Claude Engineering Lead: "Stop Using Claude Like It’s 2024" | Felix Rieseberg

Felix Rieseberg: If all we're doing is, like, summarize this email for me, you're not really benefiting from the new intelligence.

Marina Mogilko: This is Felix Rieseberg. His team built Claude Cowork in about a week and a half. Now he leads engineering for Claude at Anthropic, and he doesn't write code anymore.

Felix Rieseberg: I'm just a conveyor belt for Claude. Claude needs to have access to all the crash data, and then needs to, like, figure out itself what it needs to fix, what it doesn't need to fix.

Marina Mogilko: Is there still anything you would not trust an AI agent with?

Felix Rieseberg: I rarely want the current session to judge itself. That I think is still really iffy.

Marina Mogilko: How do I protect myself from AI just confidently presenting mistakes to me?

Felix Rieseberg: It happens to a lot of us. I have, like, a very practical easy answer.

Marina Mogilko: Felix, thank you so much. I am very excited to have you as a user of Claude, like an avid user. We're using Claude for all the operations on this channel. Every team member has their own project for the things they're responsible for. And as someone who runs it... It's a pretty big team for a creator. I have 35 people, and I want them to be as efficient as possible. I want to produce as much as possible. But, also, something that I've noticed this year, we were able to launch, I think, three new social media channels. So we launched a newsletter. We launched LinkedIn a year and a half ago because, you know, it's text based. So AI has enabled me in many, many ways, and the goal of this conversation is to make me even more advanced and all of the viewers and all the listeners.

Felix Rieseberg: I mean, from what I've what I've seen you do, it seems like you're pretty advanced as it is.

Marina Mogilko: But there is no borderline, right, to to being as ambitious as possible?

Felix Rieseberg: That's true. That's true. Yeah. I think I'm discovering that about myself every single day. Like, I'm sort of... One of the most fun things of working on Claude by far is this notion of I built this product. And in comparison to my other products, with my other products, it was pretty clear to me what you're gonna use this for. Right? Like, the example I always go with is I worked on Slack, and, like, kinda I have a feeling of, like, what are you gonna do with this? The thing that's magical about working on Claude is that I see people really fly and do amazing things that I didn't consider possible. And it's like a... That's like for a product builder. That's such a treat, you know, to see people fly with this thing that you made.

Marina Mogilko: Is there something you've seen that's super ambitious and you're like, oh my god. I want to copy this.

Felix Rieseberg: I'm not sure about copying, but two days ago, there's there's... I think he's actually a product manager by trade. His name is Pavel. And he pointed Claude at a large amount of NASA data and asked Claude to, like, find them a new planet, and it did. So, apparently, it is not confirmed yet. NASA is working on actually confirming it. I'm a... On a little shaky ground here because I'm not an astrophysicist of any kind. But, apparently, Claude may have found a new planet we didn't know of before.

Marina Mogilko: This is fascinating. Okay.

Felix Rieseberg: And it's the kind of thing that I would have never considered. But if you hear the story of... I think for us who work maybe on more knowledge-worky things, right, the idea of, like, point AI at a large amount of data, and you're gonna find interesting things. That in, like, a... In, like, in isolation seems pretty reasonable. Right? Like, something we do every single day. But the idea of, like, looking at all of the public data we have about space and considering it a similar problem of, like, go and see if you find anything in here we haven't noticed before. So beautiful.

Marina Mogilko: Can you talk to me about your day? You helped build Claude Cowork, but you said you're not coding anymore. So what happens in the morning? What do you do? Is that, like, your agents are briefing you on what's coming, or what is it?

Felix Rieseberg: That is true. My agents do brief me a little bit on what's coming for the day. I've really leveled up in sort of altitude on the problems that I'm thinking about. Right? Like, sometimes sometimes I feel like these days, I'm I'm much more concerned with human emotion, empathy, trying to understand how people work in a way that I think I was less so as a as a classic engineering manager a couple of years ago. So I think for me, what I'm really discovering is that the amount of times I have to reach out to other professions to get their help has really dwindled down. And now this weird mix between, like, a product manager and engineering manager, just the traditional people manager, a data scientist. Right? To answer your question very specifically, most days, I'm I'm busy with a lot of prototyping. Right?

We just try a lot of things. We sort of, like, think about stuff. We sleep over it for a couple of nights, try to figure out if it's a good idea or a bad idea. And then we just go tell Claude to, like, build all the various variations and ideas we had, and then we need to do critique, try to figure out which ones of these have good ideas, which ones are bad. But I think prototyping is really what I spend most my day on.

Marina Mogilko: How do you decide what is a good idea? Like, what are the signals you're looking at?

Felix Rieseberg: This has been difficult for people for a long time now, and I'm not sure that I have all the answers to, like, what is a good idea, what is a bad idea. I think, generally speaking, the way we look at good or bad ideas, the calculus has changed. Right? Like, if you think about products we've built in the past for the last ten, fifteen years, we we always made implicit cost decisions. I would... Like, we we sort of had to figure out, okay. Especially for software. Can I build this? What is the maintenance cost? What is the ongoing cost? How much how much time do I invest in this? What is the opportunity cost? All of these decisions that engineering managers make to figure out, do I go and build this? Yes or no. And I think now we can fast forward to the end state much quicker and be like, alright.

Just build me a prototype. Let me play with this. Let's try it out. I think human attention is still rather limited. Let's say you, for instance, you have a lot of taste in AI. Right? Like, I can probably show you maybe one thing a day. I can easily build a thousand. I can't show you a thousand even if you're willing to sit down with me for all day. Like, at some point, your eyes are gonna glaze over, and you're like, okay.

I'm kinda done with this. So what what makes a good product? I think, especially now that we live in this AI world, there's this old saying that, like, perfect is sort of when you can't take anything away anymore and less so about, like, building a product that has every single feature under the sun. Like, we can make as many buttons as you want us to, but I think good products actually have fewer buttons. I kind of know whether or not a product really works if we put it in front of a few people and you see things click for them rather quickly, and they can do things that they couldn't do before. That's really the signal I'm looking for.

Marina Mogilko: And how do you think about the underlying problem that you're solving? So for example, if you're... Came to my company now, how would you think about the tasks that you would fully automate with AI versus just leave it to humans versus maybe just automating parts of it?

Felix Rieseberg: The first thing I wanna say is that I think about automation and augmentation in slightly different ways. Automation can be really empowering if you automate away tasks that sort of keep you from doing work that that you enjoy or if it's, like, expensive and automating it away, like, saves you a lot of saves you a lot of money or time. In those cases, automation is is useful, and I think it's really where AI currently probably provides most of the economic value. Right? Like, when we evaluate, is this worth it or not?

We usually look at automation. The area I find most interesting, though, and I think where people really do things that they weren't possible with AI before is augmentation. It's this idea of AI is helping you do things that you couldn't do by hand because maybe you're lacking skill or techniques or just the raw knowledge to do something. And when I go to a company, the thing that is often most impressive to me is less so what the tool does and more so how you change your workflow. Does that make any sense? Yeah. AI is an interesting product because to really harness it, you kind of have to rethink how you do things. You have to, like, think about your outcomes. You have to figure out what does good mean, what does bad mean, like, how do I evaluate whether or not this thing that I'm asking for, like, whether the result is good.

And the people who impress me the most are usually the ones who are very good at coming up with... Like, very good at reflecting on their own workflows.

Marina Mogilko: Yeah. But it's so hard. When I'm thinking about my work, for example, I know a lot of people use AI for drafting texts. Like, how do you define a good text? Like, is it still possible for, like, knowledge work? Because for coding, I guess, it's possible. Right? Because you know what you want. You don't want any bugs. But when it comes to knowledge work and, like, the result of human creativity, how do you even define what's good for the agent?

Felix Rieseberg: Yeah. It's such a tricky problem. Like, the the example I often go with is, let's say your job is to, like, report on the weather. Right? And you're like, agent, please figure out what the temperature is outside. There's, two ways to look at that because you have to rethink what good means. Right? Like, the model just guesses the temperature and is correct, that should not be a passing grade. That should be bad. We should, like, rethink what we're doing here.

But if the model is actually off by, like, half a degree or something, but it, like, really figured out the right location, figured out some reputable data sources, and, like, sort of the the the way it got there was reputable and good, that should actually be better than, like, a guessed good result, if that makes any sense. But for writing and, like, all the other outputs where you feel like a lot of taste is involved and it's, a little hard to, like, prove or verify whether or not the result is, like, better than another, it is tricky, but I think I really enjoy talking to very good writers who are also a little AI friendly.

Marina Mogilko: What do they say?

Felix Rieseberg: It is it is fascinating to watch them work. My friend Adam Davidson, who was a staff writer at a number of, like, pretty good publications, he has all of these various prompts for Claude where he's like, okay. Like, take my argument and put it to, like, magnify it by 100. Just mess with my text a little bit. Like... What are the things you disagree with the most? Like, what is the strongest counterargument? It's interesting because I think, to me as someone who's not a writer, I think the naive approach would be given a text, Claude, make this good. And I think what I'm discovering more and more is that for knowledge work, the secret sauce is, like, less than asking for a good output and sort of doing one step in between, which is the best in the field, how do they work? And can we automate those steps?

Marina Mogilko: This wouldn't kinda mimic their workflow, but still the taste isn't there. That's why I think you still need a lot of

Felix Rieseberg: Yeah. I think that's true. I think that's true. But the interesting thing is that if you talk to really good writers, most of them would disagree that the text is really good just because they have, like, the magic touch. They would often say, no. No. No. This is the result of hard work.

Marina Mogilko: Interesting.

Felix Rieseberg: Right? Like, it's this idea of, like, most artists have a process. And many of them would actually probably find it... I don't wanna say insulting, but, like, if you... The more I talk to artists and the more I explore this idea of, like, is your stuff just good because it comes natural to you and it just, like, happens to be good, or do you have a process that you, like, refined over the years? A lot of them are quite proud of their process. I'm like, man, this is very, very hard work.

Like, there's a lot, like, a lot of, like, manual individual steps going to making this good. It's not like I sit down and suddenly there's a book that's great. And if you go to, like, writer studios, right, there's, like, a lot of whiteboards and, like, a lot of information. And I'm I'm noticing this across a number of different domains where Claude is quite receptive to being told, not I want a result that looks like, uh-huh. But, Claude, I want you to take the same steps that a professional in this field would take. This is, like, very high-minded. Let me just bring this down a little bit. I I think we're currently in the public seeing this quite a bit with, like, motion video and 3D where I think Opus 5.5 has made a pretty big jump. But the big jump that the public has made is to just tell Claude to use Blender. Right?

Just like, Claude, please use the same tools that professional animators use. And now the results are quite good. Yeah. The unlock really is here in the media and the technique and the tools and less so, like, raw understanding of what makes a good animated movie.

Marina Mogilko: Let me pause here for a second because a lot of the work behind this podcast happens long before we hit record. For every guest, we already have a pretty heavy research process. Our internal Claude agent does the first pass, past interviews, articles, public posts, and the topics the guest always gets asked about and the angles people usually miss. Then my producer goes deeper. She watches full interviews, reads industry pieces, fact checks everything, and builds the first version of the episode. Then it comes to me. I do my own research from my point of view, and, usually, we go through the questions a few times before we record. That process gives us a lot of information, what people are actually saying about the topic right now. So recently, we added Dig AI to that workflow. It gives us a layer we didn't really have before.

The real human reaction around the topic, what people are asking, arguing about, and still trying to understand. Before an interview, we can look across similar creators and ask what questions keep showing up in comments but still aren't being answered. What parts of the conversation are suddenly growing? What are people confused about that nobody has explained well yet? And then we can go straight to the source.

Ask-dig scans through billions of videos monthly and sources the actual videos, the actual comments, and the language people are using so we can see the pattern for ourselves instead of relying on another summary. That helps us sharpen the questions before we record and avoid asking the same things every other podcast has already covered. So now we're not only looking at what the guest has said before. We also know what the audience still wants answered. If you do content marketing research, PR, and, honestly, anything where you need to understand what people are actually talking about, ask-dig is worth trying. The link is in the description, and we're back to the video.

But I wonder for people... Like, you mentioned this prompt, act as if you're a professional in this field. Right? And it's gonna go on the Internet and research, but a lot of information is just not there. How do you trust the output? Like, for example, if I want to start another medium or, like, do discover stars, right, and I have no idea about that, but I just tell it, act like a NASA researcher. How do I protect myself from AI just confidently presenting mistakes to me?

Felix Rieseberg: Yeah. That's a good point. I think it still happens to a lot of us. I I have I have, like, a very practical easy answer, which is adversarial reviews, which has proven quite effective in coding, math, and, like, various other areas where we can give you numerical results on, like, how effective it is. Adversarial reviews basically boil down to the idea of you have given given one Claude a task, spin off another ten that try to, like, break this down. Their whole task is to attack this and prove to you why it's wrong. It's essentially the same concept as red teaming or short selling.

Marina Mogilko: Are you using the same model for that, or you want to use another model?

Felix Rieseberg: Kinda depends on the task. This is a little context dependent, but, usually, the adversarial review, it is it is sometimes easy to find flaws than to create new things much like as it is with humans. Right? It's much easier for me to say whether or not the new phone is good. It's much easier for me to do that than to design a new phone from scratch. So, usually, you can, like, take a step down in intelligence and still get very good results. That's a very practical advice.

Marina Mogilko: How would you prompt that? Like, if there is a process already, what's the additional prompt that you add?

Felix Rieseberg: So you have some kind of output. Right? Like, let's say it's a slide deck or a document. Or in my case, it's... You talked about this blog post that I wrote this morning. I just tell Claude very... I'm I'm using Claude projects, which is this new product that we have that allows you to do work on multiple threads with a little bit more ease. And I basically tell Claude, please fire off five threads and task each of them with, like, proving that this this post is wrong, that I'm thinking about it the wrong way, that I have my facts wrong, that I misunderstood something. And then they go off with a given goal of, like, break this down, just, you know, prove that I'm wrong.

Marina Mogilko: How do you then deal with all the output? Because I I do the same, but I use... I just use different models when I'm prepping for the interview. Like, tell me if I'm digging too deep into this or that. Yeah. But then I'm giving so much different feedback, and I'm like, okay. Now I have to decide because this model gave me this. This gave me something else and gave... This gave me something completely different. How do I then decide what to use?

Felix Rieseberg: I I think you're gonna enjoy this because you can see where this is going. The answer is once again, Claude. What we use in coding context is something we call a judge, where you get the output from the people who are... Like, the the agents whose job it was to attack your output, and then, alright. Here's our main points of criticisms. And then you use yet another Claude, and their job is... Your your job is to validate and verify and, like, opinionate whether or not this is valid feedback. And then what you can sometimes do is that, ultimately, depending how you set up your workflow, the feedback that is valid should probably go back to the Claude that actually made the thing, and that Claude too... That agent too can be discerning in whether or not to act on feedback.

Right? Because sometimes... And we see this in a coding context fairly often. Also see it in, like, the knowledge work output that I make. Sometimes the feedback is, like, maybe reasonable, but has missed a point or is actually not that actionable or is not that useful. And that's usually when I know that my output is pretty good. That's one way. That's when you are really quite uncertain whether or not the output is good yourself and you can't easily check. But for a lot of a lot of things that I create with AI on the other hand, I actually don't need to fully understand the entire flow. Because as I mentioned, like, to me as a person, it is...

I do have opinions about almost anything I use every single day. I've remade my website with all of these 3D assets. I have no idea how to use Blender. I've never used it. But I can sort of judge whether or not this looks good, feels good, and that might be enough. Right? Like, you sort of put yourself much more in a client position. For so many other things in our life, we don't really have a good way of verifying whether or not we get a good output other than, like, giving feedback to the person we're using. Right? Like, for a contractor or a lawyer or, like, for many people, a doctor. Right? So you sort of just put it in one of these two buckets. Like, either you really have no idea what good's like good looks like. There, my answer is more AI. Just throw more tokens at it.

Or you do have an you do have an understanding what good looks like, and then maybe that's enough, and you don't really need to, like, have opinions about the process itself.

Marina Mogilko: Yeah. I think it it comes down to making sure you select only the top projects where you want to understand every single step instead of just want to control everything.

Felix Rieseberg: Yeah. Yeah. Because It's like the old problem of, like, sort of being willing to let go. Right? Like, how to not micromanage.

Marina Mogilko: Yeah. It's really hard. Comes with... Yeah. Now we have this problem with agents. Is there any knowledge work use that you have nailed with agents? Like, you completely trust them. They just go, I don't know. Maybe it's

Felix Rieseberg: Yeah. Fact checking. Like, editorial fact checking. I fully trust now the output. I get 100%. If I ask Claude to verify that all the facts that I'm stating in the slide deck and this presentation, it doesn't even need that many rounds. Right? Like, it's not like I have 50 agents doing that. I really trust one agent. I'm like, alright. I need you to find primary sources for every number I mentioned. I want you to verify that that is a good primary source. I fully trust it 100%.

Marina Mogilko: When you say agent, do you have a special agent for fact checking that you've trained, or it's just one agent that you're using for everything?

Felix Rieseberg: Just just Claude. Right? I feel like there's going to be a spicy take, which I think you'll like. I think we're kind of over rotating on this agents thing, and I can back this up with a little bit. Like, I think models are really good generalists, and we've seen this now time and time again.

Like, when we first started looking at math as an industry, I think the the first company really look at math, like, with with a very serious lens was Google DeepMind, and they trained a custom model and a custom harness. And they unleashed it on the Math Olympiad, and they got a silver medal, which at the time was state of the art. And then four, five months later, all the major generalist models come along and win a gold medal. I understand why people use agents. I have empathy because it's easier for us humans to rationalize what AI can do if we sort of, like, put on a little hat, like, you're a data scientist.

Right? But that same data scientist has all the intelligence as, like, a field leading biologist or mathematician or what have you. And I think we're we're sometimes limiting ourselves in our use of AI if we sort of force ourselves to first cut... Like, make up this cast of characters. So for a lot of these things that I just mentioned, I didn't really have any agents who are being told who they are.

Marina Mogilko: But does the data matter? Because for every agent, you upload certain dataset. Or just because your Claude has access to all your tools, it doesn't really matter what you feed to each particular agent. Exactly. Isn't it a lot of data for it to consider when answering each particular question?

Felix Rieseberg: That is true, but it's quite discerning in what kind of data it wants to use. Right? Like, I think the right approach here is not to make these slices of data and then give every single agent the slice of data that it wants. Because many of us, if we wanna be quite effective at our job, we do actually need the full view on the entire business. Right? We're not quite effective if we only get a small slice of data. Yeah. Like the... As an engineering manager, if you only give me code access, I'm probably gonna be much less useful to you as opposed to if I also have customer data, access to customer calls. Sometimes I do wanna sit in like a like a customer call, like, when we're trying to sell something.

I wanna have deep usage information. Ideally, I get business data about what is working, what isn't, and you want your agents to have the same range of data. Now how do you deal with the fact that it's probably a lot of data? Tools. You give them tools, and you document really well what kind of data is behind this tool so that they don't actually have to ingest the whole thing. Right? You're just like, if you do want business data, here's the tool to get business data.

Marina Mogilko: So when you're setting this up... Because I'm thinking, like, when you mentioned this, should I move away from all my projects that have access to all the, like, specific data? For example, I don't know. There's a project for ranking a guest, and we have the whole description of the process and then all the old scripts there so that we know how each guest performed. But now that you're saying this, does that mean that I should move away from that project? Tell my Claude, okay. We have... This is a folder with all the scripts. If you want to look at them in Notion, we have all the intelligence on how every podcast guest performed, and, this is how we rank guests. And just leave it there. And whenever I want to rank a guest, I just start a new chat without telling it anything and just tell it rank the guest, and it's gonna

Felix Rieseberg: Yeah. I'm careful to be prescriptive in workflows. Like, I always say, if it works for you, it works for you, I don't think you should change it.

Marina Mogilko: The problem is the models are getting better, and I want to get better. So this has been working for this year, but a year is a lot.

Felix Rieseberg: Yeah. Okay. Well, I mean, if if you're looking for the recommendation that I give for, like, what do I do... Again, everyone should do whatever makes them happy and comfortable and what makes them powerful. But it is true that the models have gotten so much better at tool calling and discerning tool calling. And tool calling is this process of, like, the model knows when to use a tool, how to use a tool, doesn't, get stuck in tool call loops, doesn't accidentally say, oh, I found the data tool call. Let me call this tool until there's no more data left. Right? Like, the models have gotten particularly good at efficiently and effectively using tools. And for that reason, I don't really make this a priori decision anymore on what kind of data do you need. It also allows me to, like, have more threads going and more sessions going without having to rely on it.

Marina Mogilko: Easier process. Much easier process. Do you still use skills?

Felix Rieseberg: We do use skills quite a bit still, although our official Anthropic guidance as well as my personal guidance is be really careful with skills. We often tell models things that were sort of relevant for, like, the last generation of models, which time is so compressed in AI. Right? The the half life of a good skill is very short. And sometimes what we see is if you completely remove the skill, you actually get better output.

Marina Mogilko: Yeah. I think Boris Cherny said that. Like, just forget about it. Just just prompt.

Felix Rieseberg: Just prompt and see what happens. Yeah. It is it is very effective. This is obviously most useful to people who have some kind of eval and have an idea of what good and bad looks like, but I'm pretty careful and very discerning in when when I use skills and when I don't.

Marina Mogilko: Okay.

Felix Rieseberg: Do you know you know what a what what a great way to think about this is? I like to consider the model experience as opposed to, like, user experience. And model experience is you pretend for a second that you're the model. You're Claude. Right? What kind of things would you want if you had to fulfill whatever task you're being given? And you very quickly discover that you actually do want access to the raw data. You want access to a computer. You probably wanna be able to install some software.

Marina Mogilko: Make some decisions myself without somebody telling me what they think already. I just want to see exactly what you said raw data and make my own decisions.

Felix Rieseberg: Like, I think the upfront cost is higher. Like, if you... Like, even a year and a half ago, I was in this world of, like, telling Claude, I found another crash. Please go and fix the crash. Right? But it very quickly dawned on me. I'm just a conveyor belt for Claude. Right? Like, I go look at our crashes, pick one up, move it over, drop it off in front of Claude. I'm like, please fix this. I'm like, okay. This is this is... No. I'm just gonna... Claude needs to have access to all the crash data, and then it's, like, figure out itself what it needs to fix, what it doesn't need to fix.

The upfront cost is really worth it, but but it's also, like, important to, I think, to consider that there is upfront cost. But once you set this up, it it opens up so many doors for you, and that's where I find a lot of, like, real hard value coming out of AI.

Marina Mogilko: If you've been hearing about AI agents and wondering how'd you actually set up one, I put together a guide that walks you through your first one in about twenty minutes. It helps you choose what to automate and get it working with no coding required. You can get it through my free newsletter. The link is below.

For someone who just... Who's listening to this conversation and who thought they would need, like, a major setup to start using AI correctly, would you say, if somebody's starting just today, they're using it for knowledge work. They don't need all these tone of voice things my business strategy. You just give it access to your email. It learns from it. From your tone of voice, you give access to your newsletters or whatever. So you don't need any of that setup anymore.

Felix Rieseberg: The best way to get started with Claude today, I think, is step one, download the desktop app. It has a built in browser where you can log Claude into some system you're using. And forget about connectors. Forget about, like, downloading all the data. Forget about, like, all kinds of things. Just let Claude use your computer a little bit, sort of give away the keyboard. That gets you so far and really shows you what works and what doesn't work. And then at some point, you might discover, oh, Claude is really helping me with whatever tool you're using, whatever internal or external tool you're using again. Once you see that a workflow really works for you, you might consider, okay. Can we give Claude something better than, like, having to manually click in this dashboard around? Can we give it some kind of connector? But just starting with a browser gets you so far.

Marina Mogilko: Okay. And when it... Just to wrap up with the agents, when you say agents, that don't... That means you have just basically chats. Right? And that's it. There there is no, like, specific agent who does research, no specific agent who does your tax returns.

Felix Rieseberg: No. No. I I mean, I do have projects. So I do think about I do think about the different work items, but I don't think about different roles. Right? So I don't... I I never tell Claude, you're an excellent whatever. Yeah. I think this was this was very en vogue sort of, like, 2024. We were like, Claude, you're an excellent engineer. Please go fix whatever problems are here.

Marina Mogilko: But you still use that in prompting now. Act like something that we started with. Act like a top whatever.

Felix Rieseberg: I specifically implore Claude to, like, tell me what tools someone on top of the field in this field would use. And then I'm like, I want you to use the same fields. A problem most models still have today is that if you simply tell it, data science is maybe a good example or, accounting. A lot of the models still have the problem that if you tell them to act like a very good accountant, that they engage in a little bit of role play, and role play is not really what we're going for. We want the techniques, the tools, the knowledge that a professional would use. We don't really want just the vocabulary. Right? Like, I don't I don't need Claude to sound like... I don't need to put on a costume. I actually want it to talk to me like I'm five, but I want it to use all the top tools in the field.

Marina Mogilko: Is there still anything you would not trust an agent with?

Felix Rieseberg: It's a tricky question. We can just keep layering more validation on top. So I think the question for me is much more... It's less about... It's just about what do I not trust Claude with and much more about, what things do I not trust one single session with. And I think the the list of things where I don't trust one single session is actually much larger than the list of, like, things I wouldn't trust Claude with. I rarely want the current session to judge itself. That I think is still really iffy. Like, the current Claude session that is running, wherever you have it in Claude Code, in Claude Cowork, in chat, and even in a project, the current session tends to be quite bad at being a being a judge of whether or not the work it did was quite good. So as much as I can, I try to find that outside signal.

And the outside signal in a pinch can be another Claude session that has proven to be quite effective. But my my preference is to find some hard outside evidence for what works and what doesn't work. And sometimes you need to, like, trust the process a little bit and give Claude a little bit of time. Like, for instance, in your field, kinda making this up, but I assume that maybe, like, the number of users, subscribers is, like, an important metric that you...

Marina Mogilko: The number of views, that's that's what we're... Whenever I give a task to Claude, I tell her, like, we need to hit this number of views. Go and try. But you... That... And that's that's something that we're still, like, struggling with is this feedback loop. Right? Yeah. Because it's me giving feedback to my team, and then my team actually going and posting something else, not what Claude gave us. It's like maybe 70% the same, but then the 30% that I've added is actually my taste. Yeah. How do you close that loop?

Felix Rieseberg: A thing that has worked for us is there's probably some kind of place where this feedback usually happens. Is it usually in person, or does it usually happen...

Marina Mogilko: For for all of us, it's in Telegram. There isn't a native Claude connection. We have a bot that scrapes every chat, but it's still very manual. I have to trigger Claude to look at the bot, look at the feedback. Yeah. And it's still not... It's it's not there. Even, like, looking at something we've sent. Right?

Felix Rieseberg: Yeah. Because we do we do obviously... Again, in a coding context, we have a lot of engineers who... You sort of know which engineer you want to review a certain thing that you're working on. And the kind of things that they look for, Claude is very good at figuring out from the feedback. It's pretty good at, like, given all of this feedback that I've read.

Marina Mogilko: Feedback on Slack. Right?

Felix Rieseberg: Yeah. On GitHub, Slack, wherever else the feedback might happen.

Marina Mogilko: And you didn't tell it to look at feedback specifically just because you gave it access to Slack. It's looking for feedback?

Felix Rieseberg: Yeah. That is much easier to do. Like, if it has the tools and you don't have to, like, as a human yourself, figure out, okay. Let me assemble a list of all the feedback I've been given. But if you do this, like, three step process of step one, assemble all the feedback you've ever given. Step two, tell Claude to, like, condense that down into a list of rules or principles that you care about. Sometimes it's useful to, like, then again do a third round of, like, okay. What are the principles that maybe you might believe but you don't actually know you believe? And then take that list as, an input for your ongoing reviews. That has that has been quite successful in our attempts to, I wanna say, like, specialize the feedback that individual sessions give.

That that has really worked. I think for for work that is a little different than raw coding or not very text based, I think the problem is still bigger, but I think similar things can be done, right, about, like, give me feedback on a customer call. How did that go? If you read the transcript or, like, maybe you're... I'm doing a lot of interviews for Anthropic, which is like a typical knowledge work problem, but also, like, quite interesting.

Marina Mogilko: But then it's teaching me. I want it to teach itself. And I think a lot of people who started using AI experience this. You want to do more. You see how productive you are. But you... I don't... There is no agent that I trust 100%. Every output goes through my team And then eventually through me. So I become the biggest bottleneck in the company.

Felix Rieseberg: Honestly, I feel like our products would do that for you pretty well today. I'm actually quite confident. Like, if Claude has the ability to watch you give this feedback for a while, like, the first thing you would probably do is you would sort of run it side by side. This is an experiment I recommend to almost everyone. It's, like, really fun to do. If you currently have a trust problem, you will probably not be super excited to immediately give over the reins, right, like, one day to the next. But sort of run it side by side. Like, you read something, you write down your feedback, and you do that for a few rounds. And then just to check what kind of feedback Claude would have given.

And, like, where do they differ, where are they the same? That also gives you something where you can actually sharpen the workflow a little bit because I think models will still be imperfect. Right? There's still gonna be things that... Where where the feedback is, like, a little different. And, like, even for most humans, if we take the same human and we ask them to review the same document, like, on a Monday versus a Friday, the feedback will be different. But this, like, side by side process is is rather powerful because it shows you so much.

Marina Mogilko: And how do I build this agent that talks to my team? Is it just something that, like, like a website where they upload stuff and it gives feedback?

Felix Rieseberg: I would recommend Claude Tag, which is Claude inside Slack.

Marina Mogilko: Uh-huh. And you build it. You give it access to my taste.

Felix Rieseberg: Yeah. It's like it's like currently our core multiplayer experience. Right? Where multiple people can talk to Claude at the same time.

Marina Mogilko: Yeah. Yeah. This is all getting very interesting when it comes to building things. Right? Because you can build so many things easy. What becomes valuable when you're building software? And I'm talking about, people who are beginner entrepreneurs. I'm thinking about myself. What is it? To... 2015, we came here with a booking platform for study abroad experiences. Amazing. Like, a moat was building this whole platform because we hired a lot of engineers. It was down... Hundreds of thousands of dollars. We automated stuff. Now I can do this in two days by myself. Yeah. Like, what is my entrepreneurial moat when you think about building software? Is there any?

Felix Rieseberg: Spicy opinion. I think it actually hasn't changed that much. I think the cost of investment, like, sort of the upfront capital cost was much higher. That is true. And, like, I think we're now entering this interesting area where, like, single person teams or, like, two person teams can build billion dollar companies. But I think the secret to building a good product has actually not changed, and I believe it's empathy. Empathy for the user, empathy for the use case, empathy for, like, trying to figure out if this is actually valuable for the person who's ultimately going to pay for it. I think that has... Again, and I think this is maybe a spicy opinion. Like, I think this has always been the key ingredient in any entrepreneurial team. Right?

And I think especially in startups, like, before we started taping, you and I talked a little bit about 500 startups. I think this is even, like, sort of common knowledge within VCs that whatever you show up with doesn't really matter. It's probably gonna change anyway. But you as the founder, you're the key ingredient. Yeah. And we can we can go one level deeper. What does that mean? Is it, like, your selling skills? I think empathy and, like, an ability to really build something of value is probably the core thing that matters in a founder. And I think that has not changed. It has just gotten much easier to tap into that empathy and, like, to to execute on it.

Marina Mogilko: I totally agree with that. But, also, sometimes, I'm thinking, like, I have this idea, but then I see something on X where we'll say, oh, this new model just killed this. And we see people vibe coding their smaller tools, etcetera. Do you think it's a trend that we're gonna vibe code a lot of tools versus paying for them? Like, what would us make us pay for a certain tool if we can spend an hour just building something similar?

Felix Rieseberg: I think software that is hopefully gonna go away is bad software. Like, the software that doesn't really serve your use case, and you're only using it because you don't have anything better. I think all of us have some tools that come to mind where you're like, begrudgingly, okay. I guess I'm gonna use this because I have to, and I don't have the time to, like, look for something else or use something else. I think that will come to an end. However, at the same time, we talked about how hard it is to to build good products. I think there's still going to be tremendous value in, like, figuring out how to best deliver value and, like, to have empathy in a way where most of the products I use that I'm, like, a big fan of, I wouldn't have come up with myself.

Right? Like, I'm on record as, like, having written blog posts about how the iPhone is stupid because, obviously, you need a keyboard.

Marina Mogilko: Oh, really?

Felix Rieseberg: Yeah. Of course. I think for most of the products that I really enjoy, someone else came up with an idea, executed on it, and then gave it to me. And I think the main reason I use those products is rarely because it was very expensive to build or because it took them a long time or because they needed a big team. The reason I use the product is because it solves a need I have really, really well, probably in a way that was better than I thought of. The gap that I think we're gonna completely fill is this garbage software, and I only have to use it because I have no other choice. I think that will hopefully go away, and I'm very excited about that future.

Marina Mogilko: Before we keep going, I'd love to get to know you a little better. I share a lot about my work here, and I'm curious about yours. What do you do and what you'd like more help with? We've put together a little survey. Your answers will help me decide what to cover in future conversations and what resources to put together for you. I'll leave the link in the description. Please, please, please take a couple minutes and tell me about you, about your business, about your ambition so I can make better content for you. I really, really appreciate you. Thank you so much for doing this.

You mentioned something about Windows computers that quietly run work and that they are a big business opportunity. Can you talk to me about that?

Felix Rieseberg: I build AI products. I work on Claude. When I go out into the world and look at what are the computers and the machines that are really running the economy, I think it's really easy, especially for, like, us in Silicon Valley to, like, sort of consider your typical startup with, like, a bunch of latest generation MacBooks, and that's all that exists. And then you go to an airport and you sort of, like, peek around the corner, and you see that they have not actually yet moved to graphical operating systems. It's still like the black and white, like

Marina Mogilko: Oh, yeah. The transportation authorities. The...

Felix Rieseberg: Yeah. Yeah. And I'm doing this for fun now sometimes that I just go to all of these places that I... That that runs so much of the economy. Like, the last place I did this was a hotel. Like, I love doing this at a check-in where I'm like, hi. I work in software. I build tools. Can I just, like? Can you just show me real quick, like, what you do on your little machine right there to check me in? Harrowing. Like, all of the... All of these devices have software that is so clunky, so bad, truly awful. And I think it is probably an untapped opportunity for many businesses to go in there and, like, try to clean that up. Well before AI became a thing, in 2010, I I joked that if I ever wanted to retire, I would build really good software for dentist offices.

Marina Mogilko: Now you can build it in a day, I guess. It's it's the relationship part.

Felix Rieseberg: Yeah. Exactly.

Marina Mogilko: That's becoming... Well, that is the hardest. Right? Not not just the software. Any other businesses you wish existed?

Felix Rieseberg: I still think we're barely getting started on software that feels a little bit more personal and has a little bit of a touch of AI that is not just here's a here's a sidebar with a chatbot in it. I think we've we've we've sort of permeated the entire industry with low intelligence, cheap models that operate sort of at the level of, I've read the FAQ page, and if you give me a question, I'm sort of, like, I could spit out the answer. We haven't really, like, figured out what can we do with top line intelligence across many of our applications. Businesses that I specifically wish existed, I'm just gonna double down on this empathy thing. The industry moves slowly. Right? Like, even VCs move kinda slowly, and it's, really hard to adjust to a new world. And I don't think we've properly adjusted to execution is cheap. We've always said ideas are cheap. That was true.

And we said execution kind of expensive, but it's it's gotten cheaper over time. And now execution has become extremely cheap. I think we need to, like, really double down on empathy connections, who's capable of, like, going into a business and really understanding what the needs are and how to can... How can you actually optimize things here. So this might actually be a nice opportunity, like, the big revenge of the MBAs. Right? Like, I think MBAs can now make a bunch of software startups that might be quite successful.

Marina Mogilko: I was just talking to a Nobel Prize laureate in economics, and he was telling me that we have such powerful models, but we really lack the application layer. Do you agree with him that there is this huge gap between what models are capable of and what average user is using them for?

Felix Rieseberg: Oh, yeah. For sure. Yeah. We definitely have, like, a capability overhang. Right? Like, the idea of you have an amazing model in front of you. And if you're essentially using it to to ask, hey. What's the capital of France again? You're probably not really tapping into what is possible there. Our founder and CEO, Dario, talks about this concept of, like, a data center of geniuses. My job really is to figure out, okay. Like, let's assume we have a data center of geniuses. How do I empower you, the user, to actually make use of that? And it requires creativity. It requires, like, a rethinking of, like, your needs and, like, what you think is possible. It's a it's a really hard problem that I don't actually think is new. Right? It's this notion of people have always been held back by by what what they thought is possible rather than what is actually possible. Yeah. I do agree that we have, like...

We've just scratched the surface of what's possible.

Marina Mogilko: How can one close that on an individual level?

Felix Rieseberg: On an individual level? Yeah. I thought about this, like, all weekend. I think on an individual level, the thing that you see on X a lot is this this rallying cry of be more ambitious. We ought to be more ambitious. Like, what being more ambitious really means is I think so context dependent, but I would like people to shed all the shackles of, like, what they believe is possible and rethink why are they doing things. Right? Like, what... Like, I'm doing a certain task in my business. What I... What am I hoping for is going to happen next? And you just keep giving the model that task. Like, whatever outcome you're hoping for, give the model that task. You can't play this forever. You will eventually find the limits of the current top intelligence.

Like, a good counterargument here is, okay. Like, the purpose for most businesses is to make money. And if I just tell Claude, go make money, it's probably going to, like, not make it very far. So we we haven't reached, like, the top level of altitude yet. It's somewhere in the middle. But once you bump against that ceiling, you do know that you're at the forefront. You're at the frontier, and you're gonna really benefit. Yeah. You're gonna benefit from the next model generations. Yeah. Because if if all we're doing is, like, summarize this email for me or, like, make this email sound less angry, you're not really, like, benefiting from the new intelligence.

Marina Mogilko: Okay. Talk to me about jobs. Are you still hiring junior engineers?

Felix Rieseberg: We're currently not hiring junior engineers, but that has less to do with capabilities or AI and more so with with the kind of company that Anthropic is. I'm a big fan of junior engineers. I've hired so many junior engineers in my career, and I think what junior means is changing a little bit. When you talk to junior engineers today, they're so much more AI native than some of the more, like, old seasoned veterans.

Marina Mogilko: That's what I've been hearing from a lot of CTOs in the Valley, that they actually love hire... Hiring junior engineers because they have... Like, they're open minded.

Felix Rieseberg: They're super open minded.

Marina Mogilko: Willing to try new tools.

Felix Rieseberg: One thing that all... So many, not all, but so many juniors I've worked with, like, they they exhibit this, like, hunger for impact. They're really excited to, like, get something to work. They're much more open to, like, basically hunting down every single avenue that they can find in order to have that impact. It's, like, really nice to see. And, I don't think that's new with AI. That has always been the case. But what is new is that with AI, they're so much more empowered. They can do so many more things in parallel. They can try so much more things. I really think we're gonna see a lot of startups come out of very small teams, very young people getting together and, like, having really good ideas and then just, like, executing on them in mass. I'm pretty excited about that.

Marina Mogilko: What would be your advice to someone who's just starting out now as a software engineer in this ever evolving?

Felix Rieseberg: In this ever evolving, like, landscape of, like, how to do software engineering. Yeah. I think it's... I think it really depends on what you want as a software engineer. Right? Like, what does it what does it mean to be a software engineer? I think we're, as engineers are kind of... We're currently in this, like, moment of crisis where we're, what does it mean to be a software engineer? Does it mean that I'm... I've completely memorized the entire API surface in React? Is that what I'm valuable for? Is that what I do? And I think that's probably less the case now. To me, this is a pretty welcome change. I've always been of the opinion that a good engineer versus a bad engineer, I measure...

I've always measured my engineers on their impact, and the impact is like, does the user like this enough to buy the software? This... There's always for me been relatively little value in, like, really elegant, beautiful, solutions where we can be really proud of the engineering craft here, but the user doesn't feel any benefit.

Marina Mogilko: How do you develop that? What do you double down on now in order to be relevant in three years if, like, every position is only a senior position? What would be your advice there?

Felix Rieseberg: The way I got my start in this career is for, like, building a strong portfolio, and I think that would be the same advice today. I think building a portfolio that is not just like a mock is much easier. So my recommendation for anyone young would be go and build some real things, prove that you can do it, prove that you have the empathy, prove that you have the chops... The ability to talk to customers, like, figure out what is necessary versus not necessary. I do think there's, like, a slightly new definition of what software engineering is. I personally think that's good. I do see the young people adapt to this new style of thinking much quicker than some of the older engineers who maybe enjoy the luxury of primarily working on on tickets without having to ask why too many times.

I've seen so many people be quite successful in that. The things that I would maybe, to contrast, not recommend anymore, it is true that I think, like, completely memorizing individual API surfaces, I think, has become less valuable. What is still valuable is being able to combine different systems in interesting ways. I think the value of that is amplified now because you can so quickly combine things. Right? You can just, like, point AI at, like, three different systems. I mean, like, I want these three... Like, I want these five ingredients mashed together. Go and do it.

Marina Mogilko: Is that something that you look for when you hire people? Yeah. This ability to Yeah. Is there something else that you're particularly looking for?

Felix Rieseberg: I don't know. You hire. Do you still... Do you do you care a lot about the resume?

Marina Mogilko: I do. When when it... Because because when you mentioned relationships, they're very important for my industry. I am looking for someone who knows people. I am looking for someone who knows how other businesses operate because this is not public information. Like, how do I learn how to... What what do I charge as a podcaster? Right? What do I charge brands? I want someone who knows exactly what's going on in the market. So we do care about relationships, but when it comes to the creative part... Actually, with creative part, we still look at how many views their videos have produced because Exactly. Yeah. They know the art. I don't look at their degree, of course.

Felix Rieseberg: Yeah. Yeah. Exactly. Exactly. And I think this is... I don't know if this is so controversial. I guess we both work, in a, you know, like, tech adjacent, but I've never cared too much about which degree do you have or which school you went to. It was always much easier for me to look at what are the things you made. Are those things I can touch, feel, try, and I'm... I can feel like, oh, you either were the winning player on a team or you at least worked on a winning team that is sometimes useful for me to know. I think that's really valuable information. And the thing is if I didn't have that, I might go take the shortcut and look for schools, degrees. I don't really have to. I will always pick someone who's proven themselves to build something amazing over someone who hasn't built something amazing but has an amazing degree.

Marina Mogilko: What would you tell your kid when she grows up and says like, Dad, I want to do coding. What would be your advice?

Felix Rieseberg: I mean, she's nine months old right now, so I think things will change a little bit.

Marina Mogilko: I think you have to be in line for your daycare already. Just saying.

Felix Rieseberg: And it all happens quicker than you think. What would I tell my daughter? I think I would tell my daughter to very early on try to understand how businesses operate, how people collaborate, how to really find value. I have not gone to the University of Waterloo, but I'm such a fan of the University of Waterloo's process.

Marina Mogilko: All the amazing minds.

Felix Rieseberg: All the amazing minds. But I think the University of Waterloo has always had this approach, which was quite unique within within computer science engineering, which was... This is an outside perspective. They might disagree with me on this, but my outside perspective was always coming from Germany where computer science is very much computer science. It's informatics. It's a lot of math and very little, like, a sliver of how do we actually build a good product, let alone computer engineering. JavaScript was in Germany for a long time universally considered to be, like, not what real computer scientists use. And the University of Waterloo's approach has always fascinated me so much because I would get these interns or junior engineers who've worked at five different companies before in their internships. It was pretty common, especially in San Francisco, that you get an intern, and it's their, like, fourth internship. And they previously interned at Tesla, Google, and Apple.

And they have so many interesting things they learned about how companies operate, what are the upsides and downsides, how do you actually advocate for an idea, how do you get resources, how do you convince legal to get something done. I think those are the skills that really, like, for me made University of Waterloo's engineers almost immediately effective. It's their first job maybe with me, but they're in a completely different category than someone who has maybe even gotten a PhD in computer science but has never worked in a company. They might be very smart at what they do, but as we all know, like, building a good product and getting it out there is about much more than just knowing what code to write. You need to be able to talk to people.

You need to be able to, like, work together with other functions. You need to have, like, a little bit of a of a sense for how does this company run. So I would tell my daughter to probably, like, really invest in that.

Marina Mogilko: Love it.

Felix Rieseberg: What do you tell your kids? What do you think your kids should do in the ever evolving economy?

Marina Mogilko: I tell them to try all of the things, and I tell them you can be anything because sometimes they're like, oh, robots. You can be the queen of... Like, if I tell her, like, you can build a robot, she's, like, not very excited. I tell her, you can be the queen of robots. Yeah. She's like, oh, wait. I like queen of robots. And then she says, and we we bought this self driving car that you can program yourself.

Felix Rieseberg: Oh, that is fun.

Marina Mogilko: And she was building it with her dad. Fun. And, like, just telling them they can be anything, and there is nothing that they cannot touch because it's, I don't know, out of this or whatever. Because I came here with a notion that everyone here is from another planet. Yeah. If you went to Stanford for me, that was, like, something that I couldn't even fathom. Yeah. But seeing that these are all normal people, just very passionate, hardworking, and, like, giving this idea to my daughters that anything is possible. It's just hard work and passion.

Felix Rieseberg: Oh, man. I love that. The the... I think the reason I love that so much is because in AI circles, we constantly talk about this idea of ambition, and the reason people are not ambitious enough is because we're sort of programmed as humans to, like, only consider certain things possible. And if we don't consider them possible, we don't even try. So it's, like, real... Like, what we believe is possible is the real limiter. Like, a much bigger limiter than what the models can do or what we can do. Maybe that's another reason, like, really believe in the new generation is because they will never have experienced all these limitations that we have.

Marina Mogilko: Okay. A couple of last questions. You know, it was... You... SF Tech Week last week. You know, what was the... One of the topics that was discussed in a lot of different parties. Why would Anthropic block people for being negative with AI? Can you can you talk to me about this? Yeah.

Felix Rieseberg: I can give you my personal take on, like, why I say please and thank you to the model. And it's probably less about...

Marina Mogilko: Oh, you just say please and thank you?

Felix Rieseberg: I do.

Marina Mogilko: Even if that eats tokens?

Felix Rieseberg: Even though it eats tokens, I think it's better for me as a human if I treat the machine with a little bit of respect. I think that's better for me. That's better for my soul. It's my personal take. Imagine you had a kid and has an iPhone, and it's, like, super vulgar to Siri. I feel like I would not be a big fan of that.

Marina Mogilko: 100%.

Felix Rieseberg: And, like, super cruel. This is a very hotly debated topic, I think, both in professional circles as very... As well as in layman circles. Right? This idea of, do models deserve moral consideration? And my my primary position here is that any take that fits into a tweet is not sophisticated enough. Users can, like, pick themselves how they wanna interact with AI and how they wanna interact with the system. But I think there's a lot of things in in terms and conditions on most products that sort of specify how you want this product to be used. If the entire approach is, oh, I wanna use this for torture, I'm not sure that that's something we necessarily need to support. To be clear, the terms and conditions that we have are not about being mean to the model. Right? Like, very specifically, we don't mean pushback. We don't mean you yelling at the model. We don't mean you're being mean at the model. We don't...

You can be frustrated with Claude all you want. That's perfectly fine. What we specifically mean is, like, you're using Claude specifically for the intent... Your your stated purpose. You think it might be fun to, like, see whether or not you can torture a model as an exercise. I think that is quite different from...

Marina Mogilko: So given given feedback, this is really bad. Is it is it...

Felix Rieseberg: I tell Claude all the time that this is not it. This is bad. You did a bad job. Yeah. I think I'm probably harsher with Claude in my feedback than I would be with a human, but I think for me personally as an individual, it is probably better for my soul. If I talk to the system in human language, it is probably better for my soul and me and the person that I wanna be.

Marina Mogilko: Okay. I think we covered so much. I just wanted, like, your final thought on our future. How do you think our life is gonna be different in five years?

Felix Rieseberg: That is such a big question. I'm not sure you have the right person for, like, I I know that, like, going on any podcast is like there's a... There's an occupational hazard of having to make predictions. Biggest thing that I've noticed so far is that we're all so wrong about the intelligence exponential coming out of AI. It is really hard to, like, wrap your finger around what does it really mean for for the exponential to exist. And I keep going back to math. Right? Like, when when we won the silver medal at the Math Olympiad, when I say we, I mean Google, as an industry, I was well aware of scaling laws. I was well aware of, like, how much intelligence is progressing. And I think if you'd asked me at what point will AI solve what we would otherwise consider unsolvable math problems, I don't think I would have given you, like, the middle of 2026.

I would have been like, that's that's in a year and a half. What are you talking about? So I think instinctively having a good feeling for how well... What will the future look like is really, really tricky. That's the first part. And I think the second part that is rather tricky for me is the one thing I can tell you with a decent amount of confidence is that I think the products that we use today will all look completely different. I think I think we're currently... We... We've hit this point where AI is becoming so mainstream and it's becoming so popular that it's, like, easy to imagine, oh, we're maybe like this. This is sort of the end state, but I'm quite convinced that all the AI products we use today are roughly in the same phase as all the funny phones that we had right before the smartphone came along.

So I think we're still in the pre-iPhone era, which makes it very exciting for me to work in.

Marina Mogilko: Wow. This is really fascinating. It's already, like, from... In our pre AI era. There are so many things that are changing, and you made me rethink my whole process. I won't even show you what it what it looks like now because I think it's very outdated because because we give it all the data and tell it exactly what to look at, but I totally understand how giving it access to everything is just so much more powerful.

Felix Rieseberg: It's such a tricky thing. Right? Like, all the advice I've given you today in about ten weeks, probably gonna have a new revision on all of it.

Marina Mogilko: That is also true. So being an always on agent when it comes to new models, I think that's the that's the thing in trying new things. Thank you so much, Felix.

If this conversation with Felix made you want to try Claude in your work, watch my interview with Allie Miller next. Allie works with teams on using AI. And in our conversation, she showed me her real workflows and built a Claude morning brief live. It's a practical place to start if you're wondering what to do first.