google, Meta, Microsoft, Amazon) 👀 Children's data is handled inconsistently, with most companies not adequately protecting minors 👀 Limited transparency in privacy policies, which are complex and hard to understand and often lack crucial details about actual practices Practical Takeaways for Acceptable Use Policy and Training for nonprofits in using generative AI: ✅ Assume anything you share will be used for training - sensitive information, uploaded files, health details, biometric data, etc. ✅ Opt out when possible - proactively disable data collection for training (Meta is the one where you cannot) ✅ Information cascades through ecosystems - your inputs can lead to inferences that affect ads, recommendations, and potentially insurance or other third parties ✅ Special concern for children's data - age verification and consent protections are inconsistent Some questions to consider in acceptable use policies and to incorporate in any training. ❓ What types of sensitive information might your nonprofit staff  share with generative AI?  ❓ Does your nonprofit currently specifically identify what is considered “sensitive information” (beyond PID) and should not be shared with GenerativeAI ? Is this incorporated into training? ❓ Are you working with children, people with health conditions, or others whose data could be particularly harmful if leaked or misused? ❓ What would be the consequences if sensitive information or strategic organizational data ended up being used to train AI models? How might this affect trust, compliance, or your mission? How is this communicated in training and policy? Across the board, the Stanford research points that developers’ privacy policies lack essential information about their practices. They recommend policymakers and developers address data privacy challenges posed by LLM-powered chatbots through comprehensive federal privacy regulation, affirmative opt-in for model training, and filtering personal information from chat inputs by default. “We need to promote innovation in privacy-preserving AI, so that user privacy isn’t an afterthought." How are you advocating for privacy-preserving AI? How are you educating your staff to navigate this challenge? https://lnkd.in/g3RmbEwD

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 ]

As head of our product organization at Chase, I often think about how and what we’re delivering to customers, but I recently reflected on the vital role of product managers. While some may view it as merely administrative, in my opinion this couldn't be further from the truth. Product managers are the driving force behind strategy and exceptional experiences, whether for external customers or internal users. Our role demands a deep connection to both the product and its users. Three essential qualities we all have: Customer Obsession: Go beyond empathy by diving into data and insights to understand user behavior, pain points, and opportunities. Decisions should be data-driven, ensuring the product evolves with user needs. Strategic Leadership: Product managers must define and drive the product vision, setting strategies that align with company goals. This involves fostering alignment across cross-functional teams and building strong relationships with stakeholders to ensure everyone is working toward a shared vision. Accountability: Own the outcomes, whether good or bad. Exceptional product managers embrace challenges, learn from mistakes, and continuously iterate to improve. They step into gray areas, connecting the dots to drive cohesive and successful outcomes. This role is strategic and high-impact, requiring us to lead with intention, push boundaries, and always advocate for the user. #productmanagers #productdevelopment