Podcasts about JavaScript

You love building web apps with Python, and HTMX got you excited about the hypermedia approach -- let the server drive the HTML, skip the JavaScript build step, keep things simple. But then you hit that last 10%: You need Alpine.js for interactivity, your state gets out of sync, and suddenly you're juggling two unrelated libraries that weren't designed to work together. What if there was a single 11-kilobyte framework that gave you everything HTMX and Alpine do, and more, with real-time updates, multiplayer collaboration out of the box, and performance so fast you're actually bottlenecked by the monitor's refresh rate? That's Datastar. On this episode, I sit down with its creator Delaney Gillilan, core maintainer Ben Croker, and Datastar convert Chris May to explore how this backend-driven, server-sent-events-first framework is changing the way full-stack developers think about the modern web. Episode sponsors Sentry Error Monitoring, Code talkpython26 Command Book Talk Python Courses Links from the show Guests Delaney Gillilan: linkedin.com Ben Croker: x.com Chris May: everydaysuperpowers.dev Datastar: data-star.dev HTMX: htmx.org AlpineJS: alpinejs.dev Core Attribute Tour: data-star.dev data-star.dev/examples: data-star.dev github.com/starfederation/datastar-python: github.com VSCode: marketplace.visualstudio.com OpenVSX: open-vsx.org PyCharm/Intellij plugin: plugins.jetbrains.com data-star.dev/datastar_pro: data-star.dev gg: discord.gg HTML-ivating your Django web app's experience with HTMX, AlpineJS, and streaming HTML - Chris May: www.youtube.com Senior Engineer tries Vibe Coding: www.youtube.com 1 Billion Checkboxes: checkboxes.andersmurphy.com Game of life example: example.andersmurphy.com Watch this episode on YouTube: youtube.com Episode #537 deep-dive: talkpython.fm/537 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Chris Coyier and Stephen Shaw discuss the transition from CodeMirror 5 to CodeMirror 6, highlighting the significant improvements in accessibility, performance, and user experience. They delve into architectural changes, integration with modern JavaScript frameworks such as Next.js, and the new theming options available in the editor. Time Jumps

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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Og hvad hvis historien primært bliver fortalt af rumskibets AI – en ældre model der konstant bekymrer sig om sin “efficiency percentage” og ikke rigtig forstår mennesker? Det er præmissen i Barbara Trueloves Of Monsters and Mainframes, en science fiction-gyser der blander klassiske monstre med AI-humor og en god portion intertekstuelle referencer. Om Barbara Truelove Barbara Truelove er australsk forfatter og game designer, og hun har åbenlyst en ting med varulve. Hendes første roman Crying Wolf (2021) handlede om tvillinger der opdager de er varulve. I 2023 lavede hun det interaktive tekstspil Blood Moon, hvor plotlinjen er: “Du er en varulv.” Og så kom Of Monsters and Mainframes i 2025. Hun fortæller selv at inspirationen kom fra at læse Bram Stokers Dracula og Martha Wells’ The Murderbot Diaries samtidigt. Men sandheden er mere rodet end det: “Dracula er en del af blandingen, ja, og det samme er Murderbot, men det samme er Universal Monsters, autopiloten i en Airbus, R2D2, min erfaring med at programmere interaktive spil og (måske mest af alt) mit liv i 2022.” Bogen blev nomineret til Goodreads Choice Award i kategorien Science Fiction og har over 9.000 ratings med gennemsnit på 4,09. Demeter – rumfærgen der ikke forstår mennesker Vores “hovedperson” er Demeter. Demeter er ikke en alvidende HAL-AI. Hun er primært bygget til at styre rumfærgen sikkert mellem stjernerne. Hun kan navigere uden om kometer og håndtere tekniske kriser. Men mennesker? Det er en helt anden sag. Når varulv-angrebet rammer og børnene Agnus og Isaac flygter op på broen efter deres bedstemor har forvandlet sig, går kommunikationen ikke så godt. “It’s just a dumb AI, Isaac,” siger Agnus. Demeter reagerer prompte: “I am not lacking intelligence. You are using words marked as moderately offensive. This is antisocial behavior.” Børnene bliver stille. “I am Demeter. I am the ship. I am your friend. Report your injuries.” De begynder at lave lyde i lavt volumen. Demeters systemer kan ikke oversætte det. “How’s it going?” spørger Steward, den medicinske AI. “I wish I could lie,” svarer Demeter. “Humans are hard.” Det er denne kamp med at forstå mennesker – og begrænsningerne i hendes algoritmer – der gør Demeter interessant. Hun er dybt inkompetent til menneskelig interaktion, og det meste af tiden prøver hun bare at undgå at forholde sig til sine passagerer. Bedstemoderen med de store tænder Et af bogens bedre øjeblikke er varulv-scenen. Børnenes bedstemor forvandler sig ved et uheld, og pludselig står Demeter i en desperat kamp for at redde Agnus og Isaac. Hun får varulven lokket ind i en luftsluse. Men så forvandler den sig tilbage til bedstemor – desperat, menneskelig, helt forsvarsløs. Demeter er bundet af den første robotlov (Asimov): ingen AI må skade et menneske. Men der er et kort øjeblik hvor bedstemoderen bliver til skygge – i overgangen mellem former. I præcis det øjeblik reagerer Demeter prompte og åbner luftslussen. Bogen lader det ligge i det uvisse om bedstemoderen selv også trykker på knappen. Det er et af de øjeblikke hvor Demeter teknisk set handler inden for sine regler – men samtidig… ja, du ved. Steward overtager – og tror det er nemt Da Demeter er lukket ned, og rumfærgen skal tilbage til Jorden, bliver opgaver overladt til Steward. Den medicinske AI beslutter sig for at overtage styringen af rumskibet. Hvor svært kan det være? “You know what? Being an autopilot isn’t all that hard. I don’t know why Demeter seemed so stressed all the time. It’s day one of our journey, and we haven’t crashed yet.” Der var dog en lille bump ved afgang. Men det var ikke Stewards skyld. Dokken bevægede sig. I hvert fald tror Steward det. “I don’t exactly speak exterior sensor. They seem very alarmed all the time, constantly screaming in a strange, disjointed dialect of JavaScript.” Stewards plan? “Embrace my managerial role and endeavor to do as little as possible. The subsystems will sort it out.” Det er morsomt at følge Stewards overmodige forsøg på at være kaptajn. Som de fleste læger tror Steward de kan lidt af det hele. En leg med referencer – men måske for fragmenteret Barbara Truelove har åbenlyst haft det superhyggeligt med at skrive den her bog. Hun fortæller selv at reglerne var: smid et monster ombord, prøv at få så mange jokes og referencer til monsterets populærkulturelle historie ind som muligt, og tænk over hvordan det ville fungere i rummet. Der er masser af sjove detaljer. Skibet der transporterer Dracula til London i Bram Stokers bog hedder også Demeter. Wilhelmina Murray er Jonathan Harkers forlovede i Dracula. I bogens fem dele er der binær kode der oversættes til små jokes som “Artificial is the best kind of intelligent” og “I have never seen electric sheep.” Det er meget hyggeligt. Men det er også lidt som om bogen ikke helt selv ved hvor den er på vej hen. Anders beskriver det som om Barbara har skrevet 121 scener med monstre og rum-AI, blandet kortene, og så forsøgt at strikke en rød tråd på den måde stykkerne landede. Den fornemmelse er der lidt af. Action-scenerne er heller ikke bogens styrke. De er lidt svære at følge med i – hvem gør hvad, hvornår, hvorhenne og hvorfor. Det føles som dårlige Marvel-action-scener, hvor man mister fornemmelsen af, hvad der foregår. Det fede – og det mindre fede Det fede ved bogen er AI’erne og deres interne dynamikker. Demeter og Steward der slås om hvem der er klogere. Steward der er træt af at blive slukket midt i sætninger med “priority override.” Den scene hvor Agnus kommer tilbage efter 15 år på Jorden og skal rejse med Demeter igen? Rørende. Skibet er blevet totalt refurbished, og Agnus genkender først slet ikke Demeter. Det øjeblik hvor hun skraber overfladen af og finder sin barndoms AI-mor – det er faktisk ret godt. Men karaktererne er lidt flade. Selv Agnus, som er tættest på en hovedperson, er lidt bleg. Og monstrene? De er sjove nok som pop-kultur-jokes, men ikke særlig interessante som karakterer. Det er underholdning så længe det varer – fed til en togtur – men ikke en der skal læses igen. Vurderingen Jens: ⭐⭐⭐ (tre stjerner). “Jeg synes jeg var godt underholdt. Det var et sjovt take, og jeg hyggede mig med alle de mange referencer. Det er ikke stor litteratur. Men af og til er det rart med noget let og fornøjeligt. Synes Demeters kamp med at forstå mennesker var kongesjov og også dens kollegiale kampe med Steward AI’en.” Anders: ⭐⭐⭐ (tre stjerner). “Jeg applauderer Barbara for at have fået en sjov idé og åbenlyst have haft det superhyggeligt med at skrive bogen. Men jeg var sært ligeglad med karaktererne, selvom Demeter og Steward havde deres øjeblikke. Jeg synes der var alt for meget fokus på ligegyldig action, og historien var alt for fragmenteret uden en god fornemmelse af udvikling.” Bogen minder os om Stefano Benni’s Terra – skør, vild og kreativ science fiction. Og selvfølgelig Blindsight af Peter Watts, som også har vampyrer i rummet. Adrian Tchaikovskys Service Model har også klare paralleller med robotter der forsøger at forstå sig selv og omverden. Jens og Anders har SCIFI SNAKKET Of Monsters and Mainframes. Shownotes til episoden om Of Monsters and Mainframes Siden sidst Anders Har set Guillermo del Toro’s Frankenstein på Netflix – meget teatralsk og med store armebevægelser. Kulisserne er for vilde. Den er lidt i stil med Dracula-filmatiseringen med Gary Oldman. Meget Guillermo del Toro-stil – hvis man er til det, er den vellykket. Anders gav den 6 ud af 10. Har læst The Other Valley af Scott Alexander Howard – en tidsrejsebog med meget lidt science i den. Vi lever i et mærkeligt parallelunivers hvor en by ligger i en dal. I dalen østpå lever de 20 år ude i fremtiden, i dalen vestpå 20 år tilbage i tiden. Meget strenge regler for at man ikke må gå frem og tilbage. Velskrevet og medrivende historie. Jens Har læst The Mercy of Gods af James S.A. Corey – Expanse-forfatterne er tilbage med en helt ny verden. Anbefalet af Søren Bjørn. Mercy of Gods foregår i en fjern fremtid på en planet hvor befolkningen kun har myter om koloniseringen. Vi er blandt videnskabsfolk som forsker i hvordan inkompatible træer af liv kan samleve. Men planeten bliver pludselig invaderet af en alien race – kæmpe hummer/knæler-agtige typer. Menneskeheden bliver sat på prøve for at se om man kan være en nyttig undersåt-race. Og samtidig går det op for os at der er en kæmpe galaktisk krig igang, og en af menneskene er blevet overtaget af en sværm af nanorobotter! Trailer ude for Ryan Gosling i rollen som Ryland Grace i Project Hail Mary af Andy Weir. Kommer i biffen den 20/3. Traileren spoiler bogen helt vildt, og der er kommet en masse action-scener som ikke findes i bogen. Lytternes input Masser af gode kommentarer fra kommentarfeltet om de gode læseoplevelser i 2025. Hennings top 3/2025: “Dying inside” af Robert Silverberg, 1972, om en ældre telepat der gradvist mister sin tankelæserevne. “Hard landing” af Algis Budrys, 1993, om hvordan en besætning fra en forulykket UFO forsøger at glide ind i og camouflere sig i det jordiske samfund. “Dark is the Sun”, af Philip Jose Farmer, 1979, om en Jord millioner af år ude i fremtiden, hvor Solen er ved at brænde sammen. Som Henning selv siger: “Det er eddermame nogle deprimerende indskud.” Frederik Aarup Lauritsen delte sin top 3 for 2025: Stiftelsen af Isaac Asimov, Station 11 af Emily St. John Mandel og Efter London af Richard Jefferies – en tussegammel post-apokalyptisk bog fra 1885. Kristofferabild har ikke så meget tid til at læse Sci-Fi for tiden – er gået en lille smule i stå med Count Zero. I 2025 var det bedste han (gen)læste Rendezvous With Rama, Restaurant At The End of The Universe og Murderbot 2 og 3. Michael har ikke fået læst så meget SF sidste år, men var sært glad ved Krystalverdenen af J.G. Ballard, The Ministry of Time på vores anbefaling – “det var jo næsten en hel hjertevarm sag – sjov at komme i gang med noget romance!” – og til sidst Jordboer af Sayaka Murata, som nok er en snitter i forhold til ren SF, men en tour de force i japansk dagligliv, body horror og nogle måske rumvæsner. “Prøv det. Den er crazy!” Majbritt Høyrup gjorde opmærksom på at Elle Cordova behandler The Power i sin blogklub. Hun vil anbefale to vidunderlige novellesamlinger af Ursula K. LeGuin: The Birthday of the World og Changing Planes. Lise bidrog med sine tre bedste bøger: American Elsewhere af Robert Jackson Bennett: Starter som Twin Peaks, går over i H. P. Lovecraft. En kvinde arver et hus i en by, som ikke findes på noget kort. Cosmicomics af Italo Calvino: Vi følger universets og Jordens tilblivelse gennem væsner/grundstoffer og deres oplevelser, interaktioner og kærlighed. En fin og underfundig lille novellesamling. The Prestige af Christopher Priest: En overraskende god bog. Hun har set filmen, men bogen er meget anderledes – hele det spekulative element fylder mere, og historien er langt mere mystisk. Næste gang Anders vælger næste bog: Mary Shelley’s Frankenstein or the Modern Prometheus fra 1818. Den fås gratis som Project Gutenberg Public Domain e-pop eller PDF. Man taler tit om den som den første moderne science fiction-bog, så den er nærmest pensum for SCIFI SNAK. Jens har tidligere syntes den var røvkedelig, men er nu klar til at prøve igen – måske er han et andet menneske nu.

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss managing AI agent teams with Project Management 101. You will learn how to translate scope, timeline, and budget into the world of autonomous AI agents. You will discover how the 5P framework helps you craft prompts that keep agents focused and cost‑effective. You will see how to balance human oversight with agent autonomy to prevent token overrun and project drift. You will gain practical steps for building a lean team of virtual specialists without over‑engineering. Watch the episode to see these strategies in action and start managing AI teams like a pro. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-project-management-for-ai-agents.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In‑Ear Insights, one of the big changes announced very recently in Claude code—by the way, if you have not seen our Claude series on the Trust Insights live stream, you can find it at trustinsights. Christopher S. Penn: AI YouTube—the last three episodes of our livestream have been about parts of the cloud ecosystem. Christopher S. Penn: They made a big change—what was it? Christopher S. Penn: Thursday, February 5, along with a new Opus model, which is fine. Christopher S. Penn: This thing called agent teams. Christopher S. Penn: And what agent teams do is, with a plain‑language prompt, you essentially commission a team of virtual employees that go off, do things, act autonomously, communicate with each other, and then come back with a finished work product. Christopher S. Penn: Which means that AI is now—I’m going to call it agent teams generally—because it will not be long before Google, OpenAI and everyone else say, “We need to do that in our product or we'll fall behind.” Christopher S. Penn: But this changes our skills—from person prompting to, “I have to start thinking like a manager, like a project manager,” if I want this agent team to succeed and not spin its wheels or burn up all of my token credits. Christopher S. Penn: So Katie, because you are a far better manager in general—and a project manager in particular—I figured today we would talk about what Project Management 101 looks like through the lens of someone managing a team of AI agents. Christopher S. Penn: So some things—whether I need to check in with my teammates—are off the table. Christopher S. Penn: Right. Christopher S. Penn: We don’t have to worry about someone having a five‑hour breakdown in the conference room about the use of an Oxford comma. Katie Robbert: Thank goodness. Christopher S. Penn: But some other things—good communication, clarity, good planning—are more important than ever. Christopher S. Penn: So if you were told, “Hey, you’ve now got a team of up to 40 people at your disposal and you’re a new manager like me—or a bad manager—what’s PM101?” Christopher S. Penn: What’s PM101? Katie Robbert: Scope, timeline, budget. Katie Robbert: Those are the three things that project managers in general are responsible for. Katie Robbert: Scope—what are you doing? Katie Robbert: What are you not doing? Katie Robbert: Timeline—how long is it going to take? Katie Robbert: Budget—what’s it going to cost? Katie Robbert: Those are the three tenets of Project Management 101. Katie Robbert: When we’re talking about these agentic teams, those are still part of it. Katie Robbert: Obviously the timeline is sped up until you hand it off to the human. Katie Robbert: So let me take a step back and break these apart. Katie Robbert: Scope is what you’re doing, what you’re not doing. Katie Robbert: You still have to define that. Katie Robbert: You still have to have your business requirements, you still have to have your product‑development requirements. Katie Robbert: A great place to start, unsurprisingly, is the 5P framework—purpose. Katie Robbert: What are you doing? Katie Robbert: What is the question you’re trying to answer? Katie Robbert: What’s the problem you’re trying to solve? Katie Robbert: People—who is the audience internally and externally? Katie Robbert: Who’s involved in this case? Katie Robbert: Which agents do you want to use? Katie Robbert: What are the different disciplines? Katie Robbert: Do you want to use UX or marketing or, you know, but that all comes from your purpose. Katie Robbert: What are you doing in the first place? Katie Robbert: Process. Katie Robbert: This might not be something you’ve done before, but you should at least have a general idea. First, I should probably have my requirements done. Next, I should probably choose my team. Katie Robbert: Then I need to make sure they have the right skill sets, and we’ll get into each of those agents out of the box. Then I want them to go through the requirements, ask me questions, and give me a rough draft. Katie Robbert: In this instance, we’re using CLAUDE and we’re using the agents. Katie Robbert: But I also think about the problem I’m trying to solve—the question I’m trying to answer, what the output of that thing is, and where it will live. Katie Robbert: Is it just going to be a document? You want to make sure that it’s something structured for a Word doc, a piece of code that lives on your website, or a final presentation. So that’s your platform—in addition to Claude, what else? Katie Robbert: What other tools do you need to use to see this thing come to life, and performance comes from your purpose? Katie Robbert: What is the problem we’re trying to solve? Did we solve the problem? Katie Robbert: How do we measure success? Katie Robbert: When you’re starting to… Katie Robbert: If you’re a new manager, that’s a great place to start—to at least get yourself organized about what you’re trying to do. That helps define your scope and your budget. Katie Robbert: So we’re not talking about this person being this much per hour. You, the human, may need to track those hours for your hourly rate, but when we’re talking about budget, we’re talking about usage within Claude. Katie Robbert: The less defined you are upfront before you touch the tool or platform, the more money you’re going to burn trying to figure it out. That’s how budget transforms in this instance—phase one of the budget. Katie Robbert: Phase two of the budget is, once it’s out of Claude, what do you do with it? Who needs to polish it up, use it, etc.? Those are the phase‑two and phase‑three roadmap items. Katie Robbert: And then your timeline. Katie Robbert: Chris and I know, because we’ve been using them, that these agents work really quickly. Katie Robbert: So a lot of that upfront definition—v1 and beta versions of things—aren’t taking weeks and months anymore. Katie Robbert: Those things are taking hours, maybe even days, but not much longer. Katie Robbert: So your timeline is drastically shortened. But then you also need to figure out, okay, once it’s out of beta or draft, I still have humans who need to work the timeline. Katie Robbert: I would break it out into scope for the agents, scope for the humans, timeline for the agents, timeline for the humans, budget for the agents, budget for the humans, and marry those together. That becomes your entire ecosystem of project management. Katie Robbert: Specificity is key. Christopher S. Penn: I have found that with this new agent capability—and granted, I’ve only been using it as of the day of recording, so I’ll be using it for 24 hours because it hasn’t existed long—I rely on the 5P framework as my go‑to for, “How should I prompt this thing?” Christopher S. Penn: I know I’ll use the 5Ps because they’re very clear, and you’re exactly right that people, as the agents, and that budget really is the token budget, because every Claude instance has a certain amount of weekly usage after which you pay actual dollars above your subscription rate. Christopher S. Penn: So that really does matter. Christopher S. Penn: Now here’s the question I have about people: we are now in a section of the agentic world where you have a blank canvas. Christopher S. Penn: You could commission a project with up to a hundred agents. How do you, as a new manager, avoid what I call Avid syndrome? Christopher S. Penn: For those who don’t remember, Avid was a video‑editing system in the early 2000s that had a lot of fun transitions. Christopher S. Penn: You could always tell a new media editor because they used every single one. Katie Robbert: Star, wipe and star. Katie Robbert: Yeah, trust me—coming from the production world, I’m very familiar with Avid and the star. Christopher S. Penn: Exactly. Christopher S. Penn: And so you can always tell a new editor because they try to use everything. Christopher S. Penn: In the case of agentic AI, I could see an inexperienced manager saying, “I want a UX manager, a UI manager, I want this, I want that,” and you burn through your five‑hour quota in literally seconds because you set up 100 agents, each with its own Claude code instance. Christopher S. Penn: So you have 100 versions of this thing running at the same time. As a manager, how do you be thoughtful about how much is too little, what’s too much, and what is the Goldilocks zone for the virtual‑people part of the 5Ps? Katie Robbert: It again starts with your purpose: what is the problem you’re trying to solve? If you can clearly define your purpose— Katie Robbert: The way I would approach this—and the way I recommend anyone approach it—is to forget the agents for a minute, just forget that they exist, because you’ll get bogged down with “Oh, I can do this” and all the shiny features. Katie Robbert: Forget it. Just put it out of your mind for a second. Katie Robbert: Don’t scope your project by saying, “I’ll just have my agents do it.” Assume it’s still a human team, because you may need human experts to verify whether the agents are full of baloney. Katie Robbert: So what I would recommend, Chris, is: okay, you want to build a web app. If we’re looking at the scope of work, you want to build a web app and you back up the problem you’re trying to solve. Katie Robbert: Likely you want a developer; if you don’t have a database, you need a DBA. You probably want a QA tester. Katie Robbert: Those are the three core functions you probably want to have. What are you going to do with it? Katie Robbert: Is it going to live internally or externally? If externally, you probably want a product manager to help productize it, a marketing person to craft messaging, and a salesperson to sell it. Katie Robbert: So that’s six roles—not a hundred. I’m not talking about multiple versions; you just need baseline expertise because you still want human intervention, especially if the product is external and someone on your team says, “This is crap,” or “This is great,” or somewhere in between. Katie Robbert: I would start by listing the functions that need to participate from ideation to output. Then you can say, “Okay, I need a UX designer.” Do I need a front‑end and a back‑end developer? Then you get into the nitty‑gritty. Katie Robbert: But start with the baseline: what functions do I need? Do those come out of the box? Do I need to build them? Do I know someone who can gut‑check these things? Because then you’re talking about human pay scales and everything. Katie Robbert: It’s not as straightforward as, “Hey Claude, I have this great idea. Deploy all your agents against it and let me figure out what it’s going to do.” Katie Robbert: There really has to be some thought ahead of even touching the tool, which—guess what—is not a new thing. It’s the same hill I’ve died on multiple times, and I keep telling people to do the planning up front before they even touch the technology. Christopher S. Penn: Yep. Christopher S. Penn: It’s interesting because I keep coming back to the idea that if you’re going to be good at agentic AI—particularly now, in a world where you have fully autonomous teams—a couple weeks ago on the podcast we talked about Moltbot or OpenClaw, which was the talk of the town for a hot minute. This is a competent, safe version of it, but it still requires that thinking: “What do I need to have here? What kind of expertise?” Christopher S. Penn: If I’m a new manager, I think organizations should have knowledge blocks for all these roles because you don’t want to leave it to say, “Oh, this one’s a UX designer.” What does that mean? Christopher S. Penn: You should probably have a knowledge box. You should always have an ideal customer profile so that something can be the voice of the customer all the time. Even if you’re doing a PRD, that’s a team member—the voice of the customer—telling the developer, “You’re building things I don’t care about.” Christopher S. Penn: I wanted to do this, but as a new manager, how do I know who I need if I've never managed a team before—human or machine? Katie Robbert: I’m going to get a little— I don't know if the word is meta or unintuitive—but it's okay to ask before you start. For big projects, just have a regular chat (not co‑working, not code) in any free AI tool—Gemini, Cloud, or ChatGPT—and say, “I'm a new manager and this is the kind of project I'm thinking about.” Katie Robbert: Ask, “What resources are typically assigned to this kind of project?” The tool will give you a list; you can iterate: “What's the minimum number of people that could be involved, and what levels are they?” Katie Robbert: Or, the world is your oyster—you could have up to 100 people. Who are they? Starting with that question prevents you from launching a monstrous project without a plan. Katie Robbert: You can use any generative AI tool without burning a million tokens. Just say, “I want to build an app and I have agents who can help me.” Katie Robbert: Who are the typical resources assigned to this project? What do they do? Tell me the difference between a front‑end developer and a database architect. Why do I need both? Christopher S. Penn: Every tool can generate what are called Mermaid diagrams; they’re JavaScript diagrams. So you could ask, “Who's involved?” “What does the org chart look like, and in what order do people act?” Christopher S. Penn: Right, because you might not need the UX person right away. Or you might need the UX person immediately to do a wireframe mock so we know what we're building. Christopher S. Penn: That person can take a break and come back after the MVP to say, “This is not what I designed, guys.” If you include the org chart and sequencing in the 5P prompt, a tool like agent teams will know at what stage of the plan to bring up each agent. Christopher S. Penn: So you don't run all 50 agents at once. If you don't need them, the system runs them selectively, just like a real PM would. Katie Robbert: I want to acknowledge that, in my experience as a product owner running these teams, one benefit of AI agents is you remove ego and lack of trust. Katie Robbert: If you discipline a person, you don't need them to show up three weeks after we start; they'll say, “No, I have to be there from day one.” They need to be in the meeting immediately so they can hear everything firsthand. Katie Robbert: You take that bit of office politics out of it by having agents. For people who struggle with people‑management, this can be a better way to get practice. Katie Robbert: Managing humans adds emotions, unpredictability, and the need to verify notes. Agents don't have those issues. Christopher S. Penn: Right. Katie Robbert: The agent's like, “Okay, great, here's your thing.” Christopher S. Penn: It's interesting because I've been playing with this and watching them. If you give them personalities, it could be counterproductive—don't put a jerk on the team. Christopher S. Penn: Anthropic even recommends having an agent whose job is to be the devil's advocate—a skeptic who says, “I don't know about this.” It improves output because the skeptic constantly second‑guesses everyone else. Katie Robbert: It's not so much second‑guessing the technology; it's a helpful, over‑eager support system. Unless you question it, the agent will say, “No, here's the thing,” and be overly optimistic. That's why you need a skeptic saying, “Are you sure that's the best way?” That's usually my role. Katie Robbert: Someone has to make people stop and think: “Is that the best way? Am I over‑developing this? Am I overthinking the output? Have I considered security risks or copyright infringement? Whatever it is, you need that gut check.” Christopher S. Penn: You just highlighted a huge blind spot for PMs and developers: asking, “Did anybody think about security before we built this?” Being aware of that question is essential for a manager. Christopher S. Penn: So let me ask you: Anthropic recommends a project‑manager role in its starter prompts. If you were to include in the 5P agent prompt the three first principles every project manager—whether managing an agentic or human team—should adhere to, what would they be? Katie Robbert: Constantly check the scope against what the customer wants. Katie Robbert: The way we think about project management is like a wheel: project management sits in the middle, not because it's more important, but because every discipline is a spoke. Without the middle person, everything falls apart. Katie Robbert: The project manager is the connection point. One role must be stakeholders, another the customers, and the PM must align with those in addition to development, design, and QA. It's not just internal functions; it's also who cares about the product. Katie Robbert: The PM must be the hub that ensures roles don't conflict. If development says three days and QA says five, the PM must know both. Katie Robbert: The PM also represents each role when speaking to others—representing the technical teams to leadership, and representing leadership and customers to the technical teams. They must be a good representative of each discipline. Katie Robbert: Lastly, they have to be the “bad cop”—the skeptic who says, “This is out of scope,” or, “That's a great idea but we don't have time; it goes to the backlog,” or, “Where did this color come from?” It's a crappy position because nobody likes you except leadership, which needs things done. Christopher S. Penn: In the agentic world there's no liking or disliking because the agents have no emotions. It's easier to tell the virtual PM, “Your job is to be Mr. No.” Katie Robbert: Exactly. Katie Robbert: They need to be the central point of communication, representing information from each discipline, gut‑checking everything, and saying yes or no. Christopher S. Penn: It aligns because these agents can communicate with each other. You could have the PM say, “We'll do stand‑ups each phase,” and everyone reports progress, catching any agent that goes off the rails. Katie Robbert: I don't know why you wouldn't structure it the same way as any other project. Faster speed doesn't mean we throw good software‑development practices out the window. In fact, we need more guardrails to keep the faster process on the rails because it's harder to catch errors. Christopher S. Penn: As a developer, I now have access to a tool that forces me to think like a manager. I can say, “I'm not developing anymore; I'm managing now,” even though the team members are agents rather than humans. Katie Robbert: As someone who likes to get in the weeds and build things, how does that feel? Do you feel your capabilities are being taken away? I'm often asked that because I'm more of a people manager. Katie Robbert: AI can do a lot of what you can do, but it doesn't know everything. Christopher S. Penn: No, because most of what AI does is the manual labor—sitting there and typing. I'm slow, sloppy, and make a lot of mistakes. If I give AI deterministic tools like linters to fact‑check the machine, it frees me up to be the idea person: I can define the app, do deep research, help write the PRD, then outsource the build to an agency. Christopher S. Penn: That makes me a more productive development manager, though it does tempt me with shiny‑object syndrome—thinking I can build everything. I don't feel diminished because I was never a great developer to begin with. Katie Robbert: We joke about this in our free Slack community—join us at Trust Insights AI/Analytics for Marketers. Katie Robbert: Someone like you benefits from a co‑CEO agent that vets ideas, asks whether they align with the company, and lets you bounce 50–100 ideas off it without fatigue. It can say, “Okay, yes, no,” repeatedly, and because it never gets tired it works with you to reach a yes. Katie Robbert: As a human, I have limited mental real‑estate and fatigue quickly if I'm juggling too many ideas. Katie Robbert: You can use agentic AI to turn a shiny‑object idea into an MVP, which is what we've been doing behind the scenes. Christopher S. Penn: Exactly. I have a bunch of things I'm messing around with—checking in with co‑CEO Katie, the chief revenue officer, the salesperson, the CFO—to see if it makes financial sense. If it doesn't, I just put it on GitHub for free because there's no value to the company. Christopher S. Penn: Co‑CEO reminds me not to do that during work hours. Christopher S. Penn: Other things—maybe it's time to think this through more carefully. Christopher S. Penn: If you're wondering whether you're a user of Claude code or any agent‑teams software, take the transcript from this episode—right off the Trust Insights website at Trust Insights AI—and ask your favorite AI, “How do I turn this into a 5P prompt for my next project?” Christopher S. Penn: You will get better results. Christopher S. Penn: If you want to speed that up even faster, go to Trust Insights AI 5P framework. Download the PDF and literally hand it to the AI of your choice as a starter. Christopher S. Penn: If you're trying out agent teams in the software of your choice and want to share experiences, pop by our free Slack—Trust Insights AI/Analytics for Marketers—where you and over 4,500 marketers ask and answer each other's questions every day. Christopher S. Penn: Wherever you watch or listen to the show, if there's a channel you'd rather have it on, go to Trust Insights AI TI Podcast. You can find us wherever podcasts are served. Christopher S. Penn: Thanks for tuning in. Christopher S. Penn: I'll talk to you on the next one. Katie Robbert: Want to know more about Trust Insights? Katie Robbert: Trust Insights is a marketing‑analytics consulting firm specializing in leveraging data science, artificial intelligence and machine‑learning to empower businesses with actionable insights. Katie Robbert: Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data‑driven approach. Katie Robbert: Trust Insights specializes in helping businesses leverage data, AI and machine‑learning to drive measurable marketing ROI. Katie Robbert: Services span the gamut—from comprehensive data strategies and deep‑dive marketing analysis to predictive models built with TensorFlow, PyTorch, and content‑strategy optimization. Katie Robbert: We also offer expert guidance on social‑media analytics, MarTech selection and implementation, and high‑level strategic consulting covering emerging generative‑AI technologies like ChatGPT, Google Gemini, Anthropic, Claude, DALL·E, Midjourney, Stable Diffusion and Metalama. Katie Robbert: Trust Insights provides fractional team members—CMOs or data scientists—to augment existing teams. Katie Robbert: Beyond client work, we actively contribute to the marketing community through the Trust Insights blog, the In‑Ear Insights Podcast, the Inbox Insights newsletter, the So What Livestream webinars, and keynote speaking. Katie Robbert: What distinguishes us? Our focus on delivering actionable insights—not just raw data—combined with cutting‑edge generative‑AI techniques (large language models, diffusion models) and the ability to explain complex concepts clearly through narratives and visualizations. Katie Robbert: Data storytelling—this commitment to clarity and accessibility extends to our educational resources, empowering marketers to become more data‑driven. Katie Robbert: We champion ethical data practices and AI transparency. Katie Robbert: Sharing knowledge widely—whether you're a Fortune 500 company, a midsize business, or a marketing agency seeking measurable results—Trust Insights offers a unique blend of technical experience, strategic guidance and educational resources to help you navigate the ever‑evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

Fraudology is presented by Sardine. Request a 1:1 product demo at sardine.ai In this episode of Fraudology, Karisse Hendrick welcomes back elite fraud fighter and Stratovera CEO Robert Capps to discuss the shifting power balance in the age of AI. Robert shares a fascinating "thought experiment" where he used Large Language Models (LLMs) to reverse engineer obfuscated JavaScript, proving that even non-technical attackers can now identify and dismantle complex front-end fraud tools in real-time.The conversation dives deep into the "Build vs. Buy" debate, with Robert cautioning organizations that the true cost of building internal tools isn't just the initial code—it's the ongoing "immune response" required to fight an AI-powered adversary that never sleeps. From the "radioactive decay" of legacy device ID to the necessity of designing "entropy" into system responses, this episode is a masterclass in modern fraud strategy.Fraudology is hosted by Karisse Hendrick, a fraud fighter with decades of experience advising hundreds of the biggest ecommerce companies in the world on fraud, chargebacks, and other forms of abuse impacting a company's bottom line. Connect with her on LinkedIn She brings her experience, expertise, and extensive network of experts to this podcast weekly, on Tuesdays.

En esta entrevista, Carlos Azaustre repasa su evolución profesional, una trayectoria nada convencional que comenzó con la autoedición de cómics y pasó por gigantes como Google e IBM. Carlos explica cómo su blog nació por necesidad personal mientras vivía en Irlanda y cómo terminó convirtiéndose en un referente de SEO en español.Además, profundizamos en su faceta como profesor universitario, donde analiza el impacto de la IA generativa en los nuevos estudiantes y la importancia de no perder la capacidad de pensar antes de ejecutar. Una conversación indispensable sobre marca personal, salud mental y el futuro del desarrollo de software.Support the show

En este episodio quiero hablar sobre un par de puntos que me llamaron la atención del estado de JavaScript del 2025, compartirlos con ustedes.

Universal Salvation, part 4 Welcome back to Gnostic Insights. I'm going to do my best to wrap up this review of David Bentley Hart's book, That All Shall Be Saved, Heaven, Hell, and Universal Salvation. And I hope you understand, particularly those of you who are Christians that are listening to this, that I do all of this in the name of the Father. It's not to tear down Christianity. It's to uphold the mission of the Messiah, which has been lost over the past several hundred years of Christianity. And so this talk of universal salvation is a necessary component of believing in the glory of God. Because universal salvation of all souls, not only all humans, but the dogs, the cats, the birds, the grasses, all living things, have to return to the Father, or else the Anointed loses power. The Father loses parts of himself. Okay, let's get back to David Bentley Hart. So we're going to run through these four meditations that are the body of his book. The first meditation is, Who is God? He says, The New Testament, to a great degree, consists in the eschatological interpretation of Hebrew Scripture's story of creation, finding in Christ as eternal Logos and risen Lord, the unifying term of beginning and end. There's no more magnificent meditation on this vision than Gregory of Nyssa's description of the progress of all persons towards union with God in the one pleroma, the one fullness of the whole Christ. All spiritual wills moving, to use this loving image, from outside the temple walls to the temple precincts, and finally beyond the ages into the very sanctuary of the glory as one. Okay, let me jump in here to say, do you notice that the New Testament words, when you use the correct translations, are the same as the translations in our Tripartite Tractate of the Nag Hammadi? Logos is the eternal spirit of humanity and the risen Lord. The Fullness is the one pleroma, the whole Christ. And in this statement, it's saying that all that is spiritual, which includes the spirits that reside within each of us, will all move as one into the pleroma of the Christ. That's who Christ is to us. He's the head of our pleroma. And when I speak of pleromas, I always picture that pyramidal shape, that hierarchical shape, and the capstone is the head. We 2nd order powers are children of the 1st order powers. The 3rd order powers are the Army of Christ that have come to redeem us. When Paul spoke of this, he was applying it literally to the temple in Jerusalem, where there were the walls of the temple, and most of the people were outside of the walls, and some of the people were in the temple precincts. And finally, the very sanctuary of the glory, where only the priests were allowed. These are the three parts that were mentioned, and these are archetypal of the movement of humanity, Hart is saying, from the outside of the pleroma of the Christ, into the pleroma of the Christ, and then into the very glory of God through the Christ. On page 90, Hart says, If one truly believes that traditional Christian language about God's goodness and the theological grammar to which it belongs are not empty, then the God of eternal retribution and pure sovereignty proclaimed by so much of Christian tradition is not and cannot possibly be the God of self-outpouring love revealed in Christ. If God is the good creator of all, he must also be the savior of all without fail, who brings to himself all he has made, including all rational wills, and only thus returns to himself in all that goes forth from him. And that's the end of the chapter, Who is God? And that pretty much states my basic belief on why everyone is going to heaven, because we all come from the Father, and therefore we all must return to the Father because the Father cannot be diminished in any way. And if he lost us, he'd be diminished. Do you see? The second meditation is, What is Judgment? And the subtitle is A Reflection on Biblical Eschatology. And eschatology, that's one of those big theological words that just means the end times, the end of time. On page 93, Hart says, There's a general sense among most Christians that the notion of an eternal hell is explicitly and unremittingly advanced in the New Testament. And yet, when we go looking for it in the actual pages of the text, it proves remarkably elusive. The whole idea is, for instance, entirely absent from the Pauline corpus as even the thinnest shadow of a hint, nor is it anywhere patently present in any of the other epistolary texts. There is one verse in the Gospels, Matthew 25-46 that, traditionally understood, offers what seems the strongest evidence for the idea, but then now Hart's going to explain how that can't be true. And then he says there are also perhaps a couple of verses from Revelation, and he says nothing's clear in Revelation, so he's not going to go there. But, What in fact the New Testament provides us with are a number of fragmentary and fantastic images that can be taken in any number of ways, arranged according to our prejudices and expectations, and declared literal or figural or hyperbolic as our desires dictate. It's why people can make the case for eternal damnation, but you can also make the case for not eternal damnation, because it's so metaphorical. On page 94, Hart says, Nowhere is there any description of a kingdom of perpetual cruelty presided over by Satan, as though he were some kind of Chthonian god. On the other hand, however, there are a remarkable number of passages in the New Testament, several of them from Paul's writings, that appear instead to promise a final salvation of all persons and all things, and in the most unqualified terms. How did some images become mere images in the general Christian imagination, while others became exact documentary portraits of some final reality? If one can be swayed simply by the brute force of arithmetic, it seems worth noting that, among the apparently most explicit statements on the last things, the universalist statements are by far the more numerous. And then he lists a number of verses from the New Testament that speak of universal salvation, over 20 of them at least, and I'll give you just a couple. Romans 5.18 says, So then, just as through one transgression came condemnation for all human beings, so also through one act of righteousness came a rectification of life for all human beings. And jumping in from the Gnostic sense, he doesn't say the fall of one human, he doesn't say through Adam, he says one transgression—and we would call that one transgression the Fall of Logos, the fall of the Aeon, which is a higher order being than we are. Or Corinthians 15.22 says, For just as in Adam all die, so also in the anointed Christ all will be given life. I would say where it says for just as in Adam all die, it's not because Adam ate the apple, it's that we humans who are outside of the Christ, we're outside of the walls of the temple, we are in the pleroma of Adam—we are in the pleroma of human beings. When you accept the anointed, then you move into the pleroma, or you nest up higher into the pleroma of the Christ. That would be the Gnostic way of saying that. Second Corinthians 5.14 says, For the love of the anointed constrains us, having reached this judgment, that one died on behalf of all, all then have died. And of course that one is the Anointed, and He died on behalf of everyone. Or even Romans 11:32, For God shut up everyone in obstinacy, so that he might show mercy to everyone. And there's a long discussion in the chapter about how God's chosen—the original elect, that being the Hebrew nation—has been obstinate about accepting Jesus of Nazareth as the Anointed. And so he's saying that everyone is shut up in obstinacy, that's the Hebrews, so that he might show mercy to everyone. And that is, they're temporarily set up in obstinacy so that the message of the Anointed can be preached far and wide, before death and after death, we Gnostics would say, and not be just constrained to only the Hebrews. That's why the Hebrews are set aside for the moment, so that those outside the temple walls can also come to Christ. And then there are 19 more verses after this, and he lists them all between pages 96 and page 102. And if you are a theological scholar or a concerned Christian that wants to know if this is heresy or not, I really suggest you buy the book, That All Shall Be Saved, by David Bentley Hart, and read it carefully from cover to cover. Jumping to page 116, Hart says, There are those metaphors used by Jesus that seem to imply that the punishment of the world to come will be of only limited duration. For example, “if remanded to prison, you shall most certainly not emerge until you pay the very last pittance.” Or, “the unmerciful slave is delivered to the torturers until he should repay everything he owes.” And Hart says it seems as if this until should be taken with some seriousness. Some wicked slaves, moreover, “will be beaten with many blows, while others will be beaten with few blows.” Hart says, of course, everyone will be “salted with fire.” This fire is explicitly that of the Gehenna. But salting here is an image of purification and preservation, for salt is good. Gehenna is the Valley of Hinnom from the Old Testament, and that is where, outside of the city of Jerusalem, the refuse was burned, and even carrion and bodies were burned. And that is why it is considered to be a hellish place. And it has become a metaphor in the time of Jesus for the purging fire, the Aeonian chastening for the good. Hart says we might even find some support for the purgatorial view of the Gehenna from the Greek of Matthew 25:46, which is the supposedly conclusive verse on the side of the Infernalist Orthodoxy, where the word used for the punishment of the last day is kolasis, which most properly refers to remedial chastisement, rather than timoria, which more properly refers to retributive justice. So, the fire of the judgment. What is judgment? The fire is the chastening fire, the fire of personal guilt and remorse over the sins one has done, that causes one to repent and turn to redemption. Hart says, It is not clear in any event that the fourth gospel, [and the fourth gospel, that's the gospel of John, Matthew, Mark, Luke, and John], it is not clear in any event that the fourth gospel foretells any “last judgment,” in the sense of a real additional judgment that accomplishes more than has already happened in Christ. To see His words as pointing toward and fulfilled within his own crucifixion and resurrection, wherein all things were judged and all things redeemed. The kingdom has indeed drawn very near, and even now is being revealed. The hour indeed has come. The judge who is judged in our place is also the resurrection and the life that has always already succeeded and exceeded the time of condemnation. All of heaven and of hell meet in those three days. . . Hell appears in the shadow of the cross as what has always already been conquered, as what Easter leaves in ruins, to which we may flee from the transfiguring light of God if we so wish, but where we can never finally come to rest, for being only a shadow, it provides nothing to cling to. And he attributes that concept of hell being only a shadow to Gregory of Nyssa, although we would attribute it to the Tripartite Tractate of the Nag Hammadi which came before Gregory of Nyssa. Hell exists so long as it exists only as the last terrible residue of a fallen creation's enmity to God, the lingering effects of a condition of slavery that God has conquered universally in Christ and will ultimately conquer individually in every soul. This age has passed away already, however long it lingers on its own aftermath, and thus in the Age to Come, [and that's capital A, Age, which we would interpret as the Aeons to Come, the Aeonian Pleroma to Come], and beyond all ages, all shall come to the kingdom prepared for them from before the foundation of the world. And that's the chapter, What is Judgment? The third meditation or chapter of Hart is called What is a Person? A Reflection on the Divine Image. It says over and over in the Bible that we are made in the image of God. Man is made in the image of God. That is the divine image. On page 131, Hart says, Christians down the centuries have excelled at converting the good tidings of God's love in Christ into something dreadful, irrational, and morally horrid. [And we covered that in depth in the previous three episodes, if you want to go back there.] On page 132, Hart says, I suspect that no figure in Christian history has suffered a greater injustice as a result of the desperate inventiveness of the Christian moral imagination than the Apostle Paul, since it was the violent misprision of his theology of grace, starting with the great Augustine, it grieves me to say, that gave rise to almost all of these grim distortions of the Gospel. Aboriginal guilt, predestination, (ante praevisa merita), the eternal damnation of unbaptized infants, the real existence of vessels of wrath, and so on. All of these odious and incoherent dogmatic motifs, so to speak, and others equally nasty, have been ascribed to Paul. And yet, each and every one of them, not only is incompatible with the guiding themes of Paul's proclamation of Christ's triumph and of God's purpose in election, but is something like their perfect inversion. Well, isn't that interesting? Because we already know that the archons represent the inversions of the Aeons of the Pleroma. And so, although Hart doesn't realize he's implying this, to say that what has come down to us in Christian tradition through Augustine is the perfect inversion of what Paul was actually saying about universal salvation, which means, by definition, that it's the demiurgic or the archonic version of salvation. Isn't that interesting? I mean, that is what I have been implying, that what has been taken to be Christian tradition for the last couple of thousand years is actually a diminishment of the power of Christ and the power and love of the Father. By saying that people can be lost and condemned to eternal torture, that is sacrilegious to me. That is the heresy. And that is what Hart is saying here. He goes on to say on page 133, This is all fairly odd, really. Paul's argument in those chapters is not difficult to follow. What preoccupies him from beginning to end is the agonizing mystery that the Messiah of Israel has come, and yet so few of the children of the house of Israel have accepted the fact, even while so many from outside the covenant have. And Paul wonders, how is the promised Messiah rejected by so many, yet so many outside the temple walls have accepted the Messiah? There are far more Christians than there are Jews at the moment. Why is that? Paul was wondering. Hart says, Paul's is not an abstract question regarding which individual human beings are the saved and which are the damned. In fact, by the end of the argument, the former category, [that is the saved], proves to be vastly larger than that of the elect or the called, while the latter category, [that is the damned], makes no appearance at all. Jumping down the page, he says, “so then what if,” so now he's going to go ahead and quote Paul here, Romans 9:19, Paul says, So then what if God should show his power by preserving vessels suitable only for wrath, keeping them solely for destruction, in order to provide an instructive counterpoint to the riches of the glory he lavishes on vessels prepared for mercy, whom he has called from among the Jews and the Gentiles alike. For as it happens, rather than offering a solution to the quandary in which he finds himself, Paul is simply restating that quandary in its bleakest possible form, at the very brink of despair. He does not stop there, however, because he knows that this cannot be the correct answer. It is so obviously preposterous, in fact, that a wholly different solution must be sought, one that makes sense and that will not require the surrender either of Paul's reason or of his confidence in God's righteousness. Hence, contrary to his own warnings, Paul does indeed continue to question God's justice, and he spends the next two chapters unambiguously rejecting the provisional answer, the vessels of wrath hypothesis, altogether, so as to reach a completely different and far more glorious conclusion—God blesses everyone. Romans 10: 11, 12. And by the way, in Gnostic gospel, we would say the law is actually the Demiurge's rules for human behavior, because our self-will makes us otherwise uncontrollable. Because to the Father above, the only law is love. When we act out of love, all else follows. Going on, Hart says, As for the believing remnant of Israel, [Romans 11:5], it turns out that they have been elected not as the limited number of the saved within Israel, but as the earnest through which all of Israel will be saved. They are waiting for the Anointed to come and take the place of the King of Israel, King of the Jews. King of the Jews is one of the titles of the Messiah. That means the capstone of their pleroma. You see? It's all of these pyramidal shapes that are first designed up there in the Fullness of God, the pleroma. What Paul is saying is that the Jews that are in the pleroma of Israel, it's their remnant that makes them holy. It's their remnant that is the spiritual part, the higher part, the called part, the elect part of the pleroma of the nation of the Hebrews. And it is through those elect that all of the Jews will be saved, ultimately. Hart says, For the time being, true, a part of Israel is hardened, but this will remain the case only until the ”full entirety” [that is the pleroma] of the Gentiles enter in. The unbelievers among the children of Israel may have been allowed to stumble, but God will never allow them to fall. Hart's just saying that Israel's reluctance or slowness to believing that Jesus is the Messiah is just slowing down the progress of history to give everyone else a chance to catch up to it. Quoting Hart again, We're in Romans now, 11:11. This then is the radiant answer dispelling the shadows of Paul's grim what if in the ninth chapter of Romans. It's clarion negative. It turns out that there is no final illustrative division between the vessels of wrath and vessels of mercy. That was a grotesque, all too human thought that can now be chased away for good. God's wisdom far surpasses ours, and his love can accomplish all that it intends. “He has bound everyone in disobedience so as to show mercy to everyone.” [That's Romans 11:32.] All are vessels of wrath precisely so that all may be made vessels of mercy. . . That Paul's great attempt to demonstrate that God's election is not some arbitrary act of predilective exclusion, but instead a providential means for bringing about the unrestricted inclusion of all persons, has been employed for centuries to advance what is quite literally the very teaching that he went to such great lengths explicitly to reject. . . Yet this is still not my principal point. I want to say something far more radical. I want to say that there is no way in which persons can be saved as persons except in and with all other persons. This may seem an exorbitant claim, but I regard it as no more than an acknowledgment of certain obvious truths about the fragility, dependency, and exigency of all that make us who and what we are. Oh, this is a very interesting portion. Okay, listen to this. Jumping to page 149. No soul is who or what it is in isolation, and no soul's sufferings can be ignored without the sufferings of a potentially limitless number of other souls being ignored as well. And so it seems if we allow the possibility that even so much as a single soul might slip away unmourned into everlasting misery, the ethos of heaven turns out to be “every soul for itself”—which is also, curiously enough, precisely the ethos of hell. But Christians are obliged, it seems clear, to take seriously the eschatological imagery of scripture. And there all talk of salvation involves the promise of a corporate beatitude, a kingdom of love and knowledge, a wedding feast, a city of the redeemed, the body of Christ, which means that the hope Christians cherish must in some way involve the preservation of whatever is deepest in and most essential to personality rather than a perfect escape from personality. But finite persons are not self-enclosed individual substances. They are dynamic events of relation to what is other than themselves. And then Hart summons up the idea of a single recurrent image, he says, That of a parent whose beloved child has grown into quite an evil person, but who remains a parent nevertheless, and therefore keeps and cherishes countless tender memories of the innocent and delightful being that has now become lost in the labyrinth of that damaged soul. Is all of that, those memories, those anxieties and delights, those feelings of desperate love, really to be consigned to the fire as just so much combustible chaff? Must it all be forgotten or willfully ignored for heaven to enter into that parent's soul? And if so, is this not the darkest tragedy ever composed? And is God not then a tragedian utterly merciless in his poetic omnipotence? Who or what is that being whose identity is no longer determined by its relation to that child? [Skipping to page 153] Personhood as such is not a condition possible for an isolated substance. It is an act, not a thing. And it is achieved only in and through a history of relations with others. We are finite beings in a state of becoming, and in us there is nothing that is not an action, dynamism, an emergence into a fuller or a retreat into a more impoverished existence. And so, as I said in my first meditation, we are those others who make us. Spiritual personality is not mere individuality, nor is personal love one of its merely accidental conditions or extrinsic circumstances. A person is first and foremost a limitless capacity, a place where the all shows itself with a special inflection. We exist as the place of the other, to borrow a phrase from Michel de Certeau. Certainly, this is the profoundest truth in the doctrine of resurrection. That we must rise from the dead to be saved is a claim not simply about resumed corporeality, whatever that might turn out to be, but more crucially, about the fully restored existence of the person as socially, communally, corporately constituted. Each person is a body within the body of humanity, which exists in its proper nature only as the body of Christ. Well, that's pretty neat. See, we are nested fractal hierarchies of the pleroma of the Fullness of God. And if you've been with me a while, you know what that long and complicated sentence means. Picture a pyramidal shape, picture every living part of your body as building up the pyramid, and your conscious self is the capstone of that pleroma that makes up your body. Now, you are then nested along with all other humans into the pleroma of humanity, the body of humanity, also called the body of Adam. Just the way our cells nest up into building us, we nest up into building the great body of humanity. And then, Hart is saying this body of humanity exists in its proper nature only as the body of Christ, because when we then nest up and make Christ the king of our pleroma, we are nested into the Fullness of Christ. And that is what the final salvation resting point is. When we all finally pass through the final judgment and nest up into Christ, then we're all nested up into the pleroma, we're all nested up into the Son. And there we are. And we will still have our lives the way the Fullness has their lives. They dream together as one of paradise. And that's where we're headed. Hart says, Our personhood must truly consist not only in the immediate love of those close at hand, but also in our disposition toward those whom we, by analogy, care for from afar. Or even in the abstract, for the most essential law of charity, of love, when it is truly active, is that it must inexorably grow beyond all immediately discernible boundaries in order to be fulfilled and to continue to be active. And all of those in whom each of us is implicated, and who are implicated in each of us, are themselves in turn implicated and intertwined in countless others, and on and on without limit. We belong of necessity to an indissoluble co-inherence of souls. And I think that down here on the physical level, on the material plane, the demiurgic version of that shared coherence of all souls together is quantum entanglement. That's the Demiurge's material version of how we are implicated and intertwined with every other soul. And now he goes on to say something that's very Gnostic. On the next page, Hart says, There may be within each of us—indeed there surely is—that divine spark, that divine light or spark of nous or spirit or atman that is the abiding presence of God in us, the place of radical sustaining divine imminence, nearer to me than my inmost parts. But that light is the one undifferentiated ground of our existence, not the particularity of our personal existence, in and with one another. Oh, hey, there it is. That's what I'm always saying. This one spark, that's what we call the big S Self. And the particularity of our personal existence is what we here at Gnostic Insights label as our Ego. So we are made up of the Self that we share with all others and that we share with the Son, but we are also our own individual existence. That's why we can't just blink out into nothingness and not be missed, because we have our particularity, and it has its own place in the hierarchy. Then Hart says, But then this is to say that either all persons must be saved or that none can be. [He says,] God could, of course, erase each of the elect as whoever they once were by shattering their memories and attachments like the gates of hell and then raise up some other being in each of their places, thus converting the will of each into an idiot bliss stripped of the loves that made him or her this person, associations and attachments and pity and tenderness and all the rest. If that were the case, only in hell could any of us possess something like a personal destiny, tormented perhaps by the memories of the loves we squandered or betrayed, but not deprived of them altogether. [Jumping to 157, he says], I am not I in myself alone, but only in all others. If then anyone is in hell, I too am partly in hell. . . For the whole substance of Christian faith is the conviction that another has already and decisively gone down into that abyss for us to set all the prisoners free, even from the chains of their own hatred and despair, and hence the love that has made all of us who we are and that will continue throughout eternity to do so, cannot ultimately be rejected by anyone. Amen. And that's the end of the third meditation. Now the fourth meditation, we just don't even have time to get to. It's called, What is Freedom? And if you want to hear the fourth meditation in depth, please text me in the comments and ask for more David Bentley Hart That All Shall Be Saved. But as for now, this treatise on what is freedom? I'll actually just jump to the last page and skip all of the explanations. The fourth meditation, What is Freedom? is all about free will. I guess I'll include it in some future episode about free will and just quote Hart extensively in that episode. But to close it out, Hart says, It would make no sense to suggest that God, who is by nature not only the source of being, but also the good and the true and the beautiful and everything else that makes spirits exist as rational beings, would truly be all in all if the consummation of all things were to eventuate merely in a kind of extrinsic divine supremacy over creation. But God is not a god, [or as we would say, the God Above All Gods is not the Demiurge, is how we would put it in Gnostic terms]. And his final victory, as described in scripture, will consist not merely in his assumption of perfect supremacy over all, but also in his ultimately being all in all. Could there then be a final state of things in which God is all in all, while yet there existed rational creatures whose inward worlds consisted in an eternal rejection of and rebellion against God as the sole and consuming and fulfilling end of the rational will's most essential nature? If this fictive and perverse interiority were to persist into eternity, would God's victory over every sphere of being really be complete? Or would that small miserable residual flicker of Promethean defiance remain forever as the one space in creation from which God has been successfully expelled? Surely it would, so it too must pass away. All right, that ends this long episode, because I was trying to wrap up the entire book, which I almost did. Write to me, tell me what you think of this sort of thing. I'd especially like to hear from people who used to be Christians, or who were raised in the church, and who fell away from the church because of some of these very problems and conundrums that we've been talking about for the last four episodes. God bless us all, and onward and upward! If you find these gnostic insights meaningful, please donate to the cause. Cyd pays for these podcasts out of her retirement money, and the well is running dry. If I am to keep this up, I need your financial assistance as well as your good company. I thank my (very few) paid subscibers from the bottom of my heart to the top of my pleroma. Please help. Please enable JavaScript in your browser to complete this form.Name *FirstLastEmail *Stripe Credit Card *Choose your item *Item A - $10.00Item B - $25.00Item C - $50.00Total$0.00Submit

Mazen and Robin sit down with Kræn Hansen from ElevenLabs to break down what Node API actually is and why it could be a game-changer for React Native library authors who want to write native modules once and use them everywhere, plus what still needs to happen before it's ready for prime time. Show NotesAnnouncing Node-API Support for React Native (Callstack)Kræn Hansen's React Universe TalkKræn Hansen on Callstack's livestreamHost package: React-native-node-apiHermes implementation discussionConnect With Us!Kræn Hansen: @KrænHansenRobin Heinze: @robinheinzeMazen Chami: @mazenchamiReact Native Radio: @ReactNativeRdioThis episode is brought to you by Infinite Red!Infinite Red is an expert React Native consultancy located in the USA. With over a decade of React Native experience and deep roots in the React Native community (hosts of Chain React and the React Native Newsletter, core React Native contributors, creators of Ignite and Reactotron, and much, much more), Infinite Red is the best choice for helping you build and deploy your next React Native app.

Rich Harris joins the podcast to discuss his talk, fine-grained everything, exploring fine-grained reactivity, frontend performance, and the real costs of React Server Components and RSC payloads. Rich explains how Svelte and SvelteKit approach co-located data fetching, remote functions, and RPC to reduce server-side rendering costs, improve developer experience, and avoid unnecessary performance overhead on mobile networks. The conversation dives into async rendering, parallel async data fetching, type safety with schema validation, and why async-first frameworks may define the future of JavaScript frameworks and web performance. Links X: https://x.com/Rich_Harris Github: https://github.com/rich-harris Bluesky: https://bsky.app/profile/rich-harris.dev Resources Modern front-end frameworks like Svelte are astonishingly fast at rendering, thanks to techniques such as signal-based fine-grained reactivity. But there's more to performance than updating the screen at 60 frames per second. In this talk, we'll learn about new approaches that help you build fast, reliable, data-efficient apps. Slides: https://fine-grained-everything.vercel.app/1-1 We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com, or tweet at us at PodRocketPod. Check out our newsletter! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form, and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. ChaptersSpecial Guest: Rich Harris.

Join us for an inspiring conversation with Will Sentance - the beloved creator of "JavaScript: The Hard Parts" and one of Frontend Masters' most popular instructors!Will is so much more than a JavaScript expert. He's a passionate educator, former Codesmith CEO, Oxford Visiting Fellow, Lego enthusiast, and someone who genuinely believes everyone deserves access to great education. In this warm and honest chat, he shares his story, his teaching philosophy, and what excites him about the future of learning.  WILL'S ADVICE: "Find something in your day-to-day life you want to solve with code. The learning you'll get from solving real problems is worth 1000 tutorials."Will Sentance is the creator of "JavaScript: The Hard Parts" (one of Frontend Masters' most popular courses), former CEO of Codesmith, and current Visiting Fellow at Oxford University researching AI's impact on education. LEARN FROM WILL: Watch "JavaScript: The Hard Parts, v3" on Frontend Masters: https://frontendmasters.com/courses/javascript-hard-parts-v3 #softwareengineering #javascript #coding #AI #techeducation #careeradvice Frontend Masters Online:Twitter: https://twitter.com/FrontendMastersLinkedIn: https://www.linkedin.com/company/frontend-mastersFacebook: https://www.facebook.com/FrontendMastersInstagram: https://instagram.com/FrontendMastersAbout Us: Advance your skills with 250+ in-depth, modern software engineering courses across frontend, backend, data, cloud, and AI taught by engineers who build real systems at scale. frontendmasters.com

Maintaining software over time rarely fails because of one bad decision. It fails because teams stop getting clear signals… and start guessing.In this episode, Robby talks with Lucas Roesler, Managing Partner and CTO at Contiamo. Lucas joins from Berlin to unpack what maintainability looks like in practice when you are dealing with real constraints… limited context, missing documentation, and systems that resist understanding.A big through-line is feedback. Lucas argues that long-lived systems become easier to change when they provide fast, trustworthy signals about what they are doing. That can look like tests that validate assumptions, tooling that makes runtime behavior visible, and a habit of designing for observability instead of treating it as a bolt-on.The conversation also gets concrete. Lucas shares a modernization effort built on a decade-old tangle of database logic… views, triggers, stored procedures, and materializations… created by a single engineer who was no longer around. With little documentation to lean on, the team had to build their own approach to “reading” the system and mapping dependencies before they could safely change anything.If you maintain software that has outlived its original authors, this is a grounded look at what helps teams move from uncertainty to confidence… without heroics, and without rewriting for sport.Episode Highlights[00:00:46] What well-maintained software has in common: Robby asks Lucas what traits show up in systems that hold together over time.[00:03:25] Readability at runtime: Lucas connects maintainability to observability and understanding what a system actually did.[00:16:08] Writing the system down as code: Infrastructure, CI/CD, and processes as code to reduce guesswork and improve reproducibility.[00:17:42] How client engagements work in practice: How Lucas' team collaborates with internal engineering teams and hands work off.[00:25:21] The “rat's nest” modernization story: Untangling a legacy data system with years of database logic and missing context.[00:29:40] Making data work testable: Why testability matters even when the “code” is SQL and pipelines.[00:34:59] Pivot back to feedback loops: Robby steers into why logs, metrics, and tracing shape better decision-making.[00:35:20] Why teams avoid metrics and tracing: The organizational friction of adding “one more component.”[00:42:59] Local observability with Grafana: Using visual feedback to spot waterfalls, sequential work, and hidden coupling.[00:50:00] Non-technical book recommendations: What Lucas reads and recommends outside of software.Links & ReferencesGuest and CompanyLucas Roesler: https://lucasroesler.com/Contiamo: https://contiamo.com/SocialMastodon: https://floss.social/@theaxerBluesky: https://bsky.app/profile/theaxer.bsky.socialBooks MentionedThe Wheel of Time (Robert Jordan): https://en.wikipedia.org/wiki/The_Wheel_of_TimeAccelerando (Charles Stross): https://en.wikipedia.org/wiki/AccelerandoCharles Stross: https://en.wikipedia.org/wiki/Charles_StrossThanks to Our Sponsor!Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks.It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications.Keep your coding cool and error-free, one line at a time! Use the code maintainable to get a 10% discount for your first year. Check them out! Subscribe to Maintainable on:Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts.Keep up to date with the Maintainable Podcast by joining the newsletter.

What if you could keep Rails pages fast, accessible, and SEO‑friendly, yet still get modern interactivity without shipping a mountain of JavaScript? We sit down with Cameron Dutro to unpack Live Component, a server‑first approach that breathes life into ViewComponent by treating state as data, rendering on the server, and morphing the DOM with Hotwire. No fragile ID wiring. No React by default. Just clear state, small payloads, and focused updates.We trace the path that led here: experiments rendering Ruby in the browser with Ruby.wasm, Opal, and even a TypeScript Ruby interpreter, and why those payloads and debugging pain pushed the work back to the server. Cameron explains the Live Component mental model—initializer‑defined state, slots, and a sidecar Stimulus controller—plus how targeted re‑renders make forms and micro‑interactions feel instant. We talk transports (HTTP vs WebSockets), serialization best practices for Active Record data, and where React still shines for high‑intensity builders and editors.Beyond the code, we dig into the bigger web story: how DX‑first choices often punish users on slower devices and networks, and why a balanced, server‑driven approach can close that gap. You'll hear real‑world tradeoffs, debugging techniques that feel like home to Rails devs, and a clever fix born from a Snake game that surfaced timing issues and led to a preempt option for queued renders. If your team wants dynamic islands without adopting a full SPA, this conversation offers a practical roadmap.Explore Live Component at livecomponent.org and the GitHub org at github.com/livecomponent. If this resonated, follow, share with a Rails friend, and leave a review so more builders can find it.Send us some love.JudoscaleAutoscaling that actually works. Take control of your cloud hosting. HoneybadgerHoneybadger is an application health monitoring tool built by developers for developers.JudoscaleAutoscaling that actually works. Take control of your cloud hosting.Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Support the show

Scott and Wes run through their wishlist for the web platform, digging into the UI primitives, DOM APIs, and browser features they wish existed (or didn't suck). From better form controls and drag-and-drop to native reactivity, CSS ideas, and future-facing APIs, it's a big-picture chat on what the web could be. Show Notes 00:00 Welcome to Syntax! Wes Tweet 00:39 Exploring What's Missing from the Web Platform 02:26 Enhancing DOM Primitives for Better User Experience 03:59 Multi-select + Combobox. Open-UI 04:49 Date Picker. Thibault Denis Tweet 07:18 Tabs. 08:01 Image + File Upload. 09:08 Toggles. 10:23 Native Drag and Drop that doesn't suck. 12:03 Syntax wishlist. 12:06 Type Annotations. 15:07 Pipe Operator. 16:33 APIs We Wish to See on the Web 18:31 Brought to you by Sentry.io 19:51 Identity. 21:33 getElementByText() 24:09 Native Reactive DOM. Templating in JavaScript. 24:48 Sync Protocol. 25:52 Virtualization that doesn't suck. 27:40 Put, Patch, and Delete on forms. Ollie Williams Tweet SnorklTV Tweet 28:55 Text metrics: get bounding box of individual characters. 29:42 Lower Level Connections. 29:50 Bluetooth API. 30:47 Sockets. 31:29 NFC + RFID. 34:34 Things we want in CSS. 34:40 Specify transition speed. 35:24 CSS Strict Mode. 36:25 Safari moving to Chromium. 36:37 The Need for Diverse Browser Engines 37:48 AI Access. 44:49 Other APIs 46:59 Qwen TTS 48:07 Sick Picks + Shameless Plugs Sick Picks Scott: Monarch Wes: Slonik Headlamp Shameless Plugs Scott: Syntax on YouTube Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

In this episode of The Cybersecurity Defenders Podcast, we discuss some intel being shared in the LimaCharlie community.Researchers at Trend Micro have uncovered continued activity from China-aligned threat actors leveraging a cross-platform JavaScript-based command-and-control framework known as "PeckBirdy".Silent Push has identified an extensive phishing campaign targeting over 100 organizations, attributed to the threat actor group ShinyHunters.A malicious Visual Studio Code extension impersonating an AI coding assistant for Moltbot has been discovered distributing malware via the official VS Code Extension Marketplace.Dragos has attributed the December 2025 cyberattack on the Polish power grid to the Russian state-sponsored group known as ELECTRUM, with medium confidence.Support our show by sharing your favorite episodes with a friend, subscribe, give us a rating or leave a comment on your podcast platform.This podcast is brought to you by LimaCharlie, maker of the SecOps Cloud Platform, infrastructure for SecOps where everything is built API first. Scale with confidence as your business grows. Start today for free at limacharlie.io.

Netlify's CEO, Matt Biilmann, reveals a seismic shift nobody saw coming: 16,000 daily signups—five times last year's rate—and 96% aren't coming from AI coding tools. They're everyday people accidentally building React apps through ChatGPT, then discovering they need somewhere to deploy them. The addressable market for developer tools just exploded from 17 million JavaScript developers to 3 billion spreadsheet users, but only if your product speaks fluent AI—which is why Netlify's founder now submits pull requests he built entirely through prompting, never touching code himself, and why 25% of users immediately copy error messages to LLMs instead of debugging manually. The web isn't dying to agents; it's being reborn by them, with CEOs coding again and non-developers shipping production apps while the entire economics of software—from perpetual licenses to subscriptions to pure usage—gets rewritten in real-time. Resources:Follow Matt Biilmann on X: https://x.com/biilmannFollow Martin Casado on X: https://x.com/martin_casadoFollow Erik Torenberg on X: https://x.com/eriktorenberg Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Mazen and Robin welcome back Cedric van Putten to discuss Expo Launch, a new tool that automates deploying React Native apps to the App Store. Learn how Expo is streamlining certificates, screenshots, and submission workflows.Show NotesExpo LaunchCedric van Putten's blog post: Introducing Expo LaunchMatt Palmer's blog post: From idea to app with Replit and ExpoExpo's AI StrategyConnect With Us!Cedric van Putten: @cedric_devRobin Heinze: @robinheinzeMazen Chami: @mazenchamiReact Native Radio: @ReactNativeRdioThis episode is brought to you by Infinite Red!Infinite Red is an expert React Native consultancy located in the USA. With over a decade of React Native experience and deep roots in the React Native community (hosts of Chain React and the React Native Newsletter, core React Native contributors, creators of Ignite and Reactotron, and much, much more), Infinite Red is the best choice for helping you build and deploy your next React Native app.

What's coming for Aspire in 2026? Carl and Richard talk to Maddy Montaquila about her work as the product manager for Aspire, the tool that helps you build cloud-native, distributed applications in any language and on any platform. Maddy talks about moving beyond .NET, recognizing that modern applications are written in a number of languages, and the team has focused on ensuring excellent support for Python and JavaScript, as well as the .NET languages. The same is true for the cloud - Azure, AWS, GCP - Aspire works great with them all. And then there's the role of AI, both in building apps with Aspire and building AI into applications. Aspirify today!

In 2026, digital privacy and security reflect a global power struggle among governments, corporations, and infrastructure providers. Encryption, once seen as absolute, is now conditional as regulators and companies find ways around it. Reports that Meta can bypass WhatsApp's end-to-end encryption and Ireland's new lawful interception rules illustrate a growing tolerance for backdoors, risking weaker international standards. Meanwhile, data collection grows deeper: TikTok reportedly tracks GPS, AI-interaction metadata, and cross‑platform behavior, leaving frameworks like OWASP as the final defense against mass exploitation.Cyber risk is shifting from isolated vulnerabilities to structural flaws. The OWASP Top 10 for 2025–26 shows that old problems—access control failures, misconfigurations, weak cryptography, and insecure design—remain endemic. Supply-chain insecurity, epitomized by the “PackageGate” (Shai‑Hulud) flaw in JavaScript ecosystems, demonstrates that inconsistent patching and poor governance expose developers system‑wide. Physical systems are no safer: at Pwn2Own Automotive 2026, researchers proved that electric vehicle chargers and infotainment systems can be hacked en masse, making charging a car risky in the same way as connecting to public Wi‑Fi. The lack of hardware‑rooted trust and sandboxing standards leaves even critical infrastructure vulnerable.Corporate and national sovereignty concerns are converging around what some call “digital liberation.” The alleged 1.4‑terabyte Nike breach by the “World Leaks” ransomware group shows how centralization magnifies damage—large, unified data stores become single points of catastrophic failure. In response, the EU's proposed Cloud and AI Development Act aims to build technological independence by funding open, auditable, and locally governed systems. Procurement rules are turning into tools of geopolitical self‑protection. For individuals, reliance on cloud continuity carries personal risks: in one case, a University of Cologne professor lost years of AI‑assisted research after a privacy setting change deleted key files, revealing that even privacy mechanisms can erase digital memory without backup.At the technological frontier, risk extends beyond IT. Ethics, aerospace engineering, and sustainability intersect in new fault lines. Anthropic's “constitutional AI” reframes alignment as a psychological concept, incorporating principles of self‑understanding and empathy—but critics warn this blurs science and philosophy. NASA's decision to modify, rather than redesign, the Orion capsule's heat shield for Artemis II—despite earlier erosion on Artemis I—has raised fears of “normalization of deviance,” where deadlines outweigh risk discipline. Beyond Earth, environmental data show nearly half of the world's largest cities already face severe water stress, exposing the intertwined fragility of digital, physical, and ecological systems.Across these issues, a shared theme emerges: sustainable security now depends not just on technical patches but on redefining how society manages data permanence, institutional transparency, and the planetary limits of infrastructure. The boundary between online safety, physical resilience, and environmental stability is dissolving—revealing that long‑term survival may rest less on innovation itself and more on rebuilding trust across the systems that sustain it.

What's coming for Aspire in 2026? Carl and Richard talk to Maddy Montaquila about her work as the product manager for Aspire, the tool that helps you build cloud-native, distributed applications in any language and on any platform. Maddy talks about moving beyond .NET, recognizing that modern applications are written in a number of languages, and the team has focused on ensuring excellent support for Python and JavaScript, as well as the .NET languages. The same is true for the cloud - Azure, AWS, GCP - Aspire works great with them all. And then there's the role of AI, both in building apps with Aspire and building AI into applications. Aspirify today!

A story about users competitors can't stealThis episode is for SaaS founders wondering why their users like the product but don't love it.Second movers usually copy the leader's playbook.Pete Hunt, CEO of Dagster Labs, took a different path. He joined as Head of Engineering in 2022, became CEO ten months later, and inherited a company that was #3 or #4 in a crowded category. Today they're #2 overall—and #1 for greenfield deployments.The difference? Pete built a product with values so clear that choosing it feels like choosing sides.And this inspired me to invite Pete to my podcast. We explore what happens when users choose you for reasons competitors can't copy. Pete shares why being #2 means you have to be 10x more aggressive, why relabeling a version number created an inflection point without changing code, and what broke when his sales forecasts started slipping.You'll discover why the real challenge wasn't preserving his culture—it was changing it.We also zoom in on two of the 10 traits that define remarkable software companies: – Acknowledge you cannot please everyone – Master the art of curiosityPete's journey proves that remarkable companies don't just build tools—they build tribes.Here's one of Pete's quotes that captures his contrarian belief about technical buyers:"These technical folks connect with the values of the product in an emotional way. It's a very powerful thing. People would choose JavaScript frameworks based on their values—something that becomes their identity. People say brand marketing doesn't work on developers. I just think it's completely wrong.By listening to this episode, you'll learn:Why healthy pipeline numbers lieWhy crossing the chasm meant changing culture, not preserving itWhat a version number change did that new features couldn'tWhy sales teams hold onto deals they should killFor more information about the guest from this week: Guest: Pete Hunt, CEO of Dagster LabsWebsite: dagster.io

Your website might rank #1 on Google but be completely invisible to ChatGPT, Claude, and Perplexity. In this episode, let's break down why a huge chunk of the web is fundamentally broken for AI systems - not because of bad content, but because of technical decisions that made sense for humans but make sites invisible to the AI systems rapidly becoming the front door to the internet.Chapter Timestamps00:00:00 - Introduction: The new game your website is losing00:01:43 - The Scale of the Problem: AI crawler traffic explosion00:05:19 - The JavaScript Problem: Why AI crawlers can't see your content00:10:28 - The Bot Protection Paradox: Accidentally blocking AI00:14:40 - The Speed Requirement: Why 200ms matters00:17:46 - AI Agents Are Struggling Too: Browser agents and their limitations00:20:46 - How to Fix It: 6 things you need to do00:25:33 - Closing: The web is adapting againKey Statistics569 million GPTBot requests on Vercel's network in a single month370 million ClaudeBot requests in the same period305% growth in GPTBot traffic (May 2024 to May 2025)157,000% increase in PerplexityBot requests year-over-year33% of organic search activity now comes from AI agents~40% failure rate for the best AI browser agents on complex tasksThe 6 Things to FixImplement Server-Side Rendering (SSR) - If your site uses a JavaScript framework (React, Vue, Angular) with client-side rendering, switch to SSR or static site generation immediately. Use Next.js, Nuxt, or a pre-rendering service.Add Structured Data with JSON-LD - Expose key information in machine-readable format using schema.org markup. Microsoft confirmed Bing uses this to help Copilot understand content.Optimize for Speed - Target server response time under 200ms. First Contentful Paint under 1 second. Largest Contentful Paint under 2.5 seconds.Check Your Bot Protection Settings - Review Cloudflare, AWS WAF, or your CDN's bot management. Make a deliberate decision about GPTBot, ClaudeBot, and PerplexityBot access.Kill Infinite Scroll and Lazy Loading for Content - Use paginated URLs with standard HTML links. Ensure high-value content is in the initial HTML response.Keep Sitemaps Current - Maintain proper redirects, consistent URL patterns, and fix broken links.Tools MentionedGlimpse - Free tool to test how AI sees your website: glimpse.webperformancetools.comShow LinksSources Referenced in This EpisodeAI Crawler Statistics:Vercel Blog - The Rise of the AI CrawlerCloudflare 2025 Year in ReviewCloudflare - From Googlebot to GPTBotSearch Engine Land - AI Optimization GuideJavaScript Rendering:Prerender.io - Understanding Web CrawlersSearch Engine Journal - Enterprise SEO Trends 2026No Hacks is a podcast about web performance, technical SEO, and the agentic web. Hosted by Slobodan "Sani" Manic.

EP 276. In this week's update:Ireland has enacted sweeping new lawful interception powers, granting law enforcement expanded access to encrypted communications and raising fresh concerns among privacy advocates and tech companies.TikTok's latest U.S. privacy policy update expands location tracking, AI interaction logging, and cross-platform ad targeting, marking a significant escalation in data collection under its new American ownership structure.The newly released OWASP Top 10 (2025 edition) highlights the most critical web application security risks, providing developers and organizations with an updated roadmap to prioritize defenses against evolving threats.Security researchers have uncovered a critical bypass in NPM's post-Shai-Hulud supply-chain protections, allowing malicious code execution via Git dependencies in multiple JavaScript package managers.As Artemis II approaches, NASA defends the Orion spacecraft's unchanged heat shield design despite persistent cracking concerns from its uncrewed predecessor, while some former engineers warn the risk remains unacceptably high.Anthropic has significantly revised Claude's governing “constitution,” shifting from strict rules to high-level ethical principles while explicitly addressing the hypothetical possibility of AI consciousness and moral status.The European Parliament has adopted a strongly worded resolution urging the EU to reduce strategic dependence on American tech giants through aggressive investment in sovereign cloud, AI, and open digital infrastructure.This one's a good'n.  Let's get to it!Find the full transcript here.

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubCheck out more here:https://gotopia.tech/episodes/413Alexandre Malavasi - CTO at Marelo & Author of "Modern Full-Stack Web Development with ASP.NET Core"Albert S. Tanure - Cross Solutions Architec at Microsoft & Author of "ASP.NET Core 9 Essentials"RESOURCESAlexandrehttps://x.com/alemalavasihttps://github.com/alexandremalavasihttps://www.linkedin.com/in/alexandremalavasiAlberthttps://x.com/alberttanurehttps://github.com/tanurehttps://www.linkedin.com/in/albert-tanurehttps://www.codefc.io/enDESCRIPTIONMicrosoft Cloud Solution Architect Albert Tanure interviews Microsoft MVP Alexandre Malavasi about his fourth book, "Modern Full-Stack Web Development with ASP.NET Core". The discussion explores the challenges of writing comprehensive technical books, the importance of foundational knowledge in full stack development, and how to integrate ASP.NET Core with modern JavaScript frameworks like React, Angular, and Vue.js.Alexandre emphasizes that successful architecture decisions depend primarily on team expertise and the ability to facilitate change, rather than following trends.The conversation also highlights the critical importance of looking beyond just coding - encompassing project planning, DevOps practices, monitoring, and continuous optimization - to truly bring value to customers and become well-rounded software engineers.RECOMMENDED BOOKSAlexandre Malavasi • Modern Full-Stack Web Development with ASP.NET Core • https://amzn.to/4pvEXnYAlexandre Malavasi • Implementing Design Patterns in C# 11 and .NET 7 • https://amzn.to/49CapwnAlexandre Malavasi • Enterprise Applications with C# and .NET • https://amzn.to/4iiVidkAlexandre Malavasi • Implementing Design Patterns in C# and .NET 5 • https://amzn.to/3JU5UD2Albert Tanure • ASP.NET Core 9 Essentials • https://amzn.to/43bH73tBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

4 coole Sprachfeatures von Ada, F#, Go und PHPKennst du noch die Zeit, in der du Syntax, Standard Libraries und Edge Cases mühsam zusammengoogelt hast, statt einfach die KI zu fragen? Und wenn die KI heute sowieso Code schreibt, ist es dann überhaupt noch wichtig, mehrere Programmiersprachen zu kennen?Genau da steigen wir ein. Nicht als Sprachkrieg, sondern als Nerd-Tour durch vier Sprachfeatures, die dir Bugs, Security Incidents und Einheitenchaos ersparen können. Wir starten mit Ada und Type Ranges, also Typen mit eingebauten Wertebereichen, inklusive eines Crashes der Ariane-5-Rakete, eines Integer-Overflow und Compile-Time-Checks. Danach geht es zu F und Units of Measure, wo Meter, Sekunden oder sogar Geldbeträge Teil des Typensystems werden und der Compiler dich vor dem Mars Climate Orbiter Moment bewahrt. Dann schauen wir auf PHP und SensitiveParameters, damit Secrets nicht mehr fröhlich in Stack Traces und Logs auftauchen. Und zum Schluss landen wir bei Go: Secret Mode als Security Feature für Forward Secrecy, damit Schlüssel nach dem Handshake wirklich aus dem Speicher verschwinden. Außerdem gibt es ein GitHub-Repo mit Demos in Docker-Containern, damit du die Features in wenigen Minuten selbst anfassen kannst.Wenn du auf Open Source, Tech Community-Austausch und praktisches Knowledge Sharing stehst, wirst du hier Spaß haben. Und wenn du nach der Episode denkst, du hast noch ein besseres Sprachfeature, dann schick es rüber; wir sammeln das.Bonus: Wir schaffen es, von Raketencrash bis hin zu Secret Leaks zu kommen, ohne JavaScript als Gewinner zu küren. Knapp jedenfalls.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

In this episode of The Cybersecurity Defenders Podcast, we discuss some intel being shared in the LimaCharlie community.North Korean threat actors are targeting macOS software developers in a new malware campaign that abuses Visual Studio Code (VS Code) confi gurations to deliver JavaScript-based backdoors, according to research from Jamf.Sinkholes are usually seen as the end of a malicious campaign - the point where domains are seized and abuse stops.China's pen-testing and red-team ecosystem has always been hard to observe, especially since many teams stopped participating in international CTFs post-2018.A critical zero-day vulnerability, CVE-2025-64155, has been discovered in Fortinet's FortiSIEM platform by Horizon3.ai, allowing unauthenticated remote code execution and privilege escalation to root.Support our show by sharing your favorite episodes with a friend, subscribe, give us a rating or leave a comment on your podcast platform.This podcast is brought to you by LimaCharlie, maker of the SecOps Cloud Platform, infrastructure for SecOps where everything is built API first. Scale with confidence as your business grows. Start today for free at limacharlie.io.

jQuery is the JavaScript library that just won't quit. 20 years after its inception the team released jQuery 4.0.0, and it brings some notable modernizations including removed support for IE 10, a migration to ES modules, and support for Trusted Types. Node.js creator Ryan Dahl declared earlier this week that “the era of humans writing code is over”, and considering how good AI coding agents have gotten lately, he's probably not wrong. Cloudflare also announces it has acquired popular JavaScript framework Astro. Cloudflare has been a good steward to other OSS projects in the past, and this acquisition allows the Astro team to focus on making the framework even better.Timestamps:0:50 - jQuery 4.07:58 - Is the era of humans writing code over?18:23 - Cloudflare acquires Astro24:54 - Vertical tabs are coming to Chrome29:37 - Apple is going to use Gemini for Siri43:07 - What's making us happyNews:Paige - Ryan Dahl declares humans writing code is overJack - Cloudflare acquires AstroTJ - jQuery 4.0Lightning News: Chrome adds support for vertical tabsApple and Google enter agreement to use Gemini for Apple IntelligenceWhat Makes Us Happy this Week:Paige - Onyx Storm bookJack - Learning to play Born to Run on guitarTJ - Only Murders in the Building TV series and Dungeon Crawler Carl book seriesThanks as always to our sponsor, the Blue Collar Coder channel on YouTube. You can join us in our Discord channel, explore our website and reach us via email, or talk to us on X, Bluesky, or YouTube.Front-end Fire websiteBlue Collar Coder on YouTubeBlue Collar Coder on DiscordReach out via emailTweet at us on X @front_end_fireFollow us on Bluesky @front-end-fire.comSubscribe to our YouTube channel @Front-EndFirePodcast

Big thanks to Brilliant for sponsoring this video. To try everything Brilliant has to offer, visit https://brilliant.org/davidbombal to start your 30 day free trial or scan the QR code onscreen – You'll also get 20% off an annual premium subscription Stephen Sims joins David Bombal to discuss Operational Security (OpSec) through the lens of the "Darknet Marketplace Bible" (DNM Bible). While this document is originally designed to help criminals evade law enforcement while buying illegal goods, Stephen argues it is an excellent resource for cybersecurity professionals, journalists, and privacy advocates to learn high-level anonymity and encryption techniques. Disclaimer: Both David and Stephen repeatedly emphasize that this content is for educational, privacy, and cybersecurity research purposes only. They do not advocate illegal activity. // Stephen's Social // Twitter: / steph3nsims YouTube: / @offbyonesecurity // David's SOCIAL // Discord: discord.com/invite/usKSyzb Twitter: www.twitter.com/davidbombal Instagram: www.instagram.com/davidbombal LinkedIn: www.linkedin.com/in/davidbombal Facebook: www.facebook.com/davidbombal.co TikTok: tiktok.com/@davidbombal YouTube: / @davidbombal Spotify: open.spotify.com/show/3f6k6gE... SoundCloud: / davidbombal Apple Podcast: podcasts.apple.com/us/podcast... // MY STUFF // https://www.amazon.com/shop/davidbombal // SPONSORS // Interested in sponsoring my videos? Reach out to my team here: sponsors@davidbombal.com // MENU // 0:00 - Coming up 01:08 - Brilliant sponsored segment 03:04 - Disclaimer 03:07 - The Dark Web 07:44 - What is the Dark Web? 09:14 - The Dark Net Marketplace Bible 11:42 - DOs and DON'Ts 22:49 - Dark Net Directory 26:09 - Dread walkthrough 31:04 - Recommended Operating systems 42:07 - VPNs, Tor & PGP 53:23 - PGP // Creating key pairs 01:03:53 - How to access Dark Net Marketplaces // Black Ops marketplace 01:12 :39 - Recommended cryptocurrency for the Dark Web 01:18:43 - Shipping 01:21:12 - Communication methods 01:27:28 - JavaScript warnings 01:28:13 - Never trust external links 01:29:29 - DNM Bible summary 01:31:01 - Conclusion Please note that links listed may be affiliate links and provide me with a small percentage/kickback should you use them to purchase any of the items listed or recommended. Thank you for supporting me and this channel! Disclaimer: This video is for educational purposes only. #darkweb #opsec #tor

Today we have Andrew Northern, Principal Security Researcher at Censys, discussing "From Evasion to Evidence: Exploiting the Funneling Behavior of Injects". This research explains how modern web malware campaigns use multi-stage JavaScript injections, redirects, and fake CAPTCHAs to selectively deliver payloads and evade detection. It shows that these attack chains rely on stable redirect and traffic-distribution chokepoints that can be monitored at scale. Using the SmartApe campaign as a case study, the report demonstrates how defenders can turn those chokepoints into high-confidence detection and tracking opportunities. The research can be found here: From Evasion to Evidence: Exploiting the Funneling Behavior of Injects Learn more about your ad choices. Visit megaphone.fm/adchoices

Today we have Andrew Northern, Principal Security Researcher at Censys, discussing "From Evasion to Evidence: Exploiting the Funneling Behavior of Injects". This research explains how modern web malware campaigns use multi-stage JavaScript injections, redirects, and fake CAPTCHAs to selectively deliver payloads and evade detection. It shows that these attack chains rely on stable redirect and traffic-distribution chokepoints that can be monitored at scale. Using the SmartApe campaign as a case study, the report demonstrates how defenders can turn those chokepoints into high-confidence detection and tracking opportunities. The research can be found here: From Evasion to Evidence: Exploiting the Funneling Behavior of Injects Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of React Native Radio, Robin and Mazen are joined by Marc Rousavy to break down transforming packages to Nitro and why it's a big deal for high-performance native modules. They dig into Nitro's origins, how it stacks up against TurboModules and Expo, and what's coming next for VisionCamera. Show NotesNitroModulesChatGPT Nitro Module BuilderMarc's screencast: How to build a Nitro ModuleFrank Calise's Awesome Nitro ModulesRNR 310 - Nitro with Marc RousavyMargelo's Discord Connect With Us!Marc Rousavy: @mrousavyRobin Heinze: @robinheinzeMazen Chami: @mazenchamiReact Native Radio: @ReactNativeRdio This episode is brought to you by Infinite Red!Infinite Red is an expert React Native consultancy located in the USA. With over a decade of React Native experience and deep roots in the React Native community (hosts of Chain React and the React Native Newsletter, core React Native contributors, creators of Ignite and Reactotron, and much, much more), Infinite Red is the best choice for helping you build and deploy your next React Native app.

Package management sits at the foundation of modern software development, quietly powering nearly every software project in the world. Tools like npm and Yarn have long been the core of the JavaScript ecosystem, enabling developers to install, update, and share code with ease. But as projects grow larger and the ecosystem more complex, this older The post Next-Gen JavaScript Package Management with Ruy Adorno and Darcy Clarke appeared first on Software Engineering Daily.

Michael Hladky joins the pod to explain how CSS performance improvements like content-visibility, CSS containment, contain layout, and contain paint can dramatically outperform JavaScript virtual scrolling. The conversation explores virtual scrolling, large DOM performance, and how layout and paint work inside the browser rendering pipeline, including recalculate styles and their impact on INP Interaction to Next Paint. Michael shares real-world examples of frontend performance optimization, discusses cross-browser CSS support including Safari content-visibility, and explains why web performance issues tied to rendering are often misunderstood and overlooked. Links LinkedIn: https://www.linkedin.com/in/michael-hladky-519340148/ GitHub: https://github.com/BioPhoton X: https://x.com/Michael_Hladky Resources Conference link: https://push-based.io/event/perfnow-2025-michael-hladky-zero-js-virtual-scrolling-css Conference resource: https://github.com/push-based/css-contain-and-content-visibility-research We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com, or tweet at us at PodRocketPod. Check out our newsletter! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form, and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. Chapters 00:00 Introduction to CSS Performance and Virtual Scrolling 01:20 Why Interaction to Next Paint (INP) Changed Everything 03:00 The Real Cost of Layout and Paint 05:10 Why Large DOMs Break Performance 06:45 How CSS Containment Works 08:30 Contain Layout vs Contain Paint Explained 10:40 When Containment Breaks Your UI 12:20 Introducing Content Visibility 14:10 CSS Content Visibility vs JavaScript Virtual Scrolling 16:40 Why CSS Skips Recalculate Styles Entirely 18:50 Real Performance Gains on Desktop and Mobile 20:40 Cross-Browser Support Including Safari 22:10 Common Pitfalls and Flickering Issues 24:10 How to Measure Layout and Paint Performance 26:10 Why Frameworks Should Use This by Default 28:00 Design Systems and Low-Hanging Performance Wins 30:10 The Biggest CSS Performance Misconception 32:00 Final Takeaways on Frontend Performance

I want to build a cross-platform application. Should I just build a PWA or do I need to build a native app? Is it better to use Swift and Java or could I use C# or JavaScript? These are the questions we will answer in today's episode of Dev Questions.Website: https://www.iamtimcorey.com/ Ask Your Question: https://suggestions.iamtimcorey.com/ Sign Up to Get More Great Developer Content in Your Inbox: https://signup.iamtimcorey.com/

15 years too late but it's finally here: server-side logic in Power Pages. What does it change in practice? Unlike Azure Functions, it's just another Power Pages asset that can be added to Power Platform ALM. Perfect for anything that is logic-lite/secret-heavy. Think payments and integrations that need secrets. Server-side logic avoids awkward workarounds using plugins, Power Automate, etc. just to keep keys safe. Re-use your Javascript skills though it's not lift-n-shift from the client-side exercise. Just couple new objects to learn: HTTP client for external calls and a Dataverse object for CRUD operations. There are plenty of scenarios where client-side Web API is better, like interaction with external services requiring callbacks, for example. As Nick succulently summed it up: It doesn't make anything possible we couldn't do before. It just makes doing a lot of things we did do before a lot easier. References Power Pages server logic overview (preview) | Microsoft Learn Get in touch voice@crm.audio Nick Hayduk @Engineered_Code George Doubinski @georgedude

Package management sits at the foundation of modern software development, quietly powering nearly every software project in the world. Tools like npm and Yarn have long been the core of the JavaScript ecosystem, enabling developers to install, update, and share code with ease. But as projects grow larger and the ecosystem more complex, this older The post Next-Gen JavaScript Package Management with Ruy Adorno and Darcy Clarke appeared first on Software Engineering Daily.

Rewrites are seductive. Clean slates promise clarity, speed, and “doing it right this time.” In practice, they're often late, over budget, and quietly demoralizing.In this episode of Maintainable, Robby sits down with Brittany Ellich, a Senior Software Engineer at GitHub, to talk about a different path. One rooted in stewardship, readability, and resisting the urge to start over.Brittany's career began with a long string of rebuild projects. Over time, she noticed a pattern. The estimates were wrong. Feature development stalled. Teams burned energy reaching parity with systems they'd already had. That experience pushed her toward a strong belief: if software is in production and serving users, it's usually worth maintaining.[00:00:57] What well-maintained software actually looks likeFor Brittany, readability is the first signal. If code can't be understood, it can't be changed safely. Maintenance begins with making systems approachable for the next person.[00:01:42] Rethinking technical debtShe explains how her understanding of technical debt has evolved. Rather than a fixed category of work, it's often anything that doesn't map directly to new features. Bugs, reliability issues, and long-term risks frequently get lumped together, making prioritization harder than it needs to be.[00:05:49] Why AI changes the maintenance equationBrittany describes how coding agents have made it easier to tackle small, previously ignored maintenance tasks. Instead of waiting for debt to accumulate into massive projects, teams can chip away incrementally. (Related: GitHub Copilot and the Copilot coding agent workflow she's explored.)[00:07:16] Context from GitHub's billing systemsWorking on metered billing at GitHub means correctness and reliability matter more than flash. Billing should be boring. When it's not, customers notice quickly.[00:11:43] Navigating a multi-era codebaseGitHub's original Rails codebase is still in active use. Brittany relies heavily on Git blame and old pull requests to understand why decisions were made, treating them as a form of living documentation.[00:25:27] Treating coding agents like teammatesRather than delegating massive changes, Brittany assigns agents small, well-scoped tasks. She approaches them the same way she would a new engineer: clear instructions, limited scope, and careful review.[00:36:00] Structuring the day to avoid cognitive overloadShe breaks agent interaction into focused windows, checking in a few times a day instead of constantly monitoring progress. This keeps deep work intact while still moving maintenance forward.[00:40:24] Low-risk ways to experimentImproving test coverage and generating repository instructions are safe entry points. These changes add value without risking production behavior.[00:54:10] Navigating team resistance and ethicsBrittany acknowledges skepticism around AI and encourages teams to start with existing backlog problems rather than selling AI as a feature factory.[00:57:57] Books, habits, and staying balancedOutside of software, Brittany recommends Atomic Habits by James Clear, sharing how small routines help her stay focused.The takeaway is clear. AI doesn't replace engineering judgment. Used thoughtfully, it can support the unglamorous work that keeps software alive.Good software doesn't need a rewrite.It needs caretakers.References MentionedGitHub – Brittany's current role and the primary environment discussedGitHub Universe – Where Brittany presented her coding agent workflowAtomic Habits by James Clear – Brittany's recommended book outside of techOvercommitted - Podcast Brittany co-hostsThe Balanced Engineer Newsletter – Brittany's monthly newsletter on engineering, leadership, and balanceBrittany Ellich's website – Central hub for her writing and linksGitHub Copilot – The AI tooling discussed throughout the episodeHow the GitHub billing team uses the coding agent in GitHub Copilot to continuously burn down technical debt – GitHub blog post referencedThanks to Our Sponsor!Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks.It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications.Keep your coding cool and error-free, one line at a time! Use the code maintainable to get a 10% discount for your first year. Check them out! Subscribe to Maintainable on:Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts.Keep up to date with the Maintainable Podcast by joining the newsletter.

In this episode of PodRocket, Daniel Thompson--Yvetot joins us to break down what's new in Tauri 2.0 and how developers are using the Tauri framework to build desktop and mobile apps with Rust and JavaScript. We discuss how Tauri lets developers use frameworks like React, Vue, and Angular for the UI while handling heavy logic in Rust, resulting in smaller app binaries and better performance than Electron alternatives. The conversation covers Create Tauri App for faster onboarding, the new plugin system for controlling file system and OS access, and how Tauri improves app security by reducing attack surfaces. They also dive into mobile app development, differences between system WebViews, experiments with Chromium Embedded Framework, and why cross platform apps still need platform-specific thinking. Daniel also shares what's coming next for Tauri, including flexibility in webviews, accessibility tooling, compliance requirements in Europe, and the roadmap toward Tauri 3.0. Links Tauri: https://v2.tauri.app LinkedIn: https://www.linkedin.com/in/denjell We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey (https://t.co/oKVAEXipxu)! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com (mailto:elizabeth.becz@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Check out our newsletter (https://blog.logrocket.com/the-replay-newsletter/)! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. (https://logrocket.com/signup/?pdr) Chapters Special Guest: Daniel Thompson-Yvetot.

In this episode I talk with Sean Schertell about his return to Rails after many years in JavaScript, the pain of node module hell, Kamal for deployment, and Sean's new startup ZiaMap for land surveyors.Links:CodepilotZiaMapNonsense Monthly

Topics covered in this episode: ty: An extremely fast Python type checker and LSP Python Supply Chain Security Made Easy typing_extensions MI6 chief: We'll be as fluent in Python as we are in Russian Extras Joke Watch on YouTube About the show Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: ty: An extremely fast Python type checker and LSP Charlie Marsh announced the Beta release of ty on Dec 16 “designed as an alternative to tools like mypy, Pyright, and Pylance.” Extremely fast even from first run Successive runs are incremental, only rerunning necessary computations as a user edits a file or function. This allows live updates. Includes nice visual diagnostics much like color enhanced tracebacks Extensive configuration control Nice for if you want to gradually fix warnings from ty for a project Also released a nice VSCode (or Cursor) extension Check the docs. There are lots of features. Also a note about disabling the default language server (or disabling ty's language server) so you don't have 2 running Michael #2: Python Supply Chain Security Made Easy We know about supply chain security issues, but what can you do? Typosquatting (not great) Github/PyPI account take-overs (very bad) Enter pip-audit. Run it in two ways: Against your installed dependencies in current venv As a proper unit test (so when running pytest or CI/CD). Let others find out first, wait a week on all dependency updates: uv pip compile requirements.piptools --upgrade --output-file requirements.txt --exclude-newer "1 week" Follow up article: DevOps Python Supply Chain Security Create a dedicated Docker image for testing dependencies with pip-audit in isolation before installing them into your venv. Run pip-compile / uv lock --upgrade to generate the new lock file Test in a ephemeral pip-audit optimized Docker container Only then if things pass, uv pip install / uv sync Add a dedicated Docker image build step that fails the docker build step if a vulnerable package is found. Brian #3: typing_extensions Kind of a followup on the deprecation warning topic we were talking about in December. prioinv on Mastodon notified us that the project typing-extensions includes it as part of the backport set. The warnings.deprecated decorator is new to Python 3.13, but with typing-extensions, you can use it in previous versions. But typing_extesions is way cooler than just that. The module serves 2 purposes: Enable use of new type system features on older Python versions. Enable experimentation with type system features proposed in new PEPs before they are accepted and added to the typing module. So cool. There's a lot of features here. I'm hoping it allows someone to use the latest typing syntax across multiple Python versions. I'm “tentatively” excited. But I'm bracing for someone to tell me why it's not a silver bullet. Michael #4: MI6 chief: We'll be as fluent in Python as we are in Russian "Advances in artificial intelligence, biotechnology and quantum computing are not only revolutionizing economies but rewriting the reality of conflict, as they 'converge' to create science fiction-like tools,” said new MI6 chief Blaise Metreweli. She focused mainly on threats from Russia, the country is "testing us in the grey zone with tactics that are just below the threshold of war.” This demands what she called "mastery of technology" across the service, with officers required to become "as comfortable with lines of code as we are with human sources, as fluent in Python as we are in multiple other languages." Recruitment will target linguists, data scientists, engineers, and technologists alike. Extras Brian: Next chapter of Lean TDD being released today, Finding Waste in TDD Still going to attempt a Jan 31 deadline for first draft of book. That really doesn't seem like enough time, but I'm optimistic. SteamDeck is not helping me find time to write But I very much appreciate the gift from my fam Send me game suggestions on Mastodon or Bluesky. I'd love to hear what you all are playing. Michael: Astral has announced the Beta release of ty, which they say they are "ready to recommend to motivated users for production use." Blog post Release page Reuven Lerner has a video series on Pandas 3 Joke: Error Handling in the age of AI Play on the inversion of JavaScript the Good Parts

Wes and Scott talk about their bold predictions for web development in 2026, from WebGPU-powered design and modern CSS breakthroughs to JavaScript standards, AI-driven tooling, security risks, the future of frameworks, workflows, and more! Show Notes 00:00 Welcome to Syntax! 00:49 WebGPU and 3D experiences will finally take off Lando Norris 01:30 Web design will make a comeback Raycast shaders.com 04:03 Light mode returns (yes, really) 07:06 Modern CSS standards are about to have a huge year CSS Wrapped Graffiti 13:15 Will the Temporal API finally ship everywhere in 2026? 14:18 The rise of the standard stack 16:18 Are we headed toward standardized RPC? 19:41 What's next (and what's not) for React 21:07 Why we'll see more security failures in web dev 22:35 SvelteKit 3 lands in 2026 22:53 Where developer tooling is headed next Oxc Biome 26:44 More big acquisitions Anthropic Bun 28:02 2026: the year of durable compute 30:57 Frameworks will matter less as AI gets better 33:34 End-to-end AI workflows become the norm 36:04 Brought to you by Sentry.io 37:21 Personalized software for everyday people 39:11 MCP and MCP UI will pop 42:24 Developer skills will fall off 46:20 Crappy software will continue Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads