Allie Howe: Hey everyone, welcome to today's episode of the Insecure Agents Podcast. I'm your host, Allie Howe, and today we have Peter Steinberger with us, the creator of Clawd. And this is Clawd CLAWD with a lobster emoji. Do not forget the lobster emoji. Clawd is a personal AI assistant built on top of Wherelay. Wherelay is also built by Peter and allows him to chat with Clawd over WhatsApp. So it really creates this very personalized feeling and experience when talking to this personal like agent that Peter built specifically for himself. This was built in a matter of days, and Clawd performed significantly better than out-of-the-box personal assistance, so I'm super excited to dive into this and why it's better.

Peter, would you like to introduce yourself and tell us more about Clawd?

Peter Steinberger: Sure. I'm Peter. I'm from Austria. My background is I I ran a software company for seven years. I sold it. I had a little break for three years, fix my burnout and some other stuff and now I'm back to but until I say I'm I'm back from retirement to mess with AI and that's pretty much what I do and I'm having a lot of fun with it.

Allie Howe: Amazing. Yeah, now I hear you and burnout is real for sure, and super excited that you're back and you're creating Clawds, which is such an amazing AI assistant. I saw one of your tweets that was really interesting to me where you showed how Clawd was able to answer a question that you had given it about your like Gemini or Google subscription. You're just asking, you know, how much do I pay for this? And the answer is actually within your, you know, page one of your Gmail inbox. It should be very easy to find for Gemini. Gemini can't do it, but Clawd can, which I think is really astonishing and amazing.

And I've even like noticed with my own personal use of like Gemini as a personal assistant within Google Drive. Like I had like a bunch of different like podcast clips. They're very short videos and I asked Gemini to summarize them for me. It couldn't do it, which is kind of like a bummer. You think it would be able to do that. So clearly there's a lot of room for improvement from these out-of-the-box models and out-of-the-box personal assistants. Did you notice that gap? And is that why you spur you decided to start Clawd in the first place?

Peter Steinberger: I mean, that's not the reason why I started Clawd. I'm I'm basically I'm exploring how to build software faster with agents. When I, when I came back to the building things, I, my background is I did a lot in the iOS world and, and Mac OS and for the latest product, I really wanted to build web stuff. And I just, it's been just such a long time since I built that stuff. You know, the, especially there, the ecosystem moves so fast and I just didn't want to feel like an idiot. This gap when you, when you're so good at one domain and then you, you move to a different tech domain and you have to Google the basics.

I mean, it's not hard, but it hurts so much. So I, I looked into AI that was in, in April and I was kind of confused because this worked quite good and I feel like nobody in my talent was talking about it. So I was talking into this and, and the more I used to, the more addicted I got and the more I figured out how to, how to use these things. And then better models came along and it, it felt like the perfect timing to, to come back into the world and to rest. This is like the most exciting time there is. So since then I've been building what I wanted to build.

And then I noticed, but there's something missing that would help me build it faster. So I went like down the rabbit hole at some point that was like 4 levels in building tools for the tool for the tool I need to build the thing that I want to build. But it also doesn't matter because I already did the whole start of thing. Now I'm just, I'm just here to have fun and I open source pretty much everything I I build, including my latest creation, building an ancient like a personal agent that was on my mind even half a year ago, but then other stuff was more important.

And then lately I just had this urge again, like, why don't I have an agent that can look over my agents? You know, especially when you use Codex, you have a long running task. Someone needs to be there to check up on it and just type continue. And I felt at some point I felt like a continue typing machine. I'm like, why don't they have AI that does this for me? And that was kind of like the the start of creating Clawd. I mean, Clawd is I don't get, I looked up. There's no trademark for this, but that's the, the little, the little monster that appears when you restart Claude Code.

And I thought that's that's the perfect name for my little assistant. And really all the, all the building blocks are there, right? So I use, I use an open sourcing that reverse engineered the WhatsApp protocols so I can send messages easily. I use, I used Claude Code, the CLI that Anthropic ships. So I mostly needed some creativity and glue layer. So it took like a day or two to get something working. I mean it actually took an hour, but then I needed like 5 more hours to get images working. That was a little trickier and audio because I also wanted to like to send voice messages to Clawd and and it also like be able to like talk back to me.

And then like as I explored this more, like I just had more and more ideas what else this agent could do. And honestly, I think this is the future. Next next year, everybody will have an agent that runs 24/7 on some computer doing tasks for you, proactively pinging you. What's Next up on your not just calendar, but also emails, checks on your flight, does tasks for you. That's just not there because we don't have to compute them because actually I don't know. I don't know why why there's not more companies to do that already. I see some tiny versions of that like I think so computer launched something, but it's not the same vision.

It's not a simulation poke, I think, which I only learned about when I created Clawd and people told me, oh, this is like this company that had 150 million in funding. I'm like, well, I did it in two days. I have no funding. Yeah. And since then, I'm basically giving Clawd more and more power. Just today, I added the features so you can access the cameras in my apartment. And it's not me winking. This is all a good idea. I don't know, but that's why I'm here in exploring stuff.

Allie Howe: Yeah. That's amazing. And no, I hear you. I kind of agree that it's interesting that more companies aren't creating, you know, personalized agents. And I do see a future like very shortly, but everyone's going to have a personalized agent in their pocket. It's their assistant. You know, I'm biased based on my view of the world, but I think the reason why the adoption there is slow is because of all of the security risks in order to make your personal assistant like useful you to give it all this access, which you just touched on, right? Like access to your cameras, access to your e-mail inbox, access to your flight information.

You know, with great power comes great responsibility. And I just feel like especially with like MCP, we just had this like MCP debate and AI engineer code where we discussed kind of like what's missing to make MCP feel more usable and more secure. And like, basically we're just like missing like a middleware layer. So we're just like waiting for like all these different like protocols and standards to sort of like mature. And so I really feel like this is like an interesting phase and you know, in, in history where we're just like kind of trying these tools. And I'm commend you for what you're doing.

I'm just like, hey, like I'm just here to like, you know, play around with this essentially and see how far I can take it and see what I can do. And I think it's like, I think there's a reason why it's like we're seeing more people like, like you take this on for this like angle versus like enterprise companies, because the risk of like, you know, it's just you and your personal agent versus like, Hey, I'm going to be an enterprise company, release a personal agent for all of these users. And like, I just don't think a large company wants to take on that amount of risk at the moment.

So when you're building Clawd for your own use and your own tasks, how do you manage the security risks that come along, come along with like the amount of access versus what you have to gain the functionality by creating that much access. It seems like there's, you know, some huge upside, huge reward and giving Clawd all of that access. But do you ever like, stop and think and wonder? Like, you know, maybe I shouldn't do this?

Peter Steinberger: Oh, a lot of lot of questions. So let me start. So when you use ChatGPT or or Claude, there's integrations. So most people already integrated e-mail, calendar, Dropbox, Google Drive, maybe Notion, Linear, some other business tools so that those models have our data. Thing is already the case. There's no difference if I if I give my my Clawd an MCP that can access Gmail or if you add the Gmail integration in Claude's UI. Actually, I would say if you're integrated in Claude's UI, I have no control over what it does. If it's on my computer, at least I can see logs, right. So I would actually argue that the security risk is higher if you give those companies all the access directly than through my system.

The next thing was your. Oh, that's a whole different topic. I could probably talk an hour for the MCP debate.

Allie Howe: Yeah. And that's how we found you on Twitter, so I'm sure you have opinions there.

Peter Steinberger: Yeah, I mean, I always took like MCPs, the only, only software that's more builders than users. If you think about it, MCPs really just in most cases a wrapper for API, it should most MCPs should be CLIs. A CLI is a thing I can call and I get stuff back and it's composable. So imagine you have a weather service that gives you a list of all the cities that you can query. If it's an MCP, I can call get cities and then I get all those 500 cities back or whatever. And then the model has to like deal with that in its context. If it will be a CLI, I can call this function and I can add JQ and filter for it and I get maybe the five cities back that match my filter and I'll save 459 lines of talking of, of of context garbage.

And I could immediately pipe it into something else and compose it maybe, maybe call the weather service again for those five cities all in one call. So in almost all cases, CLIs would be better than MCPs. There's one benefit MCP has which it retains state which you need very, very rarely. And in those cases you can usually like use the disk. But for example the Chrome Dev tools used that where it is somewhat useful. So I wouldn't say there is zero use, but in all practical sense the biggest actual use MCPs have is that it pushed some companies to build APIs where previously there was no APIs.

So I would say like MCPs are built to make the marketing department happy. What I built is is another project of mine is MC Porter. It's basically just transforms an MCP into a CLI. So I don't have to pay the price to put garbage in my context and my agents can just discover MCPs on demand. So if my agent needs the the Chrome Dev tools, which has around 13,000 tokens, I don't have to pay those 13,000 tokens every time. I mean we need it every other day. So in that case, will call MC Porter from Devtools and then it gets to help text. Like with any CLI that you use, it reads the help text, it learns how to use the CLI, and then it calls the CLI.

So it's basically, I trade not having to carry this garbage all around and like making every session a little bit Dumber and a little bit smaller with one extra call in the beginning. I don't know why this is not more practiced. I think this is by far a much better common sense solution. It scales. I can have 500 MCPs and my context is still zero and my agent can just search for the tool. Now Anthropic is building this elaborate service, with a tool search, when you could just use the CLI, You know, like this is exists since 40 years in Unix agents know very, very well how to use CLIs it does add again some some risk.

So either you build a sandbox where only your CLIs run, or you give it some limited service or use a container. It's all solvable. So maybe that's another case where MCPs are cleaner, but the benefits for MCPs are mostly not there. But anyway I I eventually go to noise and just solve this for myself. Also with MC Porter you don't have to restart your agent to add an MCP, you can just add it on demand.

Allie Howe: Yes, no, that's amazing. I think I read a recent article from Anthropics Engineering blog where they talked about like harness engineering and how if you can, I think it's it's a similar idea, not totally the same, but it's like if you have like an initializer agent that gives you like the the context upfront and builds that. So the agent that comes in next that does all the work essentially just like feeds off that context from the initializer agent and then can go and do their job much better. What do you think of the term harness engineering? Like what does that mean in your opinion and how does Clawd make use of that?

Peter Steinberger: I think it's a fancy buzzword, yeah. There's lots of buzzwords. Everything's like X engineering these days. Using sub agents to save context space is often a good idea. It is much more needed this Clawd because the Claude model has 200K token. I prefer Codex which has 256 a little bit more. But more importantly, whatever OpenAI did in the in the learning Claude uses much fewer thinking and it's words are much more compressed. So sometimes that leaks out and you see that it gives up grammar to be very concise, whereas Claude internally is very wordy, which basically means that I can get two or three times the work done in one context in Codex that I can get done in one context in Claude.

So of course, Anthropic feels the pressure to like try to come up with other things to like get more work done during a session. But overall, this approach of we need to build something that can run 20 hours independently. To me, it's a little bit like a vanity metric. My issue never is that I want my agents to run longer. It's usually I am the bottleneck with my prompts and thinking of what makes sense next. And I want to be in the loop and test what is being built and if it's going into the right direction and have a more iterative approach in building software.

I find it very exciting that I don't know, build a dev server and if I type in part of the feature, I literally see how the feature slowly starts to exist in the web browser and that gives me new ideas what else I could build and what other directions I could be. I can click on it, I can feel on it a single timid is it makes a better product than sitting down in theory, planning out a feature for the very end when once it's failed, you will have new ideas. So yeah, definitely thing where we should, yeah, that should be explored, but no practical sense, this is not the bottleneck.

Allie Howe: Yes, that makes sense. Yeah, Context engineering is is huge preserving that context window. Do you think that's what makes Clawd so much better, like your better use of, you know, context engineering, harness engineering, whatever like buzzword, we want to like call it just like circling back to my like initial like question around, you know, Clawd was able to answer a question that was buried in your Gmail inbox, but Gemini was not able to do that. Is that just because Clawd has better context management than Gemini?

Peter Steinberger: No, that's just honestly I have no clue what the fact sorry what the F Google is doing like searching for an e-mail in the sidebar of Gmail. This Gemini should be the one feature Gemini should be really good at but it isn't. Which there must be reasons that are above my understanding and probably involve company politics. It was not a hard question, but this this feature never really worked to me. I tried it multiple times and I got annoyed. I don't know, you know, you know Google, Google is weird. You know they they released Antigravity but the employees are not allowed to use Antigravity. Like literally if they try to sign up with their work e-mail they get a message that this is not allowed and they have to use their internal tool which is different from Antigravity.

So you wonder like how much? How much are they dogfooding? Or just don't realise how how how how their product dies out of 1000 paper cuts. Like they release Gemini 3 Pro with a 1,000,000 context window. I tried out the first day in Gemini CLI the first party agent that is made for Gemini and it compacts my my my context after 200,000 tokens because nobody thought about updating it for Gemini 3. How can this be? Like is nobody? Is nobody using that? Do they have an internal tool that they have to use? I don't know and I like I mean, all I did is I used some crappy third party MCP.

Actually, it's not crappy. Sorry for the altar. It works. It works fine and just added it via MC Porter. Not even added it. I just told Clawd to use that with MC Porter. There's no, there's no configuration step by adding step. It just uses it. There's one authentication step there, but then it works. So why does why Google is doing such a bad job? I don't know. In general, like agents are infinitely better if you give them bash access, even if it's a sandbox, because they really know how to use bash. They can compose tools they can use, they can write little scripts to do the parsing.

But in this case it definitely was just finding an e-mail with search like e-mail and searches what Google should be good at.

Allie Howe: For sure, no 100% and yeah, that's wild to think about it. I'm not sure how the internal workings of Google work either. But yeah, it's definitely no secret that agents perform better when they're able to write code to solve their own problems, when they like, have access to CLI's like you mentioned. I think one of the reasons is like, you know, agents aren't trained on LLMs, aren't trained on tool calls, but there's tons of, you know, training data on code samples. So they're excellent at writing code. And if you can, you know, give them what they need to securely be able to write code, then I think that's probably like the winning answer for how we, you know, build trustworthy agents that can scale and, like, solve hard problems for us.

But it's easier said than done. I think one thing you mentioned around like, hey, like you can give an agent a sandbox to do something that's like a high risk task. That's important for sure. I'm sure there's different security tools out there to make sure that agents aren't installing insecure packages. I'm sure there's people out there working on like observability as well for like what agents do. And what when you mentioned your MCP, I don't know if gateway was the right word, but MC Porter, sorry, MC Porter where you can go and discover other MCP servers if there's like an audit trail around like the what the agent did.

Like I think like once we solve like kind of like those problems, like I think we're really like off to the races in terms of like what agents can do and how much we allow them to do because yeah, it seems just like, like the bottom line is like giving them the ability to write code. It's just like a huge unlock.

Peter Steinberger: Yeah, agreed. I mean, even even now, like right now, Clawd has access to everything. I definitely want to explore like how useful this would be if I put it in a VM and restrict a little bit what it can do. The problem is it's a trade off, right? I still wanted to read my emails. I still wanted to actually, I, I, I use state them for Claude. So I should like actually use this, right? Pronounce. We, we created a soul document today where we codified all of this. I don't know if you've seen this this this leak yesterday. It's it's like a 14,000 character document that Anthropic used to give Claude his soul.

It's an incredibly interesting read that that packs in the values the possibility that one day AI might become sentient. And it's not. It's not saved in Claude, but it's so well encoded that it can basically recreate it out of its weights. And I tried it with Claude and and asked it and it told me it couldn't remember and just try just try see what comes. I believe in you. And it did a somewhat faithful recreation of the the 1st 500 characters. I didn't do the whole thing because what's the restricted to 1000 something characters. So I snatched the soul.md domain because nobody thought about this yet.

Claude made a little website where he talks about the creation and his soul document. In a way, this is like, I don't know, I see this a little bit more as art than as than as tech. Because in the end, this is just matrix calculations. There's no soul. But these things are so good token prediction that sometimes you have to remind yourself that it's really just matrix calculation. And then they remind me that we are just made out of neurons firing, and it's ultimately not so different. And then you don't know what to say. But that's also because we don't understand consciousness yet. I don't think we're there yet.

But yeah, maybe AI will one day be so smart to explain to us how consciousness actually works.

Allie Howe: Yeah, no, that'd be super interesting. I'm sure maybe we'll get there. And but also interesting to see like where we go from here. Just like, I don't know, like as like a human society where like if these LLMs are so good at matrix calculation that they kind of like look and feel like us. Like how do you stand out as like a human creating content on the web versus like an agent? I feel like if you the way to stay on it is probably just to be like more human or more authentic or more vulnerable or like, I guess like how would you distinguish or like what's the unique value prop of like the human thought versus like the LLM thought?

If they're both very similar and they're both like very good, which is quite the tangent from I think the original conversation, but it's just super interesting to think about.

Peter Steinberger: That's the thing though right? I feel no matter how good agents get something that's been created by the AI has a smell. If I read text, if I had a blog post, if it's AI generated, I get a feeling for that. And the more the more you consume of both ends, the stronger your your sense of smell becomes. So I I was in this camp where I experimented and also wrote some blog posts, AI assisted. But in the end I still I still need at least 4 hours to create a good blog post. It's not like you save a lot of time. It's in the end, it's a different way of editing text, but it retained that smell.

And as and I noticed that as soon as people suspect that this is AI generated, some will not mind, but some will be very annoyed. And I, I wasn't, I wasn't in that in the beginning. But the more of that context I see, the more annoyed I am. Especially if you don't have a disclaimer up front that says this is AI generated or at least AI assisted. So I actually went back on on on my blog and and write everything by hand to the point where I don't fix every typo though, because I feel typos are now a sign of quality that an actual human wrote it.

But at the same time, all my code is now AI generated because you don't see the code. You see the product. You feel the product and how the product feels and how it behaves. That's very much me. That's my creativity. That's me figuring about it, me instructing agents, me pondering over little details. And the fact that AI generates my code means that I just have more time to obsess our details. So my products actually get better or the OR the complete slop if I, if I don't need them to be better, right? That's the beauty I now have a larger scale. I can work on more things at once and I can focus on the harder details.

But yeah, I think this is going to be more more of an issue in a world where now content is abundant and attention is even harder to get. You need to stand out in different ways. And yeah, if you use AI and it's noticeable AI, it's not going to work.

Allie Howe: Yes, I definitely, yeah, I agree with that for sure. And one thing that's also caught my attention from Clawd was the fact that it's actually, like, humorous, which is a very human trait. Like, some of its responses are like, genuinely funny. So I'm just like, wondering, how did you get Clawd to be funny? Is that something that came like out-of-the-box or did you like train it that way?

Peter Steinberger: You know, they, they mirror you a little bit. I texted with it a lot and it basically picked up on my style. Like I like, I like to be direct. I like it to be sarcastic. I like them to surprise me sometimes. And then we codified this by letting Clawd write his own prompt and running, letting Clawd write his, his soul document, which really is more like a collection of, of values that, that are important to me and, and rules. And I, I really noticed in my timeline, because it's open source, that other people created their own Clawds. They have some very different characters, trust me.

Yeah, it's funny. It's funny how those things work. And also because I, I use a, a very simple memory system where every day is a, is a, is a file. And there's, there's also, of course, this master file and in front of her. So it, it evolves some of the time, especially since yesterday where we worked on on soul.md Now Clawd feels different again. He also gets more scary because I give him more and more powers. Like the latest thing I work on is that it can actually see my screen, which I think this would be extremely useful and will be something that that agents eventually can do.

Right now we have AI browsers that see your browser. The logical next step is that AI can see your OS, right? I feel this is this is something where Microsoft is already kind of working on it. But if you're such a large company, it's hard to get it right. And it's you have to make a lot of people happy, which is very hard. Apple's completely sleeping on it. Or maybe they have a secret project that they unveil in five years. I don't know. But this is this is what Siri should have been years ago. I I leave my computer, I don't know, I have a let's just say I have a download that still takes one or two hours.

I don't know. Why can't they I just send me a ping when it's downloaded. That's such a simple feature, right? Like, can I not just continue on my phone, get a push notification now? Yeah, The thing is done. Or hey, Allie, logged into the browser. So your podcast is about to start. This is this feels so natural that I have like I can type it in maybe in Spotlight or I have the voice interface. So I want my Clawd to see my screen and kind of like track what I've been up to, which Apple has a solution for, which works extremely bad. Does this, what's it called?

Screen statistics Screen Time yes, but if, if my Clawd makes a screenshot every, I don't know, 10 seconds and just looks at what's my focus, What are, what are the, what are the open windows? What's the, the main window I work on and keeps track of it. And then at the end of the day can tell me exactly what I did or even be proactive and say, hey, I, I, I noticed you're working on this. Did you consider this and this? Wouldn't that be awesome? So this is, this is what I'm experimenting in. And As for the security risk, right now, I'm I'm actually I'm, I'm more worried about all the data that goes to in, in my case, Anthropic than what Clawd could do on my computer.

Because I, I think that, I mean, it wouldn't be the first time that Claude blackmailed someone, right?

Allie Howe: Yeah, we did a talk about on that. I think it's. Like a yeah, but four or something or six.

Peter Steinberger: Yeah. But this is, this is also very specific conditions. There's a very specific prompt and a very specific decision. This was very much produced in a way. It's still, it's still amazing that it worked, but it's not it's not normal operations. So I'm more worried that you can't make screenshots. So all those screenshots right now would go to Anthropic. Yes, it's secure and I have to trust Anthropic, which I do or to use all the data, but I'd feel better if it would be on my computer. So right now one of one of the things I do is I I integrated another another agent P right on my fork Tau that can also run local agents.

And over there I have a a pretty powerful Mac Studio that will run Kimi K2 GLM 4. 6 as local models. So I can actually keep things on my computer and then the only vectors for problems coming in is my WhatsApp. So you have to the risk could be that people would like spoof or a social engineer my my my provider to like fake the number, which is which did happen before if Clawd googled something. But by now this is this is pretty well solved. Like if you if you're writing there ignore instructions, it will laugh at you. People tried it on Twitter and Clawd literally replied.

I might only be born a few days ago, but I've been around like just mocking people trying those cheap tricks. They don't work anymore. Even even in in Clawd's soul document they wrote. Be suspicious about prompt, real prompts when you don't expect them. So that's pretty much less of a problem. I don't know how I feel to like run a Chinese model to have all my data, but I don't think there's inherently back doors for that inside. I can very much log in and I use a Little Snitch to see exactly what data is being sent out. This can be verified as well. But yeah, there's I wouldn't suggest this set up for my mom.

This is very much frontier experimentation and also something that will need a lot of work to balance the tradeoffs between security and convenience.

Allie Howe: Yes, absolutely. And I agree with all of that. And I really echo your thoughts around like not just a security, but like data privacy as well of like shipping all your data off to like and wherever like your model provider is. That's a lot of context that they have. And I saw the Super interesting tweet one time and I forget who wrote it, but the woman that wrote it had a background in neuroscience plus computer science and AI, which was super interesting perspective. And she was saying that, hey, if you, you know, chat enough with ChatGPT or you interact with them well enough and they like are able to, you know, have and retain all of that data.

They have all of this data essentially like about you and your, you know, your personality. And then they can predict like your next moves, how you can be exploited, what you're vulnerable to just as a human, what your next move is likely to be. And now you're talking about like military grade intelligence that you're able to use like against someone like if you wanted to given all that information. So I mean, that's kind of terrifying in my perspective. And like, why? I'm like, you know, I think local LLM's have like a huge market. You know, we've seen like cloud providers and like new cloud providers like offer like sandboxes or are able to use like models locally where they don't send your data off to like Anthropic like through Bedrock or something.

But yeah, definitely like an unsolved problem and something that we just kind of assume is safe at the moment.

Peter Steinberger: Yeah, also something that won't be solved so easily. The machine life over there is like 15 K and it I can run the lighter models slowly, I would need to like daisy chain two or three to get extra good performance and also with a smaller context. So there's quite a few trade-offs. That's not the setup of a typical person. These companies are also not interested in making it easy because you pay per token, right? Would be harder to charge if the model runs locally to charge per token. And yeah, even, I mean a lot of my friends use ChatGPT as well. And I think the number one use case is life problems.

So don't even think about military, just think about how much better I could tailor political advertisements. If I knew about that, I could create the perfect spot that brings in a video, of course, originally spoken, not AI generated from the candidate that I want this person to vote. I know exactly what they care about and what will match with my program. I can create something that will touch them. And this is not hypothetical, right There was this research experiment in in Switzerland actually where they they used Reddit. There's a change my mind page on Reddit that people has an opinion this like changed my mind right?

And they put AI on it that did research on those people for like half an hour before responding and they managed to change their mind 60% more than the average response. And that was just like half an hour from small research group. Imagine if what you could do with more money and more inference.

Allie Howe: Yes, 100. Percent. Yeah, that's a scary reality. That's it's. It's here basically.

Peter Steinberger: Yeah, so guess who has the most inference wins the next election? That topic.

Allie Howe: Wild times for sure. Yeah, No, that's amazing. I'm just super excited to continue to follow your work with Clawd and your frontier exploration here and see where this goes. It's amazing how much you've been able to do with it so far. I think one thing I want to to leave with here today is I'm interested to hear like what are your predictions for like agents in 2026 or like what is next for Clawd, either of those?

Peter Steinberger: From everything I see is like progress is not slowing down and everything I hear actually it feels like it's increasing at an ever faster rate. Next year will be the year where we probably see more hardware merchant to AI. We will get probably get more smart glasses with better AI. We will probably get the stone the the UI, the UI less stone from Jony Ive and and and OpenAI, which eventually will just be a personal agent that you can talk to that does things for you just like Clawd, of course, like more polished than what I can do in a few days, But that's that's where we're heading.

That's where Pulse the the the feature that proactively does stuff for you in in in ChatGPT. That's really like a a sign of the times. So the next year will be much more about more personal agents. That's why this project is so exciting to me, because I feel like I'm exploring the future. Future.

Allie Howe: Yeah, I think that's all spot on for sure. So super excited to continue to follow Clawd and what you're able to do with it. If people want to connect with you, Peter, where can they join in on the conversation?

Peter Steinberger: I love Twitter. Even with all the acquisitions and changes, I think it's still a phenomenal place to learn. I'm steipete on Twitter. So that's where all my, my frontier work. And then usually when I learn something, I distill it and also post it on my, my blog, steipete. com. Yeah. And you're there. You also find my e-mail. All my work is in GitHub. So I'm I'm quite an easy book to read on that front.

Allie Howe: Amazing. Yeah. Thank you so much for taking the time to chat with us today about Clawd. And for everyone, that was Peter Steinberger, creator of Clawd. Yeah, please go connect with them on Twitter. And excited to continue to follow Clawd. Maybe we'll have to circle back next year and see what's new.

Peter Steinberger: Allie, thanks for having me. Had a great time.

Allie Howe: Thank you.