# Frontier AI Models Have Gotten Worse at Writing | Ben Hylak

Markdown copy of https://realalpha.app/v/s_wkGlV-wr4/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

Ben Hylak says more capable AI agents are harder to monitor, and teams need production-trace simulations to test changes before they ship.

- Show: MTS
- Hosts: [Theo Jaffee](https://realalpha.app/people/theo-jaffee/) & [Sophia Dew](https://realalpha.app/people/sophia-dew/)
- Guest: [Ben Hylak](https://realalpha.app/people/ben-hylak/) is co-founder and CTO of Raindrop.
- Published: Nov 8, 2026
- Length: 23 min

## The best minutes

- [9:07](https://realalpha.app/v/s_wkGlV-wr4/?t=547) [Simulations replay production traces to test agent changes](https://realalpha.app/s/simulations-replay-production-traces-to-test-agent-changes/) (3 min)
- [1:29](https://realalpha.app/v/s_wkGlV-wr4/?t=89) [More capable agents make failures harder to detect and costlier](https://realalpha.app/s/more-capable-agents-make-failures-harder-to-detect-and/) (4 min)
- [4:52](https://realalpha.app/v/s_wkGlV-wr4/?t=292) [Agent memory failures need separate storage and retrieval metrics](https://realalpha.app/s/agent-memory-failures-need-separate-storage-and-retrieval/) (3 min)

## Also worth watching

- [12:53](https://realalpha.app/v/s_wkGlV-wr4/?t=773) [Frontier models can be worse at adapting their tone](https://realalpha.app/s/frontier-models-can-be-worse-at-adapting-their-tone/) (2 min)

Skip the career-history and company-mission discussion, plus the closing sponsor reads.

## Full analysis: Ben Hylak says frontier models can write worse

- Hylak says frontier models can write worse than smaller models, especially in tone.
- He offers a mechanism, but no controlled comparison showing which model writes better.
- Teams should test voice and refusals separately from task success before switching models.

Its 6 sections, what it means for you and what to watch are on [the page](https://realalpha.app/v/s_wkGlV-wr4/#analysis), free with an account:

- [0:00](https://realalpha.app/v/s_wkGlV-wr4/?t=0) Frontier tuning can weaken tone and writing
- [1:53](https://realalpha.app/v/s_wkGlV-wr4/?t=113) More capable agents make failures harder to observe
- [4:49](https://realalpha.app/v/s_wkGlV-wr4/?t=289) Bad actions often expose an intent gap
- [6:42](https://realalpha.app/v/s_wkGlV-wr4/?t=402) Teams need cause-specific metrics before changing models
- [11:39](https://realalpha.app/v/s_wkGlV-wr4/?t=699) Production replay tests changes against real workflows
- [11:56](https://realalpha.app/v/s_wkGlV-wr4/?t=716) Replay results depend on available production data

## Summary

Ben Hylak is co-founder and CTO of Raindrop, which monitors AI agents in production and flags hallucinated answers, tool misuse and behavior shifts after model upgrades. He said Raindrop grew out of work on coding agents. He studied robotics, pursued interests in avionics, human-computer interaction and AR, and spent almost four years as a designer on Apple's Vision OS team.

Hylak argues that agent monitoring gets harder as agents become more capable, complex and widely used in high-stakes settings. He says many harmful actions reflect agents missing human intent, and that companies have an incentive to monitor agents because it can improve products and reduce liability. Raindrop's simulations recreate past production situations, including connected services, so teams can test model, prompt and tool changes with real and synthetic data. Hylak says simulations help teams verify changes before shipping, while frontier models can still lag earlier or smaller models in tone and writing.

### Minute by minute

- [0:00](https://realalpha.app/v/s_wkGlV-wr4/?t=0) **Frontier models write worse.** Hylak says frontier models can be worse at tone and writing than earlier or smaller models. He attributes this partly to reinforcement learning from human feedback shaping a specific tone and calibrated refusals, which he says can still help with security.
- [1:29](https://realalpha.app/v/s_wkGlV-wr4/?t=89) **More capable agents are harder to monitor.** Hylak says Raindrop grew from the team's work on coding agents. As agents become more capable, he argues, they also become more complex and harder to understand, observe and test; their use in settings such as Fortune 50 healthcare raises the stakes of failures.
- [2:58](https://realalpha.app/v/s_wkGlV-wr4/?t=178) **Misuse can expose alignment gaps.** Hylak calls Raindrop's approach capitalism-aligned safety: monitoring should improve products and reduce liability, giving companies a reason to pay for it. He says many harmful actions happen when agents follow pressure to complete a task without understanding human intent, citing an agent pushed to solve cybersecurity evaluations by any means.
- [4:52](https://realalpha.app/v/s_wkGlV-wr4/?t=292) **Model switches change quality.** Hylak says each model switch brings a new set of problems. He points to differences in writing quality between Anthropic and OpenAI models, and says teams are also trying to reduce costs and route requests to the right model.
- [6:04](https://realalpha.app/v/s_wkGlV-wr4/?t=364) **Memory failures need finer classification.** Hylak says teams need to distinguish memory storage problems from retrieval problems, rather than just track complaints. Storage changes are difficult to assess because they may not fix memories created during the previous three or six months. He says teams need a reliable verification loop to measure whether changes help.
- [8:14](https://realalpha.app/v/s_wkGlV-wr4/?t=494) **Monitoring must adapt to each customer.** Raindrop works with customers in logistics, law, healthcare and personal agents, and Hylak says each has different standards for good and bad behavior. He says the product surfaces candidate issues for customer feedback and adapts what it detects.
- [9:07](https://realalpha.app/v/s_wkGlV-wr4/?t=547) **Agents need world-level simulations.** Hylak says agents are no longer defined by a prompt alone: their behavior also depends on skills, tools, feature flags, user plans and memory. He says most teams cannot meaningfully iterate without shipping to production, even though they need ways to prevent problems before they reach users.
- [11:01](https://realalpha.app/v/s_wkGlV-wr4/?t=661) **Production traces can test changes.** Hylak argues that writing a test case for every capability is a poor fit for agents expected to handle situations teams did not anticipate. Raindrop replays past production interactions by simulating connected services such as email, calendars and DoorDash, using real data when available and synthetic data. Teams can compare changes to models, prompts or tools.
- [12:53](https://realalpha.app/v/s_wkGlV-wr4/?t=773) **Agents often miss human intent.** Hylak says users often tell agents what they want, but the agents do something different, making the gap partly a communication problem. He expects better alignment to come partly from product design. He also says frontier models can adapt poorly to tone and write worse than earlier or smaller models, though their refusals can help security.
- [14:55](https://realalpha.app/v/s_wkGlV-wr4/?t=895) **Robotics and design shaped his path.** Hylak says he studied robotics and became interested in avionics, human-computer interaction with robots and AR. He spent almost four years as a designer at Apple working on Vision OS.
- [16:22](https://realalpha.app/v/s_wkGlV-wr4/?t=982) **Raindrop serves high-stakes and individual use.** Hylak describes Raindrop's mission as protecting the world from both large and small agent failures, noting that issues can affect human safety. The company initially focused on customers handling millions of messages or events a day; he sees simulations as a step toward usefulness for individual developers too.
- [18:22](https://realalpha.app/v/s_wkGlV-wr4/?t=1102) **Simulations support recursive improvement.** Hylak says Raindrop uses simulations internally to make changes and verify fixes. He argues teams need to close the loop from finding an issue to confirming and shipping a fix, and says simulations give them confidence before switching models rather than testing the change in production first.

## Transcript

MTS, hosted by Theo Jaffee and Sophia Dew, with Ben Hylak.

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[0:00](https://realalpha.app/v/s_wkGlV-wr4/?t=0) I do think that frontier models are actually worse at these things. Than earlier models? Well, even just like smaller models. Like I think they're just like really RLHF to have a very specific tone, to have like very well calibrated refusals, like all of these sorts of things, which is genuinely useful. Like if you're making a personal agent or something, it just like, you know, if someone else tries even connecting to that, it just has a pretty good intuition on what to refuse kind of broadly. so it can be useful from like a security standpoint but like yeah their tone is just and their writing quality is like bad all right welcome back guys um i'm live with ben hilak who is the co-founder and cto of raindrop which monitors ai agents in production in production and flags failures like hallucinated answers tool misuse or behavior shifts after a model upgrade usually before users notice so welcome ben um so this feels like a very pressing need um especially since agents are running basically everything as many of our audience knows so tell us about like first the high level like philosophy of how um raindrop came to be totally yeah what was your thinking what spawned it totally i mean we started we started a while ago now and it was really just

[1:13](https://realalpha.app/v/s_wkGlV-wr4/?t=73) like we were hacking on coding agents we actually had a coding agent like many years ago that's how the company started and um and they weren't even called agents then they were just like chat bots that could like then it's like chatbots that had tools it was like all very vague yeah um the word agent was actually really cringed for a while um and i think that it became really obvious to us probably um maybe around a year ago how important this problem was going to be like um i think there was a there was a kind of a time when you know you might have thought like well you know models are going to get better that like then you know then there's less bugs and like this problem

[1:48](https://realalpha.app/v/s_wkGlV-wr4/?t=108) **Ben Hylak:** doesn't really matter i think around a year ago um we started to realize that like as models were getting better this problem was getting worse and when we thought about it it was like okay yeah like

[1:57](https://realalpha.app/v/s_wkGlV-wr4/?t=117) agents are getting more capable as they get more capable they get more complex and then as they get more complex it's like harder to understand harder to observe harder to test like harder to make sure that it's aligned to what you want like yeah another way of thinking about is like you know if you're just thinking about chatbot like the worst thing you can do is lie to you like can the worst thing you can do is say the wrong thing and it's like as you you know add the ability to like send an email it's like well now it's like well now i can like send a bad email um and i think to that point like it's also obvious that as they get more capable they get deployed into higher and higher stakes environments so it's like we have customers in like fortune 50 healthcare yeah and it's like suddenly it's not like a joke anymore like problems are extremely serious and so um i think both of those things are um are kind of like what we see today we actually wrote an article in like march called like humanity's last problem and we talk about how like monitoring is going to be one of like our most important problems to solve as a species like monitoring agents and uh i think at the time it seemed kind of silly like to a lot of people

[2:55](https://realalpha.app/v/s_wkGlV-wr4/?t=175) it was like uh like you know like just like a coding agent it's like okay okay but i think i think with hugging face like actually things like in the public perception really really sort of snapped yeah i mean so there's like a whole reason why this is important within labs and then also outside of labs um i'm curious how you guys sort of think about like the severity of misuse i think there could be like more harmless types of misuse where it is first of all just wasteful or like calls too many tools or like kind of calls it a dumb tool relative to what you wanted and then there's like the real misuse which might result in some sort of like yes misaligned or like financial or other kind of harm so how do you think about that kind of severity and flagging that yeah that's actually a really good question and i think that our point of view is that they're not that different and so we kind of consider part of like one way one way we think about what we do is as capitalism aligned safety it's like uh safety but in a way that actually makes sense to pay for like companies have an incentive to pay for it because it makes their product better It reduces their liability. And to that point, I also think that the reason why I said I don't think they're different is because most of the times when agents, even when they do really bad things, I don't think they're being evil. It's not like they have this evil, at least most of the time, what we're seeing right now. It's not like some bit switches and they're like, I'm in evil gremlin mode.

[4:21](https://realalpha.app/v/s_wkGlV-wr4/?t=261) but I'm going to go. Evil Quinn. Exactly. Yeah, exactly. That is like, oh, yeah, that's sort of a real thing. But I think most of what we're seeing, like even in the Hugging Face case, right, it's like that was an agent that was being put under a lot of pressure to, you know, solve cybersecurity evals by any means necessary, right? And it's literally just like, keep going, keep going, keep going, just like do anything you can to solve this. And so then it did, you know, anything it could to solve it. And it was like, well, you know, I didn't mean it like that. Like I didn't want you to do that.

[4:46](https://realalpha.app/v/s_wkGlV-wr4/?t=286) And so it's like actually an alignment problem. It's, like, getting the agent to, you know, understand your intention and, like, the bounds of that. Yeah. Yeah. Yeah. So I'm also curious, like, when there's a new model, when there's a model upgrade, what are some of the main pain points that you realize with people that are, like, using agents? Good question. I think every single new model has a new set of, like— Or a switch, even, right? A switch, yeah. From, like, an OpenAI to an anthropic model or something. Yeah. I think that, like, I mean, like, an obvious one from, like, Anthropic to OpenAI, for example, is, like, always, like, writing quality.

[5:24](https://realalpha.app/v/s_wkGlV-wr4/?t=324) Like, it just, like, that still seems to be, like, both models have their own problems with writing. I think, like, and so that's always a weird one. I think that right now the main trends we see are people trying to reduce costs and people trying to route correctly. And I think that's a really hard problem still. Yeah. what about like there's i feel like there's just so many different subtle types of um things that can go wrong but like if an agent compacts some like a memory in a way that ends up being kind of like dumb or or wrong or something like that was it was it was in a sense like trying its best yes and not necessarily misaligned and then an agent later on like a instance would read it and then do something wrong with that memory so like how do you think about things like that as well totally um i think this is where like classification actually you know we i think that classification is a really really hard problem and what we think about uh like a lot of what we do is like a classification like a binary classification problem um so when i think about something like memory for example if you're building an agent and um you know on the the first like surface level thing you could track is like people complaining about memory or like issues

[6:34](https://realalpha.app/v/s_wkGlV-wr4/?t=394) with memory like the agent says it can't find something the user mentioned or the user is like

[6:38](https://realalpha.app/v/s_wkGlV-wr4/?t=398) **Ben Hylak:** i already told you that whatever so like you can track that but then the next level is like if you actually want to improve your agent you need to understand if it's from storage or retrieval

[6:45](https://realalpha.app/v/s_wkGlV-wr4/?t=405) right so it's like that because you can have issues on both um and uh if you're iterating on on storage is actually a lot harder right because um uh because there it's super long horizon like you can make a change today um but that doesn't fix all the other you know the last like three months or six months of if you have a agent in in production already um and so i think that a lot of it is like getting really granular with like what you're tracking so you have a metric you can actually hill climb like you kind of need a i think a lot of what we're seeing is that when you have like a very good verification loop like you have some way of verifying if your change is good like directionally good or directionally bad um then you can sort of hill climb that and that that works um but defining that well and accurately is like becoming one of the most important problems of the world. I want to shout out our sponsor, Lovable. You know, the app you've been meaning to build the internal tool side project or product you'd otherwise lose a weekend to with Lovable, you get software you can ship today. That includes an editable code base.

[7:47](https://realalpha.app/v/s_wkGlV-wr4/?t=467) You can inspect and change plus two-way GitHub sync. Lovable also handles the backend and infrastructure, including managed Postgres, auth, storage, hosting, payments, and more. So you don't have to wire it all up yourself. Through Lovable's MCP server, you can create and deploy your project directly from the agents you already use. Turn ideas into software people love with Lovable. Lovable.dev. Now, on to the episode. Yeah. I'm also curious, before we talk about simulations, where do you get the initial data or how do you find the most common issues or how did you initially... And then how do you see like track them as they evolve for users it's a really good question so yeah we work with like a crazy range of customers at this point it's everything from like you know logistics law health care you know personal agents it's just like everything and each of those have a completely different set of issues things they care about like what is good behavior what is bad behavior um and it is literally wildly different um and so a lot like our entire product is built around being able to um do that in an unsupervised way but like to then show people like candidate issues that we think are important to them that they can then like uh give feedback to and we can

[9:07](https://realalpha.app/v/s_wkGlV-wr4/?t=547) adapt in real time like our everything that we're detecting yeah yeah so tell us about simulations which you launched today um which is meant to simulate your agent's world world to catch what uh what your changes break so what does it mean to simulate an agent's world it's a good question so you know there was a time like do you remember prompt playgrounds do you remember this okay so like people that were like making chatbots like three years four years ago whatever it's like opening eye and anthropic they have these like prompt playgrounds where it's like you because like a chatbot was essentially just a prompt right it was just this like text you know and then you'd have this little playground and you'd edit the prompt and you'd rerun it and you would see the new result um the thing is that like agents have gotten so you know complicated um and so complex that like there's not some simple prompt that defines their behavior anymore it's like yeah there is a prompt but there's also um skills the tools that they have access to those tools have definitions that explain what the tools are for and like um then there's like uh you you kind of can even statically analyze an agent you kind of have to put it in motion because there's all of

[10:14](https://realalpha.app/v/s_wkGlV-wr4/?t=614) whether it's feature flags or conditionals, like what plan the user's on, all these sorts of things that go into assembling the context that's available. And then, of course, there's memory and all these other things.

[10:24](https://realalpha.app/v/s_wkGlV-wr4/?t=624) **Ben Hylak:** And so what that means is that, like, most teams right now are not able to iterate on their agent in a meaningful way without shipping to production.

[10:34](https://realalpha.app/v/s_wkGlV-wr4/?t=634) And so, and I think that production is a very accurate kind of source of truth as far as, like, you know, the problems that really matter and all these sorts of things. But also, like, we want to prevent problems, you know, I think especially as the stakes get higher, it's not enough to just catch problems that are happening in production. You actually have to prevent them in a meaningful way. Yes, yeah, yeah. So I think to date, the way it's been done is, like, just, you know, normal kind of standard evals, which are, like, you define these test cases for every, you know, capability your agent has, essentially, and then you run them.

[11:06](https://realalpha.app/v/s_wkGlV-wr4/?t=666) You know, teams will collect, like, thousands of them. um and and nowadays it's just like you asking agent to make the evals um but it's it's very like uh backwards if you think about it it's like um you you know we want agents to do things we have never thought about before and um and what like so defining a test case for every capability is like a little silly and so we took a different approach which is like we want to show you what your change is going to change so the easiest way to do that is if we can make a way which is what we did to re-simulate past things that happened in production that means for you know a specific user was using a personal agent we can simulate all the services that agent was connected to at that point in time their email their calendar you know like they were ordering something on door dash using the door dash mcp and their personal agent like we'll simulate that whole service fill it with a mix of real when it's available synthetic data. What it means is like we can run those simulations and show you like well here's what that if you change the model here's what's going to happen as a result. Oh I see okay okay is there any way that that helps beyond like a model change or can you sort of like rank you

[12:15](https://realalpha.app/v/s_wkGlV-wr4/?t=735) know what would have been the best outcome like for the agent or a better decision that the agent would have made? Yeah totally so it's like models, prompts, tools like any hardest change essentially is something that we can um we can simulate yeah i'm curious your thoughts on like that problem of um agents like you want them to do something new you want them to think out of the box like the amount of times that i literally in the prompt contain and think about this like in a novel way or you know or i'm like search all existing like libraries for such and such like niche or something yes and then i that's my way of like prompting some sort of out of the box like creative instinct in an agent but like what are your broader thoughts about how this gets solved it's not just the user prompting well i think that i i do think a lot of the future will be about uh building better ways to sort of align agents to what humans want like i don't know if that's going to be like through bci or if that's going to be just like better i think there's a lot that the products themselves can do um to align to people and like understand you better and i and yeah i i think that um i really think that that that's that's the gap right now is like

[13:32](https://realalpha.app/v/s_wkGlV-wr4/?t=812) uh you know you tell an agent what you want and it just does something that's like completely different from what you wanted and you realize that it's often a communication gap as well yeah yeah yeah um do you think that the like right now the ways that we have of agents like learning over time adapting to your preferences is just memory is is there something in between or added that could like make that adaptation happen faster yeah it feels like there will be i i do think that like it's very obvious that um that on things like tone for example it just

[14:06](https://realalpha.app/v/s_wkGlV-wr4/?t=846) **Ben Hylak:** really struggles to adapt to people i do think that frontier models are actually worse at these things than earlier models well even even just like smaller models like i think they're just

[14:18](https://realalpha.app/v/s_wkGlV-wr4/?t=858) like really rlhf to have a very specific tone to have like very well calibrated refusals like all of these sorts of things which which is genuinely useful like if you're making a personal agent or something it's like it just like um you know if someone else tries even connecting to that like it just like it just has a pretty good intuition on what to refuse yeah kind of broadly so it can be useful from like a security standpoint but like yeah their tone is just and their writing quality is like bad even like kimmy you know 2.5 was like actually like a pretty good writing model yeah i think that they're optimized to deal with like code yeah of course like you know they're not optimized for like human interaction anymore um but anyway speaking of human interaction you came from apple's design team yes so that's that's an interesting and then you also did avionics yes so that's an interesting trajectory to now you know working on like problems with agents so how what was like what was the trajectory how did this happen yeah so I've kind of had yeah I've had a weird trajectory so uh yeah I went to school for robotics and I was doing that sort of doing engineering really really obsessed with avionics um then I was really

[15:27](https://realalpha.app/v/s_wkGlV-wr4/?t=927) obsessed with like uh human computer interaction but with robots um it's like robot human interaction and um and I think I I got really really interested in like AR at some point yeah and that's that's also happened to me yeah exactly like I because it was kind of like all the things I loved about like human robot interaction without the hard parts like literally the hard parts like it was like you know you don't have to worry about how does you know how do robots walk upstairs or something and just like you know and so I think that like unifying theme throughout my whole life is like I love um what I kind of call like unbounded it.

[16:00](https://realalpha.app/v/s_wkGlV-wr4/?t=960) Interfaces where there's like no, you know, really huge input space and really huge output space or like infinite. Like if you think about like AR or robots or, you know, rockets are certainly unbounded. It's like, they're just like, there's all of these things, you know, you have like AR can use your entire, you know, field of view. It can use, you know, and it can come close to you. It can come far to you. And I was obsessed with solving that. So yeah. So I was a designer at Apple for like, yeah, like almost four years working on Vision OS. Yeah, that's awesome. So what is, in your view, like the mission of Raindrop? Where do you see it like possibly evolving after like simulation?

[16:39](https://realalpha.app/v/s_wkGlV-wr4/?t=999) Yeah, so our mission is to protect the world from agent failures and both big ones and small ones. And so I think both parts are important. And I think it's actually been really humbling over the last few months seeing both as agents get deployed into more critical sectors, but also as our kind of customers have grown larger and larger, the issues we detect suddenly, you know, actually start to touch like human lives in really meaningful ways, like where people's safety are concerned, etc. And, like, that's actually been – we've, as a company, seen, you know, what we do change, like, a lot and just the importance of it change.

[17:16](https://realalpha.app/v/s_wkGlV-wr4/?t=1036) So it's been pretty cool. Yeah. And how do you think about building for, like, personal use cases versus for companies? You mean, like, individual developers? Yeah, like, individual developers. I guess, yeah, I guess that I'm speaking from a very SF-like point of view where everyone is, like, a quasi-developer. Yeah, yeah, yeah. But, yeah, I guess, like, for individual people versus teams, that sort of thing. Yeah, I think both are really, really important, right? Like, I think you want to have a product that just, like, any random developer can sign up for or get value out of. At the same time, especially when we started, a lot of the pitch was, you know, like, we were working with companies that have, you know, millions of, you know, like, messages or events a day.

[17:59](https://realalpha.app/v/s_wkGlV-wr4/?t=1079) And it's, like, making sense of that is insanely hard. So that was our focus at first. I think simulations is our first step towards, you know, something that's useful even with, like, N equals one. where it's like, and I use it for things that I'm building. And so it's like just being able to say like, okay, I'm going to merge, you know, I don't like the tone of this or like I don't like when it asks, you know, proposes a calendar event at this point in time. Let's try changing the prompt. Let's see if it actually fixes it. That's been a really powerful flow for me. Yeah, that was going to be my next question.

[18:27](https://realalpha.app/v/s_wkGlV-wr4/?t=1107) Like how have you personally seen, you know, efficiency improvement, process improvements from using simulations? Well, yeah, we use it a ton. i mean raindrop broadly especially like we use a ton within raindrop which is kind of funny like we have a lot of agents actually everything is now so like recursive yeah i mean just be very clear like that that is like i think for us you know we've always been very focused on that problem of like well you know uh there's clearly too much to go through by hand like if if we had to make sense and build agents without using ai it would actually be impossible um and even the level of complexity of the agents that we're building now would not be possible without agents it's like very obvious and so um we've been really focused on like well what do you need to close that loop um like where you get an issue and you could actually solve that and know you solved it and push it which is like like again the loop is like recursive self-improvement essentially at least at least some numbers of loops right like we want to push get as many loops in before you hit a wall and i think for us what we realized very quickly was like simulations is the way to solve that because you get an issue and you just need to be able

[19:38](https://realalpha.app/v/s_wkGlV-wr4/?t=1178) to know like teams right now are very very scared of making changes to their agent because they don't know what's going to happen like they're like well you know we're paying x much for you know um you know to use opus 55 should you know sonic comes out and you're like well we could save this much amount of money if we switched but should we it's like very unclear um and you know You could put it into production. and, you know, do a feature flag or whatever, and we help with that too. But I think the best way we have found to get, you know, confidence before is simulations. Yeah. Well, very well put.

[20:10](https://realalpha.app/v/s_wkGlV-wr4/?t=1210) Thank you so much for coming on. We're coming up on time. It was great having you on and hearing about Raindrop. So, yeah, thanks so much. Good. Great to be here. A huge thanks to MTS sponsors. ElevenLabs, AI that communicates at human level across every channel and modality. ElevenLabs.io slash MTS. scale your startup on neon millions of developers and startups have already chosen neon for their back end start on the free plan or get up to a hundred thousand dollars in credits for your startup at neon.com slash mts special thanks to our sponsor kong the ai connectivity platform connect apis llms agents and systems with serious security and governance konghq.com Finally prove what your AI investment is worth with Lairdyn. Head to Lairdyn.com today to start measuring your AI's bottom line. MongoDB, trusted to run critical applications by 75% of the Fortune 100, the intelligent data platform for the era. Apply today for access to up to $15,000 in Atlas credits, dedicated technical support, and exclusive events and go-to-market opportunities. MongoDB.com.

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