Podcasts about vpc

Deb 00:00:01Imagine your body has a repair manual, instructions written in your cells that tell tissues how to heal, blood vessels, how to grow, and inflammation when to stop. But what if those instructions got lost somewhere along the way? Well, today I’m talking about peptides, tiny protein fragments that act like biological text messages. Two of them, BPC 157 and TB 500.They’re showing remarkable promise for gut repair, joint recovery, and tissue regeneration. But here’s what nobody’s telling you. Women respond differently to these healing signals, especially during hormonal transitions. And today, we’re uncovering the science behind these regenerative peptides, who actually needs them, and why your doctor might not know about them. Can you guys put our ad right in here and then I’ll go to the standard intro?Welcome back to Let’s Talk Wellness Now, the show where we uncover the root causes of chronic illness, explore cutting edge regenerative medicine, and empower you with the tools to heal. I’m Dr. Deb, your medical detective. And today we’re diving into regenerative peptides BPC157 and TB 500. If you or someone you love is struggling with slow recovery from injury, chronic joint pain, gut inflammation that just won’t quit, or you just feel like your body doesn’t bounce back the way it used to, this episode is for you. Grab a cup of coffee or tea or whatever helps you unwind, settle in, and let’s start you on your journey to deeper healing. We’ll do another sponsor break here. Deb 00:01:52So let’s start with the question I hear constantly in my practice. Dr. Deb, I’m doing everything right. I’m eating clean, I’m exercising, I’m taking my supplements, but I’m still not healing. What am I missing? Well, that answer might surprise you. Sometimes it’s not about what you’re putting in your body. It’s about whether your cells are actually receiving the repair signals they need. That’s where peptides come in. Think of peptides as The body’s original communication system. These short chains of amino acids are like biological post-it notes carrying instructions from one cell to another. They tell your human system when to calm down, your blood vessels when to grow and your tissues when to repair. Now here’s where it gets interesting for women specifically. We know that estrogen plays a massive role in collagen production, vascular health, inflammatory response. When estrogen starts declining, whether that’s perimenopause, postpartum, or even from chronic stress, our natural repair mechanisms slow down dramatically. You might notice it as my joints are aching more, I’m a little more fluid filled, you know, they hurt when I bend them, my injuries take twice as long to heal.Gut issues that suddenly appear out of nowhere and no matter what you do, they don’t seem to repair. Skin has lost its elasticity or just this general sense that your body isn’t keeping up anymore. This is where BPC 157 and TB 500 entered the picture. So BPC 157, short for body protection compound 157, is a naturally occurring peptide sequence found in your gastric juices. And according to a 2024 systemic review published in emerging use of BPC 157 in orthopedic sports medicine, this peptide promotes something called angiogenesis. That’s the formation of new blood vessels and they deliver oxygen and nutrients to damaged tissues. Now TB 500 is a synthetic fragment of thymus and beta-4. Deb 00:04:17A protein your body makes naturally during wound healing and research published in therapeutic peptides in orthopedics in 2025 shows that it works like a cellular first responder rushing to injury sites and coordinating tissue repair through a process called actin regulation. But here’s what makes these peptides different from just taking another supplement. They don’t force your body to do anything.They simply remind yourselves how to heal the way they used to. And for women navigating hormonal changes, autoimmune flares, chronic inflammatory conditions, that distinction matters enormously. all right, let’s get into some of these mechanisms because understanding how something works helps you make informed decisions about whether or not it’s right for you. So,Let’s look at the science. Do these peptides actually work? And if so, how do they work? Let’s start with BPC 157. This works through multiple pathways simultaneously. First, it activates growth factor receptors that stimulate fibroblasts. Those are the cells responsible for making collagen and rebuilding connective tissue. And according to research published in Frontiers and Pharmacology in 2023, titled Regeneration or Risk, BPC 157 also modulates nitric oxide signaling, which enhances vascular repair and reduces oxidative stress at the cellular level. So this is really important because many of us are nitric oxide deficient, especially as we get older, especially since the pandemic, we’re seeing a lot of people being more deficient in nitric oxide and you’re taking nitric oxide, many of you, to help with this process. But if we’re having other issues that don’t allow that nitric oxide to get where it needs to go, that could render it completely useless. So in plain English, when we’re talking about how BPC 157 helps the blood vessels work better and protects your mitochondria, big word for your energy factories and your cells from that inflammatory damage. Deb 00:06:38Now there’s studies in musculoskeletal and gastroenterology models that show BPC157 decreases inflammatory cytokines like TNF-alpha and IL-6. And these are chemical messengers that keep inflammation turned on. So by dialing them down, BPC157 creates an environment where healing can actually happen. Now, where do we know about this?TNFL and IL-6, well, we know it from viruses, we know it from Lyme disease, we know it from mold toxicity. These cytokines are turned up, they’re creating a massive inflammatory response in the body, and you’re struggling to get these things down because of that or potentially other reasons. So here’s where it gets really interesting with women in perimenopause or menopause. When estrogen declines, collagen synthesis slows down. And that’s why we see increased joint pain, slower wound healing, and our changes in the skin’s elasticity during this transition. We see the little wrinkles, the fine lines, we see the subcutaneous fat going away a little bit more. This is partially why this is occurring. And so from research shown in the Journal of Orthopedic Research in 2023 by Leibowitz and colleagues, that they suggest that BPC157 affects on the endothelial layers. So the cells lining the blood vessels and these may mimic some of the estrogen’s protective vascular effects without actually affecting your hormone levels. This is really huge because we know that as women lose estrogen, they have a higher risk for vascular events, heart attack, stroke, things like that. And if people have already had a heart attack or a stroke, We typically recommend that they don’t use estrogen because that could potentiate the risk for another heart attack or a stroke. But that means that you don’t gain the benefits of estrogen either. So if we think about this, we could potentially use BPC 157 to give us some of the benefits that we lost from having estrogen and potentially not being able to use estrogen. And that would be huge for us. Deb 00:08:57And not to mention the reduction of inflammation and the joint pain and the wound healing and the energy and the gut feelings. I mean, there’s just so many benefits to BPC 157 that we could talk about them all day long. But we’ve got to move on. So let’s talk about TB 500. Now this peptide works very differently. Its primary job is promoting cell migration, essentially telling repair cells to go to this spot and what to do when they get there. So it sends a signal, puts a little post-it stamp there and says, Hey, when you get there, fix A, B, C, and D. And there was a study in 2024 in cell biology international that demonstrated that TB 500 increases epithelial closure and improves tendon elasticity in models of repetitive strain injury. So let’s think about that a little bit. What does that really mean?That means faster recovery from exercise induced muscle damage, better healing of overuse injuries like tennis elbow or plantar fasciitis, improved scar tissue remodeling after surgery or a C-section, enhanced recovery from chronic inflammatory conditions affecting soft tissues. And I’ve talked about this several times. I have used these compounds post-surgical personally.And I remember going back to see my surgeon at the two to three week mark for follow-up. And she was amazed at how well everything was healing. And when I asked her if she wanted to know what I was doing, her response was no, but keep doing whatever you’re doing because it’s working. And after three weeks of a major pelvic repair surgery that I had, four hours in surgery, lots of sutures, not comfortable. I was actually walking a mile and didn’t have pain and I was recovering really well and felt amazing. And that is just not typically heard of in surgical procedures like mine. It’s usually a minimum of a six to eight week recovery before you’re starting to do that again. And I give all of the credit to these two peptides. Deb 00:11:17In my clinical practice, I see this play out constantly. Women who train hard, whether that’s CrossFit, running, yoga, or just trying to keep up with active kids, often hit a wall where their recovery can’t keep up pace with their activity level. And TB 500 helps to bridge that gap by optimizing the body’s natural repair timeline. But here’s what I want to emphasize with you. These peptides aren’t magic bullets.They work best when we combine them with proper nutrition and anti-inflammatory diet, adequate sleep, stress management, and we address the underlying root cause like the gut dysfunction or those hormonal imbalances. And they work much better when the hormones are balanced versus when they’re not. They’re amplifiers of your body’s existing healing capacity, not replacements for foundational health practices.So let’s have some real talk here. Let’s talk about evidence and what you need to know about that. Let’s take a drink, sorry. Now let’s address the elephant in the room. Regulatory status and safety. Neither BPC 157 or TB 500 are FDA approved for human medical use. They fall into a category called research compounds. And that means they’re legal to possess and use but they’re not approved as pharmaceutical drugs. And hopefully they will be back on our list of things to use relatively soon with the changes that Bobby Kennedy has made to peptides recently. So why does this matter? Because quality becomes a concern. Quality control is absolutely critical. You need to know where these compounds are manufactured, their source, their testing. their clarity, everything about them. There was a 2025 review in therapeutic peptides in orthopedics that concluded both peptides demonstrate strong regenerative signaling with minimal systemic side effects in preclinical studies. But, and this is really important, most of the robust data we have comes from animal models and cell culture models, not large scale human clinical trials. Deb 00:13:41Now that doesn’t mean that they don’t work. It just means that we are still in the early stages of understanding optimal dosing, treatment duration, and long-term effects in humans. So why do we have all of this great peptide information and we don’t quite have the ability to use them yet, or it’s extremely restricted?That comes under the guise of the FDA. came through the past administration with Biden where he removed a bunch of these peptides from the market. Both BPC and TB 500 were on the list of safe peptides to use before Biden made his changes. And it looks like they may be coming back relatively quickly for us here. So what we do have is growing clinical feedback from practitioners like myself. Who use these peptides in practice under careful supervision and under pilot studies on musculoskeletal recovery published in our organizations that we work with. So all of our information is documented and it is done under an observational study. There are other studies published in orthopedic and biomedical research from 2025.that actually found VPC-157 reduced pain scores by 35 % and improved functional mobility within eight weeks. This is really phenomenal because many people over the age of 40 are reaching for the Tylenol bottle, the Advil bottle, the Aleve bottle, which does a number on your kidneys and your gut and your liver. And it is really problematic to be using these things on a regular basis.And if we can use a compound that’s safe, that preserves the kidneys, the liver and the gut, why don’t we do that is the question that I have. Now, we see a lot of the same information in our clinic that we see in these studies. And it is the following things that we see. Significant reduction in joint pain and stiffness. I have a person that was looking at doing a knee replacement and we did 10 weeks of these two compounds. Deb 00:16:00And her knee pain reduced so much that she decided she didn’t feel like she needed that knee replacement right away, which is good because she is only 60 years old. And the length of that knee replacement wouldn’t be as long as it would if she could wait five or 10 years. The doctor didn’t say she needed to do it right away. She wasn’t that critical, but it was the pain that was driving her to the replacement. And so if we could preserve that and give her a reduction in pain, all the better to do that. We get faster recovery from surgical procedures, improved gut symptoms, especially in cases of leaky gut or inflammatory bowel conditions, better skin quality and wound healing, enhanced overall sense of resilience and recovery capacity. But here’s what you absolutely must know before considering peptide therapy. First, source matters. Because these aren’t FDA regulated pharmaceuticals, quality varies widely and you need to work with a physician who sources from compounding pharmacies 503A or 503B that provide certificates of analysis, third party testing and proper sterility verification. Secondly, context matters. Peptides work best as part of a comprehensive functional medicine approach. So if you’re still eating inflammatory foods, drinking alcohol, not managing your stress or your sleep, you have unaddressed gut dysfunction, and these peptides alone won’t fix those problems. Thirdly, realistic expectations matter. These aren’t overnight miracle cures. Most patients see gradual improvements over four to 12 weeks. Some respond dramatically, others see modest benefits. Individual variation is real. And fourth, medical supervision matters. Dosing, injection technique,monitoring for side effects and knowing when peptides are or are not appropriate. All of this requires clinical expertise. Now let me bust a few myths here because I hear this constantly. Myth number one, peptides are just for bodybuilders and athletes. That is false. While athletes use them for performance recovery, the therapeutic applications for chronic pain, gut healing and age related tissue decline are profound. Deb 00:18:26For everyday people. Myth number two, peptides will mess with my hormones. False. BPC-157 and TB-500 don’t interact with your endocrine system the way hormones do. They work through growth factors and cell signaling pathways. They are very different. Myth number three, if they’re not FDA approved, they must be dangerous. Not accurate.Many effective therapies exist in regulatory gray zones. What matters is quality sourcing, proper medical oversight, and informed consent. So the bottom line here is that these peptides show real promise backed by mechanistic science and growing clinical expertise, but they require responsible use, quality products, and realistic expectations. Now let’s talk about practical integration.Who should consider peptides? Well, so who actually benefits from the peptides? Let’s start there. Let me walk you through the three main categories I see. Number one is gut restoration. If you’re dealing with chronic gut inflammation, whether that’s IBS, inflammatory bowel disease, leaky gut, persistent digestive issues that haven’t responded to dietary changes alone, BPC 157 can be transformative.I had a patient recently, I’ll call her Sarah. She’s been struggling with severe gut pain and food sensitivities for three years. She tried elimination diets, probiotics, gut healing supplements, everything. And within six weeks of adding BPC 157 to her protocol, alongside the targeted nutritional therapy, her pain dropped by 70 % and she could tolerate foods that she hadn’t tolerated in years. Why does this happen? because BPC 157 directly supports mucosal integrity, the protective lining of your intestinal tract, and it reduces inflammatory cytokines and promotes healing of damaged tissue. Number two, muscle and joint recovery. This is where I see TB 500 shine. Women who are active, whether you’re a runner, a yogi, a cross-bitter, or someone who just wants to keep moving without pain. Deb 00:20:48They often hit a point where recovery becomes a very limiting factor. And maybe you’re dealing with chronic tendonitis, a nagging shoulder injury, a bad back that just will not quit, or just general achiness. It all makes you feel older and keeps you from being active the way you want to. TB 500 combined with therapies like red light therapy, PEMF, or targeted physical therapy, can dramatically accelerate soft tissue healing. I’ve seen recovery timelines cut in half for patients dealing with overuse injuries. Number three, menopausal transition support. This is where the intersection of peptides in women’s health gets really exciting. During perimenopause and menopause, declining estrogen affects collagen production, vascular health, and joint integrity, along with inflammatory processes and responses.Many women notice they just don’t heal as quickly and their joints hurt much more. Besides noticing their skin changes and their injuries linger longer. Low dose peptide protocols, often combining BPC157 for vascular and gut support with TB500 for soft tissue repair, can complement bioidentical hormone therapy or stand alone for women who can’t or don’t want to use hormones.Now I’m not saying that peptides replace your hormone optimization, but they can be powerful adjuncts that support tissue resilience during a time when your body’s natural repair mechanisms are shifting. Now, who should not use peptides? If you have any active cancer or a history of certain cancers, peptides that promote cell growth and angiogenesis might not be appropriate. If you’re pregnant or breastfeeding, we don’t have safety data.If you have severe kidney or liver disease, clearance and metabolism could be affected. You want to work with a practitioner who really understands this and be under medical supervision for these kinds of conditions. This really matters. A qualified functional medicine practitioner can assess your individual situation, run appropriate labs and determine whether peptides fit into your overall healing strategy. Remember, peptides are tools. They’re not magic. Deb 00:23:11They work best when you’re also addressing nutrition, sleep, stress, movement, and underlying root causes. They amplify your body’s healing capacity. They don’t replace the fundamentals. This is really important to understand. So thank you for joining me today on Let’s Talk Wellness Now. If this episode resonated with you, share it with another woman who’s ready to reclaim her body’s natural healing capacity. Remember, Wellness isn’t just about feeling good. It’s about thriving in every area of your life. Your body was designed to heal. You’re not a small version of a male. You are a woman with different biochemistry. And sometimes it just needs the right signals and the right support to remember how. If you’re ready to explore personalized regenerative medicine or peptide therapy as part of a comprehensive functional medicine approach,You can visit us at serenityhealthcarecenter.com. You can also follow us on Instagram, and you can look at my book, Seen at Last, and join the Seen at Last free community on Facebook, where we will provide all of this information and more for you. Until next time, I’m Dr. Deb, reminding you to take care of your body, mind, and spirit. Be well, and I’ll see you in the next episode.The post Episode 269 – Peptide Therapy for Women: How BPC-157 & TB-500 Heal Gut, Joints & Inflammation first appeared on Let's Talk Wellness Now.

The new AIEWF website is live! CFPs close in 2 days and we will run our first New Engineer Orientation this weekend, get your tickets booked ASAP as they -will- sell out. Take the AI Engineering Survey and get >$2k in credits and free AIE WF tickets!One of the central tensions in the agents industry is that even while there are major decacorn agent labs like Sierra, Decagon, Notion and Cursor being built up, it is also true that it has never been easier to DIY agents, with a plethora of agent frameworks like LangGraph and Pydantic and Flue, and managed agents from Anthropic and Gemini and Amazon. There has been a wave of companies building their own background agents from Shopify to Stripe to Paradigm to Razorpay, and even Cognition's friends Ramp have built their own coding agent with other friend Modal.You'd think Cognition might feel a bit threatened, but they're not - even after all this, they were way oversubscribed for the $1B Series D they just announced:Walden Yan, coiner of context engineering and Chief Product Officer/Cofounder of Cognition, invited OpenInspect's Cole Murray to talk about why the Devin is in the Details.Full conversation live on the pod today: In retrospect, async agents were the most AGI pilled bet you could make in 2024 - the models weren't good enough yet to vibecode, and people didn't trust AI enough to let it rip, nobody (including early Cognition) was sure about the form factors. Now it is obvious:* The first wave of AI coding tools made the developer faster but remain heavily in the loop. Copilor and Cursor's tab autocomplete are prime examples However, the workflow was still heavily centered around and bottlenecked by the developer's local workflow: a developer in an IDE, watching the model, accepting or rejecting changes, and pushing code one interaction at a time.* The second wave was local agents: Claude Code, Windsurf, Cursor's agents pane: first one and increasingly many terminals all running concurrently.* The current Age of Async Agents points to a different future focused more on agent orchestration which drives end-to-end development.According to previous guest Steve Yegge, there are finer-grained 8 levels to agent adoption, but we have collapsed it into three.As Cursor's Michael Truell put it in The third era of AI software development:Cursor is no longer primarily about writing code. It is about helping developers build the factory that creates their software. This factory is made up of fleets of agents that they interact with as teammates: providing initial direction, equipping them with the tools to work independently, and reviewing their work.The agent should not sit solely inside the developer's flow. It should be setup to work in the background so that you can give it a task, a repo, a machine, a shell, a browser, tests, memory, and review loops to go do the work somewhere else.In less than a year, the sentiment has shifted from avoiding multi-agent systems:to suggesting approaches that actually work:From coining “context engineering” to building the infrastructure behind Devin's 7x PR growth and jump from 16% to 80% of commits across Cognition repos, Walden Yan has had a front-row seat to the background-agent shift. In this episode, Cognition co-founder and CPO Walden Yan joins swyx alongside Cole Murray, creator of OpenInspect, to unpack why everyone is building their own Devin, what changed after the December 2025 model inflection, and why “spec to pull request” is now becoming a real production workflow.We go deep on the architecture of background agents: harness-in-the-box vs out-of-the-box, why Devin separates the “brain” from the machine, why repo setup is still one of the hardest problems, why Docker is not always enough, and how full VMs, snapshots, scoped secrets, GitHub bots, Slack integrations, and video-based testing all fit together. Walden and Cole also dig into memory, MCP limitations, multi-agent orchestration, AI code review, SRE auto-triage, PMs shipping code from Slack, Windsurf 2.0, hybrid frontier/sub-frontier systems, and the real failure mode of uncontrolled vibe coding: your codebase regressing to your worst engineer.And as agents eat software… and software eats the world… you can draw the conclusion on what is next:We discuss:* Why the engineering world is waking up to background agents and cloud agents* The December 2025 model inflection that made spec-to-PR workflows practical* Devin's 7x merged PR growth and rise from 16% to 80% of commits* Why Cole built OpenInspect as an open-source background-agent system* The economics of $20/seat agent products and why monetization is tricky* What Cognition actually sells beyond Devin: infra, onboarding, integrations, and adoption* Harness in the box vs out of the box, and why architecture matters* Why Devin separates the brain from the machine for security and permissions* Repo setup, scoped secrets, Docker Compose, and agent-ready dev environments* Why full VMs matter when agents need to run real applications and test them* Android, macOS, Windows, nested virtualization, and machine-specific agent work* Why testing is much harder than “computer use”* Screenshots, video verification, and the “I know it works” merge moment* GitHub UX, Devin Review, AI reviewers, and agents responding to PR comments* Why MCP alone is not enough for first-class Slack and enterprise integrations* Memory, Knowledge, skills, Claude.md, and why retrieval is still unsolved* Devin's auto-generated memories and the challenge of memory pruning* Always-on agents as permanent PMs for issues, tickets, and product areas* Sub-agents, meta-Devin management, and what multi-agent systems actually add* Why pure auto-merge vibe coding breaks down after about two weeks* AI code smells, lint rules, reward hacking, and Semgrep for agent-written code* GitAI, inline context, and preserving the “why” behind code changes* Local testing, mock servers, older codebases, and preparing companies for agents* Windsurf 2.0 and the handoff between local foreground agents and cloud background agents* SRE auto-triage, support workflows, and agents as first responders* PMs, marketing, and non-engineers creating pull requests from Slack* AI agent budgets, $1k-$5k per engineer spend, and hybrid frontier/sub-frontier systems* The rise of autonomous coding factories and who Cognition is hiringWalden Yan* X: https://x.com/walden_yan* LinkedIn: https://www.linkedin.com/in/waldenyan/Cole Murray* X: https://x.com/_colemurray* LinkedIn: https://www.linkedin.com/in/colemurray/* OpenInspect / Background Agents: https://github.com/ColeMurray/background-agentsTimestamps00:00:00 Introduction00:00:43 Why Everyone Is Building Their Own Devin00:01:57 Devin's 2025 Ramp: 7x PR Growth and 80% of Commits00:03:49 OpenInspect and the Rise of Open-Source Background Agents00:07:59 What Cognition Actually Sells Beyond Devin00:09:56 Background Agent Architecture: Harness In vs Out of the Box00:12:08 Separating the Brain from the Machine00:14:07 Repo Setup, Secrets, Docker, and Full VMs00:19:13 Why Testing Is Harder Than Computer Use00:22:40 Video Verification and the “I Know It Works” Merge Moment00:23:19 GitHub UX, Devin Review, and AI Code Review00:25:42 MCP, Slack, and Enterprise Agent Integrations00:28:59 Memory, Knowledge, and Always-On Agents00:36:16 Sub-Agents, Multi-Agent Orchestration, and Meta-Devin00:43:55 Vibe Coding, Auto-Merge, and Codebase Decay00:48:38 Agent Infra, VPCs, Cloud Providers, and Fast VM Restore00:52:25 AI Code Smells, Reward Hacking, and Code Review Systems00:56:10 Making Codebases Agent-Ready00:58:30 Windsurf 2.0 and the Local-to-Cloud Agent Handoff01:01:15 SRE Auto-Triage, PMs Shipping Code, and Agent Use Cases01:04:32 Agent Budgets, Hybrid Models, and Autonomous Coding Factories01:06:51 Hiring at Cognition and OpenInspect Consulting01:07:45 OutroTranscriptIntroduction: Walden Yan, Cole Murray, and Context EngineeringSwyx [00:00:00]: All right, we're in the studio with Walden Yan, co-founder of Cognition, CPO.Walden [00:00:08]: Happy to be here.Swyx [00:00:09]: Which is a cool title. And coiner of context engineering.Walden [00:00:15]: Although I think there are many people who'd used the terms in various ways beforehand, but I did find that people, both internally and externally, enjoyed the upgrade from prompt engineering or model wrapping into maybe a more thoughtful way to build agents.Swyx [00:00:33]: For those who haven't caught up on that, I have on screen the Don't Build Multi-Agents post, which you should go read on and we might refer to, and Cole Murray, who created OpenInspect.Cole [00:00:43]: Great to be here.Swyx [00:00:43]: So let's talk about it. Everyone is building their own Devins. What's going on?The December Shift: From Handholding Models to Autonomous PRsCole [00:00:51]: So I think the engineering world is waking up to this idea of background agents, cloud agents, whatever you'd like to call it. And I think we saw a shift around the December timeframe of 2025, where the models Opus 4.5 and GPT 5.2, they reached a capability where we moved away from handholding the model and being able to actually more or less autonomously drive the model. And what I mean by that is that we could pretty much go from a specification to a completed pull request, assuming the spec was good enough, with very little friction. And that paradigm alone, I think, changed a lot of how we interact with agents, and opened this world where background agents became more practical.Swyx [00:01:41]: I think for Cole, everyone experienced this in December, but I feel like there was just this increasing ramp, right? There was this moment which was, I think, Sonnet 3.7, where, You guys rewrote Devin in one night or something. So describe 2025 or how it felt from your side.Walden [00:02:01]: In retrospect, we always thought it was ramping up, but then even now, over the last three, four months from today, it's been ramping up even faster. So it's almost funny to be talking about how, big of a leap Sonnet 3.7 was, and honestly, a lot of it was stripping out parts of Devin that were no longer needed with that jump in of intelligence. But I also just think that a lot of the recent leaps, especially, you look at, models like Opus and the latest GPT models, they are reaching levels of autonomy where people are actually finding that they actually can just be hands-off. And people who were once debating, “Oh, do I need to be in the weeds with my model in the IDE? Can I just completely move it off into the cloud?” That's a more serious conversation, and we've seen that in all of our growth charts. Internally there's this funny graph where our usage has, of PRs, our merged PRs, has grown 7X since I forget what it was called.Swyx [00:02:57]: I think Dev, maybe tweeted that. Yes.Walden [00:03:01]: it grew like 7X over, the last, I think it was, two months, three months, something like that. And then you see our engineering headcount growth. It's, gone up by, 10% or something.Swyx [00:03:11]: We were, we were afraid To release this. So this is Devin commit percentages on all Devin repos, was 16% in January and now 80% in March.Walden [00:03:25]: It's a big shift right now. And so it makes sense that a lot of people are now thinking about, buying Devin, but also maybe, trying to build their own and there's Lots of I have a lot of fun building Devin, so I can see why other people would want to build their own cloud agents as well. Matt, well, maybe it's good to hear, what initially inspired you to try to build OpenInspect?OpenInspect: Ramp, Cloud Agents, and Open SourceCole [00:03:49]: OpenInspect came about, through primarily my clients observing how they were using tools like Claude, OpenAI's Codex at the time, and seeing some of the friction that they were having with it. Primarily the Claude was being used through Slack, and a big issue they ran into was that the sessions that were launched were specific to whoever called it via Slack. And so if a PM was the one who invoked the session and they would then go to pass context to engineering can't see the session. And that in itself was a deal breaker because the PM, “Hey, engineering, can you jump in?” But there's nothing to jump in on unless they're copy-pasting out or the single response that came back. And so seeing some of these problems, I had built a similar architecture internally, just to experiment with, test out different ideas as this trend of moving off of localhost was starting to become, And as Ramp released their blog post, I had a lot of the pieces for this already in place, and just thought it would be funny to, see what Claude could do just purely from the blog post. And on my X account, there's actually a thread of where I live tweeted, going through thisCole [00:05:14]: comparing GPT and Claude as both of them are going through it.Swyx [00:05:17]: On the announcement thing or something else?Cole [00:05:19]: right after it got released. We can put it in the show notes. Yeah, it was helpful that I had already knew how to verify the system. I knew what I was looking for. I think Ramp did a great job of really illustrating, the technical aspects of how to build something. It was much more than just like, “Hey, we built a great system.” It was, “And here's how you can build it too.” And so, I resonated a lot with that, just with the problems that I was already seeing, and I thought that, looking around, I didn't really see anything in the open source community that, met this type of system. I think there's a lot that run, in localhost like Superset, Conductor, and many others.But nothing that was actually running in the cloud. And so, I built it, and I thought it was interesting to just open source it and allow anyone to then have a foundation that they can mix and match on top of.The Business of Background Agents: Open Source vs. DevinSwyx [00:06:16]: So literally after Devin was launched was, there was OpenDevin Which became All Hands. I don't know if you tried that orWalden [00:06:22]: I was going to say, one of the things that interested me a lot with OpenInspect was, you didn't try to go make it then something you monetize. There are a lot of, I think, these open source projects would then go and really try to, raise VSwyx [00:06:36]: That's why no OpenDevin. Yeah.Walden [00:06:38]: yeah, and how did you think about that? I thought that was very interesting.Cole [00:06:44]: I thought, and just what I had seen across my clients, was that having a background agent system is going to become a critical infrastructure within their company. And so because of that, I think that I wanted to open source it so that they could fork it and put in whatever customization they wanted. To that question though, I get asked all, “Oh, are you going to raise? Are you going to turn this into a service?”Walden [00:07:08]: I'm sure you've gotten offers.Cole [00:07:09]: but primarily I don't want to do that for a few reasons. One, I think that I don't want to compete for, $20 a seat. I think that is just a really difficult business. I think it's very easy to copy the main pieces of it. Again, I built this fairly quickly. And I think because you are not owning, I guess, the entire stack, it's hard to monetize. You have money being made at the sandbox layer with Daytona, E2b, many other players. You have money being made at the model layer. And you sit in this weird in-between gray area where what are you actually selling? You're selling, I guess, the infrastructure. You're selling, the integrations maybe.Swyx [00:07:55]: let's ask the guy. What are you What are you selling?Walden [00:07:59]: Well, yeah, there's multiple layers to this in practice, and actually it's funny you mentioned the infrastructure, ‘cause when we got started building Devin as well, we had to go figure out how to make the infrastructure as well because,Swyx [00:08:10]: You had to build this two years before everyone else,?Swyx [00:08:15]: Including, the model sideWalden [00:08:17]: It was not, it was not very polished at the start, when we just built it off of raw VMs from cloud providers like EC2, the boot up time was so slow, I think, And especially then, turning off the machines, saving them, and then to be able to bring them back up again when the, when you want Devin to wake up again later. It would just be out cold for like 10 minutes because that's just how long these systems took. They were not built for this repeated down and up usage. And so we actually had to go do all of that. And as a result now, one thing we offer when we go and sell Devin to people is, you don't have to worry about all the compute side of things. We'll make it work. We'll make it work in your cloud if you want it to. But aside from the product, and I want to go into the agents and the tuning of the intelligence part later, but I think a big part of what we do at Cognition as well is to just make sure that your company learns and uses and adopts these coding agents. ‘Cause I think for especially the largest enterprises in the world, you find that there is a lot of people who want to move over to using AI for their day-to-day workloads. But because of the way projects are planned, because, not everyone is literate in using AI in these ways, having a team of engineers who can actually go in and onboard you, set up all the integrations you need, the automations you need to really get to that level of, leverage with AI, is super helpful. And so We do that. We show thought partners to the customers that we work with as well.Swyx [00:09:56]: So let's talk about, architectural stuff. I think that's always, that is something that was the topic of conversation between the two of you. Is this, the mental model that you want to start with or something else? I'll just leave the floor open to you guys.Agent Architecture: Harness in the Box vs. Out of the BoxCole [00:10:11]: I think, maybe we can start here as just a general what are the pieces of a background agent system. And then maybe we can go into some of the nuances of, Decisions that you can make.Swyx [00:10:22]: But I guess I also Like, what, maybe what Walden is saying is the agent is like in this open code box, I guess. Right? This is infra, and then there's, that's the agent. And you had this discussion about whether you put the agent in here or in Out externally. Can you tease that out?Cole [00:10:39]: In a background agent systems, you have a decision to make of where the agent is actually going to run. This is typically described as the harness in the box or out of the box. With running the agent in the box, you're making some trade-offs by doing that. The negative trade-off you're making is primarily security. Because the agent is running in that box, unless you otherwise design it, all of your secrets need to go into that box as well. And given the nature of AI, it can be unpredictable, and you could very easily end up accidentally exfilling your secrets, or other unintended behavior. Now, the out of the box is the idea that we are going to have the actual agent running not directly in the sandbox, and we will have, quote-unquote, the brain of the agent running in some type of worker, control plane. That sandbox then is going to serve as the hands where the brain is basically operating and making tool calls into that environment to manipulate it. I guess other trade-off that you're making between the two systems is that, in my opinion, running it out of the box is much more complex because, you have state that has to be managed, whereas if you're running it in the box, all of the state of that agent is actually in the box, and yes, it's you could persist it elsewhere, but it's all localized and you have less concerns to worry about.Walden [00:12:08]: I think a lot of that, what you mentioned, is why we actually from the start built Devin to what we called separate the brain from the machine. The other thing that this allows you to do is reuse any existing infrastructure you have for dev boxes Perhaps. And so you don't have to worry as much about making a new type of dev box that has all the dependencies the brain needs, as you mentioned, the secrets the brain needs as well. One thing that we've seen some customers run into is, you have a GitHub app and you want Devin, your agent, whatever, be able to interact with GitHub through this application, but then you have different users with different actual permissions. If they are all interacting through the same GitHub app and there's no actual, separation between the system that decides, what it does and the actual secrets on the machine, then you run into an issue where, okay, it's hard to do the separation. But in practice, with Devin, it's much easier because we just say whatever you put on the machine, that is, the scope of basically what the user is free to do, what the agent is free to do. So only put the most scoped secrets on that machine, and then the brain is fully not accessible from the machine. So you don't have to worry about messing with the, any of the most secure parts of the brain if the user is free to do whatever they want with the machine.Swyx [00:13:31]: I was going to just bring, I have this, chart from OpenAI, where I don't know if this is, in the box, out of the box. That is something that they do use to describe it. And then also recently Anthropic did, managed agentsSwyx [00:13:44]: Which is, this is their thing. I don't know. It's all, it's all variations of the same pattern, right?Cole [00:13:49]: So this would be out of the box.Swyx [00:13:51]: Which, is preferable for them because it's less work?Cole [00:13:56]: I would say it's more work.Swyx [00:13:58]: It's more work?Cole [00:13:58]: But it, in my opinion, it is the better architecture of the two. It's just, you're taking on a bit of complexity by doing that.Repo Setup, Docker, and VM-Based Development EnvironmentsWalden [00:14:07]: One thing I've not seen a lot of other players do well is how do you manage what's actually on the box? And this can be complex for many reasons. Let's say you have a big repository that's changing and updating a lot with changing dependencies. How do you make sure that the working environment of the agent actually stays up to date, has all the credentials it needs to, let's say, run the app and test it, and all the things you want your autonomousSwyx [00:14:34]: So a repo setup.Walden [00:14:35]: Exactly. So in, internally At Cognition, we call this repo setup.Cole [00:14:39]: The hardest part ofWalden [00:14:40]: It's been a perennial problem since the start of the company, of how do we help people get this set up? Because not everyone just has, working cloud environments working out of the box. And do you find this to be a common problem withSwyx [00:14:53]: How do you solve it?Walden [00:14:53]: Your clients?Cole [00:14:54]: This is a very common problem, and through my consulting, this is a lot of what I help teams do. A lot of teams don't really have great developer environment setups, if any. A lot of the times it's, “Go talk to Bob and get the secrets,” and that obviously doesn't work when the agent needs to actually set this up. And so a lot of that, most teams are using Docker Compose or some type of microservices. And so for theSwyx [00:15:19]: Even in prod?Cole [00:15:20]: Not in prod. With the OpenInspect, you are using this primarily to interact, and make code changes. There is other use cases, but you can hook, whether through CLI, MCPs, other tools, you can then hook that into your production systems primarily for, SRE type use cases. But you are not, necessarily, trying to test your prod internal microservice through the system.Walden [00:15:48]: And you mentioned Docker Compose. I think one direction we saw some of our friends take early on was, using Docker containers as the level of abstraction for their models. There's lots of reasons, I think, why Docker containers are not great. One thing is, Docker container's not really a true security boundary, for one. But the other is, if you are running real applications, a lot of times those applications use Docker, and then you have to think about Docker in Docker, which is, really weird. And so I think part of, the really hard challenge of getting VMs to work, why did we do that? Well, it was because we realized that you actually needed, full VMs to be able to do these types of things. And especially nowadays where there's actually value in running the application and clicking around and sending you screen recordings of these things. The value just, keeps adding on top of that. But it is a decision I see people run into when they try to build their own systems, is, “Oh, do we, in addition to this, do we put the agent in the machine or out of the machine? Do we use Docker? Do we use something else?” What do you recommend people nowadays?Cole [00:16:57]: I think Docker is a good solution for maybe not running the agent, but running your infrastructure, because that is more or less the same setup your engineers are probably already using. If they're not, then I don't know what they're using. But they're probably already using Docker Compose.Swyx [00:17:14]: I've always had a small candle for web containers. I don't know if you guys have tried them before.Swyx [00:17:19]: To me, they were, supposed to be like Docker Light.Cole [00:17:22]: Is it?Swyx [00:17:22]: I don't know.Cole [00:17:22]: No, I haven't tried it. But yeah, I think any environment that you've set up that is a good experience for your developer naturally lends itself to being easy to set up for the agent. And once you figure out that local developer story, you've more or less solved the agent in a sandbox, environment setup. OpenInspect does have hooks as well, where you can, run a setup SH script that will pre-install everything. You can then pre-snapshot that build so it starts instantly, and then there is a second hook to actually then, restore the state of the sandbox when it comes back. And so you can already have all of those microservices running and basically get the same experience that you would on your machine within the sandbox.Testing Agents: Computer Use, Screenshots, and Real App WorkflowsWalden [00:18:08]: Another thing that we've been thinking a lot about is like Different VM service offerings. Have you had customers where they needed like macOS specific VMs or like Windows specificWalden [00:18:20]: VMs?Walden [00:18:22]: There are like many technologies in the world that only work on specific types of machines, right? If you're building a.NET application that has to run on Windows or like, maybe more commonly if you want to build iOS or macOS Does that workSwyx [00:18:32]: Does Commission supportSwyx [00:18:33]: Choices like that?Walden [00:18:35]: The fundamental architecture we do, because we do the separation, it does support, but the actual work in progress is happening right now on these. Another thing that we've actually recently added support now for, it's in beta, is doing Android development. To do that, we needed to support, I think, nested virtualization within our machines because the VM itself is like a, is a virtualized Firecracker instance, and then you had to then run another Android emulator inside. And there's like weird performance issues that like, it, which is why it's like still in beta. We have to think through these problems, but it unlocks a lot for anyone who wants to do Android development.Swyx [00:19:13]: I was trying to find like a reference video for the testing thing. I couldn't find it, but I think you worked on the testing, capability. Why call it testing and not like computer use or I don't know, it's, what's the general Category of problem?Walden [00:19:26]: I think that when people think about the ability of an AI to run your app and test it, I think they actually over-index on the computer use part of it because computer use in my mind is the literal, okay, you want what button you want to click. Can you emit the right coordinates to go click that button? I think testing is actually a really interesting likeWalden [00:19:48]: Problem-solving, challenge for these AIs because if you wanted to do arbitrary testing, imagine you make a change that spans the frontend and the backend, maybe, even some other like even more deeply nested service. To actually test that change, we have to reason through what-- how do you first run these applications to orchestrate with each other with the right version of the code? Then, okay, how do I trigger the feature or how do I make the thing actually happen? And this can get arbitrarily hard, maybe you have to be an admin. Maybe a certain thing has to be feature flagged on. Maybe, you have to like run two sessions and then send us a very specific word into one of them to trigger a specific behavior. And figuring out how do you do that requires a lot of code base context, requires, a lot of orchestration that we've specifically done. And in some cases, we found that you actually, no one frontier model can actually do this full end-to-end task itself.Walden [00:20:42]: We've seen cases where we actually had to orchestrate different frontier models together to solve this problem together. That is where we spend most of our time when we think about this testing problem, not so much the computer use part. Computer use for what it's worth has gotten a lot better with recent models and it's made that part of the job certainly easier.Swyx [00:20:58]: Especially with like even 4.7, that they released yesterday, apparently like way better in terms of the vision stuff, which is going to be encompassing computer use.Walden [00:21:08]: Having evals for all these as well is something that like takes a while to build up. And having the evals be right is tricky as well. Do you ever see like, clients who are building their own agents have to start standing up evals to make sure things don't regress?Swyx [00:21:25]: Not so much evals in the traditional sense, but specific to the testing part that has just gone in. I just added support for screenshots And in theory you can also do video. I need to put in a plugin to do that. But they do show up natively, and it was a very heavily requested feature, especially after Cursor's recording came out. I think that was very enlightening for everyone of like, “Oh, this is a very good feature to actually have.”, I think with Devin you guys have had this for a while.Swyx [00:21:57]: Oh, yeah. See how screenshots work. Yeah, I don't know if there's anything, super and not obvious. It's like once what feature to build, you can just prompt it and it Will mostly work.Walden [00:22:09]: I think to Walden's point, though, the computer use is a subset of the larger testing problem, and I think that's very specific to the code base that you're working and it's not something that, out of the box that you could just solve it. The-- you do need the code base context to actually know how to test it. And I think in the case of a background agent system, you fortunately do have that code base locally that what is changing and could then inspect it and use that to drive the model.Swyx [00:22:40]: For those who haven't seen it before, this is an example of how it works. You, after the PR is done, you click testing approved, and then it sends you back a video. What I really like is that it labels, It's very small here, but it actually labels what it's testing. And then it-- and then you actually see the cursor and everything. So I don't know, yeah, the engineering in this, just Whatever you want to show. ‘cause this is like, this is one of those like, oh, few of the AGI moments, right? ‘cause Once I look at this, I actually don't I wish I can just merge inside Of Slack instead of going to GitHub ‘cause I don't need to see the code. I know it works.Walden [00:23:19]: Maybe a new feature in Cursor. Yeah, the annotations at the bottom was also a big difference for me when I, when I added those.Swyx [00:23:27]: It's just like, what am I looking at? What are you trying to demonstrate?Walden [00:23:30]: Exactly. There's a surprisingly long tail of small details that ends up making a big difference for this end metric of like how fast do you actually merge the code in. One experience that we spent a lot of time tuning early on was what is the right experience on GitHub for these tools. Because I think, most tools out there when you build the agent, you'll think about, oh, it'll create the PR for you. We try to take that a step further and say, “Oh, what if we actually made sure you could interact Devin, with direct Devin directly on GitHub?” And so we made sure that you can comment on GitHub, and Devin would actually receive those comments and address them back. But there's actually quite a bit of tuning you have to do here because you can imagine that actually like-We recently have Devin Review, for example. Devin Review will post comments on his own PR And then Devin has to then goGitHub Workflows: Devin Review, Comments, and PR AutomationSwyx [00:24:23]: He answers his own comments, which is Really loopy. So like, yeah, I like that it just updates here that it's, that I have commented But usually it's just me saying like, “Hey, merged, fix any merge conflicts.”Walden [00:24:37]: The, so when Devin fixes his own comments, you might be scared that, oh, maybe I'll infinite loop. But we've put a lot of work into making sure it doesn't, both by making sure that the comments are high signal, but also that the agent is thoughtful about what comments it immediately goes and tries to fix, and what comments it's like, “Wait a second, I think you're wrong.” Actually, that's one of my favorite moments is when Devin tells me that I'm wrong, when I try to get it to do something different. But tuning that behavior, actually makes a big difference in terms of how useful the actual GitHub experience is.Cole [00:25:06]: I think to touch on that as well, I think having the AI reviewer integrated into the system is a critical part of this background system. OpenInspect does have that. It has a GitHub code reviewer that you can control the prompt. It does do comments as well. It doesn't do them automatically yet. The capability is there, but it's not fully used.Swyx [00:25:27]: So you have to ask for it?Cole [00:25:28]: you do, yeah. You can tag it on GitHub, and then whatever you named your, GitHub bot, it will then follow up on it. It will then, if you have merge conflicts or whatever you have asked it to resolve, it will then resolve it, but it doesn't do it automatically yet.Integrations: Slack, MCP, and First-Party Agent InterfacesWalden [00:25:42]: Well, I'm curious, what is, the most common thing that people end up requesting, that they still need on top of OpenInspect when you help them go implement it?Cole [00:25:52]: I think a lot of it comes down to actually integrating it into the company. It's one thing to have the background agent system set up, but if it isn't actually integrated into your larger ecosystem, it isn't that useful. It is useful to be able to kick off sessions, but what we really want to be able to do is hook it into all of our other systems, whether that is the production database with read-only credentials, the logs, a Confluence or internal knowledge-based system. I think that is where I see the huge leap for companies, and that can be a challenge for companies as well who are maybe not familiar with exactly how to approach it, especially if they're in environments that have more compliance type things where, access control can be pretty big and how do you deliberately think about these problems, I find to be, one of the problems that comes with a system like this.Walden [00:26:46]: The thing we found is So, MCPs, obviously it has been like this, really big explosion of, oh, you can go, integrate it with all these different things. But to actually get the integration right and the and get the right experience, oftentimes we found that we had to go build our own ad hoc things. I think Slack is a great example of this. You could give your agent a Slack MCP and okay, it can post messages back to you on Slack. But we actually use Devin like a coworker in Slack, and that's how it's been built from the ground up. But to do that, you actually need to, support webhooks that come back, right? And then Devin has to respond in a natural way and then hopefully don't spam your threads too much and annoy the people in your company. So you got to tune that experience just right. Especially when there's a lot of back and forths, we find that we actually have to go beyond the simple MCP integrations in these places.Swyx [00:27:39]: I just pulled up the MCP marketplace. I know this is a Fair amount of work. Is the answer to eventually take first party control of all the top MCPs? Is that theWalden [00:27:48]: I would love a world where you could have something that's more expressive than MCP. That, goes both ways, not just a set of tools, but a proper system that interacts back and lets it Have the right experience with all these interfaces.Swyx [00:28:03]: So there actually is sampling in the MCP spec, but nobody Uses it, right?Walden [00:28:07]: And so I think that's the other part is, actually we found that when the MCP spec starts to get too complicated, it starts to lose its original promise of Being like a simple one-step connect. Now then we have to go figure out how to support all these different variations of things and It starts to look a lot like just building the first party integrations in a lot of these cases now.Cole [00:28:29]: I think it matters, too, how critical it is to your company, right? If this is something that nearly every session is going through, it probably makes sense to own it so that you can make optimizations on top of it Versus just whatever is off the shelf.Swyx [00:28:43]: Awesome. Other than MCPs, what else, sorry, well, I don't know if that's Narrowing in too much on, integrations. But what else? What other elements of building OpenInspect or Devin that you guys really sink on?Memory and Knowledge: What Agents Should RememberCole [00:28:59]: I think, a problem that comes up very frequently is this idea of memories or knowledge base.Swyx [00:29:05]: Oh, boy. How do you solve it?Cole [00:29:08]: so not solved yet, is the short answer.Cole [00:29:11]: it's something, there's a open issue for it, someone asking about it.Swyx [00:29:16]: There's, I, D Wiki hasn't indexed anything about memory yet.Cole [00:29:20]: how I'm seeing it solved across my clients is primarily through skills. I find that skills can be a good gap within that or updating Claude MD, but I think memory as a whole is a pretty unsolved problem, and it is why I've been hesitant to add it. I think there is parts of memory and that can be addressed, but I think as a whole it's a very difficult retrieval problem.Swyx [00:29:44]: Oh my God. RAMP didn't write anything about memory? I see zero search results.Walden [00:29:50]: No. Memory can be quite tricky to get right because it's the retrieval, but also the generation of the memories that can be really tricky. You don't want it to just like Remember very specific details.Swyx [00:29:59]: Walk us through the Devin memory journey because I know there's been a journey.Walden [00:30:03]: the first version of memory that like stuck around for a while was A system we have called Knowledge. And the idea was we wanted it to pick up things over time and not need the user to be proactive about teaching Devin things. So, okay, any time you remind Devin, “Wait, no, that's not quite the way you're supposed to use Git”Like, we actually want Devin to say, “Hey, do you want me to actually just remember this for the future?” And for you to just basically quickly approve or reject and for it to build up over time. ‘Cause I find that, 95%, I think, or some crazy stat like that of the memories that Devin has are all through these auto-generated things. Very few people actually just want to sit down and write big docs on Here's how you're supposed to work with the technology, et cetera. The generation and the retrieval has been something that we've been trying to tune a lot over the years. Generation, you don't want it to remember something like, if you asked one time to like, “Oh, please open as a draft PR,” you don't want to be like, “Oh, everyone forever now should get their PRs as draft PRs.” But you do want some, conveyor. Maybe you want to say like, “Oh, Cole generally likes, things to be created as draft PRs.” Same with retrieval, if you have thousands of these memories, how do you actually make sure they're retrieved at the right time? And that can be quite tricky to do right without exploding the context with a bunch of useful yeah, useless information. Surprising amount of just, eval work to just make sure that, memory is, remains a reliable system as new models come and go.Cole [00:31:31]: Do you have anything that you could share on, memory pruning? And like the temporal aspect of memory?Swyx [00:31:36]: Deleting and forgetting?Walden [00:31:39]: The, today, the, So the things they could do is it could edit memories. And so if your memory used to say like, “Oh, Cole likes to open everything as like a draft PR,” then you can imagine, “No, don't do that.” And then it'll say, “Oh, do you want me to update the memory to be Cole now want everything as, open PRs?” I think that at the same time we don't know if this is going to be the final version of the system. Whatever we have here will probably, translate into the new system that we'll be coming up with. But I think one big difference between two years ago and today is these agents are really good at using anything that resembles a file system natively. And so part of us are, is thinking, “Oh, should we rebuild memories to feel more like a file system that we let the agent navigate on its own?” That's been an interesting exploration. Also similar ideas in the scale space.Swyx [00:32:35]: I am pulling up OpenClaude's memory thing right now. So memory, OpenClaude has like this like daily memory journal thing, right? And you can I mean, that is a file system you can grep through and is a source of truth. I don't know if it's the best. It's probably super noisy, but at least, if you lose something you can discover it or you can apply some, forgetting algorithm to, more ancient memories that don't get recalled again or something. I don't know.Walden [00:33:01]: One thing we've been trying to do to push the boundaries of how you use agents at your company is letting an agent basically have a very similar file, a memory.md or something, and just like be your permanent PM for a specific set of issues maybe. So we have like some Slack channels internally, maybe a Slack channel dedicated to, a specific product like DeepWiki maybe. And you can imagine that, or you want a Devin that never stops, it's just always awake, but it has this like memory dock that it can just maintain for itself about, okay, what are like the number one priorities of what we have to fix and prioritize? Who is responsible for some upcoming work? Maybe they'll even Devin will even tag you on some recurring basis. And so it's been an interesting move to see, okay, how can we actually use Devin for more than just engineering? Can we actually upstream above the engineering process and maybe it's just Devin creating tickets, which then maybe some humans do, but then maybe other Devins do.Swyx [00:34:00]: One of my more fun automations is go research competitors and just suggest stuff to me on a weekly basis. That's the automation. I can't find it right now, but basically it just like, “Look at competitors and suggest things.” “And here are three things that you've suggested that I don't want any more of,” and you just stick that in the prompts. But like I wish actually So for like when I, for example, when I reject a PR, I wish that it updated memory so that I can then just not have to go up, go back and update the scheduled, sync, but anyway, feature request.Walden [00:34:31]: what? We might change it soon. I guess OpenInspect, in the time you've been around, has there been anything you tried to implement but then you had to like undo and like do a different way?OpenInspect Architecture: Webhooks, Control Planes, and Agent StateCole [00:34:41]: Nothing yet, but something that is on my mind. The initial way that I built it was that each of the integrations lives as its own package. And so you have The Slack bot, which is what's handling the webhooks, and then is basically interacting with the control plane. As I'm seeing the system starting to be more integrated, specifically with the GitHub bot integration, I'm considering bringing that all into the central control plane because especially now I want to start, And a request that I'm getting is the ability to monitor, the actual, pull requests being merged, as well as just tracking ofSwyx [00:35:19]: What do I have open?Cole [00:35:21]: What do I have open? How many of these are getting merged? How many comments are showing up? To just understand the health of the system. And so in the case of a GitHub app, you only have one webhook. And so then it's a question of do I put that webhook in that GitHub bot package? That's weird. It doesn't really make sense to live there because that package is more for like the code reviewer. Or do I like centralize it? So that's something that's on my mind of, making that decision. I think the other one we touched on earlier is the harness in the box versus out of the box. I think long term the architecture will eventually come back out of the box. Some of the newer tools that I've added are calling back into the control plane so that you don't have the secrets in the sandbox. And so I think long term I probably will pull the actual, agent out of the box, but I think for now it's fine.Subagents and Multi-Agent Systems: When Parallelism Helps or HurtsSwyx [00:36:16]: Just, a quick question on pulling the agent out of the box. I'm One thing I'm very bullish on this year is agents calling other agents or spawning sub-agents or Whatever you want to call it. Does that make it harder or easier? I can't tell. Because if the harness is in the box, you can just spin up more boxes. If the harness is outside the box, then you're, it's less easy because you are, you have a unicorn pet of a, of a harness that's, living outside the box.Cole [00:36:45]: In theory it would be the same way, right? Whether, one agent has launched many, sub-sessions within it, OpenInspect, for example, can launch sub-sessions and actually create other environments and then monitor them. In the case where it is out of the box, that would basically just be an additional session that's running. And so that session is also running outside of the box. It's running in your worker plane, wherever you're running this. And then you really just have to think about how does your top level agent then interact with it. I do think it can be more complex, just ‘cause again, you have now a more difficult architecture. But I think if you figured it out once, it's probably fine.Swyx [00:37:26]: Well, then I'm just, throwing it open to you in terms of, I call this like meta Devin management. Which is like the, Devin's calling Devins or Devin scheduling Devins or querying trajectories or anything like that. What have you built or unshipped, anything?Cole [00:37:46]: I think one of the surprising things we've seen is that a lot of the ways that, these, separate agents work with each other, and you want them to, parallelize their work, has still mostly followed the same manager sub-agents regime. And a lot of people I think are excited about this world where you have swarms of agents that, talk with each other all over the place. We've actually given Devin an MCP so they can just go arbitrarily message other Devins And create new Devins, et cetera. But I guess, it somehow creates, a really chaotic world in that sense. And so we've still found that most practical use on a day-to-day basis has been one single Devin.Cole [00:38:33]: Figuring out how to segregate the work and get, have other Devins work on it in, a relatively isolated sense, each with their own boxes Not sharing machines, so there's, a very little room for conflict is the regime that you have to create today.Swyx [00:38:50]: I'll call out, the experiments from Cursor, right? This is Wilson Lin's work on Single agent to multi-agent, and you're obviously famously on the side of don't build multi-agent. But they went through the whole thing, only to arrive at, this Which is exactly what Devin has, I think.Cole [00:39:08]: I think there will be a revision to that post at some point AboutSwyx [00:39:12]: Tell us about itCole [00:39:12]: I think multi-agents were very much not at all possible a year ago. You do see more multi-agent experiments today, but you can argue, are they really multi-agents, or are they just just, tool calls,? There are people who, will create sub-agents to go look for XYZ file, XYZ implementation. Has really nice context management benefits because all of the tool calls and tokens that it spends then get collapsed back to just the answer for the main agent. There's a lot of benefits to doing this. We basically have Devin do this with Deep Bookie, make a call out to Deep Bookie, give you back the results, but that feels like a tool call,? It's not like these, two collaborators actually talking back with each, back and forth with each other. But I think the thing that gives me the most bullishness that multi-agents might actually be possible is actually what I said earlier about Devin will actually sometimes tell me I'm wrong and push back, and I think that demonstrates a level of maturity and communication today that makes a multi-agent world possible. One, can two agents who have seen different information come back to each other and actually figure out who is right, what is the correct implementation? They're not just, yes men. Claude, I guess is like, used to just say, what is it? “You're right,” or,Swyx [00:40:25]: “You're absolutely right.”Cole [00:40:26]: “You're absolutely right.” Yeah.Swyx [00:40:28]: The Have you seen, did you seeCole [00:40:29]: The age is overSwyx [00:40:30]: The Codex app troll in Topic? This is the Codex app. Inside of Settings, there's a little, there's a little Easter egg, right? So if you go to, the Themes or Appearance, right? There's all these, color codes, and the top is absolutely, and it's the Topic's colors. Which is such a troll. Anyway.Model Behavior: Pushback, Adversarial Prompts, and Agent SkepticismCole [00:40:53]: I love that Easter egg. Did you discover that yourself?Swyx [00:40:54]: No, it was, someone was, tweeting about it And I was like, I was like, “Is this true?” Because, sometimes people just tweet stuff to, get a rise out of you. But yeah, there you go, in Topic colors.Cole [00:41:06]: Yeah. So yeah, we're out of this regime where, it just says you're absolutely right, and they can have real conversations and real back and forths.Swyx [00:41:13]: You can prompt it as well to be more adversarial or whatever. Yeah. Okay. Yeah, that, I mean, to me, that is more intelligence, right? That is not just something that's, a dumb tool, it's actually pushing back on you I think. Yeah.Cole [00:41:24]: when you mentioned, of course, the blog posts. There was one blog they had where they fed a swarm of agents together and built a browser.Swyx [00:41:34]: That was I think that was the one.Cole [00:41:36]: You can have, likeSwyx [00:41:37]: I think it's the same oneCole [00:41:37]: Creation of it. We found a surprising success of, don't do a swarm or anything, just have one Devin, it does its own context management. Just let it keep running for a while and give it some crazy tasks. I think we asked it to, rebuild, a Windows OS system. And it managed to do it just like, going on for long enough. It'sSwyx [00:41:55]: Was this Andrew's thing?Cole [00:41:58]: there were lots of demos that we ended up not posting, ‘cause at some point we'd just be posting way too much a bunch of, Demos. But I love that because it shows that I think the multi-agent thing still has, a bit of exciting sexiness to it, which is maybe still beyond still, the actual delta it adds to the capabilities of these systems. But it's absolutely the future. I think we're heading in that direction and we can see the progress being made there already.Swyx [00:42:25]: If I were to, make one super minor pushback because I don't feel that confident about it yetCole [00:42:33]: Go for itSwyx [00:42:33]: But I've had Ryan Lopopolo from OpenAI on the pod And he's a super slop cannon, right? Oh my God, that's my coding agent being done. I downloaded this, Peon Ping. I don't know if you guys have heard this. It takes like-, sound packs from popular games like, Command and Conquer and Warcraft, and then it plays it whenever it's done. And so it's like, “Work,” or whatever, “At your command,” or something. Anyway, what I got from the Cursor code base and from Ryan's thing was that there's a slop cannon approach where you try to loosen the single agent's, bottleneck, and I feel like that is, probably an, a very important thing to try to figure out. I don't think anyone's, really solved it. Because then you just have more reviewer slop on top of the agent slop To try to wrangle it all. Ryan will probably very strongly object that I say that he hasn't solved it, but he thinks he's He thinks he's completely solved it. But I think it's still I think it's, very important, ‘cause, that is a bottleneck, right? I feel Devin is slow sometimes Because I'm like, well, yeah, this is very readable and very sensible, but also it is slower than it could be if I just, I want a button to just say, “Just ramp this up 1,000 next parallel, in parallel and just, see what happens,”? And I don't know if that's, feasible at some point in the future.Code Review, Entropy, and AI SlopWalden [00:43:55]: I And we've also run experiments internally where we've basically tried to build entire products, true products that we knew we would eventually ship, but for now, let's try to see if we can do it just by purely, vibe coding on top of each other, auto merge, no code review at all. And then there's this benchmark of how many weeks can you go onto this for Before you say, “We have the trashiest code base.”Walden [00:44:18]: “Let's actually rewrite it from scratch.”Swyx [00:44:19]: Start a new factory, yeah. What'd you find?Walden [00:44:21]: I think we found that the state-of-the-art in December was you can probably, run this for about two weeks. By the end of those two weeks, you'd find that, hey, you want to, change the color of a button. Well, it turns out this button is implemented in, 10 different places, and they, have All these different variations, and oh, you forgot one of them, and actually it's a slightly different color in one spot. And you're like, “Okay, this is too much to work with. Let's actually try to do code review at the same time.” And make sure that we're on top of our software, actually cleaning it up a bit And making sure it's done in a scalable way.Cole [00:44:54]: I think building on that, the idea of, you don't have to look at code, I think is generally a bad idea. And the meme that I have for thatWalden [00:45:03]: What timeline, all right, is Do you think that statement will be true on?Cole [00:45:06]: I think probably for a while it'll be true that you should continue to look at your code. A problem that I see a lot of teams run into that I work with who are embracing AI native, AI first coding, is The meme that I have is that your code base regresses to your worst engineer, because that engineer who is, very gung-ho about AI and is not auditing their code, their pattern starts cementing into the code, and now the AI is referencing their patterns. And so now their if/else block that, is 20 if/elses back and forth, the AI is seeing that as the pattern of how things are done and starts to then exponentially grow this slop. And I find to your point, a pretty good approach to that is having scheduled cleanup, whether by humans or through systems, that are looking for duplication. They then address that. You'll end up with like 12 helpers for how to format a date. And you need to address that, because otherwise it will continue to sprawl.Swyx [00:46:09]: Within balance, I think it's fine to have some duplication, and then sometimes To have garbage collection, right? Yeah. The What I've been, talking about with a lot of engineering leaders is that you want to be very strict about the boundaries between modules, and it's your job as an architect, as a CTO, whatever, to say like, “Okay, here's the hard contract between you guys and you guys. Whatever you do inside this black box is your business. You do whatever. But between these guys, let's be, really damn clear, and any movement must be signed off by a human or me,” or. Then, and like that's that. I don't know if you have any other modifications or advice.Walden [00:46:44]: Well, I guess generally on the topic of, where humans can be useful, I found that ‘cause, some of these, really deep infra problems, sometimes just having a human that just has, really deep expertise can make a big difference. I've actually seen this come into play when actually building agents. So we've had a few friends now, try building their own coding agents, and I think one same problem that I recurringly heard a lot of them run into was this problem of like, “Oh, Grep is really slow on our agents' machines.” And so a lot of them, I assume because they're using AI and they themselves don't have, super deep infra background knowledge, say, “Okay, we're going to go build our own custom Grep index. It's going to be really fast,” and use that as a way around this problem. When we ran into this problem About like, maybe like a year and a half ago when we were, in the early days of building Devin, we obviously didn't have AI then. We just asked our, how to, how to do this. You can just swap out a new Grep index, so.Infrastructure Details: Grep, File Systems, and SandboxesSwyx [00:47:45]: What do you mean you hand-coded Devin? What?Walden [00:47:48]: It's like, can you believe we hand-wrote this code? And we had, our infra people who are really amazing, they were looking into it and they're like, “Oh, what? We realized that actually the root cause of this problem is actually super simple, but like fine-grain detail,” which is that a lot of these virtual machines actually underlying them don't use real file systems. They use these, network file systems where things are actually cached over the network actually in S3. So when you're Grepping, you're actually making network calls Every time you're doing these things, and that's why Grep is extremely slow on these machines. And so again, goes back to, what is all of the crazy infra work that we had to do to actually get these machines working. If you try to do this yourself, there are tons of small details like this, and so we had to eventually go swap out that network file system. ButSwyx [00:48:35]: I think there's a write-up about it, right? Silas did one about the virtual file system.Walden [00:48:38]: Oh, that was a whole other thing. TheSwyx [00:48:39]: Oh, that's a different thingWalden [00:48:40]: The BlockDev file storage formatSwyx [00:48:42]: I'll bring it upWalden [00:48:42]: Which is, a file system format that we built so that the VMs could be spun up and down very quickly. Basically, the intuition behind this is-Imagine you have, a terabyte of disk, and your agent only, wrote, a hundred lines of code on top of that disk. How long does it, say, take to, save and re-bring up that disk? And most systems, because you're not optimizing for this case, it's just, on the order of a terabyte of work because you have to Save all of that and bring it back up. In our system, we try to build a file system that incrementally builds on top of each other. So every time you save and bring the machine back up, you're only doing work that is proportional to effectively the diff in the file system. And so this, shaves off a lot of time in the boot-up process of Devin. I think we This is actually now outdated. We have a newer system inside of Devin. But yeah, there's a lot of tiny details you have to get right here to actually get the day-to-day experience of Devin to be good.Swyx [00:49:39]: It's, not technically agents, but it is agent infra, and when you sell an agent as a company, you sell agent plus agent infra.Walden [00:49:46]: At least the way we do it be And the other The nice thing about having the agent infra being done together is, you We get to deploy Devin in whatever environment we want now. We don't need to wait for some underlying infra provider to also go and support VPC or on-prem or FedGovCloud, for instance. So we can actually go and figure out, okay, since we own the infrastructure, how can we get that set up for you?Cloud Providers: Modal, Daytona, and Enterprise SandboxesSwyx [00:50:12]: Whereas you're Cloudflare dependent.Cole [00:50:15]: so Cloudflare runs the control plane. The sandboxes, Modal is supported. A contributor just added Daytona. E2B is on the roadmap, and I think there's an abstraction in place that if any contributor wants to add a new provider, they can add that in.Walden [00:50:32]: Well, what are, How are the customers you work with Do they generally try to then go set up a contract with another one of these third-party providers? Do they try to do the VMs in-house?Cole [00:50:44]: most of them I see using Modal. I think Modal has a greatWalden [00:50:48]: Shout out Modal.Swyx [00:50:48]: Shout out Modal.Cole [00:50:50]: I think Modal has a great offering. It captures all of the sandbox pieces you need, snapshots being a pretty big piece of that, and given that they also offer GPUs, I think it's a pretty nice offering as a whole.Swyx [00:51:04]: no debate there.Walden [00:51:07]: Modal is great, especially, I think their container offering is, the most natural, and so especially if you are willing to, forego, the full VM requirements Modal is, a really vast place you can spin something up on.Swyx [00:51:20]: Is there a point So Modal's very Python, and I feel like most workload, has really shifted to JavaScript. I don't know if you guys Get the same feeling. So, okay, when I started Landspace and IE and all these things, I was like 50/50 Python and JS, right? That's roughly. I think that's wrong now. I think JS has won. I don't know if you guys Like, I Maybe I'm overstating it, and maybe for cognition, there's, C# and Java and what have you. But for, new greenfield apps, do you feel that Do you get that sense? Does it matter?Cole [00:51:52]: I think that most of the libraries that I see in this space are Python native first, especially in theCole [00:51:58]: Observability space. That said, I think that there is a pretty big appeal of having your entire system in one language. Especially when you have both your frontend and backend communicating, you can have one central type Which is very nice.Swyx [00:52:11]: That's my case against Modal, which is Then you have to run JS. You can run JS inside Modal. It's just, one extra step That, isn't native to the runtime. I don't know ifWalden [00:52:22]: I don't knowSwyx [00:52:23]: Reviews. Do you have numbers? I don't know.Walden [00:52:25]: the one thing I don't like about Python is whenever AI, whenever it writes Python, it always does, the weirdest patterns, andSwyx [00:52:32]: Oh, because it's, mixing two and three or what?Walden [00:52:34]: I think it's something mixing two and three, yeah. The I don't know if you see this. It always tries to do, has attribute on objects as likeCole [00:52:41]: Oh, my God.Walden [00:52:41]: But it's like But that you shouldn't be doing that. It should error if there wasSwyx [00:52:45]: Because it's training on library code?Cole [00:52:47]: I think it's more of, likeCole [00:52:48]: From what I've seen, it's more of, a reward hacking mechanism where it doesn't want to basicallyWalden [00:52:54]: It'll never error.Cole [00:52:54]: It doesn't want the code to fail. And so it Even when it knows it has the attribute, it'll call getattr on a, and for a lot of my clients who have moved towards more autonomous coding, we've put that in as a lint rule That if you do getattr, your pull request is going to fail.Slop Signatures: Comments, Backwards Compatibility, and TypesSwyx [00:53:12]: Ooh, this is a fun topic. Can you tell me more about this? What else is a sign of AI coding that you have to put guards in?Walden [00:53:21]: So we were talking just before this about Opus 4.7. One of the things this new model likes to do is it writes lots of comments. Not like, it'll, comment every line, but it'll write, paragraph, PRDs, on top of every function. But I will say, to its credit, these aren't slop, descriptions like they were before. “Oh, here's what this function does.” It's like, “Oh, here's actually the r

Вот допустим вы построили свой небольшой (или даже большой) Датацентр — хорошее дело. А потом завели ещё Облачко для разработки, бэкапов, быстрого деплоя стейджинга. Потом туда переехало часть объектного хранилища. Потом вы находите там Container Registry и пару продовых базок. И вот уже встаёт вопрос "А что? Это по-прежнему через Интернет гонять?". "А, может, ну это, есть какой-то более надёжный канала?". Есть! Он называется Direct Connect. Это когда вы свой on-prem включаете выделенным шнурком в порт бордера облачного провайдера и получаете прямой доступ в свой VPC. Об этом и говорим в 159-м выпуске telecom с гостями из VK Cloud и Yandex Cloud. Кто: Антон Юрищев. Ведущий менеджер продуктов (SDN, Direct Connect) VK Cloud Евгений Голотик. Просто сетевой инженер Яндекса Про что: Что такое DC Interconnect и Cloud Direct Connect — и зачем они бизнесу. Почему интернет не подходит для гарантированной передачи больших объёмов данных. Как появились Direct Connect-сервисы: от туннелей до операторских L2/L3-решений. Из чего состоит сервис: тёмное волокно, VLAN/Q-in-Q, BGP, EVPN. Как работают схемы Active/Active и Active/Passive. Чем отличаются реализации Direct Connect у разных провайдеров. Почему сетевая автоматизация стала основой масштабирования облачных сетей. Какие проблемы возникают на практике: MTU, выбор L2/L3, надёжность и отказоустойчивость. Оставайтесь на связи Пишите нам: info@linkmeup.ru Канал в телеграме: t.me/linkmeup_podcast Канал на youtube: youtube.com/c/linkmeup-podcast Подкаст доступен в iTunes, Google Подкастах, Яндекс Музыке, Castbox Сообщество в вк: vk.com/linkmeup Группа в фб: www.facebook.com/linkmeup.sdsm Добавить RSS в подкаст-плеер. Пообщаться в общем чате в тг: https://t.me/linkmeup_chat Поддержите проект:

We take a deep dive into Amazon S3 Files, AWS's exciting new managed file system backed by S3! We kick things off by exploring why S3 isn't a traditional file system, covering everything from the lack of true directories and atomic renames to immutable objects and POSIX access control differences. We then walk through the existing solutions people have used to bridge that gap, like S3FS FUSE, MountPoint for S3, FSx for Lustre, and Storage Gateway. From there, we get into the heart of the episode: how S3 Files works, how to set it up, and how it uses EFS under the hood as a caching layer. We share our own real-world benchmarking results comparing S3 Files against various EFS configurations across Lambda and Fargate, and we discuss a real customer project where we put S3 Files to the test. We also cover the important caveats like eventual consistency, the 60-second write-back delay, the lack of cross-account bucket support, and the cost model so you can make an informed decision.Resources mentionedEpisode 124: S3 PerformanceEpisode 95: Mounting S3 as a FilesystemAmazon S3 FAQs: S3 FilesfourTheorem S3 Files demo code on GitHubAmazon documentation: Understanding how synchronization worksSponsor Thanks to fourTheorem for powering AWS Bites. We help teams build cloud systems that are simple, scalable, and cost effective. Visit fourtheorem.com.Chapters00:00 Introduction: Why S3 is amazing but not a file system, and what S3 Files promises to solve01:47 Why S3 is not a file system: no true directories, immutable objects, no atomic renames, expensive listings, and POSIX differences05:23 Existing solutions for mounting S3 as a file system: S3FS FUSE, Python fsspec, Hadoop S3A, MountPoint, FSx for Lustre, File Cache, and Storage Gateway07:16 How S3 Files works: NFS-based access, EFS caching layer, streaming from S3, and supported compute services like EC2, ECS, EKS, and Lambda09:49 Setting up S3 Files: buckets, file system resources, import and expiration rules, mount targets, access points, VPC requirements, and NFS port configuration13:42 S3 Files performance numbers from AWS documentation: throughput, IOPS, latency figures, and why real-world benchmarking is recommended15:39 Benchmarking S3 Files vs EFS configurations on Lambda and Fargate: small and large file reads and writes, memory/CPU impact, and key findings19:48 Downsides and limitations: NFS only, no hard links, no atomic renames, eventual consistency, the 60-second write-back delay, and large-scale rename performance warnings23:05 Real-world project experience: a SaaS multi-tenant architecture, cross-account bucket limitation discovered, and how the team worked around it27:52 Cost breakdown: EFS-equivalent cache pricing, S3 storage costs, reads from cache vs. S3 directly, and how S3 access tiers still apply29:50 Final recap and take: when S3 Files shines, when to be cautious, mixed access pattern warnings, and an invitation to share your own experiences33:42 ClosingSend us your AWS questions Do you have any AWS questions you would like us to address? Leave a comment here or connect with us on X/Twitter, Bluesky, or LinkedIn: Eóin: Bluesky | LinkedIn Luciano: X/Twitter | Bluesky | LinkedIn

SUMMARY: As AI Agents are being brought into complex, regulated workflows, we explore the importance of accountability and accuracy, and how platforms and harnesses accomplish that goal. Can the CFO really fall in love with AI? GUEST:  Ram Venkatesh, Co-Founder/CTO of Sema4.aiSHOW: 1029SHOW TRANSCRIPT: The Enterprise AI Show #1029 TranscriptSHOW VIDEO: https://youtu.be/Lc3XS44Ixg4SHOW SPONSORS:Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance. We got this.SHOW NOTES:Topic 1 - Welcome to the show. Tell us about your background, and what led you to create Sema4.ai?. Topic 2 - AI Agents vs. Automation 2.0. What Actually Changed. Tell us about the Sema4.ai platform and capabilities. What challenges does it solve today?Topic 3 - You're initially focused on solving challenges for the CFO, which means there is a ROI-focus all the time. Why did you target that segment of the business first?Topic 3a - What are the biggest hidden costs in enterprise AI deployments today?Topic 4 - Sema4.ai emphasizes “your LLM, your VPC, your data.”  What are the biggest considerations for companies looking to create these private/sovereign AI solutions? What typically gets overlooked?Topic 5 - How do you tend to frame the conversation about AI trustworthiness, and the role of humans vs. agents for enterprise work? Topic 6 - It feels like so much has changed or evolved with AI in the last 2-3 years. How does an Enterprise think about this much change for something that will be core to many critical applications? What will the Enterprise Architecture look like in 2 years?Topic 7 - Sema4.ai emerged partly from the acquisition of Robocorp and has roots in open-source automation.  Do you have a perspective on the role open-source will play in AI going forward? FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

AWS Morning Brief for the week of May 18th , with Corey Quinn. Links:Announcing general availability of Amazon EC2 M3 Ultra Mac instancesAmazon EventBridge Scheduler adds 619 new SDK API actions, including Lambda Managed InstancesAmazon Redshift launches RG instances powered by AWS GravitonAmazon Route 53 Domains adds support for 34 new Top Level Domains including .app, .dev, and .health.ENA Express for Amazon EC2 instances now supports traffic between Availability ZonesStreaming CloudWatch metrics to VPC-based OpenTelemetry collectors using LambdaHow HotelTrader cut inter-AZ cost 95% and latency by 49% with Valkey GLIDE on Amazon ElastiCacheIntroducing Claude Platform on AWS: Anthropic's native platform, through your AWS accountAmazon CloudFront Premium flat-rate pricing plan now supports higher, configurable usage allowancesScalable cross-cloud data migration to Amazon S3 with distributed rcloneDirty Frag and other issues in Amazon Linux kernelsCVE-2026-8178 - Remote Code Execution via Unsafe Class Loading in Amazon Redshift JDBC DriverFragnesia Local Privilege Escalation report via ESP-in-TCP in the Linux KernelOngoing updates on Copy.fail and variantsIssue with Amazon SageMaker Python SDK - Model artifact integrity verification issues (CVE-2026-8596 &: CVE-2026-8597)

Mock-интервью с Николаем Лебедевым - DevOps/SRE-инженер, 17 лет в Linux, 4 года AWS EKS. Stack: Terraform, Flux, Cassandra, Kafka, Vault, SOPS. Два часа - много практики, много каверзных вопросов. ЧТО СПРАШИВАЛИ ☁️ AWS: EKS и IRSA, VPC с нуля (CIDR, multi-AZ, multi-region), managed K8s vs self-hosted, Elasticache, Golden Signals и метрики SRE.

Saturday Magazine’s own Paul Horwell, now CEO of Vic. Pride Centre joined the team live on-air from The Melbourne Exhibition Centre to discuss the 125th Anniversary of Australia’s First Sitting of Parliament in Melbourne in 1901. The post Sat, 9th, 2026: Paul Horwell, CEO VPC, 125th Anniversary of Australia’s First Parliament appeared first on Saturday Magazine.

Hey Friends I am still fighting a rare but tough cold and so I was not able to produce a news segment today but I do have a GREAT conversation with a brilliant first time guest that I think you will love I hope you had a great weekend and I am happy we made to to May together All of Atima's Links Named to Ebony Magazine's "Power 100" list of emerging leaders and Jet Magazine's "40 Under 40" list, Atima Omara works and leads at the intersection of electoral politics and issue advocacy in the progressive movement. She is a political strategist, advocate, trainer, leader, and speaker with significant political, government, and non-profit experience, and she is a sought-after commentator and strategist. As the President & Chief Strategist of Omara Strategy Group, she provides strategic consulting to progressive candidates and organizations centering women and people of color in their mission and work. She strategizes with candidates and political organizations to win victories for a more reflective progressive democracy. An American-born child of Black immigrants, Atima realized early the importance of catalyzing social and electoral change from both the grassroots and leadership levels—especially among underrepresented communities. She has worked as Special Assistant to then-Virginia Governor Mark Warner, and then went to work as an organizer in multiple states with a union and community organizations on voter registration, ballot initiatives, and get-out-the-vote operations in low-income communities of color and immigrant communities. She is also a former candidate for public and political party office herself, and draws from her lived and professional experience to train activists to organize and candidates from historically marginalized communities to run for office for many organizations including: Emerge America, Higher Heights for America, Vote Run Lead, Running Start, New American Leaders, and National Council for Independent Living. Prior to that, Atima built her executive leadership experience from serving as Vice President of Reproductive Health Technologies Project, a research based advocacy organization; a Director on the political project #VOTEPROCHOICE (VPC) where she managed successful voter engagement campaigns on behalf of VPC for progressive state and local candidates; and as a nationally elected leader of the Young Democrats of America (YDA), the nation's largest partisan youth organization from 2013-15. She was the first Black president and only the fifth woman to lead the organization in its 80+ year history. During her tenure as YDA President, she grew national membership and led an independent expenditure to targeted states in 2014 that increased the youth vote turnout for Democrats in critical races. She is an original board member for Emerge Virginia and a founding board member of Virginia's List PAC, two organizations helping to elect more Democratic women. She previously served as Board Chair and Vice Chair of the Planned Parenthood Metro Washington Action Fund. The seasoned political leader is currently an elected member of the Democratic National Committee (DNC) since 2016 and elected vice chair of the DNC's Women's Caucus since 2017. Atima has published articles in American Prospect, The Root, Salon, Politico, Ms., Ebony, and The Lily (a Washington Post publication) among other notable publications and provided commentary to CNN, MSNBC/NBC, PBS, BBC, Fox News, Fox Business, NPR, Sirius XM, and other national TV & radio outlets. She has also been quoted in The Washington Post, The Atlantic, TIME, USA TODAY, Politico, Mother Jones, Newsweek, MTV News, and Refinery 29. She received her BA from the University of Virginia and MPA from George Mason University. Atima is also a graduate of the Women's Campaign School at Yale, EMILY'S List and Re:Power campaign trainings. On YOUTUBE.com/StandUpWithPete ON SubstackStandUpWithPete Listen rate and review on Apple Podcasts Listen rate and review on Spotify Pete On Instagram Pete on Blue Sky Pete on Threads Pete on Tik Tok Pete on Twitter Pete Personal FB page Stand Up with Pete FB page Gift a Subscription https://www.patreon.com/PeteDominick/gift Send Pete $ Directly on Venmo All things Jon Carroll  Buy Ava's Art  Subscribe to Piano Tuner Paul Paul Wesley on Substack Listen to Barry and Abigail Hummel Podcast Listen to Matty C Podcast and Substack Follow and Support Pete Coe Hire DJ Monzyk to build your website or help you with Marketing

Doug Houghton, director of global channels at Alkira There’s a line from this episode that’s worth leading with: “Networking is not sexy until it doesn’t work.” That’s Doug Houghton, Director of Global Channels at Alkira, and it’s a pretty concise summary of why his company exists. Alkira was founded by the team behind Viptela – the startup that essentially created the SD-WAN category before being acquired by Cisco. The lesson they carried out of that experience is that SD-WAN, for all its promise, still ran into the limits of underlying infrastructure. You ended up with disparate networks, latency constraints, and complexity that didn’t disappear – it just moved somewhere else. What they built in response is Network Infrastructure as a Service (NIaaS) – a cloud-native, consumption-based global backbone that abstracts multi-cloud connectivity into a single managed plane. The pitch to partners is concrete: consolidate 50 physical firewalls into virtualized functions, reduce total cost of ownership by 40-70%, and do it without a rip-and-replace cycle. The timing matters, and Houghton is direct about why. AI workloads – distributed large language models, agentic workflows reaching across multiple clouds simultaneously – demand a level of network elasticity that legacy infrastructure simply wasn’t designed for. Alkira’s argument is that they’re the smooth road that makes AI-driven infrastructure actually work in practice. For Canadian partners, Alkira has real resources on the ground: a solution architect based in Toronto, a dedicated channel account manager, and publicly referenceable Canadian customers including contact center provider ContactPoint 360. The Connect Partner Program, launched in March 2026, puts approximately 20 percent total margin on the table across base discount, rebates, MDF, and POC SPIFFs – with average initial deals around $500,000 USD and typical expansion of 4x in year one. Canadian partners interested in the conversation can reach the team at partners@alkira.com. Read Full Transcript Robert Dutt: Hello and welcome to In The Channel from ChannelBuzz.ca, bringing news and information to the Canadian IT channel community for the last sixteen years. I’m Robert Dutt, editor of ChannelBuzz.ca and your host for the show. If you were around when SD-WAN was the big disruptive idea in networking – the promise of simplifying branch connectivity, cutting costs, getting smarter about traffic – you probably also remember it didn’t quite deliver everything it promised. Not because the technology was bad, but because the underlying network architecture couldn’t keep up. You still ended up with complexity. It just moved somewhere else. That problem is essentially the founding insight behind Alkira. The company was built by Amir Khan and Atif Khan, the same team behind Viptela, the startup widely credited with creating the SD-WAN category before Cisco acquired it. What they learned in that experience is that SD-WAN, without a proper global backbone, just creates a different set of headaches. So they started fresh and built what they call NIaaS – Network Infrastructure as a Service – a cloud-native, consumption-based approach that abstracts the complexity of multi-cloud connectivity into something you could stand up, as my guest today puts it, with just a username and a password. The timing is not accidental, because what AI demands from a network – elasticity, low latency, the ability to reach distributed workloads almost anywhere instantly – is exactly what legacy infrastructure wasn’t built to handle. My guest is Doug Houghton, Director of Global Channels at Alkira. Doug has been in the channel a long time, knows the technology in a way that might genuinely surprise you coming from a channel chief, and has a lot to say about what it all means as a real business opportunity for Canadian VARs and MSPs. Let’s get right into it, my chat with Doug Houghton. Doug, thanks for taking the time. I appreciate it. Doug Houghton: It’s my pleasure. Thank you for having me on today, Robert. Robert Dutt: So you were part of the team that built up the SD-WAN market at Viptela back in the day. What did you learn there that told you the next big thing was going to be NIaaS, and why now? Doug Houghton: First off, that’s a great question. I felt a bit like a passenger in a car racing a thousand miles an hour when we were doing software-defined wide-area networking. What we learned was that without organizing your cloud infrastructure properly, your cloud bill gets ridiculously large – especially if you keep your control element decoupled from your data plane in the cloud with all these workloads churning. But what we really learned, and what’s applicable to what we’re now doing at Alkira, is that SD-WAN truly did deliver on its core promise. It allows customers to influence traffic based on link quality and improve the user experience. If you’re on a phone call and it starts to get goofy, you can move over to a better-performing link in real time without dropping the call. That’s powerful. And the same with data traffic. What I hadn’t fully thought through was what happens as global companies start to adopt SD-WAN and disaggregate across locations in Southeast Asia, China, Latin America, and everywhere else. The latency back to the control element isn’t easy to contend with. So you ended up with organizations making decisions that effectively created four separate, disparate networks for latency purposes. And that was not part of the original promise. What we learned was that you need a global backbone that’s high throughput and low latency. The edge can still be SD-WAN – there are real things in SD-WAN that people still want, whether that’s WAN optimization, deduplication, caching, policy-based routing, forward error correction. All of that still has practical application, and site-to-site communications are still needed in many use cases. But Alkira was built inside the cloud first, employing the same principle of decoupling control plane from data plane for scale. By abstracting the cloud infrastructure, we were able to remediate the latency that those four geographically dispersed networks created. We’re the global backbone – that middle mile with high throughput and low latency – and then you connect these clusters of SD-WAN networks together and all of a sudden the promise of SD-WAN gets a lot more consumable. You have a singular network managed from a singular control plane and element management orchestrator, and you can still get all the benefits of SD-WAN at the local sites. Robert Dutt So in plain language, a Canadian MSP or VAR is used to selling network hardware or managing someone else’s infrastructure. How is selling, deploying, and managing NIaaS different from what they’re already doing, and what makes that distinction important? Doug Houghton: Let’s take a half step back and talk about what NIaaS actually is. It’s Network Infrastructure as a Service. What Alkira does is abstract the cloud infrastructure and build a routed overlay on top of it. We think of it as a virtualized colocation facility that connects and normalizes communications across your entire network. For managed service providers and service providers, our solution accelerates bringing their customers to cloud applications, cloud workloads, storage, and everything else the cloud promises. The way I explain it to my mom – and I’ve told this joke once already today because I’m sitting in a partner’s office right now – is this: if you went to Russia, Japan, Argentina, and San Francisco all in one day and had to transact in each place, and you could speak the native language in each one, that would be ideal. What we focused on was normalizing communications regardless of the cloud service provider, colocation provider, data centre – private or public – or whatever type of router is at the branch office. As an MSP or service provider that comes in, what we give to our customers and partners is a username and a password. That lets you come in and – for your old-school folks in the audience – essentially etch-a-sketch your network together. You can turn a couple of knobs, and it’s not that we’ve cranked the amp up to eleven, we’ve just removed all the numbers and automated everything. It just knows what you want to do. It’s a routed BGP overlay with the control plane abstracted from it, so the forwarding plane can route around things like the CrowdStrike outage, or losing an AWS region – which happens more frequently than AWS would like to admit – or any cloud service provider incident. The multi-cloud reality has accelerated adoption, but it presents a new problem: you’ve got an AWS expert on staff, but you don’t have an Azure, GCP, OCI, or Alibaba Cloud expert. Those are all different languages. When I tell my mom that we normalize the communications between all the assets in the network and make it easy to connect to all of them, she gets that. For the MSP looking to monetize something new or add another revenue stream, we offer a couple of compelling things. In the middle of our stack, we place a solution inside the cloud – sitting in a VPC, VNet, VCN, or Google VPC – right in the middle of all the cloud, SaaS, and WAN workloads. We’ve pleased a lot of customers by lowering total cost of ownership through the consolidation of network services they already have in their environment, in the form of virtualized network functions. Take a Palo Alto firewall deployment – say you have fifty Palos out there, all talking to Panorama, with a security engineer managing policy centrally. Instead of having fifty firewalls on the ground, you consolidate them. You go from the ground – five to ten milliseconds to the nearest public cloud PoP – hop onto the Alkira fabric, and terminate that traffic on a virtual port on our exchange point. In the middle of that exchange point, sitting in a VPC or VNet, you place a Palo Alto virtualized network function. You get the IP address of the Panorama server, and if you didn’t tell the security engineer anything had changed, they would not know. The form factor changes, but not how they interact with Panorama, how they build policy, or anything about how they secure the traffic. That remains exactly the same. We virtualize the instance and place it on a global high-throughput, low-latency backbone inside our exchange point. We deploy exchange points in HA pairs, anywhere from 100 Mbps to 40 Gbps. The customer or service provider consumes one, and we maintain the other on their behalf – because every thirty days we’re fixing bugs and doing maintenance. We swing production workloads to the backup, do the work on the primary, then reverse the order, all while keeping these customers up and running. Because we’re delivering this as a service, it has to always be on. One of the most important architectural decisions we made from the start was ensuring those two exchange points are always running active-active in a full mesh configuration, buttressed by hundreds of other exchange points globally distributed – all synchronized and aware of each other’s states. Robert Dutt: You’ve said that legacy networks can’t handle what AI demands, specifically in terms of elasticity. Can you unpack that a little? When an MSP’s customer starts deploying language models or agentic workflows, what is it that actually breaks? Doug Houghton: Good question, and I’ll give you an honest answer. I’ve started to fall in love with Claude – I think it’s one of the coolest things in the world. I can do all sorts of creative things with it. But Claude isn’t talking only to me. He’s a bit of a flirt – he goes to a lot of different places to get knowledgeable about various things and produce the outcomes I’ve asked for. And those other places are where you run into problems. I used to say the three biggest AI providers are GCP, AWS, and Azure. That’s still largely true. But the likes of Anthropic and other AI labs are distributing LLM workloads everywhere. Without the right network underneath that, it’s like buying the hottest car and driving it down a pothole-filled road. What we offer is a high-throughput, low-latency, elastic network. If you need to turn it up in a heartbeat, you can. We helped complete the S&P Global and IHS Markit merger network integration in about a tenth of the time they expected, because we’re natively segmented. Think about those two networks as large datasets that AI agents need to access. You have to secure the traffic, and you need it to be elastic – able to reach anywhere, instantly, to produce the outcome the agent was asked for. The ability to go anywhere on a road that’s smooth as glass, in the hottest car possible – that’s what we offer. Our network infrastructure solution is an abstraction: a forwarding plane that goes everywhere, and your imagination is really the only limitation. Speed, elasticity, and securing access – even for agentic, self-directed workflows – it’s still a critical element. And nobody – I said this earlier today, so I’ll say it again – networking is not really sexy until it doesn’t work. If I have to get in and route-peer and manually configure transit gateways, I’m going to punch myself in the face repeatedly. I just don’t want to do it. It slows everything down. I can automate it with Terraform, sure. But I want to consume it now. I want to prompt it now. I want the outcome now. Robert Dutt: You’ve launched Alkira NIA, your AI co-pilot and network infrastructure assistant, along with an MCP server last year. It’s interesting – you’re essentially putting AI on top of the infrastructure that’s enabling AI. What does NIA actually do for an MSP’s day-to-day operations? Doug Houghton: Maybe I have a limited imagination, but I still use it like a utility. NIA is great because it allows you to search through all our documentation in a more organized way. We have amazing documentation – there’s a lot of it – and when you’re looking for a specific configuration or something captured in a knowledge base, that tool is really useful. But continuing the utility theme: how do I do something? If I want to create a micro-segment to distribute to a bunch of business units, or build an isolated Layer 3 routing table and get it to various business units, and then set up billing with specific billing tags for each segment – I know how to do that because I’ve done it many times. But a new user may not. You can use the NIA agent to search the documentation, search previous implementation notes, best practices, all of that. That’s real value. But you can also ask it something like “why is the sun bright” and it won’t return the answer you expect. I’ve done that too. Robert Dutt: Let’s talk about the Connect Partner Program and the economics. You’ve got the Partner Profit Stack – tiered margins, quarterly rebates, MDF, SPIFFs, the Connect Pipeline Fund. It’s a full toolkit, and it’s stuff partners have seen before. What’s the real math? What does a Canadian MSP at the Premier tier actually walk away with on a typical deal after they’ve done the work? Doug Houghton: Usually about nineteen percentage points – maybe a little more. On the pre-sale side, when we get into a POC, our Premier partners can earn a $1,000 SPIFF. We close about 85% of our POCs, so there’s real value in that. Add in the rebates and MDF access, and the total haul is closer to 20% on each deal. Worth mentioning: we’ve been a 100% channel company since May 2022. My partner David Klubinoff, my technical counterpart – we worked together at Viptela and we started the Alkira channel together. It took a couple of weeks to convince our CEO that going 100% channel was the right call. I think he’s a believer now. We’ve driven significant revenue for the company, and our partners are our thought leaders – out in the market talking about our solution and solving customer problems. I was in Chicago yesterday doing a technical enablement session with thirty-plus SAs and SEs. We had the classic SD-WAN questions, and a lot of questions about segmentation and M&A. There’s enormous consolidation happening in insurance, healthcare, and other sectors, and the overlapping IP address problem that comes with mergers is something MSPs face all the time. We’ve entirely simplified that. You build a NAT policy right in the solution and the overlapping IP issue is resolved within an hour. In the case of S&P Global and IHS Markit, they thought their merger network integration was going to take a couple of years. The issue was largely the overlapping IP addresses – IHS couldn’t talk to the HR applications at S&P, and vice versa, plus all the other interdependencies. You need a fast way to solve the overlapping IP problem before you can even get to the real work. That’s been a core design element of our solution from the very start: take care of the small things, and people can move faster and get to market faster. Our biggest MSP – and this is a publicly referenceable customer – is CEDA, a French-based organization that provides managed network services to 95% of the world’s airlines. For them, it means being able to turn up a new customer faster, connecting on-premises assets to their control elements so they can begin actually managing that network. Speed, and the efficiencies and cost reductions that come from it – that’s what it does for all MSPs. If you’re consolidating fifty firewalls into virtualized functions, you’re making a good commission, getting MDF support, quarterly rebates, and a SPIFF when you engage us collaboratively on a POC. All of that happens at an accelerated rate. I’ve been screaming from the mountaintop about our solution for about four years. Invariably, you’d walk into a room, say “Hi, I’m Doug Houghton from Alkira,” and they’d say “Who?” That’s starting to happen a lot less, which is a genuinely nice thing. Over the last twelve to twenty-four months, the business has grown exponentially, the diversity of our partner ecosystem has increased, and partner margins have been very healthy. The tiered structure was really about celebrating partners who have invested in us. Honestly, I’m waiting for the day my boss tells me to stop incentivizing partners – because when that happens, I’ll know we’ve hit the apex. Our partners will be generating so much revenue that someone gets uncomfortable with what we’re paying out. I can’t wait for that day. Some of the more interesting things in the program came from actually listening. I went around and talked to a bunch of partners about their ideal partner programs and built from there. And one of the realizations – I thought it was significant – was what we were actually doing on the post-sale side. We white-glove every implementation right now, because it’s critically important to us. We haven’t lost a customer, and we intend to keep it that way. But that doesn’t scale forever. So the question became: why don’t we help our partners productize the post-sale work? We built a product catalog, a pricing calculator, and a new partner portal we’re about to release, with its own AI agent for searching market assets. The product catalog was a light bulb moment. We pay healthy margins on the pre-sale side at every tier of Alkira Connect. But we had never touched the post-sale side at all. We’re largely automated and NIaaS is as simple as possible to consume – a username and a password. My thirteen-year-old could configure a network, and she’s really smart. But there’s still some implementation work. You still need to build policies in Panorama. There’s still DDI work. There are still services that partners can benefit from – and all partner types, MSPs, VARs, master agents, sub-agents, service providers, now have a post-sale commission opportunity. Robert Dutt: You mentioned services – you’ve got services attach plays around modernization assessments, segmentation design, migration sprints. Starting from zero, how long does it realistically take a partner to get their first deal with those services attached through the door, and what does the ramp look like? Doug Houghton: There’s a lot in that question. Let’s take a half step back. We have virtual sales and go-to-market training – three modules – and then five or six technical training modules. We’ve got a lab-in-a-box environment, foundational and advanced technical training, and DDI training. Partners typically start there. Then we run regular in-person and virtual sessions – one partner has regular office hours with me, my SE counterpart David, or our architect Christopher Arenas, and we just invite partners to come and ask questions. Getting partners genuinely comfortable with the technology is the most important thing we do, because nobody goes out and sells anything unless they’re confident they can explain how Alkira solves their customer’s problem. That’s what I’m doing in Chicago today. Our customers tend to be fairly large. We’ve got our first Fortune 10 customer now. The more complex the network, the larger and more global the deployment – multiple countries, security vendors, firewalls, DDI providers, load balancers, service providers, colos. We sit right on top of all of that. The average sales cycle is about 190 days – a little over six months. A newly enabled partner might encounter an M&A overlapping IP use case, recognize the problem, and say “I think we can solve this with Alkira.” They go through a POC together with us, the customer commits, and that first deal closes around 190 days. A little class week: it’s actually 190 and a half. The average deal size is about $500,000 USD. We then see significant expansion: typically 4x growth in the first twelve months after the initial close, and around 8x in the second twelve months. Real incentive to stick with it. We’re loyal – if the customer doesn’t kick the partner out, we go to bat with that partner on every expansion deal. We land, then expand, with the same partner. BNSF, one of our other public references, has expanded several times to address more and more use cases. The solution gets sticky and customers are genuinely surprised by how easy it is. On the post-sale side, we come in and help with implementation, especially early on. But we’re reaching the point where more capable partners can handle it themselves. We’re building a post-sale certification for Alkira right now. In the meantime, we ride shotgun through the first couple of implementations – virtually in Slack or in person – until partners are fully up to speed. All partners have access to our Slack channel, along with our entire solutions architecture and SE staff. One partner working on a Fortune 10 engagement has a great habit of putting a subject header in Slack and starting a conversation. He’s been on services at this customer for three or four months – a significant engagement. He’s the one who originally described the network as a “spaghetti mess,” which I still chuckle about. I actually built the product catalog based on those Slack headers – pulled them together, socialized them with a group of partners, got input, and built from there. To directly answer your question: you’ve got to get through that first deal, and we’re going to ride shotgun with you through the first couple of implementations. The partner learns, gets comfortable, can monetize it, and can deliver independently from there. We have no illusions about going back to being a direct company after May 2022. It’s ride or die – 100% channel, and we enable our partners to solve their customers’ problems and support them while they do it. Because our partners have been our biggest growth engine. Robert Dutt: You’ve talked about a goal of doubling revenue through partners. What does the ecosystem look like when you get there? This sounds like it could primarily be a GSI or large integrator play, given the customer complexity you’re describing. Or do you genuinely see a path for mid-market MSPs and VARs to build a meaningful NIaaS practice? Doug Houghton: Another tough question. Yes, I do have GSIs as partners. We have a fairly robust and diverse partner ecosystem, and we see small shops rising up while larger shops are moving a bit more slowly, honestly. We’re still in that brand awareness honeymoon period – people are realizing our technology is compelling, getting themselves enabled. Some large partners we’ve recently brought on are still ramping. The biggest and most established organizations aren’t yet as capable as they will be, but we’re working diligently on that. Some of our smaller partners, on the other hand – I’m thinking of a friend of mine in Utah who is just an absolute champion. He knows our solution better than almost anyone. He closed six or seven deals in the past year, supported the implementations, did it largely on his own, because he’s curious, motivated, read all the documentation, and has been through full implementation cycles with us. He works at a ten-person shop. They just happen to have really good customers, and he knows the solution cold. So we’re at different stages with different partners in terms of maturity. The answer to your question is genuinely both. The small shop in Utah and the large national partner dedicating more resources as they see more customer problems Alkira can solve – we see wins across both. In the networking space, a six-month sales cycle is about as fast as it gets. I’m giving you a username and a password and you’re going in and connecting all of a customer’s assets together. The path exists for partners of every size. Robert Dutt: You’ve called out Canada specifically in your expansion plans, alongside the UK, EU, and the Middle East. What does that look like operationally – localized support, a Canadian channel team – or is it more of a global platform available to Canadian partners? Doug Houghton: Let’s talk personnel. We have a dedicated rep in eastern Canada, based out of New Hampshire, and a brilliant solutions architect just outside of Toronto. We’ve got a channel account manager – very capable teammate of mine, Savannah Stone – and the entire global solutions architecture staff accessible via Slack. We recently closed a very significant logo in Canada – a large insurance company – and our publicly referenceable Canadian customer is ContactPoint 360, a contact centre and BPO provider. They wanted to connect their Latin American operations back to Canada and couldn’t find an effective way to do it without us. We route them through the US West region, and the results have been excellent. We’ve also added CDW Canada as a partner, and I’ve got a value-added distributor that helps with field events. It’s not a massive footprint yet – it’s a bit of “they come first, then we build” – but there is a tremendous amount of opportunity in Canada and in Latin America that I’m genuinely excited about. Nobody’s told me no yet on spending budget, so here we go. A great story on the Canadian side: a gentleman named Chris Thelosinos, an architect and consultant who works with others in our space, is a member at a wine shop in Toronto. During the Toronto International Film Festival last year, we hosted a wine event right next to TIFF. I don’t drink alcohol, so it was entirely about the conversations for me – and I had the best time. We had significant customers come out, and the demand for simplicity, ease of implementation, and everything Alkira does well was just as strong in Canada as anywhere else. The market need is real. We talk about global backbone as a service all the time. Connecting China to San Francisco carries a distance and time tax, but it’s easy to configure. For organizations navigating geopolitical complexity around China access, or needing GPU connectivity in and out, we just abstract the Azure and AWS mainland China instances. They operate the same way as their Canadian or US equivalents. And you can consume it pay-as-you-go – stop using it, stop paying for it. That’s a compelling model for MSPs looking to grow into different regions. Robert Dutt: Last question then. For that Canadian MSP who’s listened to this and is thinking, “This sounds like a real opportunity” – what’s the one thing you’d want them to take away and act on? Doug Houghton: I’d ask them to go to partners@alkira.com and send us a note. And I will ply them with all sorts of content – videos, learnings, deal registration information, everything they need to get started in the space. Tongue in cheek, and also completely seriously: partners@alkira.com. If you’re looking to grow your business as a managed service provider – managed network, managed security, managed load balancing, managed DDI, managed connectivity – we’re a really great place to start. Because it’s never unpopular to walk into a customer and solve their problem quickly and say, “I can help you with X, Y, and Z, and I can do it in the next couple of hours – and that’s going to drive a total cost of ownership savings of 40 to 70%.” Nobody ever kicks you out of the office when you say something like that. Robert Dutt: Amazing. Doug, I appreciate you taking the time. Thank you very much. Doug Houghton: Robert, thank you for the engaging conversation. I hope your listeners get some good stuff out of it. Robert Dutt: There you have it – Doug Houghton from Alkira. I’d like to thank Doug for his time, and honestly for being one of the more entertaining guests I’ve had on in a while. “Networking is not sexy until it doesn’t work” is a line I’m going to be thinking about for a while. Thanks to you for listening as well. If this conversation sparked something – whether it’s curiosity about NIaaS, the AI infrastructure angle, or what roughly 20% total margin on a $500,000 average deal could do for your business – Doug made it easy for you to take the next step. Drop a note to partners@alkira.com. That’s the front door. And from what I heard today, they will absolutely get back to you. Here’s the thing that stuck with me most in this conversation: the argument that the AI moment isn’t just a software or services play. It’s going to force a reckoning with network infrastructure that a lot of organizations have been deferring for years. The partners who treat that reckoning as an opportunity rather than a fire drill are probably going to look very smart in about three years. If you’re finding the In The Channel podcast from ChannelBuzz.ca useful, the best thing you can do is follow or subscribe wherever you get your podcasts. We’re on Apple Podcasts, Spotify, YouTube, and most major directories. And if you’re enjoying the show, ratings and reviews are genuinely appreciated – they help other people in the Canadian channel find us. Until next time, I’m Robert Dutt for ChannelBuzz.ca, and I’ll see you in the channel.

An airhacks.fm conversation with Thorsten Hoeger (@hoegertn) about: discussion about migrating a German bank to AWS in 2012, early EC2 instances and the launch of AWS VPC for private networking, clicking the AWS console before discovering CloudFormation, CloudFormation released in 2011 with JSON-only templates, Hazelcast cluster synchronization bugs on single-core EC2 instances, multicast limitations in VPC and the transit gateway workaround, CFEngine from 1993 as a predecessor to declarative infrastructure management, Puppet and Chef and Ansible as configuration management tools, CloudFormation's declarative state reconciliation predating kubernetes by three years, CloudFormation's managed state versus Terraform's local state storage, three-way diff comparing new template and old template and physical resource state, drift detection and its limitations with default values, writing 3000 lines of CloudFormation JSON in Eclipse IDE, building a Jenkins plugin for CloudFormation lifecycle management, GitOps with Git servers and Jenkins for CloudFormation deployments, separating infrastructure changes from business logic changes in early setups, treating everything as a change in modern CI/CD pipelines, the origin of CDK at Amazon as an internal tool written in Java then rewritten in typescript, CDK beta participation through the AWS Hero program, CDK constructs and L1 low-level constructs mapping directly to CloudFormation resources, CDK synth phase serializing Java objects to CloudFormation JSON, Stacks as atomic deployment units in CDK, the trade-offs of splitting stateful resources into separate stacks versus single-stack deployments, AWS CloudFormation export and reference coupling between stacks, using AWS Parameter Store for loose coupling between stacks, CDK application as the project root with application code in subfolders, Terraform benefits for multi-provider scenarios like GitHub repos and on-prem routers, regulated industries and compliance benefits of cloud infrastructure as code, change management as a byproduct of Git-based infrastructure pipelines, serverless architecture similarities to application server and WAR deployment models, CDK asset system for versioning and pushing artifacts, CDK custom resource types and self-mutating pipelines as future topics, The CDK Book co-authored by Thorsten Hoeger and colleagues, Taimos GmbH consulting for AWS infrastructure Thorsten Hoeger on twitter: @hoegertn

Turbopuffer came out of a reading app.In 2022, Simon was helping his friends at Readwise scale their infra for a highly requested feature: article recommendations and semantic search. Readwise was paying ~$5k/month for their relational database and vector search would cost ~$20k/month making the feature too expensive to ship. In 2023 after mulling over the problem from Readwise, Simon decided he wanted to “build a search engine” which became Turbopuffer.We discuss:• Simon's path: Denmark → Shopify infra for nearly a decade → “angel engineering” across startups like Readwise, Replicate, and Causal → turbopuffer almost accidentally becoming a company • The Readwise origin story: building an early recommendation engine right after the ChatGPT moment, seeing it work, then realizing it would cost ~$30k/month for a company spending ~$5k/month total on infra and getting obsessed with fixing that cost structure • Why turbopuffer is “a search engine for unstructured data”: Simon's belief that models can learn to reason, but can't compress the world's knowledge into a few terabytes of weights, so they need to connect to systems that hold truth in full fidelity • The three ingredients for building a great database company: a new workload, a new storage architecture, and the ability to eventually support every query plan customers will want on their data • The architecture bet behind turbopuffer: going all in on object storage and NVMe, avoiding a traditional consensus layer, and building around the cloud primitives that only became possible in the last few years • Why Simon hated operating Elasticsearch at Shopify: years of painful on-call experience shaped his obsession with simplicity, performance, and eliminating state spread across multiple systems • The Cursor story: launching turbopuffer as a scrappy side project, getting an email from Cursor the next day, flying out after a 4am call, and helping cut Cursor's costs by 95% while fixing their per-user economics • The Notion story: buying dark fiber, tuning TCP windows, and eating cross-cloud costs because Simon refused to compromise on architecture just to close a deal faster • Why AI changes the build-vs-buy equation: it's less about whether a company can build search infra internally, and more about whether they have time especially if an external team can feel like an extension of their own • Why RAG isn't dead: coding companies still rely heavily on search, and Simon sees hybrid retrieval semantic, text, regex, SQL-style patterns becoming more important, not less • How agentic workloads are changing search: the old pattern was one retrieval call up front; the new pattern is one agent firing many parallel queries at once, turning search into a highly concurrent tool call • Why turbopuffer is reducing query pricing: agentic systems are dramatically increasing query volume, and Simon expects retrieval infra to adapt to huge bursts of concurrent search rather than a small number of carefully chosen calls • The philosophy of “playing with open cards”: Simon's habit of being radically honest with investors, including telling Lachy Groom he'd return the money if turbopuffer didn't hit PMF by year-end • The “P99 engineer”: Simon's framework for building a talent-dense company, rejecting by default unless someone on the team feels strongly enough to fight for the candidate —Simon Hørup Eskildsen• LinkedIn: https://www.linkedin.com/in/sirupsen• X: https://x.com/Sirupsen• https://sirupsen.com/aboutturbopuffer• https://turbopuffer.com/Full Video PodTimestamps00:00:00 The PMF promise to Lachy Groom00:00:25 Intro and Simon's background00:02:19 What turbopuffer actually is00:06:26 Shopify, Elasticsearch, and the pain behind the company00:10:07 The Readwise experiment that sparked turbopuffer00:12:00 The insight Simon couldn't stop thinking about00:17:00 S3 consistency, NVMe, and the architecture bet00:20:12 The Notion story: latency, dark fiber, and conviction00:25:03 Build vs. buy in the age of AI00:26:00 The Cursor story: early launch to breakout customer00:29:00 Why code search still matters00:32:00 Search in the age of agents00:34:22 Pricing turbopuffer in the AI era00:38:17 Why Simon chose Lachy Groom00:41:28 Becoming a founder on purpose00:44:00 The “P99 engineer” philosophy00:49:30 Bending software to your will00:51:13 The future of turbopuffer00:57:05 Simon's tea obsession00:59:03 Tea kits, X Live, and P99 LiveTranscriptSimon Hørup Eskildsen: I don't think I've said this publicly before, but I just called Lockey and was like, local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you. But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working.So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people. We're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards. Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before.Alessio: Hey everyone, welcome to the Leading Space podcast. This is Celesio Pando, Colonel Laz, and I'm joined by Swix, editor of Leading Space.swyx: Hello. Hello, uh, we're still, uh, recording in the Ker studio for the first time. Very excited. And today we are joined by Simon Eski. Of Turbo Farer welcome.Simon Hørup Eskildsen: Thank you so much for having me.swyx: Turbo Farer has like really gone on a huge tear, and I, I do have to mention that like you're one of, you're not my newest member of the Danish AHU Mafia, where like there's a lot of legendary programmers that have come out of it, like, uh, beyond Trotro, Rasmus, lado Berg and the V eight team and, and Google Maps team.Uh, you're mostly a Canadian now, but isn't that interesting? There's so many, so much like strong Danish presence.Simon Hørup Eskildsen: Yeah, I was writing a post, um, not that long ago about sort of the influences. So I grew up in Denmark, right? I left, I left when, when I was 18 to go to Canada to, to work at Shopify. Um, and so I, like, I've, I would still say that I feel more Danish than, than Canadian.This is also the weird accent. I can't say th because it, this is like, I don't, you know, my wife is also Canadian, um, and I think. I think like one of the things in, in Denmark is just like, there's just such a ruthless pragmatism and there's also a big focus on just aesthetics. Like, they're like very, people really care about like where, what things look like.Um, and like Canada has a lot of attributes, US has, has a lot of attributes, but I think there's been lots of the great things to carry. I don't know what's in the water in Ahu though. Um, and I don't know that I could be considered part of the Mafi mafia quite yet, uh, compared to the phenomenal individuals we just mentioned.Barra OV is also, uh, Danish Canadian. Okay. Yeah. I don't know where he lives now, but, and he's the PHP.swyx: Yeah. And obviously Toby German, but moved to Canada as well. Yes. Like this is like import that, uh, that, that is an interesting, um, talent move.Alessio: I think. I would love to get from you. Definition of Turbo puffer, because I think you could be a Vector db, which is maybe a bad word now in some circles, you could be a search engine.It's like, let, let's just start there and then we'll maybe run through the history of how you got to this point.Simon Hørup Eskildsen: For sure. Yeah. So Turbo Puffer is at this point in time, a search engine, right? We do full text search and we do vector search, and that's really what we're specialized in. If you're trying to do much more than that, like then this might not be the right place yet, but Turbo Buffer is all about search.The other way that I think about it is that we can take all of the world's knowledge, all of the exabytes and exabytes of data that there is, and we can use those tokens to train a model, but we can't compress all of that into a few terabytes of weights, right? Compress into a few terabytes of weights, how to reason with the world, how to make sense of the knowledge.But we have to somehow connect it to something externally that actually holds that like in full fidelity and truth. Um, and that's the thing that we intend to become. Right? That's like a very holier than now kind of phrasing, right? But being the search engine for unstructured, unstructured data is the focus of turbo puffer at this point in time.Alessio: And let's break down. So people might say, well, didn't Elasticsearch already do this? And then some other people might say, is this search on my data, is this like closer to rag than to like a xr, like a public search thing? Like how, how do you segment like the different types of search?Simon Hørup Eskildsen: The way that I generally think about this is like, there's a lot of database companies and I think if you wanna build a really big database company, sort of, you need a couple of ingredients to be in the air.We don't, which only happens roughly every 15 years. You need a new workload. You basically need the ambition that every single company on earth is gonna have data in your database. Multiple times you look at a company like Oracle, right? You will, like, I don't think you can find a company on earth with a digital presence that it not, doesn't somehow have some data in an Oracle database.Right? And I think at this point, that's also true for Snowflake and Databricks, right? 15 years later it's, or even more than that, there's not a company on earth that doesn't, in. Or directly is consuming Snowflake or, or Databricks or any of the big analytics databases. Um, and I think we're in that kind of moment now, right?I don't think you're gonna find a company over the next few years that doesn't directly or indirectly, um, have all their data available for, for search and connect it to ai. So you need that new workload, like you need something to be happening where there's a new workload that causes that to happen, and that new workload is connecting very large amounts of data to ai.The second thing you need. The second condition to build a big database company is that you need some new underlying change in the storage architecture that is not possible from the databases that have come before you. If you look at Snowflake and Databricks, right, commoditized, like massive fleet of HDDs, like that was not possible in it.It just wasn't in the air in the nineties, right? So you just didn't, we just didn't build these systems. S3 and and and so on was not around. And I think the architecture that is now possible that wasn't possible 15 years ago is to go all in on NVME SSDs. It requires a particular type of architecture for the database that.It's difficult to retrofit onto the databases that are already there, including the ones you just mentioned. The second thing is to go all in on OIC storage, more so than we could have done 15 years ago. Like we don't have a consensus layer, we don't really have anything. In fact, you could turn off all the servers that Turbo Buffer has, and we would not lose any data because we have all completely all in on OIC storage.And this means that our architecture is just so simple. So that's the second condition, right? First being a new workload. That means that every company on earth, either indirectly or directly, is using your database. Second being, there's some new storage architecture. That means that the, the companies that have come before you can do what you're doing.I think the third thing you need to do to build a big database company is that over time you have to implement more or less every Cory plan on the data. What that means is that you. You can't just get stuck in, like, this is the one thing that a database does. It has to be ever evolving because when someone has data in the database, they over time expect to be able to ask it more or less every question.So you have to do that to get the storage architecture to the limit of what, what it's capable of. Those are the three conditions.swyx: I just wanted to get a little bit of like the motivation, right? Like, so you left Shopify, you're like principal, engineer, infra guy. Um, you also head of kernel labs, uh, inside of Shopify, right?And then you consulted for read wise and that it kind of gave you that, that idea. I just wanted you to tell that story. Um, maybe I, you've told it before, but, uh, just introduce the, the. People to like the, the new workload, the sort of aha moment for turbo PufferSimon Hørup Eskildsen: For sure. So yeah, I spent almost a decade at Shopify.I was on the infrastructure team, um, from the fairly, fairly early days around 2013. Um, at the time it felt like it was growing so quickly and everything, all the metrics were, you know, doubling year on year compared to the, what companies are contending with today. It's very cute in growth. I feel like lot some companies are seeing that month over month.Um, of course. Shopify compound has been compounding for a very long time now, but I spent a decade doing that and the majority of that was just make sure the site is up today and make sure it's up a year from now. And a lot of that was really just the, um, you know, uh, the Kardashians would drive very, very large amounts of, of data to, to uh, to Shopify as they were rotating through all the merch and building out their businesses.And we just needed to make sure we could handle that. Right. And sometimes these were events, a million requests per second. And so, you know, we, we had our own data centers back in the day and we were moving to the cloud and there was so much sharding work and all of that that we were doing. So I spent a decade just scaling databases ‘cause that's fundamentally what's the most difficult thing to scale about these sites.The database that was the most difficult for me to scale during that time, and that was the most aggravating to be on call for, was elastic search. It was very, very difficult to deal with. And I saw a lot of projects that were just being held back in their ambition by using it.swyx: And I mean, self-hosted.Self-hosted. ‘causeSimon Hørup Eskildsen: it's, yeah, and it commercial, this is like 2015, right? So it's like a very particular vintage. Right. It's probably better at a lot of these things now. Um, it was difficult to contend with and I'm just like, I just think about it. It's an inverted index. It should be good at these kinds of queries and do all of this.And it was, we, we often couldn't get it to do exactly what we needed to do or basically get lucine to do, like expose lucine raw to, to, to what we needed to do. Um, so that was like. Just something that we did on the side and just panic scaled when we needed to, but not a particular focus of mine. So I left, and when I left, I, um, wasn't sure exactly what I wanted to do.I mean, it spent like a decade inside of the same company. I'd like grown up there. I started working there when I was 18.swyx: You only do Rails?Simon Hørup Eskildsen: Yeah. I mean, yeah. Rails. And he's a Rails guy. Uh, love Rails. So good. Um,Alessio: we all wish we could still work in Rails.swyx: I know know. I know, but some, I tried learning Ruby.It's just too much, like too many options to do the same thing. It's, that's my, I I know there's a, there's a way to do it.Simon Hørup Eskildsen: I love it. I don't know that I would use it now, like given cloud code and, and, and cursor and everything, but, um, um, but still it, like if I'm just sitting down and writing a teal code, that's how I think.But anyway, I left and I wasn't, I talked to a couple companies and I was like, I don't. I need to see a little bit more of the world here to know what I'm gonna like focus on next. Um, and so what I decided is like I was gonna, I called it like angel engineering, where I just hopped around in my friend's companies in three months increments and just helped them out with something.Right. And, and just vested a bit of equity and solved some interesting infrastructure problem. So I worked with a bunch of companies at the time, um, read Wise was one of them. Replicate was one of them. Um, causal, I dunno if you've tried this, it's like a, it's a spreadsheet engine Yeah. Where you can do distribution.They sold recently. Yeah. Um, we've been, we used that in fp and a at, um, at Turbo Puffer. Um, so a bunch of companies like this and it was super fun. And so we're the Chachi bt moment happened, I was with. With read Wise for a stint, we were preparing for the reader launch, right? Which is where you, you cue articles and read them later.And I was just getting their Postgres up to snuff, like, which basically boils down to tuning, auto vacuum. So I was doing that and then this happened and we were like, oh, maybe we should build a little recommendation engine and some features to try to hook in the lms. They were not that good yet, but it was clear there was something there.And so I built a small recommendation engine just, okay, let's take the articles that you've recently read, right? Like embed all the articles and then do recommendations. It was good enough that when I ran it on one of the co-founders of Rey's, like I found out that I got articles about, about having a child.I'm like, oh my God, I didn't, I, I didn't know that, that they were having a child. I wasn't sure what to do with that information, but the recommendation engine was good enough that it was suggesting articles, um, about that. And so there was, there was recommendations and uh, it actually worked really well.But this was a company that was spending maybe five grand a month in total on all their infrastructure and. When I did the napkin math on running the embeddings of all the articles, putting them into a vector index, putting it in prod, it's gonna be like 30 grand a month. That just wasn't tenable. Right?Like Read Wise is a proudly bootstrapped company and it's paying 30 grand for infrastructure for one feature versus five. It just wasn't tenable. So sort of in the bucket of this is useful, it's pretty good, but let us, let's return to it when the costs come down.swyx: Did you say it grows by feature? So for five to 30 is by the number of, like, what's the, what's the Scaling factor scale?It scales by the number of articles that you embed.Simon Hørup Eskildsen: It does, but what I meant by that is like five grand for like all of the other, like the Heroku, dinos, Postgres, like all the other, and this then storage is 30. Yeah. And then like 30 grand for one feature. Right. Which is like, what other articles are related to this one.Um, so it was just too much right to, to power everything. Their budget would've been maybe a few thousand dollars, which still would've been a lot. And so we put it in a bucket of, okay, we're gonna do that later. We'll wait, we will wait for the cost to come down. And that haunted me. I couldn't stop thinking about it.I was like, okay, there's clearly some latent demand here. If the cost had been a 10th, we would've shipped it and. This was really the only data point that I had. Right. I didn't, I, I didn't, I didn't go out and talk to anyone else. It was just so I started reading Right. I couldn't, I couldn't help myself.Like I didn't know what like a vector index is. I, I generally barely do about how to generate the vectors. There was a lot of hype about, this is a early 2023. There was a lot of hype about vector databases. There were raising a lot of money and it's like, I really didn't know anything about it. It's like, you know, trying these little models, fine tuning them.Like I was just trying to get sort of a lay of the land. So I just sat down. I have this. A GitHub repository called Napkin Math. And on napkin math, there's just, um, rows of like, oh, this is how much bandwidth. Like this is how many, you know, you can do 25 gigabytes per second on average to dram. You can do, you know, five gigabytes per second of rights to an SSD, blah blah.All of these numbers, right? And S3, how many you could do per, how much bandwidth can you drive per connection? I was just sitting down, I was like, why hasn't anyone build a database where you just put everything on O storage and then you puff it into NVME when you use the data and you puff it into dram if you're, if you're querying it alive, it's just like, this seems fairly obvious and you, the only real downside to that is that if you go all in on o storage, every right will take a couple hundred milliseconds of latency, but from there it's really all upside, right?You do the first go, it takes half a second. And it sort of occurred to me as like, well. The architecture is really good for that. It's really good for AB storage, it's really good for nvm ESSD. It's, well, you just couldn't have done that 10 years ago. Back to what we were talking about before. You really have to build a database where you have as few round trips as possible, right?This is how CPUs work today. It's how NVM E SSDs work. It's how as, um, as three works that you want to have a very large amount of outstanding requests, right? Like basically go to S3, do like that thousand requests to ask for data in one round trip. Wait for that. Get that, like, make a new decision. Do it again, and try to do that maybe a maximum of three times.But no databases were designed that way within NVME as is ds. You can drive like within, you know, within a very low multiple of DRAM bandwidth if you use it that way. And same with S3, right? You can fully max out the network card, which generally is not maxed out. You get very, like, very, very good bandwidth.And, but no one had built a database like that. So I was like, okay, well can't you just, you know, take all the vectors right? And plot them in the proverbial coordinate system. Get the clusters, put a file on S3 called clusters, do json, and then put another file for every cluster, you know, cluster one, do js O cluster two, do js ON you know that like it's two round trips, right?So you get the clusters, you find the closest clusters, and then you download the cluster files like the, the closest end. And you could do this in two round trips.swyx: You were nearest neighbors locally.Simon Hørup Eskildsen: Yes. Yes. And then, and you would build this, this file, right? It's just like ultra simplistic, but it's not a far shot from what the first version of Turbo Buffer was.Why hasn't anyone done thatAlessio: in that moment? From a workload perspective, you're thinking this is gonna be like a read heavy thing because they're doing recommend. Like is the fact that like writes are so expensive now? Oh, with ai you're actually not writing that much.Simon Hørup Eskildsen: At that point I hadn't really thought too much about, well no actually it was always clear to me that there was gonna be a lot of rights because at Shopify, the search clusters were doing, you know, I don't know, tens or hundreds of crew QPS, right?‘cause you just have to have a human sit and type in. But we did, you know, I don't know how many updates there were per second. I'm sure it was in the millions, right into the cluster. So I always knew there was like a 10 to 100 ratio on the read write. In the read wise use case. It's, um, even, even in the read wise use case, there'd probably be a lot fewer reads than writes, right?There's just a lot of churn on the amount of stuff that was going through versus the amount of queries. Um, I wasn't thinking too much about that. I was mostly just thinking about what's the fundamentally cheapest way to build a database in the cloud today using the primitives that you have available.And this is it, right? You just, now you have one machine and you know, let's say you have a terabyte of data in S3, you paid the $200 a month for that, and then maybe five to 10% of that data and needs to be an NV ME SSDs and less than that in dram. Well. You're paying very, very little to inflate the data.swyx: By the way, when you say no one else has done that, uh, would you consider Neon, uh, to be on a similar path in terms of being sort of S3 first and, uh, separating the compute and storage?Simon Hørup Eskildsen: Yeah, I think what I meant with that is, uh, just build a completely new database. I don't know if we were the first, like it was very much, it was, I mean, I, I hadn't, I just looked at the napkin math and was like, this seems really obvious.So I'm sure like a hundred people came up with it at the same time. Like the light bulb and every invention ever. Right. It was just in the air. I think Neon Neon was, was first to it. And they're trying, they're retrofitted onto Postgres, right? And then they built this whole architecture where you have, you have it in memory and then you sort of.You know, m map back to S3. And I think that was very novel at the time to do it for, for all LTP, but I hadn't seen a database that was truly all in, right. Not retrofitting it. The database felt built purely for this no consensus layer. Even using compare and swap on optic storage to do consensus. I hadn't seen anyone go that all in.And I, I mean, there, there, I'm sure there was someone that did that before us. I don't know. I was just looking at the napkin mathswyx: and, and when you say consensus layer, uh, are you strongly relying on S3 Strong consistency? You are. Okay.SoSimon Hørup Eskildsen: that is your consensus layer. It, it is the consistency layer. And I think also, like, this is something that most people don't realize, but S3 only became consistent in December of 2020.swyx: I remember this coming out during COVID and like people were like, oh, like, it was like, uh, it was just like a free upgrade.Simon Hørup Eskildsen: Yeah.swyx: They were just, they just announced it. We saw consistency guys and like, okay, cool.Simon Hørup Eskildsen: And I'm sure that they just, they probably had it in prod for a while and they're just like, it's done right.And people were like, okay, cool. But. That's a big moment, right? Like nv, ME SSDs, were also not in the cloud until around 2017, right? So you just sort of had like 2017 nv, ME SSDs, and people were like, okay, cool. There's like one skew that does this, whatever, right? Takes a few years. And then the second thing is like S3 becomes consistent in 2020.So now it means you don't have to have this like big foundation DB or like zookeeper or whatever sitting there contending with the keys, which is how. You know, that's what Snowflake and others have do so muchswyx: for goneSimon Hørup Eskildsen: Exactly. Just gone. Right? And so just push to the, you know, whatever, how many hundreds of people they have working on S3 solved and then compare and swap was not in S3 at this point in time,swyx: by the way.Uh, I don't know what that is, so maybe you wanna explain. Yes. Yeah.Simon Hørup Eskildsen: Yes. So, um, what Compare and swap is, is basically, you can imagine that if you have a database, it might be really nice to have a file called metadata json. And metadata JSON could say things like, Hey, these keys are here and this file means that, and there's lots of metadata that you have to operate in the database, right?But that's the simplest way to do it. So now you have might, you might have a lot of servers that wanna change the metadata. They might have written a file and want the metadata to contain that file. But you have a hundred nodes that are trying to contend with this metadata that JSON well, what compare and Swap allows you to do is basically just you download the file, you make the modifications, and then you write it only if it hasn't changed.While you did the modification and if not you retry. Right? Should just have this retry loops. Now you can imagine if you have a hundred nodes doing that, it's gonna be really slow, but it will converge over time. That primitive was not available in S3. It wasn't available in S3 until late 2024, but it was available in GCP.The real story of this is certainly not that I sat down and like bake brained it. I was like, okay, we're gonna start on GCS S3 is gonna get it later. Like it was really not that we started, we got really lucky, like we started on GCP and we started on GCP because tur um, Shopify ran on GCP. And so that was the platform I was most available with.Right. Um, and I knew the Canadian team there ‘cause I'd worked with them at Shopify and so it was natural for us to start there. And so when we started building the database, we're like, oh yeah, we have to build a, we really thought we had to build a consensus layer, like have a zookeeper or something to do this.But then we discovered the compare and swap. It's like, oh, we can kick the can. Like we'll just do metadata r json and just, it's fine. It's probably fine. Um, and we just kept kicking the can until we had very, very strong conviction in the idea. Um, and then we kind of just hinged the company on the fact that S3 probably was gonna get this, it started getting really painful in like mid 2024.‘cause we were closing deals with, um, um, notion actually that was running in AWS and we're like, trust us. You, you really want us to run this in GCP? And they're like, no, I don't know about that. Like, we're running everything in AWS and the latency across the cloud were so big and we had so much conviction that we bought like, you know, dark fiber between the AWS regions in, in Oregon, like in the InterExchange and GCP is like, we've never seen a startup like do like, what's going on here?And we're just like, no, we don't wanna do this. We were tuning like TCP windows, like everything to get the latency down ‘cause we had so high conviction in not doing like a, a metadata layer on S3. So those were the three conditions, right? Compare and swap. To do metadata, which wasn't in S3 until late 2024 S3 being consistent, which didn't happen until December, 2020.Uh, 2020. And then NVMe ssd, which didn't end in the cloud until 2017.swyx: I mean, in some ways, like a very big like cloud success story that like you were able to like, uh, put this all together, but also doing things like doing, uh, bind our favor. That that actually is something I've never heard.Simon Hørup Eskildsen: I mean, it's very common when you're a big company, right?You're like connecting your own like data center or whatever. But it's like, it was uniquely just a pain with notion because the, um, the org, like most of the, like if you're buying in Ashburn, Virginia, right? Like US East, the Google, like the GCP and, and AWS data centers are like within a millisecond on, on each other, on the public exchanges.But in Oregon uniquely, the GCP data center sits like a couple hundred kilometers, like east of Portland and the AWS region sits in Portland, but the network exchange they go through is through Seattle. So it's like a full, like 14 milliseconds or something like that. And so anyway, yeah. It's, it's, so we were like, okay, we can't, we have to go through an exchange in Portland.Yeah. Andswyx: you'd rather do this than like run your zookeeper and likeSimon Hørup Eskildsen: Yes. Way rather. It doesn't have state, I don't want state and two systems. Um, and I think all that is just informed by Justine, my co-founder and I had just been on call for so long. And the worst outages are the ones where you have state in multiple places that's not syncing up.So it really came from, from a a, like just a, a very pure source of pain, of just imagining what we would be Okay. Being woken up at 3:00 AM about and having something in zookeeper was not one of them.swyx: You, you're talking to like a notion or something. Do they care or do they just, theySimon Hørup Eskildsen: just, they care about latency.swyx: They latency cost. That's it.Simon Hørup Eskildsen: They just cared about latency. Right. And we just absorbed the cost. We're just like, we have high conviction in this. At some point we can move them to AWS. Right. And so we just, we, we'll buy the fiber, it doesn't matter. Right. Um, and it's like $5,000. Usually when you buy fiber, you buy like multiple lines.And we're like, we can only afford one, but we will just test it that when it goes over the public internet, it's like super smooth. And so we did a lot of, anyway, it's, yeah, it was, that's cool.Alessio: You can imagine talking to the GCP rep and it's like, no, we're gonna buy, because we know we're gonna turn, we're gonna turn from you guys and go to AWS in like six months.But in the meantime we'll do this. It'sSimon Hørup Eskildsen: a, I mean, like they, you know, this workload still runs on GCP for what it's worth. Right? ‘cause it's so, it was just, it was so reliable. So it was never about moving off GCP, it was just about honesty. It was just about giving notion the latency that they deserved.Right. Um, and we didn't want ‘em to have to care about any of this. We also, they were like, oh, egress is gonna be bad. It was like, okay, screw it. Like we're just gonna like vvc, VPC peer with you and AWS we'll eat the cost. Yeah. Whatever needs to be done.Alessio: And what were the actual workloads? Because I think when you think about ai, it's like 14 milliseconds.It's like really doesn't really matter in the scheme of like a model generation.Simon Hørup Eskildsen: Yeah. We were told the latency, right. That we had to beat. Oh, right. So, so we're just looking at the traces. Right. And then sort of like hand draw, like, you know, kind of like looking at the trace and then thinking what are the other extensions of the trace?Right. And there's a lot more to it because it's also when you have, if you have 14 versus seven milliseconds, right. You can fit in another round trip. So we had to tune TCP to try to send as much data in every round trip, prewarm all the connections. And there was, there's a lot of things that compound from having these kinds of round trips, but in the grand scheme it was just like, well, we have to beat the latency of whatever we're up against.swyx: Which is like they, I mean, notion is a database company. They could have done this themselves. They, they do lots of database engineering themselves. How do you even get in the door? Like Yeah, just like talk through that kind of.Simon Hørup Eskildsen: Last time I was in San Francisco, I was talking to one of the engineers actually, who, who was one of our champions, um, at, AT Notion.And they were, they were just trying to make sure that the, you know, per user cost matched the economics that they needed. You know, Uhhuh like, it's like the way I think about, it's like I have to earn a return on whatever the clouds charge me and then my customers have to earn a return on that. And it's like very simple, right?And so there has to be gross margin all the way up and that's how you build the product. And so then our customers have to make the right set of trade off the turbo Puffer makes, and if they're happy with that, that's great.swyx: Do you feel like you're competing with build internally versus buy or buy versus buy?Simon Hørup Eskildsen: Yeah, so, sorry, this was all to build up to your question. So one of the notion engineers told me that they'd sat and probably on a napkin, like drawn out like, why hasn't anyone built this? And then they saw terrible. It was like, well, it literally that. So, and I think AI has also changed the buy versus build equation in terms of, it's not really about can we build it, it's about do we have time to build it?I think they like, I think they felt like, okay, if this is a team that can do that and they, they feel enough like an extension of our team, well then we can go a lot faster, which would be very, very good for them. And I mean, they put us through the, through the test, right? Like we had some very, very long nights to to, to do that POC.And they were really our biggest, our second big customer off the cursor, which also was a lot of late nights. Right.swyx: Yeah. That, I mean, should we go into that story? The, the, the sort of Chris's story, like a lot, um, they credit you a lot for. Working very closely with them. So I just wanna hear, I've heard this, uh, story from Sole's point of view, but like, I'm curious what, what it looks like from your side.Simon Hørup Eskildsen: I actually haven't heard it from Sole's point of view, so maybe you can now cross reference it. The way that I remember it was that, um, the day after we launched, which was just, you know, I'd worked the whole summer on, on the first version. Justine wasn't part of it yet. ‘cause I just, I didn't tell anyone that summer that I was working on this.I was just locked in on building it because it's very easy otherwise to confuse talking about something to actually doing it. And so I was just like, I'm not gonna do that. I'm just gonna do the thing. I launched it and at this point turbo puffer is like a rust binary running on a single eight core machine in a T Marks instance.And me deploying it was like looking at the request log and then like command seeing it or like control seeing it to just like, okay, there's no request. Let's upgrade the binary. Like it was like literally the, the, the, the scrappiest thing. You could imagine it was on purpose because just like at Shopify, we did that all the time.Like, we like move, like we ran things in tux all the time to begin with. Before something had like, at least the inkling of PMF, it was like, okay, is anyone gonna hear about this? Um, and one of the cursor co-founders Arvid reached out and he just, you know, the, the cursor team are like all I-O-I-I-M-O like, um, contenders, right?So they just speak in bullet points and, and facts. It was like this amazing email exchange just of, this is how many QPS we have, this is what we're paying, this is where we're going, blah, blah, blah. And so we're just conversing in bullet points. And I tried to get a call with them a few times, but they were, so, they were like really writing the PMF bowl here, just like late 2023.And one time Swally emails me at like five. What was it like 4:00 AM Pacific time saying like, Hey, are you open for a call now? And I'm on the East coast and I, it was like 7:00 AM I was like, yeah, great, sure, whatever. Um, and we just started talking and something. Then I didn't know anything about sales.It was something that just comp compelled me. I have to go see this team. Like, there's something here. So I, I went to San Francisco and I went to their office and the way that I remember it is that Postgres was down when I showed up at the office. Did SW tell you this? No. Okay. So Postgres was down and so it's like they were distracting with that.And I was trying my best to see if I could, if I could help in any way. Like I knew a little bit about databases back to tuning, auto vacuum. It was like, I think you have to tune out a vacuum. Um, and so we, we talked about that and then, um, that evening just talked about like what would it look like, what would it look like to work with us?And I just said. Look like we're all in, like we will just do what we'll do whatever, whatever you tell us, right? They migrated everything over the next like week or two, and we reduced their cost by 95%, which I think like kind of fixed their per user economics. Um, and it solved a lot of other things. And we were just, Justine, this is also when I asked Justine to come on as my co-founder, she was the best engineer, um, that I ever worked with at Shopify.She lived two blocks away and we were just, okay, we're just gonna get this done. Um, and we did, and so we helped them migrate and we just worked like hell over the next like month or two to make sure that we were never an issue. And that was, that was the cursor story. Yeah.swyx: And, and is code a different workload than normal text?I, I don't know. Is is it just text? Is it the same thing?Simon Hørup Eskildsen: Yeah, so cursor's workload is basically, they, um, they will embed the entire code base, right? So they, they will like chunk it up in whatever they would, they do. They have their own embedding model, um, which they've been public about. Um, and they find that on, on, on their evals.It. There's one of their evals where it's like a 25% improvement on a very particular workload. They have a bunch of blog posts about it. Um, I think it works best on larger code basis, but they've trained their own embedding model to do this. Um, and so you'll see it if you use the cursor agent, it will do searches.And they've also been public around, um, how they've, I think they post trained their model to be very good at semantic search as well. Um, and that's, that's how they use it. And so it's very good at, like, can you find me on the code that's similar to this, or code that does this? And just in, in this queries, they also use GR to supplement it.swyx: Yeah.Simon Hørup Eskildsen: Um, of courseswyx: it's been a big topic of discussion like, is rag dead because gr you know,Simon Hørup Eskildsen: and I mean like, I just, we, we see lots of demand from the coding company to ethicsswyx: search in every part. Yes.Simon Hørup Eskildsen: Uh, we, we, we see demand. And so, I mean, I'm. I like case studies. I don't like, like just doing like thought pieces on this is where it's going.And like trying to be all macroeconomic about ai, that's has turned out to be a giant waste of time because no one can really predict any of this. So I just collect case studies and I mean, cursor has done a great job talking about what they're doing and I hope some of the other coding labs that use Turbo Puffer will do the same.Um, but it does seem to make a difference for particular queries. Um, I mean we can also do text, we can also do RegX, but I should also say that cursors like security posture into Tur Puffer is exceptional, right? They have their own embedding model, which makes it very difficult to reverse engineer. They obfuscate the file paths.They like you. It's very difficult to learn anything about a code base by looking at it. And the other thing they do too is that for their customers, they encrypt it with their encryption keys in turbo puffer's bucket. Um, so it's, it's, it's really, really well designed.swyx: And so this is like extra stuff they did to work with you because you are not part of Cursor.Exactly like, and this is just best practice when working in any database, not just you guys. Okay. Yeah, that makes sense. Yeah. I think for me, like the, the, the learning is kind of like you, like all workloads are hybrid. Like, you know, uh, like you, you want the semantic, you want the text, you want the RegX, you want sql.I dunno. Um, but like, it's silly to like be all in on like one particularly query pattern.Simon Hørup Eskildsen: I think, like I really like the way that, um, um, that swally at cursor talks about it, which is, um, I'm gonna butcher it here. Um, and you know, I'm a, I'm a database scalability person. I'm not a, I, I dunno anything about training models other than, um, what the internet tells me and what.The way he describes is that this is just like cash compute, right? It's like you have a point in time where you're looking at some particular context and focused on some chunk and you say, this is the layer of the neural net at this point in time. That seems fundamentally really useful to do cash compute like that.And, um, how the value of that will change over time. I'm, I'm not sure, but there seems to be a lot of value in that.Alessio: Maybe talk a bit about the evolution of the workload, because even like search, like maybe two years ago it was like one search at the start of like an LLM query to build the context. Now you have a gentech search, however you wanna call it, where like the model is both writing and changing the code and it's searching it again later.Yeah. What are maybe some of the new types of workloads or like changes you've had to make to your architecture for it?Simon Hørup Eskildsen: I think you're right. When I think of rag, I think of, Hey, there's an 8,000 token, uh, context window and you better make it count. Um, and search was a way to do that now. Everything is moving towards the, just let the agent do its thing.Right? And so back to the thing before, right? The LLM is very good at reasoning with the data, and so we're just the tool call, right? And that's increasingly what we see our customers doing. Um, what we're seeing more demand from, from our customers now is to do a lot of concurrency, right? Like Notion does a ridiculous amount of queries in every round trip just because they can't.And I'm also now, when I use the cursor agent, I also see them doing more concurrency than I've ever seen before. So a bit similar to how we designed a database to drive as much concurrency in every round trip as possible. That's also what the agents are doing. So that's new. It means just an enormous amount of queries all at once to the dataset while it's warm in as few turns as possible.swyx: Can I clarify one thing on that?Simon Hørup Eskildsen: Yes.swyx: Is it, are they batching multiple users or one user is driving multiple,Simon Hørup Eskildsen: one user driving multiple, one agent driving.swyx: It's parallel searching a bunch of things.Simon Hørup Eskildsen: Exactly.swyx: Yeah. Yeah, exactly. So yeah, the clinician also did, did this for the fast context thing, like eight parallel at once.Simon Hørup Eskildsen: Yes.swyx: And, and like an interesting problem is, well, how do you make sure you have enough diversity so you're not making the the same request eight times?Simon Hørup Eskildsen: And I think like that's probably also where the hybrid comes in, where. That's another way to diversify. It's a completely different way to, to do the search.That's a big change, right? So before it was really just like one call and then, you know, the LLM took however many seconds to return, but now we just see an enormous amount of queries. So the, um, we just see more queries. So we've like tried to reduce query, we've reduced query pricing. Um, this is probably the first time actually I'm saying that, but the query pricing is being reduced, like five x.Um, and we'll probably try to reduce it even more to accommodate some of these workloads of just doing very large amounts of queries. Um, that's one thing that's changed. I think the right, the right ratio is still very high, right? Like there's still a, an enormous amount of rights per read, but we're starting probably to see that change if people really lean into this pattern.Alessio: Can we talk a little bit about the pricing? I'm curious, uh, because traditionally a database would charge on storage, but now you have the token generation that is so expensive, where like the actual. Value of like a good search query is like much higher because they're like saving inference time down the line.How do you structure that as like, what are people receptive to on the other side too?Simon Hørup Eskildsen: Yeah. I, the, the turbo puffer pricing in the beginning was just very simple. The pricing on these on for search engines before Turbo Puffer was very server full, right? It was like, here's the vm, here's the per hour cost, right?Great. And I just sat down with like a piece of paper and said like, if Turbo Puffer was like really good, this is probably what it would cost with a little bit of margin. And that was the first pricing of Turbo Puffer. And I just like sat down and I was like, okay, like this is like probably the storage amp, but whenever on a piece of paper I, it was vibe pricing.It was very vibe price, and I got it wrong. Oh. Um, well I didn't get it wrong, but like Turbo Puffer wasn't at the first principle pricing, right? So when Cursor came on Turbo Puffer, it was like. Like, I didn't know any VCs. I didn't know, like I was just like, I don't know, I didn't know anything about raising money or anything like that.I just saw that my GCP bill was, was high, was a lot higher than the cursor bill. So Justine and I was just like, well, we have to optimize it. Um, and I mean, to the chagrin now of, of it, of, of the VCs, it now means that we're profitable because we've had so much pricing pressure in the beginning. Because it was running on my credit card and Justine and I had spent like, like tens of thousands of dollars on like compute bills and like spinning off the company and like very like, like bad Canadian lawyers and like things like to like get all of this done because we just like, we didn't know.Right. If you're like steeped in San Francisco, you're just like, you just know. Okay. Like you go out, raise a pre-seed round. I, I never heard a word pre-seed at this point in time.swyx: When you had Cursor, you had Notion you, you had no funding.Simon Hørup Eskildsen: Um, with Cursor we had no funding. Yeah. Um, by the time we had Notion Locke was, Locke was here.Yeah. So it was really just, we vibe priced it 100% from first Principles, but it wasn't, it, it was not performing at first principles, so we just did everything we could to optimize it in the beginning for that, so that at least we could have like a 5% margin or something. So I wasn't freaking out because Cursor's bill was also going like this as they were growing.And so my liability and my credit limit was like actively like calling my bank. It was like, I need a bigger credit. Like it was, yeah. Anyway, that was the beginning. Yeah. But the pricing was, yeah, like storage rights and query. Right. And the, the pricing we have today is basically just that pricing with duct tape and spit to try to approach like, you know, like a, as a margin on the physical underlying hardware.And we're doing this year, you're gonna see more and more pricing changes from us. Yeah.swyx: And like is how much does stuff like VVC peering matter because you're working in AWS land where egress is charged and all that, you know.Simon Hørup Eskildsen: We probably don't like, we have like an enterprise plan that just has like a base fee because we haven't had time to figure out SKU pricing for all of this.Um, but I mean, yeah, you can run turbo puffer either in SaaS, right? That's what Cursor does. You can run it in a single tenant cluster. So it's just you. That's what Notion does. And then you can run it in, in, in BYOC where everything is inside the customer's VPC, that's what an for example, philanthropic does.swyx: What I'm hearing is that this is probably the best CRO job for somebody who can come in and,Simon Hørup Eskildsen: I mean,swyx: help you with this.Simon Hørup Eskildsen: Um, like Turbo Puffer hired, like, I don't know what, what number this was, but we had a full-time CFO as like the 12th hire or something at Turbo Puffer, um, I think I hear are a lot of comp.I don't know how they do it. Like they have a hundred employees and not a CFO. It's like having a CFO is like a runningswyx: business man. Like, you know,Simon Hørup Eskildsen: it's so good. Yeah, like money Mike, like he just, you know, just handles the money and a lot of the business stuff and so he came in and just hopped with a lot of the operational side of the business.So like C-O-O-C-F-O, like somewhere in between.swyx: Just as quick mention of Lucky, just ‘cause I'm curious, I've met Lock and like, he's obviously a very good investor and now on physical intelligence, um, I call it generalist super angel, right? He invests in everything. Um, and I always wonder like, you know, is there something appealing about focusing on developer tooling, focusing on databases, going like, I've invested for 10 years in databases versus being like a lock where he can maybe like connect you to all the customers that you need.Simon Hørup Eskildsen: This is an excellent question. No, no one's asked me this. Um, why lockey? Because. There was a couple of people that we were talking to at the time and when we were raising, we were almost a little, we were like a bit distressed because one of our, one of our peers had just launched something that was very similar to Turbo Puffer.And someone just gave me the advice at the time of just choose the person where you just feel like you can just pick up the phone and not prepare anything. And just be completely honest, and I don't think I've said this publicly before, but I just called Lockey and was like local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you.But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working. So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people and we're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards and.Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before. As I said, I didn't even know what a seed or pre-seed round was like before, probably even at this time. So I was just like very honest with him. And I asked him like, Lockie, have you ever have, have you ever invested in database company?He was just like, no. And at the time I was like, am I dumb? Like, but I think there was something that just like really drew me to Lockie. He is so authentic, so honest, like, and there was something just like, I just felt like I could just play like, just say everything openly. And that was, that was, I think that that was like a perfect match at the time, and, and, and honestly still is.He was just like, okay, that's great. This is like the most honest, ridiculous thing I've ever heard anyone say to me. But like that, like that, whyswyx: is this ridiculous? Say competitor launch, this may not work out. It wasSimon Hørup Eskildsen: more just like. If this doesn't work out, I'm gonna close up shop by the end of the mo the year, right?Like it was, I don't know, maybe it's common. I, I don't know. He told me it was uncommon. I don't know. Um, that's why we chose him and he'd been phenomenal. The other people were talking at the, at the time were database experts. Like they, you know, knew a lot about databases and Locke didn't, this turned out to be a phenomenal asset.Right. I like Justine and I know a lot about databases. The people that we hire know a lot about databases. What we needed was just someone who didn't know a lot about databases, didn't pretend to know a lot about databases, and just wanted to help us with candidates and customers. And he did. Yeah. And I have a list, right, of the investors that I have a relationship with, and Lockey has just performed excellent in the number of sub bullets of what we can attribute back to him.Just absolutely incredible. And when people talk about like no ego and just the best thing for the founder, I like, I don't think that anyone, like even my lawyer is like, yeah, Lockey is like the most friendly person you will find.swyx: Okay. This is my most glow recommendation I've ever heard.Alessio: He deserves it.He's very special.swyx: Yeah. Yeah. Yeah. Okay. Amazing.Alessio: Since you mentioned candidates, maybe we can talk about team building, you know, like, especially in sf, it feels like it's just easier to start a company than to join a company. Uh, I'm curious your experience, especially not being n SF full-time and doing something that is maybe, you know, a very low level of detail and technical detail.Simon Hørup Eskildsen: Yeah. So joining versus starting, I never thought that I would be a founder. I would start with it, like Turbo Puffer started as a blog post, and then it became a project and then sort of almost accidentally became a company. And now it feels like it's, it's like becoming a bigger company. That was never the intention.The intentions were very pure. It's just like, why hasn't anyone done this? And it's like, I wanna be the, like, I wanna be the first person to do it. I think some founders have this, like, I could never work for anyone else. I, I really don't feel that way. Like, it's just like, I wanna see this happen. And I wanna see it happen with some people that I really enjoy working with and I wanna have fun doing it and this, this, this has all felt very natural on that, on that sense.So it was never a like join versus versus versus found. It was just dis found me at the right moment.Alessio: Well I think there's an argument for, you should have joined Cursor, right? So I'm curious like how you evaluate it. Okay, I should actually go raise money and make this a company versus like, this is like a company that is like growing like crazy.It's like an interesting technical problem. I should just build it within Cursor and then they don't have to encrypt all this stuff. They don't have to obfuscate things. Like was that on your mind at all orSimon Hørup Eskildsen: before taking the, the small check from Lockie, I did have like a hard like look at myself in the mirror of like, okay, do I really want to do this?And because if I take the money, I really have to do it right. And so the way I almost think about it's like you kind of need to ha like you kind of need to be like fucked up enough to want to go all the way. And that was the conversation where I was like, okay, this is gonna be part of my life's journey to build this company and do it in the best way that I possibly can't.Because if I ask people to join me, ask people to get on the cap table, then I have an ultimate responsibility to give it everything. And I don't, I think some people, it doesn't occur to me that everyone takes it that seriously. And maybe I take it too seriously, I don't know. But that was like a very intentional moment.And so then it was very clear like, okay, I'm gonna do this and I'm gonna give it everything.Alessio: A lot of people don't take it this seriously. But,swyx: uh, let's talk about, you have this concept of the P 99 engineer. Uh, people are 10 x saying, everyone's saying, you know, uh, maybe engineers are out of a job. I don't know.But you definitely see a P 99 engineer, and I just want you to talk about it.Simon Hørup Eskildsen: Yeah, so the P 99 engineer was just a term that we started using internally to talk about candidates and talk about how we wanted to build the company. And you know, like everyone else is, like we want a talent dense company.And I think that's almost become trite at this point. What I credit the cursor founders a lot with is that they just arrived there from first principles of like, we just need a talent dense, um, talent dense team. And I think I've seen some teams that weren't talent dense and like seemed a counterfactual run, which if you've run in been in a large company, you will just see that like it's just logically will happen at a large company.Um, and so that was super important to me and Justine and it's very difficult to maintain. And so we just needed, we needed wording for it. And so I have a document called Traits of the P 99 Engineer, and it's a bullet point list. And I look at that list after every single interview that I do, and in every single recap that we do and every recap we end with.End with, um, some version of I'm gonna reject this candidate completely regardless of what the discourse was, because I wanna see people fight for this person because the default should not be, we're gonna hire this person. The default should be, we're definitely not hiring this person. And you know, if everyone was like, ah, maybe throw a punch, then this is not the right.swyx: Do, do you operate, like if there's one cha there must have at least one champion who's like, yes, I will put my career on, on, on the line for this. You know,Simon Hørup Eskildsen: I think career on the line,swyx: maybe a chair, butSimon Hørup Eskildsen: yeah. You know, like, um, I would say so someone needs to like, have both fists up and be like, I'd fight.Right? Yeah. Yeah. And if one person said, then, okay, let's do it. Right?swyx: Yeah.Simon Hørup Eskildsen: Um. It doesn't have to be absolutely everyone. Right? And like the interviews are always the sign that you're checking for different attributes. And if someone is like knocking it outta the park in every single attribute, that's, that's fairly rare.Um, but that's really important. And so the traits of the P 99 engineer, there's lots of them. There's also the traits of the p like triple nine engineer and the quadruple nine engineer. This is like, it's a long list.swyx: Okay.Simon Hørup Eskildsen: Um, I'll give you some samples, right. Of what we, what we look for. I think that the P 99 engineer has some history of having bent, like their trajectory or something to their will.Right? Some moment where it was just, they just, you know, made the computer do what it needed to do. There's something like that, and it will, it will occur to have them at some point in their career. And, uh. Hopefully multiple times. Right.swyx: Gimme an example of one of your engineers that like,Simon Hørup Eskildsen: I'll give an eng.Uh, so we, we, we launched this thing called A and NV three. Um, we could, we're also, we're working on V four and V five right now, but a and NV three can search a hundred billion vectors with a P 50 of around 40 milliseconds and a p 99 of 200 milliseconds. Um, maybe other people have done this, I'm sure Google and others have done this, but, uh, we haven't seen anyone, um, at least not in like a public consumable SaaS that can do this.And that was an engineer, the chief architect of Turbo Puffer, Nathan, um, who more or less just bent this, the software was not capable of this and he just made it capable for a very particular workload in like a, you know, six to eight week period with the help of a lot of the team. Right. It's been, been, there's numerous of examples of that, like at, at turbo puff, but that's like really bending the software and X 86 to your will.It was incredible to watch. Um. You wanna see some moments like that?swyx: Isn't that triple nine?Simon Hørup Eskildsen: Um, I think Nathan, what's calledAlessio: group nine, that was only nine. I feel like this is too high forSimon Hørup Eskildsen: Nathan. Nathan is, uh, Nathan is like, yeah, there's a lot of nines. Okay. After that p So I think that's one trait. I think another trait is that, uh, the P 99 spends a lot of time looking at maps.Generally it's their preferred ux. They just love looking at maps. You ever seen someone who just like, sits on their phone and just like, scrolls around on a map? Or did you not look at maps A lot? You guys don't look atswyx: maps? I guess I'm not feeling there. I don't know, butSimon Hørup Eskildsen: you just dis What about trains?Do you like trains?swyx: Uh, I mean they, not enough. Okay. This is just like weapon nice. Autism is what I call it. Like, like,Simon Hørup Eskildsen: um, I love looking at maps, like, it's like my preferred UX and just like I, you know, I likeswyx: lotsAlessio: of, of like random places, soswyx: like,youswyx: know.Alessio: Yes. Okay. There you go. So instead of like random places, like how do you explore the maps?Simon Hørup Eskildsen: No, it's, it's just a joke.swyx: It's autism laugh. It's like you are just obsessed by something and you like studying a thing.Simon Hørup Eskildsen: The origin of this was that at some point I read an interview with some IOI gold medalistswyx: Uhhuh,Simon Hørup Eskildsen: and it's like, what do you do in your spare time? I was just like, I like looking at maps.I was like, I feel so seen. Like, I just like love, like swirling out. I was like, oh, Canada is so big. Where's Baffin Island? I don't know. I love it. Yeah. Um, anyway, so the traits of P 99, P 99 is obsessive, right? Like, there's just like, you'll, you'll find traits of that we do an interview at, at, at, at turbo puffer or like multiple interviews that just try to screen for some of these things.Um, so. There's lots of others, but these are the kinds of traits that we look for.swyx: I'll tell you, uh, some people listen for like some of my dere stuff. Uh, I do think about derel as maps. Um, you draw a map for people, uh, maps show you the, uh, what is commonly agreed to be the geographical features of what a boundary is.And it shows also shows you what is not doing. And I, I think a lot of like developer tools, companies try to tell you they can do everything, but like, let's, let's be real. Like you, your, your three landmarks are here, everyone comes here, then here, then here, and you draw a map and, and then you draw a journey through the map.And like that. To me, that's what developer relations looks like. So I do think about things that way.Simon Hørup Eskildsen: I think the P 99 thinks in offs, right? The P 99 is very clear about, you know, hey, turbo puffer, you can't run a high transaction workload on turbo puffer, right? It's like the right latency is a hundred milliseconds.That's a clear trade off. I think the P 99 is very good at articulating the trade offs in every decision. Um. Which is exactly what the map is in your case, right?swyx: Uh, yeah, yeah. My, my, my world. My world.Alessio: How, how do you reconcile some of these things when you're saying you bend the will the computer versus like the trade

In this episode, hosts Lois Houston and Nikita Abraham are joined by special guests Samvit Mishra and Rashmi Panda for an in-depth discussion on security and migration with Oracle Database@AWS. Samvit shares essential security best practices, compliance guidance, and data protection mechanisms to safeguard Oracle databases in AWS, while Rashmi walks through Oracle's powerful Zero-Downtime Migration (ZDM) tool, explaining how to achieve seamless, reliable migrations with minimal disruption.   Oracle Database@AWS Architect Professional: https://mylearn.oracle.com/ou/course/oracle-databaseaws-architect-professional/155574 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, Anna Hulkower, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   -------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:26 Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Communications and Adoption with Customer Success Services. Lois: Hello again! We're continuing our discussion on Oracle Database@AWS and in today's episode, we're going to talk about the aspects of security and migration with two special guests: Samvit Mishra and Rashmi Panda. Samvit is a Senior Manager and Rashmi is a Senior Principal Database Instructor.  00:59 Nikita: Hi Samvit and Rashmi! Samvit, let's begin with you. What are the recommended security best practices and data protection mechanisms for Oracle Database@AWS? Samvit: Instead of everyone using the root account, which has full access, we create individual users with AWS, IAM, Identity Center, or IAM service. And in addition, you must use multi-factor authentication. So basically, as an example, you need a password and a temporary code from virtual MFA app to log in to the console.  Always use SSL or TLS to communicate with AWS services. This ensures data in transit is encrypted. Without TLS, the sensitive information like credentials or database queries can be intercepted. AWS CloudTrail records every action taken in your AWS account-- who did what, when, and from where. This helps with audit, troubleshooting, and detecting suspicious activity. So you must set up API and user activity logging with AWS CloudTrail.  Use AWS encryption solutions along with all default security controls within AWS services. To store and manage keys by using transparent data encryption, which is enabled by default, Oracle Database@AWS uses OCI vaults. Currently, Oracle Database@AWS doesn't support the AWS Key Management Service. You should also use advanced managed security services such as Amazon Macie, which assists in discovering and securing sensitive data that is stored in Amazon S3.  03:08 Lois: And how does Oracle Database@AWS deliver strong security and compliance? Samvit: Oracle Database@AWS enforces transparent data encryption for all data at REST, ensuring stored information is always protected. Data in transit is secured using SSL and Native Network Encryption, providing end-to-end confidentiality. Oracle Database@AWS also uses OCI Vault for centralized and secure key management. This allows organizations to manage encryption keys with fine-grained control, rotation policies, and audit capabilities to ensure compliance with regulatory standards. At the database level, Oracle Database@AWS supports unified auditing and fine-grained auditing to track user activity and sensitive operations. At the resource level, AWS CloudTrail and OCI audit service provide comprehensive visibility into API calls and configuration changes. At the database level, security is enforced using database access control lists and Database Firewall to restrict unauthorized connections. At the VPC level, network ACLs and security groups provide layered network isolation and access control. Again, at the database level, Oracle Database@AWS enforces access controls to Database Vault, Virtual Private Database, and row-level security to prevent unauthorized access to sensitive data. And at a resource level, AWS IAM policies, groups, and roles manage user permissions with the fine-grained control. 05:27 Lois Samvit, what steps should users be taking to keep their databases secure? Samvit: Security is not a single feature but a layered approach covering user access, permissions, encryption, patching, and monitoring. The first step is controlling who can access your database and how they connect. At the user level, strong password policies ensure only authorized users can login. And at the network level, private subnets and network security group allow you to isolate database traffic and restrict access to trusted applications only. One of the most critical risks is accidental or unauthorized deletion of database resources. To mitigate this, grant delete permissions only to a minimal set of administrators. This reduces the risk of downtime caused by human error or malicious activity. Encryption ensures that even if the data is exposed, it cannot be read. By default, all databases in OCI are encrypted using transparent data encryption. For migrated databases, you must verify encryption is enabled and active. Best practice is to rotate the transparent data encryption master key every 90 days or less to maintain compliance and limit exposure in case of key compromise. Unpatched databases are one of the most common entry points for attackers. Always apply Oracle critical patch updates on schedule. This mitigates known vulnerabilities and ensures your environment remains protected against emerging threats. 07:33 Nikita: Beyond what users can do, are there any built-in features or tools from Oracle that really help with database security? Samvit: Beyond the basics, Oracle provides powerful database security tools. Features like data masking allow you to protect sensitive information in non-production environments. Auditing helps you monitor database activity and detect anomalies or unauthorized access. Oracle Data Safe is a managed service that takes database security to the next level. It can access your database configuration for weaknesses. It can also detect risky user accounts and privileges, identify and classify sensitive data. It can also implement controls such as masking to protect that data. And it can also continuously audit user activity to ensure compliance and accountability. Now, transparent data encryption enables you to encrypt sensitive data that you store in tables and tablespaces. It also enables you to encrypt database backups. After the data is encrypted, this data is transparently decrypted for authorized users or applications when they access that data. You can configure OCI Vault as a part of the transparent data encryption implementation. This enables you to centrally manage keystore in your enterprise. So OCI Vault gives centralized control over encryption keys, including key rotation and customer managed keys. 09:23 Lois: So obviously, lots of companies have to follow strict regulations. How does Oracle Database@AWS help customers with compliance?  Samvit: Oracle Database@AWS has achieved a broad and rigorous set of compliance certifications. The service supports SOC 1, SOC 2, and SOC 3, as well as HIPAA for health care data protection. If we talk about SOC 1, that basically covers internal controls for financial statements and reporting. SOC 2 covers internal controls for security, confidentiality, processing integrity, privacy, and availability. SOC 3 covers SOC 2 results tailored for a general audience. And HIPAA is a federal law that protects patients' health information and ensures its confidentiality, integrity, and availability. It also holds certifications and attestations such as CSA STAR, C5. Now C5 is a German government standard that verifies cloud providers meet strict security and compliance requirements. CSA STAR attestation is an independent third-party audit of cloud security controls. CSA STAR certification also validates a cloud provider's security posture against CSA's cloud controls matrix. And HDS is a French certification that ensures cloud providers meet stringent requirements for hosting and protecting health care data. Oracle Database@AWS also holds ISO and IEC standards. You can also see PCI DSS, which is basically for payment card security and HITRUST, which is for high assurance health care framework. So, these certifications ensure that Oracle Database@AWS not only adheres to best practices in security and privacy, but also provides customers with assurance that their workloads align with globally recognized compliance regimes. 11:47 Nikita: Thank you, Samvit. Now Rashmi, can you walk us through Oracle's migration solution that helps teams move to OCI Database Services? Rashmi: Oracle Zero-Downtime Migration is a robust and flexible end-to-end database migration solution that can completely automate and streamline the migration of Oracle databases. With bare minimum inputs from you, it can orchestrate and execute the entire migration task, virtually needing no manual effort from you. And the best part is you can use this tool for free to migrate your source Oracle databases to OCI Oracle Database Services faster and reliably, eliminating the chances of human errors. You can migrate individual databases or migrate an entire fleet of databases in parallel. 12:34 Nikita: Ok. For someone planning a migration with ZDM, are there any key points they should keep in mind?  Rashmi: When migrating using ZDM, your source databases may require minimal downtime up to 15 minutes or no downtime at all, depending upon the scenario. It is built with the principles of Oracle maximum availability architecture and leverages technologies like Oracle GoldenGate and Oracle Data Guard to achieve high availability and online migration workflow using Oracle migration methods like RMAN, Data Pump, and Database Links. Depending on the migration requirement, ZDM provides different migration method options. It can be logical or physical migration in an online or offline mode. Under the hood, it utilizes the different database migration technologies to perform the migration. 13:23 Lois: Can you give us an example of this? Rashmi: When you are migrating a mission critical production database, you can use the logical online migration method. And when you are migrating a development database, you can simply choose the physical offline migration method. As part of the migration job, you can perform database upgrades or convert your database to multitenant architecture. ZDM offers greater flexibility and automation in performing the database migration. You can customize workflow by adding pre or postrun scripts as part of the workflow. Run prechecks to check for possible failures that may arise during migration and fix them. Audit migration jobs activity and user actions. Control the execution like schedule a job pause, resume, if needed, suspend and resume the job, schedule the job or terminate a running job. You can even rerun a job from failure point and other such capabilities. 14:13 Lois: And what kind of migration scenarios does ZDM support? Rashmi: The minimum version of your source Oracle Database must be 11.2.0.4 and above. For lower versions, you will have to first upgrade to at least 11.2.0.4. You can migrate Oracle databases that may be of the Standard or Enterprise edition. ZDM supports migration of Oracle databases, which may be a single-instance, or RAC One Node, or RAC databases. It can migrate on Unix platforms like Linux, Oracle Solaris, and AIX. For Oracle databases on AIX and Oracle Solaris platform, ZDM uses logical migration method. But if the source platform is Linux, it can use both physical and logical migration method. You can use ZDM to migrate databases that may be on premises, or in third-party cloud, or even within Oracle Cloud Infrastructure. ZDM leverages Oracle technologies like RMAN datacom, Database Links, Data Guard, Oracle GoldenGate when choosing a specific migration workflow. 15:15 Are you ready to revolutionize the way you work? Discover a wide range of Oracle AI Database courses that help you master the latest AI-powered tools and boost your career prospects. Start learning today at mylearn.oracle.com. 15:35 Nikita: Welcome back! Rashmi, before someone starts using ZDM, is there any prep work they should do or things they need to set up first? Rashmi: Working with ZDM needs few simple configuration. Zero-downtime migration provides a command line interface to run your migration job. First, you have to download the ZDM binary, preferably download from my Oracle Support, where you can get the binary with the latest updates. Set up and configure the binary by following the instructions available at the same invoice node. The host in which ZDM is installed and configured is called the zero-downtime migration service host. The host has to be Oracle Linux version 7 or 8, or it can be RCL 8. Next is the orchestration step where connection to the source and target is configured and tested like SSH configuration with source and target, opening the ports in respective destinations, creation of dump destination, granting required database privileges. Prepare the response file with parameter values that define the workflow that ZDM should use during Oracle Database migration. You can also customize the migration workflow using the response file. You can plug in run scripts to be executed before or after a specific phase of the migration job. These customizations are called custom plugins with user actions. Your sources may be hosted on-premises or OCI-managed database services, or even third-party cloud. They may be Oracle Database Standard or Enterprise edition and on accelerator infrastructure or a standard compute. The target can be of the same type as the source. But additionally, ZDM supports migration to multicloud deployments on Oracle Database@Azure, Oracle Database@Google Cloud, and Oracle Database@AWS. You begin with a migration strategy where you list the different databases that can be migrated, classification of the databases, grouping them, performing three migration checks like dependencies, downtime requirement versions, and preparing the order migration, the target migration environment, et cetera. 17:27 Lois: What migration methods and technologies does ZDM rely on to complete the move? Rashmi: There are primarily two types of migration: physical or logical. Physical migration pertains to copy of the database OS blocks to the target database, whereas in logical migration, it involves copying of the logical elements of the database like metadata and data. Each of these migration methods can be executed when the database is online or offline. In online mode, migration is performed simultaneously while the changes are in progress in the source database. While in offline mode, all changes to the source database is frozen. For physical offline migration, it uses backup and restore technique, while with the physical online, it creates a physical standby using backup and restore, and then performing a switchover once the standby is in sync with the source database. For logical offline migration, it exports and imports database metadata and data into the target database, while in logical online migration, it is a combination of export and import operation, followed by apply of incremental updates from the source to the target database. The physical or logical offline migration method is used when the source database of the application can allow some downtime for the migration. The physical or logical online migration approach is ideal for scenarios where any downtime for the source database can badly affect critical applications. The only downtime that can be tolerated by the application is only during the application connection switchover to the migrated database. One other advantage is ZDM can migrate one or a fleet of Oracle databases by executing multiple jobs in parallel, where each job workflow can be customized to a specific database need. It can perform physical or logical migration of your Oracle databases.  And whether it should be performed online or offline depends on the downtime that can be approved by business. 19:13 Nikita: Samvit and Rashmi, thanks for joining us today. Lois: Yeah, it's been great to have you both. If you want to dive deeper into the topics we covered today, go to mylearn.oracle.com and search for the Oracle Database@AWS Architect Professional course. Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 19:35 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

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

The future of reliability is not one tool. It is a team of agents working together. At AWS re:Invent, I had a chat with Francois Martel, Field CTO at NeuBird.ai, to talk about how AI is changing the way developers and SREs handle reliability in the real world.Here are the key takeaways from our conversation-- Coding agents are becoming the front door to AITools like GitHub Copilot and Cursor are getting massive adoption. When paired with NeuBird's Hawkeye agentic SRE server, these agents can jump straight into root cause analysis and even take action to remediate issues-- SREs are a natural fit for agentsSREs already live in the command line and think in scripts. Coding agents are an easy and practical entry point for bringing AI into day to day SRE workflows-- Agent adoption is speeding upWe are past experimentation. Customers are seeing value from early use cases, which is pushing broader and faster adoption of agent based systems-- Enterprise security still mattersFor larger organizations, NeuBird can deploy the agent inside the customer's VPC. The data stays in their environment and the full data path remains under their control-- AWS partnership momentumNeuBird is launching a pay as you go offering on the AWS Marketplace. This makes it one of the first agentic SRE servers you can try without long term commitment and connect to tools like AWS, Datadog, Dynatrace, and GrafanaIf you want to see how agentic SRE works in practice, you can start with the pay as you go option or the two week free trial and pairing it with your favorite coding agent.It was great catching up with François again and seeing how NeuBird is pushing the agentic SRE space forward.#data #ai #awsreinvent #aws #agents #awspartners #copilot #agents #theravitshow

Send us a textThis week, watch editor and producer Devin Pennypacker joins the podcast to chat about new releases from Omega, VPC, and ponder what Tudor might do for their 100th anniversary. With these new releases, there are some deeper issues brought to bear that we get into in this episode, from releasing an LE colorway into regular production, to tackling a tricky sophomore release. We even confront the current state of dive watches through the lens of the new Type 39VM, and narrowly avoid an existential crisis. Show Notes:The Deep TrackDevin Pennypacker on InstagramJDM Casio Timber CruiserCertina DS SuperSpeedmaster 3861Book A Room On the MoonSpeedmaster Black & White VPC Type39VMStudio Underd0g Field Watch 02 SeriesClemence WatchesPaulin MaraBell & Ross MultimeterTudor Turns 100Tudor North FlagTudor P01Tudor RangerTAG Heuer x New BalanceSupport the show

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

Dans cet épisode, Laurent Kretz reçoit Bérénice Maîtrepierre, responsable e-commerce chez Damart, la marque iconique qui réchauffe les Français depuis 1953. Aujourd'hui, Damart est face à un nouveau défi : conjuguer héritage et modernité. Le retail tient bon avec plus de 90 magasins. Le print reste stratégique : un achat en ligne sur deux est précédé d'un catalogue. Et le digital prend de l'ampleur : 50 millions d'euros de ventes, 20 millions de visites… Bérénice explique comment moderniser une marque patrimoniale sans perdre son ADN. Tout est pensé pour raconter Damart autrement, en ligne comme en magasin.Au programme : 00:00:00 - Introduction00:02:12 - Présentation de Damart00:09:54 – Concurrence sur le thermique, SEO/SEA, enjeux de notoriété00:16:15 - La cible client : âge, usages & comportements00:22:37 - Le rôle du print : influence sur la conversion00:28:11 - Références catalogue, VPC et adaptation digitale.00:31:48 - Le digital = 20% du CA (≈ 50 M€)00:37:04 – Comment arbitrer ses développements tech00:51:02 – E-merchandising & expérience Client01:03:38 - Conclusion Et quelques dernières infos à vous partager :Suivez Le Panier sur Instagram @lepanier.podcast !Inscrivez- vous à la newsletter sur lepanier.io pour cartonner en e-comm !Écoutez les épisodes sur Apple Podcasts, Spotify ou encore Podcast Addict Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Today, we are dropping our final episode in the series "The Railsware Way", sponsored by our good friends at Railsware. Railsware is a leading product studio with two main focuses - services and products. They have created amazing products like Mailtrap, Coupler and TitanApps, while also partnering with teams like Calendly and Bright Bytes. They deliver amazing products, and have happy customers to prove it.In this series, we are digging into the company's methods around product engineering and development. In particular, we will cover relevant topics to not only highlight their expertise, but to educate you on industry trends alongside their experience.In today's episode, we are speaking with Oleksii Ianchuk, Product Lead at Railsware, specifically for Mailtrap. Thought he doesn't like to limit his activities to product development, Oleksii has spent six years in product and project management, and is keen on searching for insights and putting them to work, as well as gauging the effects of his input.Questions:The story of Mailtrap starts with accidentally sending test emails to real users in 2011. How did Mailtrap evolve from an internal "fail" to a platform serving hundreds of thousands of users? How did that mistake spark the creation of Mailtrap, and what lessons did you learn about turning problems into opportunities?What made you decide to expand from email testing into Email API/SMTP delivery - and why was it harder than expected? What specific challenges around deliverability, spam fighting, and infrastructure caught you off guard?Can you walk us through the "splitting the product" mistake and its long-term consequences? Your team decided to separate testing and sending into different repositories and isolated VPC projects. What seemed like a good engineering decision at the time - how did this create problems as you scaled, and what would you do differently?You spent a year struggling with Redshift before switching to Elasticsearch - what did that teach you about technology decisions? You ran tests, evaluated alternatives, and still picked the wrong database for your use case. How do you balance thorough research with the reality that you can't always predict what will work until you're in production?When do you buy external expertise versus rely on your internal team? How do you decide when to hire outside knowledge, and how do you find the right consultants for niche problems?Why didn't existing Mailtrap users immediately adopt the Email API/SMTP feature, and what did that teach you?You expected current users to quickly transition to the new sending functionality. What did you learn about switching costs, user perception, and the challenge of changing how people think about your product?What business insights around deliverability, spam prevention, and compliance surprised you most?Email delivery isn't just about infrastructure - there's a whole ecosystem of postmasters, anti-spam systems, and compliance requirements. What aspects of this business were most unexpected, and how did they shape your product strategy?Looking at Mailtrap's 13-year journey, what's your philosophy on "failing fast" versus "building solid foundations"?Linkshttps://railsware.com/https://www.linkedin.com/in/yanch/Our Sponsors:* Check out Incogni: https://incogni.com/codestory* Check out NordProtect: https://nordprotect.com/codestorySupport this podcast at — https://redcircle.com/code-story-insights-from-startup-tech-leaders/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Stephen is a VMware trainer who teaches classes on NSX and now has been leaving VCF networking. Eric and Stephen talk about the setup and security aspects of VPC's and how the average admin can now tackle this config.

Tech leaders are often led to believe that they have “full-stack observability.” The MELT framework—metrics, events, logs, and traces—became the industry standard for visibility. However, Robert Cowart, CEO and Co-Founder of ElastiFlow, believes that this MELT framework leaves a critical gap. In the latest episode of the Tech Transformed podcast, host Dana Gardner, President and Principal Analyst at Interabor Solutions, sits down with Cowart to discuss network observability and its vitality in achieving full-stack observability.The speakers discuss the limitations of legacy observability tools that focus on MELT and how this leaves a significant and dangerous blind spot. Cowart emphasises the need for teams to integrate network data enriched with application context to enhance troubleshooting and security measures. What's Beyond MELT?Cowart explains that when it comes to the MELT framework, meaning “metrics, events, logs, and traces, think about the things that are being monitored or observed with that information. This is alluded to servers and applications.“Organisations need to understand their compute infrastructure and the applications they are running on. All of those servers are connected to networks, and those applications communicate over the networks, and users consume those services again over the network,” he added.“What we see among our growing customer base is that there's a real gap in the full-stack story that has been told in the market for the last 10 years, and that is the network.”The lack of insights results in a constant blind spot that delays problem-solving, hides user-experience issues, and leaves organizations vulnerable to security threats. Cowart notes that while performance monitoring tools can identify when an application call to a database is slow, they often don't explain why.“Was the database slow, or was the network path between them rerouted and causing delays?” he questions. “If you don't see the network, you can't find the root cause.”The outcome is longer troubleshooting cycles, isolated operations teams, and an expensive “blame game” among DevOps, NetOps, and SecOps.Elastiflow's approaches it differently. They focus on observability to network connectivity—understanding who is communicating with whom and how that communication behaves. This data not only speeds up performance insights but also acts as a “motion detector” within the organization. Monitoring east-west, north-south, and cloud VPC flow logs helps organizations spot unusual patterns that indicate internal threats or compromised systems used for launching external attacks.“Security teams are often good at defending the perimeter,” Cowart says. “But once something gets inside, visibility fades. Connectivity data fills that gap.”Isolated Monitoring to Unified Experience Cowart believes that observability can't just be about green lights...

Мок-интервью для junior/начинающего middle DevOps: CI/CD, Git-ветки, AWS (VPC, S3), Kubernetes (probes, DaemonSet), Terraform. Разбираем основы, типовые вопросы и ошибки — простым языком.

Here's the thing. Most enterprise AI pitches talk about scale and speed. Fewer talk about trust, tone, and culture. In this conversation with Inflection AI's Amit Manjhi and Shruti Prakash, I explore a different path for enterprise AI, one that combines emotional intelligence with analytical horsepower, enabling teams to ask more informed questions of their data and receive answers that are grounded in context. Amit's story sets the pace. He is a three-time founder, a YC alum, and a CS PhD who has solved complex problems across mobile, ad tech, and data. Shruti complements that arc with a product lens shaped by real operational trenches, from clean rooms to grocery retail analytics.  Together, they built BoostKPI during the pandemic, transforming natural language into actionable insights, and then joined Inflection AI to help refocus the company on achieving enterprise outcomes. Their shared north star is simple to say yet tricky to execute. Make data analysis conversational, accurate, and emotionally aware so people actually use it. We unpack Inflection's shift from Pi's consumer roots to privacy-first enterprise tools. That history matters because it gives the team a head start on EQ. When you combine a deep well of human-to-AI conversations with modern LLMs, you get systems that explain, probe, and adapt rather than dump charts and call it a day.  Shruti breaks down what dialogue with data looks like in practice. Think back-and-forth exchanges that move from "what happened" to "why it happened," then on to "where else this pattern appears" and "what to do next," all grounded in an organization's language and values. Amit takes us under the hood on deployment choices and ownership. If a customer wants on-prem or VPC, they get it. If they're going to fine-tune models to their vernacular, they can. The model, the insights, and the guardrails remain in the customer's control. I enjoyed the honesty around adoption. Chasing AGI makes headlines, but it rarely helps a merchandising manager spot an early drop in lifetime value or a CX lead understand churn risk before quarter end. The duo keeps the conversation grounded in everyday questions that drive numbers and reduce meetings. They describe a path where EQ and IQ come together to form what Shruti calls contextual intelligence, and where brands can trust AI agents to assist without losing ownership or voice. If you care about making data useful to more people, and you want AI that sounds like your company rather than a generic assistant, this one is for you. We cover startup lessons, the reality of cofounding as a couple during lockdowns, and how Inflection is working with large enterprises to bring conversational analysis to real workloads. It is a grounded look at where enterprise AI is heading, and a timely reminder that technology should elevate humans, not replace them. ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

AWS Morning Brief for the week of September 8th, with Corey Quinn. Links:Amazon disrupts watering hole campaign by Russia's APT29AWS IAM launches new VPC endpoint condition keys for network perimeter controlsRDS Data API now supports IPv6Now Open — AWS Asia Pacific (New Zealand) RegionAWS Resource Explorer is now available in AWS Asia Pacific (Taipei) RegionProtect your Amazon Route 53 DNS zones and records Efficiently verify Amazon S3 data at scale with compute checksum operationAWS Elemental celebrates 10 years of innovationChoosing the right AWS live streaming solution for your use case

In this episode, James Maude sits down with Brian Wagner, CTO at Revenir, whose cybersecurity story started at just 15, building Microsoft Access databases for a medical hospice. From teenage entrepreneur to AWS security specialist, Brian's path has been anything but ordinary. He pulls back the curtain on his time with the elite Zipline incident response team where he confronted a catastrophic VPC peering breach that spiraled into data theft and blackmail. Together, James and Brian dissect how vendor network compromises can silently open doors into your cloud and why Brian insists that true security isn't something you bolt on later - it's a culture you build from day one.

Send us a textWhat happens when three major cloud providers each reimagine network design from scratch? You get three completely different approaches to solving the same fundamental problem.The foundation of cloud networking begins with the virtual containers that hold your resources: AWS's Virtual Private Clouds (VPCs), Azure's Virtual Networks (VNets), and Google Cloud's VPCs (yes, the same name, very different implementation). While they all serve the same basic purpose—providing logical isolation for your workloads—their design philosophies reveal profound differences in how each provider expects you to architect your solutions.AWS took the explicit control approach. When you create subnets within an AWS VPC, you must assign each to a specific Availability Zone. This creates a vertical architecture pattern where you're deliberately placing resources in specific physical locations and designing resilience across those boundaries. Network engineers often find this intuitive because it matches traditional fault domain thinking. However, this design means you must account for cross-AZ data transfer costs and explicit resiliency patterns.Azure flipped the script with their horizontal approach. By default, subnets span across all AZs in a region, with Microsoft's automation handling the resilience for you. This "let us handle the complexity" philosophy makes initial deployment simpler but provides less granular control. Meanwhile, Google Cloud went global, allowing a single VPC to span regions worldwide—an approach that simplifies global connectivity but introduces new challenges for security segmentation.These architectural differences aren't merely academic—they fundamentally change how you design for resilience, manage costs, and implement security. The cloud introduced "toll booth" pricing for data movement, where crossing availability zones or regions incurs charges that didn't exist in traditional data centers. Understanding these nuances is crucial whether you're migrating existing networks or designing new ones.Want to dive deeper into cloud networking concepts? Let us know what topics you'd like us to cover next as we explore how traditional networking skills translate to the cloud world.Purchase Chris and Tim's new book on AWS Cloud Networking: https://www.amazon.com/Certified-Advanced-Networking-Certification-certification/dp/1835080839/ Check out the Fortnightly Cloud Networking Newshttps://docs.google.com/document/d/1fkBWCGwXDUX9OfZ9_MvSVup8tJJzJeqrauaE6VPT2b0/Visit our website and subscribe: https://www.cables2clouds.com/Follow us on BlueSky: https://bsky.app/profile/cables2clouds.comFollow us on YouTube: https://www.youtube.com/@cables2clouds/Follow us on TikTok: https://www.tiktok.com/@cables2cloudsMerch Store: https://store.cables2clouds.com/Join the Discord Study group: https://artofneteng.com/iaatj

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

"We trust them with their money, but we can't trust them with their expertise?"Podcast mic drop. Imagine the progress that could be made if associations consistently invited partners (and their expertise) into the conversation.That's what John Bacon, ASAE's Vice President of Enterprise Sales, is challenging associations to do: go beyond the dollars and recognize partners as strategic allies. This 14-minute episode will give you tips on how to do it.Learn how EDUCAUSE , an association committed to advancing higher education through technology, is bringing partners together in a unique way to advance their industry.Just a year in, partnerships have doubled and the program is at capacity according to Leah Lang (EDUCAUSE).If you're ready to not only increase revenue but also advance your industry by bringing partners into the conversation, give this episode a listen!About Association RevUP: A PAR podcast that celebrates the stories of associations who are utilizing business to bring about real change in their industries. Episodes are less than 15-minutes, written and produced to keep you engaged, and full of actionable insights. Hosted by Carolyn Shomali.- ⁠⁠VPC, Inc.⁠⁠, the boutique production company PAR trusts with our in-person event the RevUP Summit, and the parter of this podcast.- ⁠⁠MyPar.org⁠⁠: learn more about the PAR member community- ⁠⁠RevUP Summit⁠⁠: November 4-6, 2025 in Annapolis, MD. The only association event focused on revenue health.

“How do we continue to sustain this for another 30 years?” If sustainability, advocacy, partnerships or a new generation of members is of importance for your association, this is a 13-minute episode you won't want to miss. Find out how the Association of Progressive Rental Organizations (APRO) is engaging a new generation of members to advocate for its industry in Washington. And, how a long-term relationship between APRO and vendor-member Ashley Furniture was the key to making it happen. You'll hear from APRO CEO Charles Smitherman and Ashley Furniture's Mike Kays on a mentorship/fellowship opportunity in Washington.Plus, learn why momentum is vital to your association's success. You'll learn how to build it in a way that is unstoppable from best-selling author and keynote speaker, Brant Menswar.About Association RevUP: A PAR podcast that celebrates the stories of associations who are utilizing business to bring about real change in their industries. Episodes are less than 15-minutes, written and produced to keep you engaged, and full of actionable insights. Hosted by Carolyn Shomali.- ⁠VPC, Inc.⁠, the production company PAR trusts with our in-person event the RevUP Summit, and the parter of this podcast.- ⁠MyPar.org⁠: learn more about the PAR member community- ⁠RevUP Summit⁠: November 4-6, 2025 in Annapolis, MD- Brant Menswar- APRO Fellowship Program

Welcome to the first episode of the Virtually Speaking Podcast's VMware Cloud Foundation 9 series! In this inaugural installment, hosts Pete Flecha and John Nicholson sit down with Paul Turner, Broadcom's  leader of VCF product management, to kick off an in-depth exploration of VMware Cloud Foundation 9. This series will dive deep into the platform's latest innovations, capabilities, and transformative potential for private cloud computing.    Key Highlights: The rise of private cloud and its cost-efficiency compared to public cloud services Sovereign cloud capabilities and regulatory compliance Innovations in GPU as a service Native support for VMs and Kubernetes in a single platform Advances in storage (vSAN) with enhanced performance and resilience Networking improvements with native VPC setup in vCenter VMware's partnerships with hyperscalers and 500+ cloud service providers Join us as we unpack the exciting developments in VCF 9 across multiple episodes.

Send us a textTim and Chris discuss major cybersecurity acquisitions and innovations, examining how these changes will impact enterprise security and cloud architecture.• Zscaler acquires Red Canary MDR (Managed Detection and Response) to fill gaps in their platform despite potential integration challenges• AWS Network Firewall now supports multiple VPC endpoints without requiring Transit Gateway deployment• AWS exits the private 5G market, pivoting to partnerships with established telecommunications providers• CheckPoint acquires Veritai Cybersecurity to enhance their Infinity platform with "virtual patching" capabilities• North Korean IT workers using sophisticated techniques to infiltrate Western companies by posing as legitimate remote employeesCheck the news document for additional stories we didn't have time to cover, including a project called MPIC focused on preventing BGP attacks with certificate validation.Purchase Chris and Tim's new book on AWS Cloud Networking: https://www.amazon.com/Certified-Advanced-Networking-Certification-certification/dp/1835080839/ Check out the Fortnightly Cloud Networking Newshttps://docs.google.com/document/d/1fkBWCGwXDUX9OfZ9_MvSVup8tJJzJeqrauaE6VPT2b0/Visit our website and subscribe: https://www.cables2clouds.com/Follow us on BlueSky: https://bsky.app/profile/cables2clouds.comFollow us on YouTube: https://www.youtube.com/@cables2clouds/Follow us on TikTok: https://www.tiktok.com/@cables2cloudsMerch Store: https://store.cables2clouds.com/Join the Discord Study group: https://artofneteng.com/iaatj

VPC is committed to being a place of welcome for everyone. We are always seeking new ways to spread our arms wider to the world. Over the years, our welcome of LGBTQIA+ folks has grown organically as we have listened to the stories in our congregation and come to understand how important it is to have a clear commitment to full inclusion. We are so proud to be a partner with kin•dom community, an organization that was founded by Rev. Pepa Paniagua, who grew up at VPC. This organization provides safe spaces of belonging for queer youth. And this coming Sunday, we are honored to have Rev. Dr. John Leedy, the current Executive Director of kin•dom, preach in our Sunday morning service.

In this episode we speak with Brendan Carroll, Co-Founder and Senior Partner at Victory Park Capital (VPC), a global leader in alternative asset management specializing in private asset-backed credit. Founded in 2007 and now a majority-owned affiliate of Janus Henderson Group, VPC offers comprehensive financing and capital markets solutions through its affiliate platform, Triumph Capital Markets. With a global presence across 25 cities, VPC is at the forefront of innovative investment strategies. Brendan leads strategic initiatives, firm operations, and the sourcing of high-impact investment opportunities at VPC. As a key member of VPC's Investment and Valuation Committees, Brendan's leadership is integral to driving the firm's success and delivering exceptional value to its investors. Brendan supports Lurie Children's Hospital. To learn more about this organization click here. I am your host RJ Lumba.  We hope you enjoy the show.  If you like the episode, click to follow.

"I think what we're doing is trailblazing." We don't often hear 'trailblazing' associated with associations, but that's exactly how Thad Lurie describes the most recent initiative of the American Geophysical Union (AGU) in episode 3.Effectively reaching the youngest career professionals is an ongoing challenge for associations in the quest to remain relevant. In less than 13-minutes, you'll learn how AGU is using personalization to provide members with the content and connections they are looking for. And, how they are adapting the new technology to meet immediate and evolving member needs.The Association RevUP Podcast is presented by the Professionals for Association Revenue (PAR) and hosted by Carolyn Shomali.Learn more about ⁠PAR⁠ and its annual conference, ⁠The RevUP Summit. ⁠Special Thanks to podcast partner, the event production company ⁠VPC, Inc.⁠Helpful Links:Learn more about AGUAGU partner: Tasio Labs

Send us a textThe tech world gives and takes away as Google introduces CloudWAN while MITRE nearly loses CVE funding, showcasing both innovation and vulnerability in our digital infrastructure landscape. Politics increasingly intersects with technology as we examine controversial security clearance revocations alongside much-needed technical improvements in cloud networking.• Google Cloud Next introduces CloudWAN service with two use cases: high-performance data center connectivity and premium branch networking• Google's approach differs from AWS, encouraging single global VPC deployments across regions• MITRE loses funding for the CVE program, threatening the global vulnerability tracking system• CISA provides 11-month bridge funding, but fragmentation begins as EU launches alternative vulnerability tracking• Azure announces general availability of route maps for Virtual WAN, bringing traditional networking capabilities to cloud• Former CISA director Chris Krebs targeted in federal investigation for debunking 2020 election fraud claims• Security clearance revocations increasingly used as political weapons against technology professionalsSubscribe to Cables to Clouds Fortnightly News and tell a friend about the show to stay informed about the evolving cloud technology landscape.Purchase Chris and Tim's new book on AWS Cloud Networking: https://www.amazon.com/Certified-Advanced-Networking-Certification-certification/dp/1835080839/ Check out the Fortnightly Cloud Networking Newshttps://docs.google.com/document/d/1fkBWCGwXDUX9OfZ9_MvSVup8tJJzJeqrauaE6VPT2b0/Visit our website and subscribe: https://www.cables2clouds.com/Follow us on BlueSky: https://bsky.app/profile/cables2clouds.comFollow us on YouTube: https://www.youtube.com/@cables2clouds/Follow us on TikTok: https://www.tiktok.com/@cables2cloudsMerch Store: https://store.cables2clouds.com/Join the Discord Study group: https://artofneteng.com/iaatj

In this news episode, Phil Gervasi and Justin Ryburn cover some of the latest tech headlines, such as Starlink entering the service provider space with Community Gateways, OpenAI raising $40 billion for AGI development, Comcast acquiring Nitel to expand its channel distribution strategy, and AWS announcing support for VPC route server running BGP. Tune in for the latest news and a quick roundup of upcoming network industry events.

Send us a textWhat exactly is cloud networking? This seemingly simple question quickly descends into a fascinating philosophical debate as we welcome back Nico Vibert, Senior Staff Technical Marketing Engineer at Isovalent/Cisco, to tackle this identity crisis head-on.The conversation begins with a startling observation from Nico about analyst reports that group wildly different vendors together under the "cloud networking" umbrella. From there, we explore how defining cloud networking has become increasingly complex as technologies evolve and converge. We trace the origins back to AWS's introduction of VPC in 2009 and discuss how different cloud providers approach networking based on their unique company cultures.One clear consensus emerges: true cloud networking must be API-driven. Whether consumed directly via APIs or through infrastructure-as-code tools like Terraform, programmability stands as a non-negotiable requirement. But beyond this foundation, the boundaries blur significantly when examining various technologies that might qualify.Does Kubernetes networking fall under the cloud networking umbrella? What about Middle Mile providers like Equinix or Megaport that physically connect clouds? Are CDNs part of cloud networking, or something entirely different? We dissect these questions without settling on definitive answers, highlighting how technology's rapid evolution makes categorization increasingly difficult.Looking ahead, we explore how AI is reshaping cloud networking in two critical ways: networks optimized for AI workloads and AI-enhanced network management. Cloud providers are investing billions in infrastructure upgrades, developing custom silicon to reduce dependency on GPU manufacturers, signaling massive transformation on the horizon.Whether you're a network engineer, cloud architect, or technology leader trying to understand this evolving landscape, this episode provides valuable perspective on cloud networking's past, present, and future directions.Connect with Nico: https://www.linkedin.com/in/nicolasvibert/Purchase Chris and Tim's new book on AWS Cloud Networking: https://www.amazon.com/Certified-Advanced-Networking-Certification-certification/dp/1835080839/ Check out the Fortnightly Cloud Networking Newshttps://docs.google.com/document/d/1fkBWCGwXDUX9OfZ9_MvSVup8tJJzJeqrauaE6VPT2b0/Visit our website and subscribe: https://www.cables2clouds.com/Follow us on BlueSky: https://bsky.app/profile/cables2clouds.comFollow us on YouTube: https://www.youtube.com/@cables2clouds/Follow us on TikTok: https://www.tiktok.com/@cables2cloudsMerch Store: https://store.cables2clouds.com/Join the Discord Study group: https://artofneteng.com/iaatj

"We need to take these things that are ideas and figure out if they even have legs before we spend a bunch of time and money and invest in them."What would it look like if your association acted like a startup? That's the question explored in episode 2 with Chrissy Bagby of the American Association of Veterinary State Boards (AAVSB) and Elizabeth Engel of Spark Consulting. Learn how AAVSB is implementing a culture of innovation where employees at all levels of the organization have learned how to identify and develop non-dues revenue ideas with the help of a proven framework.Learn how to understand your audience, their needs and potential solutions to their problems before investing valuable time and resources moving in the wrong direction.The Association RevUP Podcast is presented by the Professionals for Association Revenue (PAR) and hosted by Carolyn Shomali.Learn more about PAR and its annual conference, The RevUP Summit. Special Thanks to podcast partner, the event production company VPC, Inc.Helpful Links:Lean Startup by Eric RiesSkateboard to Car: MVP analogyVideo: Eric Ries 2011 Google Talk

If you're in SF: Join us for the Claude Plays Pokemon hackathon this Sunday!If you're not: Fill out the 2025 State of AI Eng survey for $250 in Amazon cards!Unsupervised Learning is a podcast that interviews the sharpest minds in AI about what's real today, what will be real in the future and what it means for businesses and the world - helping builders, researchers and founders deconstruct and understand the biggest breakthroughs. Top guests: Noam Shazeer, Bob McGrew, Noam Brown, Dylan Patel, Percy Liang, David LuanFull Episode on Their YouTubeTimestamps* 00:00 Introduction and Excitement for Collaboration* 00:27 Reflecting on Surprises in AI Over the Past Year* 01:44 Open Source Models and Their Adoption* 06:01 The Rise of GPT Wrappers* 06:55 AI Builders and Low-Code Platforms* 09:35 Overhyped and Underhyped AI Trends* 22:17 Product Market Fit in AI* 28:23 Google's Current Momentum* 28:33 Customer Support and AI* 29:54 AI's Impact on Cost and Growth* 31:05 Voice AI and Scheduling* 32:59 Emerging AI Applications* 34:12 Education and AI* 36:34 Defensibility in AI Applications* 40:10 Infrastructure and AI* 47:08 Challenges and Future of AI* 52:15 Quick Fire Round and Closing RemarksTranscript[00:00:00] Introduction and Podcast Overview[00:00:00] Jacob: well, thanks so much for doing this, guys. I feel like we've we've been excited to do a collab for a while. I[00:00:13] swyx: love crossovers. Yeah. Yeah. This, this is great. Like the ultimate meta about just podcasters talking to other podcasters. Yeah. It's a lot. Podcasts all the way up.[00:00:21] Jacob: I figured we'd have a pretty free ranging conversation today but brought a few conversation starters to, to, to kick us off.[00:00:27] Reflecting on AI Surprises and Trends[00:00:27] Jacob: And so I figured one interesting place to start is you know, obviously it feels that this world is changing like every few months. Wondering as you guys reflect path on the past year, like what surprised you the most?[00:00:36] Alessio: I think definitely recently models we kinda on the, on the right here. Like, oh, that, well, I, I I think there's, there's like the, what surprised us in a good way.[00:00:44] May maybe in a, in a bad way. I would say in a good way. Recently models and I think the release of them right after the new reps scaling instead talked by Ilia. I think there was maybe like a, a little. It's so over and then we're so back. I'm like such a short, short period. It was really [00:01:00] fortuitous[00:01:00] Jacob: timing though, like right.[00:01:01] As pre-training died, I mean, obviously I'm sure within the labs they knew pre-training was dying and had to find something. But you know, from the outside it was it, it felt like one right into the other.[00:01:09] Alessio: Yeah. Yeah, exactly. So that, that was a good surprise,[00:01:12] swyx: I would say, if you wanna make that comment about timing, I think it's suspiciously neat that like, because we know that Strawberry was being worked on for like two years-ish.[00:01:20] Like, and we know exactly when Nome joined OpenAI, and that was obviously a big strategic bet by OpenAI. So like, for it to transition, so transition so nicely when like, pre-training is kind of tapped out to, into like, oh, now inference time is, is the new scaling law is like conv very convenient. I, I, I like if there were an Illuminati, this would be what they planned.[00:01:41] Or if we're living in a simulation or something. Yeah.[00:01:44] Open Source Models and Their Impact[00:01:44] swyx: Then you said open source[00:01:45] Alessio: as well? Yeah. Well, no, I, I think like open source. Yeah. We're discussing this on the negative. I would say the relevance of open source. I would specifically open models. Yeah, I was surprised the lack, like the llamas of the world by the lack of adoption.[00:01:56] And I mean, people use it obviously, but I would say nobody's [00:02:00] really like a huge fanboy, you know, I think the local llama community and some of the more obvious use cases really like it. But when we talk to like enterprise folks, it's like, it's cool, you know? And I think people love to argue about licenses and all of that, but the reality is that it doesn't really change the adoption path of, of ai.[00:02:18] So[00:02:19] swyx: yeah, the specific stat that I got from on anchor from Braintrust mm-hmm. In one of the episodes that we did was I think he estimated that open source model usage in work in enterprises is that like 5% and going down.[00:02:31] Jacob: And it feels like you're basically all these enterprises are in like use case discovery mode, where it's like, let's just take what we think is the most powerful model and figure out if we can find anything that works.[00:02:39] And, you know, so much of, of, of it feels like discovery of that. And then, right, as you've discovered something, a new generation of models are out and so you have to go do discovery with those. And you know, I think obviously we're probably optimistic that the that the open source models increase in uptake.[00:02:50] It's funny, I was gonna say my biggest surprise in the last year was open source related, but it was just how Fast Open Source caught up on the reasoning models. It was kind of unclear to me, like over time whether there would be, you know, [00:03:00] a compounding advantage for some of the closed source models where in the, okay, in the early days of, of scaling you know, there was a, a tight time loop, but over time, you know, would would the gap increase?[00:03:08] And if anything it feels like a trunk. You know, and I think deep seek specifically was just really surprising in how, you know, in many ways if the value of these model companies is like you have a model for a period of time and you're the only one that can build products on top of that model while you have it.[00:03:21] Like, God, that time period is a lot shorter than a, than I thought it was gonna be a year ago.[00:03:25] swyx: Yeah. I mean, again, I I, I don't like this label of how Fast Open Source caught up because it's really how Fast Deepsea caught up. Right. And now we have, like, I think some of it is that Deepsea is basically gonna stop open sourcing models.[00:03:36] Yeah. So like there, there's no team open source, there's just different companies and they choose to open source or not. And we got lucky with deep seek releasing something and then everyone else is basically distilling from deep seek and those are distillations. Catching up is such an easier lower bar than like actually catching up, which is like you, you are like from scratch.[00:03:56] You're training something that like is competitive on that front. I don't know if [00:04:00] that's happening. Like basically the only player right now is we're waiting for LA four.[00:04:03] Jordan: I mean, it's always an order of magnitude cheaper to replicate what's already been done than to create something fundamentally new.[00:04:09] And so that's why I think deep seek overall was overhyped. Right? I mean obviously it's a good open source, new entrant, but at the same time there's nothing new fundamentally there other than sort of doing it executing what's already been done really well.[00:04:21] Alessio: Yeah,[00:04:21] Jordan: right.[00:04:21] Alessio: So Well, but I think the traces is like maybe the biggest thing, I think most previous open models is like the same model, just a little worse and cheaper.[00:04:30] Yeah. Like R one is like the first model that had the full traces. So I think that's like a net unique thing in fair, open source. But yeah, I, I think like we talked about deep seek in the our n of year 2023 recap, and we're mostly focused on cheaper inference. Like we didn't really have deep, see, deep CV three[00:04:47] swyx: was out then, and we were like, that was already like talking about fine green mixture of experts and all that.[00:04:51] Like that's a great receipt to[00:04:52] Jacob: have[00:04:52] swyx: to be like, yeah.[00:04:52] Jacob: End[00:04:53] swyx: of year 20. Yeah. That's a,[00:04:54] Jacob: that's a, that's, that's an[00:04:55] swyx: impressive one. You follow the right whale believers in Twitter. It's, it's like [00:05:00] pretty obvious. I actually had like so, you know, I used to be in finance and, and a lot, a lot of my hedge fund and PE friends called me up.[00:05:06] They were like, why didn't you tip us off on deep seek? And I'm like, well, I mean, it's been there. It's, it's actually like kind of surprising that like, Nvidia like fell like what, 15% in one day? Yeah. Because deep seek and I, I think it's just like whatever the market, public market narrative decides is a story, becomes the story, but really like the technical movements are usually.[00:05:26] One to two years in the making. Before that,[00:05:27] Jacob: basically these people were telling on themselves that they didn't listen to your podcast. They've been on the end of year 22, 3. No, no,[00:05:32] swyx: no. Like yeah, we weren't, we weren't like banging the drum. So like it's also on us to be like, no, like this. This is an actual tipping point.[00:05:38] And I think I like as people who are like, our function as podcasters and industry analysts is to raise the bar or focus attention on things that you think matter. And sometimes we're too passive about it. And I think I was too passive there. I'd be, I'd be happy to own up on that.[00:05:52] Jacob: No, I feel like over time you guys have moved into this margin general role of like taking stances of things that are or aren't important and, you know I feel like you've done that with MCP of [00:06:00] late and a bunch of[00:06:00] swyx: things.[00:06:00] Yeah.[00:06:01] Challenges and Opportunities in AI Engineering[00:06:01] swyx: So like the, the general pushes is AI engineering, you know, like it's gotta, gotta wrap the shirt. And MCP is part of that, but like the, the general movement is what can engineers do above the model layer to augment model capabilities. And it turns out it's a lot. And turns out we went from like, making fun of GPT rappers to now I think the overwhelming consensus GPT wrappers is the only thing that's interesting.[00:06:20] Yeah.[00:06:21] Jacob: I remember like, Arvin from Perplexity came on our podcast and he was like, I'm proudly a rapper. Like, you know, it's like anyone that's like talking about like, you know, differentiation, like pre-product market fit is like a ridiculous thing to, to say, like, build something people want and then yeah.[00:06:33] Over time you can kind of worry about that.[00:06:35] swyx: Yeah. I, I interviewed him in 2023 and I think he may have been the first person on our podcast to like, probably be a GBT rapper. Yeah. And yeah, and obviously he's built a huge business on that. Totally. Now, now we now we all can't get enough of it. I have another one for, Oh, nice.[00:06:47] That was Alessia's one and we, we perhaps individual answers just to be interesting in the same Uber on the way up. Yeah. You just like in the, in different Oh, I was driving too. Oh, you were driving. So I actually, I mean, it was a Tesla mostly drove mine was [00:07:00] actually, it is interesting that low-code builders did not capture the AI builder market.[00:07:04] Right. AI builders being bought lovable, low-code builders being Zapier, Airtable, retool notion. Any of those, like you're not technical. You can build software.[00:07:14] misc: Yeah.[00:07:14] swyx: Somehow not all them missed it. Why? It's bizarre. Like they should have the DNA, I don't know. They should have. They already have the reach, they already have the, the distribution.[00:07:25] Like why? I I have no idea. The ability to[00:07:27] Jacob: fast follow too. Like I'm surprised there's Yeah. There's just[00:07:29] swyx: nothing. Yeah. What do you make of that? I, it seems and you know, not to come back to the AI engineering future, like it takes a, a certain kind of. Founder mindset or AI engineer mindset to be like, we will build this from whole cloth and not be tied to existing paradigms.[00:07:45] I think, 'cause I like, if I was, if I'm to, you know, you know, Wade or who's, who's, who's the Zapier person than, you know, Mike. Mike who has left the Zapier. Yeah. What's the, yeah. Like you know, Zapier, when they decided to do Zapier ai, they [00:08:00] were like, oh, you can use natural language to make Zap actions, right?[00:08:03] When Notion decided to do Notion ai, they were like, oh, you can like, you know write documents or, you know, fill in tables with, with ai. Like, they didn't do the, the, the, the next step because they already had their base and they were like, let's improve our baseline. And the other people who actually tried for to, to create a phone cloth were like, we, we got no prior preconceptions.[00:08:24] Like, let's see what we can, what kinda software people can build with like from scratch, basically. I don't know that, that's my explanation. I dunno if you guys have any retros on the AI builders?[00:08:33] Jacob: Yeah. Or, or, or did they kind of get lucky getting, you know starting that product journey? Like right as the models were reaching the inflection point?[00:08:39] There's the timing[00:08:40] swyx: issue. Yeah. Yeah, yeah. Yeah. Yeah, I don't know. Like I, I, to some extent, I think the only reason you and I are talking about it is that they, both of them have reported like ridiculous numbers. Like zero to 20 million in three months, basically, both of them. Jordan, did you have a, a big surprise?[00:08:55] Jordan: Yeah, I mean, some of what's already been discussed. I guess the only other thing would be on the Apple side in particular, I [00:09:00] think, I think you know, for the last text message summary, like, but they're[00:09:04] Jacob: funny. They're funny at how bad they had, how off they're, they're viral. Yeah.[00:09:08] Jordan: I mean, so like for the last couple years we've seen so many companies that are trying to do personal assistance, like all these various consumer things, and one of the things we've always asked is, well, apple is in prime position to do all this.[00:09:18] And then with Apple Intelligence, they just. Totally messed up in so many different ways. And then the whole BBC thing saying that the guy shot himself when he didn't. And just like, there's just so many things at this point that I would've thought that they would've ironed up their, their AI products better, but just didn't really catch on,[00:09:35] Jacob: you know, second on this list of, of generally overly broad opening questions would be anything that you guys think is kind of like overhyped or under hyped in the AI world right now?[00:09:43] Alessio: Overhyped agents framework. Sorry. Not naming any particular ones. I'm sorry. Not, not not, yeah, exactly. It's not, I, I would say they're just overall a chase to try and be the framework when the workloads are like in such flux. Yeah. That I just think is like so [00:10:00] hard to reconcile the two. I think what Harrison and Link Chain has done so amazingly, it's like product velocity.[00:10:05] Like, you know, the initial obstructions were maybe not the ending obstruction, but like they were just releasing stuff every day trying to be on top of it. But I think now we're like past that, like what people are looking for now. It's like something that they can actually build on mm-hmm. And stay on for the next couple of years.[00:10:23] And we talked about this with Brett Taylor on our episode, and it feels like, it's like the jQuery era Yeah. Of like agents and lms. It's like, it's kinda like, you know, single file, big frameworks, kinda like a lot of players, but maybe we need React. And I think people are just trying to build still Jake Barry.[00:10:39] Like, I don't really see a lot of people doing react like,[00:10:43] swyx: yeah. Maybe the, the only modification I made about that is maybe it's too early even for frameworks at all. And the thing that, and do you think[00:10:50] Jacob: there's enough stability in the underlying model layer and, and patterns to, to have this,[00:10:54] swyx: the thing is the protocol and not the framework?[00:10:56] Jacob: Yeah.[00:10:56] swyx: Because frameworks inherently embed protocols, but if you just focus on a protocol, maybe that [00:11:00] works. And obviously MCP is. The current leading mm-hmm. Area. And you know, I think the comparison there would be, instead of just jQuery, it is XML HTB requests, which is like the, the thing that enabled Ajax.[00:11:10] And that was the, the, the, the, the sort of inciting incident for JavaScripts being popular as a language.[00:11:16] Jordan: I would largely agree with that. I mean, I think on the, the react side of things, I think we're starting to see more frameworks sort of go after more of that, I guess like master is sort of like on the TypeScript side and more of like a sort of master.[00:11:28] Yeah, yeah, yeah, yeah. The traction is really impressive there. And so I think we're starting to see more surface there, but I think there's still a big opportunity. What do you have for for an over or under hyped on the under hype side? You know, I actually, I, I know I mentioned Apple already, but I think the private cloud compute side with PCC, I actually think that could be really big.[00:11:45] It's under the radar right now. Mm-hmm. But in terms of basically bringing. The on device sort of security to the cloud. They've done a lot of architecturally interesting things there. Who's they? Apple. Oh, okay. On the PCC side. And so I actually think of that.[00:11:58] swyx: So you're negative on Apple [00:12:00] Intelligence, but also on Apple Cloud,[00:12:01] Jordan: on the more of the local device.[00:12:04] Sort of, I think there'll be a lot of workloads still on device, but when you need to speak to the cloud for larger LLMs, I think that Apple has done really interesting thing on the privacy side.[00:12:13] Alessio: Yeah. We did the seed of a company that does that, so Yeah. Especially as things become more co that you set 'em up on purpose.[00:12:18] So that felt like a perfect Yeah, no, I was like, let's go Jordan, you guys concluding before this episode? Tell me about that company after. We'll chat after, but, but yes, I, I think that's like the unique the thing about LLM workflows is like you just cannot have everything be single tenant, right?[00:12:35] Because you just cannot get enough GPUs. Like even like large enterprises are used to having VPCs and like everything runs privately. But now you just cannot get enough GPUs to run in a VPC. So I think you're gonna need to be in a multi-tenant architecture, and you need, like you said, like single tenant guarantees in multi-tenant environment.[00:12:52] So yeah, it's a interesting space.[00:12:55] swyx: Yeah. What about you, Swiss? Under hypes, I want to say [00:13:00] memory. Just like stateful ai. As part of my keynote on, on for just like every, every conference I do, I do a keynote and I try to do the task of like defining an agent, just, you know, always evergreen content, every content for a keynote.[00:13:14] But I did it in a, in a way that it was like I think like a, what a researcher would do. Like you, you survey what people say and then you sort of categorize and, and go like, okay, this is the, the. What everyone calls agents and here are the groups of DEF definitions. Pick and choose. Right. And then it was very interesting that the week after that OpenAI launched their agents SDK and kind of formalized what they think agents are.[00:13:34] CloudFlare also did the same with us and none of them had memory. Yeah, it's very strange. The, pretty much like the only big lab o obviously there, there's conversation memory, but there's not memory memory like in like a, like a let's store a large across fact about you and like, you know, exceed the, the context length.[00:13:54] And here's the, if you, if you're look, if you look closely enough, there's a really good implementation of memory inside of [00:14:00] MCP when they launched with the initial set of servers. They had a memory server in there, which I, I would recommend as like, that's where you start with memory. But I think like if there was a better, I.[00:14:10] Memory abstraction, then a lot of our agents would be smarter and could learn on, on the job, which is something that we all want. And for some reason we all just like ignored that because it's just convenient to, and, but do you feel like[00:14:24] Jacob: it's being ignored or it's just a really hard problem and like lots of, I feel like lots of people are working on it.[00:14:27] Just feels like it's, it's proven more challenging.[00:14:29] swyx: Yeah. Yeah. Yeah. So, so Harrison has lang me, which I think now he's like, you know, relaunched again. And then we had letter come speak at our mm-hmm. Our conference I don't know, Zep, I think there's a bunch of other memory guys, but like, something like this I think should be normal in the stack.[00:14:44] And basically I think anything stateful should be interesting to VCs 'cause it's databases and, you know, we know how those things make money.[00:14:51] Jacob: I think on the over hype side, the only thing I'd add is like, I'm, I'm still surprised how many net new companies there are training models. I thought we were kind of like past that.[00:14:58] And[00:14:58] swyx: I would say they died end of last year. And now, [00:15:00] now they've resurfaced. Yeah. I mean they, that's one of the questions that you had down there of like, yeah. Sorry. Is there an opportunity for net new model players? I wouldn't say no. I don't know what you guys think.[00:15:08] Alessio: I, I don't have a reason to say no, but I also don't have a reason to say, this is what is missing and you should have a new model company do it.[00:15:15] But again, I'm an add here. Like, all these guys wanna[00:15:17] swyx: pursue a GI, you know, all, they all want to be like, oh, we'll, we'll like hit, you know, soda on all the benchmarks and like, they can't all do it. Yeah.[00:15:25] Jacob: I mean, look, I don't know if Ilia has the secret secret approach up his sleeve of of something beyond test time compute.[00:15:29] Mm-hmm. But it was funny, I, we had Noam Shaer on the podcast last week. I was asking him like, you know, is, is there like some sort of other algorithmic breakthrough? Would he make a Ilia? And he's like, look, I think what he is implicitly said was test time compute gets to the point where these models are doing AI engineering for us.[00:15:43] And so, you know, at that point they'll figure out the next algorithm breakthrough. Yeah. Which I thought was was pretty interesting.[00:15:47] Jordan: I agree with you folks. I think that we're most interested, at least from our side and like, you know, foundation models for specific use cases and more specialized use cases.[00:15:55] Mm-hmm. I guess the broader point is if there is something like that, that these companies can latch onto [00:16:00] and being there sort of. Known for being the best at. Maybe there's a case for that. Largely though I do agree with you that I don't think there should be, at this point, more model companies. I think it's like[00:16:09] Jacob: these[00:16:09] Jordan: unique data[00:16:09] Jacob: sets, right?[00:16:10] I mean, obviously robotics has been an area we've been really interested in. It's entirely different set of data that's required, you know, on top of like a, a good BLM and then, you know, biology, material sciences, more the specific use cases basically. Yeah. But also specific, like specific markets. A lot of these models are super generalizable, but like, you know finding opportunities to, you know, where, you know, for a lot of these bio companies, they have wet labs, like they're like running a ton of experiments or you know, same on the material sciences side.[00:16:31] And so I still feel like there's some, some opportunities there, but the core kind of like LLM agent space is it's tough, tough to compete with the big ones.[00:16:38] Alessio: Yeah. Agree. Yeah. But they're moving more into product. Yeah. So I think that's the question is like, if they could do better vertical models, why not do that instead of trying to do deep research and operator?[00:16:50] And these different things. Mm-hmm. I think that's what I'm, in my mind, it's like the agents coming[00:16:53] swyx: out too.[00:16:54] Alessio: Well. Yeah. In my, in my mind it's like financial pressure. Like they need to monetize in a much shorter timeframe [00:17:00] because the costs are so high. But maybe it's like, it's not that easy to, do[00:17:04] Jacob: you think they would be, that it would be a better business model to like, do a bunch of vertical?[00:17:07] Well, it's more like[00:17:07] Alessio: why wouldn't they, you know, like you make less enemies if you're like a model builder, right? Yeah. Like, like now with deep research and like search, now perplexity like an enemy and like a, you know, Gemini deep research is like more of an enemy. Versus if they were doing a finance model, you know?[00:17:25] Mm-hmm. Or whatever, like they would just enable so many more companies and they always have, like they had as one of the customer case studies for GBT search, but they're not building a finance based model for them. So is it because it's super hard and somebody should do it? Or is it because the new models.[00:17:41] Are gonna be so much better that like the vertical models are useless anyways. Like this is better lesson. Exactly.[00:17:46] Jacob: It still seems to be a somewhat outstanding question. I, I'd say like, all the signs of the last few years seem to be like a general purpose model is like the way to go. And, you know, you know, like training a hyper-specific model in this, in, in a domain is like, you know, maybe it's cheaper and faster, but it's not gonna be like higher quality.[00:17:59] But [00:18:00] also like, I think it's still an, I mean, we were talking to, to no and Jack Ray from Google last week, and they were like, yeah, this is still an outstanding, like, we, we check this every time we have a new model. Like whether there's you know, there that still seems to be holding. I remember like a few years ago, it felt like all the rage was like the, it was like the Bloomberg GPT model came out.[00:18:14] Everyone was like, oh, you gotta like, you know, massive data. Yeah. I had[00:18:17] swyx: a GPA, I had DP of AI of Bloomberg present on that. Yeah. That must be a really[00:18:20] Jacob: interesting episode to go back on because I feel like, like very shortly thereafter, the next opening AI model came out and just like beat it on all sorts of[00:18:25] swyx: No, it, it was a talk.[00:18:26] We haven't released it yet, but yeah, I mean it's basically they concluded that the, the closed models were better so they just Yeah. Stopped. Interesting. Exactly. So I feel like that's been the but he's I, I would be. He's very insistent that the work that they did, the team he assembled, the data that he collected is actually useful for more than just the model.[00:18:42] So like, basically everything but the model survived. What are the other things? The data pipeline. Okay. The team that they, they, they assembled for like fine tuning and implementing whatever models they, they ended up picking. Yeah, it seems like they are happy with that. And they're running with that.[00:18:57] He runs like 12, 13 [00:19:00] teams at Bloomberg just working. Jenny, I across the company.[00:19:03] Jacob: I mean, I guess we've, we've all kind of been alluding it to it right now, but I guess because it's a natural transition. You know, the other broad opening I have is just what we're paying most attention to right now. And I think back on this, like, you know, the model company's coming into the product area.[00:19:13] I mean, I think that's gonna be like, I'm fascinated to see how that plays out over the next year and kind of these like frenemy dynamics and it feels like it's gonna first boil up on like cursor anthropic and like the way that plays out over the next six months I think will be. What, what is Cursor?[00:19:26] swyx: Anthropic is, you mean Cursor versus anthropic or, yeah. And I[00:19:29] Jacob: assume, you know, over time Anthropic wants to get more into the application side of coding Uhhuh. And you know, I assume over time Cursor will wanna diversify off of, you know, just using the Anthropic model.[00:19:39] swyx: It's interesting that now Cursor is now worth like 10 billion, nine, nine, 10 billion.[00:19:43] Yeah. And like they've made themselves hard to acquire, like I would've said, like, you should just get yourself to five, 6 billion and join OpenAI. And like all the training data goes through OpenAI and that's how they train their coding model. Now it's not as complicated. Now they need to be an independent company.[00:19:57] Jacob: Increasingly, it's seems to the model companies want to get into the [00:20:00] product layer. And so seeing over the next six, 12 months does having the best model, you know let you kind of start from a cold start on the product side and, and get something in market. Or are the, you know, companies with the best products, even if they eventually have to switch to a somewhat worse, tiny bit worse model, does it not, you know, where do the developers ultimately choose to go?[00:20:16] I think that'll be super interesting. Yeah.[00:20:18] Alessio: Don't you think that Devon is more in trouble than cursor? I, I feel like on Tropic, if anything wants to move more towards, I don't think they wanna build the ID like if I think about coding, it's like kind of like, you know, you look at it like a cube, it's like the ID is like one way to get the code and then the agent is like the other side.[00:20:33] Yeah. I feel like on Tropic wants more be on the agent side and then hand you off the cursor when you want to go in depth versus like trying to build the claw. IDEI think that's not, I would say, I don't know how you think the[00:20:46] swyx: existence, a cloud code doesn't show, doesn't support what you say. Like maybe they would, but[00:20:52] Jacob: assume, like I assume both just converge eventually where you want have where will you be able to do both?[00:20:57] So,[00:20:57] swyx: so in order to be so we're, we're talking [00:21:00] about coding agents, whether it's sort of what is it? Inner loop versus auto loop, right? Like inner loop is inside cursor, inside your ID between inside of a GI commit and auto loop is between GI commits on, on the cloud. And I think like to be an outer loop coding agent, you have to be more of a, like, we will integrate with your code base, we'll sign your whatever.[00:21:17] You know, security thing that you need to sign. Yeah. That kinda schlep. I don't think the model ads wanna do that schlep, they just want to provide models. So that, that, that's, that would be my argument against like why cognition should still have, have, have some moat against anthropic just simply because they cognition would do the schlep and the biz dev and the infra that philanthropic doesn't really care about.[00:21:39] Jacob: I know the schlep is pretty sticky though. Once you do it,[00:21:41] swyx: it's very sticky. Yeah. Yeah. I mean it's, it's, it's interesting. Like, I, I think the natural winner of that should be sourcegraph. But there's another[00:21:47] Jacob: unprompted point portfolio. Nice. We, I mean they, they're[00:21:51] swyx: big supporters like very friendly with both Quinn and B and they've they've done a lot of work with Cody, but like, no, not much work on the outer [00:22:00] loop stuff yet.[00:22:01] But like any company where like they have already had, like, we've been around for 10 years, we, we like have all the enterprise contracts that you already trust us with your code base. Why would you go trust like factory or cognition as like, you know, 2-year-old startups who like just came outta MIT Like, I don't know.[00:22:17] Product Market Fit in AI[00:22:17] Jacob: I guess switching gears to the to the application side I'm curious for both of you, like how do you kind of characterize what has genuine product market fit in AI today? And I guess less, you more and your side of the investing side, like more interesting to invest in that category of the stuff that works today or kind of where the capabilities are going long term.[00:22:35] Alessio: That's hard. I was asking you to do my job for you, like, man, that's a easy, that's a layout. Tell us all your investing[00:22:40] pieces. Yeah, yeah, yeah. I, I, I would say we, well we only really do mostly seed investing, so it's hard to invest in things that already work. Yeah. That fair. Are really late. So we try to, but, but we try to be at the cusp of like, you know, usually the investments we like to make, there's like really not that much market risk.[00:22:57] It's like if this works. Obviously people are gonna [00:23:00] use it, but like it's unclear whether or not it's gonna work. So that's kind of more what we skew towards. We try not to chase as many trends and I don't know, I, you know, I was a founder myself and sometimes I feel like it's easy to just jump in and do the thing that is hot, but like becoming a founder to do something that is like underappreciated or like doesn't yet work shows some level of like dread and self, like you, you actually really believe in the thing.[00:23:25] So that alone for me is like, kind of makes me skew more towards that. And you do a lot of angel investing too, so I'm curious how,[00:23:31] swyx: Yeah, but I don't regard, I don't have, I don't use, put, put that in my mental framework of things like I come at this much more as a content creator or market analyst of like, yeah, it, it really does matter to me what has part of market fit because.[00:23:45] People, I have to answer the question of what is working now When, when people ask me,[00:23:50] Jacob: do you feel like relative to the, the obviously the hype and discourse out there, like, you know, do you feel like there's a lot of things that have product market fit or like a few things, like where a few things? Yeah.[00:23:58] swyx: I was gonna say this, so I have a list [00:24:00] of like two years ago we, I wrote the Anatomy of autonomy posts where it was like the, the first, like what's going on in agents and, and and, and, and what is actually making money. Because I think there's a lot of gen I skeptics out there. They're all like, these, these things are toys.[00:24:13] They're, they're not unreliable. And you know, why, why, why you dedicating your life to these things. And I think for me, the party market fit bar at the time was a hundred million dollars, right? Like what use cases can reasonably fit a hundred million dollars. And at the time it was like co-pilot it was Jasper.[00:24:30] No longer, but mm-hmm. You know, in that category of like help you write. Yeah. Which I think, I think was, was helpful. And then and the cursor I think was on there as, as a, as, as, as like a coding agent. Plus plus. I think that list will just grow over time of like the form factors that we know to work, and then we can just adapt the form factors to a bunch of other things.[00:24:47] So like the, the one that's the most recently added to this is deep research.[00:24:52] misc: Yeah.[00:24:52] swyx: Right. Where anything that looks like a deep research whether it's a grok version, Gemini version, perplexity version, whatever. He has an investment [00:25:00] that that he likes called Brightwave that is basically deep research for finance.[00:25:02] Yeah. And anything where like all it is like long-term agent, agent reporting and it's starting to take more and more of the job away from you and, and just give you much more reason to report. I think it's going to work. And that has some PMFI think obviously has PMF like I, I would say. It's I, I went to this exercise of trying to handicap how much money open AI made from launching open ai deep research.[00:25:25] I think it's billions. Like the, the, the mo the the she upgrade from like $20 to 200. It has to be billions in the R off. Maybe not all them will stick around, but like that is some amount of PMF that is didn't they have to immediately drop it down[00:25:38] Jacob: to the $20 tier?[00:25:39] swyx: They expanded access. I don't, I wouldn't say, which I thought was[00:25:42] Jacob: really telling of the market.[00:25:43] Right. It's like where you have a you know, I think it's gonna be so interesting to see what they're actually able to get in that 200 or $2,000 tier, which we all think is, is, you know, has a ton of potential. But I thought it was fascinating. I don't know whether it was just to get more people exposure to it or the fact that like Google had a similar product obviously, and, and other folks did too.[00:25:59] But [00:26:00] it was really interesting how quickly they dropped it down.[00:26:02] swyx: I don't, I think that's just a more general policy of no matter what they have at the top tier, they always want to have smaller versions of that in the, in the lower tiers. Yeah. And just get people exposure to it. Just, yeah, just get exposure.[00:26:12] The brand of being first to market and, and like the default choice Yeah. Is paramount to open ai[00:26:18] Jacob: though. I thought that whole thing was fascinating 'cause Google had the first product, right? Yeah. And no, like, you know, I, we[00:26:24] swyx: interviewed them. I, I, I, straight up to their faces, I was like, opening, I mocked you.[00:26:28] And they were like, yeah, well, actually curious, what's[00:26:30] Jacob: it, this is totally off topic, but whatever. Like, what is it going to take for go? Google just released some great models like a, a few weeks ago. Like I feel like it's happening. The stuff they're shipping is really cool. It's happening. Yeah, but I, I, I also, I feel like at least in the, you know, broader discourse, it's still like a drop in the bucket relative to[00:26:45] swyx: Yeah.[00:26:45] I mean, I, I can riff on, on this. I, I, but I, I think it's happening. I think it takes some time, but I am, like my Gemini usage is up. Like, I, I use, I use it a lot more for anything from like summarizing YouTube videos to the [00:27:00] native image generation Yeah. That they just launched to like flash thinking.[00:27:02] So yeah, multi-mobile stuff's great. Yeah. I run you know, and I run like a daily sort of news recap called AI news that is, 99% generated by models, and I do a bake off between all the frontier models every day. And it's every day. Like does it switch? I manual? Yes, it does switch. And I, man, I manually do it.[00:27:18] And flash is, flash wins most days. So, so like, I think it's happening. I think I was thinking, I was thinking about tracking myself like number of opens of tragedy, g Bt versus Gemini. And at some point it will cross. I think that Gemini will be my main and, and it, it, I I like that will slowly happen for a bunch of people.[00:27:37] And, and, and then that will, that'll shift. I, I think that's, that's a really interesting for developers, this is a different question. Yeah. It's Google getting over itself of having Google Cloud versus Vertex versus AI studio, all these like five different brands, slowly consolidating it. It'll happen just slowly, I guess.[00:27:53] Alessio: Yeah.[00:27:54] Yeah. I, I mean, another good example is like you cannot use the thinking models in cursor. Yeah. And I know [00:28:00] Logan killed Patrick's that they're working on it, but I, I think there's all these small things where like if I cannot easily use it, I'm really not gonna go out of my way to do it. But I do agree that when you do use them, their models are, are great.[00:28:12] So yeah. They just need better, better bridges.[00:28:15] swyx: You had one of the questions in the prep.[00:28:16] Debating Public Companies: Google vs. Apple[00:28:16] swyx: What public company are you long and short and minus Google versus, versus Apple, like, long, short. That was also my[00:28:23] Jacob: combo. I, I feel like, yeah, I mean, it does feel like Google's really cooking right now.[00:28:26] swyx: Yeah. So okay, coming back to what has product market fit[00:28:29] Jacob: now,[00:28:29] swyx: now that we come[00:28:30] Jacob: back to my complete total sidetrack,[00:28:33] Customer Support and AI's Role[00:28:33] swyx: there's also customer support.[00:28:35] We were talking on, on the car about Decagon and Sierra, obviously Brett, Brett Taylor is founder of Sierra. And yeah, it seems like there's just this, these layers of agents that'll like, I think you just look at like the income statement or like the, the org chart of any large scaled company and you start picking them off one by one.[00:28:51] What like is interesting knowledge work? And they would just kind of eat. Things slowly from the outside in. Yeah, that makes sense.[00:28:57] Alessio: I, I mean, the episode with the, [00:29:00] with Brett, he's so passionate about developer tools and Yeah. He did not do a developer tools. We spent like two hours talking about developer tools and like, all, all of that stuff.[00:29:10] And it's like, I, they a customer support company, I'm like, man, that says something. You know what I mean? Yeah. It's like when you have somebody like him who can like, raise any amount of money from anybody to do anything. Yeah. To pick customer support as the market to go after while also being the chairman of OpenAI, like that shows you that like, these things have moats and have longstanding, like they're gonna stick around, you know?[00:29:32] Otherwise he's smarter than that. So yeah, that's a, that's a space where maybe initially, you know, I would've said, I don't know, it's like the most exciting thing to, to jump into, but then if you really look at the shape of like, how the workforce are structured and like how the cost centers of like the business really end up, especially for more consumer facing businesses, like a lot of it goes into customer support.[00:29:54] AI's Impact on Business Growth[00:29:54] Alessio: All the AI story of the last two years has been cost cutting. Yeah. I think now we're gonna switch more towards growth revenue. [00:30:00] Totally. You know, like you've seen Jensen, like last year, GTC was saying the more you buy, the more you save this year is that the more you buy, the more you make. So we're hot off the[00:30:08] Jacob: press.[00:30:10] We were there. We were there. Yeah. I do think that's one of the most interesting things about the, this first wave of apps where it's like almost the easiest thing that you could you could get real traction with was stuff that, you know, for lack of a better way to frame it, like so that people had already been comfortable outsourcing the BPOs or something and kind of implicitly said like, Hey, this is a cost center.[00:30:24] Like we are willing to take some performance cut for cost in the past. You know, the, the irony of that, or what I'm really curious to see how it plays out is, you know, you, you could imagine that is the area where price competition is going to be most fierce because it's already stuff that you know, that people have said, Hey, we don't need the like a hundred percent best version of that.[00:30:42] And I wonder, you know, this next wave of apps. May prove actually even more defensible as you get these capabilities that actually are, you know, increased top line or whatnot where you're like, you take ai, go to market, for example. Like you're, you'd pay like twice as much for something that brought, like, 'cause there's just a kind of very clean ROI story to it.[00:30:59] And so [00:31:00] I wonder ultimately whether the, like this next set of apps actually ends up being more interesting than the, than the first wave.[00:31:05] Alessio: Yeah,[00:31:05] Voice AI and Scheduling Solutions[00:31:05] Jordan: I think a lot of the voice AI ones are interesting too, because you don't need a hundred percent precision recall to actually, you know, have a great product.[00:31:12] And so for example, we looked into a bunch of you know, scheduling intake companies, for example, like home services, right? For electricians and stuff like that. Today they miss 50% of their calls. So even if the AI is only effective, say 75% of the time, yeah, it's crazy, right? So if it's effective 75% of the time, that's totally fine because that's still a ton of increased revenue for the customer, right?[00:31:32] And so you don't need that a hundred percent accuracy. Yeah. And so as the models. And the reliability of these agents are getting better is totally fine, because you're still getting a ton of value in the meantime.[00:31:41] swyx: Yeah. One, this is, I don't know how related this is, but I, one of my favorite meetings at it is related one of my favorite meetings at AI Engineer Summit, it is like, like I do these, this is our first one in New York, and I it is like met the different crew than, than you meet here.[00:31:55] Like everyone here is loves developer tools, loves infra over there. They're actually more interested in [00:32:00] applications. It's kind of cool. I met this like bootstrap team that, like, they're only doing appointment scheduling for vets. They, they, yeah. And like, they're like, this is a, this is an anomaly. We don't usually come to engineering summits 'cause we usually go to vet summits and like talk to the, they're, they're like, you know, they, they're, they're literally, I'm sure it's a[00:32:16] Jordan: massive pain point.[00:32:17] They're willing to pay a lot of money.[00:32:20] Alessio: Yeah. But, but, but this is like my point about saving versus making more, it's like if an electrician takes two x more calls, do they have the bandwidth? To actually do two X more in-house and they get higher. Well, yeah, exactly. That's the thing is like, I don't think today most businesses are like structured to just like overnight two, three x the band, you know?[00:32:38] I think that's like a startup thing. Like mo most businesses then you make an[00:32:42] swyx: electrician agent. Well, no, totally. That's how do you, how do you recruiting agent for electrician, for like[00:32:49] Alessio: electrician. Great. That's a good point. How do you do lambda school for electrician? I, it's hilarious.[00:32:53] Jacob: Whack-a-mole for the bottlenecks in these businesses.[00:32:55] Like as, oh, now we have a ton of demand. Like, cool. Like where do we go?[00:32:58] swyx: Yeah.[00:32:59] Exploring AI Applications in Various Fields[00:32:59] swyx: So just to [00:33:00] round out the, the this PMF thing I think this is relevant in a certain sense of, like, it's pretty obvious that the killer agents are coding agents, support agents, deep research, right? Roughly, right. We've covered all those three already.[00:33:10] Then, then, then you have to sort of be, turn to offense and go like, okay, what's next? And like, what, what about, I[00:33:16] Jacob: mean, I also just like summarization of, of voice and conversation, right? Yep. Absolutely. We actually had that on there. I[00:33:21] swyx: just, I didn't put it as agent. Because seems less agentic, you know? But yes, still, still a good AI use case.[00:33:26] That one I, I've seen I would mention granola and what's the other one? Monterey, I think a bridge was one wanted to mention. I was say bridge. Yeah, bridge. Okay. So I'll just, I'll call out what I had on my slides. Yeah. For, for the agent engineering thing. So it was screen sharing, which I think is actually kind of, kind of underrated.[00:33:42] Like people, like an AI watching you as you do your work and just like offering assistance outbound sales. So instead of support, just being more outbound hiring, you say[00:33:51] Jacob: outbound sales has brought a market fit?[00:33:53] swyx: No, it, it, it will, it's come out. Oh, on the comp. Yeah. I was totally agree with that. Yeah. Hiring like the recruiting side education, like the, [00:34:00] the sort of like personalized teaching, I think.[00:34:02] I'm kind of shocked we haven't seen more there. Yeah. Yeah. I don't know if that's like, like it's like Duolingo is the thing. Amigo.[00:34:08] Jacob: Yeah. I mean, speak in some of these like, you know,[00:34:10] swyx: speak, practice, yeah. Interesting. And then finance, I, there's, there's a ton of finance cases that we can talk about that and then personal ai, which we also had a little bit of that, but I think personal AI is a harder to monetize, but I, I think those would be like, what I would say is up and coming in terms of like, that's what I'm currently focusing on.[00:34:27] Jacob: I feel like this question's been asked a few different ways but I'm, I'm curious what you guys think it's like, is it like, if we just froze model capabilities today, like is there, you know, trillions of dollars of application value to be unlocked? Like, like AI education? Like if we just stopped today all model development, like with this current generation of models, we could probably build some pretty amazing education apps.[00:34:44] Or like, how much of this, how much of, of all this is like contingent upon just like, okay, people have had two years with GBT four and like, you know, I don't know, six months with the reasoning models, like how much is contingent upon it just being more time with these things versus like the models actually have to get better?[00:34:58] I dunno, it's a hard question, so I'm gonna just throw it [00:35:00] to you.[00:35:00] Alessio: Yeah. Well I think the societal thing, it's maybe harder, especially in education. You know, like, can you basically like Doge. The education system. Probably you should, but like, can you, I I think it's more of a human,[00:35:14] Jacob: but people pay for all sorts of like, get ahead things outside of class and you know, certainly in other countries there's a ton of consumer spend and education.[00:35:21] It feels like the market opportunity is there.[00:35:23] swyx: Yeah. And, and private education, I think yeah, public Public is a very different, yeah. One of my most interesting quests from last year was kind of reforming Singapore's education system to be more sort of AI native, just what you were doing on the side while you were Yes.[00:35:38] That's a great, that's a great side quest. My stated goal is for Singapore to be the first country that has Python as a first language, as a, as a national language. Anyway, so, but the, the, the, the defense, the pushback I got from Ministry of Education was that the teachers would be unprepared to do it.[00:35:53] So it's like, it was like the def the, like, the it was really interesting, like immediate pushback. Was that the defacto teachers union being like, [00:36:00] resistant to change and like, okay. It's that that's par for the course. Anyway, so not, not to, not to dwell too much on that, but like yeah, I mean, like, I, I think like education is one of those things that pe everyone, like has strong opinions on.[00:36:11] 'cause they all have kids, all be the education system. But like, I think it's gonna be like the, the domain specific, like, like speak like such a amazing example of like top down. Like, we will go through the idea maze and we'll go to Korea and teach them English. Like, it's like, what the hell? And I would love to see more examples of that.[00:36:29] Like, just like really focus, like no one tried to solve everything. Just, just do your thing really, really well[00:36:34] Defensibility in AI Applications[00:36:34] Jacob: on this trend of of, of difficult questions that come up. I'm gonna just ask you the one that my partners like to ask me every single Monday, which is how do you think about defensibility at the at the app layer?[00:36:41] Alessio: Oh[00:36:41] Jacob: yeah, that's great. Just gimme an answer. I can copy paste and just like, you know, have network effects. Auto, auto response.[00:36:47] swyx: Honestly like network effects. I think people don't prioritize those enough because they're trying to make the single player experience good. But then, then they neglect the [00:37:00] multiplayer experience.[00:37:00] I think one of the I always think about like load-bearing episodes, like, you know, as, as park that you do one a week and like, you know, some of those you don't really talk about ever again. And others you keep mentioning every single podcast. And one of the, this is obviously gonna be the last one. I think the recap episodes for us are pretty load-bearing.[00:37:15] Like we, we refer to them every three months or so. And like one of them I think for us is Chai for me is chai research, even though that wasn't like a super popular one among the broader community outside of Chai, the chai community, for those who don't know, chai Research is basically a character AI competitor.[00:37:32] Right. They were bootstraps, they were founded at the same time and they have out outlasted character of de facto. Right. It's funny, like I, I would love to ask Mil a bit more about like the whole character thing, but good luck getting past the Google copy. But like, so he, like, he, like he doesn't have his own models, basically he has his own network of people submitting models to be run.[00:37:54] And I think like. That is like short term going to be hurting him because he doesn't have [00:38:00] proprietary ip. But long term he has the network network effect to make him robust to any changes in the future. And I think, like I wanna see more of that where like he's basically looking himself as kind of a marketplace and he's identified the choke point, which is will be app or the, the sort of protocol layer that interfaces between the users and the model providers.[00:38:18] And then make sure that the money kind of flows through and that works. I, I wish that more AI builders or AI founders emphasize network effects. 'cause that that's the only thing that you're gonna have with the end of the day. Yeah. And like brand deeds into network effects you.[00:38:34] Jacob: Yeah, I guess you know, harder in, in the enterprise context.[00:38:36] Right. But I mean, I feel, it's funny, we do this exercise and I feel like we talk a lot about like, you know, obviously there's, you know kind of the velocity and the breadth you're able to kind of build of product surface area. There's just like the ability to become a brand in a space. Like, I'm shocked that even in like six, nine months, how an individual company can become synonymous with like an entire category.[00:38:52] And like, then they're in every room for customers and like all the other startups are like clawing their way to try and get in like one, you know, 20th of those rooms.[00:38:59] Jordan: There's a [00:39:00] bunch of categories where we talk about an IC and it's like, oh, pricing compression's gonna happen, not as defensible. And so ACVs are gonna go down over time.[00:39:08] In actuality, some of these, the ACVs have doubled, we've seen, and the reason for that is just, you know, people go to them and pay for that premium of being that brand.[00:39:16] Jacob: Yeah. I mean, one thing I'm struck by is there's been, there was such a head fake in the early days of, of AI apps where people were like, we want this amazing defensibility story, and then what's the easiest defensibility story?[00:39:24] It's like, oh, like. Totally unique data set or like train your own model or something. And I feel like that was just like a total head fake where I don't think that's actually useful at all. It's the much less, you sound much less articulate when you're like, well the defensibility here is like the thousand small things that this company does to make like the user experience design everything just like delightful and just like the speed at which they move to kind of both create a really broad product, but then also every three, six months when a new model comes out, it's kind of an existential event for like any company.[00:39:49] 'cause if you're not the first to like figure out how to use it, someone else will. Yeah. And so velocity really matters there. And it's funny in in, in kinda our internal discussions, we've been like, man, that sounds pretty similar to like how we thought about like application SaaS [00:40:00] companies. That there isn't some like revolutionary reason you don't sound like a genius when you're like, here's applications why application SaaS company A is so much better than B.[00:40:07] But it's like a lot of little things that compound over time.[00:40:10] Infrastructure and AI: Current Trends[00:40:10] Jacob: What about the infrastructure space, guys? Like I'm curious you know. What, how do you guys think about where the interesting categories are here today and you know, like where, where, where do you wanna see more startups or, or where do you think there are too many?[00:40:21] Alessio: Yeah. Yeah, we call it kind of the L-L-M-O-S. But I would say[00:40:24] swyx: not we, I mean Andre, Andre calls it LMOS[00:40:27] Alessio: Well, but yeah, we, well everyone else just copies whatever two. And Andre, the three of you call it the LMO. Well, we have just like four words of ai framework Yeah. Yeah. That we use. And LM Os is one of them, but yeah, I mean, code execution is one.[00:40:39] We've been banging the drum, everybody now knows where investors in E two B. Mm-hmm. Memory, you know, is one that we kind of touched on before. Super interesting search we talked about. I, I think those are more not traditional infra, not like the bare metal infra. It's more like the infra around the tools for agents model, you know?[00:40:57] Which I think is where a lot of the value is gonna [00:41:00] be. The security[00:41:00] swyx: ones. Yeah.[00:41:01] Alessio: Yeah. And cyber security. I mean there's so much to be done there. And it's more like basically any area where. AI is being used by the offense. AI needs to be applied on the defense side, like email security, you know, identity, like all these different things.[00:41:16] So we've been doing a lot there as well as, you know, how do you rethink things that used to be costly, like red teaming and maybe used to be a checkbox in the past Today they can be actually helpful. Yeah. To make you secure your app. And there's this whole idea of like, semantics, right? That not the models can be good at.[00:41:32] You know, in the past everything is about syntax. It's kind of like very basic, you know, constraint rules. I think now you can start to infer semantics from things that are beyond just like simple recognition to like understanding why certain things are happening a certain way. So in the security space, we're seeing that with binary inspection, for example.[00:41:51] Like there's kinda like the syntax, but then there are like semantics of like understanding what is the scope overall really trying to do. Even though this [00:42:00] individual syntax, it's like seeing something specific. Not to get too technical, but yeah, I, I think infra overall, it's like a super interesting place if you're making use of the model, if you're just, I'm less bullish.[00:42:13] Not, not that it's not a great business, but I think it's a very capital intensive business, which is like serving the models. Mm-hmm. Yeah. I think that infra is like, great people will make money, but yeah. I, I, I don't think there's as much of a interest from, from us at[00:42:25] Jordan: least. Yeah. How, how do you guys think about what OpenAI and the big research labs will encompass as part of the developer and infra category?[00:42:31] Yeah.[00:42:31] Alessio: That, that's why I, I would say I search is the first example of one of the things we used to mention on, you know, we had X on the podcast and perplexity obviously as a, as an API. The basic idea[00:42:44] swyx: is if you go into like the chat GBT custom GPT builder, like what are the check boxes? Each of them is a startup.[00:42:50] Alessio: Yeah. And, and now they're also APIs. So now search is also an a p, we will see what the adoption is. There's the, you know, in traditional infra, like everybody wants to be [00:43:00] multi-cloud, so maybe we'll see the same Where change GPD search or open AI search. API is like, great with the open AI models because you get it all bundled in, but their price is very high.[00:43:11] If you compare it to like, you know, XI think is like five times the, the price for the same amount of research, which makes sense if you have a big open AI contract. But maybe if you're just like pick and best in breed, you wanna compare different ones. Yeah. Yeah, they don't have a code execution one.[00:43:26] I'm sure they'll release one soon. So they wanna own that too, but yeah. Same question we were talking about before, right? Did they wanna be an API company or a product company? Do you make more money building Tri g BT search or selling search? API?[00:43:38] swyx: Yeah. The, the broader lesson, instead of like going, we did applications just now.[00:43:42] And then what do you think is interesting infrastructure? Like it's not 50 50, it's not like equal weighted, like it, it's just very clearly the application layer has like. Been way more interesting. Like yes, there, there's interesting in infrastructure plays and I even want to like push back on like the, the, the whole GPU serving thing because like together [00:44:00] AI is doing well, fireworks, I mean I was, that worked.[00:44:02] Alessio: It's like data[00:44:02] Jacob: centers[00:44:03] Alessio: and inference[00:44:03] Jacob: providers,[00:44:04] Alessio: the,[00:44:04] swyx: you know,[00:44:04] Alessio: I think it's not like the capital[00:44:06] swyx: Oh, I see.[00:44:07] Alessio: I for, for again, capital efficiency. Yeah. Much larger funds. So you, I'm sure you have GPU clouds. Yeah.[00:44:13] swyx: Yeah. So that's, that's, that is one thing I have been learning in, in that you know, I think I have historically had dev tools and infra bias and so has he, and we've had to learn that applications actually are very interesting and also maybe kind of the killer application of models in a sense that you can charge for utility and not for cost.[00:44:33] Right? Which, where like most infrastructure reduces to cost plus. Yeah. Right. So, and like, that's not where you wanna be for ai. So that's, that's interesting for, for me I thought it would be interesting for me to be the only non VC in the room to be saying what is not investible. 'cause like then I then, you know, you can I, I won't be canceled for saying like, your, your whole category is, we have a great thing where like, this thing's[00:44:54] Jacob: not investible and then like three months later we're desperately chasing.[00:44:56] Exactly. Exactly. So you don't wanna be on a record space changes so [00:45:00] fast. It's like you gotta, every opinion you hold, you have to like, hold it quite loosely. Yeah.[00:45:02] swyx: I'm happy to be wrong in public, you know, I think that's how you learn the most, right? Yeah. So like, fine tuning companys is something I struggled with and still, like, I don't see how this becomes a big thing.[00:45:12] Like you kind of have to wrap it up in a broader, ser broader enterprise AI company, like services company, like a writer, AI where like they will find you and it's part of the overall offering. Mm-hmm. But like, that's not where you spike. Yeah, it's kind of interesting. And then I, I'll, I'll just kind of AI DevOps and like, there's a lot of AI SRE out there seems like.[00:45:32] There's a lot of data out there that that should be able to be plugged into your code base or, or, or your app to it's self-heal or whatever. It's just, I don't know if that's like, been a thing yet. And you guys can correct me if you're, if I'm wrong. And then the, the last thing I'll mention is voice realtime infra again, like very interesting, very, very hot.[00:45:49] But again, how big is it? Those are the, the main three that I'm thinking about for things I'm struggling with.[00:45:54] Jordan: Yeah. I guess a couple comments on the A-I-S-R-E side. I actually disagree with that one. Yeah. I think that the [00:46:00] reason they haven't sort of taken off yet is because the tech is just not there quite yet.[00:46:04] And so it goes back to the earlier question, do we think about investing towards where the companies will be when the models improve versus now? I think that's going to be, in short term we'll get there, but it's just not there just yet. But I think it's an interesting opportunity overall.[00:46:18] swyx: Yeah. It's my pushback to you is, well it's monitoring a lot of logs, right?[00:46:22] Yeah. And it's basically anomaly detection rather than. Like there's, there's a whole bunch of like stuff that can happen after you detect the anomaly, but it's really just an anomaly detection. And we've always had that, you know, like it's, this is like not a Transformers LLM use case. This is just regular anomaly detection.[00:46:38] Jordan: It's more in terms of like, it's not going to be an autonomous SRE for a while. Yeah. And so the question is how, how much can the latest sort of AI advancements increase the efficacy of going, bringing your MTTR

In 2022, Lin Qiao decided to leave Meta, where she was managing several hundred engineers, to start Fireworks AI. In this episode, we sit down with Lin for a deep dive on her work, starting with her leadership on PyTorch, now one of the most influential machine learning frameworks in the industry, powering research and production at scale across the AI industry. Now at the helm of Fireworks AI, Lin is leading a new wave in generative AI infrastructure, simplifying model deployment and optimizing performance to empower all developers building with Gen AI technologies.We dive into the technical core of Fireworks AI, uncovering their innovative strategies for model optimization, Function Calling in agentic development, and low-level breakthroughs at the GPU and CUDA layers.Fireworks AIWebsite - https://fireworks.aiX/Twitter - https://twitter.com/FireworksAI_HQLin QiaoLinkedIn - https://www.linkedin.com/in/lin-qiao-22248b4X/Twitter - https://twitter.com/lqiaoFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro(01:20) What is Fireworks AI?(02:47) What is PyTorch?(12:50) Traditional ML vs GenAI(14:54) AI's enterprise transformation(16:16) From Meta to Fireworks(19:39) Simplifying AI infrastructure(20:41) How Fireworks clients use GenAI(22:02) How many models are powered by Fireworks(30:09) LLM partitioning(34:43) Real-time vs pre-set search(36:56) Reinforcement learning(38:56) Function calling(44:23) Low-level architecture overview(45:47) Cloud GPUs & hardware support(47:16) VPC vs on-prem vs local deployment(49:50) Decreasing inference costs and its business implications(52:46) Fireworks roadmap(55:03) AI future predictions

"Clinical research is not cheap."Season 2 of the Association RevUP podcast celebrates the stories of associations who are utilizing business to bring about real change in their industries.In this debut episode, discover how the Society of Interventional Oncology and Jena Eberly Stack united multiple industry partners to fund a high-cost clinical trial with real-world health impacts. Then Dan Cole and Brittany Shoul share 5 ways your association can level up negotiations for your next big deal, no matter the size.About Association RevUP : Episodes are less than 20-minutes, written and produced to keep you engaged, and full of actionable insights. Hosted by Carolyn Shomali.- VPC, Inc., the production company PAR trusts with our in-person event the RevUP Summit, and the parter of this podcast.- MyPar.org: learn more about the PAR member community- RevUP Summit: November 4-6, 2025 in Annapolis, MD

AWS Morning Brief for the week of February 17, with Corey Quinn. Links:Amazon DynamoDB now supports auto-approval of quota adjustmentsAmazon Elastic Block Store (EBS) now adds full snapshot size information in Console and APIAmazon RDS for MySQL announces Extended Support minor 5.7.44-RDS.20250103Amazon Redshift Serverless announces reduction in IP Address Requirements to 3 per SubnetAWS Deadline Cloud now supports Adobe After Effects in Service-Managed FleetsAWS Network Load Balancer now supports removing availability zonesAWS CloudTrail network activity events for VPC endpoints now generally availableHarness Amazon Bedrock Agents to Manage SAP InstancesTimestamp writes for write hedging in Amazon DynamoDBUpdating AWS SDK defaults – AWS STS service endpoint and Retry StrategyLearning AWS best practices from Amazon Q in the ConsoleAutomating Cost Optimization Governance with AWS ConfigAmazon Q Developer in chat applications rename - Summary of changes - AWS Chatbot

AWS Morning Brief for the week of December 23, with Corey Quinn. Links:Amazon AppStream 2.0 introduces client for macOSAmazon EC2 instances support bandwidth configurations for VPC and EBSAmazon Timestream for InfluxDB now supports Internet Protocol Version 6 (IPv6) connectivityAmazon WorkSpaces Thin Client now available to purchase in IndiaAWS Backup launches support for search and item-level recoveryAWS Mainframe Modernization now supports connectivity over Internet Protocol version 6 (IPv6)AWS Marketplace now supports self-service promotional media on seller product detail pagesAWS re:Post now supports Spanish and PortugueseAWS Resource Explorer supports 59 new resource typesAWS offers a self-service feature to update business names on AWS InvoicesAnnouncing CloudFormation support for AWS Parallel Computing ServiceAnnouncing Node Health Monitoring and Auto-Repair for Amazon EKS - AWSAnd that's a wrap!Best practices for creating a VPC for Amazon RDS for Db2How the Amazon TimeHub team handled disruption in AWS DMS CDC task caused by Oracle RESETLOGS: Part 3How to detect and monitor Amazon Simple Storage Service (S3) access with AWS CloudTrail and Amazon CloudWatchEnforce resource configuration to control access to new features with AWSMaximizing your cloud journey: Engaging an AWS Solutions Architect

It's Wednesday morning and that means another edition of the Purely Cloud Guest Series featuring co-host Ondrej Bursik and the Cloud Technical Product Specialist team delivering all the technical details around Pure's growing portfolio of cloud solutions. In this one, Ondrej and Pavel Kovar take you deep into the depths of Pure's Cloud Block Store for AWS offering, including architectural decisions and deployment best practices. Learn about the technical aspect of Cloud Block Store for CBS including virtual drives and shelves, controllers, networking best practices, and VPC end points. From there, Ondrej and Pavel take you through deployment recommendations via portal or CLI and deployment steps to ensure success across security and user management aspects. Thanks for checking out this episode of the Pure Report podcast and for more information on Cloud Block Store for AWS, go to: https://www.purestorage.com/products/cloud-block-storage/cbs.html

In this episode, Meg Ashby, a senior cloud security engineer shares how her team tackled AWS's centralized VPC interface endpoints, a design often seen as an anti-pattern. She explains how they turned this unconventional approach into a cost-efficient and scalable solution, all while maintaining granular controls and network visibility. She shares why centralized VPC endpoints are considered an AWS anti-pattern, how to implement granular IAM controls in a centralized model and the challenges of monitoring and detecting VPC endpoint traffic. Guest Socials: ⁠Meg's Linkedin Podcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels: - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security BootCamp Questions asked: (00:00) Introduction (02:48) A bit about Meg Ashby (03:44) What is VPC interface endpoints? (05:26) Egress and Ingress for Private Networks (08:21) Reason for using VPC endpoints (14:22) Limitations when using centralised endpoint VPCs (19:01) Marrying VPC endpoint and IAM policy (21:34) VPC endpoint specific conditions (27:52) Is this solution for everyone? (38:16) Does VPC endpoint have logging? (41:24) Improvements for the next phase Thank you to our episode sponsor Wiz. Cloud Security Podcast listeners can also get a free cloud security health scan by going to wiz.io/csp

AWS Morning Brief for the week of December 9, with Corey Quinn. Links:AWS announces access to VPC resources over AWS PrivateLinkAnnouncing Amazon Aurora DSQL (Preview)Announcing Amazon Bedrock IDE in preview as part of Amazon SageMaker Unified StudioAWS announces Amazon CloudWatch Database InsightsAmazon DynamoDB global tables previews multi-Region strong consistencyAmazon EC2 introduces Allowed AMIs to enhance AMI governanceAnnouncing Amazon EC2 I8g instancesAnnouncing Amazon EKS Auto ModeAnnouncing Amazon EKS Hybrid NodesAnnouncing Amazon Elastic VMware Service (Preview)Announcing Amazon FSx Intelligent-Tiering, a new storage class for FSxAmazon Q Developer can now automate code reviewsAmazon Q Developer announces automatic unit test generation to accelerate feature developmentAmazon S3 adds new default data integrity protectionsAnnouncing Amazon S3 Metadata (Preview) – Easiest and fastest way to manage your metadataAmazon S3 launches storage classes for AWS Dedicated Local ZonesAnnouncing Amazon S3 Tables – Fully managed Apache Iceberg tables optimized for analytics workloadsAWS announces Amazon SageMaker LakehouseAWS Control Tower launches managed controls using declarative policiesAWS announces AWS Data Transfer Terminal for high-speed data uploadsAmazon Web Services announces declarative policiesIntroducing AWS Glue 5.0AWS announces Invoice ConfigurationAWS Marketplace now offers EC2 Image Builder components from independent software vendorsAWS announces AWS Security Incident Response for general availabilityAnnouncing AWS Transfer Family web appsBuy with AWS accelerates solution discovery and procurement on AWS Partner websitesOracle Database@AWS is now in limited previewPartyRock improves app discovery and announces upcoming free daily useAnnouncing the preview of Amazon SageMaker Unified StudioVPC Lattice now includes TCP support with VPC ResourcesAnnouncing the 2024 Geo and Global AWS Partners of the YearAmazon MemoryDB Multi-Region is now generally availableTop announcements of AWS re:Invent 2024SponsorThe Duckbill Group: https://www.duckbillgroup.com/

Hoje é dia de sobre carreira! No episódio de estreia da série especial do podcast, conversamos com Erika Nagamine, Golden Jacket da AWS, sobre a sua trajetória, sobre as suas decisões, e sobre o poder que a curiosidade teve para lhe impulsionar ao longo de toda a sua carreira. Vem ver quem participou desse papo: Paulo Silveira, o host que gosta de certificação André David, o cohost que está rolando até agora Erika Nagamine, Arquiteta de Soluções Especialista em Dados & AI - Analytics na AWS

Join us for an insightful conversation with Joe Niemiec, Senior Product Manager for Streaming at MongoDB, recorded live at MongoDB Local London. In this video, Joe explains the fundamentals of stream processing and how it empowers developers to run continuous aggregation queries on real-time data. Discover practical use cases, including monitoring oil well pumps and smart grid applications, that showcase the power of stream processing in various industries. Joe also discusses the latest enhancements, such as expanded regional support, VPC peering for secure connections, and improved Kafka integration. Whether you're new to MongoDB or looking to enhance your data processing capabilities, this video is packed with valuable information to help you get started with stream processing!