Welcome to today's functional group update for the platform backend team. My name is Auman and I'm the engineering manager and today as it says there and as I hope you are aware it is the first of May 2018. The last update was actually only three weeks ago. Usually we do five but because of some moving around of updates it has only been three weeks. Not all that much has happened since but we did release get that 10.7 on April 22nd. Of course platform team contributed a lot of things to this release and the blog posts that are linked to covers that in great detail. But there were a couple things that I don't think deserved quite the attention they or didn't quite get to the attention they deserved. So I wanted to correct that with this slide. Couple things I think are very much worth mentioning. On the one hand we have the create commit API that now respects LFS tracking rules which means that if you use the API to create a file upload file with a certain extension and we know that that file should be tracked by LFS. Instead of just putting the file in the repository anyway we actually on the back end go through the trouble of uploading it to LFS and the file that ends up in the repo is just the LFS pointer. Thanks James and for Jones for working on this. We also have better LDAP connection feel your hand. The other three couple times and when it still doesn't work after a couple retires if we still cannot connect to LDAP we interpret that as the user no longer existing in LDAP which in the past would result in user being blocked or being removed from a group those kinds of things. Now we are a little bit better about realizing that it's just a connection failure and that we will have another chance at underline. Of course if there's a certain time period where you haven't been able to verify if the user should still have access to certain resources we do block them but not just because there is a momentary connection error. The future of the project is also pretty much the you can see this in the actual thank you Tiago. The archive projects which in the past only had a read only repository are now completely really their issues their merge requests you cannot comment you cannot change you cannot create anything in there which makes them a lot more archived than they previously were and it's just something that Bob worked on. Then in the next five weeks hopefully the next update will be in five weeks so this should cover everything that happened until then. The first thing we're going to do on May 7th is finalize the development of GitLab 10.8 and a couple of the things that are going to be in that piece are listed here. I'm not going to go into too much detail because of the links and read up all about them there. The one I wanted to call out which might sound all too interesting is important mirroring relays without project stable. Projects in GitLab that are mirrors of an external project posted on some other repository of course updates frequently they update on some schedule. Apparently I'm having some connection issues let me just retry sharing the sites and otherwise I hope that everyone will just be able to read the sites for themselves and go from there. Give me one second I'll try to restart this and we're going to give it another try. Okay I think you should see my screen again if not then please let me know. I'm also going to open up the chat window again so I can know if someone says something's broken. All right Bob says for back I guess that's good news. So yeah now I was talking about important mirroring related columns moved out of the project stable. On GitLab.com we have a lot of projects that are mirrors of externally hosted projects and periodically GitLab will sync up and get any latest changes from those remote resources, those remote locations and we have a lot of those projects doing that and every time they do that they go through a couple state transitions they go from scheduled to be updated currently updating finished or updating filled and at some point they go back through that cycle again and we're pretty much constantly doing that for at this point around 15 different 15 mirrors per second to get updated and this of course means that there's a lot of actual updates to the status field happening in the database. Right now these status fields are stored right on the project stable which is a quite large table which stores a lot of project related data mostly settings and some cash stuff but it's still quite a wide table with a lots of columns that means that every time that we just switch the state from one value to another we actually write a whole new row into Postgres and mark the other one as as old as still which means that we are actually accumulating a lot of old still data because we're doing this in this pretty big table so one thing we're going to do is to move these fields to a separate table which will be much smaller which will only store mirroring and import related data which means that if we update it as frequently as we do we will not be building this huge amount of still rows so this will be you will be able to see this not directly in the application but hopefully you'll be able to experience performance benefits and just you know everything on get the outcome will be a little bit faster hopefully you won't notice which means that everything is going right and there's a couple more issues there that you might want to check out if you're interested in specific people for doing for 10.8 then on May 8 we will be kicking off development of GitLab 11.0 what's going to go in there is not quite clear yet but it will be announced on the public kickoff on May the 8th and this is available to everyone everyone can join if you check out that link you will be able to see the details of where you can follow along and then another happy event that's going to take place in the next couple weeks is that Imre Farkas will be joining the team as a senior developer on May 9th and I would like to specifically thanks Jan who I think worked with him previously and who referred to GitLab and as Sean mentioned to me a couple times when Jan referred Imre he told Sean he's a pretty good developer and Sean immediately said like oh yeah if Jan thinks he's pretty good we have to get him and it turns out that he is absolutely great and I'm very much looking forward to having him join the team that's everything that I had to share for now like I mentioned it's only been three weeks since the last update so not too much has happened hopefully the next third will be a little bit more interesting if anyone has any questions though I'd be more than happy to answer them and if not I'm going to give everyone about 10 seconds and then I will give you about 23 minutes every day left back


You know early in my career thinking about leadership. You just did the job and so from this seat looking backwards I would say advocate for professional development understand what professional development is I'll be outside of your area of expertise Advocate for that look for attach yourself to mentors who can help you Grow so the second thing I would do when I think about what technology is today in the organization is I would be a much stronger advocate for the user and what the user is trying to accomplish within the organization I would really make sure that I was bringing back what I was learning from user I'd make sure I was out with our users and our colleagues making sure I understood it and just really focus on building that voice because The leaders that grow up in IT and become IT leaders have to understand that that's what we do The third thing if you think you want to be a leader within an IT organization, don't stay in your niche in IT Understand what your colleagues are working on what projects are they a part of how are they serving the community? How is it all tied together be that team member that can actually have a conversation about what the others are doing? So you understand holistically what is happening in IT? What do we do what are our roles in an IT organization? If you get in a large one you may become fairly segregated from some of those roles so be intentional about learning What does my what does my team do?


Hi, everyone. I'm Kevin, a researcher from Google, and today I'll talk to you guys about a new recent work on lower bounds for encrypted multi-maps and searchable encryption in a new model that we call the leakage software model. This is a joint work of my fabulous collaborators, Sarver Patel and Pino Perceiano from Google and the University of Salerno, respectively. All right, so let's get into it. So the problem we're trying to solve in this scenario with the problem we're considering is sort of a user or data owner that wishes to outsource its data to a third party on the right or a server that's potentially untrusted. Anyone wants to do it in sort of a privacy-preserving way. So let's suppose, let's consider an example where let's say the user has already uploaded a map or a key value store, a key associated with values. So what happens is let's suppose the user wants a query for a key KI, it might send it into plain text, and it would get the associated to go VI. So I guess I want to get to quickly, sort of even if all the values were encrypted using a client-side key, during sort of these queries or requests by the user, the server will learn which key was being queried. So okay, it might learn it for a single query, it might not seem so bad, but over a large sequence of operations of queries and inserts, the server will get more statistics and more detailed information. So for example, maybe the second key was never queried, and the 15th key was the most frequently queried key. And sort of it's been shown in the past that using this sort of complex information, it can be used to try to understand what data is being outsourced even if the values are encrypted using a client-side key. And also it can also learn what kind of intent or algorithm the user is trying to run by just looking at these keys. So in an ideal world, what we would like is something like the following, where the user gets a key KI once a query, maybe does a sequence of complicated sort of retrievals from the server, is able to get the tuple of values VI associated with KI, but it does it in such a way that the server doesn't know what the requested key is. All right, so try to understand, to further understand this problem, what we can do is consider this map data structure and consider the privacy spectrum that's achieved by various solutions. So sort of the most basic solution is what we call point text map. So we usually just call it map data structures. You know, this has been a very classic problem. So it's actually called the dictionary problem many years ago, and it's been considered for a long period of time, and it's been solved very well. So there's many solutions such as perfect hashing, the FTS hashing scheme, and cuckoo hashing and many, many more. But sort of what you get with these plain text maps is a constant overhead. So when you want to retrieve a key KI for a value, for values VI, you would get that value associated with that key. And similarly, it only requires order and storage. If you want to store a table consisting of n keys and n values, you only require order and storage. On the other hand, in terms of privacy, it ends up leaking all keys and values because there's no privacy was not even a requirement for this case. So what we can then do is consider a slightly different primitive called structured encryption. So this will obviously have stronger privacy but slightly less efficiency. So what is structured encryption? Sort of the idea is to encrypt the data structure while maintaining its operations. So the classical example that's been very heavily studied is this idea of sociable encryption, where you're essentially encrypting a search index while maintaining the operations of the search index. And there's many works in the past two decades I've listed here that consider static, dynamic, and various privacy settings. But sort of at a high level, what we what is obtained by structured encryption from maps is you sort of typically get order one efficiency, but of course it can be higher depending on the leakage function that you get. And the privacy is obviously, it doesn't leak everything about the keys and values, but it leaks some sort of well-defined leakage function that contains various components. So things like the number of values that are associated with keys, for example. You might learn key quality between operations, which we'll get to a little more later, but it's sort of learning whether two operations are performed the same key. And so you might also learn the number of operations the user performs. So sort of this is on the spectrum, this is sort of a slightly more less, slightly more private but less efficient primitive. So finally to complete the spectrum, we can go even further to the right and consider something that's much more private but much less efficient, which can be built from things like oblivious RAMs. So what is oblivious RAM? Essentially it was introduced by Goldrack and Astrosky in the 90s. There's been many works that, you know, that led to something of the order log and optimal overhead constructions, but sort of what happens is they can actually in an oblivious RAM can actually implement a map using log and overhead. And this turns out to be tight based on lower bounds and various models that over the past a couple decades. But for privacy, what you end up getting is something very, very strong. What it turns out is that the adversary cannot distinguish any two sequences of the same length that could have been performed by the user. So, you know, translating this to the world of leakage functions, what essentially it says is that the leakage function is only of either the length of the operational sequence or an upper bound of the operational sequence. So in essence, what happens is you're sort of have this to the spectrum where, you know, the left is the most efficient but the least private. And as you move to the right, you get less you trade efficiency for more privacy. Alright, so in this work, what we're really going to focus on is actually this area between structured encryption and oblivious RAM. As you can see, there is a there's a gap in efficiency from constant to log n. And similarly for leakage for privacy where oblivious RAMs are in some ways the ideal or the optimal privacy, whereas the structured encryption ends up leaking some very non trivial, very informative leakage that the adversary could use to try to get information to try to compromise the data. And what we're essentially trying to do is trying to figure out how this is how this sort of ramp goes, is it sort of a direct job? Are there more things here that are there more things we can construct that are have less than log and efficiency, but better privacy than the structured encryption schemes? Of course, to study this, I stopped, I have to I sort of have to tell you what structured encryption actually obtained because right now I just said it's a non trivial leakage function. So it turns out that structured encryption can actually be defined using a very simple hash and encrypting pilot or many of these structured encryption schemes. So the idea of a hash encrypting pilot is to take any plain text operations with let's say three operations insert keys values get keys delete keys. You know, these are this is a plain text map that has that's only trying to get to implement these operations efficiently, and without any privacy. So for example, let's suppose we we've implemented this using a using the keys and values in plain text that we did before. So the idea of the hash encrypt compiler is sort of simply store a client side private key that consists of actually two keys of a hash key and an encryption key and simply replace each of the keys with a hash of the key and replace all the values with an in with an in CPA encryption of the values. And it turns out, you know, it's very this is not a very complicated transformation, but it still enables both for example, queries, if you want to query for a key AI, the user simply hashes low locally and sends the hash to the server. And the server will look up the hash in the plain text map and return the associated encryption values, or if it doesn't exist, return null. Similarly, if you wanted to insert a key with some value VI, you just send a hash of the key and an in CPA encryption of the value. And the server would simply perform the plain text operation of inserting the hashed key to be associated with encryption of the value VI, such as that. So let's take a look at what the leakage of the hash and encrypt compiler is. So when you do, let's say an insert for cats with the string zero one, what ends up have what ends up being revealed is that the server sees a hash of cats, an encryption of the string zero one, and the fact that you're doing insertion because the server has to know whether it's doing a query to either to do a query to the online plain text map or insertion to the online plain text map. And maybe you do another insertion for dogs.  So let's let's analyze what leakage or what the adversary can learn by viewing this information. So first, of course, it learns a type of operation performed. Like I said earlier, the server has to know what it was doing a get or an insert into the underlying plain text map. So obviously has to learn the type of operation the user is performing. It also learns the length of the query response. So here, for example, you know, in the first query, it learns that there's one associated encryption with with the with the query keyword. Whereas for the fifth, you know, for the for the fifth operation, this query for cats, it learns that there's two encryptions actually associated with the with the query. And finally, this last but what's very important leakage is this key quality pattern. So what the key quality pattern states is essentially that the server can identify which operations are being performed for the same on the same plain text key. So take an example of these the first, the fourth and the fifth operation. In all these operations, the server learn the server is given a hash of cat, which is deterministic. And since it's deterministic, what the server can quickly didn't infer is that all three operations are being performed on the same keyword plain text keyword. But what I want to iterate is the reiterate is that the server doesn't learn which plain text keyword it was it was being performed. So it wouldn't know it's cat, but it knows that all three of these operations are performed on the same key. Similarly, for the other two operations, they're both performed on dog. The server learns that they're both performed on the same keyword. So essentially a long story show what the key quality pattern says is that for any two operations, you learn whether they're performed on the same key or not. So it turns out actually that surprisingly, this ends up matching the leakage of many of almost all of the best structured encryption schemes with constant overhead. So you know, it's this area. It's sort of this, you know, in the private spectrum, it's this red box. So a very good question is, can we do better? So let's take a look at the three leakage functions and see what we can mitigate in terms of leakage. Chances of the type of operation performed is actually quite easy to mitigate because what the user could always do is perform all possible operation types and sort of replace everything that's not a real operation with a mock one. So for example, if you're doing a query, you can also do a mock insert. If you're doing an insert, you can also do a mock query. The length of the query response is a little more tricky. It's, in fact, it's very hard to mitigate because you sort of have to pad the responses to always be the maximum. And there's been several works recently, like Camara Muatz has in EuroCrypt 19, as well as work from our group in CCS 19 that sort of show various solutions to volume hiding structured encryption schemes. But the whole point is that sort of it gets very messy if you want to hide the length of query responses because you have to pad. So what we can then do is actually focus on this third leakage pattern called the key quality pattern and try to figure out what happens if we try to consider a slightly weaker notion of a key quality or a slightly smaller leakage or a stronger privacy. So let's take a look again at the key quality pattern. And what I'm going to do is I'm going to just define to you something we call the decoupled key quality pattern. So like we said before, in the first, fourth and fifth operations, the first and fourth operations are inserts and the fifth operation is a query, the adversarial server learns that all three of them, all these operations are performed on the same key cat, even though it doesn't know what cat is. The decoupled key quality pattern essentially decouples the key qualities between inserts and query operations. So what would happen now is that this adversarial server will still learn whether two insert operations are performed on the same key or not and whether the same, whether two query operations are performed on the same key or not. However, we decoupled the information that the adversary can see between inserts and queries. So for example, for an insert operation in a query operation, the server would never learn whether these two operations are performed on the same key or not. And sort of this is denoted by the fact that we changed the color of this cat for the query into a different color. Similarly, we can do the same thing for this dog operation where, again, even though they're queried for the same keyword, now we can try to construct a scheme where somehow the adversary server would not learn that this insert and query are performed for the same key. All right. So actually, this leads us sort of to our main result. So what we actually end up proving is that any encrypted multi-map with leakage at most the decoupled key equality pattern must have a mega log in overhead. And actually, this turns out to be a very, this lower bound is tight because of their exist order log in all RAM based encrypted multi-maps that end up leaking much less than the decoupled key equality pattern. So sort of going back to this privacy spectrum, what we end up showing is actually that essentially everything just slightly to the right of the structured encryption schemes because like I said, we showed before earlier that it's actually a compiler that any sort of a scheme that is able to leak the key quality pattern can have order one efficiency. But as soon as you aim for something even slightly stronger in terms of privacy, let's say the decoupled key quality pattern, which doesn't really have many real world implications, doesn't actually harden any sort of system. Theoretically, it ends up requiring these schemes to you could use log in overhead, which ends up matching oblivious ramps. So in other words, what I'm trying to say is if you're going to if you want anything slightly stronger than the structure encryption in terms of theoretical asymptotic, you might as well go all the way to oblivious ramps. All right, so this is our result and let's let's go about trying to prove it. So to prove this lower bound, we end up using something called the cell pro model or the leakage cell pro model where we incorporate leakage functions. So the idea of the cell pro model is sort of you have a again, you have the user on the left and the server on the right and the server's memory essentially split up into something called cells and each cell is the same length. The only cost in the cell pro model is essentially accessing any parts of server memory. So accessing. So one unit cost in this model essentially is means either reading or writing, which we call also probing a cell in this of the server memory. So it turns out that's the only cost we consider in the cell pro model. Everything else is free. So things like computation, random oracles, accessing client storage is all free. So you can solve your favorite and if you're hard problem, if you wanted to for free stuff like that and sort of, okay, why we consider a very weak cost models that ends up being very strong lower bounds. If you were to consider a more realistic model where computation is is cost some amount of, you know, is some it's cost some expense, same thing as randomness generation or access to the client storage, our lower bounds will still hold. In other words, the cell pro models is in some ways a holy grail lower bounds as it's the weakest cost model. So the technique we use to prove our lower bound is the information transfer technique introduced by Petrascu and Domain in 2006. And sort of the high level idea of the information transfer technique is to sort of arrange how information is transferred between various operations that are performed. So sort of the idea is let's suppose you do an operations, you know, which I've listed sort of top bottom top to bottom here operation one operation and what we do is we're going to build a virtual binary tree over these operations. What does that mean? Essentially what we mean is we're going to build a binary tree with n leaves such that each operation is assigned to a unique leaf in sort of chronological order. So the most the first operations assigned to the top most leaf and second operations assigned to the second most top leaf and so on and so forth. So like I said in the cell pro model, each of these operations, you know, so let's say you're reading a writing to the encrypted multi map is actually implemented using cell reads and cell writes. So for example, whatever operation one was, it's actually it may it's implemented using for example, reading the cell at the address 15, writing something to address a cell address 17, 72, and writing something to cell address 220, and so on and so forth. So what we're actually going to do now is is sort of go iterate through every single cell read that occurs in this operation in this operational sequence and assign the red cell address to some unique node in this information transfer tree. So for example, let's suppose we took an arbitrary cell read, let's say cell read 15, that's to perform an operation three. What we then do is we go back and find the most recent operation that ended up writing to address to cell address 15. So that might be operation one. And what we end up doing then is we're going to assign cell address 15 to the lowest common ancestor of the leaf nodes that are associated with operation one and operation three. And in this case, it happens to be the root of the tree. And we do this sort of for every single address that's read in all and operations. So what I will this is this is sort of the key point of the information transfer technique is sort of that the way we define and assign these cell addresses ends up defining the total amount of information that's being transferred. So let's to explain this carefully. Let's consider a concrete example of this specific red node in this tree. And this red node will have a seat will have like several cell addresses that are assigned to it. And what these cell addresses signify is the total amount of information that's transferred from operations that are performed in the top subtree. So this top subtree of this red node that would be used by operations in the bottom subtree of this red node. So for example, let's say somehow operation, you know, an operation in the top subtree wrote something to a specific overwrote of value for a specific key. And that key is later read in the bottom subtree. Obviously that information for the for the data structure to be correct that information must be transferred from the top subtree to the bottom subtree. And what I'm trying is and what the information transfer technique shows is that actually the total amount of information that that's that's transferred is must exist in the cells assigned to this red node. And to sort of see this what we can go through is sort of try some examples. So let's say we take this blue this blue node and try to say maybe any cell address is assigned here somehow transfer nodes from the from the from the top subtree of this red node to the bottom subtree of this red node. And if you sort of think about it quickly, the answer is it can't because of any cell address is assigned to this blue node would be read in an operation that is already performed in the top subtree of this red node. So in fact, there can't be any information in cell address that are assigned here that can be transferred that's that's if that's transferred from the top subtree of the red node to the bottom subtree of the red node. Similarly, you can do another analysis, maybe pick another tree that pick another node that's a parent of the red node. And again, let's suppose some cell address is assigned here, that would mean it's it's written in, you know, in some in the top subtree of this of the blue node, which consists of the whole tree rooted at the red node. And it would be we read somewhere in the bottom subtree, which is sort of not important for any any operations that performed in this this performed in the bottom subtree of this red node. So in other words, what what we end up what you will what you end up seeing is that all the information transfer from the top subtree of the red node to the bottom subtree of the red node must exist in the cells that are assigned in the red node. So why is this important? Well, it turns out what we're going to find is we're going to find operations that maximize that the number of cell addresses that are assigned to eat to to each internal node. So again, let's look at this red node and what we can essentially do is try to find out a sequence of operations that would maximize the number of cell addresses that have to be assigned to the red node. So in fact, it's actually very simple to do this. What you can do is in the top subtree, put a bunch of inserts in the bottom subtree, we can do a bunch of queries and sort of what we're going to do is, you know, we insert to to unique indices in the top subtree with these values that are completely random. And then in the bottom subtree, you simply query them. So trying to so using this idea of what we're going to do is now actually construct our lower bound. So for our lower bound, we have to find some hard sequence. And the hard sequence we essentially choose is something very simple. It's like, you know, insert to one or random value V and then subsequently read it, insert to index two and subsequently read it so on and so forth. And we assume that how we generate these values has a large amount of entropy. So OK, you might ask right off the back, isn't this operation easy to handle? And so the key is that the sequence was indistinguishable from many sequences with identical leakage. So taking a look, let's look at the let's look at this hard, this hard distribution. We can quickly see is that this hard sequence ends up maximizing, you know, all of these red notes number of subjects that we assign to this red note. For example, let's look at this, this top red note. Clearly, you know, all of the all the operations in this top subtree are inserts that are subsequently queried in the bottom subtree. So OK, this hard sequence, at least maximizes the cell addresses for maximum cell is assigned to, you know, this this whole this whole level of notes. But what we can then do is actually slightly modify the operation. So for example, let's say we take the queries and change them. So what we do is we change these two queries to be dummies. And we actually query, you know, the key one and key two in the bottom subtree. And maybe to make sure the leakage remains the same between the two operations, you might have to add some sort of header where you insert a bunch of dummies that can be queried later. So if you take a look at this new construct, at this new sequence, which has the same leakage as the original hard distribution which I just described, it turns out that this sequence ends up maximizing the number of cell addresses up to a constant factory of two. The number of cell addresses that have to be assigned to this red, red note. And you can do this repetitively for each internal node in the tree. So sort of what ends up happening, how we prove this lower bound is sort of, you know, we use these ideas to show that many probes must be assigned to half the internal nodes for this easy hard, for this easy hard distribution. And then what we have what we also notice that each cell read or probe is assigned to at most one internal node in the tree. So by simply summing up the probes assigned over all nodes provides a lower bound. So what I'm trying to say is sort of if for some reason there was not enough cell cells that are assigned to this red note, we would know that this specific sequence couldn't exist. So for privacy, you have to assign the maximum cell addresses, the maximum cells addresses that have to be at this red note, according to this distribution.  All right. So it turns out that so that's actually completes our lower bound from a very high level, the information transfer technique, but it turns out that you can actually modify the lower bound to get the more modified the proof to get stronger lower bounds. So it turns out that we can prove lower bounds in our paper even when one of the insert operations are formed in plain text or the core operations are performed with plain text. Furthermore, we can actually use this uses lower bound for encrypted multi maps to end up proving lower bounds for dynamic searchable encryption. And in fact, what we show is that by this this decoupled key quality ends up actually being something called response hiding dynamic searchable encryption where you hide the actual documents that are that are associated with any query key. And what we prove is that such response hiding dynamic searchable encryption schemes end up requiring log in overhead. And again, this is tight because they're actually end up being ORAM based solutions for these for these the dynamic searchable encryption schemes that are response hiding. So OK, so that that completes sort of what we did in this paper. There turned out to be recently a large number of other works that the cryptographic lower bound in the cell pro model. So there's the seminal oblivious RAM paper by Lars and Neilson, as well as several follow up that I list here that if you're interested in you should you should go explore. All right, so thanks for listening to my talk and I hope you enjoyed and learn something.


Everybody forced here ripping 2022 dynasty baseball a five box case number 48 left side serial numbers Let's do the random right now, and then we'll open the case. Good luck everybody There's a four in the back and a one up front five times names first one two three four and five Chad p done a mod and then five times on the numbers one two three four and five eight down to one All right chad p8 viviana 2 miles h4 john a3 tyler b10 john a9 scott m7 zong han 5 viviana 6 mod 1 Those results just got posted to the site. You can always find them there at any time There the link is coming to you in chat currently as well if you want to check it out I'm also going to drop you a link for elite court contenders at 16 to go now Honest brushes came in. Thank you. Uh christopher. Oh Appreciate that order man. Thank you. Thank you. Thank you. All right That one's gonna have something huge in right it fell over I said don't for last Oh, that really slight Maybe that's what it was. Maybe it's a gigantic hit. We'll see first up jaren diran on the horizontal rookie patch out of three of five Nice diran hit here john a with the three spots Some scratches that won't come off of that mag unfortunately they might with like Not just my hand Nice hit john A jaschism junior On the four color patch auto six of ten six spot is viviana We had duran and jazz in the in the in the last days too Oh, maybe that means there's another shohei in here Ain't nobody be mad about that The goat dude It's an alex manoa patch auto two out of ten three color patch on the manoa viviana as well with the two spot It's having a bit of a down here for sure You probably get it right though. He's a good pitcher Got vladi jr. On the patch auto six of ten six spot viviana again the third hit for viviana Third in a row actually a couple of blue jays In the jazz All right, here we go. It is alex braggman. Whoa. What is that patch? What is that five out of five going to zhung han l On the patch auto is that like a houston sort of patch like what it's not a laundry tag Does anyone know what that is it's some kind of cool-looking patch i'm assuming but i just don't know what specifically that's awesome though congrats five spot zhung han Now i'll do it. Thanks again everybody moving on to basketball


Old Man Raisin' are actually my favorite. There you go, cheers. Yeah. I was spitting, but he decided, well, let's open up a cookie shop. He would always tell me, I want to work because I want to, and not because I have to. And he asked me to quit my job to come help him bake these cookies. What were you thinking?  You know, it's a lot of work. But I love being in charge of my life. You know, I love being in charge of how much money I can make, how far I can go, how successful I can be. But I didn't realize that it was in me. I never thought I would be an entrepreneur. It never crossed my mind, you know, that I would have on my own business. Never crossed my mind. Thank you so much for your time and for sharing your story. Well, thank you. Thank you so much for this thing. Yeah, I wanted to know.


TfNN, headline news update. Good afternoon everybody, I'm Tommy O'Brien, coming to you live from TfNN headquarters in St. Petersburg, Florida, 1pm Eastern time on Monday, three hours left to go on the trading day. We've got markets hanging around positive territory, S&Ps right now, positive by eight points, trading at $29.49, we've got the NASDAQ right now up 22 points, trading at $81.69, Dow Jones up 32 points, trading at $26,576. We're still big in earning season, after the bell tonight, we get the, I was going to say Google, Alphabet, the parent of Google, after the close tonight, tomorrow, Apple. And continuing in the week, Wednesday we get a Fed decision, that meeting starting tomorrow and Friday we get jobs data for last month, this month. Jobs data for April should be quite an action-packed week. With that in mind, jump over to the futures markets. We'll start it off with the Dow, as we get up there. So we're nearing the high where we were at about 4am this morning, $26,577 was the high in the Dow, we're currently about 20 points from that level, $26,555, pretty tight trading range, all things considered the low being $26,484, should be only talking about 70 Dow points. NASDAQ 100, up approaching that high as well, we just got above that level briefly in the last 10 minutes, $78.61 being the high, currently trading $78.56, low of the day, about 7am, $78.28, S&Ps, now far above that high that we made the high overnight, $29.46, we're now 3 points above that level, trading $29.49 just off the high as we made a few minutes ago. Crude oil didn't take long to bounce off of that pullback, early, early this morning, you had crude down there at $62.46, we're now a solid dollar over that level with Crude trading at $63.58, gold contract pulling back a bit, $12.80, $81, the price of June Gold and Euro US Dollar, we're seeing a little bit of dollar weakness, Euro strength, Euro trading $11.81, and one of the stories I was just reading, coming on the air prior to this, this breaking, pretty remarkable that they just don't stop in terms of the stories relating to how this is unfolded with the 737 MAX and whether I don't think it's appropriate how Boeing has behaved, but Boeing waiting until after the lion air crash to tell Southwest that the safety alert was turned off for the 737 MAX, pretty remarkable, so it just goes into the specifics of that, and if you get a chance, check it out, we'll break it down, of course, but Boeing nonetheless, pretty much flat for the day, down about 43 cents, trading at $3.80, $36. Stay tuned folks, Steve Rhodes coming up right now at the Trader's Edge, Dave White live at two o'clock with the power trading hour and Tom O'Brien live from 3.0.5. Stay tuned folks, have great money.


Hello and welcome to this presentation of the STM32 Advanced Encryption Standard Hardware Accelerator. It covers the features of the AES interface, which is widely used for cryptographic applications. The AES algorithm is a symmetric block cipher used to encrypt and decrypt information using a secret cryptographic key that is 128 or 256 bits long. Encryption converts data to an unintelligible format called ciphertext. Decrypting the ciphertext converts the data back into its original format called plaintext. Applications benefit from the NIST FIPS 197 compliant implementation of the AES algorithm to protect the confidentiality of data as well as its low processing time. The AES accelerator supports four operation modes, encryption, key derivation, decryption, and key derivation plus decryption. It processes 128-bit data blocks using an encryption key that is either 128 or 256 bits long based on the selected chaining mode as shown on the next slide. This simplified block diagram of the AES shows the basic functional and control modules. The AES accelerator processes 128-bit data blocks using an encryption key with a length of either 256 bits or 128 bits, with or without a data swapping option. The AES accelerator has four operating modes. Mode 1, encryption using the encryption key stored in the AES key register. Mode 2, key derivation which derives a new key based on the value stored in the AES key register before enabling the AES accelerator. This mode is independent from the AES chaining mode selection. Module 3, decryption using a given or pre-computed decryption key stored in the AES key register. And Mode 4, key derivation plus decryption using an encryption key stored in the AES key registers, not used when the AES is configured in counter mode for performing a chaining algorithm. The AES accelerator supports six chaining algorithms or modes. Electronic Code Book or ECB. This is the default mode. This mode does not use the AES IVR register. There are no chaining operations. The message is divided into blocks and each block is encrypted separately. Cypher Block Chaining, CVC. Each block of plain text is XOR'd with the previous Cypher text block before being encrypted. To make each message unique, an initialization vector is used when processing the first block. Counter Mode, CTR. A 32-bit counter is used in addition to a nonce value for the XOR operation with the Cypher text or plain text. Galois Counter Mode, GCM. Used to encrypt and authenticate the plain text generating the corresponding Cypher text and the tag, also known as the Message Authentication Code or Message Integrity Check. It is based on the AES's counter mode for confidentiality and uses a multiplier over a fixed finite field for generating the tag. It requires an initialization vector at the beginning. Galois Message Authentication Code Mode or GMAC. GMAC is the same as GSM applied on a message composed of only the header. All steps and headings are the same except the payload phrase will not be used. Cypher Message Authentication Code Mode, CMAC. CMAC is used to authenticate the plain text generating the corresponding tag. The message is composed of only the header phrase and the tag phrase. The CCM standard defines specific encoding rules for the first authentication block called B0 in the standard. In particular, the first block includes flags, a nonce, and the payload length expressed in bytes. The error flags block checks the behavior of the AES accelerator via two different flags. The read error flag or RDERR is set in the AES status register when an unexpected read operation is detected during the computation phase or during the input phase. The write error flag or WRERR is set in the AES status register when an unexpected write operation is detected during the output phase or during the computation phase. An interrupt can be generated when one of these two error flags is set if the error interrupt enable or ERRIE bit in the AES control register was previously set. Two extra flags are available for the AES accelerator to give the status of current operation. The computation complete flag, CCF, is set by hardware when the computation is complete. An interrupt is generated if the CCF interrupt enable bit was previously set. The busy flag used only with GCM mode indicates that a higher priority message can interrupt the current message during GCM payload phase for encryption mode. The following slides give the processing times for each of the operating modes according to the selected chaining mode. Here are the processing times depending on the key size and algorithms. To complete the feature, here are the processing times for GCM and GMAC algorithms. Here is a summary of the events able to trigger an interrupt in the nested vectored interrupt controller. AES computation completed. AES read error and AES write error. Direct memory access requests are generated internally for both incoming and outgoing data. The DMA channel must be configured in memory to peripheral or peripheral to memory mode with a data size equal to 32 bits. Here is an overview of the status of the AES accelerator in each of the low power modes. AES operations are not possible when the device is in stop mode. The AES encryption and decryption algorithms are suitable for a variety of applications such as secure networking routers, wireless communications, encrypted data storage including secure smart cards, secure video surveillance systems, secure electronic financial transactions, etc. The sender sends a plain text message encrypted with a secret key and the receiver decrypts the message with the same secret key. This is a list of peripherals related to the AES accelerator. Please refer to these peripheral trainings for more information if needed. For more details, please refer to these application notes and user manual available on our website.


so here's a new development board the fastest 96 board so far so hi so who are you hi my name is Rinshath I work for Ho-Pran Rung Ho-Ruan Jain and who are you oh my name is Kongfei I'm the foreign high city I'm the project manager of the high-key 970 so this development board is a consumer extended consumerization right yes so can you show around the ports okay there are many interfaces on the board for example two USB type A and two USB type C and an HDMI and a Microsoft micro storage SD card then then we can find the many extended extended parallels and KCIE network interface and this is the Wi-Fi chip and under this departure there is the key ring SOC and memory storage chip so this is the the curing very powerful CPU it has also AI a neural network processing unit so what is this chipset this is the same this is in the Mate 10 the same chipset right yes so curing 970 the fastest ARM processor in the world yes one of them yes yes well the fastest ARM Cortex a 73 yes 53 yes lead the car yes 33 10 nanometer oh yes 10 nanometer so very very powerful and it's PCIe what can what can people do with the PCIe people can use PCIe to extend the storage for example oh there are many PCIe card so you can use it so what can do for example GPU know what GPU what can add on the PCIe for example we can add the PCIe card on this interface so any PCIe any PCIe PCIe this is this is the mini PCIe interface and uh there's no SATA right uh not SATA the PCIe card can maybe use in the SATA card maybe the PCIe has SSD card SSD card for example can add a hard disk yes SSD yes you can add uh many different things yes extra extra GPU extra yes maybe connected can connect to Nvidia or AMD GPU or not a good idea yes maybe as well so what kind of market is this for uh this is mainly focusing on yeah uh robotic industry automobile industry and smart city so uh how soon is this available in the month it will be available to purchase in the month here and uh anybody can just buy it online yeah yeah probably you can buy it on amazon is any price it's around 299 dollars 299 dollars so this is the most powerful um this most powerful 96 boards right yeah and um uh what do you work with hope run you have a good partnership with them yes i see you can hope for a pattern us we we develop these boards together especially in a software uh hope run to a great job yeah and so this will be supported with the Linaro uh with all the software support uh i have the how's the puzzle to do the ai what is this uh high ai sdk yes uh let me introduce it uh in this board it uh it has kiri 97 in the kiri 97 we Huawei has set up a sdk ai sdk uh it named Huawei high ai sdk in this sdk we can support and many neural network uh applications and we can use the dedicated uh neural network processing for it named mpu so you can accelerate the calculation it is a great uh and efficient uh uh processor so there's a heterogeneous processing included with all the different different parts of the sdc right here you're running uh actually you're running debian yes can you show right yeah so there's a mouse so debian do you want to show please show it can can you explain uh how much it's developed already in the debian how much there's no uh gpu acceleration right maybe you can jump in yeah so this is the early demo yeah of debian support um we go in here you have all this stuff going on right here it seems smooth can you type something cool okay there's no the keyboard somewhere but so so actually it's right here it's running yeah it's very very fast how can you how can you do so fast after the phone come out only three months ago how you work so fast because we have many partners for example hobo ran and uh Huawei has silicon engineers both hardware engineers and software engineers and system engineers we work together and we have overcome very difficulties so that we can get it ready now in the past three years so not only android it'll be every linux distro yes linux support yes and uh but this this ai the neural network engine is not open right uh only sdk the sdk will be opened and in the uh in the Huawei platform and we we will put Huawei ai to the open source community and we want many developers to use it and we will we have right many files about how to use it all right and this is a 4k 4k hdmi yes it is okay 4k 60 okay hd65 yes very very powerful so this is so quickly in the market because it's very important for high silicon and Huawei to have a ai community right yes we want this to uh used by many development developers all right yes and do you think this will help to make what maybe it will help to make a smart car self-driving car robot what will do yes drone yes what's possible it's possible it all possible for example in the deep learning in the uh what kind of deep learning uh for them like what deep learning like because you you use this to do the uh imaging or the videos or the audios rolling all right like uh so there could be robots some really cool robots yes smart city what smart city smart city is that you can use it to do the uh uh Imager area everywhere to know how the how what happened in the city is and this is 50 percent 50 times power efficient yes compared to what uh compared to only using cpu compared yes compared to cpu or gpu yes so it's much better for ai only for ai yes yes but it's not a dsp it's not a gpu it's a dedicated uh it's a dedicated processor all right yes well


Great to have you here Kate. Thanks to be yeah, it's great to be here We've got some huge Congratulations in order first and foremost Maven has closed and announced on Monday. It's 90 million dollar series e-round So huge congratulations for that Led by you. Yes We are general catalyst We are could not be more thrilled to have led this round and partner with you on this journey And this is a significant investment not only in this funding climate But also as a testament to everything you and your team have accomplished and we're just thrilled And I'd like to start at the beginning of that journey You know where it's slush which which attracts some of the highest potential founders across Europe at the early stage So for you, when did you know you wanted to start Maven and why why this idea? so First of all, it's so great to be here. I was working in London Maven started in London About eight years ago and and I was starting to hear about this conference slush and it seems so cool So eight years later. It's really awesome to be on stage So I started Maven You know eight years ago in London because there was a clear gap in care for women and families Not just in the US but globally You know when you get on that journey of parenthood and so I had just kind of entered my 30s and And I was starting to see a lot of stories from my my friends as they were going through that that family building journey around Infertility postpartum depression miscarriage You know if you were gay you didn't have main core like main fertility benefits through your employer or your you know A lot of the the system wasn't covering kind of you know surrogacy and adoption and those pathways to parenthood And so there was just such an incredible need and it was right at the early stages of digital health And so all these companies, you know, I was working at this venture capital firm in London and all of these companies were coming to pitch Healthcare innovation and no one was talking about women and no one was talking about families And this was such an obvious entry point for for so much of the healthcare journey. And so I thought very naively This is something that I can definitely do for the next five ten twenty years. And so Maven was born Beautiful, there's always a bit of naivete in those moments You need it to push yourself forward and you mentioned I mean, it's been an eight-year journey when you reflect back Are there certain major milestones that you believe led you to this moment now? You know of Maven success. Yes, exactly. Um, so I would say, you know working in health care You know you are go to market has been B to C to B So we we sold the consumer and then we went into business and I know we'll talk about that, right? You know, I think when that first big client really takes a risk on you That's really where and and they say oh, we're going to bring Maven to our population That's really when I think that one of the big milestones occurs.  Let our series be with them And so that was also an inflection point in the VC community got it And you would of course Kevin your exposure to that at the beginning of your career You know that quite well you mentioned yourself, you know Maven started as a direct-to-consumer business has since evolved a partner with employers and insurers Can you walk us through that transition because I've heard you say that Starting the business with the consumer first has been a key to your success. So maybe you can expand a bit about that sure so Because I am a patient on the Maven platform. So I have three kids I actually started my pregnancy and parenthood journey with a miscarriage and then since went to have three pregnancies on Maven And so I in some ways, I just wouldn't know how to do it any other way I think building one of the big things you have to look at when you're a founder particularly entering a complicated Market like healthcare is what's your unique kind of insight and what's your unique competitive advantage? And so, you know given that women and families have been so underserved for so long I mean honestly my unique insight was like, how do we build a better system particularly for women? And so and so that's really where You know, we have had a very product-focused company for a very long time And so the thinking was if we can really build a compelling product for consumers There's probably not going to be as many of them on the platform in the beginning because this is largely a B2B market from a Sales standpoint if we build though an amazing product That's going to be our competitive advantage when we walk into a benefits team an employer team or a health plan And we say listen, you know You really should take a bet on us because look at all these engagement metrics Look at all this impact we're having and so again, it takes a while and it took a little bit longer You know, we were mainly we sold our first benefits contracts in the six months after we launched But they were really small and so the first two years most of our users were consumer not coming through the enterprise But it was an we were able to really just continue to hone the product and and uniquely craft it to their needs And and so then by the time the scale started to hit, you know, we had a pretty unique value prop So we were one of the first companies we first company to and this seems so obvious for any women or parents sitting in the room But to bring postpartum and return to work care as part of the core prenatal care model And that was just I mean, it's like obvious need that that members have or you know, we were the one of the first companies Actually, no again I think the first to bring the fertility journey into you know and tie it together with the pregnancy journey and again That's just so obvious because that's what's happening in real life But then when we then we went B2B and we talked about that with the buyers You know, we had these insights but then we had the data to prove it from our products Got it because that's an interesting piece actually in the US because they're obviously your benefits managers your mercers You're well as towers Watson's that that help companies think strategically about which benefits they should choose Were you part of that education for why women's and family health should be part of the conversation and how did you lead some of that dialogue? Oh, yeah, I mean we I think we part of the the go-to market in the beginning. It was very clear We just had to evangelize the problem and so you know We were I think always talking about clinical impact and having a clinical strategy early on is really important So we had a lot of doctors and we had a lot of providers saying, you know This is critical for outcomes. This model is critical and so we didn't have them yet But you know, there was like that we had a plan around how to get them And then we would survey our early members and and kind of tie it to their self-reported data in the app to say look Directionally, this is going in the right direction So we always had that kind of clinical impact story and we just used a lot of patient stories as well to talk about Hey, I had you know, I had a baby and then I had nothing to support me after that or hey I actually really didn't want to go back to work, you know in the US Sadly, we there's no federally mandated paid leave And so there are some companies in the US where people have to go back to work after six weeks now What we see from our European clients, too is on the other side you have 12 month leaves But that also is really really hard for women to go back after being at home for for a year And so they're it's on both sides an issue And so to be able to talk about some of those real patient stories Really helped evangelize kind of the early days and so then what you know, what happens in markets? We went from being in a very kind of we were like the only solution Ty and we're talking about You know this one thing and then all of a sudden all these other companies came into the market Which was great because they put pressure on the buyer But then now you know the most important thing in a more competitive market like we're in today is it's not You know you tell the patient stories, but then bringing it back to the clinical impact in the data Is really where Maven continues to stand apart Would you say that those are the key differentiating factors when a Company or an insurer anyone says, you know, why should we work with Maven? Is it really the data? Is there anything else that you use to say this when you think about the whole Landscape of women's or family health products? Well, it's both the data But I think for particularly for employers employers are looking for breadth So they players are really busy and they they don't want just kind of one-point solution They want, you know, if they don't want just a fertility benefit They want a whole kind of family care platform And so I think what you know for us to just following that member and following that consumer in terms of what they want Has propelled us in our product roadmap to kind of not just stay in one place one area So, you know in our product roadmap, it's always a trade-off between breadth and depth some years We go deep to get the clinical data other years we go broader and so so that's kind of I think also Where Maven stands apart. That's a that's a perfect segue to to talk just a bit about the platform You know when I think about women's and family health broadly There are so many areas that are underserved and those are global issues and we'll talk about that as well and Maven is already addressing gaps in Parenting and pediatrics and fertility and family planning, etc And earlier this year you launched the first platform for you in in menopause How did you decide that menopause was the next product and maybe a bit more broadly? How do you decide which areas to invest in and prioritize? Yeah, it's a great question So actually menopause originated a lot of the global demand originated here in Europe So in the UK, I think about a year ago They had a commission the government did on How menopause shows up in the workplace and how it affects women and so ever since then a lot of Employees at a lot of companies including our own kind of big multinationals. We're starting to say, okay If health equity is truly kind of a a value here and DEI is a value then You know, we're investing a ton in fertility and family building, but what about menopause and so So for us, I mean, it's obvious there's a clear need and because we have so many OBGYNs on our platform They've always as well asked us and so we have a client advisory board and for years We were asking the client advisory board Do you want menopause do you want menopause and that just wasn't at the top of the list and then all of a Sudden in Q1 we asked our client advisory board. Do you want menopause and they're like, yes And it was because a lot of this kind of grassroots demand That originated in Europe that then made its way back to kind of you know, big global benefits teams in the US as well And so, you know because we're a technology company and we have a set of core capabilities We we already had the care delivery. So we already had the OBGYNs, right? We have an in-house content team We have virtual classes. We had all that so we quickly package that up We we built it in four months and then we actually launched it a month ago And we already serve over a million lives much of it is global Over a hundred of our clients bought it. So it was it was, you know, healthcare doesn't move that fast So it was it was kind of an amazing an amazing story and we're projecting, you know Many more clients to adopt it next year. Okay, that's excellent and you mentioned this is an idea that came from You're now launching that in the US but reverse Maven's actually focusing quite a lot on expanding its presence outside of the US you have members already in over a hundred and seventy five countries Can you tell us today a bit more about your global strategy? Sure So global is just also one of those incredible stories where if I if I look back at our numbers and our pipeline and our user base In 2020 it was still I think a lot of companies were talking about global, but it was still in its nascent stages now You know over 60% of our clients offer Maven globally We have some of our big kind of Fortune 50 multi nationals launching Maven globally They just our Microsoft just launched us globally. I think two months ago, which was incredible Yeah, and and just the demand and the uptake I mean we didn't you know It was our first Fortune 50 launching globally and we didn't know what it was gonna look like But we've since now can you have a bunch more launches in a few months and I think what we're seeing Globally is you know fertility and family building benefits similar to the US have also been left out of care models And so it's a complex web of laws where it comes to surrogacy and adoption and IVF You know everything's changing all the time Some IVF is supported publicly others people are going to private clinics surrogacy is illegal in some some countries So there's medical tourism aspects and so for a lot of big multi nationals and and you know Who want one team across the globe? You know, they can't really say okay? We're gonna help our US employees build families, but not right our global so so we have some enormous launches You know, we've grown 10x globally in the past 24 months and we're projecting to grow another 10x in the next 12 months So we're now you know, we have local care advocates around the world. We're now hiring a local Sales team on the ground in the UK But it is just it's it's awesome to see and it's starting with fertility and family building benefits But but then the conversation is more broader right it's broader about parents in the workforce and pregnancy and you know help gender equity at work and some of our clients in Japan Particularly, you know, they they they're all in on return to work because there is they're having you know Big problems around keeping women in the workforce and having senior women So every country is a little bit different, but it definitely get you know It's it's really really exciting to see or and and you started in London. I mean you mentioned it yourself You know, you came up with the idea for maven in London not far from where we are now And then you decided to start the company in the US, you know There are lots of founders here who are probably thinking very carefully about where to build their businesses How did you make that decision at that time? Yeah, so I it's so great to be back here because some of our best friends live in Europe Many of our amazing angel investors are our European Brent Hoberman from first minute and And so, you know, we were in London my husband and I were like, oh my goodness. We love London However, there are two reasons that we moved. I mean, of course, I'm American So was able to move very easily from a visa standpoint But but number one I have three kids today and so personally I wanted to I knew I was gonna have kids on this journey and My parents live an hour for me So my mother is with my children right now and I'm so grateful and so I think that is so important particularly a female founders To make sure if you're gonna have kids on this journey that you get that care team around you so that you know I I miss my kids, of course, but they're really happy with their grandparents reading more sugar than they've had probably in a month So so anyway, so that's number one number two I think for all founders, you know, you have to look at the TAM and the TAM is really what allows you I think to build big businesses in the US healthcare is nearly 20% of GDP Yep, it's a multi trillion dollar market And so it was a pretty obvious thing, you know if product market fit takes a second if you're operating in a big TAM, you know, you can kind of figure it out and so, you know, and also there the US Maybe you have seen that the headlines recently, but you know, Roe v. Wade was just overturned There's always been I think challenges to access to women's health care in the US And so wanted to make sure that, you know, we started there But you know, we signed our first global contracts in 2018 2019. So actually Starting in London has become a big competitive advantage because we lean on our investors We lean on you know, we understand global in a way that I think some of our US competitors do not that's very important It's something, you know, we're very hands-on with our early-stage founders There's a reason that we're in London and in New York and in Boston and in San Francisco and Palo Alto You want to be able to support companies everywhere and making that decision about when you launch in the US is something that we think about very, very carefully And it sounds like you did the same from quite early on And bringing you back maybe to your VC roots as well I mean you're a very active angel investor and I know that making Time for other women who are building companies is also a huge priority for you has been for years What is a frequent piece of advice that you give to early-stage founders when they're starting their journey? Yeah, um, well, first of all, you have to really like what you're doing because it's really hard. Um, I think that, um, you know Probably a Pete my dad's an entrepreneur Um, and so it's been amazing to have his kind of coaching and guidance along the way So some of these with the nuggets of wisdom are his one of the things they said really early on because I think When you're a seed stage founder, you know, everything is just so heavy and the highs are high and the lows are low and You know at one point he was like, you know Nothing and no one is ever as good as you think they are Also, not as bad as you think they are and so I think it's it's um, you know riding those highs and lows and I think the other The other thing that that at least in healthcare has been so important But I would imagine in every industry, you know I think one of the the things about being a founder is that your learning curve has to remain high and you have to remain Humble because you're constantly being thrown at things, you know, that you've never seen before And so for us at maven, um, I I'm an ex journalist actually And so I have a a huge network of advisors We have four board independence which was really important to me across all these different sectors And so I kind of say it's like my network of sources And so when I encounter and it's you know almost every day There's some new situation whether it's a go-to-market Question or a product question or a financial question or just kind of I mean a management question We're you know over 500 people now You know, they're I'm able to ask them and pick up the phone and call them and text them And so um and and then of course having an amazing executive team who who so every decision I make Usually I have inputs from Three to four people who are experts in that field and and that has helped me from the very early days until you know It continues to help me a lot. Maybe we can talk just a bit about team for a minute as well I mean you've been So thoughtful about surrounding yourself with extraordinary people not only from an advisor perspective But on your executive team and frankly among the 500 people at maven Is there any secret sauce to that any thought about building a team? Especially a global one with such a significant strategy Yeah, I mean the the questions around team are are so um, you know There's so many these days because we're in this hybrid work environment now I think though that for us Having values is so important And having values that are co-created by your team and so I think some people kind of think Oh, the founder has to create the values and it's this top-down kind of approach But for us, you know, yes kind of I have a point of view But I also, you know, we just refreshed our values we refresh them every two years I think also hearing from the team hearing what's most important from them, you know has has helped inform them and so Um, and then as you as you kind of have values to be able to lean on them as well If you're announcing a tough decision or or there is an issue say well, you know a value of ours Some of maven values are embrace the service mindset or you know continuously learn keep healthcare human That's been a maven value for eight years And so uh, so I think that's really important and then the other thing um that you know I I personally look at is uh on our executive team But but also our senior leadership team and like the managers of the company that are really leading You know the the charge on a day-to-day basis You know that that incredible growth mindset and humility that they have to have like, you know working in healthcare You have deep expertise and so many people that work in the industry today But for them to be successful at maven and we've had some stories where they haven't been successful You have to come in with an open mind And you have to be able to say oh the way I did something here I might have to do it a little bit differently and then same from tech right We have a lot of people people are from the tech side And you know when they come in to maven 2 the way they did in their old company We want them to come to maven to teach us that but then they need to be open to kind of You know doing things in a new way because we're you know continuing to create a category and so um So anyway, so I think that that growth mindset and humility is something that we even test for in the interview and hiring process Beautiful, it's um, I'm smiling because it's something that general catalyst takes so seriously We talk about growth mindset all the time Um because our business is changing incredibly quickly as well I mean look at this environment that we're in and general catalyst has been around for over 20 years So there are many mds at gc who have seen these cycles and we're able to help our founders think very carefully about it But anyone in this industry be it a founder or an operator or a venture investor has to think very carefully about Growth and change and evolution and you guys have been doing it for a very long time and it's heartening to hear um You mentioned this briefly you you mentioned rovey wade for example There are many depressing headlines across the world when it comes to women's and family health For you at a 10,000 foot overview Are you hopeful and what do you think it takes for us to actually move women's and family health forward? You know, um, I think founders are always optimistic, right? So I am hopeful actually because I think that um You know human ingenuity always sees us through and when bad things happen I think you know rovey wade is just basically in a front to access and health care All the major american medical associations have said this is terrible You know nearly one in four women In the u.s. Get an abortion by the time they're 45 and you know sometimes it's for medical reasons Sometimes it's for private reasons. So it truly is just you know part of the core care model and it and and so I think though that in this moment You also all of the data coming out the clinical data the economic data on on something like this Is is very bad But maybe it's going to take this to kind of you know build a better future and so I I think I am optimistic because At the highest levels of industry in um in america, uh, at least you know, there are people that Are talking about how to change this So I wish it were being changed tomorrow because people are suffering every single day because of this But I do think that we're going to get to a point where there's going to be consensus About you know a better kind of you know system for for women and families and you know, I was I was just actually reading this essay It was from the 1950s. Oh, wow By katherinan porter this great excellent, you know american writer and it was right after the atomic bomb was dropped And it was this horrific time when everyone was kind of questioning the future of humanity and like who we are as a species Right, but I think the whole point was like but then you figure out that you figure out your way out of it And you know you take a step back and then you go forward And so I think all the entrepreneurs here in this room I and and particularly all the entrepreneurs innovating in women's and family health I think the cats out of the bag the doors box has been open and you know We together are going to create a much much better system globally beautiful beautifully said well Thank you for joining us kate for your wisdom for your candor and we're energized to see what happens with maven next We're so thank you to general callus for leading the last round. We could not be happy or so appreciate it great Well, thank you everybody


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