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Latent Space: The AI Engineer Podcast â€” CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0

Play Episode Listen Later Feb 24, 2026 25:15

Send a textThis episode covers the latest updates in cloud computing, including AWS Spot Interruption metrics, new EC2 instances, Azure and GCP enhancements, and cost management innovations. Perfect for cloud professionals seeking actionable insights and industry trends.keywordsKey topics:AWS Spot Interruption MetricsNew EC2 Memory-Optimized InstancesAzure and GCP Cloud EnhancementsCost Management and Optimization StrategiesCapacity Constraints and AI Impact

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Bitter Lessons in Venture vs Growth: Anthropic vs OpenAI, Noam Shazeer, World Labs, Thinking Machines, Cursor, ASIC Economics — Martin Casado & Sarah Wang of a16z

Play Episode Listen Later Feb 19, 2026 55:18

Tickets for AIEi Miami and AIE Europe are live, with first wave speakers announced!From pioneering software-defined networking to backing many of the most aggressive AI model companies of this cycle, Martin Casado and Sarah Wang sit at the center of the capital, compute, and talent arms race reshaping the tech industry. As partners at a16z investing across infrastructure and growth, they've watched venture and growth blur, model labs turn dollars into capability at unprecedented speed, and startups raise nine-figure rounds before monetization.Martin and Sarah join us to unpack the new financing playbook for AI: why today's rounds are really compute contracts in disguise, how the “raise → train → ship → raise bigger” flywheel works, and whether foundation model companies can outspend the entire app ecosystem built on top of them. They also share what's underhyped (boring enterprise software), what's overheated (talent wars and compensation spirals), and the two radically different futures they see for AI's market structure.We discuss:* Martin's “two futures” fork: infinite fragmentation and new software categories vs. a small oligopoly of general models that consume everything above them* The capital flywheel: how model labs translate funding directly into capability gains, then into revenue growth measured in weeks, not years* Why venture and growth have merged: $100M–$1B hybrid rounds, strategic investors, compute negotiations, and complex deal structures* The AGI vs. product tension: allocating scarce GPUs between long-term research and near-term revenue flywheels* Whether frontier labs can out-raise and outspend the entire app ecosystem built on top of their APIs* Why today's talent wars ($10M+ comp packages, $B acqui-hires) are breaking early-stage founder math* Cursor as a case study: building up from the app layer while training down into your own models* Why “boring” enterprise software may be the most underinvested opportunity in the AI mania* Hardware and robotics: why the ChatGPT moment hasn't yet arrived for robots and what would need to change* World Labs and generative 3D: bringing the marginal cost of 3D scene creation down by orders of magnitude* Why public AI discourse is often wildly disconnected from boardroom reality and how founders should navigate the noiseShow Notes:* “Where Value Will Accrue in AI: Martin Casado & Sarah Wang” - a16z show* “Jack Altman & Martin Casado on the Future of Venture Capital”* World Labs—Martin Casado• LinkedIn: https://www.linkedin.com/in/martincasado/• X: https://x.com/martin_casadoSarah Wang• LinkedIn: https://www.linkedin.com/in/sarah-wang-59b96a7• X: https://x.com/sarahdingwanga16z• https://a16z.com/Timestamps00:00:00 – Intro: Live from a16z00:01:20 – The New AI Funding Model: Venture + Growth Collide00:03:19 – Circular Funding, Demand & “No Dark GPUs”00:05:24 – Infrastructure vs Apps: The Lines Blur00:06:24 – The Capital Flywheel: Raise → Train → Ship → Raise Bigger00:09:39 – Can Frontier Labs Outspend the Entire App Ecosystem?00:11:24 – Character AI & The AGI vs Product Dilemma00:14:39 – Talent Wars, $10M Engineers & Founder Anxiety00:17:33 – What's Underinvested? The Case for “Boring” Software00:19:29 – Robotics, Hardware & Why It's Hard to Win00:22:42 – Custom ASICs & The $1B Training Run Economics00:24:23 – American Dynamism, Geography & AI Power Centers00:26:48 – How AI Is Changing the Investor Workflow (Claude Cowork)00:29:12 – Two Futures of AI: Infinite Expansion or Oligopoly?00:32:48 – If You Can Raise More Than Your Ecosystem, You Win00:34:27 – Are All Tasks AGI-Complete? Coding as the Test Case00:38:55 – Cursor & The Power of the App Layer00:44:05 – World Labs, Spatial Intelligence & 3D Foundation Models00:47:20 – Thinking Machines, Founder Drama & Media Narratives00:52:30 – Where Long-Term Power Accrues in the AI StackTranscriptLatent.Space - Inside AI's $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z[00:00:00] Welcome to Latent Space (Live from a16z) + Meet the Guests[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast, live from a 16 z. Uh, this is Alessio founder Kernel Lance, and I'm joined by Twix, editor of Latent Space.[00:00:08] swyx: Hey, hey, hey. Uh, and we're so glad to be on with you guys. Also a top AI podcast, uh, Martin Cado and Sarah Wang. Welcome, very[00:00:16] Martin Casado: happy to be here and welcome.[00:00:17] swyx: Yes, uh, we love this office. We love what you've done with the place. Uh, the new logo is everywhere now. It's, it's still getting, takes a while to get used to, but it reminds me of like sort of a callback to a more ambitious age, which I think is kind of[00:00:31] Martin Casado: definitely makes a statement.[00:00:33] swyx: Yeah.[00:00:34] Martin Casado: Not quite sure what that statement is, but it makes a statement.[00:00:37] swyx: Uh, Martin, I go back with you to Netlify.[00:00:40] Martin Casado: Yep.[00:00:40] swyx: Uh, and, uh, you know, you create a software defined networking and all, all that stuff people can read up on your background. Yep. Sarah, I'm newer to you. Uh, you, you sort of started working together on AI infrastructure stuff.[00:00:51] Sarah Wang: That's right. Yeah. Seven, seven years ago now.[00:00:53] Martin Casado: Best growth investor in the entire industry.[00:00:55] swyx: Oh, say[00:00:56] Martin Casado: more hands down there is, there is. [00:01:00] I mean, when it comes to AI companies, Sarah, I think has done the most kind of aggressive, um, investment thesis around AI models, right? So, worked for Nom Ja, Mira Ia, FEI Fey, and so just these frontier, kind of like large AI models.[00:01:15] I think, you know, Sarah's been the, the broadest investor. Is that fair?[00:01:20] Venture vs. Growth in the Frontier Model Era[00:01:20] Sarah Wang: No, I, well, I was gonna say, I think it's been a really interesting tag, tag team actually just ‘cause the, a lot of these big C deals, not only are they raising a lot of money, um, it's still a tech founder bet, which obviously is inherently early stage.[00:01:33] But the resources,[00:01:36] Martin Casado: so many, I[00:01:36] Sarah Wang: was gonna say the resources one, they just grow really quickly. But then two, the resources that they need day one are kind of growth scale. So I, the hybrid tag team that we have is. Quite effective, I think,[00:01:46] Martin Casado: what is growth these days? You know, you don't wake up if it's less than a billion or like, it's, it's actually, it's actually very like, like no, it's a very interesting time in investing because like, you know, take like the character around, right?[00:01:59] These tend to [00:02:00] be like pre monetization, but the dollars are large enough that you need to have a larger fund and the analysis. You know, because you've got lots of users. ‘cause this stuff has such high demand requires, you know, more of a number sophistication. And so most of these deals, whether it's US or other firms on these large model companies, are like this hybrid between venture growth.[00:02:18] Sarah Wang: Yeah. Total. And I think, you know, stuff like BD for example, you wouldn't usually need BD when you were seed stage trying to get market biz Devrel. Biz Devrel, exactly. Okay. But like now, sorry, I'm,[00:02:27] swyx: I'm not familiar. What, what, what does biz Devrel mean for a venture fund? Because I know what biz Devrel means for a company.[00:02:31] Sarah Wang: Yeah.[00:02:32] Compute Deals, Strategics, and the ‘Circular Funding' Question[00:02:32] Sarah Wang: You know, so a, a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What, what sort of partner are you looking at? Is there a go-to market arm to that? Um, and these are just things on this scale, hundreds of millions, you know, maybe.[00:02:50] Six months into the inception of a company, you just wouldn't have to negotiate these deals before.[00:02:54] Martin Casado: Yeah. These large rounds are very complex now. Like in the past, if you did a series A [00:03:00] or a series B, like whatever, you're writing a 20 to a $60 million check and you call it a day. Now you normally have financial investors and strategic investors, and then the strategic portion always still goes with like these kind of large compute contracts, which can take months to do.[00:03:13] And so it's, it's very different ties. I've been doing this for 10 years. It's the, I've never seen anything like this.[00:03:19] swyx: Yeah. Do you have worries about the circular funding from so disease strategics?[00:03:24] Martin Casado: I mean, listen, as long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.[00:03:29] swyx: Exactly. All right. This, this is like the, the whole pyramid scheme bubble thing, where like, as long as you mark to market on like the notional value of like, these deals, fine, but like once it starts to chip away, it really Well[00:03:41] Martin Casado: no, like as, as, as, as long as there's demand. I mean, you know, this, this is like a lot of these sound bites have already become kind of cliches, but they're worth saying it.[00:03:47] Right? Like during the internet days, like we were. Um, raising money to put fiber in the ground that wasn't used. And that's a problem, right? Because now you actually have a supply overhang.[00:03:58] swyx: Mm-hmm.[00:03:59] Martin Casado: And even in the, [00:04:00] the time of the, the internet, like the supply and, and bandwidth overhang, even as massive as it was in, as massive as the crash was only lasted about four years.[00:04:09] But we don't have a supply overhang. Like there's no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if, if someone invests in a company that, um. You know, they'll actually use the GPUs. And on the other side of it is the, is the ask for customer. So I I, I think it's a different time.[00:04:25] Sarah Wang: I think the other piece, maybe just to add onto this, and I'm gonna quote Martine in front of him, but this is probably also a unique time in that. For the first time, you can actually trace dollars to outcomes. Yeah, right. Provided that scaling laws are, are holding, um, and capabilities are actually moving forward.[00:04:40] Because if you can put translate dollars into capabilities, uh, a capability improvement, there's demand there to martine's point. But if that somehow breaks, you know, obviously that's an important assumption in this whole thing to make it work. But you know, instead of investing dollars into sales and marketing, you're, you're investing into r and d to get to the capability, um, you know, increase.[00:04:59] And [00:05:00] that's sort of been the demand driver because. Once there's an unlock there, people are willing to pay for it.[00:05:05] Alessio: Yeah.[00:05:06] Blurring Lines: Models as Infra + Apps, and the New Fundraising Flywheel[00:05:06] Alessio: Is there any difference in how you built the portfolio now that some of your growth companies are, like the infrastructure of the early stage companies, like, you know, OpenAI is now the same size as some of the cloud providers were early on.[00:05:16] Like what does that look like? Like how much information can you feed off each other between the, the two?[00:05:24] Martin Casado: There's so many lines that are being crossed right now, or blurred. Right. So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So like what is a model company?[00:05:35] Mm-hmm. Like, it's clearly infrastructure, right? Because it's like, you know, it's doing kind of core r and d. It's a horizontal platform, but it's also an app because it's um, uh, touches the users directly. And then of course. You know, the, the, the growth of these is just so high. And so I actually think you're just starting to see a, a, a new financing strategy emerge and, you know, we've had to adapt as a result of that.[00:05:59] And [00:06:00] so there's been a lot of changes. Um, you're right that these companies become platform companies very quickly. You've got ecosystem build out. So none of this is necessarily new, but the timescales of which it's happened is pretty phenomenal. And the way we'd normally cut lines before is blurred a little bit, but.[00:06:16] But that, that, that said, I mean, a lot of it also just does feel like things that we've seen in the past, like cloud build out the internet build out as well.[00:06:24] Sarah Wang: Yeah. Um, yeah, I think it's interesting, uh, I don't know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, um.[00:06:33] You raise money for compute, you pour that or you, you pour the money into compute, you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GBT, that could be cloud code, you know, whatever it is. You massively gain share and get users.[00:06:49] Maybe you're even subsidizing at that point. Um, depending on your strategy. You raise money at the peak momentum and then you repeat, rinse and repeat. Um, and so. And that wasn't [00:07:00] true even two years ago, I think. Mm-hmm. And so it's sort of to your, just tying it to fundraising strategy, right? There's a, and hiring strategy.[00:07:07] All of these are tied, I think the lines are blurring even more today where everyone is, and they, but of course these companies all have API businesses and so they're these, these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there.[00:07:23] But they're competing on the app layer.[00:07:24] Martin Casado: Yeah. So this is a really, really important point. So I, I would say for sure, venture and growth, that line is blurry app and infrastructure. That line is blurry. Um, but I don't think that that changes our practice so much. But like where the very open questions are like, does this layer in the same way.[00:07:43] Compute traditionally has like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not, might not be the case here. It may be the case that you actually can't verticalize on the token string. Like you can't build an app like it, it necessarily goes down just because there are no [00:08:00] abstractions.[00:08:00] So those are kinda the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is. In the past, if you raised money, then you basically had to wait for engineering to catch up. Which famously doesn't scale like the mythical mammoth. It take a very long time.[00:08:18] But like that's not the case here. Like a model company can raise money and drop a model in a, in a year, and it's better, right? And, and it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before.[00:08:39] And I think everybody's trying to understand what the consequences are. So I think it's less about like. Big companies and growth and this, and more about these more systemic questions that we actually don't have answers to.[00:08:49] Alessio: Yeah, like at Kernel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage [00:09:00] market is very different because today you're investing X amount of capital to win a deal because of price structure and whatnot, and you're kind of pot committing.[00:09:07] Yeah. To a certain strategy for a certain amount of time. Yeah. But if you could like iteratively spin out companies and products and just throw, I, I wanna spend a million dollar of inference today and get a product out tomorrow.[00:09:18] swyx: Yeah.[00:09:19] Alessio: Like, we should get to the point where like the friction of like token to product is so low that you can do this and then you can change the Right, the early stage venture model to be much more iterative.[00:09:30] And then every round is like either 100 k of inference or like a hundred million from a 16 Z. There's no, there's no like $8 million C round anymore. Right.[00:09:38] When Frontier Labs Outspend the Entire App Ecosystem[00:09:38] Martin Casado: But, but, but, but there's a, there's a, the, an industry structural question that we don't know the answer to, which involves the frontier models, which is, let's take.[00:09:48] Anthropic it. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share. And let's say that, uh, uh, uh, you know, uh, a company's building smaller models [00:10:00] that, you know, use the bigger model in the background, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more.[00:10:10] Every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it. And if that's the case, they can expand beyond everything built on top of it. It's like imagine like a star that's just kind of expanding, so there could be a systemic. There could be a, a systemic situation where the soda models can raise so much money that they can out pay anybody that bills on top of ‘em, which would be something I don't think we've ever seen before just because we were so bottlenecked in engineering, and this is a very open question.[00:10:41] swyx: Yeah. It's, it is almost like bitter lesson applied to the startup industry.[00:10:45] Martin Casado: Yeah, a hundred percent. It literally becomes an issue of like raise capital, turn that directly into growth. Use that to raise three times more. Exactly. And if you can keep doing that, you literally can outspend any company that's built the, not any company.[00:10:57] You can outspend the aggregate of companies on top of [00:11:00] you and therefore you'll necessarily take their share, which is crazy.[00:11:02] swyx: Would you say that kind of happens in character? Is that the, the sort of postmortem on. What happened?[00:11:10] Sarah Wang: Um,[00:11:10] Martin Casado: no.[00:11:12] Sarah Wang: Yeah, because I think so,[00:11:13] swyx: I mean the actual postmortem is, he wanted to go back to Google.[00:11:15] Exactly. But like[00:11:18] Martin Casado: that's another difference that[00:11:19] Sarah Wang: you said[00:11:21] Martin Casado: it. We should talk, we should actually talk about that.[00:11:22] swyx: Yeah,[00:11:22] Sarah Wang: that's[00:11:23] swyx: Go for it. Take it. Take,[00:11:23] Sarah Wang: yeah.[00:11:24] Character.AI, Founder Goals (AGI vs Product), and GPU Allocation Tradeoffs[00:11:24] Sarah Wang: I was gonna say, I think, um. The, the, the character thing raises actually a different issue, which actually the Frontier Labs will face as well. So we'll see how they handle it.[00:11:34] But, um, so we invest in character in January, 2023, which feels like eons ago, I mean, three years ago. Feels like lifetimes ago. But, um, and then they, uh, did the IP licensing deal with Google in August, 2020. Uh, four. And so, um, you know, at the time, no, you know, he's talked publicly about this, right? He wanted to Google wouldn't let him put out products in the world.[00:11:56] That's obviously changed drastically. But, um, he went to go do [00:12:00] that. Um, but he had a product attached. The goal was, I mean, it's Nome Shair, he wanted to get to a GI. That was always his personal goal. But, you know, I think through collecting data, right, and this sort of very human use case, that the character product.[00:12:13] Originally was and still is, um, was one of the vehicles to do that. Um, I think the real reason that, you know. I if you think about the, the stress that any company feels before, um, you ultimately going one way or the other is sort of this a GI versus product. Um, and I think a lot of the big, I think, you know, opening eyes, feeling that, um, anthropic if they haven't started, you know, felt it, certainly given the success of their products, they may start to feel that soon.[00:12:39] And the real. I think there's real trade-offs, right? It's like how many, when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new re research? Right? Is it, or long-term research, is it toward, um, n you know, near to midterm research? And so, um, in a case where you're resource constrained, um, [00:13:00] of course there's this fundraising game you can play, right?[00:13:01] But the fund, the market was very different back in 2023 too. Um. I think the best researchers in the world have this dilemma of, okay, I wanna go all in on a GI, but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to a GI. And so it does make, um, you know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right?[00:13:27] And certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.[00:13:32] Martin Casado: I would say that because, ‘cause we're keeping track of all of the things that are different, right? Like, you know, venture growth and uh, app infra and one of the ones is definitely the personalities of the founders.[00:13:45] It's just very different this time I've been. Been doing this for a decade and I've been doing startups for 20 years. And so, um, I mean a lot of people start this to do a GI and we've never had like a unified North star that I recall in the same [00:14:00] way. Like people built companies to start companies in the past.[00:14:02] Like that was what it was. Like I would create an internet company, I would create infrastructure company, like it's kind of more engineering builders and this is kind of a different. You know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider a GI, but others have not.[00:14:20] And so like there is always this tension with personnel. And so I think we're seeing more kind of founder movement.[00:14:27] Sarah Wang: Yeah.[00:14:27] Martin Casado: You know, as a fraction of founders than we've ever seen. I mean, maybe since like, I don't know the time of like Shockly and the trade DUR aid or something like that. Way back in the beginning of the industry, I, it's a very, very.[00:14:38] Unusual time of personnel.[00:14:39] Sarah Wang: Totally.[00:14:40] Talent Wars, Mega-Comp, and the Rise of Acquihire M&A[00:14:40] Sarah Wang: And it, I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right? No. Yeah. Very rarely can you see someone get poached for $5 billion. That's hard to compete with. And then secondly, if you're a founder in ai, you could fart and it would be on the front page of, you know, the information these days.[00:14:59] And so there's [00:15:00] sort of this fishbowl effect that I think adds to the deep anxiety that, that these AI founders are feeling.[00:15:06] Martin Casado: Hmm.[00:15:06] swyx: Uh, yes. I mean, just on, uh, briefly comment on the founder, uh, the sort of. Talent wars thing. I feel like 2025 was just like a blip. Like I, I don't know if we'll see that again.[00:15:17] ‘cause meta built the team. Like, I don't know if, I think, I think they're kind of done and like, who's gonna pay more than meta? I, I don't know.[00:15:23] Martin Casado: I, I agree. So it feels so, it feel, it feels this way to me too. It's like, it is like, basically Zuckerberg kind of came out swinging and then now he's kind of back to building.[00:15:30] Yeah,[00:15:31] swyx: yeah. You know, you gotta like pay up to like assemble team to rush the job, whatever. But then now, now you like you, you made your choices and now they got a ship.[00:15:38] Martin Casado: I mean, the, the o other side of that is like, you know, like we're, we're actually in the job hiring market. We've got 600 people here. I hire all the time.[00:15:44] I've got three open recs if anybody's interested, that's listening to this for investor. Yeah, on, on the team, like on the investing side of the team, like, and, um, a lot of the people we talk to have acting, you know, active, um, offers for 10 million a year or something like that. And like, you know, and we pay really, [00:16:00] really well.[00:16:00] And just to see what's out on the market is really, is really remarkable. And so I would just say it's actually, so you're right, like the really flashy one, like I will get someone for, you know, a billion dollars, but like the inflated, um, uh, trickles down. Yeah, it is still very active today. I mean,[00:16:18] Sarah Wang: yeah, you could be an L five and get an offer in the tens of millions.[00:16:22] Okay. Yeah. Easily. Yeah. It's so I think you're right that it felt like a blip. I hope you're right. Um, but I think it's been, the steady state is now, I think got pulled up. Yeah. Yeah. I'll pull up for[00:16:31] Martin Casado: sure. Yeah.[00:16:32] Alessio: Yeah. And I think that's breaking the early stage founder math too. I think before a lot of people would be like, well, maybe I should just go be a founder instead of like getting paid.[00:16:39] Yeah. 800 KA million at Google. But if I'm getting paid. Five, 6 million. That's different but[00:16:45] Martin Casado: on. But on the other hand, there's more strategic money than we've ever seen historically, right? Mm-hmm. And so, yep. The economics, the, the, the, the calculus on the economics is very different in a number of ways. And, uh, it's crazy.[00:16:58] It's cra it's causing like a, [00:17:00] a, a, a ton of change in confusion in the market. Some very positive, sub negative, like, so for example, the other side of the, um. The co-founder, like, um, acquisition, you know, mark Zuckerberg poaching someone for a lot of money is like, we were actually seeing historic amount of m and a for basically acquihires, right?[00:17:20] That you like, you know, really good outcomes from a venture perspective that are effective acquihires, right? So I would say it's probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way.[00:17:33] Alessio: Yeah.[00:17:33] What's Underfunded: Boring Software, Robotics Skepticism, and Custom Silicon Economics[00:17:33] Alessio: Um, let's talk maybe about what's not being invested in, like maybe some interesting ideas that you would see more people build or it, it seems in a way, you know, as ycs getting more popular, it's like access getting more popular.[00:17:47] There's a startup school path that a lot of founders take and they know what's hot in the VC circles and they know what gets funded. Uh, and there's maybe not as much risk appetite for. Things outside of that. Um, I'm curious if you feel [00:18:00] like that's true and what are maybe, uh, some of the areas, uh, that you think are under discussed?[00:18:06] Martin Casado: I mean, I actually think that we've taken our eye off the ball in a lot of like, just traditional, you know, software companies. Um, so like, I mean. You know, I think right now there's almost a barbell, like you're like the hot thing on X, you're deep tech.[00:18:21] swyx: Mm-hmm.[00:18:22] Martin Casado: Right. But I, you know, I feel like there's just kind of a long, you know, list of like good.[00:18:28] Good companies that will be around for a long time in very large markets. Say you're building a database, you know, say you're building, um, you know, kind of monitoring or logging or tooling or whatever. There's some good companies out there right now, but like, they have a really hard time getting, um, the attention of investors.[00:18:43] And it's almost become a meme, right? Which is like, if you're not basically growing from zero to a hundred in a year, you're not interesting, which is just, is the silliest thing to say. I mean, think of yourself as like an introvert person, like, like your personal money, right? Mm-hmm. So. Your personal money, will you put it in the stock market at 7% or you put it in this company growing five x in a very large [00:19:00] market?[00:19:00] Of course you can put it in the company five x. So it's just like we say these stupid things, like if you're not going from zero to a hundred, but like those, like who knows what the margins of those are mean. Clearly these are good investments. True for anybody, right? True. Like our LPs want whatever.[00:19:12] Three x net over, you know, the life cycle of a fund, right? So a, a company in a big market growing five X is a great investment. We'd, everybody would be happy with these returns, but we've got this kind of mania on these, these strong growths. And so I would say that that's probably the most underinvested sector.[00:19:28] Right now.[00:19:29] swyx: Boring software, boring enterprise software.[00:19:31] Martin Casado: Traditional. Really good company.[00:19:33] swyx: No, no AI here.[00:19:34] Martin Casado: No. Like boring. Well, well, the AI of course is pulling them into use cases. Yeah, but that's not what they're, they're not on the token path, right? Yeah. Let's just say that like they're software, but they're not on the token path.[00:19:41] Like these are like they're great investments from any definition except for like random VC on Twitter saying VC on x, saying like, it's not growing fast enough. What do you[00:19:52] Sarah Wang: think? Yeah, maybe I'll answer a slightly different. Question, but adjacent to what you asked, um, which is maybe an area that we're not, uh, investing [00:20:00] right now that I think is a question and we're spending a lot of time in regardless of whether we pull the trigger or not.[00:20:05] Um, and it would probably be on the hardware side, actually. Robotics, right? And the robotics side. Robotics. Right. Which is, it's, I don't wanna say that it's not getting funding ‘cause it's clearly, uh, it's, it's sort of non-consensus to almost not invest in robotics at this point. But, um, we spent a lot of time in that space and I think for us, we just haven't seen the chat GPT moment.[00:20:22] Happen on the hardware side. Um, and the funding going into it feels like it's already. Taking that for granted.[00:20:30] Martin Casado: Yeah. Yeah. But we also went through the drone, you know, um, there's a zip line right, right out there. What's that? Oh yeah, there's a zip line. Yeah. What the drone, what the av And like one of the takeaways is when it comes to hardware, um, most companies will end up verticalizing.[00:20:46] Like if you're. If you're investing in a robot company for an A for agriculture, you're investing in an ag company. ‘cause that's the competition and that's surprising. And that's supply chain. And if you're doing it for mining, that's mining. And so the ad team does a lot of that type of stuff ‘cause they actually set up to [00:21:00] diligence that type of work.[00:21:01] But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for, for purpose. And so we kinda like to look at software. Solutions or horizontal solutions like applied intuition. Clearly from the AV wave deep map, clearly from the AV wave, I would say scale AI was actually a horizontal one for That's fair, you know, for robotics early on.[00:21:23] And so that sort of thing we're very, very interested. But the actual like robot interacting with the world is probably better for different team. Agree.[00:21:30] Alessio: Yeah, I'm curious who these teams are supposed to be that invest in them. I feel like everybody's like, yeah, robotics, it's important and like people should invest in it.[00:21:38] But then when you look at like the numbers, like the capital requirements early on versus like the moment of, okay, this is actually gonna work. Let's keep investing. That seems really hard to predict in a way that is not,[00:21:49] Martin Casado: I think co, CO two, kla, gc, I mean these are all invested in in Harvard companies. He just, you know, and [00:22:00] listen, I mean, it could work this time for sure.[00:22:01] Right? I mean if Elon's doing it, he's like, right. Just, just the fact that Elon's doing it means that there's gonna be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just ‘cause you have this North star who's Elon with a humanoid and that's gonna like basically willing into being an industry.[00:22:17] Um, but we've just historically found like. We're a huge believer that this is gonna happen. We just don't feel like we're in a good position to diligence these things. ‘cause again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. Like that's like that competitive equilibrium with a human being is what's important.[00:22:34] It's not like the core tech and like we're kind of more horizontal core tech type investors. And this is Sarah and I. Yeah, the ad team is different. They can actually do these types of things.[00:22:42] swyx: Uh, just to clarify, AD stands for[00:22:44] Martin Casado: American Dynamism.[00:22:45] swyx: Alright. Okay. Yeah, yeah, yeah. Uh, I actually, I do have a related question that, first of all, I wanna acknowledge also just on the, on the chip side.[00:22:51] Yeah. I, I recall a podcast that where you were on, i, I, I think it was the a CC podcast, uh, about two or three years ago where you, where you suddenly said [00:23:00] something, which really stuck in my head about how at some point, at some point kind of scale it makes sense to. Build a custom aic Yes. For per run.[00:23:07] Martin Casado: Yes.[00:23:07] It's crazy. Yeah.[00:23:09] swyx: We're here and I think you, you estimated 500 billion, uh, something.[00:23:12] Martin Casado: No, no, no. A billion, a billion dollar training run of $1 billion training run. It makes sense to actually do a custom meic if you can do it in time. The question now is timelines. Yeah, but not money because just, just, just rough math.[00:23:22] If it's a billion dollar training. Then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save 20%, which you could save much more than that with an ASIC 20%, that's $200 million. You can tape out a chip for $200 million. Right? So now you can literally like justify economically, not timeline wise.[00:23:41] That's a different issue. An ASIC per model, which[00:23:44] swyx: is because that, that's how much we leave on the table every single time. We, we, we do like generic Nvidia.[00:23:48] Martin Casado: Exactly. Exactly. No, it, it is actually much more than that. You could probably get, you know, a factor of two, which would be 500 million.[00:23:54] swyx: Typical MFU would be like 50.[00:23:55] Yeah, yeah. And that's good.[00:23:57] Martin Casado: Exactly. Yeah. Hundred[00:23:57] swyx: percent. Um, so, so, yeah, and I mean, and I [00:24:00] just wanna acknowledge like, here we are in, in, in 2025 and opening eyes confirming like Broadcom and all the other like custom silicon deals, which is incredible. I, I think that, uh, you know, speaking about ad there's, there's a really like interesting tie in that obviously you guys are hit on, which is like these sort, this sort of like America first movement or like sort of re industrialized here.[00:24:17] Yeah. Uh, move TSMC here, if that's possible. Um, how much overlap is there from ad[00:24:23] Martin Casado: Yeah.[00:24:23] swyx: To, I guess, growth and, uh, investing in particularly like, you know, US AI companies that are strongly bounded by their compute.[00:24:32] Martin Casado: Yeah. Yeah. So I mean, I, I would view, I would view AD as more as a market segmentation than like a mission, right?[00:24:37] So the market segmentation is, it has kind of regulatory compliance issues or government, you know, sale or it deals with like hardware. I mean, they're just set up to, to, to, to, to. To diligence those types of companies. So it's a more of a market segmentation thing. I would say the entire firm. You know, which has been since it is been intercepted, you know, has geographical biases, right?[00:24:58] I mean, for the longest time we're like, you [00:25:00] know, bay Area is gonna be like, great, where the majority of the dollars go. Yeah. And, and listen, there, there's actually a lot of compounding effects for having a geographic bias. Right. You know, everybody's in the same place. You've got an ecosystem, you're there, you've got presence, you've got a network.[00:25:12] Um, and, uh, I mean, I would say the Bay area's very much back. You know, like I, I remember during pre COVID, like it was like almost Crypto had kind of. Pulled startups away. Miami from the Bay Area. Miami, yeah. Yeah. New York was, you know, because it's so close to finance, came up like Los Angeles had a moment ‘cause it was so close to consumer, but now it's kind of come back here.[00:25:29] And so I would say, you know, we tend to be very Bay area focused historically, even though of course we've asked all over the world. And then I would say like, if you take the ring out, you know, one more, it's gonna be the US of course, because we know it very well. And then one more is gonna be getting us and its allies and Yeah.[00:25:44] And it goes from there.[00:25:45] Sarah Wang: Yeah,[00:25:45] Martin Casado: sorry.[00:25:46] Sarah Wang: No, no. I agree. I think from a, but I think from the intern that that's sort of like where the companies are headquartered. Maybe your questions on supply chain and customer base. Uh, I, I would say our customers are, are, our companies are fairly international from that perspective.[00:25:59] Like they're selling [00:26:00] globally, right? They have global supply chains in some cases.[00:26:03] Martin Casado: I would say also the stickiness is very different.[00:26:05] Sarah Wang: Yeah.[00:26:05] Martin Casado: Historically between venture and growth, like there's so much company building in venture, so much so like hiring the next PM. Introducing the customer, like all of that stuff.[00:26:15] Like of course we're just gonna be stronger where we have our network and we've been doing business for 20 years. I've been in the Bay Area for 25 years, so clearly I'm just more effective here than I would be somewhere else. Um, where I think, I think for some of the later stage rounds, the companies don't need that much help.[00:26:30] They're already kind of pretty mature historically, so like they can kind of be everywhere. So there's kind of less of that stickiness. This is different in the AI time. I mean, Sarah is now the, uh, chief of staff of like half the AI companies in, uh, in the Bay Area right now. She's like, ops Ninja Biz, Devrel, BizOps.[00:26:48] swyx: Are, are you, are you finding much AI automation in your work? Like what, what is your stack.[00:26:53] Sarah Wang: Oh my, in my personal stack.[00:26:54] swyx: I mean, because like, uh, by the way, it's the, the, the reason for this is it is triggering, uh, yeah. We, like, I'm hiring [00:27:00] ops, ops people. Um, a lot of ponders I know are also hiring ops people and I'm just, you know, it's opportunity Since you're, you're also like basically helping out with ops with a lot of companies.[00:27:09] What are people doing these days? Because it's still very manual as far as I can tell.[00:27:13] Sarah Wang: Hmm. Yeah. I think the things that we help with are pretty network based, um, in that. It's sort of like, Hey, how do do I shortcut this process? Well, let's connect you to the right person. So there's not quite an AI workflow for that.[00:27:26] I will say as a growth investor, Claude Cowork is pretty interesting. Yeah. Like for the first time, you can actually get one shot data analysis. Right. Which, you know, if you're gonna do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before. And our team, the other, it was like midnight and the three of us were playing with Claude Cowork.[00:27:47] We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like, aha moment. That sounds so boring. But you know, that's, that's the kind of thing that a growth investor is like, [00:28:00] you know, slaving away on late at night. Um, done in a few seconds.[00:28:03] swyx: Yeah. You gotta wonder what the whole, like, philanthropic labs, which is like their new sort of products studio.[00:28:10] Yeah. What would that be worth as an independent, uh, startup? You know, like a[00:28:14] Martin Casado: lot.[00:28:14] Sarah Wang: Yeah, true.[00:28:16] swyx: Yeah. You[00:28:16] Martin Casado: gotta hand it to them. They've been executing incredibly well.[00:28:19] swyx: Yeah. I, I mean, to me, like, you know, philanthropic, like building on cloud code, I think, uh, it makes sense to me the, the real. Um, pedal to the metal, whatever the, the, the phrase is, is when they start coming after consumer with, uh, against OpenAI and like that is like red alert at Open ai.[00:28:35] Oh, I[00:28:35] Martin Casado: think they've been pretty clear. They're enterprise focused.[00:28:37] swyx: They have been, but like they've been free. Here's[00:28:40] Martin Casado: care publicly,[00:28:40] swyx: it's enterprise focused. It's coding. Right. Yeah.[00:28:43] AI Labs vs Startups: Disruption, Undercutting & the Innovator's Dilemma[00:28:43] swyx: And then, and, but here's cloud, cloud, cowork, and, and here's like, well, we, uh, they, apparently they're running Instagram ads for Claudia.[00:28:50] I, on, you know, for, for people on, I get them all the time. Right. And so, like,[00:28:54] Martin Casado: uh,[00:28:54] swyx: it, it's kind of like this, the disruption thing of, uh, you know. Mo Open has been doing, [00:29:00] consumer been doing the, just pursuing general intelligence in every mo modality, and here's a topic that only focus on this thing, but now they're sort of undercutting and doing the whole innovator's dilemma thing on like everything else.[00:29:11] Martin Casado: It's very[00:29:11] swyx: interesting.[00:29:12] Martin Casado: Yeah, I mean there's, there's a very open que so for me there's like, do you know that meme where there's like the guy in the path and there's like a path this way? There's a path this way. Like one which way Western man. Yeah. Yeah.[00:29:23] Two Futures for AI: Infinite Market vs AGI Oligopoly[00:29:23] Martin Casado: And for me, like, like all the entire industry kind of like hinges on like two potential futures.[00:29:29] So in, in one potential future, um, the market is infinitely large. There's perverse economies of scale. ‘cause as soon as you put a model out there, like it kind of sublimates and all the other models catch up and like, it's just like software's being rewritten and fractured all over the place and there's tons of upside and it just grows.[00:29:48] And then there's another path which is like, well. Maybe these models actually generalize really well, and all you have to do is train them with three times more money. That's all you have to [00:30:00] do, and it'll just consume everything beyond it. And if that's the case, like you end up with basically an oligopoly for everything, like, you know mm-hmm.[00:30:06] Because they're perfectly general and like, so this would be like the, the a GI path would be like, these are perfectly general. They can do everything. And this one is like, this is actually normal software. The universe is complicated. You've got, and nobody knows the answer.[00:30:18] The Economics Reality Check: Gross Margins, Training Costs & Borrowing Against the Future[00:30:18] Martin Casado: My belief is if you actually look at the numbers of these companies, so generally if you look at the numbers of these companies, if you look at like the amount they're making and how much they, they spent training the last model, they're gross margin positive.[00:30:30] You're like, oh, that's really working. But if you look at like. The current training that they're doing for the next model, their gross margin negative. So part of me thinks that a lot of ‘em are kind of borrowing against the future and that's gonna have to slow down. It's gonna catch up to them at some point in time, but we don't really know.[00:30:47] Sarah Wang: Yeah.[00:30:47] Martin Casado: Does that make sense? Like, I mean, it could be, it could be the case that the only reason this is working is ‘cause they can raise that next round and they can train that next model. ‘cause these models have such a short. Life. And so at some point in time, like, you know, they won't be able to [00:31:00] raise that next round for the next model and then things will kind of converge and fragment again.[00:31:03] But right now it's not.[00:31:04] Sarah Wang: Totally. I think the other, by the way, just, um, a meta point. I think the other lesson from the last three years is, and we talk about this all the time ‘cause we're on this. Twitter X bubble. Um, cool. But, you know, if you go back to, let's say March, 2024, that period, it felt like a, I think an open source model with an, like a, you know, benchmark leading capability was sort of launching on a daily basis at that point.[00:31:27] And, um, and so that, you know, that's one period. Suddenly it's sort of like open source takes over the world. There's gonna be a plethora. It's not an oligopoly, you know, if you fast, you know, if you, if you rewind time even before that GPT-4 was number one for. Nine months, 10 months. It's a long time. Right.[00:31:44] Um, and of course now we're in this era where it feels like an oligopoly, um, maybe some very steady state shifts and, and you know, it could look like this in the future too, but it just, it's so hard to call. And I think the thing that keeps, you know, us up at [00:32:00] night in, in a good way and bad way, is that the capability progress is actually not slowing down.[00:32:06] And so until that happens, right, like you don't know what's gonna look like.[00:32:09] Martin Casado: But I, I would, I would say for sure it's not converged, like for sure, like the systemic capital flows have not converged, meaning right now it's still borrowing against the future to subsidize growth currently, which you can do that for a period of time.[00:32:23] But, but you know, at the end, at some point the market will rationalize that and just nobody knows what that will look like.[00:32:29] Alessio: Yeah.[00:32:29] Martin Casado: Or, or like the drop in price of compute will, will, will save them. Who knows?[00:32:34] Alessio: Yeah. Yeah. I think the models need to ask them to, to specific tasks. You know? It's like, okay, now Opus 4.5 might be a GI at some specific task, and now you can like depreciate the model over a longer time.[00:32:45] I think now, now, right now there's like no old model.[00:32:47] Martin Casado: No, but let, but lemme just change that mental, that's, that used to be my mental model. Lemme just change it a little bit.[00:32:53] Capital as a Weapon vs Task Saturation: Where Real Enterprise Value Gets Built[00:32:53] Martin Casado: If you can raise three times, if you can raise more than the aggregate of anybody that uses your models, that doesn't even matter.[00:32:59] It doesn't [00:33:00] even matter. See what I'm saying? Like, yeah. Yeah. So, so I have an API Business. My API business is 60% margin, or 70% margin, or 80% margin is a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that's using it, I will consume them whether I'm a GI or not.[00:33:14] And I will know if they're using it ‘cause they're using it. And like, unlike in the past where engineering stops me from doing that.[00:33:21] Alessio: Mm-hmm.[00:33:21] Martin Casado: It is very straightforward. You just train. So I also thought it was kind of like, you must ask the code a GI, general, general, general. But I think there's also just a possibility that the, that the capital markets will just give them the, the, the ammunition to just go after everybody on top of ‘em.[00:33:36] Sarah Wang: I, I do wonder though, to your point, um, if there's a certain task that. Getting marginally better isn't actually that much better. Like we've asked them to it, to, you know, we can call it a GI or whatever, you know, actually, Ali Goi talks about this, like we're already at a GI for a lot of functions in the enterprise.[00:33:50] Um. That's probably those for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn't [00:34:00] coming from the model itself. There's probably a rich enterprise business to be built there. I mean, could be wrong on that, but there's a lot of interesting examples.[00:34:08] So, right, if you're looking the legal profession or, or whatnot, and maybe that's not a great one ‘cause the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from. Services. It comes from implementation, right? It comes from all these things that actually make it useful to the end customer.[00:34:24] Martin Casado: Sorry, what am I, one more thing I think is, is underused in all of this is like, to what extent every task is a GI complete.[00:34:31] Sarah Wang: Mm-hmm.[00:34:32] Martin Casado: Yeah. I code every day. It's so fun.[00:34:35] Sarah Wang: That's a core question. Yeah.[00:34:36] Martin Casado: And like. When I'm talking to these models, it's not just code. I mean, it's everything, right? Like I, you know, like it's,[00:34:43] swyx: it's healthcare.[00:34:44] It's,[00:34:44] Martin Casado: I mean, it's[00:34:44] swyx: Mele,[00:34:45] Martin Casado: but it's every, it is exactly that. Like, yeah, that's[00:34:47] Sarah Wang: great support. Yeah.[00:34:48] Martin Casado: It's everything. Like I'm asking these models to, yeah, to understand compliance. I'm asking these models to go search the web. I'm asking these models to talk about things I know in the history, like it's having a full conversation with me while I, I engineer, and so it could be [00:35:00] the case that like, mm-hmm.[00:35:01] The most a, you know, a GI complete, like I'm not an a GI guy. Like I think that's, you know, but like the most a GI complete model will is win independent of the task. And we don't know the answer to that one either.[00:35:11] swyx: Yeah.[00:35:12] Martin Casado: But it seems to me that like, listen, codex in my experience is for sure better than Opus 4.5 for coding.[00:35:18] Like it finds the hardest bugs that I work in with. Like, it is, you know. The smartest developers. I don't work on it. It's great. Um, but I think Opus 4.5 is actually very, it's got a great bedside manner and it really, and it, it really matters if you're building something very complex because like, it really, you know, like you're, you're, you're a partner and a brainstorming partner for somebody.[00:35:38] And I think we don't discuss enough how every task kind of has that quality.[00:35:42] swyx: Mm-hmm.[00:35:43] Martin Casado: And what does that mean to like capital investment and like frontier models and Submodels? Yeah.[00:35:47] Why “Coding Models” Keep Collapsing into Generalists (Reasoning vs Taste)[00:35:47] Martin Casado: Like what happened to all the special coding models? Like, none of ‘em worked right. So[00:35:51] Alessio: some of them, they didn't even get released.[00:35:53] Magical[00:35:54] Martin Casado: Devrel. There's a whole, there's a whole host. We saw a bunch of them and like there's this whole theory that like, there could be, and [00:36:00] I think one of the conclusions is, is like there's no such thing as a coding model,[00:36:04] Alessio: you know?[00:36:04] Martin Casado: Like, that's not a thing. Like you're talking to another human being and it's, it's good at coding, but like it's gotta be good at everything.[00:36:10] swyx: Uh, minor disagree only because I, I'm pretty like, have pretty high confidence that basically open eye will always release a GPT five and a GT five codex. Like that's the code's. Yeah. The way I call it is one for raisin, one for Tiz. Um, and, and then like someone internal open, it was like, yeah, that's a good way to frame it.[00:36:32] Martin Casado: That's so funny.[00:36:33] swyx: Uh, but maybe it, maybe it collapses down to reason and that's it. It's not like a hundred dimensions doesn't life. Yeah. It's two dimensions. Yeah, yeah, yeah, yeah. Like and exactly. Beside manner versus coding. Yeah.[00:36:43] Martin Casado: Yeah.[00:36:44] swyx: It's, yeah.[00:36:46] Martin Casado: I, I think for, for any, it's hilarious. For any, for anybody listening to this for, for, for, I mean, for you, like when, when you're like coding or using these models for something like that.[00:36:52] Like actually just like be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it. And so like, you're, I [00:37:00] think like, like the best Soto ish model. You know, it is going to remain very important no matter what the task is.[00:37:06] swyx: Yeah.[00:37:07] What He's Actually Coding: Gaussian Splats, Spark.js & 3D Scene Rendering Demos[00:37:07] swyx: Uh, speaking of coding, uh, I, I'm gonna be cheeky and ask like, what actually are you coding?[00:37:11] Because obviously you, you could code anything and you are obviously a busy investor and a manager of the good. Giant team. Um, what are you calling?[00:37:18] Martin Casado: I help, um, uh, FEFA at World Labs. Uh, it's one of the investments and um, and they're building a foundation model that creates 3D scenes.[00:37:27] swyx: Yeah, we had it on the pod.[00:37:28] Yeah. Yeah,[00:37:28] Martin Casado: yeah. And so these 3D scenes are Gaussian splats, just by the way that kind of AI works. And so like, you can reconstruct a scene better with, with, with radiance feels than with meshes. ‘cause like they don't really have topology. So, so they, they, they produce each. Beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great.[00:37:50] It's just never, you know, it's always been meshes and like, things like unreal use meshes. And so I work on a open source library called Spark js, which is a. Uh, [00:38:00] a JavaScript rendering layer ready for Gaussian splats. And it's just because, you know, um, you, you, you need that support and, and right now there's kind of a three js moment that's all meshes and so like, it's become kind of the default in three Js ecosystem.[00:38:13] As part of that to kind of exercise the library, I just build a whole bunch of cool demos. So if you see me on X, you see like all my demos and all the world building, but all of that is just to exercise this, this library that I work on. ‘cause it's actually a very tough algorithmics problem to actually scale a library that much.[00:38:29] And just so you know, this is ancient history now, but 30 years ago I paid for undergrad, you know, working on game engines in college in the late nineties. So I've got actually a back and it's very old background, but I actually have a background in this and so a lot of it's fun. You know, but, but the, the, the, the whole goal is just for this rendering library to, to,[00:38:47] Sarah Wang: are you one of the most active contributors?[00:38:49] The, their GitHub[00:38:50] Martin Casado: spark? Yes.[00:38:51] Sarah Wang: Yeah, yeah.[00:38:51] Martin Casado: There's only two of us there, so, yes. No, so by the way, so the, the pri The pri, yeah. Yeah. So the primary developer is a [00:39:00] guy named Andres Quist, who's an absolute genius. He and I did our, our PhDs together. And so like, um, we studied for constant Quas together. It was almost like hanging out with an old friend, you know?[00:39:09] And so like. So he, he's the core, core guy. I did mostly kind of, you know, the side I run venture fund.[00:39:14] swyx: It's amazing. Like five years ago you would not have done any of this. And it brought you back[00:39:19] Martin Casado: the act, the Activ energy, you're still back. Energy was so high because you had to learn all the framework b******t.[00:39:23] Man, I f*****g used to hate that. And so like, now I don't have to deal with that. I can like focus on the algorithmics so I can focus on the scaling and I,[00:39:29] swyx: yeah. Yeah.[00:39:29] LLMs vs Spatial Intelligence + How to Value World Labs' 3D Foundation Model[00:39:29] swyx: And then, uh, I'll observe one irony and then I'll ask a serious investor question, uh, which is like, the irony is FFE actually doesn't believe that LMS can lead us to spatial intelligence.[00:39:37] And here you are using LMS to like help like achieve spatial intelligence. I just see, I see some like disconnect in there.[00:39:45] Martin Casado: Yeah. Yeah. So I think, I think, you know, I think, I think what she would say is LLMs are great to help with coding.[00:39:51] swyx: Yes.[00:39:51] Martin Casado: But like, that's very different than a model that actually like provides, they, they'll never have the[00:39:56] swyx: spatial inte[00:39:56] Martin Casado: issues.[00:39:56] And listen, our brains clearly listen, our brains, brains clearly have [00:40:00] both our, our brains clearly have a language reasoning section and they clearly have a spatial reasoning section. I mean, it's just, you know, these are two pretty independent problems.[00:40:07] swyx: Okay. And you, you, like, I, I would say that the, the one data point I recently had, uh, against it is the DeepMind, uh, IMO Gold, where, so, uh, typically the, the typical answer is that this is where you start going down the neuros symbolic path, right?[00:40:21] Like one, uh, sort of very sort of abstract reasoning thing and one form, formal thing. Um, and that's what. DeepMind had in 2024 with alpha proof, alpha geometry, and now they just use deep think and just extended thinking tokens. And it's one model and it's, and it's in LM.[00:40:36] Martin Casado: Yeah, yeah, yeah, yeah, yeah.[00:40:37] swyx: And so that, that was my indication of like, maybe you don't need a separate system.[00:40:42] Martin Casado: Yeah. So, so let me step back. I mean, at the end of the day, at the end of the day, these things are like nodes in a graph with weights on them. Right. You know, like it can be modeled like if you, if you distill it down. But let me just talk about the two different substrates. Let's, let me put you in a dark room.[00:40:56] Like totally black room. And then let me just [00:41:00] describe how you exit it. Like to your left, there's a table like duck below this thing, right? I mean like the chances that you're gonna like not run into something are very low. Now let me like turn on the light and you actually see, and you can do distance and you know how far something away is and like where it is or whatever.[00:41:17] Then you can do it, right? Like language is not the right primitives to describe. The universe because it's not exact enough. So that's all Faye, Faye is talking about. When it comes to like spatial reasoning, it's like you actually have to know that this is three feet far, like that far away. It is curved.[00:41:37] You have to understand, you know, the, like the actual movement through space.[00:41:40] swyx: Yeah.[00:41:40] Martin Casado: So I do, I listen, I do think at the end of these models are definitely converging as far as models, but there's, there's, there's different representations of problems you're solving. One is language. Which, you know, that would be like describing to somebody like what to do.[00:41:51] And the other one is actually just showing them and the space reasoning is just showing them.[00:41:55] swyx: Yeah, yeah, yeah. Right. Got it, got it. Uh, the, in the investor question was on, on, well labs [00:42:00] is, well, like, how do I value something like this? What, what, what work does the, do you do? I'm just like, Fefe is awesome.[00:42:07] Justin's awesome. And you know, the other two co-founder, co-founders, but like the, the, the tech, everyone's building cool tech. But like, what's the value of the tech? And this is the fundamental question[00:42:16] Martin Casado: of, well, let, let, just like these, let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I'm a venture for, you know, so like, ventures always, always like kind of wild west type[00:42:24] swyx: stuff.[00:42:24] You, you, you, you paid a dream and she has to like, actually[00:42:28] Martin Casado: I'm gonna say I'm gonna mar to reality, so I'm gonna say the venture for you. And she can be like, okay, you a little kid. Yeah. So like, so, so these diffusion models literally. Create something for, for almost nothing. And something that the, the world has found to be very valuable in the past, in our real markets, right?[00:42:45] Like, like a 2D image. I mean, that's been an entire market. People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, a, to turn me into a whatever, like an image would cost a hundred bucks in an hour. The inference cost [00:43:00] us a hundredth of a penny, right? So we've seen this with speech in very successful companies.[00:43:03] We've seen this with 2D image. We've seen this with movies. Right? Now, think about 3D scene. I mean, I mean, when's Grand Theft Auto coming out? It's been six, what? It's been 10 years. I mean, how, how like, but hasn't been 10 years.[00:43:14] Alessio: Yeah.[00:43:15] Martin Casado: How much would it cost to like, to reproduce this room in 3D? Right. If you, if you, if you hired somebody on fiber, like in, in any sort of quality, probably 4,000 to $10,000.[00:43:24] And then if you had a professional, probably $30,000. So if you could generate the exact same thing from a 2D image, and we know that these are used and they're using Unreal and they're using Blend, or they're using movies and they're using video games and they're using all. So if you could do that for.[00:43:36] You know, less than a dollar, that's four or five orders of magnitude cheaper. So you're bringing the marginal cost of something that's useful down by three orders of magnitude, which historically have created very large companies. So that would be like the venture kind of strategic dreaming map.[00:43:49] swyx: Yeah.[00:43:50] And, and for listeners, uh, you can do this yourself on your, on your own phone with like. Uh, the marble.[00:43:55] Martin Casado: Yeah. Marble.[00:43:55] swyx: Uh, or but also there's many Nerf apps where you just go on your iPhone and, and do this.[00:43:59] Martin Casado: Yeah. Yeah. [00:44:00] Yeah. And, and in the case of marble though, it would, what you do is you literally give it in.[00:44:03] So most Nerf apps you like kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.[00:44:08] swyx: Yeah.[00:44:08] Martin Casado: Um, things like marble, just that the whole generative 3D space will just take a 2D image and it'll reconstruct all the like, like[00:44:16] swyx: meaning it has to fill in. Uh,[00:44:18] Martin Casado: stuff at the back of the table, under the table, the back, like, like the images, it doesn't see.[00:44:22] So the generator stuff is very different than reconstruction that it fills in the things that you can't see.[00:44:26] swyx: Yeah. Okay.[00:44:26] Sarah Wang: So,[00:44:27] Martin Casado: all right. So now the,[00:44:28] Sarah Wang: no, no. I mean I love that[00:44:29] Martin Casado: the adult[00:44:29] Sarah Wang: perspective. Um, well, no, I was gonna say these are very much a tag team. So we, we started this pod with that, um, premise. And I think this is a perfect question to even build on that further.[00:44:36] ‘cause it truly is, I mean, we're tag teaming all of these together.[00:44:39] Investing in Model Labs, Media Rumors, and the Cursor Playbook (Margins & Going Down-Stack)[00:44:39] Sarah Wang: Um, but I think every investment fundamentally starts with the same. Maybe the same two premises. One is, at this point in time, we actually believe that there are. And of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?[00:44:56] So, uh, we're not investing in every, you know, now the term is NEO [00:45:00] lab, but every foundation model, uh, any, any company, any founder trying to build a foundation model, we're not, um, contrary to popular opinion, we're

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What is Oracle Database@AWS?

Oracle University Podcast

Play Episode Listen Later Feb 10, 2026 16:36

In this episode, hosts Lois Houston and Nikita Abraham take you inside how Oracle brings its industry-leading database technology directly to AWS customers. Senior Principal OCI Instructor Susan Jang unpacks what the OCI child site is, how Exadata hardware is deployed inside AWS data centers, and how the ODB network enables secure, low-latency connections so your mission-critical workloads can run seamlessly alongside AWS services. Susan also walks through the differences between Exadata Database Service and Autonomous Database, helping teams choose the right level of control and automation for their cloud databases. Oracle Database@AWS Architect Professional: https://mylearn.oracle.com/ou/course/oracle-databaseaws-architect-professional/155574 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, Anna Hulkower, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:26 Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Communications and Adoption with Customer Success Services. Lois: Hi there! Last week, we talked about multicloud and the partnerships Oracle has with Microsoft Azure, Google Cloud, and Amazon Web Services. If you missed that episode, do listen to it as it sets the foundation for today's discussion, which is going to be about Oracle Database@AWS. 00:59 Nikita: That's right. And we're joined by Susan Jang, a Senior Principal OCI Instructor. Susan, thanks for being here. To start us off, what is Oracle Database@AWS? Susan: Oracle Database@AWS is a service that allows Oracle Exadata infrastructure that is managed by Oracle Cloud Infrastructure, or OCI, to run directly inside an AWS data center. 01:25 Lois: Susan, can you go through the key architecture components and networking relationships involved in this? Susan: The AWS Cloud is the Amazon Web Service. It's a cloud computing platform. The AWS region is a distinct, isolated geographic location with multiple physically separated data center, also known as availability zone. The availability zone is really a physically isolated data center with its own independent power, cooling, and network connectivity. When we speak of the AWS data center, it's a highly secured, specialized physical facility that houses the computing storage, the compute servers, the storage server, and the networking equipment. The VPC, the Virtual Private Cloud, is a logical, isolated virtual network. The AWS ODB network is a private user-created network that connects the virtual private cloud network of Amazon resources with an Oracle Cloud Infrastructure Exadata system. This is all within an AWS data center. The AWS-ADB peering is really an established private network connection that's between the Oracle VPC, the Virtual Private Cloud, and the Oracle Database@AWS network. And that would be the ODB. Within the AWS data center, you have something that you see called the child site. Now, an OCI child site is really a physical data center that is managed by Oracle within the AWS data center. It's a seamless extension of the Oracle Cloud Infrastructure. The site is hosting the Exadata infrastructure that's running the Oracle databases. The Oracle Database@AWS service brings the power as well as the performance of an Oracle Exadata infrastructure that is managed by Oracle Cloud Infrastructure to run directly in an AWS data center. 03:57 Nikita: So essentially, Oracle Database@AWS lets you to run your mission-critical Oracle data load close to your AWS application, while keeping management simple. Susan, what advantages does Oracle Database@AWS bring to the table? Susan: Oracle Database@AWS offers a powerful and flexible solution for running Oracle workloads natively within AWS. Oracle Database@AWS streamlines the process of moving your existing Oracle Database to AWS, making migration faster as well as easier. You get direct, low latency connectivity between your application and Oracle databases, ensuring a high performance for your mission-critical workloads. Billing, resource management, and operational tasks are unified, allowing you to manage everything through similar tools with reduce complexity. And finally, Oracle Database@AWS is designed to integrate smoothly with your AWS environments' workloads, making it so much easier to build, deploy, and scale your solutions. 05:15 Lois: You mentioned the OCI child site earlier. What part does it play in how Oracle Database@AWS works? Susan: The OCI child site really gives you the capability to combine the physical proximity and resources of AWS with the logical management and the capability of Oracle Cloud Infrastructure. This integrated approach allows us to enable the ability for you to run and manage your Oracle databases seamlessly in your AWS environment while still leveraging the power of OCI, our Oracle Cloud Infrastructure. 06:03 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure for subscribers! Whether you're interested in multicloud, databases, networking, security, AI, or machine learning, there's something for everyone. So, what are you waiting for? Pick your topic and get started by visiting mylearn.oracle.com. 06:29 Nikita: Welcome back! Susan, I'm curious about the Exadata infrastructure inside AWS. What does that setup look like? Susan: The Exadata Infrastructure consists of physical database, as well as storage servers. That is deployed-- the database and the storage servers are interconnected using a high-speed, low-latency network fiber, ensuring optimal performance and reliable data transfer. Each of the database server runs one or more Virtual Machines, or VMs, as we refer to them, providing flexible compute resources for different workloads. You can create, as well as manage your virtual machine, your VM clusters in this infrastructure using various methods. Your AWS console, Command-Line Interface, CLI, or Application Program Interface, that's your API, giving you various options, several options for automating, as well as integrating your existing tools. When you're creating your Exadata Infrastructure, there are a few things you need to define and set up. You need to define the total number of your database servers, the total number of your storage server, the model of your Exadata system, as well as the availability zone where all these resources will be deployed. This architecture delivers a high-performance resiliency and flexible management capability for running your Oracle Database on AWS. 08:18 Lois: Susan, can you explain the network architecture for Oracle Database deployments on AWS? Susan: The ODB network is an isolated network within the AWS that is designed specifically for Exadata deployments. It includes both the client, as well as the backup subnet, which are essential for securing and efficient database operations. When you create your Exadata Infrastructure, you need to specify the ODB network as you need the connectivity. This network is mapped directly to the corresponding network in the OCI child site. This will enable seamless communication between AWS, as well as the Oracle Cloud Infrastructure. The ODB network requires two separate CIDR ranges. And in addition, the client subnet is used for the Exadata VM cluster, providing connectivity for database operations. Well, you do also have another subnet. And that subnet is the backup subnet. And it's used to manage database backups of those VM cluster, ensuring not only data protection, but also data recovery. Within your AWS region and availability zone, the ODB network contains these dedicated client, as well as backup subnet. It basically isolates the Exadata traffic for both the day-to-day access, and that would be for the client, as well as the backup operations, and that would be for the backup subnet. This network design supports secure, high performance, and connectivity in a reliable backup management of the Oracle Database deployments that is running on AWS. 10:23 Nikita: Since we're on the topic of networking, can you tell us about ODB peering within the Oracle Database architecture? Susan: The ODB peering establishes a secure private connection between your AWS Virtual Private Cloud, your VPC, then the Oracle Database, the ODB network that contains your Exadata Infrastructure. This connection makes it possible for application servers that's running in your VPC, such as your Amazon EC2 instances to access your Oracle databases that is being hosted on Exadata within your ODB network. You specify the ODB network when you set up your infrastructure, specifically the Exadata Infrastructure. This network includes dedicated client, as well as backup subnets for an efficient and secure connectivity. If you wish to enable multiple VPCs to connect to the same ODB network and access the Oracle Database@AWS resources, you can leverage AWS Transit Gateways or even an AWS Cloud WAN for scalable and centralized connectivity. The virtual private cloud contains your application server, and that's securely paired with the Oracle Database network, creating a seamless, high-performance path to your application to interact with your Oracle Database. ODB peering simplifies the connectivity between the AWS application environments and the Oracle Exadata Infrastructure, thus supporting a flexible, high performance, and secure database access. 12:23 Lois: Now, before we close, can you compare two key databases that are available with Oracle Database@AWS: Oracle Exadata Database Service and Oracle Autonomous Database Service? Susan: The Exadata Database Service offers a fully managed and dedicated infrastructure with operational monitoring that is handled by you, the customer. In contrast, the Autonomous Database is fully managed by Oracle, taking care of all the operational monitoring. Exadata provides very high scalability though resources, such as disk and compute, must be sized manually. Where in the Autonomous Database, it offers high scalability through automatic elastic scaling. When we speak of performance, both service deliver strong results. Exadata offers ultra-low latency and Exadata-level performance, while the Autonomous Database delivers optimal performance with automation. Both services provide high migration capability. Exadata offers full compatibility and the Autonomous Database includes a robust set of migration tools. When it comes to management, Exadata requires manual management and administration. And that's really in a way to provide you the ability to customize it in the manner you desire, making it meets your very specific business needs, especially your database needs. In contrast, the Autonomous Database is fully managed by Oracle, including automated administration tasks, optimal self-tuning features to further reduce any management overhead. When we speak of the feature sets, the Exadata delivers a full suite of Oracle features, including the RAC application cluster, or the Real Application Cluster, RAC, whereas the Autonomous offers a complete feature set, but specifically that is designed for optimized Autonomous operations. Finally, when we speak of integration, integration for both of this service integrates seamlessly with AWS service, such as your EC2, your network, the VPC, your policies, the Identity and Access Management, your IAM, the monitoring with your CloudWatch, and of course, your storage, your SC, ensuring a consistent experience within your AWS ecosystem. 15:21 Nikita: So, you could say that the Exadata Database Service is better for customers who want dedicated infrastructure with granular control, while the Autonomous Database is built for customers who want a fully automated experience. Thank you, Susan, for taking the time to talk to us about Oracle Database@AWS. Lois: That's all we have for today. If you want to learn more about the topics we discussed, head over to mylearn.oracle.com and search for the Oracle Database@AWS Architect Professional course. In our next episode, we'll find out how to get started with the Oracle Database@AWS service. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 16:06 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

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WNiCF - December 2025 - News

The Straits Times Audio Features

Play Episode Listen Later Jan 30, 2026 48:43

Send us a textIn this episode, Frank and Stephen share the latest in cloud computing. They focus on AWS innovations, like new EC2 instances, generative AI advancements, and cost management strategies. They explore how AI integrates into cloud services and introduce Amazon Nova 2 for advanced reasoning tasks. They also discuss improvements in billing and support services. The conversation includes updates from Azure and Google Cloud, showcasing the changing cloud technology landscape and its impact on businesses.

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S1E63: Forced to kowtow until he bled: Why some men stay in abusive marriages

Play Episode Listen Later Jan 27, 2026 38:03

A counsellor of a support group reveals why some men choose to tolerate physical, emotional and psychological abuse in their marriages. Synopsis: On Wednesdays, The Straits Times takes a hard look at Singapore's social issues of the day with guests. Spousal abuse cases in Singapore rose significantly in 2024 to 2,136, up from 2,008 such cases in 2023, according to the Ministry of Social and Family Development (MSF). While official figures did not give a breakdown of these cases by gender, the defamation case of Johnny Depp against his ex-wife Amber Heard and post-Covid-19 awareness have encouraged more men to step forward, said a counsellor who runs a specialised support group for battered husbands. To find out more in this episode, assistant podcast editor Lynda Hong speaks with Josiah Yeo, a counsellor at Lutheran Community Care Services (LCCS), who started its support group for abused husbands in 2022. Mr Yeo details how one of the abused husbands in the support group was beaten with golf clubs and made to slap himself in public. He was even locked out of the house and made to kowtow in front of the door 30 times, before he could get into his home. Apart from abusive marriages, Mr Yeo also gives some pre-marital advice on what are some red flags to look out for, especially in the dating stage. Highlights (click/tap above): 1:49 Impact of Johnny Depp’s defamation suit on men in Singapore 9:31 When do abused husbands finally walk away from a marriage? 14:05 Why men don’t confide in their friends 18:40 Three reasons men stay in abusive marriage 19:11 Breaking the "bro code": How to check on a friend without making it awkward 32:39 Dating red flags - how to spot a potential abusive spouse Read more on abused spouses: https://str.sg/aqjX Helplines Family violence National Anti-Violence & Sexual Harassment Helpline: 1800-777-0000 (24 hours) AWARE Helpline: 1800-777-5555 (weekdays, 10am to 6pm) Care Corner Project StART: 6476-1482 (weekdays 10am-1pm, 2pm-5pm; except public holidays) TOUCH Family Support: 6317-9998 Lutheran Community Care Services: 6441-3906 / connect@lccs.org.sg Mental well-being National Mindline: 1771 (24 hours) / 6669-1771 (via WhatsApp) Samaritans of Singapore: 1-767 (24 hours) / 9151-1767 (24 hours CareText via WhatsApp) Singapore Association for Mental Health: 1800-283-7019 Silver Ribbon Singapore: 6386-1928 Chat, Centre of Excellence for Youth Mental Health: 6493-6500/1 Women’s Helpline (Aware): 1800-777-5555 (weekdays, 10am to 6pm) The Seniors Helpline: 1800-555-5555 (weekdays, 9am to 5pm) Tinkle Friend (for primary school-age children): 1800-2744-788 Counselling Touchline (Counselling): 1800-377-2252 Touch Care Line (for caregivers): 6804-6555 Counselling and Care Centre: 6536-6366 We Care Community Services: 3165-8017 Shan You Counselling Centre: 6741-9293 Clarity Singapore: 6757-7990 Online resources mindline.sg/fsmh eC2.sg chat.mentalhealth.sg carey.carecorner.org.sg (for those aged 13 to 25) limitless.sg/talk (for those aged 12 to 25) Read ST’s Opinion section: https://str.sg/w7sH Host: Lynda Hong (lyndahong@sph.com.sg) Produced and edited by: Hadyu Rahim Executive producers: Ernest Luis & Lynda Hong Follow In Your Opinion Podcast here and get notified for new episode drops: Channel: https://str.sg/w7Qt Apple Podcasts: https://str.sg/wukb Spotify: https://str.sg/w7sV Feedback to: podcast@sph.com.sg SPH Awedio app: https://www.awedio.sg --- Follow more ST podcast channels: All-in-one ST Podcasts channel: https://str.sg/wvz7 Get more updates: http://str.sg/stpodcasts The Usual Place Podcast YouTube: https://str.sg/theusualplacepodcast --- Get The Straits Times app, which has a dedicated podcast player section: The App Store: https://str.sg/icyB Google Play: https://str.sg/icyX --- #inyouropinionSee omnystudio.com/listener for privacy information.

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S1E63: Forced to kowtow until he bled: Why some men stay in abusive marriages

In Your Opinion

Play Episode Listen Later Jan 27, 2026 38:03

AWS Security Specialty (SCS-C03) Exam Guide: Mastery & Practice

InfosecTrain

Play Episode Listen Later Jan 21, 2026 67:09

Is the AWS Security Specialty a beginner certification? How does the new SCS-C03 version differ from its predecessor? In this session, we break down the entire AWS certification hierarchy and pinpoint exactly where the Security Specialty stands. We explore the shifting weight of exam domains; like the increased focus on IAM and the introduction of the Open Cybersecurity Schema Framework (OCSF). Beyond the theory, we walk through real-world exam scenarios, from bypassing the internet for private service communication to mitigating large-scale DDoS attacks.

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151. EC2 ❤️ Lambda - Lambda Managed Instances

AWS Bites

Play Episode Listen Later Jan 16, 2026 35:59

AWS just made Lambda… less serverless. Lambda Managed Instances (Lambda MI) brings managed EC2 capacity into Lambda, and it changes the rules: environments stay warm, a single environment can handle multiple concurrent invocations, and scaling becomes proactive and asynchronous instead of “spin up on demand when traffic hits.”In this episode of AWS Bites, Eoin and Luciano break down what Lambda MI unlocks (and what it costs): fewer traditional cold starts, but a new world of capacity planning, headroom, and potential throttling during fast spikes. We compare it to Default Lambda, explain how the new scaling signals work, and what “ACTIVE” really means when publishing can take minutes on a new capacity provider.To make it real, we built a video-processing playground: an API, a CPU-heavy processor, and a Step Functions workflow that scales up before work and back down after. We share the practical lessons, the rough edges (regions, runtimes, mandatory VPC, minimum 2 GB + 1 vCPU, concurrency pitfalls), and the pricing reality: requests + EC2 cost + a 15% management fee.In this episode, we mentioned the following resources:Lambda Managed Instances official docs: https://docs.aws.amazon.com/lambda/latest/dg/lambda-managed-instances.htmlOur example repo (video processing playground): https://github.com/fourTheorem/lambda-miConcurrency mental model reference (Vercel Fluid Compute): https://vercel.com/fluidLambda MI Node.js runtime best practices (concurrency considerations): https://docs.aws.amazon.com/lambda/latest/dg/lambda-managed-instances-nodejs-runtime.html Do you have any AWS questions you would like us to address?Leave a comment here or connect with us on X/Twitter, BlueSky or LinkedIn:- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/eoins⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bsky.app/profile/eoin.sh⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/eoins/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/loige⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bsky.app/profile/loige.co⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/lucianomammino/

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spotify education real change cloud lift timeline developers intel reducing hundreds switching aws optimization amd automatically processor cost savings cloud migration ec2

Play Episode Listen Later Jan 16, 2026 20:45

Today we tackle the biggest misconception in enterprise IT: that moving to cloud automatically saves money. Chris from EPI-USE explains why treating cloud like "just another data center" becomes expensive, and how strategic assessment before migration prevents costly surprises.Hosted by Jon Myer → The Jon Myer Podcast | Recorded at AWS re:Invent 2025Key Topics Discussed:✅ Why 20-30% waste is typical (sometimes 60-70% in extreme cases)✅ WHALE Assessment: 2-week timeline, minimal client effort, AWS-funded✅ Real savings: Hundreds of thousands annually (30-40% reductions)✅ On-prem pays for capacity; cloud pays for provisioning—critical shift✅ AMD Epic processors: 11-15% efficiency gains without re-architecting✅ T-shirt sizing mistakes: Developers over-provisioning based on on-prem thinking✅ Finding forgotten NT4 systems and reducing footprint pre-migration✅ Processor flexibility: Last year Intel, this year AMD—change annually✅ Cloud Center of Excellence: Essential for guiding decisions✅ Not "Can it move?" but "When can it move?"—5-10 year strategic roadmapYouTube Timeline:0:00 - Welcome & Introduction1:05 - Why cloud migration doesn't automatically mean savings2:11 - Lift-and-shift requires immediate post-migration plan3:05 - Not everything should migrate (legacy, proprietary systems)3:53 - How much waste exists in pre-migration environments?5:10 - T-shirt sizing mistakes and over-provisioning6:36 - Pre-migration assessment: Reducing footprint before moving7:21 - Finding forgotten NT4 systems (turn it off, see who screams)9:03 - What is the WHALE Assessment?9:49 - Why AWS funds assessments that reduce spend10:40 - Timeline: 2 weeks, minimal client effort11:40 - Real savings: Hundreds of thousands annually (30-40%)12:37 - Optimization without re-architecting applications13:28 - AMD Epic processors: 11% efficiency gains generation-to-generation15:18 - Real-world examples: Switching processor types17:14 - Education gap: People forget about EC2 optimization18:49 - Cloud flexibility: Change processor types monthly if needed19:30 - How to get started with EPI-USE20:27 - Closing thoughts & thank you

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Play Episode Listen Later Jan 16, 2026 20:45

Matt Klein - cofounder of Bitdrift: meeting developers where they are and early days of AWS

Scaling DevTools

Play Episode Listen Later Dec 19, 2025 48:42 Transcription Available

In this episode, Matt Klein (Bitdrift, Envoy) reflects on building EC2 in the early days of AWS, the reality behind AWS's origins, and what Amazon's customer obsession looks like from the inside. He then dives into creating Envoy at Lyft, the challenges of open source at scale, and spinning Bitdrift out of Lyft to focus on mobile observability. He shares how to meet developers where they are and what it takes to find product market fit. This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs.Links: • Matt's Linkedin • Bitdrift

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Ep#242 AI-Assisted Workplace: Jeff Barr on the Future of Development & AWS Innovation

Play Episode Listen Later Dec 19, 2025 27:53

Joining us today is Jeff Barr, Chief Evangelist for AWS, recorded live at AWS re:Invent 2025. For two decades, Jeff has documented the evolution of cloud computing through the AWS blog, sharing insights on everything from the launch of EC2 instances to the latest breakthroughs in generative AI and machine learning.In this episode, we explore what's next in the AI-assisted workplace, where this technology is heading, how professionals can navigate the shift to AI-native work, and what guidance Jeff has for individuals and organizations trying to turn AI capabilities into competitive advantages.Key Topics Discussed:✅ The rapid global adoption of AI-assisted development (50%+ in 14 countries)✅ Why developers need to become better human-to-human communicators✅ The shift from writing code to reading and validating AI-generated code✅ Balancing productivity with AI hallucinations and errors✅ The art of prompt engineering and verbose specifications✅ Disposable apps: Building to solve, then rebuilding better next time✅ Why some skills are becoming more valuable in the AI era✅ Swimming upstream: Being an early adopter and giving feedback✅ Language barriers disappearing with AI translation capabilities✅ Jeff's 14 years at re:Invent and stepping back from the AWS blogWhether you're deep in your cloud journey or just getting started, this conversation gives you a front-row seat to the future being built at re:Invent.Guest: Jeff Barr, Chief Evangelist, AWSHost: Jon MyerRecorded at: AWS re:Invent 2025, Las VegasSubscribe for more conversations about cloud, AI, and the future of technology innovation.#AWS #ArtificialIntelligence #CloudComputing #reInvent #AIAssistedDevelopment #GenerativeAI #TechInnovation #JonMyerPodcastYouTube Timeline:0:00 - Welcome & Introduction1:19 - Annual tradition at re:Invent with Jeff Barr2:25 - Surprising speed of global AI adoption3:51 - Jeff's year traveling the world talking to developers4:45 - Key guidance: Step in and use AI tools6:18 - Learning to read code, not just write it7:51 - The personal chef analogy for prompt engineering9:28 - When AI gets it incredibly wrong: The college fund story11:12 - Balancing productivity with hallucinations13:18 - Why the same prompt gives different outputs14:29 - Embracing change: 50 years in tech16:01 - AI breaking down language barriers17:10 - When companies aren't AI-ready18:24 - From experimentation to problem-solving19:42 - Disposable apps: Build, use, toss, rebuild better20:29 - Essential skills for AI-assisted work21:15 - You have to experience it, not just read about it21:40 - What Jeff looks forward to at re:Invent23:42 - Stepping away from the AWS blog after 20 years26:15 - Advice for re:Invent attendees27:19 - Closing thoughts

Ep#242 AI-Assisted Workplace: Jeff Barr on the Future of Development & AWS Innovation

The Tech Blog Writer Podcast

Play Episode Listen Later Dec 19, 2025 27:53

3518: AWS re:Invent: The New Playbook For Detection, Response, And Secure AI

Play Episode Listen Later Dec 14, 2025 28:12

How do you move faster with AI and cloud innovation without losing control of security along the way? Recorded live from the show floor at AWS re:Invent in Las Vegas, this episode of Tech Talks Daily features a timely conversation with Kimberly Dickson, Worldwide Go-To-Market Lead for AWS Detection and Response Services. As organizations race to adopt agentic AI, modernize applications, and manage sprawling cloud environments, Kimberly offers a grounded look at why security must still sit at the center of every decision. Kimberly explains how her role bridges two worlds at AWS. On one side are customers dealing with prioritization fatigue, fragmented security signals, and growing pressure to do more with fewer resources. On the other hand, there are the internal service teams building products like Amazon GuardDuty, Amazon Inspector, and AWS Security Hub. Her job is to connect those realities, shaping services based on what customers actually struggle with day to day. That perspective sets the tone for a conversation focused less on hype and more on practical outcomes. We unpack how AWS thinks about security culture at scale, from infrastructure and encryption through to threat intelligence gathered across Amazon's global footprint. Kimberly shares how AWS uses large-scale honeypots to observe attacker behavior in real time, feeding that intelligence back into detection services while also working with governments and industry partners to take down active threats. It is a reminder that cloud security is no longer just about protecting individual workloads, but about contributing to a safer internet overall. The conversation also dives into new announcements from re:Invent, including the launch of AWS Security Hub, extended threat detection for EC2 and EKS, and the emergence of security-focused AI agents. Kimberly explains how these tools shift security teams away from manual investigation and toward faster, higher-confidence decisions by correlating risks across vulnerabilities, identity, network exposure, and sensitive data. The goal is clear visibility, clearer priorities, and remediation that fits naturally into existing workflows. We also explore how AWS approaches security in multi-cloud and hybrid environments, why foundational design principles still matter in an AI-driven world, and how open standards are helping normalize security data across vendors. Kimberly's reflections on re:Invent itself bring a human close to the episode, highlighting the pride and responsibility felt by teams building systems that millions of organizations depend on. As AI adoption accelerates and security teams are asked to keep pace without slowing innovation, what would it take for your organization to move faster while still trusting the foundations you are building on?

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AWS RE:Invent 2025

Patoarchitekci

Play Episode Listen Later Dec 12, 2025 25:31

“Billions of Agents - każdy z nas będzie miał minimum 10 agentów.” Łukasz cytuje oficjalny przekaz AWS re:Invent 2025, ale prawdziwy przekaz konferencji jest głębszy: hyperscalerzy mają realną przewagę w dostępie do GPU i infrastruktury do trenowania modeli. AWS Clean Rooms, Nova Forge, AI Factory - to ekosystem do łatwego fine-tuningu bez konieczności budowania własnej farmy GPU. A adopcja własnych modeli AWS? Wszyscy używają Anthropic Claude na Bedrocku, nikt Amazon Nova. Szymon wybiera Nova Act jako najlepsze ogłoszenie: “Najciekawsze podejście z całej konferencji” - RPA z agentami AI do automatyzacji starych systemów przez przeglądarkę. AWS Transform? “Eksplozje, wybuchy, beżowe pecety z lat 90. I disclaimer: AWS realnie nie wykorzystuje materiałów wybuchowych.” Modernizacja COBOL-a, mainframe'ów, webforms do Blazora (“Ktoś poza Microsoftem używa słowa Blazor”), aktualizacja SDK, Terraform, CloudFormation. Lambda na EC2 to gwoździem do trumny: “Pamiętasz migrację Prime'a z Lambdy na EC2, bo koszty były za duże? Już by nie musieli.” 97 newsów, w tym Bedrock z 18 modelami open source, wektory w S3, EKS z Argo CD, Kiro jako kolejny fork VS Code. 97 newsów z AWS re:Invent - ale czy któryś naprawdę zmieni coś w Twojej pracy?

WNiCF - November 2025 - News

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Play Episode Listen Later Dec 11, 2025 51:11

Send us a textIn this episode, SteveO and Frank discuss the latest updates in cloud computing, focusing on AWS and Azure innovations. They cover new features in EC2, Lambda, and Workspaces, as well as advancements in data management and AI. The conversation also highlights cost management tools and strategies, flexible cost allocation, and storage solutions. The hosts emphasize the importance of FinOps in managing cloud costs and conclude with insights into future developments in cloud technology.

Graviton5, le processeur surpuissant d'Amazon pour le cloud ?

Choses à Savoir TECH

Play Episode Listen Later Dec 9, 2025 2:46

Pour la dernière grande journée de son événement annuel AWS re:Invent, Amazon Web Services a levé le voile sur une annonce stratégique : l'arrivée de Graviton5, son tout nouveau processeur maison. Une puce ARM de cinquième génération, gravée en 3 nanomètres, embarquant… 192 cœurs. Un monstre de calcul, pensé pour bouleverser l'équation performance-prix du cloud.AWS ne s'en cache pas : le pari Graviton est déjà un succès. Plus de la moitié des nouvelles capacités de calcul de la plateforme reposent désormais sur cette architecture, et près de 90 % des 1 000 plus gros clients EC2 utilisent déjà ces processeurs. Avec Graviton5, Amazon ne se contente plus d'optimiser : il change d'échelle. Cette nouvelle génération marque un véritable saut technologique. En concentrant 192 cœurs sur une seule puce, Amazon augmente considérablement la densité de calcul. Résultat : les échanges entre cœurs sont 33 % plus rapides. Le cache L3, cette mémoire ultra-rapide essentielle aux performances, a été multiplié par cinq. Chaque cœur dispose ainsi de 2,6 fois plus d'espace immédiat que sur Graviton4. Concrètement, cela accélère fortement les bases de données, l'analyse de données massives, mais aussi les jeux en ligne ou les services temps réel.Côté réseau, les progrès sont tout aussi notables : +15 % de bande passante en moyenne, jusqu'à un doublement sur les instances les plus puissantes. L'accès au stockage cloud progresse aussi de 20 %. Graviton5 promet au total 30 % de performances supplémentaires et 20 % de latence en moins par rapport à la génération précédente. La gravure en 3 nm, l'une des plus avancées du marché — dominée par TSMC, Samsung et Intel — permet d'augmenter la puissance tout en réduisant la consommation. Un point crucial à l'heure où les data centers pèsent de plus en plus lourd dans la consommation électrique mondiale. AWS va même jusqu'à refroidir directement la puce, sans boîtier intermédiaire.Autre avancée majeure : la sécurité. Avec le Nitro Isolation Engine, AWS ne se contente plus d'affirmer l'isolation des données… il en apporte une preuve mathématique. Une garantie très recherchée par les banques, les hôpitaux et les administrations. Les premiers retours sont enthousiastes. Airbnb gagne 25 % sur ses moteurs de recherche. Atlassian observe 30 % de rapidité en plus sur Jira. SAP annonce même jusqu'à 60 % d'accélération sur ses bases de données. Les premières instances EC2 M9g sont déjà disponibles en test. Les déclinaisons C9g et R9g, dédiées au calcul intensif et à la mémoire, arriveront en 2026. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

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Ep180: Agent-Based Learning Systems - How Scale AI Transforms Enterprise Knowledge into Business Value

AWS for Software Companies Podcast

Play Episode Listen Later Dec 4, 2025 26:58

Raviteja Yelamanchili shares how Scale AI transformed banking cycles from one year to real-time and why your most valuable enterprise data isn't being collected.Topics Include:Scale evolved from data annotations company to enterprise AI solutions providerHealthcare system transformed patient transcriptions into value using reinforcement learning researchBlank slate customer problems allow Scale to experiment with latest methodsMany customers propose solutions before explaining their actual underlying business problemsBiggest AI misconception: technology will replace jobs rather than augment productivityDon't wait for perfect AI—start learning through iteration and evolution nowBanking credit cycle transformed from one-year process to real-time strategic insightsScale deploys flexibly across EC2, EKS, or Bedrock based on customer requirementsEnterprises want business value generation more than academic research papers aloneNext 12-24 months focus: making data consumable and leveraging unused datasetsTribal knowledge from experienced SMEs represents most valuable yet uncollected dataAgent-based learning captures expertise through feedback loops on Scale's SGP platformParticipants:Raviteja Yelamanchili - Head of Solution Engineering, Scale AISee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

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Kodsnack 676 - Maps will get you fired, with Simon Wardley

Kodsnack in English

Play Episode Listen Later Dec 4, 2025 26:12

Recorded on-stage at Øredev 2025, Fredrik talks to Simon Wardley about maps (not the same as charts!), stories, commodotization, digital sovereignty, getting labeled a heretic by all sides, and a lot more. Among other things, Simon discusses how you can map things out and thereby find new ways to present and challenge the current state within and organization. Not that it will necessarily be very popular, hence the bit about being called a heretic. Many thanks to Øredev for inviting Kodsnack again, they paid for the trip and the editing time of these keynote recordings, but have no say about the content of these or any other episodes. Thank you Cloudnet for sponsoring our VPS! Comments, questions or tips? We a re @kodsnack, @tobiashieta, @oferlund and @bjoreman on Twitter, have a page on Facebook and can be emailed at info@kodsnack.se if you want to write longer. We read everything we receive. If you enjoy Kodsnack we would love a review in iTunes! You can also support the podcast by buying us a coffee (or two!) through Ko-fi. Links Øredev All the presentation videos from Øredev 2025 Simon Wardley From here to there and back again - Simon’s keynote Rewilding software engineering - AI, tools and human decisions - Simon’s other talk at Øredev Wardley mapping Simon’s writings on Medium Innovate-leverage-commoditize model Reaching cloud velocity - AWS book EC2 Mapreduce Extreme programming Six sigma Lean Titles We only had 50 minutes A map, not a graph The map is wrong Maps will get you fired As long as everyone else is just as bad Look at the entire map The size of Malmö Sick-care systems Shocks to the system Wardleyconf

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150. Exploring All-New ECS Managed Instances (MI) Mode

AWS Bites

Play Episode Listen Later Nov 28, 2025 26:59

Love AWS Fargate, but occasionally hit the “I need more control” wall (GPUs, storage, network bandwidth, instance sizing)? In this episode of AWS Bites, Eoin and Luciano put the brand-new Amazon ECS Managed Instances (ECS MI) under the microscope as the “middle path” between Fargate simplicity and ECS on EC2 flexibility. We unpack what ECS MI actually is and where it fits in the ECS spectrum, especially how it changes the way you think about clusters and capacity providers. From there we get practical: we talk through the pricing model (EC2 pricing with an additional ECS MI fee that can be a bit counterintuitive if you rely heavily on Reserved Instances or Savings Plans), and we share what it feels like to finally get GPU support in an experience that's much closer to Fargate than to “full EC2 fleet management”. To make it real, we walk through what we built: a GPU-enabled worker that transcribes podcast audio using OpenAI Whisper, including the end-to-end setup in CDK (roles, capacity provider wiring, task definitions, and service configuration). Along the way we call out the rough edges we ran into, like configuration options that look like they might enable Spot-style behavior, and the operational realities you should expect, such as tasks taking roughly 3–4 minutes to start when ECS needs to provision fresh capacity. We close by mapping out the workloads where ECS MI shines (queue-driven GPU jobs, HPC-ish compute, tighter storage/network control) and the scenarios where it's probably the wrong choice, like when you need custom AMIs, SSH access, or stricter isolation guarantees.In this episode, we mentioned the following resources: Amazon ECS Managed Instances: ⁠https://aws.amazon.com/ecs/managed-instances/⁠ ECS Managed Instances documentation: ⁠https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ManagedInstances.html⁠ Amazon Bottlerocket (what it is): ⁠https://aws.amazon.com/bottlerocket/⁠ Our CDK ECS MI template: ⁠https://github.com/fourTheorem/cdk-ecs-mi-template⁠ Ep 42. How do you containerise and run your API with Fargate?: ⁠https://awsbites.com/42-how-do-you-containerise-and-run-your-api-with-fargate/⁠ Ep 72. How do you save cost with ECS?: ⁠https://awsbites.com/72-how-do-you-save-cost-with-ecs/⁠ Ep 10. Lambda or Fargate for containers?: ⁠https://awsbites.com/10-lambda-or-fargate-for-containers/⁠ Ep 38. How do you choose the right compute service on AWS?: ⁠https://awsbites.com/38-how-do-you-choose-the-right-compute-service-on-aws/⁠ Ep 143. Is App Runner better than Fargate?: ⁠https://awsbites.com/143-is-app-runner-better-than-fargate/⁠ Do you have any AWS questions you would like us to address?Leave a comment here or connect with us on X/Twitter, BlueSky or LinkedIn:- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/eoins⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bsky.app/profile/eoin.sh⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/eoins/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/loige⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bsky.app/profile/loige.co⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/lucianomammino/

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505 Bumpers 89

Reversim Podcast

Play Episode Listen Later Nov 22, 2025

פרק מספר 505 של רברס עם פלטפורמה - באמפרס מספר 89, שהוקלט ב-13 בנובמבר 2025, רגע אחרי כנס רברסים 2025 [יש וידאו!]: רן, דותן ואלון (והופעת אורח של שלומי נוח!) באולפן הוירטואלי עם סדרה של קצרצרים מרחבי האינטרנט: הבלוגים, ה-GitHub-ים, ה-Claude-ים וה-GPT-ים החדשים מהתקופה האחרונה.

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Don't Hit Pause: How Top Earners Work With The Holiday Season, Not Against It | Andrea Ebert

consultants holiday season ebert ec2 hit pause top earners

Play Episode Listen Later Nov 17, 2025 27:49

The holidays aren't a time to power down—they're a chance to power up smartly. Learn how top earners stay consistent, create momentum, and use the holiday season to build connection, joy, and growth that carries into the new year. LifeVantage does not guarantee success. Consultants will earn incentives depending on individual diligence, work effort, and market conditions. See qualifications for additional information. Since the beginning of the program in 2018, approximately 230 Consultants have earned the My LifeVenture award by achieving EC2 and maintaining that rank for 6 consecutive months within the 18 months of advancing to EC2. The average number of active consultants over this period of time is 62,407.  The average 12-month earnings of a typical US Consultant who earned in 2024 are $683. These earnings represent gross income and do not account for expenses incurred in building a business. Visit the LifeVantage Income Disclosure Statement for more details.

WNiCF - October 2025 - News

AWS for Software Companies Podcast

Play Episode Listen Later Nov 12, 2025 50:15 Transcription Available

Send us a textIn this episode of What's New in Cloud FinOps, hosts SteveO and Frank discuss the latest updates from the TBM conference, then moving on to their usual FinOps news, with many AWS news: new EC2 instances, cost management innovations, and sustainability initiatives in cloud services. They explore the implications of Lambda billing changes, real-time bidding technology, and the evolving landscape of cloud financial management training. Chapters00:00 Introduction and Conference Insights02:07 AWS News Highlights06:45 New EC2 Instances and Performance Improvements10:41 Data Management and Optimization Tools14:07 Storage Innovations and Cost Efficiency18:34 Capacity Management and Kubernetes Integration21:57 Pricing Changes and Licensing Optimization27:39 Emerging Technologies and Cost Savings28:46 AWS RTB Fabric for Real-Time Advertising30:50 AWS Service Availability Updates33:19 Sunsetting AWS Services34:41 Amazon Q Developer: Cost Management Tool38:27 Kronos 2: Advanced Forecasting Model39:20 Upcoming FinOps Sessions at reInvent 202541:30 GCP Anomaly Detection Features43:19 Azure's Environmental Sustainability Features44:54 AWS Customer Carbon Footprint Tool Update46:21 New FinOps Training Courses

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Ep168: Scaling Agentic Workloads: Why Reliable Infrastructure is Non-Negotiable for Enterprise AI by Anyscale

Play Episode Listen Later Nov 7, 2025 24:57

** AWS re:Invent 2025 Dec 1-5, Las Vegas - Register Here! **Learn how Anyscale's Ray platform enables companies like Instacart to supercharge their model training while Amazon saves heavily by shifting to Ray's multimodal capabilities.Topics Include:Ray originated at UC Berkeley when PhD students spent more time building clusters than ML modelsAnyscale now launches 1 million clusters monthly with contributions from OpenAI, Uber, Google, CoinbaseInstacart achieved 10-100x increase in model training data using Ray's scaling capabilitiesML evolved from single-node Pandas/NumPy to distributed Spark, now Ray for multimodal dataRay Core transforms simple Python functions into distributed tasks across massive compute clustersHigher-level Ray libraries simplify data processing, model training, hyperparameter tuning, and model servingAnyscale platform adds production features: auto-restart, logging, observability, and zone-aware schedulingUnlike Spark's CPU-only approach, Ray handles both CPUs and GPUs for multimodal workloadsRay enables LLM post-training and fine-tuning using reinforcement learning on enterprise dataMulti-agent systems can scale automatically with Ray Serve handling thousands of requests per secondAnyscale leverages AWS infrastructure while keeping customer data within their own VPCsRay supports EC2, EKS, and HyperPod with features like fractional GPU usage and auto-scalingParticipants:Sharath Cholleti – Member of Technical Staff, AnyscaleSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

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Monthly News Update: DNS Did That Thing Again...

Cables2Clouds

Play Episode Listen Later Nov 5, 2025 32:20 Transcription Available

Send us a textStart with a simple truth: when the platform breaks, your clever architecture won't save you. We dig into the AWS US‑East‑1 outage where DynamoDB's role in DNS planning for load balancers collided with a race condition, leaving empty records and stalled EC2 instances. Forget the finger‑wagging about “well‑architected” apps—this was a platform failure with limited customer escape routes. We weigh multi‑region and multi‑cloud trade‑offs with a sober look at cost, complexity, and operational burden.Security took center stage with two high‑risk stories you need to act on. First, a critical WSUS flaw enabling remote unauthenticated code execution against the very servers meant to protect fleets. If WSUS is still live, patch immediately or take it offline until you can. Then, the F5 source code theft: not a cloning threat, but a blueprint for discovering subtle bugs and crafting precise exploits. Attribution points toward Chinese state‑sponsored actors, which means targeted, quiet use rather than noisy mass exploitation. The risk isn't gone when headlines fade; it's just harder to see.We connect this to rising exploitation of vSock across hypervisors like VMware ESXi. With public PoCs and active abuse, vSock opens covert channels from host to guest, making segmentation and management plane isolation non‑negotiable. Patch aggressively, gate access through jump hosts, enforce MFA, and consider disabling vSock where viable on QEMU stacks. These are concrete steps that cut real risk.Then we turn to the elephant in the data center: AI ROI. Vendors keep shipping agentic assistants and copilots, but few can show durable returns outside a subsidized token economy. We share a pragmatic lens for measuring value—cycle time, MTTR, defect rates—while acknowledging the dot‑com‑style arc ahead: hype, correction, then durable wins that prioritize efficiency. As AI demand drives massive new builds, the physical footprint of the cloud is showing up in local power grids and skylines. Infrastructure choices now carry community and energy implications leaders can't ignore.Subscribe, share with a colleague who owns platform reliability or security, and leave a review with your biggest takeaway or question—what will you patch, segment, or measure first?Purchase Chris and Tim's book on AWS Cloud Networking: https://www.amazon.com/Certified-Advanced-Networking-Certification-certification/dp/1835080839/ Check out the Monthly Cloud Networking Newshttps://docs.google.com/document/d/1fkBWCGwXDUX9OfZ9_MvSVup8tJJzJeqrauaE6VPT2b0/Visit our website and subscribe: https://www.cables2clouds.com/Follow us on BlueSky: https://bsky.app/profile/cables2clouds.comFollow us on YouTube: https://www.youtube.com/@cables2clouds/Follow us on TikTok: https://www.tiktok.com/@cables2cloudsMerch Store: https://store.cables2clouds.com/Join the Discord Study group: https://artofneteng.com/iaatj

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APIs to Tell You What You Already Paid For

çŸ½è°·è¼•é¬†è«‡ Just Kidding Tech

Play Episode Listen Later Nov 3, 2025 6:28

AWS Morning Brief for the week of November 3rd, with Corey Quinn. Links:Beyond pilots: A proven framework for scaling AI to productionNew Amazon CloudWatch metrics to monitor EC2 instances exceeding I/O performanceProcessing Amazon S3 objects at scale with AWS Step Functions Distributed Map S3 prefixWhat's the difference between AWS ParallelCluster and AWS Parallel Computing Service?France Télévisions prepared for 2024 Olympic Games with AWS Countdown PremiumAmazon Kinesis Data Streams now supports 10x larger record sizesAmazon DocumentDB (with MongoDB compatibility) announces upgraded query planner that can run queries up to 10x fasterAnnouncing AWS X-Ray SDKs/Daemon End-of-Support and OpenTelemetry MigrationAmazon S3 adds conditional write functionality to copy operationsIntroducing the Capacity Reservation Topology API for AI, ML, and HPC instance typesHow to deploy a SQL Server Failover Cluster Instance across three Availability Zones using Storage Spaces Direct Reduce CAPTCHAs for AI agents browsing the web with Web Bot Auth (Preview) in Amazon Bedrock AgentCore BrowserUsing Kubernetes Labels to Split and Track Application Costs on Amazon EKSIntroducing AWS Lambda event source mapping tools in the AWS Serverless MCP Server Split Cost Allocation Data for Amazon EKS supports Kubernetes labels

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S2E35 AWS 大當機內幕：Race Condition 拖垮全球網路

Play Episode Listen Later Oct 27, 2025 25:48

10 月 20 號星期一，亞馬遜雲端服務 AWS 的核心區域 us-east-1 爆出一個 Race Condition，導致 DynamoDB 的 DNS 被清空，結果連帶拖垮了 113 項內部與外部服務。從社群平台、交易所、航空公司、政府單位，甚至英超足球聯盟，全都中標。這場十五小時的大當機，不只是 AWS 的災難，更是「雲端集中化」的一次警訊。這集我們就來聊聊：☁️ 為什麼 us-east-1 這麼關鍵？⚙️ Race Condition 到底怎麼讓 DNS 全毀？

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WNiCF - September 2025 - News

airhacks.fm podcast with adam bien

Play Episode Listen Later Oct 24, 2025 36:13

Send us a textIn this episode of What's New in Cloud FinOps, Stephen Old and Frank discuss the latest updates in cloud computing, including AWS Outposts' integration with third-party storage, new Amazon EC2 Mac instances, Azure's managed services, and Google Cloud VM Engine updates. They also explore pricing changes in Azure, the deprecation of Azure Machine Learning data labeling, and the introduction of new metrics in software development. The conversation highlights the importance of sustainability in cloud services and concludes with reflections on the podcast's five-year anniversary.TakeawaysAWS Outposts now supports third-party storage integration with Dell and HPE.Amazon EC2 introduces new Mac instances for developers.Azure managed services now include Grafana dashboards at no extra cost.Google Cloud VM Engine V1 SKUs are now end of sale.Azure UltraDisk pricing has been reduced significantly in specific regions.Azure Machine Learning data labeling will be deprecated by 2026.AWS Transform Assessment helps visualize storage migration benefits.New cost to serve software metric introduced by AWS.Cortex Framework now deploys sustainability modules for SAP.AWS Lambda cold start billing changes will take effect in 2025.

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From Cloud Networking to Powertools for AWS Lambda (Java)

Play Episode Listen Later Oct 22, 2025

An airhacks.fm conversation with Philipp Page (@PagePhilipp) about: early computing experiences with Windows XP and Intel Pentium systems, playing rally car games like Dirt with split-screen multiplayer, transitioning from gaming to server administration through Minecraft, running Minecraft servers at age 13 with memory limitations and out-of-memory exceptions, implementing caching mechanisms with cron jobs and MySQL databases, learning about SQL injection attacks and prepared statements, discovering connection pooling advantages over PHP approaches, appreciating type safety and Object-oriented programming principles in Java, the tendency to over-abstract and create unnecessary abstractions as junior developers, obsession with avoiding dependencies and implementing frameworks from scratch, building custom Model-View-Controller patterns and dependency injection systems, developing e-learning platform for aerospace industry using PHP Symfony framework, implementing time series forecasting in pure Java without external dependencies, internship and employment at AWS Dublin in Frontier Networking team, working on AWS Outposts and Ground Station hybrid cloud offerings, using python and rust for networking control plane development, learning to appreciate Python despite initial resistance to dynamically typed languages, joining AWS Lambda Powertools team as Java tech lead, maintaining open-source serverless development toolkit, providing utilities for observability including structured JSON logging with Lambda-specific information, implementing metrics and tracing for distributed event-driven architectures, mapping utilities to AWS Well-Architected Framework serverless lens recommendations, caching parameters and secrets to improve scalability and reduce costs, debate about AspectJ dependency and alternatives like Micronaut and quarkus approaches, providing both annotation-based and programmatic interfaces for utilities, newer utilities like Kafka consumer avoiding AspectJ dependency, comparing Micronaut's compiler-based approach and Quarkus extensions for bytecode generation, AspectJ losing popularity in enterprise Java projects, preferring Java standards over external dependencies for long-term maintainability, agents in electricity trading simulations for renewable energy scenarios, comparing on-premise Java capabilities versus cloud-native AWS features, default architecture pattern of Lambda with S3 for persistent storage, using AWS Calculator for cost analysis before architecture decisions, event-driven architectures being native to AWS versus artificially created in traditional Java projects, everything in AWS emitting events naturally through services like EventBridge, filtering events rather than creating them artificially, avoiding unnecessary microservices complexity when simple method calls suffice, directly wiring API Gateway to DynamoDB without Lambda for no-code solutions, using Java for CDK infrastructure as code while minimizing runtime dependencies, maximizing cloud-native features when in cloud versus on-premise optimization strategies, starting with simplest possible architecture and justifying complexity, blue-green deployments and load balancing handled automatically by Lambda, internal AWS teams using Lambda for orchestration and event interception, Lambda as foundational zero-level service across AWS infrastructure, preferring highest abstraction level services like Lambda and ECS Fargate, only dropping to EC2 when specific requirements demand lower-level control, contributing to Powertools for AWS Lambda Python repository before joining team, compile-time weaving avoiding Lambda cold start performance impacts, GraalVM compilation considerations for Quarkus and Micronaut approaches, customer references available on Powertools website, contrast between low-level networking and serverless development, LinkedIn as primary social media platform for professional connections, Powertools for AWS Lambda (Java) Philipp Page on twitter: @PagePhilipp

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S1E141: Grieving matters: How to cope with the loss of a loved one

The Straits Times Audio Features

Play Episode Listen Later Sep 30, 2025 38:42

Grief is a painful, individual emotional and physical response to a significant loss. But it can be managed. Synopsis: Every first Wednesday of the month, The Straits Times helps you make sense of health matters that affect you. Grief is a painful, individual emotional and physical response to a significant loss.Death, divorce, the loss of a home or a job, fast declining health are among the major events that people grieve. To learn more about coping with grief, ST senior health correspondent Joyce Teo speaks to Lin Jing, a counsellor from the Singapore Association for Mental Health. SAMH is one of the few social service agencies focusing on mental health here that operates a general helpline for the public at 1800-283-7019 They also discuss what is grief counselling about. If your grief feels like it's too much to bear, please reach out for help. We have included more helplines below. Highlights (click/tap above): 9:00 When should you consider grief counselling? 12:45 When guilt is thrown into the picture 23:00 Understanding cognitive behaviourial therapy, grief counselling and grief therapy 29:10 Building a life around the loss of a child… 32:00 Appearing strong and unaffected by grief, when you are crumbling inside Check out ST's new series, No health without mental health: https://str.sg/mentalhealthmatters Read Joyce Teo's stories: https://str.sg/JbxN Host: Joyce Teo (joyceteo@sph.com.sg) Produced and edited by: Amirul Karim Executive producers: Ernest Luis and Lynda Hong Follow Health Check Podcast here and get notified for new episode drops: Channel: https://str.sg/JWaN Apple Podcasts: https://str.sg/JWRX Spotify: https://str.sg/JWaQ Feedback to: podcast@sph.com.sg SPH Awedio app: https://www.awedio.sg --- Follow more ST podcast channels: All-in-one ST Podcasts channel: https://str.sg/wvz7 Get more updates: http://str.sg/stpodcasts The Usual Place Podcast YouTube: https://str.sg/4Vwsa --- Get The Straits Times app, which has a dedicated podcast player section: The App Store: https://str.sg/icyB Google Play: https://str.sg/icyX --- Helplines Mental well-being National helpline: 1771 (24 hours) / 6669-1771 (via WhatsApp) Samaritans of Singapore: 1-767 (24 hours) / 9151-1767 (24 hours CareText via WhatsApp) Singapore Association for Mental Health: 1800-283-7019 Silver Ribbon Singapore: 6386-1928 Chat, Centre of Excellence for Youth Mental Health: 6493-6500/1 Women’s Helpline (Aware): 1800-777-5555 (weekdays, 10am to 6pm) The Seniors Helpline: 1800-555-5555 (weekdays, 9am to 5pm) Tinkle Friend (for primary school-age children): 1800-2744-788 Counselling Touchline (Counselling): 1800-377-2252 Touch Care Line (for caregivers): 6804-6555 Counselling and Care Centre: 6536-6366 We Care Community Services: 3165-8017 Shan You Counselling Centre: 6741-9293 Clarity Singapore: 6757-7990 Online resources mindline.sg/fsmh eC2.sg chat.mentalhealth.sg carey.carecorner.org.sg (for those aged 13 to 25) limitless.sg/talk (for those aged 12 to 25) --- #healthcheckSee omnystudio.com/listener for privacy information.

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S1E141: When coping with grief is not about the death of a loved one

Health Check

Play Episode Listen Later Sep 30, 2025 38:42

How to cope with losing a sense of normalcy in your life. Synopsis: Every first Wednesday of the month, The Straits Times helps you make sense of health matters that affect you. Loss is an inevitable part of life, and grief is our response to any significant loss. To learn more about coping with grief, ST senior health correspondent Joyce Teo speaks to Lin Jing, a counsellor from the Singapore Association for Mental Health. SAMH is one of the few social service agencies focusing on mental health here that operates a general helpline for the public at 1800-283-7019 They also discuss what is grief counselling about. If your grief feels like it's too much to bear, please reach out for help. We have included more helplines below. Highlights (click/tap above): 9:00 When should you consider grief counselling? 12:45 When guilt is thrown into the picture 23:00 Understanding cognitive behaviourial therapy, grief counselling and grief therapy 29:10 Building a life around the loss of a child… 32:00 Appearing strong and unaffected by grief, when you are crumbling inside Check out ST's new series, No health without mental health: https://str.sg/mentalhealthmatters Read Joyce Teo's stories: https://str.sg/JbxN Host: Joyce Teo (joyceteo@sph.com.sg) Produced and edited by: Amirul Karim Executive producers: Ernest Luis and Lynda Hong Follow Health Check Podcast here and get notified for new episode drops: Channel: https://str.sg/JWaN Apple Podcasts: https://str.sg/JWRX Spotify: https://str.sg/JWaQ Feedback to: podcast@sph.com.sg SPH Awedio app: https://www.awedio.sg --- Follow more ST podcast channels: All-in-one ST Podcasts channel: https://str.sg/wvz7 Get more updates: http://str.sg/stpodcasts The Usual Place Podcast YouTube: https://str.sg/4Vwsa --- Get The Straits Times app, which has a dedicated podcast player section: The App Store: https://str.sg/icyB Google Play: https://str.sg/icyX --- Helplines Mental well-being National helpline: 1771 (24 hours) / 6669-1771 (via WhatsApp) Samaritans of Singapore: 1-767 (24 hours) / 9151-1767 (24 hours CareText via WhatsApp) Singapore Association for Mental Health: 1800-283-7019 Silver Ribbon Singapore: 6386-1928 Chat, Centre of Excellence for Youth Mental Health: 6493-6500/1 Women’s Helpline (Aware): 1800-777-5555 (weekdays, 10am to 6pm) The Seniors Helpline: 1800-555-5555 (weekdays, 9am to 5pm) Tinkle Friend (for primary school-age children): 1800-2744-788 Counselling Touchline (Counselling): 1800-377-2252 Touch Care Line (for caregivers): 6804-6555 Counselling and Care Centre: 6536-6366 We Care Community Services: 3165-8017 Shan You Counselling Centre: 6741-9293 Clarity Singapore: 6757-7990 Online resources mindline.sg/fsmh eC2.sg chat.mentalhealth.sg carey.carecorner.org.sg (for those aged 13 to 25) limitless.sg/talk (for those aged 12 to 25) --- #healthcheckSee omnystudio.com/listener for privacy information.

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Gartner Phones It In

Play Episode Listen Later Sep 29, 2025 4:25

AWS Morning Brief for the week of September 29th, 2025, with Corey Quinn. Links:You can now preview Amazon S3 Tables in the S3 consoleAWS Organizations supports full IAM policy language for service control policies (SCPs)Amazon Route 53 Resolver Query Logging now available in Asia Pacific (New Zealand)A guide to reducing waste and improving efficiency with AWSAmazon RDS announces cross-Region and cross-account snapshot copyAWS announces EC2 instance attestationHumane World for Animals uses AWS to scale global animal welfare programs deprecating its SOAP APIPrompt Library | AWS StartupsAWS named as a Leader in the 2025 Gartner Magic Quadrant for AI Code Assistants

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LifeVantage + LoveBiome | Tyler Daniels, Valencia Pamphile, & Steve Fife

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Play Episode Listen Later Sep 29, 2025 36:34

Join us next week for a special announcement about the Q4 Drive ERA Quarterly Incentive! The average 12-month earnings of a typical US Consultant who earned in 2024 are $683. These earnings represent gross income and do not account for expenses incurred in building a business. Visit the LifeVantage Income Disclosure Statement for more details. This statement has not been evaluated by the Food and Drug Administration. This product is not intended to diagnose, treat, cure, or prevent any disease. Of the 60,000 active LifeVantage Consultants, approximately 250 earn company trips.  Since the beginning of the program in 2018, approximately 230 Consultants have earned the My LifeVenture award by achieving EC2 and maintaining that rank for 6 consecutive months within the 18 months of advancing to EC2. The average number of active consultants over this period of time is 62,407. 

LifeVantage + LoveBiome | Kristen Cunningham, Steve Fife, & Kelly Olsen

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Play Episode Listen Later Sep 22, 2025 41:38

The average 12-month earnings of a typical US Consultant who earned in 2024 are $683. These earnings represent gross income and do not account for expenses incurred in building a business. Visit the LifeVantage Income Disclosure Statement for more details. This statement has not been evaluated by the Food and Drug Administration. This product is not intended to diagnose, treat, cure, or prevent any disease. Of the 60,000 active LifeVantage Consultants, approximately 250 earn company trips.  Since the beginning of the program in 2018, approximately 230 Consultants have earned the My LifeVenture award by achieving EC2 and maintaining that rank for 6 consecutive months within the 18 months of advancing to EC2. The average number of active consultants over this period of time is 62,407. 

Mindset Upgrades: Letting Go To Grow | Courtney Watkins

mindset consultants letting go watkins upgrades ec2

Play Episode Listen Later Sep 8, 2025 35:56

Since the beginning of the program in 2018, approximately 230 Consultants have earned the My LifeVenture award by achieving EC2 and maintaining that rank for 6 consecutive months within the 18 months of advancing to EC2. The average number of active consultants over this period of time is 62,407. The average 12-month earnings of a typical US Consultant who earned in 2024 are $683. These earnings represent gross income and do not account for expenses incurred in building a business. Visit the LifeVantage Income Disclosure Statement for more details. Of the 60,000 active LifeVantage Consultants, approximately 250 earn company trips.

#095 AWS costs are like fingernails ...

cloudonaut

Play Episode Listen Later Aug 25, 2025 37:09

Michael shares a trick to reduce AWS Config costs for volatile workloads. Andreas talks about EC2 instance families and their availability in the different AWS regions. On top of that, the Wittig brothers share insights into their work and business.

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The Most Expensive Toggle In The World

airhacks.fm podcast with adam bien

Play Episode Listen Later Aug 11, 2025 5:07

Episode Summary:AWS Morning Brief for the week of August 11th, 2025, with Corey Quinn.Links: AWS Cloud Visibility Best PracticesThis Ars articleAWS European Sovereign Cloud to be operated by EU citizensAmazon killing a user's accountMountpoint for Amazon S3 CSI driver v2: Accelerated performance and improved resource usage for Kubernetes workloadsStreamlining outbound emails with Amazon SES Mail ManagerAWS Lambda now supports GitHub Actions to simplify function deploymentAnthropic's Claude Opus 4.1 now in Amazon BedrockAmazon CloudWatch introduces organization-wide VPC flow logs enablementUnderstanding and Remediating Cold Starts: An AWS Lambda PerspectiveAmazon SQS increases maximum message payload size to 1 MiBOpenAI open weight models now available on AWS Best practices for analyzing AWS Config recording frequenciesAmazon EKS adds safety control to prevent accidental cluster deletionAWS Console Mobile App now offers access to AWS SupportAmazon EC2 now supports force terminate for EC2 instances Amazon DynamoDB adds support for Console-to-CodeUsing generative AI for building AWS networksSimplify network connectivity using Tailscale with Amazon EKS Hybrid NodesCost tracking multi-tenant model inference on Amazon Bedrock

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Adam && Adam == true

Play Episode Listen Later Aug 9, 2025 71:26

An airhacks.fm conversation with Adam Dudczak (@maneo) about: early programming experiences with Commodore 64 and Pascal, demo scene participation through postal mail swapping of floppy disks, writing assembly code for 64K intros with music and graphics, developing digital library systems using Java Servlets and Hibernate, involvement in reactivating Poznan Java User Group in 2007, NetBeans Dream Team and NetBeans World Tour, appearing on Polish breakfast TV to discuss Java programming, working at Supercomputing Center on cultural heritage digitization projects, transitioning to EJB 3.0 and Glassfish based on conference inspirations, joining allegro in 2014 to rewrite search functionality from PHP to Java microservices, handling 14K requests per second with Solr-based search infrastructure, migrating big data stack from on-premise Hadoop to Google Cloud Platform, developing private banking application for children using Spring and Hibernate then migrating to Google Sheets with 70 lines of JavaScript, discussing public cloud cost optimization strategies, comparing AWS Lambda versus EC2 versus container services based on traffic patterns, emphasizing removal of code when moving to public cloud to leverage managed services, standardization benefits of Java EE for long-term maintenance and migration, quarkus as modern framework supporting old Jakarta EE code with fast startup times, importance of choosing appropriate persistence layer (S3 vs relational databases) based on cloud costs, serverless architectures for enterprise applications with predictable low traffic, differences between AWS Azure and GCP service offerings and pricing models, Turbo assembler project klatwa Adam Dudczak on twitter: @maneo

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S1E54: Kpod vapes, zombie kids: Why it's time to raise the alarm

The Straits Times Audio Features

Play Episode Listen Later Jul 17, 2025 36:58

Kpods, a street term for drug-laced e-vaporisers, has been much-talked about this week. Synopsis: Join Natasha Ann Zachariah at The Usual Place as she unpacks the latest current affairs with guests. Videos of vape users taken by the public have been surfacing online – in particular, of younger people – turning into “zombies” and behaving erratically after using etomidate-laced vapes. An anaesthetic used in hospitals during medical procedures, etomidate is classified as a poison, which can only be used by licensed medical professionals. In this episode of The Usual Place podcast, I spoke with my colleague and crime reporter Nadine Chua; Yio Chu Kang SMC MP Yip Hon Weng, who has raised multiple questions in Parliament on vaping over the past few years; and executive director of youth mental health charity Impart, Narasimman Tivasiha Mani, who has encountered teens using Kpods. Highlights (click/tap above): 5:37 You don’t need to tell sellers your age, you just need money, notes Mr Narasimman 6:37 Vape sellers market the devices to look trendy or innocuous 14:01 “If he continues down this path, it’s like he’s gone anyway.”: Ms Chua on how a mother shared about her struggle with her son who is hooked on Kpods 14:54 The roles – and limitations – of different government agencies such as the Central Narcotics Bureau and Health Sciences Authority 27:36 What could happen in the long run if we fail to act on Kpods today? Read ST's coverage on the invisible vaping crisis: https://str.sg/JpFev Read Nadine Chua's articles: https://str.sg/3z8M3 Host: Natasha Ann Zachariah (natashaz@sph.com.sg) Read Natasha’s articles: https://str.sg/iSXm Follow Natasha on her IG account and DM her your thoughts on this topic: https://www.instagram.com/theusualplacepodcast Follow Natasha on LinkedIn: https://str.sg/v6DN Filmed by: Studio+65 Edited by: Teo Tong Kai, Eden Soh & Natasha Liew Executive producers: Ernest Luis & Lynda Hong Helplines: Mental well-being National helpline: 1771 (24 hours) / 6669-1771 (via WhatsApp) Samaritans of Singapore: 1-767 (24 hours) / 9151-1767 (24 hours CareText via WhatsApp) Singapore Association for Mental Health: 1800-283-7019 Silver Ribbon Singapore: 6386-1928 Chat, Centre of Excellence for Youth Mental Health: 6493-6500/1 Women’s Helpline (Aware): 1800-777-5555 (weekdays, 10am to 6pm) The Seniors Helpline: 1800-555-5555 (weekdays, 9am to 5pm) Counselling Touchline (Counselling): 1800-377-2252 Touch Care Line (for caregivers): 6804-6555 Counselling and Care Centre: 6536-6366 We Care Community Services: 3165-8017 Shan You Counselling Centre: 6741-9293 Clarity Singapore: 6757-7990 Online resources mindline.sg/fsmh eC2.sg tinklefriend.sg chat.mentalhealth.sg carey.carecorner.org.sg (for those aged 13 to 25) limitless.sg/talk (for those aged 12 to 25) shanyou.org.sg Follow The Usual Place Podcast and get notified for new episode drops every Thursday:Channel: https://str.sg/5nfmApple Podcasts: https://str.sg/9ijXSpotify: https://str.sg/cd2PYouTube: https://str.sg/theusualplacepodcastFeedback to: podcast@sph.com.sg SPH Awedio app: https://www.awedio.sg --- Follow more ST podcast channels: All-in-one ST Podcasts channel: https://str.sg/wvz7 Get more updates: http://str.sg/stpodcasts The Usual Place Podcast YouTube: https://str.sg/4Vwsa --- Get The Straits Times app, which has a dedicated podcast player section: The App Store: https://str.sg/icyB Google Play: https://str.sg/icyX --- #tup #tuptrSee omnystudio.com/listener for privacy information.

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Momentum Is A Choice: How To Stay Ready And Relevant | Dawn Bures

AWS for Software Companies Podcast

Play Episode Listen Later Jul 7, 2025 24:40

Since the beginning of the program in 2018, approximately 230 Consultants have earned the My LifeVenture award by achieving EC2 and maintaining that rank for 6 consecutive months within the 18 months of advancing to EC2. The average number of active consultants over this period of time is 62,407.  The average 12-month earnings of a typical US Consultant who earned in 2024 are $683. These earnings represent gross income and do not account for expenses incurred in building a business. Visit the LifeVantage Income Disclosure Statement for more details. Of the 60,000 active LifeVantage Consultants, approximately 250 earn company trips. 

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Ep102: 500 Billion Connected Devices: Intel's Investment in improving Enterprise AI

Play Episode Listen Later May 29, 2025 16:35

Akanksha Bilani of Intel shares how businesses can successfully adopt generative AI with significant performance gains while saving on costs.Topics Include:Akanksha runs go-to-market team for Amazon at IntelPersonal and business devices transformed how we communicateForrester predicts 500 billion connected devices by 20265,000 billion sensors will be smartly connected online40% of machines will communicate machine-to-machineWe're living in a world of data delugeAI and Gen AI help make data effectiveGoal is making businesses more profitable and effectiveVarious industries need Gen AI and data transformationIntel advises companies as partners with AWSThree factors determine which Gen AI use cases adoptFactor one: availability and ease of use casesHow unique and important are they for business?Does it have enough data for right analytics?Factor two: purchasing power for Gen AI adoption70% of companies target Gen AI but lack clarityLeaders must ensure capability and purchasing power existFactor three: necessary skill sets for implementationNeed access to right partnerships if lacking skillsIntel and AWS partnered for 18 years since inceptionIntel provides latest silicon customized for Amazon servicesEngineer-to-engineer collaboration on each processor generation92% of EC2 runs on Intel processorsIntel powers compute capability for EC2-based servicesIntel ensures access to skillsets making cloud aliveAWS services include Bedrock, SageMaker, DLAMIs, KinesisPerformance is the top three priorities for successNot every use case requires expensive GPU acceleratorsCPUs can power AI inference and training effectivelyEvery GPU has a CPU head node component Participants:Akanksha Bilani – Global Sales Director, IntelSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

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The Evolution Of Platform Engineering With Massdriver CEO Cory O'Daniel

The Secure Developer

Play Episode Listen Later May 27, 2025 40:01

Episode SummaryDive into the ever-evolving world of platform engineering with Cory O'Daniel, CEO and co-founder of Massdriver. This episode explores the journey of DevOps, the challenges of building and scaling infrastructure, and the crucial role of creating effective abstractions to empower developers. Cory shares his insights on the shift towards platform engineering as a means to build more secure and efficient software by default.Show NotesIn this episode of The Secure Developer, host Danny Allan sits down with Cory O'Daniel, CEO and co-founder of Massdriver, to discuss the dynamic landscape of platform engineering. Cory, a seasoned software engineer and first-time CEO, shares his extensive experience in the Infrastructure as Code (IaC) space, tracing his journey from early encounters with EC2 to founding Massdriver. He offers candid advice for developers aspiring to become CEOs, emphasizing the importance of passion and early customer engagement. The conversation delves into the evolution of DevOps over the past two decades, highlighting the constant changes in how software is run, from mainframes to serverless containers and now AI. Cory argues that the true spirit of DevOps lies in operations teams producing products that developers can easily use. He points out the challenge of scaling operations expertise, suggesting that IT and Cloud practices need to mature in software development to create better abstractions for developers, rather than expecting developers to become infrastructure experts. A significant portion of the discussion focuses on the current state of abstractions in IaC. Cory contends that existing public abstractions, like open-source Terraform modules, are often too generic and don't account for specific business logic, security, or compliance requirements. He advocates for operations teams building their own prescriptive modules that embed organizational standards, effectively shifting security left by design rather than by burdening developers. The episode also touches upon the potential and limitations of AI in the operations space, with Cory expressing skepticism about AI's current ability to handle the contextual complexities of infrastructure without significant, organization-specific training data. Finally, Cory shares his optimism for the future of platform engineering, viewing it as a return to the original intentions of DevOps, where operations teams ship software with ingrained security and compliance, leading to more secure systems by default.LinksMassDriverAnsibleChefTerraformDevOps is BullshitElephant in the CloudDockerPostgresOpenTofuHelmRedisElixirSnyk - The Developer Security Company Follow UsOur WebsiteOur LinkedIn

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The Art of Amazon Q Developer

Paul's Security Weekly TV

Play Episode Listen Later Apr 28, 2025 4:31

AWS Morning Brief for the week of April 28th, with Corey Quinn. Links:Amazon CloudWatch agent now supports Red Hat OpenShift Service on AWS (ROSA) Amazon Cognito now supports refresh token rotation Amazon Q Developer releases state-of-the-art agent for feature development AWS Account Management now supports IAM-based account name updates AWS CodeBuild adds support for specifying EC2 instance type and configurable storage size AWS Console Mobile Application adds support for Amazon Lightsail AWS STS global endpoint now serves your requests locally in Regions enabled by default AWS Transfer Family introduces Terraform module for deploying SFTP server endpoints How Smartsheet reduced latency and optimized costs in their serverless architecture In the works – New Availability Zone in Maryland for US East (Northern Virginia) Region CVE-2025-3857 – Infinite loop condition in Amazon.IonDotnet I annotated Amazon CEO Andy Jassy's 2024 Letter to Shareholders

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Win95, Shuckworm, Ottokit, DCs, EC2, IAB, OSS, Recall, Josh Marpet, and More... - SWN #467

Paul's Security Weekly

Play Episode Listen Later Apr 11, 2025 35:45

Win95, Shuckworm, Ottokit, DCs, EC2, IAB, OSS, Recall, Josh Marpet, and More, on this edition of the Security Weekly News. Visit https://www.securityweekly.com/swn for all the latest episodes! Show Notes: https://securityweekly.com/swn-467

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Win95, Shuckworm, Ottokit, DCs, EC2, IAB, OSS, Recall, Josh Marpet, and More... - SWN #467

Play Episode Listen Later Apr 11, 2025 35:45

Win95, Shuckworm, Ottokit, DCs, EC2, IAB, OSS, Recall, Josh Marpet, and More, on this edition of the Security Weekly News. Show Notes: https://securityweekly.com/swn-467

recall oss dcs iab ec2 marpet

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