RedMonk’s James Governor sits down with Mina Ilieva, AI engineer at TurinTech AI, to talk about a problem everyone in 2026 recognizes: we’re shipping a lot of code slop. Ilieva explains how TurinTech’s platform, Artemis, fights back. It began life running a genetic algorithm that scores candidate code against a fitness function, and it now has a newer tool, Discovery, built on an empirical loop where agents form hypotheses, turn them into experiments, and verify every change before a human approves it. The two get into what clients actually optimize for — throughput, latency, memory, runtime — and why none of it is free. Ilieva walks through real wins with Intel’s vLLM work, QuantLib, BLAKE3, and a quantized Nemotron model. They also take apart “token maxxing,” the habit of burning tokens to look busy, and make the case that verification skill, not raw output, is what keeps engineers employable.
This RedMonk conversation is sponsored by TurinTech AI.
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Transcript
James Governor
hi, this is James Governor, co-founder of RedMonk. We’re here for another MonkCast. I’m here today with Mina Ilieva, an AI engineer with TurinTech. So today the topic is going to be, of course, AI related. It’s 2026. What else would we talk about? But yeah, TurinTech is a UK startup, an AI company. It’s been around since before the LLM revolution actually kicked in. we’re in Liverpool Street here today, so pretty close to that.
Cluster around King’s Cross. There is a ton of AI activity happening in London at the moment. I’d say that the the city was now AI pilled. That’s a good phrase to use. Yeah, yeah, yeah. There’s a lot going on. tell us a bit about TurinTech and your role at the company.
Mina Ilieva
Right, sure. well first and foremost, thank you so much for having me on the podcast. so yeah, indeed, as you said, I’m an AI engineer, I work for TurinTech, I’ve been Interim tech for maybe on a year and a half now. as I said, I’m an AI engineer, so I work more on the research site as of recently. So I assist clients with driving optimization work on our platform. And when I say optimization, this is sort of like the key thing that we do, right? So we leverage code optimization across various organizations. We work with banks, trading firms, but also you know, companies like Intel that deal with LM inference work.
And so that’s it. So I’m working more on the research side and you know obviously kind of fixing bugs on the platform as well, making sure that the agents are working, making sure that everything’s fine and yeah, a bit of everything, but mostly research.
James Governor
Tell me a bit about this code optimization question, because at the moment we’re shipping a lot of slop. we maybe don’t want to do that. So TurinTech’s about code optimization, what does that mean and how does it work? What is it that what what sort of algorithm is you using? How does that work in practice?
Mina Ilieva
Right. Great question. I mean, you know, and you touched upon like the one the most important, like the pressing issues nowadays, right? You say we generate a lot of code slop. And that’s essentially what TurinTech is, or our Artemis being central to TurinTech TurinTech’s product is trying to solve as an optimization platform because AI is everywhere, as you say. AI is in Finsbury Park, it’s here, everything in the room. Everyone says agentic and they just, you know, sometimes they know what they mean, sometimes they don’t. That’s a part of the issue.
But most generally code is being generated at a Like large speed, a lot of sorry, a lot of money is being wasted on tokens. and that’s good. And essentially it does enhance productivity somewhere, but the main thing is like, do we use large language model models or harnesses around them, such as agents, to actually produce code that matters, code of value, code that ships? And that’s what Artemis and TurinTech essentially aims to solve for organizations, and we have. And I want to talk a bit about. bit more about like the use cases that we’ve briefly but you know some of the work that we’ve done that highlights that community.
James Governor
Well people that so one question so you call yourself you say sort of in research, you’re working with the clients. I mean should we be calling you a forward deployed engineer? I think that work is handled by so we work as a team. We work as a team. but yeah we do handle deployment.
Yep. we we deploy a platform in various ways. So it can be SaaS deployment, it can be on prem, on you know, people’s cloud, et cetera. And that’s a part of the thing that it’s very important. So we we make sure that people run their code locally, so they ensure that the data that is produced by by the platform that you know they work with is only preserv only preserved on their machines. Right. It’s pretty important.
James Governor
So th this is You’re talking about those customers that have regulated industries or certainly banking always cares a lot about privacy.
Mina Ilieva
Any customer really. I mean, you know, we we work with banks, and they quite enjoy that level of anonymity, I guess, you know, data anonymity if I may say so. but we work with startups as well. we work with individual customers as well. We’ve assisted a lot of researchers in universities complete their projects, which has been pretty cool. Mm-hmm.
And yeah, so literally anyone interested in code optimization, and I want to actually get back to your first question. I mean, what is code optimization? How does Artemis handle code optimization? Yep. So famously Artemis started out as a platform leveraging sort of a genetic algorithm, right? So it’s the kind of model that creates populations of potential candidates, in our case code candidates, right? Would score them and technically get out via a fitness function the best candidate that will fit your problem.
Right the most. Okay. Right. And that was something that was working for for for a long time. Now we’ve kind of built up on that logic of evolution. And we’ve we have a new tool that we’re we’re coming out with. It’s called Discovery. So here we’re leveraging more on the empirical method for code optimization, right? So it’s our solution, not just you know, to to kind of the endless tracing of code changes that developers will want to have, but also
systems that hallucinate quite a lot and ours doesn’t right because what we do is we have a we have a multi-agent system that generates hypotheses that turn into experiments that turn into code versions that are manually verified. Well sorry, what what is done manually is just the approval. They are automate the the ver verification is automated. And what happens really is you know you the user defines the generations and so therefore you have this full experimental empirical loop that makes sure that any idea
That an LLM agent or yourself will have will be verified and you know you see if that the code compiles tests and you know it it survives the benchmark and then you know shippable to the end. Right. So this is what we do.
James Governor
So tell me about this. You well, you mentioned benchmark. What are the things that your clients are optimizing for? So I guess this gets brings us to the use cases, like correct. So it could be performance of the code, but I’m assuming the the some of those use cases are changing. So let yeah, let’s talk about what you all the work that you’re doing in clients and and a bit more about those use cases that you wanted to get to.
Mina Ilieva
Sure, yeah, of course. So great question is like what is you what are we optimizing for? I mean what are you improving is is the real question. So when when it comes to benchmarking, which is technically just a command that can be consumed by a runner, which is a software that we ship with our platform, so again locally, the benchmark kind of defines the metrics.
That our platform traces for the users. So when you say performance, I mean we can also distill performance in kind of different submetrics as well, right? So we can talk about throughput, we can talk about latency, time to first token, et cetera, et cetera. So these are like metrics that are widely used in LM inference, right? And crucially, there are also trade-offs between those metrics that we also leverage. I really liked a quote from a client recently that was like, well, optimization is never free bread, you know, because every time you change something in your code.
you kind of are have to be okay with some trade-offs. Yes.
James Governor
Optimization is always about trade-offs. Yes.
Mina Ilieva
Yep. Yes. And so this is what we also deliver by allowing clients to define the metrics they care about. So primarily on the platform, if you just come in, right, your your developer, you just want to try it out, we do measure for memory, runtime, and performance. So like you know, CPU performance. So this is this is like the primary three metrics that we measure code performance by, but users can
Absolutely come with come in with more. And the platform can account for them. So I mean, the metrics or whatever we optimize for are tailored to the use cases that we work with.
James Governor
See, I’m I’m getting the title for this podcast. It’s fitness and free bread. Awesome. Love it. Love it. Love it. yeah, those are evocative themes. The free bread thing is interesting because that that’s it’s it’s I mean it’s not an English saying necessarily.
Mina Ilieva
I guess so. I mean the team wasn’t of English origin, so I guess
James Governor
Okay, that right that that that might be why, but I but I do like it. I’m gonna be thinking about free bread from now on. okay, so let’s let’s aga so interesting enough, you mentioned a client, you’re working with Intel. Is that is is that a client that you yourself are working with? Or or just the team is working with?
Mina Ilieva
I guess the team is working with it. I’ve been in calls and also personal meetings with Intel. They partner with us quite quite heavily.
for for for both the benefits of of Turing Check and Intel themselves. so when it comes to our Intel work, for example, the latest kind of optimization that we’re really proud of, right, if I may say so, is actually optimizing VLLM, which is their you know inference engine repository. we’ve managed to boost throughput for I believe f within the range of six to thirty six percent amongst quantized and non-quantized models.
and that was huge, you know, for them because it was actually beyond the value that they requested in the first place. And we’ve used Artemis Discovery, right, to do that. So it was great. So that’s just one of the optimization we’ve done for them. We also work with OpenVINO, which is another kind of like very low-level code type repository. we have had some optimizations and some work there, I believe, that Intel are now interested to use the platform because of the OpenVINO work for more diagnostic purposes.
And that’s also a a kind of way you can use Artemis for, right? So, you know, repositories range from like very non-optimized to highly optimized one ones, right? And when you talk about, you know, companies like Intel, you would surely think that their code is good, and it is. but you know, as changes progress on the repository, you can use Artemis, you can use our discovery tool, our empirical method kind of tool, to keep, you know.
To keep up to date, to maintain your code, and to make sure that nothing breaks along the way, and to make sure that your ideas for optimizations are valid. Okay. You know, which is a part of the workflow developers usually enjoy.
James Governor
Okay. Okay. So actually, so I think VLLM is an open source project.
Mina Ilieva
It is, I believe.
James Governor
So therefore those benefits will potentially
Mina Ilieva
transpire into a PR.
James Governor
Will be used exactly across the industry. So that would be that would be great. We’ll we’ll see about that. I’m gonna have to look into the status of that actually.
Talking about me, yeah, it’s always obviously w London financial services. I mean, at the risk of a bad joke given the context, you know, why does one rob banks? That’s where the money is. Why does one sell software to l London-based financial services companies? Because that’s where the money is. What are the real optimization? what are the areas that you’re seeing the most demand for in terms of optimization today? And is it changing? you know, I I think as I say, the pre and post-LLM revolution before you were selling.
A different set of benchmarks. Now you’re focusing more on sort of inference. Yeah, what are what what are you seeing? Or maybe you’re not working. I I mean I’m asking about banks. Maybe you’ve been working with other
Mina Ilieva
I guess, yeah, I’m on the this is a really good question. I was trying to think deeply about it. So I guess the areas for optimization have been changing. I mean by the month, not just like by the year, right? ‘Cause ’cause AI is getting better and better, or maybe sometimes worse and worse when it comes to adoption, right? And you know, that’s what we’re trying to help with. but yeah, the areas I believe
There’s a general when it comes to inference optimization, there’s a general direction in the way of local models being deployed, right? So like deploying models locally on machines, right? Because technically the value well the the pricing of proprietary models has been increasing a lot.
James Governor
Is this because they’re looking to make savings on tokens?
Mina Ilieva
I do believe so. I mean this is the you know, this is probably the aim for everyone, but you know, some people might say otherwise.
James Governor
No, I mean I I got an like The degree to which that has now become a mainstream concern, I think is really interesting. Like I I got a call from a reporter at the in fact an editor at the the Daily Telegraph to ask me about like this what’s this token maxxing thing? And you know, does it make sense? It seems like people are wasting money on tokens. And I think it’s one thing for a like a tech mazine to be asking, but when it’s the Daily Telegraph, you know that this has become very much a
front level concern for organizations.
Mina Ilieva
Do you know who I think? I mean, maybe that’s a bit of a tangent, but I’d like to kind of like chime in here when it comes to like token like token maxxing. Did you say tok tok
James Governor
yeah the the phrase is and the that these that’s when you’ve got organizations or individuals that are claiming like they’re so good because they burned so many tokens. So you’ve got the the the we had Amazon which was literally you you were measured on how many tokens you burned, which is I not a great way to deliver efficiency.
but we’ve certainly seen individuals, you know, it’s the classic Silicon Valley sort of like, you know, if you’re not spending three thousand dollars a week on your agents, you’re not gonna make it. And and and so the the the the the claims and you look at some of the maths, it it it that’s just not gonna fly with a lot of enterprises. You start talking about thousands of dollars per I mean, per developer is one thing, they’re already like, wait a minute. Right you add that onto a swarm of agents and you want every agent to be burning that many tokens.
yeah, the economics swiftly get out of control. So on the one hand you’ve got people like, Yeah, token maxxing is awesome because it shows how, you know
Look at me with all these swarms of agents. I’m so productive. I’ve rebuilt this whole system myself. Exactly. But I think for for a lot of organizations, they’re more interested in they’re probably token maxxing is not what you want to do if you are Barclays bank.
Mina Ilieva
Right. and in general I feel like token maxxing is it what you want to do if you want to be genuinely productive, if you want to raise PRs that actually get approved. So what I would say is absolutely my firm belief that a developer.
James Governor
for you, token maxxing is is about maximizing efficiency of tokens.
Mina Ilieva
That’s exactly what I think. Yeah. It’s about yeah. It’s it’s about using tokens the right way. Yep. Right. and you know, it’s about also measuring, you know, the kind of changes you’re trying to install, like you’re trying to t like create in your code base, right? and Artemis does that. and we’re kind of following that workflow in general when it comes to our own work.
You know, and we we almost sometimes even like create that harness around validation, which is like, okay, if I make this change, will it compile? Will it pass unit tests? Will it produce better metrics? Right. And that’s something that, you know, even even if someone thinks about workflows like that, similarly to it still generates a red herring every now and again, right? Because LLMs are very opportunistic, right? So if you just if you just talk about token maxxing, the first thing I think about is quote code, right?
Have like 10 terMina Ilievals open, you know, have like three sub agents here, five skills over here, don’t know what you’re doing, but you feel like you’re on top of the world. Like that’s you know, that’s the kind of like lonesome kind of entrepreneurial development.
James Governor
But that’s the token maxxer right there.
Mina Ilieva
Yeah, that’s the token maxxer. Yeah. we w we definitely support that. You can definitely use Artemis with Claude Code to get us. It’s definitely not c competition there, but it’s all about code validation at the end of the day, right? So, you know.
You have maybe you have some workflows and some skills that you’ve you’ve installed somewhere, but you people forget that LMs still hallucinate. People forget that LMs are opportunistic machines, right? So if you wanna work if you wanna use or employ a multi agent system, right, that is gonna ensure your work gets shipped at the end of the day ’cause that’s your aim, then you know, I’m gonna say rely on Artemis or, you know, rely on, you know, the developers as good as ours, yeah, technically to build that for you.
James Governor
We’ve certainly been seeing on that note, worth talking about. So if we look at the Dora report, which is has has been going on for some years now and it has now begun to spend more time looking at these AI issues, kind of this interesting disjunction where we if we go back a few years, it was definitely moving faster was correlated with higher quality and less downtime. That was because the organizations that were moving faster had invested in the testing, the documentation.
They were just more effective software development organizations. So yes, they were moving faster, but it you know they had production excellence. So therefore they were able to take advantage and and and have, as I say, fewer bugs, better MTTR and so on. that’s broken down, and what we’re now seeing is a degradation in quality. Right. So speed goes up, quality goes down a bit, and then CircleCI have got some really interesting research, and we’re always at RedMonk.
questionnaires are great, And Dora’s great, but we love telemetry data when it’s actually based on on on machine data, right? And so CircleCI was looking at at their builds, what they’ve seen is interesting in their clients is that the the branches, there’s an increase in in in work. But whether that’s that’s not convert that’s not correlated with the trunk. So we’re seeing stuff that we’re not shipping as opposed to stuff that we are shipping. So it’s a sort of
I hesitate to say h hesitate to say a fake productivity. Yeah. But we we are not we are not mate the LLM revolution has not yet been a team sport. It’s been about the individual. It’s been about that person. You’re talking about using Claude. I agree with you. And I think that’s the transition that we need to make as an industry.
Mina Ilieva
Yes. I I see we see eye to eye here, definitely. I think it’s one of the things that Artemis does really for organizations is enables kind of working in a team when it comes to using our agentic platform, you know, optimizations, you know, you raise PRs, you get these reviews, reviewed, sorry. and we give the user a lot of control as well, which is sometimes something that, you know, cloud code users or like, you know, mass code shippers that don’t actually like, you know, get that kind of past that gate into production. they’re they’re not doing that, right? Because what they do is they just keep vibe coding like all the time because it’s so good. And I feel
Feel like maybe I should also say something here as a neuroscience background type person going into going into AI, you know, dopamine is on all time high in general in the world. And I feel like there’s there’s a lot of that combined also with the fear of
James Governor
we used to talk about the mean time to dopamine of using containers. Right. But the mean time to dopamine when when when you’re using something like Claude unbelievable. Yeah, the the the feedback loop is so quick and you feel amazing. You feel you feel incredibly productive. Yeah. Yeah, yeah, yeah. But it’s yeah, dopamine is at an all time high, you’re not wrong.
Mina Ilieva Yeah. and even if you feel I’m gonna start again here, and even if you feel hopeless about something, right, you have some agent somewhere to encourage you that changing somewhere, something, you know, whatever, can can yield better results.
James Governor
You’re absolutely right.
Mina Ilieva
Thank you so much, Sonnet four point five was it? I guess this is you know, but this this as I said in the past, you know, we as as I said in a I think a minute ago, is it that kind of activity breeds a lot of red herrings in people’s workflows. it it’s not very healthy, I believe, unless you know exactly what you’re doing. And I believe it’s a good sport for very experienced engineers. Which leads me to a point I really wanted to make about, you know, there’s a wide ranging fear now in the industry that developers slash engineers, slash researchers, whoever really will be replaced by AI.
You know, I think that’s that’s absolutely false. I actually don’t think that anyone with a good eye for verification, for code, you know, good people skills and you know, also as you said, teamworking skills will be made redundant. And you know, and I think yeah, it’s just like a bit of a noise being made. but you know, and we in turn tech we aim to also provide a product that that tells users and actually gives them evidence for that.
same story because what we do is we just save time for exploration. We let developers find code changes you know, on their own, technically, either running by running our systems manually or in auto mode, regardless of what they want to do, whatever whatever they have time for. But there’s always the human eye validation thing. And there’s always of course, you know, setting up that flow where you compile test and benchmark whatever kind of code you’re trying to run or
James Governor
I gotta say I mean I I agree with you, but but the the rubric now has gone so far towards, no, we don’t, you know, we don’t we’re not gonna review the code. We can’t scale to that. Right. now, sensible people that are saying that are definitely you you do need review, but they’re like, review will be via AI. I mean, we’ve seen this very large code bases. I think Chrome from Google is a good example. Where initially they’re like everything needs to be human reviewed, and and that’s beginning to change. Right. Where it’s like we cannot.
We we can’t review the scale of work that’s being done. but I guess that’s where you need ever I mean, verification becomes ever more important and I guess AI tooling becomes super relevant. Tell me about your customers though. You go to a bank and if you say things like, you don’t need to do human review or like we
Mina Ilieva
we don’t say that to banks We don’t say that to bankk. We we do say that they don’t might don’t need to write those PRs as much as they
would previously do that and you know developers really enjoy that ’cause everyone hates writing PRs. So we do that automatically for for clients. but we don’t we we say that they should review the code and they the the engineers we work with have an actual aptitude to do that. What I’ve also noticed though is, you know, more experienced engineers work more quickly with our platform and and they adopt it you know, quite quite happily as well. and they’re better able to trace what it does, you know, which is further, you know, enhancing the argument that, you know
AI is just here to help you out. But yeah, so they do review on their own. And what we do, what Artemis does is you connect your code base, connect your branch to the platform on the cloud, or you know, you can also connect to it locally now via the CLI. And any code change can get shipped into a PR very quickly. But the review happens by humans in the end. Yep. Right. If you have a review agent that you have as part of your internal workflow, that’s pretty cool too. I personally would be interested to see that agent.
I quite like creating agents, but you know, no. We we leave reviews to humans at the end. We we want to hand control to users.
James Governor
Okay. So we talk about use case. You’re working with clients. You mentioned Intel, which was great. Are there any other customers that you can name today? ‘Cause that would be super awesome.
Mina Ilieva
I that would be pretty cool indeed. so I guess not at the moment. Okay. I could I wanted to talk about Quantlib though. So Quantlib rings a bell in you know in the audiences ears in general. It’s
It’s the major kind of quant finance repository and it’s it’s a it’s a change that we actually shipped and we’re actually kind of proud of. It’s a single code change run basically finding Artemis at the time, I think it was like a year or something ago, found that there was a nested loop, kind of annuity annuities. Do you say annuities are recomputed in a nested loop? And what I believe happened was it it started recomputing them in advance, stored them in a vector, and
it yielded around I think thirty percent you know less time on testing. Yep. and that was shipped. That was immediately shipped. And yeah that’s what’s one of the things we’ve done. and we have also worked with hashing libraries similar to you know Quantlib. one of them is BLAKE3 . Yep. which is fastest, you know, hashing fast fastest hashing function there is do you know what a try saying that quickly a bunch of times. Do you know what a hashing function is?
James Governor
Yes I do.
Mina Ilieva
Okay, cool. Well, okay. So what we did is
James Governor
But I’ll tell you what, why don’t you explain? Because maybe not everyone knows what a hash is.
Mina Ilieva
Right. And it’s kind of like the the the backbone of blockchain, you know, how all that operates. and what we’ve done there is we have introduced, I believe like there was a parallelization technique to reduce time.
and yielded better latency. So we made it slightly faster. I think it was like two or three percent. But for BLAKE3, that’s huge, right? You save you save a lot of a lot of time and money. So that was another optimization that we did. the most recent one, I think, it’s quite proudly, we optimized a mixture of experts model. Nemotron 3 Nano uses about 30 billion parameters, but only three billion of these get used by the experts to compute weights, which creates a bottleneck. So what we did, or you know, a colleague of ours and our team did is it shrunk those weights, the computation of those weights from Bx sixteen to integer eight, which is lace kind of a quantization technique there, boosting throughput, I believe, by forty percent and reducing latency by thirty. I’m not really sure about those numbers, but that would actually
Definitely two digit two digits. So, you know. So this is this is the most recent one. That was last week. Okay. Right. So we do amazing things with Discovery. We do amazing things in the team and we we’re learning a lot as well. And I think that brings me also to like the no free bread. No yeah, no no free bread. There we go. Right. I mean technically when it comes to optimizations, we do trade we we we can also say we there’s an accuracy trade off, right?
That we talk about. and we have observed these trade-offs, but what I think clients have been quite okay with is seeing that there’s a slight accuracy degradation in favor, for example, of more like toolput or you know, like less like on latency. Yep. And I think, you know, I think this just kind of speaks about I just want to shed light a bit more on like the truth behind optimization, which is, there’s always a trade-off in the metrics you measure, and you’re gonna have to sacrifice something. But you know, Artemis kind of gives you the sheds light onto what exactly the sacrifice is and whether or not it’s worth it for you up front, right? And quickly.
James Governor
And I think So Artemis is some free bread.
Mina Ilieva
Artemis is some free bread.
James Governor
Just a little bit. Just a little bit of free bread. Okay. Well, I’m I mean, I’m gluten free, so the whole bread thing, I mean, sounds great, but I mean I I I I I don’t even I don’t even need free bread, but but that’s that’s that’s that’s all we need to say about that. But so Mina Ilieva, I think this is you know, the no free bread, great place to you know, close the conversation. Okay. that’s been super interesting.
I think particularly I think for you know our audience, you know, thank you so much for sharing the information about those specific projects where you’ve improved performance. as I say, hopefully I mean, well frankly, that’s a great marketing opportunity, the VLM stuff, I think that’s significant, that’s used all across the industry now.
Mina Ilieva
And we validated it on different types of hardware as well. So it’s hardware agnostic is great.
James Governor
wait, see I I was gonna finish and then I wanted to ask you what one last question. I’m gonna do a Columbo thing. Sure. models. So you you you talked about Claude. what what are the key what are the main and also you but then you were talking about like your customers maybe using models running their own models, open weights models. Yes. What are you seeing most of? Tell us about what should what models should we be watching or or
Mina Ilieva
I believe anything that works for you, I mean depending on your workflow. If you’re an individual developer, I believe you should try and
If you could afford it, use as many models as you want, right? But if you’re for more an organization, if you do want to cut costs, then yeah, I think I feel like you know that’s kind of where the future of AI is. And it is a personal opinion, but we are also prepared for it, right? Here in TurinTech. But I believe that yeah, deploying your local LLMs, optimizing them, quantizing them, doing whatever you want with them is a good at least practice to start. Like it’s a good exercise. so that would be my recommendation.
James Governor
Yeah. I mean I think as an industry we’re certainly moving to in in in the in your view of of token maxxing which is token efficiency we’re certainly seeing an understanding and it’s important that we drive this understanding that you should be choosing the right model for the right job and frankly there is a lot of model choice out there and you don’t always want to default to the latest, greatest and most expensive. so on that note, I think that was really great Monkcast conversation.
thanks so much for joining me today. for those of you that are listening and or watching, please do all the smash like, subscribe, share it with your friends. I think it was a really interesting episode. so once again, thank you, Mina Ilieva. Thank you, James Governor. Have a good day.
Good. Nice. Yeah. I mean it has to be edited a little bit, let me know. Yeah, mean
