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Welcome to Season 4 of Discussing Stupid! This podcast has always been about the same thing: the bad habits, lazy defaults, and inherited decisions that make digital work harder than it needs to be. Season 3 narrowed in on AI for a full run of episodes. This season we're back to the whole field. That means accessibility, user experience, content, search, design, and the way organizations actually make decisions about all of it. Some episodes will be about things that have been broken for fifteen years and nobody has bothered to fix. Others will be about brand new mistakes people are making with very new tools. AI is still in the mix, but it's one topic among many instead of the whole map. The format isn't changing - two people who do this work for a living, talking through what's going wrong and what to do instead. No theory for its own sake and absolutely no pretending there's a clean answer when there isn't one. We've got a decent list of topics for this season going, but we'd love to hear what's driving you nuts as well in the digital world. Drop a comment on YouTube, hit us up on social, or use the contact form at highmonkey.com/podcast. Also, in this trailer you'll hear a preview of our new theme music, written and produced by Cole. Episode 1 drops October 6! (0:00) - Welcome to Season 4 (0:26) - Setting the Season 4 scene (2:07) - Send us your topics (2:29) - Original music for Season 4 (3:32) - Music sneak peek (4:09) - Brought to you by High Monkey (4:20) - We have a video version (4:50) - Season 4 starts October 6 Subscribe for email updates on our website: https://www.discussingstupid.com/ Watch us on YouTube: https://www.youtube.com/@discussingstupid Listen on Apple Podcasts, Spotify, or Soundcloud: https://podcasts.apple.com/us/podcast/discussing-stupid-a-byte-sized-podcast-on-stupid-ux/id1428145024 https://open.spotify.com/show/0c47grVFmXk1cco63QioHp?si=87dbb37a4ca441c0 https://soundcloud.com/discussing-stupid Check Us Out on Socials: https://www.linkedin.com/company/discussing-stupid https://www.instagram.com/discussingstupid/ https://www.facebook.com/discussingstupid https://x.com/DiscussStupid

Season 3 of the Intentional AI series started with a simple premise: AI is a powerful tool, but the way it gets marketed and the way most people actually use it are two very different things. Eighteen episodes later, that premise has held up. In this finale, Virgil and Cole take stock of what the season actually taught them. Not the hype, not the headlines - what they observed firsthand through testing, building, and paying attention to how AI performs when real work is on the line. A big part of this episode centers on a real-world example: Virgil's experience building an internal business application using an AI low-code tool called Zite. What made it work wasn't the tool. It was the month of planning that happened before a single line of code was written - database diagrams, stakeholder conversations, process mapping, all of it done before AI touched anything. The app ended up replacing four SaaS systems. That kind of result doesn't come from letting AI lead. It comes from knowing exactly what you need before you ask AI for help. The flip side of that shows up in a small but telling moment: a teenager at the gym using AI to generate a workout plan. It probably gave him something reasonable. But here's the problem - he won't know whether it worked for three to six months, and he likely has no framework to evaluate it either way. That's the same trap organizations fall into when they hand big process decisions to AI without the expertise to judge the output. Cole frames it simply: AI is a mirror. Whatever you bring to it, it reflects back. Strong process knowledge, clear goals, and domain expertise get amplified. Gaps and blind spots get amplified too. The tool doesn't know the difference. Virgil runs through where AI held up the most across the season - SEO, analytics, research, coding, wireframes - and where it didn't, including original content, image generation, video, and design. Eighteen episodes in, the picture is a lot clearer than when we started. Previously in the Intentional AI series: Episode 1: Intentional AI and the Content LifecycleEpisode 2: Maximizing AI for Research and AnalysisEpisode 3: Smarter Content Creation with AIEpisode 4: The role of AI in content managementEpisode 5: How much can you trust AI for accessibilityEpisode 6: You’re asking AI to solve the wrong problems for SEO, GEO, and AEOEpisode 7: Why AI can make your content personalization worseEpisode 8: The real value of AI wireframes is NOT the wireframesEpisode 9: Just because AI can create images doesn't mean you should use themEpisode 10: The Super Bowl didn't sell AI, it exposed itEpisode 11: AI video rewards planning, not your ideasEpisode 12: AI might struggle with creativity, but coding isn't creativeEpisode 13.1: What the rise of conversational search means for your websiteEpisode 14: AI agents are only as good as your workflowEpisode 15: AI can't fix your social media if you have nothing to sayEpisode 16: The most important operation in analytics - understanding your "why?"Episode 17: How to get better results from AI prompts That's a wrap on Season 3. Thanks for coming along for the ride - see you in Season 4! For more conversations about AI, design, and digital strategy, visit https://www.highmonkey.com/podcast and subscribe on your favorite podcast platform. (0:00) - Intro (1:15) - Season 3 milestone: 2,000 listeners (2:06) - Why we started this series (3:09) - The AI backlash: justified or not? (6:33) - Virgil's AI-built internal app (7:29) - AI is only as powerful as the person using it (9:56) - AI at the gym! (13:00) - The workout is a microcosm of how AI gets misused (15:24) - AI's real value: saving time (16:42) - Plan first, build second (21:04) - Security: the thing low-code builders miss (24:59) - AI is a mirror: it reflects what you bring to it (25:32) - Where we would use AI (28:07) - Cheers to Season 3 + what's next (28:48) - Outro Subscribe for email updates on our website: https://www.discussingstupid.com/ Watch us on YouTube: https://www.youtube.com/@discussingstupid Listen on Apple Podcasts, Spotify, or Soundcloud: https://podcasts.apple.com/us/podcast/discussing-stupid-a-byte-sized-podcast-on-stupid-ux/id1428145024 https://open.spotify.com/show/0c47grVFmXk1cco63QioHp?si=87dbb37a4ca441c0 https://soundcloud.com/discussing-stupid Check Us Out on Socials: https://www.linkedin.com/company/discussing-stupid https://www.instagram.com/discussingstupid/ https://www.facebook.com/discussingstupid https://x.com/DiscussStupid

Near the end of Season 3, Virgil and Cole sit down to look back at the prompts used throughout the Intentional AI series, what worked, what did not, and what that gap says about how most people approach AI. The conversation starts with a simple premise: the prompt itself is not really the point. The words you type are just the output of a more important process, which is knowing clearly what you want before you start. When that clarity is missing, the prompt cannot compensate. When it is there, even a basic prompt tends to work. One of the more durable takeaways from across the season is that asking AI what to ask it is a legitimate strategy, not a workaround. When you do not have a clear framework for your task, AI can actually help you build one. Ask it what information it needs to do the thing you want done. From wireframes to strategic planning, that back-and-forth approach consistently produced better results than leading with a one-shot prompt and hoping for the best. The episode also draws a clear line between creative and analytical tasks. Across the season, analytical and pattern-based tasks like coding, research, and schema building tended to produce more reliable results. Creative work was another story. Not because the tools are broken, but because creativity requires judgment the AI does not have and the human has to supply. And that supply only works if the human actually knows the domain they are working in. That last point carries the most weight. Domain expertise is not just helpful when using AI - it is the variable that determines whether you can evaluate the output at all. If you do not know what good looks like in a given area, the AI can produce something plausible and you will not know whether it actually helped. That reality is a big part of why so many AI rollouts have underdelivered, and plays a part in a lot of the AI backlash we're seeing. Previously in the Intentional AI series: Episode 1: Intentional AI and the Content LifecycleEpisode 2: Maximizing AI for Research and AnalysisEpisode 3: Smarter Content Creation with AIEpisode 4: The role of AI in content managementEpisode 5: How much can you trust AI for accessibilityEpisode 6: You’re asking AI to solve the wrong problems for SEO, GEO, and AEOEpisode 7: Why AI can make your content personalization worseEpisode 8: The real value of AI wireframes is NOT the wireframesEpisode 9: Just because AI can create images doesn't mean you should use themEpisode 10: The Super Bowl didn't sell AI, it exposed itEpisode 11: AI video rewards planning, not your ideasEpisode 12: AI might struggle with creativity, but coding isn't creativeEpisode 13.1: What the rise of conversational search means for your websiteEpisode 14: AI agents are only as good as your workflowEpisode 15: AI can't fix your social media if you have nothing to sayEpisode 16: The most important operation in analytics - understanding your "why?" New episodes drop every other Tuesday. For more conversations about AI, design, and digital strategy, visit https://www.highmonkey.com/podcast and subscribe on your favorite podcast platform. (0:00) - Intro (0:55) - Today's topic: AI prompts (2:45) - You never know where a prompt will take you (5:10) - Ask AI what to ask it (7:05) - The repeatability problem (9:00) - Creative vs. analytical: two very different conversations (11:20) - One area where AI delivers: Research (14:00) - Domain expertise = major missing variable (16:45) - The AI backlash was predictable (19:05) - As AI models continue to evolve, so will our workflows (20:45) - Closing thoughts & finale preview (21:27) - Outro Subscribe for email updates on our website: https://www.discussingstupid.com/ Watch us on YouTube: https://www.youtube.com/@discussingstupid Listen on Apple Podcasts, Spotify, or Soundcloud: https://podcasts.apple.com/us/podcast/discussing-stupid-a-byte-sized-podcast-on-stupid-ux/id1428145024 https://open.spotify.com/show/0c47grVFmXk1cco63QioHp?si=87dbb37a4ca441c0 https://soundcloud.com/discussing-stupid Check Us Out on Socials: https://www.linkedin.com/company/discussing-stupid https://www.instagram.com/discussingstupid/ https://www.facebook.com/discussingstupid https://x.com/DiscussStupid

If there is one thing AI should be genuinely good at, it is analytics. Pattern recognition across large data sets is more or less what it was built for. But just because AI can look at your data does not mean it knows what you are actually trying to learn from it. That is the part most people skip. In this episode, Cole brings in a framework from a LinkedIn post by Tim Stoddart that puts the problem into clear terms: data is cheap, insight is expensive, storytelling is priceless. The Lego analogy Stoddart uses is a good one. You can sort a pile of bricks by color, arrange them beautifully, and end up with something completely meaningless if you started with the wrong bricks. The same is true with analytics. Before AI can help you, you have to be honest about whether you are even pulling from the right data to begin with. Virgil has been testing this directly using the podcast's own analytics across Google Analytics, Captivate, YouTube, Apple Podcasts, Spotify, SoundCloud, and their mailing list. The challenge is not a lack of data. It is that the data lives in separate places, each with its own reporting logic, and none of them talk to each other. When he ran actual queries against the data he could access, the results were uneven. One question surfaced a genuinely useful insight about engagement that he would not have found on his own. Another hit a wall that no amount of follow-up prompting could get past. The bigger point underneath all of it is about starting with the outcome rather than the data. Virgil has applied this same logic to web strategy for years. The last page you should build is the homepage. The same principle applies here. If you cannot clearly name what you want to understand before you open your analytics, the data is not going to organize itself into an answer. The tools for cross-platform AI analytics are not quite where they need to be yet, but the direction is clear. AI is already starting to suggest its own follow-up questions, which changes the dynamic considerably for people who do not know what to ask next. The dashboard as a destination is fading. What replaces it is a conversation with your data - one that only works if you walk in knowing what you are trying to find out. Previously in the Intentional AI series: Episode 1: Intentional AI and the Content Lifecycle Episode 2: Maximizing AI for Research and Analysis Episode 3: Smarter Content Creation with AI Episode 4: The role of AI in content management Episode 5: How much can you trust AI for accessibility Episode 6: You’re asking AI to solve the wrong problems for SEO, GEO, and AEO Episode 7: Why AI can make your content personalization worse Episode 8: The real value of AI wireframes is NOT the wireframes Episode 9: Just because AI can create images doesn't mean you should use them Episode 10: The Super Bowl didn't sell AI, it exposed it Episode 11: AI video rewards planning, not your ideas Episode 12: AI might struggle with creativity, but coding isn't creative Episode 13.1: What the rise of conversational search means for your website Episode 14: AI agents are only as good as your workflow Episode 15: AI can't fix your social media if you have nothing to say New episodes drop every other Tuesday. For more conversations about AI, design, and digital strategy, visit https://www.highmonkey.com/podcast and subscribe on your favorite podcast platform. (0:00) - Intro (0:48) - Today's topic: AI and data analytics (1:51) - Virgil example: 3 million rows, one question (4:02) - The Lego analogy: from a pile of bricks to a story (5:04) - What if you're sorting the wrong bricks? (6:26) - Building with Legos from multiple sets (8:59) - You have to know what you're building (11:00) - A live example with podcast analytics (13:59) - Where AI can name the problem but not solve it (16:04) - AI that tells you what to ask next (17:40) - Stop reading your data, start asking it questions (20:36) - The tools landscape today and what's coming (22:00) - Outro Subscribe for email updates on our website: https://www.discussingstupid.com/ Watch us on YouTube: https://www.youtube.com/@discussingstupid Listen on Apple Podcasts, Spotify, or Soundcloud: https://podcasts.apple.com/us/podcast/discussing-stupid-a-byte-sized-podcast-on-stupid-ux/id1428145024 https://open.spotify.com/show/0c47grVFmXk1cco63QioHp?si=87dbb37a4ca441c0 https://soundcloud.com/discussing-stupid Check Us Out on Socials: https://www.linkedin.com/company/discussing-stupid https://www.instagram.com/discussingstupid/ https://www.facebook.com/discussingstupid https://x.com/DiscussStupid

AI-generated social media content is everywhere right now. The question is not whether you should use AI for it. The question is whether what you are producing is actually worth putting out there. In this episode, Cole and Virgil get into the honest version of this conversation. Social media is already oversaturated. The algorithm rewards activity, but activity without a message is just noise at scale. The real problem with a lot of AI-assisted social content is not that it sounds like AI. It is that there was nothing behind it to begin with. Cole ran the same prompt across three tools - Gemini, ChatGPT, and Claude - asking each to write a LinkedIn post promoting an article from earlier in the series. The source material was not great, the prompt was intentionally minimal, and the results reflected that. Each tool handled it differently, and the gap between them comes down to how well each one understood what LinkedIn actually requires from a post. The takeaway is not which tool won. It is that the output ceiling is set before you open the tool. Know what you actually want to say, then use AI to help you say it faster and across more formats. That is where it earns its place. Previously in the Intentional AI series: Episode 1: Intentional AI and the Content LifecycleEpisode 2: Maximizing AI for Research and AnalysisEpisode 3: Smarter Content Creation with AIEpisode 4: The role of AI in content managementEpisode 5: How much can you trust AI for accessibilityEpisode 6: You’re asking AI to solve the wrong problems for SEO, GEO, and AEOEpisode 7: Why AI can make your content personalization worseEpisode 8: The real value of AI wireframes is NOT the wireframesEpisode 9: Just because AI can create images doesn't mean you should use themEpisode 10: The Super Bowl didn't sell AI, it exposed itEpisode 11: AI video rewards planning, not your ideasEpisode 12: AI might struggle with creativity, but coding isn't creativeEpisode 13.1: What the rise of conversational search means for your websiteEpisode 14: AI agents are only as good as your workflow New episodes drop every other Tuesday. For more conversations about AI, design, and digital strategy, visit https://www.highmonkey.com/podcast and subscribe on your favorite podcast platform. (0:00) - Intro (1:17) - Today's topic: AI social post creation strategy (1:58) - The social media volume crisis (4:33) - Start with your message, not the tool (6:14) - Good AI use case for social media (6:56) - On standing out (8:04) - Focusing on small wins (10:48) - AI has evolved and so have our perspectives on it (14:28) - We tested 3 tools for AI social posts (15:42) - Testing Gemini (17:49) - Testing Claude (20:39) - Testing ChatGPT (22:52) - Replacing monotony is not replacing creativity (24:23) - Closing thoughts (25:14) - Outro Subscribe for email updates on our website: https://www.discussingstupid.com/ Watch us on YouTube: https://www.youtube.com/@discussingstupid Listen on Apple Podcasts, Spotify, or Soundcloud: https://podcasts.apple.com/us/podcast/discussing-stupid-a-byte-sized-podcast-on-stupid-ux/id1428145024 https://open.spotify.com/show/0c47grVFmXk1cco63QioHp?si=87dbb37a4ca441c0 https://soundcloud.com/discussing-stupid Check Us Out on Socials: https://www.linkedin.com/company/discussing-stupid https://www.instagram.com/discussingstupid/ https://www.facebook.com/discussingstupid https://x.com/DiscussStupid

Agentic AI has been one of the most repeated phrases in marketing and technology for the past year. Most people using it cannot tell you what it means. Virgil and Cole bring in Sean Wright, Lead Product Evangelist at Kentico, to cut through the noise and talk about what agentic AI actually does inside a content management workflow. The starting point is a distinction that gets lost in the hype. Standard AI chat requires you to keep driving it. You ask, it answers, it stops. Agents do not stop when you do. They make decisions, take action, and iterate toward a goal using whatever tools and data they have access to. Sean makes the case that this shift in behavior is significant, but only if the agent has something real to work with. Context is the deciding factor every time. Sean walks through two areas where Kentico's built-in AI engine, AIRA (AI Recommendations and Assistance), is already handling real workflow tasks. The first is image management - optimization, cropping, focal point detection, alt text generation, and taxonomy tagging, all happening in the background without the marketer stepping in. The second is a content strategist agent that evaluates web content against your organization's content strategy document, checking for consistency in tone, style, and voice before anything goes live. The conversation closes on evals, a concept that does not get enough attention outside of product development circles. As models change and context evolves, teams need a way to verify that output quality is holding steady. Sean makes the case that this applies to individual marketers and marketing teams, not just vendors. If you rely on a repeatable AI-assisted workflow, you should have a way to know when something has quietly shifted. Previously in the Intentional AI series: Episode 1: Intentional AI and the Content LifecycleEpisode 2: Maximizing AI for Research and AnalysisEpisode 3: Smarter Content Creation with AIEpisode 4: The role of AI in content managementEpisode 5: How much can you trust AI for accessibilityEpisode 6: You’re asking AI to solve the wrong problems for SEO, GEO, and AEOEpisode 7: Why AI can make your content personalization worseEpisode 8: The real value of AI wireframes is NOT the wireframesEpisode 9: Just because AI can create images doesn't mean you should use themEpisode 10: The Super Bowl didn't sell AI, it exposed itEpisode 11: AI video rewards planning, not your ideasEpisode 12: AI might struggle with creativity, but coding isn't creativeEpisode 13.1: What the rise of conversational search means for your website New episodes every other Tuesday. For more conversations about AI, design, and digital strategy, visit https://www.highmonkey.com/podcast and subscribe on your favorite podcast platform. (0:00) - Intro (0:44) - Meet Sean Wright from Kentico (1:45) - What does "agentic" actually mean? (3:13) - The agent doesn't stop when you do (4:11) - It all depends on the tools (5:49) - AI output is non-deterministic. Plan for it. (8:11) - What AIRA handles behind the scenes (11:17) - Where AI works best: logic over creativity (12:17) - Evaluating content against your own strategy (15:24) - Consistency is harder than creation (16:03) - AI still requires planning (16:58) - Tools that can help across the content lifecycle (18:48) - Cognitive load and the 80/20 rule (20:16) - Will AI replace your job? (21:45) - The Wall-E chair question (23:19) - Agentic AI is intentional AI (24:17) - What are evals and why do they matter? (27:01) - Wrapping up (28:00) - Outro Subscribe for email updates on our website: https://www.discussingstupid.com/ Watch us on YouTube: https://www.youtube.com/@discussingstupid Listen on Apple Podcasts, Spotify, or Soundcloud: https://podcasts.apple.com/us/podcast/discussing-stupid-a-byte-sized-podcast-on-stupid-ux/id1428145024 https://open.spotify.com/show/0c47grVFmXk1cco63QioHp?si=87dbb37a4ca441c0 https://soundcloud.com/discussing-stupid Check Us Out on Socials: https://www.linkedin.com/company/discussing-stupid https://www.instagram.com/discussingstupid/ https://www.facebook.com/discussingstupid https://x.com/DiscussStupid

Conversational search changes the fundamental contract of how users interact with a website. Instead of returning links, it returns answers. That sounds like a clean upgrade. What it actually does is make the quality of everything sitting underneath the AI impossible to ignore. In this episode, Virgil and Cole bring in their first-ever guest for the series, Will Noble from Squiz, who has spent over a decade working in the information discovery space for large enterprise organizations. Will explains the shift with a clean analogy early in the episode. Traditional search is like asking a librarian for help and getting handed a stack of encyclopedias. Conversational search is that same librarian reading every book in the library and handing you a direct answer. The user experience improvement is real. But so is what it depends on, because the AI reads everything, including the outdated policy documents buried in a subdomain that nobody has touched in a decade. When dormant content gets surfaced as a confident answer, the gap between what was published and what is actually true becomes a reputational and legal problem. The practical guidance that emerges from the conversation is to start with a defined slice of content you know is solid. Will walks through a real example of a university with 250,000 pieces of content that scoped its initial conversational search implementation to 50 pages focused on student life. Questions related to that area got clean, accurate answers. Everything else defaulted to traditional keyword search. That controlled scope is what allowed the project to prove value before expanding, and it is what kept stakeholders from pulling the plug the moment a bad result surfaced. The garbage-in, garbage-out principle has always been true in search. What this episode makes clear is that it has never carried higher stakes. LLMs do not skip the bad content. They find it, surface it, and present it with confidence. The first step toward getting conversational search right is the same step the rest of this series keeps coming back to: know what you are working with before you deploy. Previously in the Intentional AI series: Episode 1: Intentional AI and the Content LifecycleEpisode 2: Maximizing AI for Research and AnalysisEpisode 3: Smarter Content Creation with AIEpisode 4: The role of AI in content managementEpisode 5: How much can you trust AI for accessibilityEpisode 6: You’re asking AI to solve the wrong problems for SEO, GEO, and AEOEpisode 7: Why AI can make your content personalization worseEpisode 8: The real value of AI wireframes is NOT the wireframesEpisode 9: Just because AI can create images doesn't mean you should use themEpisode 10: The Super Bowl didn't sell AI, it exposed itEpisode 11: AI video rewards planning, not your ideasEpisode 12: AI might struggle with creativity, but coding isn't creative New episodes every other Tuesday. For more conversations about AI, design, and digital strategy, visit https://www.highmonkey.com/podcast and subscribe on your favorite podcast platform. (0:00) - Intro (0:51) - Meet Will Noble from Squiz (2:14) - Today's topic: Conversational search (4:14) - Welcome to the new era of information seeking (7:10) - The dormant content problem (9:48) - You can ignore the problem, but it won't ignore you (11:45) - Where do you start with thousands of pages? (14:36) - Start small, don't go big (17:14) - AI's opportunity as a content auditing tool (20:39) - Search is the foundation of everything AI does (22:13) - How do you keep up when AI moves this fast? (25:43) - Outro Subscribe for email updates on our website: https://www.discussingstupid.com/ Watch us on YouTube: https://www.youtube.com/@discussingstupid Listen on Apple Podcasts, Spotify, or Soundcloud: https://podcasts.apple.com/us/podcast/discussing-stupid-a-byte-sized-podcast-on-stupid-ux/id1428145024 https://open.spotify.com/show/0c47grVFmXk1cco63QioHp?si=87dbb37a4ca441c0 https://soundcloud.com/discussing-stupid Check Us Out on Socials: https://www.linkedin.com/company/discussing-stupid https://www.instagram.com/discussingstupid/ https://www.facebook.com/discussingstupid https://x.com/DiscussStupid

Most of the Intentional AI series has tested AI in areas where judgment, creativity, and context matter a lot. This episode is a little different. Virgil, Cole, and returning guest Chad take a look at AI and web development, a domain where patterns, repetition, and known best practices are the whole point. On paper, that should be where AI shines. Chad makes clear that for working developers, AI is already genuinely useful. Generating boilerplate, producing code blocks for well-understood functionality, and cutting down on time spent typing out repetitive structures. These are real wins. The catch is that getting value out of AI-generated code still requires knowing what you're looking at. If you can't read the output, you can't catch the errors, and you can't fix what's wrong. That gap becomes more visible when you consider who these tools are being marketed to. The pitch is often aimed at business users and non-developers, promising a fast path from idea to working product. The episode digs into why that gap -- between what gets generated and what is actually usable -- is harder to close in code than it is in content or images. A piece of writing that's 80% there can be polished. Code that's 80% there can be a liability, especially if the person using it doesn't know what the other 20% is. Virgil tested Claude, ChatGPT, and GenSpark against the same prompt: build a visually appealing, fully accessible accordion web component using the series source article. All three produced something workable. None were perfect. Claude handled screen reader accessibility well but had a JavaScript bug that prevented the drawers from opening and used a low-contrast color scheme. ChatGPT produced the most functional but visually flat result, with the worst screen reader compliance. GenSpark produced the most polished visual, with the most helpful follow-up prompts, and landed in the middle on accessibility. As Virgil put it, these were the least failures the series has generated, which is saying something. Previously in the Intentional AI series: Episode 1: Intentional AI and the Content Lifecycle Episode 2: Maximizing AI for Research and Analysis Episode 3: Smarter Content Creation with AI Episode 4: The role of AI in content management Episode 5: How much can you trust AI for accessibility Episode 6: You’re asking AI to solve the wrong problems for SEO, GEO, and AEO Episode 7: Why AI can make your content personalization worse Episode 8: The real value of AI wireframes is NOT the wireframes Episode 9: Just because AI can create images doesn't mean you should use them Episode 10: The Super Bowl didn't sell AI, it exposed it Episode 11: AI video rewards planning, not your ideas New episodes every other Tuesday. For more conversations about AI, design, and digital strategy, visit https://www.highmonkey.com/podcast and subscribe on your favorite podcast platform. (0:00) - Intro (0:44) - Today's topic: Intersection of AI & coding (2:46) - The "just type and get a website" myth (3:44) - Where AI actually helps with coding (6:08) - When "good enough" works and when it doesn't (8:13) - The real win: Using AI as a developer (11:06) - Tool test: building an accordion with AI (13:14) - Testing Claude (16:23) - Testing ChatGPT (Codex) (18:25) - Testing GenSpark (20:25) - Using AI to fix AI code (23:11) - Tons of opportunity with AI and coding (24:16) - Outro Subscribe for email updates on our website: https://www.discussingstupid.com/ Watch us on YouTube: https://www.youtube.com/@discussingstupid Listen on Apple Podcasts, Spotify, or Soundcloud: https://podcasts.apple.com/us/podcast/discussing-stupid-a-byte-sized-podcast-on-stupid-ux/id1428145024 https://open.spotify.com/show/0c47grVFmXk1cco63QioHp?si=87dbb37a4ca441c0 https://soundcloud.com/discussing-stupid Check Us Out on Socials: https://www.linkedin.com/company/discussing-stupid https://www.instagram.com/discussingstupid/ https://www.facebook.com/discussingstupid https://x.com/DiscussStupid

AI video tools promise fast, cheap production. But what you get back depends entirely on how much thinking you did before you hit enter. In Episode 11 of Season 3's Intentional AI series, Virgil and Cole take on AI video generation, arguably the most complex and most hyped area of AI content creation. Video production has always been expensive, often running thousands of dollars per minute through traditional workflows. AI tools are pitched as the solution to that cost. The reality is more complicated. The core question Cole raises early: are you using AI for speed, or for creativity? With video, that matters even more than with text or images, because the ability to edit what AI generates is extremely limited. You are largely working with what comes back. Virgil tested three tools, Claude, Artlist, and Sora, using the same prompt and the same source article the series has been following. The results varied wildly. Some tools produced clean, factually grounded output that could serve as a foundation with additional editing. Others burned through resources quickly and delivered results that raised more questions than they answered (to put it lightly). Each tool had tradeoffs between creative quality, turnaround time, cost, and practical usability. The pattern held across the board: AI video does not reward vague ideas. It rewards storyboarding, defined objectives, and clear constraints. The most realistic use case is not generating entire videos from scratch, but using AI for individual pieces -- a specific animation, a graphic element, a rough draft to react to. AI video is getting better. But better does not mean ready. Previously in the Intentional AI series: Episode 1: Intentional AI and the Content Lifecycle Episode 2: Maximizing AI for Research and Analysis Episode 3: Smarter Content Creation with AI Episode 4: The role of AI in content management Episode 5: How much can you trust AI for accessibility Episode 6: You’re asking AI to solve the wrong problems for SEO, GEO, and AEO Episode 7: Why AI can make your content personalization worse Episode 8: The real value of AI wireframes is NOT the wireframes Episode 9: Just because AI can create images doesn't mean you should use them Episode 10: The Super Bowl didn't sell AI, it exposed it New episodes every other Tuesday. For more conversations about AI, design, and digital strategy, visit https://www.highmonkey.com/podcast and subscribe on your favorite podcast platform. (0:00) - Intro (1:04) - The good & bad of AI video generation (1:30) - Are you using AI for speed or creativity? (3:56) - Structure up front = your best friend (7:28) - How Coinbase used simplicity to stand out (8:42) - More Super Bowl AI narrative unpacking (10:20) - We tested 3 tools for AI video generation (11:47) - Testing Claude (14:24) - Testing Artlist (16:32) - Testing Sora (19:03) - Closing thoughts & takeaways (21:40) - Outro Subscribe for email updates on our website: https://www.discussingstupid.com/ Watch us on YouTube: https://www.youtube.com/@discussingstupid Listen on Apple Podcasts, Spotify, or Soundcloud: https://podcasts.apple.com/us/podcast/discussing-stupid-a-byte-sized-podcast-on-stupid-ux/id1428145024 https://open.spotify.com/show/0c47grVFmXk1cco63QioHp?si=87dbb37a4ca441c0 https://soundcloud.com/discussing-stupid Check Us Out on Socials: https://www.linkedin.com/company/discussing-stupid https://www.instagram.com/discussingstupid/ https://www.facebook.com/discussingstupid https://x.com/DiscussStupid

In Episode 10 of Intentional AI, we are taking a short detour in our Intentional AI series to talk about the Super Bowl. Not the game. The ads. A noticeable chunk of them leaned hard into AI. On the surface, it felt like a big moment for the industry. But when you look closer, it raises a different question. Are we watching real progress, or just very expensive hype? We unpack what was actually being sold, what was implied, and what gets left out when AI is positioned as effortless. AI has value. We are not arguing that it does not. But it works best when it is used intentionally and within clear boundaries. When it is marketed as a replacement for thinking, planning, or strategy, that is where things fall apart. If you are trying to separate signal from noise, this one is for you. Previously in the Intentional AI series: Episode 1: Intentional AI and the Content LifecycleEpisode 2: Maximizing AI for Research and AnalysisEpisode 3: Smarter Content Creation with AIEpisode 4: The role of AI in content managementEpisode 5: How much can you trust AI for accessibilityEpisode 6: You’re asking AI to solve the wrong problems for SEO, GEO, and AEOEpisode 7: Why AI can make your content personalization worseEpisode 8: The real value of AI wireframes is NOT the wireframesEpisode 9: Just because AI can create images doesn't mean you should use them New episodes every other Tuesday. For more conversations about AI, design, and digital strategy, visit www.discussingstupid.com and subscribe on your favorite podcast platform. (0:00) - Intro (0:42) - We had to talk about the Super Bowl (2:05) - The numbers behind AI in the Super Bowl (3:55) - How AI is marketed vs reality of AI (7:30) - This is why we started Intentional AI (8:30) - Reflections on the current realities of AI (13:20) - Where does AI make the most sense? (15:30) - Our reaction to the AI generated ads (17:30) - Join us and learn to be responsible with AI (19:00) - Outro **Also disclaimer: there is a math error at 18:15 - the correct calculation is closer to $100-150 million.** Subscribe for email updates on our website: https://www.discussingstupid.com/ Watch us on YouTube: https://www.youtube.com/@discussingstupid Listen on Apple Podcasts, Spotify, or Soundcloud: https://podcasts.apple.com/us/podcast/discussing-stupid-a-byte-sized-podcast-on-stupid-ux/id1428145024 https://open.spotify.com/show/0c47grVFmXk1cco63QioHp?si=87dbb37a4ca441c0 https://soundcloud.com/discussing-stupid Check Us Out on Socials: https://www.linkedin.com/company/discussing-stupid https://www.instagram.com/discussingstupid/ https://www.facebook.com/discussingstupid https://x.com/DiscussStupid

In Episode 9 of the Intentional AI series, Cole and Virgil take on one of the most common and misunderstood uses of AI today: image and graphic generation. From social media visuals to promotional graphics, AI images are fast, easy, and everywhere. The conversation focuses on why images became the public on ramp to AI and why that familiarity creates risk. Visuals feel harmless, but the moment AI starts generating finished looking images, teams inherit decisions around ownership, ethics, and trust that they are often unprepared to make. A central theme of the episode is responsibility escalation. As AI reduces the effort required to create images, the importance of human judgment increases. Treating AI generated visuals as final work can quickly introduce legal, ethical, and reputational problems. Virgil shares a practical experiment where he used a simple prompt to generate three social media promotional graphics from an existing article and tested the results across three tools: Canva, Claude, and Artlist. Canva produced the most generic and repetitive designs. Claude delivered cleaner structure and stronger messaging but struggled with fonts, formats, and variation. Artlist created the most visually interesting outputs, though it introduced workflow limitations and cost concerns. The episode reinforces a consistent conclusion across the series. AI can help jumpstart visual work, but it cannot replace judgment, intent, or responsibility. In this episode, they explore: Why AI images are so tempting to useWhere AI generated graphics actually helpWhy most AI visuals fall flatEthical and ownership risks teams overlookA comparison of Canva, Claude, and Artlist A downloadable Episode Companion Guide is available below with example outputs and tool takeaways. https://links.discussingstupid.com/s3e9companion Previously in the Intentional AI series: Episode 1: Intentional AI and the Content LifecycleEpisode 2: Maximizing AI for Research and AnalysisEpisode 3: Smarter Content Creation with AIEpisode 4: The role of AI in content managementEpisode 5: How much can you trust AI for accessibilityEpisode 6: You’re asking AI to solve the wrong problems for SEO, GEO, and AEOEpisode 7: Why AI can make your content personalization worseEpisode 8: The real value of AI wireframes is NOT the wireframes New episodes every other Tuesday. For more conversations about AI, design, and digital strategy, visit www.discussingstupid.com and subscribe on your favorite podcast platform. (0:00) - Intro (1:40) - You can’t escape AI imagery (3:18) - Why AI images are risky (4:40) - The legal and ethical line (6:15) - Creativity vs time and cost (9:28) - Every tool has hopped on the AI bandwagon (13:20) - The slippery slope of AI visuals (15:35) - We tested 3 tools for AI visuals (17:30) - Testing Canva (20:40) - Testing Claude (Opus) (22:15) - Testing Artlist (24:15) - Tool testing takeaways (26:45) - Closing thoughts (28:00) - Outro Subscribe for email updates on our website: https://www.discussingstupid.com/ Watch us on YouTube: https://www.youtube.com/@discussingstupid Listen on Apple Podcasts, Spotify, or Soundcloud: https://podcasts.apple.com/us/podcast/discussing-stupid-a-byte-sized-podcast-on-stupid-ux/id1428145024 https://open.spotify.com/show/0c47grVFmXk1cco63QioHp?si=87dbb37a4ca441c0 https://soundcloud.com/discussing-stupid Check Us Out on Socials: https://www.linkedin.com/company/discussing-stupid https://www.instagram.com/discussingstupid/ https://www.facebook.com/discussingstupid https://x.com/DiscussStupid

In Episode 8 of the Intentional AI series, Cole, Virgil, and Chad explore one of the most tempting uses of AI in digital work: wireframing and page layout. With AI now able to generate full wireframes in minutes or even seconds, the promise of speed is undeniable. But speed alone is not the point. The conversation focuses on where AI genuinely helps in the wireframing process and where it introduces new risks. Wireframes are meant to establish structure, hierarchy, and intent, not just visual output. While AI can quickly generate layouts, components, and patterns, it still requires strong human judgment to evaluate what is correct, what is missing, and what could cause problems downstream. A key theme of the episode is escalation of responsibility. As AI reduces the time required to create wireframes, the importance of human review, direction, and decision making increases. Treating AI generated wireframes as finished work can introduce serious risks, especially around accessibility, content fidelity, maintainability, and overall project direction. Virgil shares an experiment where he used AI to first generate a detailed prompt for wireframing, then tested that prompt across three tools: Claude, Google Gemini 3, and Figma Make. The results reveal clear differences in layout quality, accessibility handling, content retention, and how easily the outputs could be integrated into real workflows. Claude produced the strongest layout and structural patterns but failed badly on accessibility and removed large portions of content. Gemini generated simpler wireframes with clearer structure, but used even less content and still struggled with accessibility. Figma Make stood out for workflow integration, retaining all content and allowing direct editing inside Figma, though it also failed accessibility requirements and relied heavily on generic styling and placeholder imagery. Throughout the episode, the group returns to the same conclusion. AI is extremely effective at getting the first portion of wireframing done quickly. It is far less effective at making judgment calls, enforcing standards, or understanding context without guidance. In this episode, they explore: How wireframing fits into the content lifecycleWhy speed changes the risk profile of design workUsing AI to generate prompts instead of starting from scratchWhere AI wireframes succeed and where they failAccessibility and content risks in AI generated layoutsA wireframing comparison of Claude, Gemini 3, and Figma Make A downloadable Episode Companion Guide is available below with tool comparisons and key takeaways. DS-S3-E8-CompanionDoc.pdf Previously in the Intentional AI series: Episode 1: Intentional AI and the Content LifecycleEpisode 2: Maximizing AI for Research & AnalysisEpisode 3: Smarter Content Creation with AIEpisode 4: The role of AI in content managementEpisode 5: How much can you trust AI for accessibility?Episode 6: You’re asking AI to solve the wrong problems for SEO/GEO/AEOEpisode 7: Why AI can make your content personalization worse New episodes every other Tuesday. For more conversations about AI, design, and digital strategy, visit www.discussingstupid.com and subscribe on your favorite podcast platform. (0:00) - Intro (1:12) - Why wireframing belongs in the content lifecycle (2:24) - Wireframing is hard / The appeal of AI here (4:08) - Using AI to create the prompt for wireframing (5:27) - Why prompt creation unlocks the real value (7:15) - AI wireframing = filling in blanks & reacting (10:34) - Risks for teams without wireframing expertise (12:21) - Using AI to ask better questions, not skip thinking (13:57) - Iterating prompts and adding constraints (15:24) - We tested 3 AI tools for wireframing (15:56) - Testing Claude (19:41) - Testing Gemini (21:05) - Testing Figma Make (24:56) - Practical takeaways and best use cases (26:50) - Outro Subscribe for email updates on our website: https://www.discussingstupid.com/ Watch us on YouTube: https://www.youtube.com/@discussingstupid Listen on Apple Podcasts, Spotify, or Soundcloud: https://podcasts.apple.com/us/podcast/discussing-stupid-a-byte-sized-podcast-on-stupid-ux/id1428145024 https://open.spotify.com/show/0c47grVFmXk1cco63QioHp?si=87dbb37a4ca441c0 https://soundcloud.com/discussing-stupid Check Us Out on Socials: https://www.linkedin.com/company/discussing-stupid https://www.instagram.com/discussingstupid/ https://www.facebook.com/discussingstupid https://x.com/DiscussStupid

In Episode 7 of the Intentional AI series, Cole and Virgil focus on content personalization and why it is one of the most overpromised areas of AI. While personalization is often positioned as simple and automated, doing it well requires far more clarity and intent than most tools suggest. They break personalization into two main approaches. Role based personalization tailors messages for specific audiences or job functions, while behavioral personalization adapts experiences based on how people interact with content over time. The conversation also touches on predictive analysis and where AI may eventually help interpret patterns across analytics data. A central theme of the episode is trust. Using AI for personalization assumes the system understands audience priorities and pain points. Without clear direction, AI fills in the gaps with assumptions. Cole and Virgil explain why personalization has always been difficult to implement, why adoption remains low, and why AI does not remove the need for strategy, measurement, or human judgment. The episode also addresses the risks of personalization. Messages that are too generic get ignored, while messages that feel overly personal can cross into uncomfortable territory. Finding the right balance is still a human responsibility. In the second half of the episode, they continue their ongoing experiment using the same AI written accessibility article from earlier episodes. This time, they test three tools by asking them to generate role based promotional emails for a head of web marketing, a director of information technology, and a C level executive. The results highlight meaningful differences in tone, structure, and assumptions across tools. The takeaway is consistent with the Intentional AI series. AI can support personalization, but only when you define goals, outcomes, and boundaries first. In this episode, they explore: What content personalization actually meansRole based versus behavioral personalizationWhy personalization adoption remains lowThe balance between relevance and creepinessHow AI supports personalization without replacing strategyA role based email comparison of Perplexity, Copilot, and Claude A downloadable Episode Companion Guide is available below with tool comparisons and practical takeaways. DS-S3-E7-CompanionDoc.pdf Previously in the Intentional AI series: Episode 1: Intentional AI and the Content LifecycleEpisode 2: Using AI for Research and AnalysisEpisode 3: AI and Content CreationEpisode 4: Content Management and AIEpisode 5: How much can you trust AI for accessibility?Episode 6: You’re asking AI to solve the wrong problems for SEO, GEO, and AEO New episodes every other Tuesday. For more conversations about AI and digital strategy, visit www.discussingstupid.com and subscribe on your favorite podcast platform. (0:00) - Intro (0:56) - Delivering tailored content with AI (1:30) - Different kinds of AI personalization (4:10) - Why personalization can be tricky (5:00) - The need for measurement and outcomes (7:45) - The Personalization Pendulum™ (10:00) - The work doesn’t go away! (13:10) - We tested 3 AI tools for personalization (16:10) - Testing Perplexity (18:10) - Testing Copilot & Claude (19:20) - Explaining our prompting process (21:25) - The topic of AI replacing human labor (24:30) - Outro Subscribe for email updates on our website: https://www.discussingstupid.com/ Watch us on YouTube: https://www.youtube.com/@discussingstupid Listen on Apple Podcasts, Spotify, or Soundcloud: https://podcasts.apple.com/us/podcast/discussing-stupid-a-byte-sized-podcast-on-stupid-ux/id1428145024 https://open.spotify.com/show/0c47grVFmXk1cco63QioHp?si=87dbb37a4ca441c0 https://soundcloud.com/discussing-stupid Check Us Out on Socials: https://www.linkedin.com/company/discussing-stupid https://www.instagram.com/discussingstupid/ https://www.facebook.com/discussingstupid https://x.com/DiscussStupid

In Episode 6 of the Intentional AI series, Cole, Virgil, and Seth move into the visibility stage of the content lifecycle and tackle a common mistake they see everywhere. Teams keep treating SEO, GEO, and AEO as optimization problems, when in reality they are content quality, structure, and clarity problems. Search engines andgenerative models have both gotten smarter. Keyword tricks, shortcuts, and “secret sauce” tactics no longer work the way they once did. Instead, visibility now depends on clear intent, strong structure, accessible language, and content that actually helps people. The group looks at how SEO history is repeating itself, why organizations keep chasing hacks, and how that mindset actively works against long-term discoverability. They also dig into how SEO, GEO, and AEO overlap, where they differ, and why writing exclusively for AI can backfire by alienating human readers. The conversation covers content modeling, headless-style structures, and why these approaches help machines understand relationships without sacrificing usability. A major focus of the episode is schema. The team explains why schema is becoming increasingly important for generative engines, why it is difficult and error-prone to manage at scale, and where AI can help draft complex schema structures without fully understanding context. This leads to a broader point. AI can accelerate specific tasks, but it cannot replace judgment, prioritization, or review. In the second half of the episode, they continue their ongoing experiment using the same AI-written accessibility article from earlier episodes. They test how three tools approach GEO-focused improvements. Each tool surfaces different insights, none of them are complete on their own, and all of them require human decision-making to be useful. The takeaway is consistent with the theme of the series. AI is powerful when you ask it to solve the right problems, and dangerous when you expect it to fix foundational issues for you. In this episode, they explore: Why SEO, GEO, and AEO fail when treated as optimization tricksHow search has shifted from keywords to clarity, structure, and intentWhere SEO and GEO overlap and where they meaningfully divergeThe risk of writing for AI instead of for peopleWhy content modeling supports both search engines and generative enginesHow AI can assist with schema creation and where humans must interveneWhy repeating the same schema everywhere weakens its valueA GEO-focused comparison of Writesonic, Grammarly, and ClaudeWhy broad prompts underperform and targeted prompts lead to better outcomes A downloadable Episode Companion Guide is available below. It includes tool notes, schema examples, prompt guidance, and practical takeaways for applying AI to search without losing clarity or control. DS-S3-E6-CompanionDoc.pdf Previously in the Intentional AI series: Episode 1: Applying AI to the content lifecycleEpisode 2: Maximizing AI for research and analysisEpisode 3: Smarter content creation with AIEpisode 4: The role of AI in content management AIEpisode 5: How much can you trust AI for accessibility? Upcoming episodes in the Intentional AI series: Jan 6, 2026 – Content PersonalizationJan 20, 2026 – Wireframing and LayoutFeb 3, 2026 – Design and MediaFeb 17, 2026 – Back End DevelopmentMar 3, 2026 – Conversational Search (with special guest)Mar 17, 2026 – Chatbots and Agentic AIMar 31, 2026 – Series Finale and Tool Review Holiday break notice Discussing Stupid will be taking a short break for the holidays. The next new episode will be released on January 6th. Whether you work on websites, structured content, or digital strategy, this episode is about recognizing when AI is being asked to solve the wrong problems. The goal is not more optimization. It is clearer intent, better structure, and content that actually deserves to be found. New episodes every other Tuesday. For more conversations about AI, digital strategy, and all the ways we get it wrong (and how to get it right), visit www.discussingstupid.com and subscribe on your favorite podcast platform. Chapters (0:00) - Intro (0:37) - Boosting your SEO, GEO & AEO with AI (1:10) - Virgil on how SEO history is repeating itself (4:08) - Defining SEO & GEO overlaps (7:04) - Is a headless CMS better for GEO? (8:27) - Schema generation is awesome with AI (13:54) - If you tag everything, you’ve tagged nothing (15:18) - We tested 3 AI tools for SEO/GEO/AEO (16:39) - Testing Writesonic (18:16) - Testing Grammarly (19:33) - Testing Claude (20:54) - Every AI tool has gaps & you’re the filler (23:49) - Next episode preview… (24:55) - Outro Subscribe for email updates on our website: https://www.discussingstupid.com/ Watch us on YouTube: https://www.youtube.com/@discussingstupid Listen on Apple Podcasts, Spotify, or Soundcloud: https://podcasts.apple.com/us/podcast/discussing-stupid-a-byte-sized-podcast-on-stupid-ux/id1428145024 https://open.spotify.com/show/0c47grVFmXk1cco63QioHp?si=87dbb37a4ca441c0 https://soundcloud.com/discussing-stupid Check Us Out on Socials: https://www.linkedin.com/company/discussing-stupid https://www.instagram.com/discussingstupid/ https://www.facebook.com/discussingstupid https://x.com/DiscussStupid

In Episode 5 of the Intentional AI series, Cole, Virgil, and Seth shift into another part of the content lifecycle. This time, they focus on accessibility and how AI fits into that work. Accessibility is more than code checks. It is making sure people can actually use and understand what you create. The team walks through what happened when they ran the High Monkey website through an AI accessibility review, where the tool gave helpful guidance, and where it completely misread the page. They also talk about the pieces of accessibility that AI handles surprisingly well, especially language, metaphors, and readability, and why these areas are often missed by standard scanners. In the second half of the episode, they continue the ongoing experiment from earlier episodes. Using the same AI written article from before, they test how three tools handle rewriting it to an adult eighth grade reading level, then compare the results with a readability checker. The differences across models show why simple writing, clear prompts, and human review are still necessary. In this episode, they explore: How AI evaluates accessibility on a real websiteWhere AI tools give useful insights and where they misinterpret contentWhy conversational explanations can help non technical teamsHow to prompt AI to look for the issues you actually care aboutThe importance of plain language and readable writing in accessibilityA readability comparison using Copilot, Perplexity, and GrammarlyWhy simple content supports both accessibility and AI performance A downloadable Episode Companion Guide is available below. It includes key takeaways, tool notes, prompt examples, and practical advice for using AI in accessibility work. DS-S3-E5-CompanionDoc.pdf Upcoming episodes in the Intentional AI series: Dec 16, 2025 - SEO / AEO / GEOJan 6, 2026 - Content PersonalizationJan 20, 2026 - Front End Development and WireframingFeb 3, 2026 - Design and MediaFeb 17, 2026 - Back End DevelopmentMar 3, 2026 - Conversational Search (with special guest)Mar 17, 2026 - Chatbots and Agentic AIMar 31, 2026 - Series Finale and Tool Review Whether you work on websites, content workflows, or internal digital tools, this conversation is about using AI with care. The goal is to work smarter, keep content readable, and avoid handing all of your judgment over to automation. New episodes every other Tuesday. For more conversations about AI, digital strategy, and all the ways we get it wrong (and how to get it right), visit www.discussingstupid.com and subscribe on your favorite podcast platform. Chapters (0:00) - Intro (0:46) - Today’s focus: Accessibility with AI (1:20) - We let AI audit HighMonkey.com (4:00) - Finding the human value in AI feedback (6:25) - The power of strategic prompting (12:33) - We tested 3 AI tools for accessibility (14:49) - AI Tool findings (18:17) - Keep all your readers in mind (20:50) - Next episode preview Subscribe for email updates on our website: https://www.discussingstupid.com/ Watch us on YouTube: https://www.youtube.com/@discussingstupid Listen on Apple Podcasts, Spotify, or Soundcloud: https://podcasts.apple.com/us/podcast/discussing-stupid-a-byte-sized-podcast-on-stupid-ux/id1428145024 https://open.spotify.com/show/0c47grVFmXk1cco63QioHp?si=87dbb37a4ca441c0 https://soundcloud.com/discussing-stupid Check Us Out on Socials: https://www.linkedin.com/company/discussing-stupid https://www.instagram.com/discussingstupid/ https://www.facebook.com/discussingstupid https://x.com/DiscussStupid