 So, we start from last year building a decentralized service of drones that could fly autonomously and capture data and then move this data into data insights that we can sell. So we did a poor concept in Papua New Guinea, where we had this drone that autonomously flew over the plantation of coconuts and then analyzed our mini-coconut petri. Then we used this data to sell to Hedgefond. So then we had the problem of scanning the drone due to regulation and all this stuff. So lately, over the past six months, we've been working on something. Instead of having problems with data collection, now we use the four biggest satellites worldwide. So now we don't have a problem of data collection. So I'm going to show you. So we're building a protocol, but the concept is you've got a huge value of data from space, geospatial analytics. This is growing very fast, but no one is trying to actually capture this data. So you can actually make better decisions, decision triggers, and capture data analytics to sell to Hedgefond or to predict the production of caca worldwide or to do parametric insurance, for example. Cool. So what we do, we build like Numeri or like Kaggle. You guys know about Kaggle and Numeri. We build a competition of data scientists that are incentivized to build a specific model for a specific use case. So we pay data scientists $25,000 die to build a model and we give them 10% stake in this model. So then those models can be used by different companies to fetch data. For example, we're building a model that can predict the number of cacao plantation in Brazil. So the data scientist build this model and then you get $25,000 die and also 10% ownership of this model. So every time Hedgefond wants to collect data from the specific model, the data scientist receive 10% stake. So we got a company in Paris, for example, they're doing this. They build a model that can calculate the position of the ship, the containers in the ocean and they sell the analytics to Hedgefond. They're making between $3 and $5 million a month. So I'll let you know how much it would be if we decentralize this entire market cap. So what we do, we tokenize this model, we turn them into a non-fungible token and then we give opportunity for anyone here to invest in this model. And then the clients pay directly. So the interest value is the money that is paid by the client and then you can own a stake of this model. We can also turn this model into ovicles. So what we're doing, for example, for the biggest insurance company in the world, we connect this, for example, a farmer can link his land, can sign his crops and policy through the AXA insurance company. And whatever happened to this land, we trigger the ovicle and we verify there's nothing, for example, we calculate the damages that happened to this crop between zero and five. And we give a number. So then we can do automation claim. The farmer can actually receive it automatically. So this is what we're building, for example, as model dashboard. The design is a bit taken from 0x. But you can see all the different models, for example, coffee yield. We're building something in Mexico Beach. Sorry, I'm a bit... We're building something in Mexico Beach, for example, you have rooftop anomalies. So we have this model that can use, it's a machine learning model, that can fetch data from the satellite. And we calculate on GPS-based location where all these anomalies prove top. That you can, for example, you can connect to a farm or stuff like that. We're building an interesting project which is carbon offset for forest density. We can monitor from space what is the density of the forest. And then we can talk on as this, we can basically use a carbon offset. This project is called Reforest Room. And by doing so, what you can do, you can buy a piece of this forest and it will show through a certificate how much you've been participating in carbon offsets. So then you can actually reclaim a tax incentive at the end of the year. So you can see on this dashboard that we get NTN token, which is called the nitrogen token. But then we've got something else, which is called nitrogen X. And this nitrogen X is actually captured by using a bounding curve. And this bounding curve lets you... I'm going to show you. Is that loading? I see the slide on the internet, so it's a bit slow. Do I have access? Okay, let's go to the next one. Okay, cool. So we've got all these different models. And then we can use the model as an Oracle API. So we can actually link this Oracle, this model, to Prediction Market, like Noziz or Augur. And we can also link it to different smart contracts. We can do decentralized insurance company, like e-service. We can also build a DAO. And a DAO will be something that will be specialized for renewable energy or climate change. And this climate change DAO will have all the different models about the climate change. So for example, we're building a model. We send someone in Antarctica that we can capture how fast is the ice in melting. So we can actually capture this data. And we can also have exchange market with private people that can actually trade in this model. So we can actually apply any kind of financial instrument on the top of this model. So derivative, future, cap, swap, catch swap. So this is like, for example, in this example, we've got a DAO that's specialized in agriculture. So we've got a coffee yield model, carbon credits, cacao biomass, crops analysis. So we got those five, four different biggest satellites. They give us access to five or six different bands, which is RGB colors. We've got a infrared. We've got biomass spectral camera. So we can extract. You've got the different layers into the protocol you can actually capture your data and turn them into useful information for those models. I'm sorry for the starting. So yeah, the token sticking is interesting. So what we do, the nitrogen token, you stake it against a model, and it gives you a return based on the burden curve. It gives you this nitrogen X. Those nitrogen X cannot be trade. They can only be used in this model. So you can actually capture and you can invest in this model as an investor and then capture a revenue income. For example, we can have this landing page of all the different models that have been generating money over the past three months. And what is the expectation or the prediction of this model to generate money in the next three months? For example, we know there's a catastrophe going to happen in Caribbean Island. And we have this specific model that can capture what is the stage, what is the degree of damages in Caribbean Island. So we know this model will be used and generate a lot of money. So you can actually stake it into this model. So that's what I was explaining. You invest, you stake your token. And then based on if your early stage in the model, so the model was just created, you get the most of nitrogen X. And you can actually use it to spend in the model. But at some point, the model will get up in value so then you can always sell your nitrogen X. It would be more advantageous to sell them to actually consume the data. One nitrogen X gives you one access to a computational power for running those data into the model. So per model, you can see how much you've been staking against them. And how much if you get the stake back, how much it will give you in nitrogen X. So yeah, you can actually build all-vehicles mechanism. Also, so this is the five different applications that we're building. We're building a co-op parametric and trans-mechanism. So you can actually link this to Easter risk. So this is kind of like building a proof of observation from space. So this is a protocol layer to give you data inside from space. We're building carbon offset, roof plop anomalies for insurance industry, community trading, and tracking for climate change. So those models are being built internally and we are planning to give access for anyone to invest in those models to the first two, like in the coming weeks. But the plan is to let anyone build models through a competition, like the same as Numeroi is doing. Numeroi is doing this for the financial stock market. We're doing this kind of same mechanism for visual data coming from space. Yeah, this is just like, for example, catastrophe insurance. So you can have this model that gives you insight or give you a binary input. So those models, the plan is long-term, you can also train or run the computational of those models into Golem or into other platform. So we're going to see this model for rooftop anomalies. So this is the kind of information you can get from space. And this can also be plugged into different models. Yeah, thank you. I'd like some other slide, but it's not loading, so.