011: Future Signals 2026: The Architecture of Intent

You went into 2025 excited about AI. You ended it exhausted. You weren't alone. And this conversation knows exactly what that cost you.

There's a moment in this episode where everything clicks. It happens when Brittany and Adal land on the question most executives are quietly carrying: if everyone now has access to the same AI tools, who am I going to be? That question is the heartbeat of this entire conversation. And it might be the most important leadership question of 2026.

The Future Signals 2026 report isn't a list of predictions. It's a map built on patents, VC reports, financial data, and a two-layer signal system that separates what is happening now from where things are cooking. Last year's signals? 85% became reality. So when the signals show up in your industry, you'll already know what they mean.

THE FIVE SIGNALS

Signal 01 — The agent that handles it
AI isn't a new hire. It's infrastructure, like electricity. The question isn't who to replace. It's how to wield it.

Signal 02 — A fragmented world
Borders, regulations, and restrictions are multiplying. The companies that prepared for flexibility won't just survive. They'll sprint.

Signal 03 — Programmable biology
AI isn't just accelerating research. It's redesigning from the atomic level up. Every industry that touches living things is affected.

Signal 04 — AI beyond the screen
AI is developing a physical language. It's not smarter robots. It's AI that can understand and navigate the world around you.

Signal 05 — The human premium
When everything can be built, what you choose to build and why becomes the ultimate differentiator. Human intent is the scarce resource.

THE NUMBER THAT WILL STAY WITH YOU
88% of companies are just accelerating old workflows with AI.
6% are fundamentally redesigning how they work and who they are.

The conversation gets honest about something you've probably seen on LinkedIn. Influencers claiming their 37 agents made them millionaires overnight while you were just having breakfast. It's almost funny. Until you realize how much that noise is quietly eroding your confidence.

The point was never the number of agents. It's the right technology, in the right hands, pointed at something that actually matters.

"When execution becomes abundant, intent becomes scarce."

This episode ends with a question worth sitting with: where are you on the maturity map? Just starting? Building? Scaling? The signals meet you there, designed for the stage you're actually in. Not the stage someone's performing on LinkedIn.


GO DEEPER
→ Download the Future Signals 2026 report: https://designthinkingjapan.com/thinking
→ Explore the open methodology on GitHub : https://github.com/DesignThinkingJapan/future-signals-2026-report
→ AI Leadership Academy, executive fluency for real decisions: https://designthinkingjapan.com/academy

AI is a marathon but it can feel like a sprint. That's why it's important to go with intent.

Transcript

SPEAKER_01 0:00

Welcome to Future Signals 2026. Now, did we write the Future Signals or did we create the Future Signals 2026 simply to have more research out there? Of course not. There's really one core reason why we developed the Future Signals. It's similar to 2025, but even more so in 2026, and it's to make sure that we're able to give real confidence to executives in their hands. So 2025, you would be hard-pressed to meet someone that wasn't enthusiastic around AI, but equally, you probably those same people ended up being quite exhausted. So they went from enthusiasm to exhaustion by the end of 2026 because there was just simply so much noise. So what we wanted to do is take all of that noise and give it some kind of meaning so that people can make some really good decisions.

SPEAKER_00 0:46

Yeah, the report serves uh a couple of purposes. One of them is to provide a navigation uh point, uh a playbook that is equally uh equipped with evidence, with with actions. This uh information that we're gonna that is being gathered in the report. It serves that. Okay, okay, I've read this, this is what is my what might come in the next year. So it serves as your preparation playbook for this year.

SPEAKER_01 1:13

Right. I really like that, particularly if you think about um what the structure looks like. So it's at the end of each chapter, it'll say, for example, this is how we know the signal is calibrating, so recalibrating, as in like it's not right at all. And this is how we know if the signal is accelerating. The thing that I also really like is if we think about AI and the maturity map of AI, people are really in very different positions. There are people that are honestly in 2026 just beginning their AI journey. There are certainly companies out there that are quite, you know, a lot on in their way when it comes to maturity and they're in their building phase. And then there are certainly companies that are leading and scaling, and that AI is a s is a simultaneous infrastructure throughout their entire organization.

SPEAKER_00 1:54

What can we learn from them?

SPEAKER_01 1:55

Exactly. And so in the report, there's those three steps. So matter no matter where you are, there's something to learn, right? But the thing that I think I like most about this report is that it's not us taking a guess and saying, you know, we this is our best guess, you know, believe us. Um if anything, it's it's a it's us taking deep research from trends that are already happening out there and then giving meaning to it to people. So it's not, hey, believe us, you know, we're fortune tellers. We're taking data that already exists out there, but we've been able to identify what's the common thread between them.

SPEAKER_00 2:31

So the the all of this has to do with the way we created the report. The 2025 Future Signals Report, Latis Report, was created deliberately by the Sensing Japan team and openly, like uh saying that it was created with the the assisted eyeball dragon tape. And that has to do a lot with the way the the future signal report is is is uh created. Starting from 2025, in that year the Design in Japan team created the report and openly saying that it was in collaboration with AI. So it was not just uh a bunch of analysts, a bunch of uh people or experts as SMEs trying to figure it out or to predict what's coming up. But how do we use the AI to accelerate it? And for the first time, that architecture was open. So it's available on the Sentinel Japan GitHub for people to have a look. And of course, when that report was created, we put it to the test. At the end of the year, we launched 13 independent sources to analyze and to test how many of the predictions that were there back then became truth. And we were very happy to observe that 85% of those predictions are becoming a reality, which means that the richness of the information is uh a solid uh uh source of information. Right?

SPEAKER_01 3:55

Uh 2025, like all things, I mean human-centered design is at the core of everything that we do. Um, and iterations obviously key. Um and in 2025, we learned that it might be uh helpful, valuable, uh to add another layer of research. So we had our context layer, which is what's going on right now. So we pulled everything from filed patents to uh voices to VC reports to financial analysis, kind of what's going on right now, so the context layer. And then we moved to also the signal layer, which is where are things going? And so we were able to then triangulate, right? So between what's happening but also where are things going.

SPEAKER_00 4:34

So um what does it what about it, right? So what? Yeah, who cares? So what? The the so what is that the these two signals, which is kind of uh one of the early stages, means that we're observing the current state of activities, industrial and societal activities. However, the second signal tells you where things are cooking, like things are getting ready. Like, for example, there are uh the robotic, let's say, uh revolution that we're observing right now. All of those uh products were starting to get prepared five years ago. Five years ago they were researched, and this year they launched all together. So the analogy and the importance of this new architecture is that it starts with the human intention, and then we launch these uh signal researches from two different angles. Then at each step there's a human validating the outcome. And when we have these signals, we clean them. They got cleaned. But not only that, they got challenged. How do we know if they're just a phase? How do we know if this doesn't become true? And through the data gathering, the data analysis, the data challenging, the diverse sources that challenge each other, and the combination of thousands of thousands of uh bits and bytes of information, we came up with something, uh the Scientist Japan Research team came up with something very interesting. Very interesting, and we believe uh uh also uh fun to read.

SPEAKER_01 6:05

Right. Um so we went from four signals to five signals this year. Um and the five signals we're going to uh go through maybe the obvious read and then kind of go a little bit deeper.

SPEAKER_00 6:16

So we will yeah, we'll share each one of them just a little bit.

SPEAKER_01 6:19

Oh, just exactly just a little bit. Um so first things first, we began with the agent that just handles it. So when people think agents, I've heard time and time again people say something like, Should my next hire be a VA or an AI? Which is fundamentally the wrong question to ask because you are thinking that AI is in fact an employee when we found out that's not really true at all. In fact, it has a total nother uh materialization being that it is not an employee, it is very much infrastructure. You do not hire an AI any more than you hire electricity. You obviously pay for electricity, you hire electricity for your uh team to use, but it's not necessarily on your org chart, right? So dive more into um with that initial read of, oh my gosh, it's agents. Agents are just going to like essentially replace all of the people into rethinking the inversion of uh the AI.

SPEAKER_00 7:18

We have seen uh how how long do you want to run the chartic? I thought it was just snippets.

SPEAKER_01 7:25

Yeah like just a couple minutes, like each one.

SPEAKER_00 7:30

Okay, okay, okay. Um the inversion, the obvious part is that AIS are here and we will start uh firing people and get wins from that potential savings. However, what we're observing is that uh most product companies are taking the human, equipping with these tools and multiplying their output. What it means that they will have a aggressive, hyper-aggressive production, right? We're leaving behind the competitors but also providing faster value or bigger value at lower cost to the clients, which of course is a is a great advantage. However, observing this as infrastructure is critical because you can, if you're in construction, you can break down a house with a hammer, or you could get one of these uh you know caterpillar machines and pfft brick holes and move faster. But the machine needs intention, needs direction, needs a point of view, needs decision, right? And needs someone to wield it. It's an amazing sword. And today, seeing how this infrastructure connects, right? Has to be aligned with the purpose of the company, with the goal of the company, with the values, and of course, combining all that aspects is what makes it a good infrastructure.

SPEAKER_01 8:52

Right. So moving AI not being uh an employee, but rather infrastructure. And then if we think about Signal 2, which is the idea of a fragmented world. So interestingly, I uh went to university during during the age of globalization. You know, the world is flat, um, you know, you can have breakfast in Berlin, dinner in New York, that kind of that was very much um the mode of the time. Um, but we've seen very much in 2026 just how the universe always swings back and forth between when one thing is true, very soon the actual opposite becomes true. So if we think about the fragmented world, the idea that you have regulatory frameworks, for example, but actually that's not really what we're talking about, that just each country has their own regulations. Um, and that you know, sometimes one market might be more expensive than the other, it's actually deeper than that, right?

SPEAKER_00 9:44

Yeah, the obvious the the the as we call it, the obvious read is like we're having regulations and more restrictions. Uh the non-obvious read is that these new restrictions create opportunity. Yes. Because the opportunity goes to those companies that get prepared, that get ready to jump. What happens when I'm developing in some infrastructure, let's say with uh some country preferences, and then I move abroad because my company wants to be or is international and I cannot operate with the same infrastructure? How do I anticipate this to become this seamless transition? How do I become transparent in those movements? How do I allocate resources, not in the old way, which was uh let's choose one vendor, one provider, and this is good for all, like one fits all. But how do I equip myself in an agile way, in a very flexible way, to anticipate changes, not only changes on regulations, but changes on preferences, what the people might like, what the technology might bring. How do I make my own operation um non-restrictive but adaptative?

SPEAKER_01 10:52

So, signal two, we knew to we had enough data to show us obviously that it is going to be true, which is why we listed it as a signal. But we didn't expect for it to be proven this quickly, right? So in tw I mean, we're this is um a couple of weeks ago, yeah, March 2026 today. Um but we saw even you know late February um that there was a clear mandate from the US government that, hey, we're no longer going to be uh using anthrop where there was a there was a call or a suggestion to no longer use anthropic um technologies. Uh and then this this was really like a switch overnight, right?

SPEAKER_00 11:28

And this is Yeah, that's uh one of the challenges. What happens when because most companies are building on multiple stacks of technologies. And I even heard some some uh let's say more uh novice voices that says, yeah, but we have our own software developer teams. So we're okay because we develop our own technologies. But those technologies are built on top of something else, in the terms of software, IDEs, licenses, APIs, infrastructure, chipset, electricity, we go to the electron level. So knowing that some of these pieces depend on each other and how do you become flexible creates creates reality. And what happened with that tropic being flagged as uh as the supply chain risk, uh, what happened with the government, and right now, of course, then in this debate creates a clear evidence of the importance of this signal.

SPEAKER_01 12:16

Right. And that the signal, obviously, we meant as a obvious as a geographical level, being so, for example, we have like the US uh mandate to things, or for example, German or Japan, whatever. But we even saw that this is not just like a foreign adversary thing, this can even happen within a nation state itself. So super interesting stuff.

SPEAKER_00 12:33

This can be a single bit of.

SPEAKER_01 12:35

Yeah, that's gonna be another episode in and of itself. So signal three being about biology, I think the obvious thing that people kind of go to is that AI can make you know drug research faster. So if you equip an a scientist, so if we talk about the idea of an AI scientist, the idea that you'll have a scientist, you know, being you know using AI really well, but there's actually an inversion to that, right? That it's not a scientist using AI, rather something quite different.

SPEAKER_00 12:59

Yeah, when you start w mo most uh uh most companies use uh RD directly or indirectly, regardless of the industry that you are, right? But when you think about the the obvious path, which is how do I use my research and development team to find something versus how do I use first principle and redesign from bottom up from bottom up from the material level, the things that I'm going to need changes fundamentally. And I'm going to probably hint into the next into the next signal. We're going from assisted uh activities, from uh borders, right? We're going to biology, which is understanding the chemistry, but also going to the physics. So when AI is understanding the physics, it's also understanding what I can do to manipulate the chemistry to make better outcomes. So programmable biology is not only at the chemistry level, it's at the infrastructure level. How do I create the elements that I need to operate in a better way?

SPEAKER_01 14:09

Right. So this is obviously if we think about like an industrial application. So this is everything from if any any person that uses uh living living uh or organic materials, right? So it's whether you're in agriculture or whether you're in manufacturing, if you can go back down to the pr physics first principles layer and say, how can we fundamentally do this differently or more effectively? I mean, it's an interesting question, which as you said suggests to then signal four being that AI takes on a physical context. So I think the f the most obvious kind of thing that comes to mind is, oh, well, everyone's gonna have a robot or um jumping into signal number four. Right, which I right, and so jumping into signal number four, the idea of that AI is beyond the screen. AI has a physical context, which means that we've gone from AI as infrastructure, AI having borders, AI being chemistry, and then the floor signal, AI being physics, understanding that AI not only is uh well, if you take a step back, I guess one one of the more simple or the most obvious read is that robots can do more physical tasks. Robots get better at doing things. But if you think about it, AI, uh, unlike, for example, if you think about traditional software or the internet, can live beyond your phone or your actual screen, your computer screen, TV screen, whatever. It can be in so many different things, really anything. It's developing an actual physical language in order to live and interact with the world, right? So it's not just we've got smarter robots, it's actually AI understanding and being able to navigate our physical world.

SPEAKER_00 15:43

Yeah, the understanding is passing from the paper to the physical things, and not only having a robot that manipulates things, but could be an assistant, could be you could could be your LLM. When you're having a conversation, the advice, the back and forward that you get, it's accurately because understand the physics of the world. That you're wearing these kind of shoes and the floor is kind of wet, right? Take that to motorsports, to manufacturing, take that to uh healthcare, to any industry. How these uh physical constraints and the physical combination of all those elements have affect on the desired outcome. Having that is the vocabulary that we will start sharing with them. Because they start getting this understanding of the world and goes to the first signal, right? Things that we're starting to prepare become a reality. Right, exactly. And that cycle repeats.

SPEAKER_01 16:33

And usually when we get to about signal four, this is when people kind of like put their pens down and say, Do I give up? I'm a human. Does that mean that I think that I'm completely irrelevant? And interestingly, even though our first four signals highlight the maturity of AI as a technology, our fifth signal, if anything, is really the connective tissue, which is the idea of a human premium. So this means that it's not the fact that AI can do more and then con human contribution becomes less, and then you know what, you should just like, you know, turn around, go home. Everybody have uh, you know, like universal basic income, you know, and everyone go home. That's actually not what we're saying. We're actually saying something quite different here.

SPEAKER_00 17:11

Yeah, when you can build anything, what would you like to build? Right. And as we as we mentioned, if we connect the four signals, it's about having uh AI that can augment, multiplies you. You have things that can be programmed to the atomic level, you have physical uh robots or physical elements that can be added into that equation, right? You have different frontiers that could allow you to manipulate and to operate with different contexts. Means like uh the the first intuition is as you as you mentioned, I give up. But that also means that knowing what to build, having the right person to direct will change things. You probably won't have five operators, but you have one person directing a small team or a big team of call be robots, physical robots, or uh digital robots, taking action, taking activity. That's what makes the differentiator. Imagine this analogy, like uh and we share this in other episodes. If all of us ask our favorite uh Claude, Gemini, uh Perplexy, whatever, we said uh give me 10 good ideas to make for a party. Yeah. All of us will have 10 good ideas. But because it's a probabilistic model, all of us will have mostly the same ideas. So how do we differentiate at industrial level when we receive the same? Right. That's where the human comes from.

SPEAKER_01 18:29

It's our guidance.

SPEAKER_00 18:30

That that's the human intention.

SPEAKER_01 18:32

Right. Now, when people talk about that, when we say where's the opportunity, right? Where is actually the opportunity? I think this is when we have their 88.6 uh percentage come in. So 88% being that 88% of companies are simply taking existing workflows and then accelerating them with AI, right? Which isn't wrong. However, it's limited. It's limited, exactly. So it's but if you look at the 6% that are going deeper and they're actually fundamentally redesigning the idea of uh their their workflow, so not just how have we done it and then how do we make it faster, they're thinking how might we completely do it differently? So for 88.6, when we're looking at where is the opportunity, I think the opportunity is not just in making your traditional workflows faster. I think the real opportunity is redesigning through human intent your your future workflow. One of my favorite questions that we often ask is well, when everyone has access to the same technology, who are you going to be? What do you want people to say about you? How do you want your customers to feel? These are all the kinds of conversations that ironically I think are the most important in the age of AI.

SPEAKER_00 19:40

I was uh I was having some some conversation out there and some of them were like, okay, I need to do AI. Tell me what's your what do you want to do? Oh well, I have this thing and I think AI is here. So just the exercise of reimagining was my my let's say my reflection with them. But what do you need to do the same? Because if we had the AI, we could just create this engine that replaces all the seven steps. You just need one. Like what do you need to manufacture? Imagine if this is manufacturing for for uh the pieces for an engine. What do you need to manufacture all of this if you can, let's say, redesign and create just one, reduce it to simple things. And I think that's the the the human ingenuity, right? The human intention.

SPEAKER_01 20:21

Yeah. This is also very much also courage as well. This is where, for example, leadership um is most effective, impactful, maybe, when you actually take courageous decisions, when you have a compelling vision for the future. Because if you're just talking about AI in your organization as, hey, it can help you write faster emails or more polite emails, or it can check some documents for you, people go, oh, that sounds okay. But if you until you fundamentally rethink, how might this reimagine not only how we work, but who we are, I mean, I think that's where people start really leaning into the idea of our fifth signal being the human um the human premium.

SPEAKER_00 21:02

So combining with combining with the fact that you can visualize things faster than ever. Right. The c the cost to see things tangible has reduced. So you don't have to wait six months, four months, uh one a year to see a prototype. If you're in BC, you're not waiting, you know, you should not be waiting months to see a prototype from your from your uh the teams that are pitching to you. If you have an idea, the courage, the courage becomes uh, let's say, easier to to let's say to keep. It it becomes easier to keep the momentum because the risk is reduced by the accelerated execution. Right? You can contain it, analyze it, validate it, prove it, and move forward. Right. The risk of uh what are you going to lose is less is what if we're right. Right.

SPEAKER_01 21:51

And I think this is the the exact moment to circle back and think about your own personal context. So, of course, when we think about these. The five signals, there's a lot, there's a lot going on. But the number one thing that we always say in human-centered AI is what's your problem? What are you trying to do? What's your context? So when you're reading the report, really reflect on which signal is applying most to me. And then also think about, you know, even in terms of the maturity, where are you? Are you starting? Are you building? Are you scaling? And I think having these, um having the report, being able to triangulate your position through what's what's the signal, where am I at on my maturity, and also if the signal is even um recalibrating or even accelerating. So being able to identify where you are and what's really relevant to you, I find personally really helpful. And it really just begins with that very simple step of what's my problem? Because you may look at the signals and think, oh my gosh, how does you know the physical context apply to me? But there's some really great examples and some really great case studies. So my number one recommendation to people is to reflect not on just the signals just as a whole, but think about you, your business. Where are you going? Where do you want to go? Who do you want to be?

SPEAKER_00 23:06

So maybe it's a good opportunity to remind the viewers. Yeah, there's uh resources. One resource is the uh feature signals report, and every signal points to the same the same direction. When execution becomes abundant, intent becomes scarce. Other resources, of course, can be found in the Scientist Japan website, like uh AI custom development development, like uh could be accelerated, but also the academy to have bigger conversations about this.

SPEAKER_01 23:37

Yes, yes, exactly.

SPEAKER_00 23:38

Maybe you should say that or me.

SPEAKER_01 23:40

No, I mean you said it already, so it's fine. Yeah, so I think it's a good thing.

SPEAKER_00 23:42

No, you say it better. I think you could say it.

SPEAKER_01 23:44

Hang on a second. Um Right, which is also um uh let me just take a minute to remind everybody that uh we always we always say um at at DTJ that AI is a marathon. It feels like a sprint, but it is most definitely not a sprint. It is most certainly a marathon.

SPEAKER_00 24:00

George Hoots had a good point. What did he say? George Hoots. Uh actually you mentioned it. That he was saying like uh there's a lot of of uh messages out there, like these influencers. I was building with 400.

SPEAKER_01 24:14

Yeah, so I was reading, so it was George Hoots' LinkedIn, which I thought was interesting that he has a LinkedIn and he like it wasn't on Twitter or it wasn't his GitHub or something like a little bit more happy.

SPEAKER_00 24:22

I love going to Link Link Thing and and important things.

SPEAKER_01 24:26

Yeah, so I was looking at one of his LinkedIn posts, and and one of them said, I'm so sick of like waking up in the morning and somebody saying, I've just I had my 37 agents work overnight and you know now I'm doing whatever, like you know, 10 million, you know, uh monthly, you know, recurring revenue or something like that.

SPEAKER_00 24:44

And he said, And what were you doing?

SPEAKER_01 24:46

And the point was he said, and what were you doing? Yeah, and what were you what were you doing? Like again.

SPEAKER_00 24:50

Having breakfast.

SPEAKER_01 24:51

Exactly. And then what were you guys doing while my you know 37 agents were making me these millions of dollars? Well, you were just having your breakfast like a pleb. And I laughed because I thought that's very much kind of what it feels like that you have this absolute disconnect between some people that are kind of making more noise around AI.

SPEAKER_00 25:09

They're actually doing.

SPEAKER_01 25:11

Exactly. And then his point was you people are gonna get found out.

SPEAKER_00 25:15

The people that are saying, I am doing all this with 400 agents, maybe you don't need 400, you just need a few ones well, well equipped. So remember, this is the era when you have startups that were built with five people, the one billion, the one billion dollar company, the one person billion dollar company is not about having 300 agents. It's having the right amount of technology. Agentic, biology, robotic, uh in in terms of uh uh let me try again. It's it's about having the right amount of of technology. It's it's having the right um uh amount of of resources and technology, agentic, biological, like the in terms of frontiers, in terms of of uh of uh photos. Let me try again. Let me try again.

SPEAKER_01 25:58

You go go in go in order. You have to go frontier clinical and physical.

SPEAKER_00 26:02

Yeah, well it's it's not about having like the this vast amount of of those, it's having them in the right moment in terms of agentic, in terms of the frontiers, in terms of a biology, in terms of the robotics, in terms of the physical context, not just robotics. The physical context and in terms of the people who's wielding it. What are the right amount and the good intent that you're going to do? That that means that you have an unstoppable force. Not only 400 agents to the one, I don't know, just to be an influencer on LinkedIn on Twitter.

SPEAKER_01 26:32

Yes, exactly. And so I think this is a another moment. Um if this is something that r uh resonates with you, this kind of you know conversation around AI, um stay with us. Of course, we have our uh human-centered AI series, um, which is always free. Um if you're in Tokyo, drop by, um, come and uh come and visit us. We also have our academy, which is both remote and online now, um, where we develop an executive level fluency in people. And I think this is really interesting because we have lots of people that are sitting on on budgets, on resources, and they're thinking, I really want to make a good decision. You know, I want to make a good decision. Where do I invest? How do I invest?

SPEAKER_00 27:09

I don't like you say sitting on budgets. Feel like a very like a like you want to remove the budget from them. Yes, but that's a consequence.

SPEAKER_01 27:17

So we have uh and so for example with our academy, it's really focused on executive fluency, which I really like because it's not fake fluency, it's actual technical fluency. You will be able to then identify what does an AI stack look like. Uh it's not just kind of fake fluency, like it's actual real fluency, which I think is really cool.

SPEAKER_00 27:36

It's a strategic fluency for decision making.

SPEAKER_01 27:39

Exactly, exactly. Knowing what to build, right? That's all what it's all about. And then of course you can come, you can call us anytime that you need any also software builds as or any AI application builds as well. So with that, this was our uh 2026 Signals. Uh we are very proud of this work. I think it is for me, huh?

SPEAKER_00 27:58

Uh nice fact uh during this week, there's been demand of downloads. I have already passed the downloads that we can do. From last year, yeah.

SPEAKER_01 28:09

Yeah, exactly. Um, yeah, yeah, which which is um really exciting. So definitely go over and and check it out. And the thing that I like really a lot is um how many people jump to the methodology that they're like, I want to check out your GitHub. I didn't think that would be like as interesting to people, but there are plenty of people that are like start from the GitHub and they're like, give me the technical architecture first, um, which I think is also cool. I mean, but there's there's either way. So um, no, we are here anytime that you are looking for a more signal than noise, come to us. This is where you're gonna find it, and we will see you in our next episode. Thank you. Thanks, everyone.