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I played around with the new Google Agentic IDE, Antigravity, on launch day and created a few features for an app I’ve been playing with.If you’re unfamiliar this video is a helpful overview of the features:My initial impressions offer a more nuanced experience than the chipper attitude of this presentation which should help you get a balanced perspective.This Substack is reader-supported. To receive new posts and support our work, consider becoming a free or paid subscriber.Using AntigravityAgent ManagerThis interface feels like a move in the right direction. It offers a means of managing the work done by an agent, ability to see and respond to plans easily, and clear indication of changes made. I like the Agent Manager’s UI but it’s been a little buggy so far. I made some good changes but it is missing some of the context I have in my CLAUDE.md files on how I wanted it to build the app. I’m not sure if it’s reading some of my core information files or docs.KnowledgeThe knowledge base feature looks interesting but in the two medium-sized features I created it didn’t seem to think this needed to be updated. Unsure when it will feel there’s something worth of it. As with all AI memory systems I do worry about it getting the wrong idea and storing that idea for later use.Intelligent Tool ApprovalAntigravity lets the model choose when to run a tool in some cases rather than stopping to ask for permission. This is a cool concept if it works well and I’m curious to explore with this further. I worry that seemingly innocent commands may look non-destructive and get called anyway by the model despite causing some destructive change. This may require a metadata layer similar to how MCP servers have expanded their interfaces to include if a tool mutates data.Browser ToolIt’s great to see them add a tool for interacting with the code as it’s actually run. However, my initial setup of their browser didn’t seem to workout very well. I installed the extension but the agent had difficulty finding the extension. Eventually it seemed to work when I opened up a new tab after closing Chrome.Since Chrome isn’t my main browser, it’s not setup quite right for the application I was testing but it worked well on a later project. It seems to be able to record, capture screenshots, and read the console.CommentingHaving a highlight and comment system on AI plans is plain great UX. In existing solutions I find that there are often times where I’m opening a note or something to put my feedback in and then scrolling down and pasting that feedback into the chat input at the bottom of a agent chat window. When I made comments, it seemed like they were factored in appropriately when I asked the model to apply them.The pitch of this feature sounded good but I’m curious how the model thinks about incorporating feedback like this in practice.Gemini 3 Pro (High)I’ll pay it a good compliment in saying that this model felt a lot like Claude Sonnet 4.5 to the point that I felt like it was working with me in the same way I’m used to. It’s not often that happens when switching models. That said, I still didn’t get my context appropriately in the conversation and worry a bit if I have to start all of my conversations with it saying Read @CLAUDE.md before we can start working.On release day there’s always a lot of strain on these models and this was no different. There were a couple times I needed to leave and come back when working through the features to let the global limit cool off a bit. Hopefully the usage gets a bit more predictable and lets more people use this regularly.OnboardingWhen I launched Antigravity and onboarded, the onboarding crashed on the final step. Then I had to go through it again and even though I had indicated I wanted it to pull in settings from my Cursor, it ignored that and didn’t pull in any of my extensions or config. This is definitely a headwind in my adoption journey but something that is likely to get fixed with time.TakeawaysI built out two features at the same time to explore the agentic capabilities of the Antigravity IDE. One feature was to add notes for users in a feed and the other is to allow users to upload images to Google Cloud Storage using a rich text field. However, I think having the changes happening in parallel was a bad idea.Part of me assumed these changes would exist in separate worktrees or branches so that they wouldn’t conflict. Some of the demo videos made that seem like it might be the case but no, it’s the same as running two separate Claude Code instances in the same repo. Just a new UI.Ultimately, I wanted to put Antigravity through its paces, but running two things at once confused me a bit while learning a new tool. It also seems to have confused the interface too because one of the agents just stopped responding to my prompts after it tried unsuccessfully to test the feature in the browser. The other agent completed it’s work fine but in the Review tab was still showing both sets of changes which was, again, confusing.At the end of my coding session I had problems determining how to progress and what to do with the resulting conversations. There’s an ability to review the changes and provide feedback but it’s still a bit confusing how to get the IDE to commit the change from the Agent Manager view. When I did merge I also didn’t know what to do with the conversations. I wish there was an archive feature or something as deleting these conversations doesn’t feel great especially when the Knowledge doesn’t seem to update.To complete my changes, I ended up just switching back over to Claude Code as that seemed to have overall better context on what I was building and I had better muscle-memory as to how to progress a late stage change.In Antigravity, there’s a lot of really great intentionality around context but some of the control around context does feel limited. Because it’s a “smart system” there’s a lot less control. That’s helpful in someways but also makes it more difficult to understand exactly what’s going on at any time.I keep noticing some things I’ve come to appreciate about other interfaces that are missing here. One example is queued changes in Claude Code. If there’s a string of commands that make sense to just run one after another, it’ll queue them up. I find that while the auto approval works well in Antigravity, but I find there are times where I need to wait to approve several changes that were clearly known in advance that could have been approved without delays between each approval.Release Article & VideosThe release blog mentions “Gemini 3 is also much better at figuring out the context and intent behind your request” but I haven’t found this to be the case. Jumping into an existing codebase some of the core NextJS architecture I had in place for version 16 was ignored despite clear indications. That said, many of the solutions create were well done. They just didn’t retain the high context as this blog post might indicate.In the getting started video, it was refreshing to see the a Google engineer directly trust the AI with his API key. I think that’s honestly the norm in a lot of cases depending on how permissive the keys are. It enabled the AI to explore and investigate the API with context of the interface due to some Googling of the interface from the web. In all honesty, this is a pretty likely use case for most devs.Agents Testing During ResearchIt’s amazing to see the impact of the Intelligent Tool Approval when it comes to agents doing research. That’s where there’s a bit of magic in this release. This makes me think that this might be one of the better agentic interfaces to do coding research within.Nano Banana Image GenIt’s awesome to have an image generation module as powerful as Nano Banana running directly in the IDE. It can generate assets and directly add them to the application. That’s pretty incredible. (no transparent backgrounds though unfortunately)NextI’m intrigued by Antigravity but it’s still feeling a lot like a Beta of something that could be cool. It’s going to be interesting to watch other competitors in the space learn from these solutions and find ways to improve their services based on these changes. I wouldn’t recommend rolling Antigravity as your main editor for a few weeks while bugs get ironed out but I think it’s great to experiment with and potentially run research tasks on existing projects.This Substack is reader-supported. To receive new posts and support our work, consider becoming a free or paid subscriber. Get full access to Stable Discussion at blog.stablediscussion.com/subscribe

In my latest video, I share a high-level summary of building a full feature with Claude Code and AI-assisted coding — from rapid prototyping to a frustrating production bug that ate up 20% of my time. If you haven’t seen it yet, you can catch the overview here:Videos are great for telling the story at a glance focusing on the high-level summary — but they don’t always have the space to unpack the details that really outline the feel of building something with an AI.That’s what this publication is for. Unpacking the hype and diving deep.If you’re building with AI tools or just curious about the real-world bumps on the road, this deeper dive will give you practical insights and a more nuanced perspective.Lessons Learned in the Coding SessionI’ve captured all of the 88 messages I sent in this coding session in one consistent webpage where they can all be browsed at your convenience. There’s also this companion site where I outline some of the high level takeaways.In this post I’ve pulled out a few gems that highlight some tips and tricks that I think are pretty critical to having a good experience doing AI Assisted Coding.For each note, you’ll notice the note number (message number), a quoted text that indicates the message I sent the AI, and a description that details what I was doing at each step. For example, the first major message I sent was this one:Here we’re catching Claude Code up to some of the research I did to begin the feature. You’ll see the comment there and a quick note with the details. This is everything I needed to give the AI to get the feature started.Let’s look at a few other notes that are more tellingProviding Context7 to DocumentationContext is super important to get right when working with AI, so much so that there’s an entire Context Engineering movement. As such, I often leverage Context7 to pull docs from technologies I’m coding with to leverage in my code.Today’s AI models are trained at some point in the past and all contain a timestamped view of the world and pulling this context ensures we’re aligned with the latest version of any technology we’re using. This can have a huge impact on how well a feature is integrated in our code.I’m using this to ensure we’re correctly calling the API and ensuring that we’re downloading the image from that API in the right format.Note: I’m also not generating code right away. I’m using this research to put together a document that we can reference later when we build out the feature. I find this helpful to ensure we keep available key technical considerations as Compaction in our conversation (generated summaries of conversations) may cause some details to get lost.Prompt to PromptAs I’m building features that, themselves, leverage Large Language Models, I’m needing to create some prompts and tweak them as I go. I generally will tweak a bit by hand but I’ve found as I continue working with Claude Code that it’s often more explicit and creative at coming up with good prompts.Anthropic themselves also have a really great prompt creation tool on their Claude Console page for developers to take their prompts and refine them into more optimized prompts for working with their models.Generate Test DataI’ve found AI uniquely helpful in getting some test data spun up quickly for testing out features. If you let it go a bit wild you’ll sometimes be rewarded with something cool or unique! Try it out sometime!Regular Planning is KeyUsing Claude Code, planning is a pretty critical step.I will generally add planning in as a step whenever my direction shifts fundamentally. Here I’ve realized I have a new page that I need to develop that leverages the feature we’re working on but will require a much wider sense of the application to be able to pull off well. At these times I want Claude to be investigative and curious rather than ambitious and assuming. If I just let it go on a feature like this it could easily create a page I don’t want or add unrelated changes that don’t line up with the direction I’m moving towards.Give Claude Code Your Tedious and HungryI love tasking Claude Code with fixing the code around the changes that have occurred. When I use AI Assisted Coding tools I also couple those tools with a suite of analysis tools to test the code that is generated for common errors and mistakes.This is key to going fast and projects that I don’t have setup well in this space move slowly even when I use AI Assisted coding practices. It’s plain painful to get them moving as I need to be much more diligent as to what changed and why.That said, you may need to poke it a few times to do what you want. AI will often take shortcuts in order to try to focus on what it sees as it’s main goals and priorities. This means occasionally you’ll need to redirect those goals a bit to align with your own.Regularly Document WinsWhen I finally pushed through the major defect that had come up, outlined in the video, I realized we needed to update our documents to capture this. It wasn’t captured anywhere else yet and it is critical to get this captured so we can refer to the fix in the future.An Image is worth a lot of WordsPassing images to Claude Code is an amazing way to tell it exactly what you have in mind. I regularly pass images to it when I’m working to have it understand something a bit more complex or need to simply point at something for it to understand. Reference images can help a lot when coming up with a design or theming around a color.I will regularly leverage a visual vibe coding tool like Magic Patterns to build out ideas for me to quickly reference via screenshots or code snippets. I’ve got a bit more about how I use that tool here:The Overall JourneyBuilding with Claude Code is exhilarating because you can go from idea to implementation so fast. But as I’ve learned, it’s just as important to slow down sometimes — to observe, orient, and decide — before you act. AI can supercharge your coding, but it can’t replace the human insight that keeps your code cohesive and aligned with your goals.Going back through these messages has been a great way to surface some of the ways that I work with Claude Code to prototype. As you go through them, I’d love to know if you found anything interesting about the way that I work with Claude Code that I didn’t mention. Additionally, I’d love to hear how you’re working with AI-assisted coding tools. What bumps have you hit? What tricks have you found? Drop a comment or reach out — let’s keep unpacking the hype and learning together.This Substack is reader-supported. To receive new posts and support our work, consider becoming a free or paid subscriber. Get full access to Stable Discussion at blog.stablediscussion.com/subscribe

I wanted to collect a few thoughts on the recent OpenAI Dev Day 2025 announcements from my initial investigation into the tech behind the announcement. Based on a couple years of building AI integrations into applications these are my gut reactions to the presentation.If you’re unfamiliar with their presentation, it might be valuable to skim through it first.Now let’s get into it.Our TakeAgentKit and Agent Builder feels great and looks like what the future of tools for building Agents around real products and services will look like. It’s like an extension on n8n’s existing capabilities (another agent builder tool) but AgentKit is streamlined around OpenAI’s offering. This could be serendipitous if you’re already leveraging their existing file storage solution or other core features.The kit is streamlined for building quickly but I don’t really think it’s quite the powerhouse people think it is. We can see why by reviewing some of the other features in the announcement.ChatKit is an application layer toolkit for delivering a chat interface to end users by directly leveraging ChatGPT’s methodology. It has a nice set of features that manages to do many of the things that the Vercel AI SDK was already doing well. I’m a fan of some of their direction which is clearly inspired by that library.Similar to building with Swift for the Apple iOS, to leverage this kit a team will be aligning with a design system of visual components created by OpenAI. Until we get a chance to play with these components, it’s unclear how far these components can be pushed and where the limitations are. Additionally, we’ll need to wait and see how this library will evolve over time since this product space is certainly very new.OpenAI is looking to own more of the experience layer by providing an ecosystem of UX and UI tooling. Applications leveraging their agent platform will need to keep pace with changes and adjust accordingly if they adopt this approach to building. That can be an uncomfortable place to be longterm and might be a worry for early adopters.OpenAI mentioned that there would be a capability to publish Apps in ChatGPT in the future but no word yet on exactly when. The actual guidelines to publish an app are quite extensive however. They remind me of an Apple App Store-like approval process blocking publishing and becoming featured. Adherence to the style and intent of ChatGPT will be directly rewarded here.AgentKit doesn’t directly land users into ChatGPT, which can be a bit misleading if you watched the presentation. It seems like AgentKit has everything setup to build something into ChatGPT itself but in actuality AgentKit is for creating agentic experiences on separate company-specific websites.As OpenAI leans into adopting MCP, there seems to be some underlying messaging that companies don’t have a vendor lock-in around OpenAI. However, the MCP ecosystem is still missing many core services and is still maturing. I’d argue there is a lot of inherent vendor locking with AgentKit. That much is clear.Evals is one of the most compelling reasons to be excited about AgentKit. But to leverage it well teams will need a very clear vision of what an agent does and what it looks like when it does something well. That continues to be a difficult spot for product builders to define.Overall, I think AgentKit shows an interesting perspective on what agentic platforms should look like. Unless there’s a clear path towards Apps in ChatGPT I think the main adopters of these releases are going to be B2B application builders. While there’s room for a B2C path, losing brand seems like it lacks competitiveness and limits the upside potential.Existing options for building agents continue to be available and, given a team with some frontend engineering capability, those solutions aren’t as complex as OpenAI makes it sound. Get full access to Stable Discussion at blog.stablediscussion.com/subscribe

I was deep in a World of Warcraft inventory crisis the other day—bags full of random items with cryptic names. “Tangy Clam Meat” sat there taunting me. What do I even use this for?This simple gaming question sent me down two very different paths that perfectly illustrate how search has fundamentally changed. And if you’re running any kind of online business or content operation, this shift is about to upend everything you know about visibility, traffic, and revenue.A Tale of Two SearchesPath 1: The Traditional Google JourneyWhen I typed “what do I do with Tangy Clam Meat wow classic” into Google, I entered a familiar but exhausting maze:Step 1: Google shows me search results (after scrolling past AI summaries I don’t trust yet)Step 2: I click on WowHead because it ranks firstStep 3: I’m bombarded with ads—top, bottom, sides, pop-upsStep 4: I navigate their specific UI, hunting for informationStep 5: I discover I need to click on “reagent” (who knew that’s what cooking ingredients are called?)Step 6: Finally find my answer buried in the middle of the pageTotal time: Several minutes.Mental energy: Depleted.Ads encountered: Countless.Path 2: The AI ConversationThen I tried Dia‘s AI search. Same question, completely different experience:Step 1: I type my question naturallyStep 2: AI searches multiple sources simultaneouslyStep 3: I get a direct, synthesized answerStep 4: DoneBut here’s where it gets magical—and this is the part that changes everything.Context-Aware Follow-UpsNow, I had more items to check in my inventory. In the traditional model, I’d have to either:* Start a completely new search for each item* Try to navigate the same cluttered website to find more information* Open multiple tabs and repeat the entire processBut with AI search watch what happens:Me: “what about tender crocolisk meat”AI: Immediately understands I’m still asking about WoW Classic recipes and provides the answerMe: “raptor egg”AI: Knows the context (a directory of results), gives me recipe detailsMe: “small venom sac”AI: Tells me it’s not for cooking but for alchemy insteadI didn’t have to specify the game. Didn’t have to say “recipe” or “wow classic” again. The AI maintained our conversation context. I literally just typed item names—sometimes misspelled—and got exactly what I needed.This isn’t just convenience. This is a fundamental reimagining of how we interact with information.Why This MattersThe Click-Through Economy is CollapsingMany a website’s business model assumes one thing: you’ll click through to their site. But when AI provides answers directly, that assumption crumbles. Here’s what’s at stake:Revenue Streams in Critical Danger:* Display advertising (no clicks = no ad views)* Affiliate links (AI won’t pass these through)* Sponsored content (less attractive with declining user counts)But this isn’t just a story of decline. New revenue opportunities are emerging for those willing to adapt—from data licensing to AI-specific services. Watch my detailed breakdown of the revenue transformation matrix and emerging opportunities →The Paradox of More Content, Fewer VisitorsHere’s the mind-bending reality of AI Engine Optimization (AEO): You need to create more content to get fewer visitors.Why? Because AI systems need comprehensive information to reference. You’re no longer optimizing for one perfect landing page. You’re building an entire knowledge ecosystem that AI can traverse.Example: Instead of one “Tangy Clam Meat” page, gaming wikis now need:* “Where to farm Tangy Clam Meat in Westfall for Alliance players”* “Is Tangy Clam Meat worth keeping for leveling cooking 1-300?”* “Tangy Clam Meat vs Clam Meat - which recipes need which?”* “Best grinding spots for Tangy Clam Meat for level 15-20 characters”* “Can Horde players get Tangy Clam Meat or is it Alliance only?”* “Auction House pricing guide for Tangy Clam Meat by server type”Each page might only get a handful of direct visits, but they all contribute to the wiki’s visibility when someone asks an AI “what should I do with this random meat in my WoW inventory?”Admittedly this is a contrived example and I’m not sure how beneficial these questions would be for World of Warcraft directly but it’s illustrative of the kinds of content that may answer AI questions.The Metrics That Actually Matter NowThe challenge with measuring AI visibility is real. As HubSpot discovered, AI results vary based on conversation context, user history, and countless variables you can’t control. The same query produces different results depending on what questions came before it, whether memory is enabled, which AI you’re using. You can’t A/B test AI responses like Google rankings.Here’s what we do know and can measure:1. Traditional SEO Remains Your Foundation AI systems pull from search-indexed content. Without SEO visibility, you likely have no AI visibility:* Organic rankings (your baseline for being discoverable)* Indexed pages (comprehensive coverage = more AI reference material)* Domain authority (trusted sites get cited more often)2. The Volume-to-Visit Paradox Track the new reality HubSpot describes—more content, fewer visitors:* Total pages published vs. traffic per page* Coverage of long-tail questions in your space* Visitor qualification metrics (conversion rate, time to purchase)3. Visitor Quality Indicators The few humans who arrive have already done their research in AI. Monitor:* Conversion rates (should increase)* Pages per session (should decrease—they know what they want)* Support ticket sophistication (fewer “what is this?” questions)4. Competitive AI Visibility Manual checks remain your best option. Weekly sample queries about your category:* Do you appear in AI responses?* How prominently versus competitors?* Which of your pages get cited as sources?5. Content Architecture for Agents You’re now publishing for machines first. Measure:* Question-answer pairs created per topic* Structural clarity of your content (can an AI easily parse it?)* Topic interconnection (how well you link related concepts)The uncomfortable truth: we’re measuring proxy metrics because the real metric—influence within AI conversations—is largely invisible. As HubSpot notes, this is marketing for agents versus humans. The agents don’t click, don’t convert, and don’t fill out forms. But they determine whether humans ever hear about you at all.We’re in uncharted territory where success might mean accepting lower traffic while betting that the traffic you do get is exponentially more valuable.The Two-Audience StrategyYou’re now designing for two completely different consumers:AI Agents* Need structured, comprehensive data* Consume hundreds of pages to form opinions* Prefer clear, factual information* Value completeness over creativityHighly Qualified Humans* Already know about you from AI conversations* Ready to buy, not research* Need immediate value demonstration* Want streamlined conversion pathsThis is a fundamental shift from offering content (mostly for free) and profiting off of advertising. Now it’s likely if you’re making content it needs to be paid. (oh! Maybe this is a good time to check if you’re subscribed 🙃)This Substack is reader-supported. To receive new posts and support my work, consider becoming a free or paid subscriber.Practical Survival Strategies1. Build Your Answer ArchiveTransform your existing content into Q&A format. Every blog post should answer specific questions people might ask AI about your space.2. Create Conversation ChainsDesign content that naturally leads to follow-up questions. Think about the customer journey as a conversation, not a funnel.3. Establish Direct RelationshipsEmail lists, apps, communities—anything that bypasses search becomes exponentially more valuable. You need to own your audience relationship.4. Structure for MachinesWell-organized, schematized data becomes a competitive advantage. Structured data that AI can easily parse and cite will win over beautiful but chaotic content.5. Monitor AI MentionsSet up systems to track when and how AI systems mention your brand. This is your new SEO ranking.What This Means for Content CreatorsThe comfortable era of “write content → rank in Google → get traffic → monetize” is over. The new reality:* Your content might be read entirely by machines* Success happens inside AI conversations, not on your website* The few humans who visit are ready to buy, not browse* Brand building occurs in AI memory, not human memoryThis isn’t just another algorithm update. It’s a fundamental rewiring of how information flows online.The Bottom LineThe shift from search to conversation, from clicks to context, from keywords to knowledge graphs—it’s all happening now. That simple gaming question about Tangy Clam Meat revealed a seismic shift in how we find and consume information.Sites that adapt will thrive by becoming invaluable data sources for AI while creating exceptional conversion experiences for the few humans who visit. Sites that don’t adapt will simply become invisible.The question isn’t whether this change is coming—it’s already here. The question is whether you’ll evolve your strategy in time.What’s your experience with AI search? Are you seeing changes in your traffic patterns? Drop a comment below with other Substack members or join our Discord to discuss.For more deep dives into AI’s impact on digital business, subscribe to Stable Discussion. And if you want to experiment with AI-powered research yourself, check out Benny Chat. Get full access to Stable Discussion at blog.stablediscussion.com/subscribe

Over the last week, I’ve been captivated by the idea that Designers are best positioned to leverage AI on development teams. AI is changing how products are built, but there's a blind spot: designers are still standing on the sidelines, even as the tools finally let them take center stage. Most teams treat AI as the domain of engineers and data scientists, and for designers, this technical barrier makes AI seem unapproachable and out of reach.On the other end of the spectrum, there’s a subculture of “vibe coding” and hustle culture. Small teams or solo builders are cranking out rough AI prototypes, often without rigorous product development practices.But even as these experiments multiply, they rarely result in thoughtful, user-centered products—often sacrificing quality and vision for speed. This highlights a gap: while engineers and hackers can rapidly iterate on technical possibilities, what's too often missing in the process is the guiding hand of design.I’ve noted that the teams closest to the customer are best positioned to deliver real value. Designers, more than anyone, bridge the gap between WHAT a customer wants and HOW the business delivers it. This makes designers uniquely well-placed to drive and shape how AI is applied to solve practical, customer-centric problems.What’s new, and what too few teams have noticed: the AI toolchain has finally become accessible enough that designers themselves can prototype, test, and iterate—without waiting for engineering or hunting down a Python wizard.Design-Driven Product DevelopmentOver the years, I’ve formed a straightforward operating model for developing great products on engineering teams:On most teams, "Make it Work" means building quick, rough prototypes—getting something functional before worrying about polish or coherence. That may sound efficient, but by relegating design and user experience to the end of the process, these products often inherit all the awkwardness, missed opportunities, and makeshift decisions of their early versions. Design becomes an exercise in damage control and technical compromise—not in envisioning or elevating what’s possible.Teams can attempt to avoid this list order by doing big planning cycles, documenting ahead of time, or other attempts to "shortcut" the process. However, when they run into problems, these approaches often revert to doing things in this order. This is just the tried and true way of getting things done.Why not flip this script? What if, from the very beginning, designers were the ones to shape the prototype—not as a surface afterthought, but as the driving force for both how the product functions and feels? If prototyping is the process where key decisions are made, designers should be there, guiding what’s built, not just decorating it after the fact.Now, as AI makes prototyping more accessible and immediate, designers can move from concept to interactive demo without the traditional bottlenecks. This shift helps ensure that design considerations aren’t an afterthought, but baked in from the earliest steps.Some of the most innovative solutions come from design-led exploration—where a designer, by understanding both the user and the technology’s constraints, proposes an approach no one else saw. By leading with design, teams can reduce costly rework, discover what users really want earlier, and prevent soulless or awkward interfaces from ever making it out into the world.Representing Business and TechnologyDesigners bridge the gap between development and business teams. They translate technical constraints into user-centric solutions that meet business objectives. They also transform high-level business requirements into wireframes, prototypes, and visual designs for developers to build out.Negotiation is essential, not just to the design role, but across the triad of product, design, and engineering. Each group brings its own perspective, priorities, and blind spots: designers may champion user needs but sometimes underestimate technical effort; developers possess crucial implementation insight but can occasionally lose sight of broader business or user aims; even product or business leaders may bring great vision but stumble on feasibility. The healthiest teams recognize these dynamics and lean into the creative tension, surfacing their assumptions and sharing context early and often.When these disciplines disconnect, you often see familiar breakdowns: designers shut out of early technical decisions; product obsessing over features without clarity on what’s possible; and developers, at their worst, retreating into reactive “IT mode,” simply processing tickets and change requests rather than partnering in the product vision. Nearly everyone working in tech will have seen these patterns and felt the frustration they create.The opportunity, then, isn’t for designers to take over prototyping alone, but to pull the process closer to multidisciplinary influence—helping organizations build better products faster by dissolving long-standing silos.AI PrototypingAI prototyping is better than ever before. With a competitive landscape of new tools, there are many great solutions that improve over time. And with so many people looking at leveraging these tools, a variety of new techniques are being explored that continue to push what they are capable of.While coders will likely leverage IDEs (Integrated Development Environments) like Cursor or Windsurf, web-based solutions that don’t require a complex development setup tend to be easier to use. These web-based tools additionally offer the ability for teams to remix solutions with others and share prototypes across a team.These days, I prefer v0 because of their direct connection to the Next.js technology and their integrated Vercel deployments which are familiar to me. Finding a tool that matches your experience offers a significant advantage. Additionally, the design aesthetics of v0’s solution seem to be pretty good for my needs.Other tools like bolt.new and lovable.dev offer a similar suite of tools but focus differently to best match the needs of their customers. As this space continues to show huge revenue growth and remains novel to market to users, additional solutions continue to be released.Designers Building with AI PrototypingI was able to run a workshop on AI for the design team at Compass Digital. This workshop provided AI fundamentals for building personalized AI design workflows but also provided guidance on prototyping using vibe coding techniques. By the conclusion of the session, the team felt familiar with the concepts and were putting together some really interesting designs that immediately pushed at the limits of what’s possible with these prototyping tools.Designers often need to guide coders and product managers to understand what’s possible. Because coders are more focused on the code, a user experience that aims at a specific visual style often gets lost on them. Product managers are excited about what they know but are usually a bit more concrete-minded and need to be shown what’s possible. Once they get it they’re usually fully behind an idea.At my first startup, I saw firsthand how designers can expand what teams believe is possible. We were stuck on a UI detail—a post-it note display the developer thought couldn’t be built with CSS. I took a stab at it and sent over a solution. When my cofounder saw it working, he realized more was possible than he’d assumed. That moment not only solved our immediate problem but also deepened our collaborative approach to product development.Designers frequently bridge gaps between vague ideas and concrete solutions. With AI prototyping tools, they're even better equipped to overcome blockers and build stronger, more collaborative relationships with other teams.The New World of Design-Led ImplementationWe’re now establishing a new operating model:* Design to make it work* Develop to make it right* Collaborate further to make it goodWith new AI code generation tools, designers are better positioned than ever to build incredible things. These initial solutions can be the foundation of great features that enable the rest of the teams to better build and establish solutions that bring it to life.If designers remain sidelined, teams will keep shipping products that feel disjointed, generic, or frustrating to use. But if design leads without staying grounded in technical and business realities, solutions may end up beautiful but impractical or impossible to bring through to production. This new approach depends on design remaining tightly connected to both business goals and engineering constraints, stepping beyond old silos to work collaboratively from the start. Rather than throwing things over the fence to development and back again, we must establish clear outcomes and follow them all the way to conclusion.While this is likely only possible with web development teams due to the limitations of these tools, it’s extremely likely that code generation tools for apps are just around the corner. For now, web teams are living in the future and should look to benefit.If you feel like your team is excited about this future or you’d like to learn more about what AI can do for your design team, we’d love to hear from you. At Hint Services, we run workshops specifically for design teams and offer advice for clients looking to leverage AI tooling in their organizations. If this resonates with you, please drop us a line!Stable Discussion is reader-supported. To receive new posts and support our work, consider becoming a free or paid subscriber. Get full access to Stable Discussion at blog.stablediscussion.com/subscribe