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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

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

Della and Ben return to the podcast and get caught up with months of news bringing together their highlights just in time for the holidays. Stay tuned for an interesting run through the latest major changes that excite and inspire us about AI.LinksDALL·E 3SDXL TurboPikaGPT-4 TurboAWS Bedrockllamafiletwominutepapers $1 GPT gameAda Customer ExperienceThanks for listening to Stable Discussion! Subscribe for free to receive new posts and support our work. Get full access to Stable Discussion at blog.stablediscussion.com/subscribe

With the changes at OpenAI this week, I'm assuming your newsfeed is being flooded with speculation and drama. Maybe you've been missing Game of Thrones and that's exactly what you're hoping to see. However, if you'd rather find other sources of information about AI outside the tabloids, then I have a model for you.This continues our prior post from last week that covered the first part of this model. Those were the Commentators, Professionals and Innovators creating AI content. This week's post covers the remaining two groups: Leaders and Misadopters.LeadersThese are engineers and scientists from industry-leading AI companies who form the direction of AI advancement. Seeking to compete with other companies, these groups look for highly scalable solutions that promote their businesses. The latest major releases from OpenAI this week fall directly into this category.There are some seriously incredible things being done by the major companies leading AI. But you can't separate the work from the motivations. Those companies have a specific mission and may look toward that mission over exploring the space that they're helping to develop. Sometimes this can make their announcements feel disconnected from the real world or more important than they actually are.Industry leaders are the best at marketing information about what new technologies are being created. The content is entertaining and informative and provides a good introductory view of these concepts. But if you're looking for a more objective measure of the technology, you might not find that view being publicized by those with the most to gain.Leaders to watch:OpenAI - Pretty clear driver of major change and has "GPT"-of-things as its brand, which has become somewhat synonymous with AI.GitHub Next - A team at GitHub looking at developer applications of LLM software to determine what is possible.MisadoptersThis group largely focuses on shouting down the hype surrounding AI. While some of the content that they create is diligently documented or reviewed, much of the content can be opinionated or situational. Similar to other interest groups, this group is often looking for a place to lead the conversation on technology and the development of a brand.As with many technologies, people question the need for a new technology and its ability to solve problems better than the "tried and true" way. In infancy, the technology seems to have many limitations, and that can often make it seem like we should abandon any further investment. However, those with longer-term vision can see that there is opportunity, despite the advice of Misadopters.This advice can be well-intentioned for many reasons as well. A feature may not be well understood or incorrectly posted on a forum. Teams of engineers may try to take AI practices and integrate them into their existing applications. If it doesn't work as intended, they feel they have evidence that "AI doesn't work". These teams instead need to take a step back and look at their approach to see if maybe there is something that is missing. But that can be hard when you need to release software on a deadline.AI today can be a gimmick that gets rudely tacked onto an existing application like Notion or Snapchat. The service doesn't feel like it fully aligns with the mission of the application and isn't bringing something that the users were looking for. Product leaders need to reevaluate their objectives and find solutions where AI fits more naturally into the product, rather than shoehorning it into their existing solutions.Misadopters provide a great place to find negative opinions about AI. There will continue to be a growing wealth of this type of knowledge on the internet. However, these conversations aren't the most inspirational or pragmatic. As with any of the information produced by any of these groups, be careful to evaluate why the information was created and for what purpose.I try to forget Misadopters exist.While it would be great to have a few negative points to look at, I generally don't get that excited about shouting down ideas even if they're wrong. I don't believe AI fits every niche or possibility, but I also don't think it's just hype.My assumption is that you, the reader, can sniff out what kind of content I'm referring to. If not, that's ok too. Start looking and second-guessing broad statements about how "AI is this" or "AI will do that". Keep in mind that there is a wider range of possibilities if you know where to look.Bringing It All TogetherNow that we have the definitions out of the way it's time to decide what kind of content speaks to you. You've probably already checked out one or more of the sources included, but if not, let me try to point you to where you'll be interested.If you're interested in discussing AI in general and don't want to get too technical, I think Commentators are a good source for you. You'll get conflicting viewpoints often, but that can also be the exciting drama you're looking for.For news straight from the source and to better understand the latest updates that are being released, Leaders offer a one-stop shop. Most people with some interest in AI will be keeping up to date here, and it's good to stay apprised of the latest announcements coming from this group.As someone looking to dive deep into one topic or learn something particular about AI, Professionals are the best to look towards. They've turned teaching a skill into a business and there are a lot of people hungry for your attention and putting a lot of effort into ensuring that you succeed, so you can maybe buy a course or two.If you're looking to be inspired by the things people are building and better understand how and why, look to Innovators. These are the people testing the outer limits of AI and trying things nobody has tried before. You'll feel inspired and maybe a little dumb part of the time. But that's always good motivation to get started building your own thing.Thanks for reading Stable Discussion! Subscribe for free to receive new posts and support our work. Get full access to Stable Discussion at blog.stablediscussion.com/subscribe

As I board a plane on a trip to Korea, I can’t help but think how little AI helped to prepare me for this trip. I researched destinations on travel sites and with Google Maps. Purchasing was straightforward on booking sites where I was able to use my points. Based on previous trips, I created a list on my iPad of everything I needed to pack. Thinking back on it I can’t help thinking… as someone with such a big focus on AI, why don’t I use these tools more?To start, it’s important to think about why we use tools. We pick tools depending on the job we’re looking to complete. If you’re tired, you might look to make coffee or tea. Hungry? Maybe it’s time for a burger or some ramen. Common tools help us solve the issues we experience each day. AI tools like ChatGPT are just another tool to use.The choice of tool is at times personal but is usually determined by what we know a tool can or can’t do. I am not usually interested in using a shoe as a hammer (unless I’m missing a hammer). So the fact that I’m not using AI tools for my trip means that I don’t find them very useful for the problem I have and I have better tools. Or, at least I perceive that I have better tools.But what problem am I trying to solve? My biggest challenge is filling in the details of the trip. I know where I’m flying, I know what hotels I’ll be staying in, and I know a few places I want to go. I’m pretty decisive when I make the big decisions for this trip but the smaller details are harder for me. What train do I take to get between cities, what’s a good spot for dinner each day, or what would be a good gift to get my friends?The small details are where I need help. I’d like this help to be based on my preferences too. General help on the small stuff gets you stuck in a tourist-infested restaurant with a waiting line around the block. I need an itinerary manager to fill in the gaps in my knowledge/research and give me recommendations.I don’t find ChatGPT to be a very good itinerary manager. While the ability to converse and problem-solve is incredible, it still needs to get better to be able to fill the role. While there might be other options besides ChatGPT, the travel options on There’s An AI For That are a disappointing and too commercial. They also don’t fill out the role I have in mind either.An itinerary manager needs to have good access to verified information, adjust their recommendations to my preferences, and keep track of key pieces of information.Verified travel information online in countries outside North America is difficult to find. While ”every company has become an internet company” in the States, much of the rest of the world is still playing catch up. In Japan, software is considered a pretty boring career and much more emphasis is put on hardware development. This results in apps and websites that don’t offer the same level of services you might expect in Western countries. Google Maps still struggles to understand dense areas of many Asian cities.Further complicating things, aggregation websites can be completely different across regions. While Google is popular with English speakers, KakaoTalk is the go-to in Korea. Unless you can read Korean, it’s likely that any of the information there is going to be inaccessible even with the amazing photo translation available in Google Translate. A good itinerary manager needs to be able to combine these different sources of information to come up with a good plan.If you’re looking for better data sources, reviews and peer-to-peer forums like Reddit are often the most accurate. But this information is hard to get at without extra effort. Someone looking to learn more about any location will need to sort through relevant information that is quite old or recent information that isn’t relevant. Combing through the comments requires research, either by a person or by an AI.ChatGPT’s Bing Search isn’t going to be able to really find most of this information. While ChatGPT Plugins can provide some travel integrations to make things a little easier, the ones that currently exist for travel are lightly-veiled service offerings for renting cars or booking flights.You can paste in data that you find elsewhere on the internet but you may find that the results ChatGPT can glean from your research are too simplified to help in many cases. Hallucinations based on partial answers cause even more trouble.The most popular AI tool, ChatGPT, remains untrustworthy for the task of finding accurate data. But say we were okay with some research on our end, we’d still like to have ChatGPT give us some suggestions that are relevant to us. For that we need personalization. The ability for us to have our itinerary manager remember what kind of places we like, how we like to get places, what we should bring with us, and what we’re willing to spend money on.ChatGPT is pretty good at getting feedback. If we ask for a suggestion of activities to do in a city and then ask for specifically indoor activities, it will take that feedback and iterate on it. But the number of add-on suggestions it can remember is limited. Keep asking it to tweak things and it may forget earlier tweaks you asked it to remember.Researchers have discussed the limited memory capabilities of ChatGPT and other AI models. Even after extensions to the total size of memory and text it can work with, there are still many problems remembering details. It’s believed that training data is to blame and companies are already working to adjust training data that can work with larger text and keep more context in memory. For now, the current workaround for memory problems is to put the most important information at the beginning and end of any prompt. ChatGPT manages how prompts incorporate the history of the conversation and may inject its content into these highly coveted positions. This limits the control power users have and can make the AI more forgetful than directly interfacing with GPT-4.Because you’re working with a generalized chat application when you use ChatGPT rather than GPT-4 directly, the underlying prompts and algorithms do some work to remember things that might be important for future prompts. You don’t control what is remembered. While planning a trip, it may just completely forget important details that, if forgotten, could cause chaos to your trip plans.Until memory problems are resolved in a generalizable way, ChatGPT remains an unreliable, inaccurate, and forgetful itinerary manager. While I’m trying to plan a trip, you can extrapolate this example into many other fields and roles. It highlights just how limiting AI applications are today.The story of AI is a compelling one. We’re seeing incredible things happen all of the time. But it might take ten years or more before we get to grasp this technology to do more than generate art and writing. I’m excited and compelled to keep looking at how close we’re getting after a bit of relaxation on my trip. Catch you on the other side!Thanks for reading Stable Discussion! Subscribe for free to receive new posts and support our work. Get full access to Stable Discussion at blog.stablediscussion.com/subscribe

Welcome, and thanks for reading Stable Discussion! We appreciate the support we’ve received and are honoured that you’re tuning in roughly every week to see what we've been learning and discussing!As we quickly started sharing what we've been thinking, we may have missed introducing ourselves. This publication, Stable Discussion, shares expert and approachable insights about modern artificial intelligence. We post articles and discuss these exciting changes on our podcast. We aim to make our content approachable and fun while helping to spread ideas and share our perspectives.Our hosts, Ben Hofferber and Abdella Ali are both experienced consultants and engineers focused on web and mobile customer experience. We've built services and websites for many Fortune 500 companies and have seen how difficult it can be to implement cutting-edge technical solutions in real-world environments. We use our experience to take a critical look at AI and how companies and people are looking to use it.This excitement and learning encouraged us to found Hint Services. We help advise and consult with companies looking to create user experiences leveraging AI. To get a taste of what we do, look to the AI UI/UX patterns page, where we highlight the patterns we're currently excited about. We believe that generative AI enables much more than just creating chatbots.Building AI tools and sharing our discussions and ideas with you, our audience, helps us to better understand what AI is really changing. We're always trying new tools and testing those tools here to see what everyone thinks. This gives us a unique perspective that we're excited to keep sharing and developing with the larger community.Thank you again for reading and listening. You'll be hearing from us again very soon!Our Discord CommunityWe’ve recently launched a discord community for AI Enthusiasts. It’s a paid community requiring an application process. We’re looking to connect with others excited about building User Interfaces and Experiences using LLM Generation and other AI tools.Please apply if you’re interested! Subbb.me/stablediscussionContent to Get StartedBelow is some of the content we're most proud of. It's been an amazing 8 months for us, and as we reflect, these are some of the high points that most stand out.This has been our most popular and trending post so far. A great introduction to thinking about what AI really is that helps to defuse some of the hype and ground yourself in the real interesting capabilities on display in the world today.This post is one of our favourites for discussing how creatives look to use AI. We have a few follow-up posts like The Mental and Creative Cost of GPT, but this is an excellent introduction to our ideas around AI art and creativity.If this post wasn't enough of an introduction, check out our Podcast. On the latest episode, we share what we've been up to recently and help round out this introduction with some more of our focuses and ideas.This post helps shed light on the strange realities of AI and how they work today. It's surreal that these AI models act so human in ways that keep them from being our familiar cold and calculating computers. For a shift in perspective, we think this helps to better understand them.This post got our parents to try out AI and ask us many questions about how they could do new things! I can't imagine a better endorsement for an article. If you have not found an excellent way to use AI or are still learning, check it out. Get full access to Stable Discussion at blog.stablediscussion.com/subscribe

Today’s post is a guest post by friend of the show, Michael Fraser. Michael is an AI Evangelist and Innovation Strategist at Action Insight. He posts regularly on The Local Model where you can find more of his insights and work using Generative Models to change how companies think about art, workflows, and their future.Being a leader means making decisions. Lots of them. And each decision is fueled by information. But there's so much information coming to us, it can be overwhelming and cause us to miss opportunities or fail to identify problems. The solution? Tools that can help us quickly transform our messy information into clear, easy-to-understand insights. It's all about making better decisions, faster.Keep reading to find out how you can use simple approaches to get big results out of AI & GPT4.GPT can apply any framework to your problemGPT-4 is an adaptable AI model, capable of learning and applying a wide range of frameworks to various problems. The beauty of this model lies in its flexibility. No matter what framework you choose to use - whether it's a widely known one or a unique system you've devised - if you can clearly explain its structure and rules, GPT-4 can implement it. This allows for comprehensive, diverse problem-solving possibilities, catering to the specific needs and nuances of different challenges. Just remember, the key is clear and concise instruction. With that, GPT-4 can become a dynamic tool in your problem-solving arsenal.Here are three examples to illustrate:* Facts and Logic: This approach involves separating objective data (facts) from author's interpretation or reasoning (logic) in a given text.* Edward De Bono's Six Thinking Hats: This method applies different 'hats' or perspectives (white, red, black, yellow, green, blue) to analyze an issue comprehensively.* Structured Data: This framework involves turning unstructured information into a structured format like a spreadsheet, useful for data analysis and organization.Facts and Logic FrameworkGood writing often intertwines facts and logic to make compelling and persuasive arguments. As leaders, sometimes you need a clear separation to think through the matter carefully. Just ask GPT to separate them out for you.First let’s generate an article:Now let’s ask GPT to transform the same article into Facts and Logic:The article may not be as compelling to read, but its much better at helping us clearly distinguish what’s going on in their argument. Remember - you can do this with any piece of text you can copy into GPT - news, research, reports - anything.Edward De Bono’s Six Thinking HatsExploring different perspectives is a powerful way to overcome bias, ensuring we consider all angles before drawing conclusions or making decisions. However, even the most experienced leaders can find this challenging. That's where GPT-4 shines, with its ability to adopt diverse points of view based on the context it's given.A particularly powerful tool for embracing these multiple perspectives is Edward De Bono's six thinking hats framework. My partner at Action/Insight, Kate Bowers, suggested this approach, and it has proven extremely effective. The beauty of using this model with GPT-4 is its ease of application. GPT-4 already understands the six thinking hats and their associated perspectives, allowing you to tap into the breadth of insights this framework offers at will.By invoking the different 'hats', you can guide GPT-4 to scrutinize a problem from all possible angles, thus helping to combat inherent biases and promoting a more balanced understanding. In short, this tool provides a shortcut to comprehensive, insightful analysis, setting the stage for well-informed, holistic decision-making.Let’s ask GPT to wear the blue hat and give some advice on this post:Organize your data into a table or spreadsheetOne of the most powerful ways to transform your data is to have GPT take a long, rambling block of text (e.g. a transcript or customer call) and have it break it down into a structured table with whichever columns you want. Now you can quickly review all the information that was provided in a way that is clear and can support decision making going forward.Here’s a simple example, recording some details about some children’s books:This is a very powerful tool for collecting and capturing data that would otherwise be lost, and once you understand this capability you might start seeing a whole world of lost data in your organization. Get collecting!AI is for leadersMore than any technology in recent memory, AI is a technology that directly benefits you as a leader, no matter what field you are in. It helps you organize your information, transform it into any presentation that suits your purposes, and ultimately leads you to better decisions.There’s no better time than now to get started.Thanks for reading Stable Discussion! Subscribe for free to receive new posts and support our work and those in our community. Get full access to Stable Discussion at blog.stablediscussion.com/subscribe

You're late to class, didn't study, and must sit through a lecture on advanced mathematics (or a compliance training in an office, whichever you find most boring). You forgot your notebook and end up without any lecture notes. Now, an hour later, you're doing homework and try to remember what you learned.You remember things at the beginning of the lecture. Topics connect to your previous learning quite easily. But then things get a bit murky... You've got something written down, but it looks wrong. You remember, at the end of the lecture, there are a few ways to check your results. Running through these checks, you can confirm that you're wrong, but you don't know why...Without notes to reference, you're a bit lost. This is exactly the way AI feel when given too much data and no way to take notes!The Challenge of Noteless AIIn a recent paper, Lost in the Middle - How Language Models Use Long Contexts, researchers found that when ChatGPT, and other AI, are given a lot of text they tend to forget or misremember some of it. Specifically, the information in the middle of the text. Similar to our lecture, the AI are less certain about information in the middle of any given data. It's uncanny that AI continue to act so human.To help explain how this happened, the paper points to AI training. AI haven't been trained on larger chunks of information paired with accurate reflections over this information. Instead, they have been trained on shorter sections of text, often derived from how humans respond to information.Where could we get this training data? Perfect recall isn't something we expect until we think about a machine "learning to read"” Then we want exact results. We want machines to do the drudgery of figuring everything out and just tell us what we need to know or what might have missed in our own reading. We don't want machines to read how we read or act like us!But, hey. This is science, not science fiction. Because humans don't act like a prefect machine, we unfortunately don't have anything to model these machines after. It's becoming more obvious that, because these AIs are trained by humans, AI have even copied our faults.How would we help a human get over these faults? How would you keep track of key pieces of information when interacting with a large and complex set of information?Taking a Cue from Human MemoryIn another Percy Liang paper titled, Generative Agents - Interactive Simulacra of Human Behaviour, the research team simulated a world full of AI-driven people. They woke up, ate, worked, socialized, planned, and slept.A major challenge when creating this simulation was getting the citizens of this experiment to think like people. To solve this, the team constructed a complex set of memory systems. Short-term memory helped them remember where important items were, track immediate needs, and engage in conversations. Long-term memory was used to plan and remember important facts (among other things). These enabled the AI people in this simulation to act like people and even plan an ad hoc Valentine's Day parties with others.It's a fascinating paper and I recommend you read it.The AI were able to organize and stay on task when they were given some place to keep their memories stored. Memories tracked, at a high level, are summaries of what was happening around them. These memories are essentially notes on their environment!Getting AI to Remember with NotesThis all leads us to some creative thinking about how to use AI to solve problems. While we'd love to treat the AI like machines, we need to assist their ability to remember and provide them with notes that direct their actions. Helping them take notes is essential to developing good AI workflows.When we interact with ChatGPT, we're usually already keeping track of what the AI is thinking. They're using us as their long term memory, and we can remind them when they forget important information. In part, this is why chat interfaces are beneficial for this era of AI. They can sort of rely on us to perform well.However, say we want an AI to read a large section of text and help build a summary of that text. Then we have a more challenging role to fill because the AI is going to misremember or forget information. We need to help give the AI note-taking capabilities.If we split the text into smaller chunks, we can ask the AI to give us short summaries of each chunk. When we're finished, the AI can combine those pieces together to summarize what was covered. Just be careful that the pieces aren't too large when combined, otherwise we're right back where we were and the AI is being forgetful again.Helping AI build plans isn't new, and there's already been a lot of research done to understand how to get AI to follow plans that help them accomplish tasks. A popular tool for building a plan is Chain-of-Thought (CoT) Prompting. We ask the AI to write out a plan for steps to take, which it will try to follow:Question: Marty has 100 centimeters of ribbon that he must cut into 4 equal parts. Each of the cut parts must be divided into 5 equal parts. How long will each final cut be?Answer: Let's think step by step.Give this a try in ChatGPT, and you'll get back a sequence of steps and, usually, a pretty accurate result. We can leverage this pattern to get the AI to make plans for itself which can be referred back to later. These plans help AI to better organize its approach to solving problems.The Path AheadWhile this only covers a few methods to help AI handle larger sets of text, many others methods exist and continue to be explored. While many believed that providing AI with a larger "context window" (the ability to be given more text) would solve many limitations, it seems that those limitations continue to exist. At this point, I won't use the longer context windows of these models until these memory problems are resolved with new modeling or better training data.For the time being, the AI development community is going to be innovating around these limitations to engineer systems and solutions rather than just relying on LLMs to solve these problems. If you're doing something interesting to help AI better understand and remember larger sections of text, we'd love to hear about it!We'll continue to post interesting ideas about how to use AI here and on our main site, Stable Discussion. Keep checking back to keep updated on our latest thinking around these ideas!Thanks for reading Stable Discussion! Subscribe for free to receive new posts and support our work. Get full access to Stable Discussion at blog.stablediscussion.com/subscribe

In our discussions with many different people, how to get started has come up quite a bit. Many of these people aren’t working in technology and haven’t programmed before. When they try to use AI it doesn’t work for them and the results aren’t all that helpful. This gave us the idea of putting together a short guide for anyone who identifies with these problems and would like some helpful pointers to get started.Getting SetupI think the easiest place to start is by getting an account setup to experiment with ChatGPT. You can go on their site, create an account, and get ready in just a few minutes. They also have a mobile app that you can download which may be easier if you don’t usually use a computer or laptop.These same ideas will carry through to other systems like Midjourney, DALL·E, or Bard. We just find ChatGPT is one of the easier interfaces to use when getting started.For our examples we’re going to get ChatGPT to write a letter to our friend that updates them on the latest news in our life and breaks the bad news to them that we won’t be able to visit this summer in as kindly as possible. I highly recommend you follow along on ChatGPT.Don’t Look for A Single Right AnswerThe most common challenge we find people struggle with is looking for the “right answer” to using AI. If you did well in school or took standardized tests you might be geared toward finding one correct answer to how you should use AI. Unfortunately there isn’t just one correct answer. Many different questions can get you the same result. It’s similar to asking your friend a question, they’ll likely give you the same response even if you ask your friend a question in a few different ways.Instead, using AI is a lot like trying to ask a question that the AI can understand best to answer your question. If you ask someone “What is the capital?”, they might not know what you’re talking about. The AI is the same, you’ll need to be a bit more specific.The examples we give below are some good ways to get started but you might find that for what you’re trying to do there’s another better way that works for you. Don’t get too attached and try to have fun finding new ways to get what you want back from the AI.Initial PromptPrompts are the questions or queries that we send to a service like ChatGPT. For our initial prompt, we’re going to go ahead and get started writing a letter to our friend using the following:“Write a letter to my friend, Paul, telling him I got a promotion to Manager at my company, my dog died, and I won’t be able to visit them this summerIf you put this into ChatGPT you’ll notice that it does a great job getting a letter created but it likely isn’t at all what you imagined. It probably doesn’t sound like you, it likely includes details that we didn’t give, and it might not be very interesting to read.You might be tempted to then just say:“Make it better”The result is a letter that likely uses a more complex vocabulary, might have fixes some grammatical issues, and might be a little longer but doesn’t seem to address any of our issues. It’s at this point most people give up and call it a day. But if we push just a little further I think ChatGPT will surprise you!You can find my chat example hereAdding SpecificsSo we can immediately solve some of the problems we had just by providing a few more details. Let’s start a new chat and add some details that can be used to fix some of the challenges we’re having:“Write a casual letter to my friend, Paul. Tell him I got a promotion to Manager at my company, Nintendi, because of my hard work over the last year. My dog, Mari, died from old age and my family is still grieving. I won’t be able to visit him this summer due to the new responsibilities at work.”Testing this out we’ll immediately see much better results. Describing the tone of letter (casual) helps a lot and can definitely help ChatGPT know what kind of language I want to use. The other specifics help to fill the letter with details that make the letter’s contents a lot closer to what we were hoping for.You can find this chat example hereIf you imagine talking to your friend and asking them to write a letter for you, these are the same details you’d need to provide them. It helps a bit to think of an AI as just an assistant that is trying to figure out what you want and can only use what you give it to help you.Adding specifics to your prompt might be just enough to get what you’re looking for especially if the letter isn’t very personal. However, this letter still doesn’t have the personal touch you might like it to have. Let’s look at some other ideas for how to make our examples just a little better.Adding an ExampleIf we were to hire someone to write letters for us, something we might do is provide them with some good examples of how to do it. That would help them understand what we’re looking for and when we gave them feedback they could continue to learn with those examples as a foundation. For this improvement, we’re going to give the AI the same thing: an example of a letter for them to use.Write a casual letter to my friend, Paul. Tell him I got a promotion to Manager at my company, Nintendi, because of my hard work over the last year. My dog, Mari, died from old age and my family is still grieving. I won’t be able to visit him this summer due to the new responsibilities at work.Use the following example as a reference for my writing style:Hey!It’s been a crazy and amazing week. I’m at the Seville airport, getting ready to board flight TP1105 to Lisboa before TP259 to Toronto. It’s a really small airport here and security took me 5 minutes!My favourite memory is this dinner of the amazing view of Ronda with local wine, salmon tartar, croquettes, artichokes and Iberian ham. Amazing views, delicious pairings, and incredible company. Looking forward to being home now. 9.5 hours in the air and I’m there.Love you,BenAdding this example, we see the style of the result change to be more inline with the way that we talk and we see our formatting style in the resulting letter. However, you still might not be too happy with the result. Don’t worry, there are a couple other ideas we can try out!You can see the result of adding an example here.There are a number of other ways to provide an example that might provide even better results in your testing. One prompt engineering technique is called a **One-Shot Prompt** where we give it an example of a prompt and a resulting letter. You can see an example of this here.Adding RulesOne thing AI are great at is following rules that we provide. They can factor in a large number of factors that are hard for us to track. Because of this, rules are a great way for us to control their behavior and get better results. Let’s add some rules that the AI should follow when they help us write our letter.Write a casual letter to my friend, Paul. Tell him I got a promotion to Manager at my company, Nintendi, because of my hard work over the last year. My dog, Mari, died from old age and my family is still grieving. I won’t be able to visit him this summer due to the new responsibilities at work.Follow these rules when writing the letter:* Don’t be too emotive and stay somewhat reserved* Try to start and end with good news* Don’t add any details I didn’t include* End with “wishing you the best”Use the following example as a reference for my writing style:Hey!It’s been a crazy and amazing week. I’m at the Seville airport, getting ready to board flight TP1105 to Lisboa before TP259 to Toronto. It’s a really small airport here and security took me 5 minutes!My favourite memory is this dinner of the amazing view of Ronda with local wine, salmon tartar, croquettes, artichokes and Iberian ham. Amazing views, delicious pairings, and incredible company. Looking forward to being home now. 9.5 hours in the air and I’m there.Love you,BenThese rules help to reinforce our tone, style, and help keep the AI from adding details that we don’t want added to our letter. Rules are easy to add and remove to see different results in the letter’s style and offer great direction for the AI. Sometimes you can jump right to adding rules for a great result, but it very much depends on the task you’re trying to do.While it may not be perfect, this really gets us quite close to a letter that we’d be happy to send. After a few small tweaks and edits you have something that you can send to Paul and a great template you might use for future letters!You can see the final result here!Turning it off and on againSimilar to many technologies, AI sometimes get a little bit overwhelmed and need to be restarted. Consider opening a new chat conversation with ChatGPT after a while.You can keep a conversation going for a long time in ChatGPT but you will start to lose control of the conversation and the AI might start behaving strangely. This is especially true If you’re trying to do a lot of different things in a single chat. Previous responses may leak into other questions. For example if I’m writing another letter to a friend in the same chat that we were chatting with Paul, it may bring up my promotion even through I didn’t say that it should just because the AI knew about it from earlier in the conversation.Use a more powerful modelAnother great way to get better results is to use a more advanced version of the AI. If you’re struggling to get GPT-3.5 to return something, using GPT-4 could be all you need for the AI to understand what you want. Later models are a little slower but they’re smarter and able to better understand complex requests. The GPT-4 announcement gives some great examples for how much the model improved over time.TakeawaysPrompting can be a frustrating experience as we try to get an AI to understand just what we want. When struggling, refer back to these improvements that can help AI better understand what to do and how to produce something a little closer to what you’re looking for. AI act a lot like assistants and we need to provide them with the details that help them to do the jobs that we give them. Without that feedback, they’ll continue to struggle to understand how to make their results any better.Thanks for reading Stable Discussion! Subscribe for free to receive new posts and support our work. Get full access to Stable Discussion at blog.stablediscussion.com/subscribe

Topics discussed in this episode include:Latest AI News* Sam Altman visits Toronto on his world tour: https://open.substack.com/pub/stablediscussion/p/discussions-about-chatgpt-with-sam* Google I/O Announcements* PALM2: https://blog.google/technology/ai/google-palm-2-ai-large-language-model/* Anthropic 100K context window: https://www.anthropic.com/index/100k-context-windows* Geoffry Hinton interfview: https://www.technologyreview.com/2023/05/02/1072528/geoffrey-hinton-google-why-scared-ai/* Drag your GAN paper: https://arxiv.org/abs/2305.10973* Tree of Thoughts (ToT): https://arxiv.org/pdf/2305.10601.pdfEthics of AI* What is the future like for artists and what do we think about rights and usage of AI tooling?* What does the future of jobs look like and where should we be looking for people that may be losing the current way they work?* Do we know what a good future looks like if it doesn’t look like the world we live in today?* Geoffrey Hinton’s warnings about the potential future of AIMisc References:* Simulated Towns Research paper: https://arxiv.org/abs/2304.03442* Fall by Neil Stevenson: https://en.wikipedia.org/wiki/Fall;_or,_Dodge_in_Hell* Sapiens (book): https://en.wikipedia.org/wiki/Sapiens:_A_Brief_History_of_Humankind* Lex Fridman Interview with Sam Altman: https://www.youtube.com/watch?v=L_Guz73e6fw Get full access to Stable Discussion at blog.stablediscussion.com/subscribe

Note tooling is something that I've been obsessing over for years. I've never thought of myself as a particularly good note taker but, as I started working more, I realized that I needed to offload information from my brain. It's hard to keep track of all the things you have on the go, your passion projects, and even things you might be trying to learn about outside of life. It's easy to feel overwhelmed with all the demands on your mind.After filling many physical notebooks, Evernotes, One Notes, Bears, Notions, and Roams I've finally settled on Obsidian as my tool of choice for handling notes. This is in no small part because of its ability to facilitate a Second Brain note-taking system.A Second Brain is an approach to help capture, organize, and retrieve information effectively. There are many different techniques for taking notes to build a Second Brain, including the famous Zettelkasten Method of note-taking. These systems reduce mental overload, improving learning, reinforcing memory, and making you a better problem solver and decision maker.Obsidian is a great tool for any of these Second Brain techniques because of its ability to create links and references between content, structure content, build note-taking workflows, and quickly search the notes. Using it as a Second Brain creates a centralized location where you can keep track of all of your information. Having all of your knowledge in one place constructs a "Data Lake" you can analyze and search to help surface ideas, patterns, and generate new content.Many writers use these pools of knowledge to "slow burn" their content creation, which leads to better research and more complete ideas being published. They start writing multiple articles at the same time, collecting research bit by bit, so that they can benefit from the serendipitous discovery of new information as they write. It basically avoids that feeling where you just finished publishing something and later realized that there's one or two things you wish you'd added.I've been looking into using my Second Brain as a source for an AI chatbot. While access to the newest version of ChatGPT's Plugins will allow you to query against documents (like PDFs), I want to use hundreds of documents including highlights from articles, diagrams, and even referencing images. Anthropic announced a 100k token limit for their AI, Claude, this week but it’ll be interesting to see what the research shows about the AI’s ability to focus with a larger context space.After I created a D&D chat bot to converse with the rules of a very old version of the game, I've tried to create the same thing, but for my notes. I dabbled at building a system to do this myself, but then I came across a plugin for Obsidian written by wfhbrian called Smart Connections. The plugin adds an AI-powered chat client right into Obsidian! It's great when I don't have to waste time building because someone else has already beat me to it! 😄I can ask the Smart Connections chat questions, and it will reference notes that I've added, or I can directly reference it specific notes for it to work with. The comparison tool that comes alongside the chat can also show which notes are similar to help create better connections between notes. This is great for creating maps over your Obsidian notes, which could come together into digital gardens.You'll need a bit of technical knowledge to get Smart Connections setup. I've linked to Github where you can refer to the setup procedure, but you'll need to get an OpenAI API key setup, and you should be ok with spending a little money to index all of your notes. My ~500 notes took less than $1 to process, and then a cent or two every time I converse with it.The ability to use AI with my Obsidian Second Brain has grounded my belief that this is the best note-taking system around. Being able to use your own Second Brain as a source is incredible. You can chat with everything you've learned and ask questions that maybe only you would know! Better yet, the answer will likely be familiar to you or written in a way that's clear to you because the explanations were written by you! Tools like Gitbook Lens are already realizing that chat build against Knowledge Management Systems is a revolutionary idea.There's a future where we have the power to use our own personal data to improve our lives. Rather than selling data to create services like Google or Facebook, AI offer a way to use that data without needing any special training to be better able to leverage that data. With AI running on-device, we won't need to worry about an AI system keeping our confidential information. This will empower us to fully leverage what we know and our own strengths to be able to improve our lives without the need to sacrifice our data to do it.While Smart Connections isn't perfect, it's a great first step in being able to create a generic documentation search system for your Second Brain. AI solutions can always be improved and optimized by adopting a less generalized approach. We'd love to work together if you're interested in creating a more optimized documentation system for your data, or are interested in what other capabilities you'd like to incorporate.Thanks for reading Stable Discussion! Subscribe for free to receive new posts and support our work. Get full access to Stable Discussion at blog.stablediscussion.com/subscribe

I've edited this first paragraph about a hundred times before arriving at a place where I like it. I kept revisiting it as I wrote further down the page. I keep thinking: Are all these changes really going to make it better? Could an AI get that out of the way so I can write?An AI could have started this post off great. It certainly would have been way faster than me. I could regenerate the first paragraph as I wrote the rest of the post. A couple visits to ChatGPT, and I'd be done! No need to tweak or tune things.You can feel the tension in that statement. I've just spent a lot of time doing something, and I'm realizing an AI could have done it better. Worse, I'm telling you about it! You can empathize with me on this though. It feels like I wasted my time.Everyone is marketing these incredible AI tools every day. It feels like we have to use them or get left behind. And a trap is waiting for us as we adopt these tools. Where do we fit in? Where can we put our creativity? That question hints at the mental tax AIs can have for creatives.It's unbelievably demoralizing to feed ChatGPT three paragraphs that I've spent hours on just for it to return one great paragraph in a matter of seconds that gets my point across more clearly. It's like being an author who sees their magnum opus distilled into a handful of PowerPoint slides. It doesn't exactly inspire you to keep writing.What's worse, when generating some writing based on some outline that you've written, you've just given yourself the most tedious part of the job: Editing! While I like a lot of AI tools and prompt techniques, it's easy to find yourself doing work so that AIs can have all the fun.Let's get back to why we are writing. We write to say something or have some impact on the reader. If you add another barrier to writing something, you might never get those ideas written down. Worse, leave all the ideas to some AI. (I gotta say, left to their own devices, AIs can make a god awful video plot)With some intent behind your writing, there are a lot of compositional elements that are aligned with your message. You had an idea in mind, and there are things you don't usually have to check. Looking at your writing, you don't have to say, "Where did this come from?" or "What does this have to do with the points I was trying to get across?" or "Is this what I had in mind?" (At least, hopefully not too often)But if you've sloughed off the responsibility for writing to your AI Assistant, these are things you have to review and edit out. While you can resolve these problems slightly with prompting techniques, now you're engineering and not writing, which is a process that may take longer than just writing in the first place.What's the trick?There's a book that I really like called Non-violent Communication by Marshall B. Rosenberg. The book covers our lives conflicts and presents some opportunities to solve them. One radical idea presented was, "Don't do anything that isn't play!".The book tells the story of a doctor who hates submitting a form for clinical studies. It's one of the things they don't like doing. When pressed on why they don't stop, they say it's because it brings in some money, not a lot but a little. But by thinking about it further, the doctor decides that life is generally better not doing that thing and continuing other parts of the practice that are more enjoyable.I love this idea and the personal empowerment it drives. Many parts of daily life are taxing and may not even be necessary to live happy lives. These are precisely the kind of tasks AIs should and can help us with. Hopefully, they can also help doctors have less paperwork.Problems of morale and creative challenges occur when AI take away the fun parts of our craft. Just because AIs can write emails, create artwork, or even direct movies doesn't mean we have to use them for that. We found those interesting because we thought computers couldn't produce anything artistic. I expect we'll have higher standards for art as a result but likely will still be attracted to media that "has a soul."I really like writing. I like the feeling of ideas flowing out of me and onto the page clearly and directly to the reader. Imagining someone reading what I'm writing makes me feel a sense of responsibility and excitement that pushes me to do as much as possible for that person. Help them understand what I'm seeing and tell it in a way they can understand.When I use AIs, I use them for editing and, at times, brainstorming. I've dabbled with using it to create a panel of experts that I can use to poke holes in ideas, but I have yet to find the way I want to use it for that. I might write a prompt like this:You are an expert marketing editor for an AI blog aimed at a general audience. Provide helpful feedback that is consistent with the voice of any writing provided. --- --- Provide ideas around the pacing, order of parts, and give me a summary of the main points a general reader would get from this text.While I might not use much of what is provided in its feedback, it is nice to hear actionable feedback quickly. This process takes much longer with people, and I can easily catch dumb mistakes or missteps in my approach using an AI. Also, I just don't like editing very much!Now that you know my secrets, I hope you'll give yourself another attempt with ChatGPT or your AI tool of choice. Find the play in your work that makes your days more enjoyable and the process easier. Then mercilessly hand off as many of those dreaded tasks to the AI as you can. Who knows, maybe it'll give you back a bit of time for something else you've been meaning to pick up.Thanks for reading Stable Discussion! Subscribe for free to receive new posts and support our work. Get full access to Stable Discussion at blog.stablediscussion.com/subscribe