Podcasts about Datadog

The dominant structural mechanism highlighted is the industry-wide shift toward liability transfer and governance gaps in AI procurement, deployment, and incident response. According to Dave Sobel, both vendors and organizations are accelerating AI adoption without corresponding investments in oversight, training, or clear accountability structures. This is reflected across multiple sectors, from software vendors such as Grammarly, Eightfold.ai, Cohesity, and Rubrik, to business leaders and policymakers, where risk is systematically deferred downstream rather than managed at the point of adoption. The most consequential evidence is the quantitative disconnect between stated AI priorities and functional oversight. Research cited by Dave Sobel from Economist Impact and HR Dive found that while 38% of organizations budget for AI and 86% of executives rate AI as essential, only 16% offer internal training and over half of department-level AI initiatives lack formal oversight (Ernst & Young). Additionally, 88% of AI vendors limit their liability, and only 17% align with regulatory compliance, per cited surveys, leaving substantial legal and operational risk for end users and service providers. Supporting this trend, Dave Sobel points to Grammarly's opt-out identity usage in new features and a class action lawsuit against Eightfold.ai regarding AI-driven employment decisions. Vendors such as Cohesity, Rubrik, ServiceNow, and Datadog are responding by building tools focused on remediation and recovery from AI-driven incidents, underscoring a shift from preventive governance to reactive containment. Policy moves—such as expanded operational cyber roles for the private sector—further offload accountability without addressing contractual and insurance exposure. For MSPs and technology leaders, these developments create practical risks: unclear service scope around AI tool usage in contracts, increased exposure to billable incidents and legal action, and rising labor costs for incident recovery. Service providers must audit agreements for AI-specific language, distinguish AI-related incidents from standard SLAs, and treat AI governance as a managed risk service. The pressure will increasingly fall on MSPs to account for training gaps, audit trails, compliance attestations, and recovery procedures—not simply the technology itself. Three things to know today 00:00 ROI Reality Check 02:12 Governance Gap Widens 03:14 Cleanup Economy Rises 05:45 Why Do We Care?  Supported by:  CometBackup 

Episode #534 consacré à « Shai-Hulud » Avec Christophe Tafani-Dereeper Références : Shai-Hulud:  https://securitylabs.datadoghq.com/articles/shai-hulud-2.0-npm-worm/ https://github.com/DataDog/indicators-of-compromise/blob/main/shai-hulud-2.0/README.md https://www.wiz.io/blog/shai-hulud-2-0-aftermath-ongoing-supply-chain-attack https://www.cert.ssi.gouv.fr/actualite/CERTFR-2025-ACT-051/    Evoqué pendant l'épisode : Précédent épisode NLS sur la sécurité de la chaîne d'approvisionnement : https://www.nolimitsecu.fr/securisation-de-la-chaine-dapprovisionnement-logicielle/ Attaque sur le mainteneur npm « Qix » : https://socket.dev/blog/npm-author-qix-compromised-in-major-supply-chain-attack Exemples d'autres attaques par phishing npm en 2025 : https://bsky.app/profile/bad-at-computer.bsky.social/post/3lydioq5swk2y https://www.aikido.dev/blog/npm-debug-and-chalk-packages-compromised https://www.mimecast.com/threat-intelligence-hub/npm-phishing-campaign/ PoC de ver npm en […] The post Shai-Hulud appeared first on NoLimitSecu.

All speakers are announced at AIE EU, schedule coming soon. Join us there or in Miami with the renowned organizers of React Miami! Singapore CFP also open!We've called this out a few times over in AINews, but the overwhelming consensus in the Valley is that “the IDE is Dead”. In November it was just a gut feeling, but now we actually have data: even at the canonical “VSCode Fork” company, people are officially using more agents than tab autocomplete (the first wave of AI coding):Cursor has launched cloud agents for a few months now, and this specific launch is around Computer Use, which has come a long way since we first talked with Anthropic about it in 2024, and which Jonas productized as Autotab:We also take the opportunity to do a live demo, talk about slash commands and subagents, and the future of continual learning and personalized coding models, something that Sam previously worked on at New Computer. (The fact that both of these folks are top tier CEOs of their own startups that have now joined the insane talent density gathering at Cursor should also not be overlooked).Full Episode on YouTube!please like and subscribe!Timestamps00:00 Agentic Code Experiments00:53 Why Cloud Agents Matter02:08 Testing First Pillar03:36 Video Reviews Second Pillar04:29 Remote Control Third Pillar06:17 Meta Demos and Bug Repro13:36 Slash Commands and MCPs18:19 From Tab to Team Workflow31:41 Minimal Web UI Philosophy32:40 Why No File Editor34:38 Full Stack Cursor Debate36:34 Model Choice and Auto Routing38:34 Parallel Agents and Best Of N41:41 Subagents and Context Management44:48 Grind Mode and Throughput Future01:00:24 Cloud Agent Onboarding and MemoryTranscriptEP 77 - CURSOR - Audio version[00:00:00]Agentic Code ExperimentsSamantha: This is another experiment that we ran last year and didn't decide to ship at that time, but may come back to LM Judge, but one that was also agentic and could write code. So it wasn't just picking but also taking the learnings from two models or and models that it was looking at and writing a new diff.And what we found was that there were strengths to using models from different model providers as the base level of this process. Basically you could get almost like a synergistic output that was better than having a very unified like bottom model tier.Jonas: We think that over the coming months, the big unlock is not going to be one person with a model getting more done, like the water flowing faster and we'll be making the pipe much wider and so paralyzing more, whether that's swarms of agents or parallel agents, both of those are things that contribute to getting much more done in the same amount of time.Why Cloud Agents Matterswyx: This week, one of the biggest launches that Cursor's ever done is cloud agents. I think you, you had [00:01:00] cloud agents before, but this was like, you give cursor a computer, right? Yeah. So it's just basically they bought auto tab and then they repackaged it. Is that what's going on, or,Jonas: that's a big part of it.Yeah. Cloud agents already ran in their own computers, but they were sort of site reading code. Yeah. And those computers were not, they were like blank VMs typically that were not set up for the Devrel X for whatever repo the agents working on. One of the things that we talk about is if you put yourself in the model shoes and you were seeing tokens stream by and all you could do was cite read code and spit out tokens and hope that you had done the right thing,swyx: no chanceJonas: I'd be so bad.Like you obviously you need to run the code. And so that I think also is probably not that contrarian of a take, but no one has done that yet. And so giving the model the tools to onboard itself and then use full computer use end-to-end pixels in coordinates out and have the cloud computer with different apps in it is the big unlock that we've seen internally in terms of use usage of this going from, oh, we use it for little copy changes [00:02:00] to no.We're really like driving new features with this kind of new type of entech workflow. Alright, let's see it. Cool.Live Demo TourJonas: So this is what it looks like in cursor.com/agents. So this is one I kicked off a while ago. So on the left hand side is the chat. Very classic sort of agentic thing. The big new thing here is that the agent will test its changes.So you can see here it worked for half an hour. That is because it not only took time to write the tokens of code, it also took time to test them end to end. So it started Devrel servers iterate when needed. And so that's one part of it is like model works for longer and doesn't come back with a, I tried some things pr, but a I tested at pr that's ready for your review.One of the other intuition pumps we use there is if a human gave you a PR asked you to review it and you hadn't, they hadn't tested it, you'd also be annoyed because you'd be like, only ask me for a review once it's actually ready. So that's what we've done withTesting Defaults and Controlsswyx: simple question I wanted to gather out front.Some prs are way smaller, [00:03:00] like just copy change. Does it always do the video or is it sometimes,Jonas: Sometimes.swyx: Okay. So what's the judgment?Jonas: The model does it? So we we do some default prompting with sort. What types of changes to test? There's a slash command that people can do called slash no test, where if you do that, the model will not test,swyx: but the default is test.Jonas: The default is to be calibrated. So we tell it don't test, very simple copy changes, but test like more complex things. And then users can also write their agents.md and specify like this type of, if you're editing this subpart of my mono repo, never tested ‘cause that won't work or whatever.Videos and Remote ControlJonas: So pillar one is the model actually testing Pillar two is the model coming back with a video of what it did.We have found that in this new world where agents can end-to-end, write much more code, reviewing the code is one of these new bottlenecks that crop up. And so reviewing a video is not a substitute for reviewing code, but it is an entry point that is much, much easier to start with than glancing at [00:04:00] some giant diff.And so typically you kick one off you, it's done you come back and the first thing that you would do is watch this video. So this is a, video of it. In this case I wanted a tool tip over this button. And so it went and showed me what that looks like in, in this video that I think here, it actually used a gallery.So sometimes it will build storybook type galleries where you can see like that component in action. And so that's pillar two is like these demo videos of what it built. And then pillar number three is I have full remote control access to this vm. So I can go heat in here. I can hover things, I can type, I have full control.And same thing for the terminal. I have full access. And so that is also really useful because sometimes the video is like all you need to see. And oftentimes by the way, the video's not perfect, the video will show you, is this worth either merging immediately or oftentimes is this worth iterating with to get it to that final stage where I am ready to merge in.So I can go through some other examples where the first video [00:05:00] wasn't perfect, but it gave me confidence that we were on the right track and two or three follow-ups later, it was good to go. And then I also have full access here where some things you just wanna play around with. You wanna get a feel for what is this and there's no substitute to a live preview.And the VNC kind of VM remote access gives you that.swyx: Amazing What, sorry? What is VN. AndJonas: just the remote desktop. Remote desktop. Yeah.swyx: Sam, any other details that you always wanna call out?Samantha: Yeah, for me the videos have been super helpful. I would say, especially in cases where a common problem for me with agents and cloud agents beforehand was almost like under specification in my requests where our plan mode and going really back and forth and getting detailed implementation spec is a way to reduce the risk of under specification, but then similar to how human communication breaks down over time, I feel like you have this risk where it's okay, when I pull down, go to the triple of pulling down and like running this branch locally, I'm gonna see that, like I said, this should be a toggle and you have a checkbox and like, why didn't you get that detail?And having the video up front just [00:06:00] has that makes that alignment like you're talking about a shared artifact with the agent. Very clear, which has been just super helpful for me.Jonas: I can quickly run through some other Yes. Examples.Meta Agents and More DemosJonas: So this is a very front end heavy one. So one question I wasswyx: gonna say, is this only for frontJonas: end?Exactly. One question you might have is this only for front end? So this is another example where the thing I wanted it to implement was a better error message for saving secrets. So the cloud agents support adding secrets, that's part of what it needs to access certain systems. Part of onboarding that is giving access.This is cloud is working onswyx: cloud agents. Yes.Jonas: So this is a fun thing isSamantha: it can get super meta. ItJonas: can get super meta, it can start its own cloud agents, it can talk to its own cloud agents. Sometimes it's hard to wrap your mind around that. We have disabled, it's cloud agents starting more cloud agents. So we currently disallow that.Someday you might. Someday we might. Someday we might. So this actually was mostly a backend change in terms of the error handling here, where if the [00:07:00] secret is far too large, it would oh, this is actually really cool. Wow. That's the Devrel tools. That's the Devrel tools. So if the secret is far too large, we.Allow secrets above a certain size. We have a size limit on them. And the error message there was really bad. It was just some generic failed to save message. So I was like, Hey, we wanted an error message. So first cool thing it did here, zero prompting on how to test this. Instead of typing out the, like a character 5,000 times to hit the limit, it opens Devrel tools, writes js, or to paste into the input 5,000 characters of the letter A and then hit save, closes the Devrel tools, hit save and gets this new gets the new error message.So that looks like the video actually cut off, but here you can see the, here you can see the screenshot of the of the error message. What, so that is like frontend backend end-to-end feature to, to get that,swyx: yeah.Jonas: Andswyx: And you just need a full vm, full computer run everything.Okay. Yeah.Jonas: Yeah. So we've had versions of this. This is one of the auto tab lessons where we started that in 2022. [00:08:00] No, in 2023. And at the time it was like browser use, DOM, like all these different things. And I think we ended up very sort of a GI pilled in the sense that just give the model pixels, give it a box, a brain in a box is what you want and you want to remove limitations around context and capabilities such that the bottleneck should be the intelligence.And given how smart models are today, that's a very far out bottleneck. And so giving it its full VM and having it be onboarded with Devrel X set up like a human would is just been for us internally a really big step change in capability.swyx: Yeah I would say, let's call it a year ago the models weren't even good enough to do any of this stuff.SoSamantha: even six months ago. Yeah.swyx: So yeah what people have told me is like round about Sonder four fire is when this started being good enough to just automate fully by pixel.Jonas: Yeah, I think it's always a question of when is good enough. I think we found in particular with Opus 4 5, 4, 6, and Codex five three, that those were additional step [00:09:00] changes in the autonomy grade capabilities of the model to just.Go off and figure out the details and come back when it's done.swyx: I wanna appreciate a couple details. One 10 Stack Router. I see it. Yeah. I'm a big fan. Do you know any, I have to name the 10 Stack.Jonas: No.swyx: This just a random lore. Some buddy Sue Tanner. My and then the other thing if you switch back to the video.Jonas: Yeah.swyx: I wanna shout out this thing. Probably Sam did it. I don't knowJonas: the chapters.swyx: What is this called? Yeah, this is called Chapters. Yeah. It's like a Vimeo thing. I don't know. But it's so nice the design details, like the, and obviously a company called Cursor has to have a beautiful cursorSamantha: and it isswyx: the cursor.Samantha: Cursor.swyx: You see it branded? It's the cursor. Cursor, yeah. Okay, cool. And then I was like, I complained to Evan. I was like, okay, but you guys branded everything but the wallpaper. And he was like, no, that's a cursor wallpaper. I was like, what?Samantha: Yeah. Rio picked the wallpaper, I think. Yeah. The video.That's probably Alexi and yeah, a few others on the team with the chapters on the video. Matthew Frederico. There's been a lot of teamwork on this. It's a huge effort.swyx: I just, I like design details.Samantha: Yeah.swyx: And and then when you download it adds like a little cursor. Kind of TikTok clip. [00:10:00] Yes. Yes.So it's to make it really obvious is from Cursor,Jonas: we did the TikTok branding at the end. This was actually in our launch video. Alexi demoed the cloud agent that built that feature. Which was funny because that was an instance where one of the things that's been a consequence of having these videos is we use best of event where you run head to head different models on the same prompt.We use that a lot more because one of the complications with doing that before was you'd run four models and they would come back with some giant diff, like 700 lines of code times four. It's what are you gonna do? You're gonna review all that's horrible. But if you come back with four 22nd videos, yeah, I'll watch four 22nd videos.And then even if none of them is perfect, you can figure out like, which one of those do you want to iterate with, to get it over the line. Yeah. And so that's really been really fun.Bug Repro WorkflowJonas: Here's another example. That's we found really cool, which is we've actually turned since into a slash command as well slash [00:11:00] repro, where for bugs in particular, the model of having full access to the to its own vm, it can first reproduce the bug, make a video of the bug reproducing, fix the bug, make a video of the bug being fixed, like doing the same pattern workflow with obviously the bug not reproducing.And that has been the single category that has gone from like these types of bugs, really hard to reproduce and pick two tons of time locally, even if you try a cloud agent on it. Are you confident it actually fixed it to when this happens? You'll merge it in 90 seconds or something like that.So this is an example where, let me see if this is the broken one or the, okay, this is the fixed one. Okay. So we had a bug on cursor.com/agents where if you would attach images where remove them. Then still submit your prompt. They would actually still get attached to the prompt. Okay. And so here you can see Cursor is using, its full desktop by the way.This is one of the cases where if you just do, browse [00:12:00] use type stuff, you'll have a bad time. ‘cause now it needs to upload files. Like it just uses its native file viewer to do that. And so you can see here it's uploading files. It's going to submit a prompt and then it will go and open up. So this is the meta, this is cursor agent, prompting cursor agent inside its own environment.And so you can see here bug, there's five images attached, whereas when it's submitted, it only had one image.swyx: I see. Yeah. But you gotta enable that if you're gonna use cur agent inside cur.Jonas: Exactly. And so here, this is then the after video where it went, it does the same thing. It attaches images, removes, some of them hit send.And you can see here, once this agent is up, only one of the images is left in the attachments. Yeah.swyx: Beautiful.Jonas: Okay. So easy merge.swyx: So yeah. When does it choose to do this? Because this is an extra step.Jonas: Yes. I think I've not done a great job yet of calibrating the model on when to reproduce these things.Yeah. Sometimes it will do it of its own accord. Yeah. We've been conservative where we try to have it only do it when it's [00:13:00] quite sure because it does add some amount of time to how long it takes it to work on it. But we also have added things like the slash repro command where you can just do, fix this bug slash repro and then it will know that it should first make you a video of it actually finding and making sure it can reproduce the bug.swyx: Yeah. Yeah. One sort of ML topic this ties into is reward hacking, where while you write test that you update only pass. So first write test, it shows me it fails, then make you test pass, which is a classic like red green.Jonas: Yep.swyx: LikeJonas: A-T-D-D-T-D-Dswyx: thing.No, very cool. Was that the last demo? Is thereJonas: Yeah.Anything I missed on the demos or points that you think? I think thatSamantha: covers it well. Yeah.swyx: Cool. Before we stop the screen share, can you gimme like a, just a tour of the slash commands ‘cause I so God ready. Huh, what? What are the good ones?Samantha: Yeah, we wanna increase discoverability around this too.I think that'll be like a future thing we work on. Yeah. But there's definitely a lot of good stuff nowJonas: we have a lot of internal ones that I think will not be that interesting. Here's an internal one that I've made. I don't know if anyone else at Cursor uses this one. Fix bb.Samantha: I've never heard of it.Jonas: Yeah.[00:14:00]Fix Bug Bot. So this is a thing that we want to integrate more tightly on. So you made it forswyx: yourself.Jonas: I made this for myself. It's actually available to everyone in the team, but yeah, no one knows about it. But yeah, there will be Bug bot comments and so Bug Bot has a lot of cool things. We actually just launched Bug Bot Auto Fix, where you can click a button and or change a setting and it will automatically fix its own things, and that works great in a bunch of cases.There are some cases where having the context of the original agent that created the PR is really helpful for fixing the bugs, because it might be like, oh, the bug here is that this, is a regression and actually you meant to do something more like that. And so having the original prompt and all of the context of the agent that worked on it, and so here I could just do, fix or we used to be able to do fixed PB and it would do that.No test is another one that we've had. Slash repro is in here. We mentioned that one.Samantha: One of my favorites is cloud agent diagnosis. This is one that makes heavy use of the Datadog MCP. Okay. And I [00:15:00] think Nick and David on our team wrote, and basically if there is a problem with a cloud agent we'll spin up a bunch of subs.Like a singleswyx: instance.Samantha: Yeah. We'll take the ideas and argument and spin up a bunch of subagents using the Datadog MCP to explore the logs and find like all of the problems that could have happened with that. It takes the debugging time, like from potentially you can do quick stuff quickly with the Datadog ui, but it takes it down to, again, like a single agent call as opposed to trolling through logs yourself.Jonas: You should also talk about the stuff we've done with transcripts.Samantha: Yes. Also so basically we've also done some things internally. There'll be some versions of this as we ship publicly soon, where you can spit up an agent and give it access to another agent's transcript to either basically debug something that happened.So act as an external debugger. I see. Or continue the conversation. Almost like forking it.swyx: A transcript includes all the chain of thought for the 11 minutes here. 45 minutes there.Samantha: Yeah. That way. Exactly. So basically acting as a like secondary agent that debugs the first, so we've started to push more andswyx: they're all the same [00:16:00] code.It is just the different prompts, but the sa the same.Samantha: Yeah. So basically same cloud agent infrastructure and then same harness. And then like when we do things like include, there's some extra infrastructure that goes into piping in like an external transcript if we include it as an attachment.But for things like the cloud agent diagnosis, that's mostly just using the Datadog MCP. ‘Cause we also launched CPS along with along with this cloud agent launch, launch support for cloud agent cps.swyx: Oh, that was drawn out.Jonas: We won't, we'll be doing a bigger marketing moment for it next week, but, and you can now use CPS andswyx: People will listen to it as well.Yeah,Jonas: they'llSamantha: be ahead of the third. They'll be ahead. And I would I actually don't know if the Datadog CP is like publicly available yet. I realize this not sure beta testing it, but it's been one of my favorites to use. Soswyx: I think that one's interesting for Datadog. ‘cause Datadog wants to own that site.Interesting with Bits. I don't know if you've tried bits.Samantha: I haven't tried bits.swyx: Yeah.Jonas: That's their cloud agentswyx: product. Yeah. Yeah. They want to be like we own your logs and give us our, some part of the, [00:17:00] self-healing software that everyone wants. Yeah. But obviously Cursor has a strong opinion on coding agents and you, you like taking away from the which like obviously you're going to do, and not every company's like Cursor, but it's interesting if you're a Datadog, like what do you do here?Do you expose your logs to FDP and let other people do it? Or do you try to own that it because it's extra business for you? Yeah. It's like an interesting one.Samantha: It's a good question. All I know is that I love the Datadog MCP,Jonas: And yeah, it is gonna be no, no surprise that people like will demand it, right?Samantha: Yeah.swyx: It's, it's like anysystemswyx: of record company like this, it's like how much do you give away? Cool. I think that's that for the sort of cloud agents tour. Cool. And we just talk about like cloud agents have been when did Kirsten loves cloud agents? Do you know, in JuneJonas: last year.swyx: June last year. So it's been slowly develop the thing you did, like a bunch of, like Michael did a post where himself, where he like showed this chart of like ages overtaking tap. And I'm like, wow, this is like the biggest transition in code.Jonas: Yeah.swyx: Like in, in [00:18:00] like the last,Jonas: yeah. I think that kind of got turned out.Yeah. I think it's a very interest,swyx: not at all. I think it's been highlighted by our friend Andre Kati today.Jonas: Okay.swyx: Talk more about it. What does it mean? Yeah. Is I just got given like the cursor tab key.Jonas: Yes. Yes.swyx: That's that'sSamantha: cool.swyx: I know, but it's gonna be like put in a museum.Jonas: It is.Samantha: I have to say I haven't used tab a little bit myself.Jonas: Yeah. I think that what it looks like to code with AI code generally creates software, even if you want to go higher level. Is changing very rapidly. No, not a hot take, but I think from our vendor's point at Cursor, I think one of the things that is probably underappreciated from the outside is that we are extremely self-aware about that fact and Kerscher, got its start in phase one, era one of like tab and auto complete.And that was really useful in its time. But a lot of people start looking at text files and editing code, like we call it hand coding. Now when you like type out the actual letters, it'sswyx: oh that's cute.Jonas: Yeah.swyx: Oh that's cute.Jonas: You're so boomer. So boomer. [00:19:00] And so that I think has been a slowly accelerating and now in the last few months, rapidly accelerating shift.And we think that's going to happen again with the next thing where the, I think some of the pains around tab of it's great, but I actually just want to give more to the agent and I don't want to do one tab at a time. I want to just give it a task and it goes off and does a larger unit of work and I can.Lean back a little bit more and operate at that higher level of abstraction that's going to happen again, where it goes from agents handing you back diffs and you're like in the weeds and giving it, 32nd to three minute tasks, to, you're giving it, three minute to 30 minute to three hour tasks and you're getting back videos and trying out previews rather than immediately looking at diffs every single time.swyx: Yeah. Anything to add?Samantha: One other shift that I've noticed as our cloud agents have really taken off internally has been a shift from primarily individually driven development to almost this collaborative nature of development for us, slack is actually almost like a development on [00:20:00] Id basically.So Iswyx: like maybe don't even build a custom ui, like maybe that's like a debugging thing, but actually it's that.Samantha: I feel like, yeah, there's still so much to left to explore there, but basically for us, like Slack is where a lot of development happens. Like we will have these issue channels or just like this product discussion channels where people are always at cursing and that kicks off a cloud agent.And for us at least, we have team follow-ups enabled. So if Jonas kicks off at Cursor in a thread, I can follow up with it and add more context. And so it turns into almost like a discussion service where people can like collaborate on ui. Oftentimes I will kick off an investigation and then sometimes I even ask it to get blame and then tag people who should be brought in. ‘cause it can tag people in Slack and then other people will comeswyx: in, can tag other people who are not involved in conversation. Yes. Can just do at Jonas if say, was talking to,Samantha: yeah.swyx: That's cool. You should, you guys should make a big good deal outta that.Samantha: I know. It's a lot to, I feel like there's a lot more to do with our slack surface area to show people externally. But yeah, basically like it [00:21:00] can bring other people in and then other people can also contribute to that thread and you can end up with a PR again, with the artifacts visible and then people can be like, okay, cool, we can merge this.So for us it's like the ID is almost like moving into Slack in some ways as well.swyx: I have the same experience with, but it's not developers, it's me. Designer salespeople.Samantha: Yeah.swyx: So me on like technical marketing, vision, designer on design and then salespeople on here's the legal source of what we agreed on.And then they all just collaborate and correct. The agents,Jonas: I think that we found when these threads is. The work that is left, that the humans are discussing in these threads is the nugget of what is actually interesting and relevant. It's not the boring details of where does this if statement go?It's do we wanna ship this? Is this the right ux? Is this the right form factor? Yeah. How do we make this more obvious to the user? It's like those really interesting kind of higher order questions that are so easy to collaborate with and leave the implementation to the cloud agent.Samantha: Totally. And no more discussion of am I gonna do this? Are you [00:22:00] gonna do this cursor's doing it? You just have to decide. You like it.swyx: Sometimes the, I don't know if there's a, this probably, you guys probably figured this out already, but since I, you need like a mute button. So like cursor, like we're going to take this offline, but still online.But like we need to talk among the humans first. Before you like could stop responding to everything.Jonas: Yeah. This is a design decision where currently cursor won't chime in unless you explicitly add Mention it. Yeah. Yeah.Samantha: So it's not always listening.Yeah.Jonas: I can see all the intermediate messages.swyx: Have you done the recursive, can cursor add another cursor or spawn another cursor?Samantha: Oh,Jonas: we've done some versions of this.swyx: Because, ‘cause it can add humans.Jonas: Yes. One of the other things we've been working on that's like an implication of generating the code is so easy is getting it to production is still harder than it should be.And broadly, you solve one bottleneck and three new ones pop up. Yeah. And so one of the new bottlenecks is getting into production and we have a like joke internally where you'll be talking about some feature and someone says, I have a PR for that. Which is it's so easy [00:23:00] to get to, I a PR for that, but it's hard still relatively to get from I a PR for that to, I'm confident and ready to merge this.And so I think that over the coming weeks and months, that's a thing that we think a lot about is how do we scale up compute to that pipeline of getting things from a first draft An agent did.swyx: Isn't that what Merge isn't know what graphite's for, likeJonas: graphite is a big part of that. The cloud agent testingswyx: Is it fully integrated or still different companiesJonas: working on I think we'll have more to share there in the future, but the goal is to have great end-to-end experience where Cursor doesn't just help you generate code tokens, it helps you create software end-to-end.And so review is a big part of that, that I think especially as models have gotten much better at writing code, generating code, we've felt that relatively crop up more,swyx: sorry this is completely unplanned, but like there I have people arguing one to you need ai. To review ai and then there is another approach, thought school of thought where it's no, [00:24:00] reviews are dead.Like just show me the video. It's it like,Samantha: yeah. I feel again, for me, the video is often like alignment and then I often still wanna go through a code review process.swyx: Like still look at the files andSamantha: everything. Yeah. There's a spectrum of course. Like the video, if it's really well done and it does like fully like test everything, you can feel pretty competent, but it's still helpful to, to look at the code.I make hep pay a lot of attention to bug bot. I feel like Bug Bot has been a great really highly adopted internally. We often like, won't we tell people like, don't leave bug bot comments unaddressed. ‘cause we have such high confidence in it. So people always address their bug bot comments.Jonas: Once you've had two cases where you merged something and then you went back later, there was a bug in it, you merged, you went back later and you were like, ah, bug Bot had found that I should have listened to Bug Bot.Once that happens two or three times, you learn to wait for bug bot.Samantha: Yeah. So I think for us there's like that code level review where like it's looking at the actual code and then there's like the like feature level review where you're looking at the features. There's like a whole number of different like areas.There'll probably eventually be things like performance level review, security [00:25:00] review, things like that where it's like more more different aspects of how this feature might affect your code base that you want to potentially leverage an agent to help with.Jonas: And some of those like bug bot will be synchronous and you'll typically want to wait on before you merge.But I think another thing that we're starting to see is. As with cloud agents, you scale up this parallelism and how much code you generate. 10 person startups become, need the Devrel X and pipelines that a 10,000 person company used to need. And that looks like a lot of the things I think that 10,000 person companies invented in order to get that volume of software to production safely.So that's things like, release frequently or release slowly, have different stages where you release, have checkpoints, automated ways of detecting regressions. And so I think we're gonna need stacks merg stack diffs merge queues. Exactly. A lot of those things are going to be importantswyx: forward with.I think the majority of people still don't know what stack stacks are. And I like, I have many friends in Facebook and like I, I'm pretty friendly with graphite. I've just, [00:26:00] I've never needed it ‘cause I don't work on that larger team and it's just like democratization of no, only here's what we've already worked out at very large scale and here's how you can, it benefits you too.Like I think to me, one of the beautiful things about GitHub is that. It's actually useful to me as an individual solo developer, even though it's like actually collaboration software.Jonas: Yep.swyx: And I don't think a lot of Devrel tools have figured that out yet. That transition from like large down to small.Jonas: Yeah. Kers is probably an inverse story.swyx: This is small down toJonas: Yeah. Where historically Kers share, part of why we grew so quickly was anyone on the team could pick it up and in fact people would pick it up, on the weekend for their side project and then bring it into work. ‘cause they loved using it so much.swyx: Yeah.Jonas: And I think a thing that we've started working on a lot more, not us specifically, but as a company and other folks at Cursor, is making it really great for teams and making it the, the 10th person that starts using Cursor in a team. Is immediately set up with things like, we launched Marketplace recently so other people can [00:27:00] configure what CPS and skills like plugins.So skills and cps, other people can configure that. So that my cursor is ready to go and set up. Sam loves the Datadog, MCP and Slack, MCP you've also been using a lot butSamantha: also pre-launch, but I feel like it's so good.Jonas: Yeah, my cursor should be configured if Sam feels strongly that's just amazing and required.swyx: Is it automatically shared or you have to go and.Jonas: It depends on the MCP. So some are obviously off per user. Yeah. And so Sam can't off my cursor with my Slack MCP, but some are team off and those can be set up by admins.swyx: Yeah. Yeah. That's cool. Yeah, I think, we had a man on the pod when cursor was five people, and like everyone was like, okay, what's the thing?And then it's usually something teams and org and enterprise, but it's actually working. But like usually at that stage when you're five, when you're just a vs. Code fork it's like how do you get there? Yeah. Will people pay for this? People do pay for it.Jonas: Yeah. And I think for cloud agents, we expect.[00:28:00]To have similar kind of PLG things where I think off the bat we've seen a lot of adoption with kind of smaller teams where the code bases are not quite as complex to set up. Yes. If you need some insane docker layer caching thing for builds not to take two hours, that's going to take a little bit longer for us to be able to support that kind of infrastructure.Whereas if you have front end backend, like one click agents can install everything that they need themselves.swyx: This is a good chance for me to just ask some technical sort of check the box questions. Can I choose the size of the vm?Jonas: Not yet. We are planning on adding that. Weswyx: have, this is obviously you want like LXXL, whatever, right?Like it's like the Amazon like sort menu.Jonas: Yes, exactly. We'll add that.swyx: Yeah. In some ways you have to basically become like a EC2, almost like you rent a box.Jonas: You rent a box. Yes. We talk a lot about brain in a box. Yeah. So cursor, we want to be a brain in a box,swyx: but is the mental model different? Is it more serverless?Is it more persistent? Is. Something else.Samantha: We want it to be a bit persistent. The desktop should be [00:29:00] something you can return to af even after some days. Like maybe you go back, they're like still thinking about a feature for some period of time. So theswyx: full like sus like suspend the memory and bring it back and then keep going.Samantha: Exactly.swyx: That's an interesting one because what I actually do want, like from a manna and open crawl, whatever, is like I want to be able to log in with my credentials to the thing, but not actually store it in any like secret store, whatever. ‘cause it's like this is the, my most sensitive stuff.Yeah. This is like my email, whatever. And just have it like, persist to the image. I don't know how it was hood, but like to rehydrate and then just keep going from there. But I don't think a lot of infra works that way. A lot of it's stateless where like you save it to a docker image and then it's only whatever you can describe in a Docker file and that's it.That's the only thing you can cl multiple times in parallel.Jonas: Yeah. We have a bunch of different ways of setting them up. So there's a dockerfile based approach. The main default way is actually snapshottingswyx: like a Linux vmJonas: like vm, right? You run a bunch of install commands and then you snapshot more or less the file system.And so that gets you set up for everything [00:30:00] that you would want to bring a new VM up from that template basically.swyx: Yeah.Jonas: And that's a bit distinct from what Sam was talking about with the hibernating and re rehydrating where that is a full memory snapshot as well. So there, if I had like the browser open to a specific page and we bring that back, that page will still be there.swyx: Was there any discussion internally and just building this stuff about every time you shoot a video it's actually you show a little bit of the desktop and the browser and it's not necessary if you just show the browser. If, if you know you're just demoing a front end application.Why not just show the browser, right? Like it Yeah,Samantha: we do have some panning and zooming. Yeah. Like it can decide that when it's actually recording and cutting the video to highlight different things. I think we've played around with different ways of segmenting it and yeah. There's been some different revs on it for sure.Jonas: Yeah. I think one of the interesting things is the version that you see now in cursor.com actually is like half of what we had at peak where we decided to unshift or unshipped quite a few things. So two of the interesting things to talk about, one is directly an answer to your [00:31:00] question where we had native browser that you would have locally, it was basically an iframe that via port forwarding could load the URL could talk to local host in the vm.So that gets you basically, so inswyx: your machine's browser,likeJonas: in your local browser? Yeah. You would go to local host 4,000 and that would get forwarded to local host 4,000 in the VM via port forward. We unshift that like atswyx: Eng Rock.Jonas: Like an Eng Rock. Exactly. We unshift that because we felt that the remote desktop was sufficiently low latency and more general purpose.So we build Cursor web, but we also build Cursor desktop. And so it's really useful to be able to have the full spectrum of things. And even for Cursor Web, as you saw in one of the examples, the agent was uploading files and like I couldn't upload files and open the file viewer if I only had access to the browser.And we've thought a lot about, this might seem funny coming from Cursor where we started as this, vs. Code Fork and I think inherited a lot of amazing things, but also a lot [00:32:00] of legacy UI from VS Code.Minimal Web UI SurfacesJonas: And so with the web UI we wanted to be very intentional about keeping that very minimal and exposing the right sum of set of primitive sort of app surfaces we call them, that are shared features of that cloud.Environment that you and the agent both use. So agent uses desktop and controls it. I can use desktop and controlled agent runs terminal commands. I can run terminal commands. So that's how our philosophy around it. The other thing that is maybe interesting to talk about that we unshipped is and we may, both of these things we may reship and decide at some point in the future that we've changed our minds on the trade offs or gotten it to a point where, putswyx: it out there.Let users tell you they want it. Exactly. Alright, fine.Why No File EditorJonas: So one of the other things is actually a files app. And so we used to have the ability at one point during the process of testing this internally to see next to, I had GID desktop and terminal on the right hand side of the tab there earlier to also have a files app where you could see and edit files.And we actually felt that in some [00:33:00] ways, by restricting and limiting what you could do there, people would naturally leave more to the agent and fall into this new pattern of delegating, which we thought was really valuable. And there's currently no way in Cursor web to edit these files.swyx: Yeah. Except you like open up the PR and go into GitHub and do the thing.Jonas: Yeah.swyx: Which is annoying.Jonas: Just tell the agent,swyx: I have criticized open AI for this. Because Open AI is Codex app doesn't have a file editor, like it has file viewer, but isn't a file editor.Jonas: Do you use the file viewer a lot?swyx: No. I understand, but like sometimes I want it, the one way to do it is like freaking going to no, they have a open in cursor button or open an antigravity or, opening whatever and people pointed that.So I was, I was part of the early testers group people pointed that and they were like, this is like a design smell. It's like you actually want a VS. Code fork that has all these things, but also a file editor. And they were like, no, just trust us.Jonas: Yeah. I think we as Cursor will want to, as a product, offer the [00:34:00] whole spectrum and so you want to be able to.Work at really high levels of abstraction and double click and see the lowest level. That's important. But I also think that like you won't be doing that in Slack. And so there are surfaces and ways of interacting where in some cases limiting the UX capabilities makes for a cleaner experience that's more simple and drives people into these new patterns where even locally we kicked off joking about this.People like don't really edit files, hand code anymore. And so we want to build for where that's going and not where it's beenswyx: a lot of cool stuff. And Okay. I have a couple more.Full Stack Hosting Debateswyx: So observations about the design elements about these things. One of the things that I'm always thinking about is cursor and other peers of cursor start from like the Devrel tools and work their way towards cloud agents.Other people, like the lovable and bolts of the world start with here's like the vibe code. Full cloud thing. They were already cloud edges before anyone else cloud edges and we will give you the full deploy platform. So we own the whole loop. We own all the infrastructure, we own, we, we have the logs, we have the the live site, [00:35:00] whatever.And you can do that cycle cursor doesn't own that cycle even today. You don't have the versal, you don't have the, you whatever deploy infrastructure that, that you're gonna have, which gives you powers because anyone can use it. And any enterprise who, whatever you infra, I don't care. But then also gives you limitations as to how much you can actually fully debug end to end.I guess I'm just putting out there that like is there a future where there's like full stack cursor where like cursor apps.com where like I host my cursor site this, which is basically a verse clone, right? I don't know.Jonas: I think that's a interesting question to be asking, and I think like the logic that you laid out for how you would get there is logic that I largely agree with.swyx: Yeah. Yeah.Jonas: I think right now we're really focused on what we see as the next big bottleneck and because things like the Datadog MCP exist, yeah. I don't think that the best way we can help our customers ship more software. Is by building a hosting solution right now,swyx: by the way, these are things I've actually discussed with some of the companies I just named.Jonas: Yeah, for sure. Right now, just this big bottleneck is getting the code out there and also [00:36:00] unlike a lovable in the bolt, we focus much more on existing software. And the zero to one greenfield is just a very different problem. Imagine going to a Shopify and convincing them to deploy on your deployment solution.That's very different and I think will take much longer to see how that works. May never happen relative to, oh, it's like a zero to one app.swyx: I'll say. It's tempting because look like 50% of your apps are versal, superb base tailwind react it's the stack. It's what everyone does.So I it's kinda interesting.Jonas: Yeah.Model Choice and Auto Routingswyx: The other thing is the model select dying. Right now in cloud agents, it's stuck down, bottom left. Sure it's Codex High today, but do I care if it's suddenly switched to Opus? Probably not.Samantha: We definitely wanna give people a choice across models because I feel like it, the meta change is very frequently.I was a big like Opus 4.5 Maximalist, and when codex 5.3 came out, I hard, hard switch. So that's all I use now.swyx: Yeah. Agreed. I don't know if, but basically like when I use it in Slack, [00:37:00] right? Cursor does a very good job of exposing yeah. Cursors. If people go use it, here's the model we're using.Yeah. Here's how you switch if you want. But otherwise it's like extracted away, which is like beautiful because then you actually, you should decide.Jonas: Yeah, I think we want to be doing more with defaults.swyx: Yeah.Jonas: Where we can suggest things to people. A thing that we have in the editor, the desktop app is auto, which will route your request and do things there.So I think we will want to do something like that for cloud agents as well. We haven't done it yet. And so I think. We have both people like Sam, who are very savvy and want know exactly what model they want, and we also have people that want us to pick the best model for them because we have amazing people like Sam and we, we are the experts.Yeah. We have both the traffic and the internal taste and experience to know what we think is best.swyx: Yeah. I have this ongoing pieces of agent lab versus model lab. And to me, cursor and other companies are example of an agent lab that is, building a new playbook that is different from a model lab where it's like very GP heavy Olo.So obviously has a research [00:38:00] team. And my thesis is like you just, every agent lab is going to have a router because you're going to be asked like, what's what. I don't keep up to every day. I'm not a Sam, I don't keep up every day for using you as sample the arm arbitrator of taste. Put me on CRI Auto.Is it free? It's not free.Jonas: Auto's not free, but there's different pricing tiers. Yeah.swyx: Put me on Chris. You decide from me based on all the other people you know better than me. And I think every agent lab should basically end up doing this because that actually gives you extra power because you like people stop carrying or having loyalty with one lab.Jonas: Yeah.Best Of N and Model CouncilsJonas: Two other maybe interesting things that I don't know how much they're on your radar are one the best event thing we mentioned where running different models head to head is actually quite interesting becauseswyx: which exists in cursor.Jonas: That exists in cur ID and web. So the problem is where do you run them?swyx: Okay.Jonas: And so I, I can share my screen if that's interesting. Yeahinteresting.swyx: Yeah. Yeah. Obviously parallel agents, very popal.Jonas: Yes, exactly. Parallel agentsswyx: in you mind. Are they the same thing? Best event and parallel agents? I don't want to [00:39:00] put words in your mouth.Jonas: Best event is a subset of parallel agents where they're running on the same prompt.That would be my answer. So this is what that looks like. And so here in this dropdown picker, I can just select multiple models.swyx: Yeah.Jonas: And now if I do a prompt, I'm going to do something silly. I am running these five models.swyx: Okay. This is this fake clone, of course. The 2.0 yeah.Jonas: Yes, exactly. But they're running so the cursor 2.0, you can do desktop or cloud.So this is cloud specifically where the benefit over work trees is that they have their own VMs and can run commands and won't try to kill ports that the other one is running. Which are some of the pains. These are allswyx: called work trees?Jonas: No, these are all cloud agents with their own VMs.swyx: Okay. ButJonas: When you do it locally, sometimes people do work trees and that's been the main way that people have set out parallel so far.I've gotta say.swyx: That's so confusing for folks.Jonas: Yeah.swyx: No one knows what work trees are.Jonas: Exactly. I think we're phasing out work trees.swyx: Really.Jonas: Yeah.swyx: Okay.Samantha: But yeah. And one other thing I would say though on the multimodel choice, [00:40:00] so this is another experiment that we ran last year and the decide to ship at that time but may come back to, and there was an interesting learning that's relevant for, these different model providers. It was something that would run a bunch of best of ends but then synthesize and basically run like a synthesizer layer of models. And that was other agents that would take LM Judge, but one that was also agentic and could write code. So it wasn't just picking but also taking the learnings from two models or, and models that it was looking at and writing a new diff.And what we found was that at the time at least, there were strengths to using models from different model providers as the base level of this process. Like basically you could get almost like a synergistic output that was better than having a very unified, like bottom model tier. So it was really interesting ‘cause it's like potentially, even though even in the future when you have like maybe one model as ahead of the other for a little bit, there could be some benefit from having like multiple top tier models involved in like a [00:41:00] model swarm or whatever agent Swarm that you're doing, that they each have strengths and weaknesses.Yeah.Jonas: Andre called this the council, right?Samantha: Yeah, exactly. We actually, oh, that's another internal command we have that Ian wrote slash council. Oh, and they some, yeah.swyx: Yes. This idea is in various forms everywhere. And I think for me, like for me, the productization of it, you guys have done yeah, like this is very flexible, but.If I were to add another Yeah, what your thing is on here it would be too much. I what, let's say,Samantha: Ideally it's all, it's something that the user can just choose and it all happens under the hood in a way where like you just get the benefit of that process at the end and better output basically, but don't have to get too lost in the complexity of judging along the way.Jonas: Okay.Subagents for ContextJonas: Another thing on the many agents, on different parallel agents that's interesting is an idea that's been around for a while as well that has started working recently is subagents. And so this is one other way to get agents of the different prompts and different goals and different models, [00:42:00] different vintages to work together.Collaborate and delegate.swyx: Yeah. I'm very like I like one of my, I always looking for this is the year of the blah, right? Yeah. I think one of the things on the blahs is subs. I think this is of but I haven't used them in cursor. Are they fully formed or how do I honestly like an intro because do I form them from new every time?Do I have fixed subagents? How are they different for slash commands? There's all these like really basic questions that no one stops to answer for people because everyone's just like too busy launching. We have toSamantha: honestly, you could, you can see them in cursor now if you just say spin up like 50 subagents to, so cursor definesswyx: what Subagents.Yeah.Samantha: Yeah. So basically I think I shouldn't speak for the whole subagents team. This is like a different team that's been working on this, but our thesis or thing that we saw internally is that like they're great for context management for kind of long running threads, or if you're trying to just throw more compute at something.We have strongly used, almost like a generic task interface where then the main agent can define [00:43:00] like what goes into the subagent. So if I say explore my code base, it might decide to spin up an explore subagent and or might decide to spin up five explore subagent.swyx: But I don't get to set what those subagent are, right?It's all defined by a model.Samantha: I think. I actually would have to refresh myself on the sub agent interface.Jonas: There are some built-in ones like the explore subagent is free pre-built. But you can also instruct the model to use other subagents and then it will. And one other example of a built-in subagent is I actually just kicked one off in cursor and I can show you what that looks like.swyx: Yes. Because I tried to do this in pure prompt space.Jonas: So this is the desktop app? Yeah. Yeah. And that'sswyx: all you need to do, right? Yeah.Jonas: That's all you need to do. So I said use a sub agent to explore and I think, yeah, so I can even click in and see what the subagent is working on here. It ran some fine command and this is a composer under the hood.Even though my main model is Opus, it does smart routing to take, like in this instance the explorer sort of requires reading a ton of things. And so a faster model is really useful to get an [00:44:00] answer quickly, but that this is what subagent look like. And I think we wanted to do a lot more to expose hooks and ways for people to configure these.Another example of a cus sort of builtin subagent is the computer use subagent in the cloud agents, where we found that those trajectories can be long and involve a lot of images obviously, and execution of some testing verification task. We wanted to use that models that are particularly good at that.So that's one reason to use subagents. And then the other reason to use subagents is we want contexts to be summarized reduced down at a subagent level. That's a really neat boundary at which to compress that rollout and testing into a final message that agent writes that then gets passed into the parent rather than having to do some global compaction or something like that.swyx: Awesome. Cool. While we're in the subagents conversation, I can't do a cursor conversation and not talk about listen stuff. What is that? What is what? He built a browser. He built an os. Yes. And he [00:45:00] experimented with a lot of different architectures and basically ended up reinventing the software engineer org chart.This is all cool, but what's your take? What's, is there any hole behind the side? The scenes stories about that kind of, that whole adventure.Samantha: Some of those experiments have found their way into a feature that's available in cloud agents now, the long running agent mode internally, we call it grind mode.And I think there's like some hint of grind mode accessible in the picker today. ‘cause you can do choose grind until done. And so that was really the result of experiments that Wilson started in this vein where he I think the Ralph Wigga loop was like floating around at the time, but it was something he also independently found and he was experimenting with.And that was what led to this product surface.swyx: And it is just simple idea of have criteria for completion and do not. Until you complete,Samantha: there's a bit more complexity as well in, in our implementation. Like there's a specific, you have to start out by aligning and there's like a planning stage where it will work with you and it will not get like start grind execution mode until it's decided that the [00:46:00] plan is amenable to both of you.Basically,swyx: I refuse to work until you make me happy.Jonas: We found that it's really important where people would give like very underspecified prompt and then expect it to come back with magic. And if it's gonna go off and work for three minutes, that's one thing. When it's gonna go off and work for three days, probably should spend like a few hours upfront making sure that you have communicated what you actually want.swyx: Yeah. And just to like really drive from the point. We really mean three days that No, noJonas: human. Oh yeah. We've had three day months innovation whatsoever.Samantha: I don't know what the record is, but there's been a long time with the grantsJonas: and so the thing that is available in cursor. The long running agent is if you wanna think about it, very abstractly that is like one worker node.Whereas what built the browser is a society of workers and planners and different agents collaborating. Because we started building the browser with one worker node at the time, that was just the agent. And it became one worker node when we realized that the throughput of the system was not where it needed to be [00:47:00] to get something as large of a scale as the browser done.swyx: Yeah.Jonas: And so this has also become a really big mental model for us with cloud, cloud agents is there's the classic engineering latency throughput trade-offs. And so you know, the code is water flowing through a pipe. The, we think that over the coming months, the big unlock is not going to be one person with a model getting more done, like the water flowing faster and we'll be making the pipe much wider and so ing more, whether that's swarms of agents or parallel agents, both of those are things that contribute to getting.Much more done in the same amount of time, but any one of those tasks doesn't necessarily need to get done that quickly. And throughput is this really big thing where if you see the system of a hundred concurrent agents outputting thousands of tokens a second, you can't go back like that.Just you see a glimpse of the future where obviously there are many caveats. Like no one is using this browser. IRL. There's like a bunch of things not quite right yet, but we are going to get to systems that produce real production [00:48:00] code at the scale much sooner than people think. And it forces you to think what even happens to production systems. Like we've broken our GitHub actions recently because we have so many agents like producing and pushing code that like CICD is just overloaded. ‘cause suddenly it's like effectively weg grew, cursor's growing very quickly anyway, but you grow head count, 10 x when people run 10 x as many agents.And so a lot of these systems, exactly, a lot of these systems will need to adapt.swyx: It also reminds me, we, we all, the three of us live in the app layer, but if you talk to the researchers who are doing RL infrastructure, it's the same thing. It's like all these parallel rollouts and scheduling them and making sure as much throughput as possible goes through them.Yeah, it's the same thing.Jonas: We were talking briefly before we started recording. You were mentioning memory chips and some of the shortages there. The other thing that I think is just like hard to wrap your head around the scale of the system that was building the browser, the concurrency there.If Sam and I both have a system like that running for us, [00:49:00] shipping our software. The amount of inference that we're going to need per developer is just really mind-boggling. And that makes, sometimes when I think about that, I think that even with, the most optimistic projections for what we're going to need in terms of buildout, our underestimating, the extent to which these swarm systems can like churn at scale to produce code that is valuable to the economy.And,swyx: yeah, you can cut this if it's sensitive, but I was just Do you have estimates of how much your token consumption is?Jonas: Like per developer?swyx: Yeah. Or yourself. I don't need like comfy average. I just curious. ISamantha: feel like I, for a while I wasn't an admin on the usage dashboard, so I like wasn't able to actually see, but it was a,swyx: mine has gone up.Samantha: Oh yeah.swyx: But I thinkSamantha: it's in terms of how much work I'm doing, it's more like I have no worries about developers losing their jobs, at least in the near term. ‘cause I feel like that's a more broad discussion.swyx: Yeah. Yeah. You went there. I didn't go, I wasn't going there.I was just like how much more are you using?Samantha: There's so much stuff to be built. And so I feel like I'm basically just [00:50:00] trying to constantly I have more ambitions than I did before. Yes. Personally. Yes. So can't speak to the broader thing. But for me it's like I'm busier than ever before.I'm using more tokens and I am also doing more things.Jonas: Yeah. Yeah. I don't have the stats for myself, but I think broadly a thing that we've seen, that we expect to continue is J'S paradox. Whereswyx: you can't do it in our podcast without seeingJonas: it. Exactly. We've done it. Now we can wrap. We've done, we said the words.Phase one tab auto complete people paid like 20 bucks a month. And that was great. Phase two where you were iterating with these local models. Today people pay like hundreds of dollars a month. I think as we think about these highly parallel kind of agents running off for a long times in their own VM system, we are already at that point where people will be spending thousands of dollars a month per human, and I think potentially tens of thousands and beyond, where it's not like we are greedy for like capturing more money, but what happens is just individuals get that much more leverage.And if one person can do as much as 10 people, yeah. That tool that allows ‘em to do that is going to be tremendously valuable [00:51:00] and worth investing in and taking the best thing that exists.swyx: One more question on just the cursor in general and then open-ended for you guys to plug whatever you wanna put.How is Cursor hiring these days?Samantha: What do you mean by how?swyx: So obviously lead code is dead. Oh,Samantha: okay.swyx: Everyone says work trial. Different people have different levels of adoption of agents. Some people can really adopt can be much more productive. But other people, you just need to give them a little bit of time.And sometimes they've never lived in a token rich place like cursor.And once you live in a token rich place, you're you just work differently. But you need to have done that. And a lot of people anyway, it was just open-ended. Like how has agentic engineering, agentic coding changed your opinions on hiring?Is there any like broad like insights? Yeah.Jonas: Basically I'm asking this for other people, right? Yeah, totally. Totally. To hear Sam's opinion, we haven't talked about this the two of us. I think that we don't see necessarily being great at the latest thing with AI coding as a prerequisite.I do think that's a sign that people are keeping up and [00:52:00] curious and willing to upscale themselves in what's happening because. As we were talking about the last three months, the game has completely changed. It's like what I do all day is very different.swyx: Like it's my job and I can't,Jonas: Yeah, totally.I do think that still as Sam was saying, the fundamentals remain important in the current age and being able to go and double click down. And models today do still have weaknesses where if you let them run for too long without cleaning up and refactoring, the coke will get sloppy and there'll be bad abstractions.And so you still do need humans that like have built systems before, no good patterns when they see them and know where to steer things.Samantha: I would agree with that. I would say again, cursor also operates very quickly and leveraging ag agentic engineering is probably one reason why that's possible in this current moment.I think in the past it was just like people coding quickly and now there's like people who use agents to move faster as well. So it's part of our process will always look for we'll select for kind of that ability to make good decisions quickly and move well in this environment.And so I think being able to [00:53:00] figure out how to use agents to help you do that is an important part of it too.swyx: Yeah. Okay. The fork in the road, either predictions for the end of the year, if you have any, or PUDs.Jonas: Evictions are not going to go well.Samantha: I know it's hard.swyx: They're so hard. Get it wrong.It's okay. Just, yeah.Jonas: One other plug that may be interesting that I feel like we touched on but haven't talked a ton about is a thing that the kind of these new interfaces and this parallelism enables is the ability to hop back and forth between threads really quickly. And so a thing that we have,swyx: you wanna show something or,Jonas: yeah, I can show something.A thing that we have felt with local agents is this pain around contact switching. And you have one agent that went off and did some work and another agent that, that did something else. And so here by having, I just have three tabs open, let's say, but I can very quickly, hop in here.This is an example I showed earlier, but the actual workflow here I think is really different in a way that may not be obvious, where, I start t

Jason and Jeff go completely off-script to discuss what they are actually doing in their portfolios right now. Jeff breaks down why he bought more Datadog on a recent dip and his strategy for adding to Enphase, while Jason explains how he is using options (specifically selling puts) to generate income while waiting for better valuations. Plus, they analyze the risk vs. reward of QuantumScape, debate the new CEO at PayPal, and play a game of "buy or get off the pot" that ends with a live stock purchase.00:53 Unscripted Earnings Season04:34 Jeff Did a Thing06:43 Why Buy Datadog Now09:14 AI Risks for SaaS11:34 Selling Puts Strategy17:06 Market Psychology Chegg19:41 Market Efficiency Long Term23:15 Enphase DCA Dilemma25:46 How to Time Adds28:08 Quarterly Info Is Noise28:30 Enphase Through the Cycle29:49 QuantumScape Solid State Promise31:50 Timing the Entry Price33:53 Asymmetric Upside vs Risk36:24 PayPal CEO Shakeup38:59 Reframing the PayPal Thesis44:48 Small Positions to Fix46:36 Airbnb Growth Proof PointsCompanies mentioned: ABNB, ADBE, CHGG, DDOG, ENPH, PYPL, QS, UPSTFind where to listen & subscribe,  portfolio contests, and contact information at https://investingunscripted.com*****************************************To get 15% off any paid plan at fiscal.ai, visit https://fiscal.ai/unscriptedListen to the Chit Chat Stocks Podcast for discussions on stocks, financial markets, super investors, and more. Follow the show on Spotify, Apple Podcasts, or YouTube*****************************************Join our PatreonSubscribe to our portfolio on Savvy Trader

In der heutigen Folge sprechen die Finanzjournalisten Anja Ettel und Holger Zschäpitz über einen Absturz bei Nvidia, einen Rebound bei Software und eine Wende im Warner Brothers Drama. Außerdem geht es um Atlassian, Zscaler, Datadog, Applovin, Crowdstrike, Workday, Salesforce, Opendoor, Intuitive Machines, Carvana, IonQ, Rigetti, Netflix, Paramount Skydance, Allianz, Deutsche Telekom, Münchener Rück (Munich Re), Scout24, Heidelberg Materials, Deutsche Börse, Kion, Hensoldt, Puma, Block (Square), WiseTech, Amazon, Nike, Verizon, Papa Johns, Pinterest, Autodesk, Ebay, UPS, Hypoport, Xtrackers MSCI World Industrials ETF (WKN: A113FN), Amundi S&P World Industrials Screened ETF (WKN: A3DSTE), iShares MSCI Europe Industrials Sector ETF (WKN: A2QBZ6), iShares S&P 500 Industrials Sector ETF (WKN: A142N0). Wir freuen uns an Feedback über aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts und AAA-Newsletter. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Der Börsen-Podcast Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

Today we sit down with Doug May, SVP of Productivity at Harness, to discuss one of the most critical yet overlooked aspects of a healthy organization: Sales Productivity. Doug has had an illustrious career at elite organizations including Datadog and Databricks, and he brings that expertise to Harness, where he has cut ramp time in half and increased per-rep contribution by 43%. We explore the "F1 engineering team" analogy of GTM support, why productivity metrics are the ultimate indicator of a company's health, and the specific questions every candidate should ask to de-risk their next career move.

Le Recruiting Operations (ou RecOps pour les intimes), ça peut vite sembler un peu obscur. Que fait-il exactement ?Process ? Outils ? Data ? Compliance ? Sécurité ? Onboarding ? Spoiler : c'est tout ça à la fois. Et même bien plus encore.Alors pour y voir un peu plus clair, j'ai décidé de dédier une série d'épisode à ce sujet. Et on commence avec Chloé Morisson, Program Manager Recruiting Operations chez Datadog. Recruteuse repentie devenue architecte de l'écosystème recrutement, Chloé est ultra-pragmatique, et surtout très lucide sur ce qui fait (vraiment) tourner une équipe recrutement dans la durée.Alors au programme de cet échange :

Erfahre hier mehr über unseren Partner Scalable Capital - dem Broker mit einem der besten YouTube-Kanäle zu Aktien & Investments. https://www.youtube.com/@scalable.capital/videos Spotify freut sich über mehr Nutzer als gedacht. Coca-Cola freut sich über mehr Absatz. Datadog mag KI. Alibaba macht neue KI. Cintas will UniFirst kaufen. Ferraris kosten über 470.000 €. Hasbro setzt auf Harry Potter. Prediction-Market Kalshi hat Super-Bowl-Rekord. Gucci-Mutter Kering (WKN: 851223) ist im wichtigen Weihnachtsquartal geschrumpft. Trotzdem war die Aktie gestern um die 10% im Plus. Erstens: Die Zahlen waren nicht so schlecht wie befürchtet. Zweitens: Alle hoffen auf den neuen CEO Luca de Meo. Canon (WKN: 853055) war 2025 auf dem Online-Marktplatz StockX beliebt ohne Ende, gerade bei der Gen Z. Kann die Aktie davon profitieren? Diesen Podcast vom 11.02.2026, 3:00 Uhr stellt dir die Podstars GmbH (Noah Leidinger) zur Verfügung.

In today's episode of Motley Fool Money, host Emily Flippen is joined by analysts Jason Hall and Toby Bordelon to break down earnings from three of the most volatile Rule-Breaking stocks out there. They discuss: - How Spotify continues to convert free to paid users, and how monetization efforts are evolving in a more cost-conscious environment - Whether or not DataDog's usage-based business model is under threat as software companies see pullbacks across the board - Ferrari's attempt to reassure investors that it has growth left in it, even as its EV ambitions evolve Companies discussed: SPOT, DDOG, RACE Host: Emily Flippen, Jason Hall, Toby Bordelon Producer: Anand Chokkavelu Engineer: Dan Boyd Disclosure: Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, “TMF”) do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. We're committed to transparency: All personal opinions in advertisements from Fools are their own. The product advertised in this episode was loaned to TMF and was returned after a test period or the product advertised in this episode was purchased by TMF. Advertiser has paid for the sponsorship of this episode. Learn more about your ad choices. Visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠megaphone.fm/adchoices Learn more about your ad choices. Visit megaphone.fm/adchoices

A busy morning of when it comes to earnings:Carl Quintanilla, Sara Eisen, and David Faber kicked off the hour with two of them - Coca-Cola & Marriott... The CEOs of both companies joined the team with their read on the consumer, the numbers, and more. Plus: why Evercore still sees a higher S&P ahead - despite growing AI debt concerns - with the firm's Head of Equity Strategy. Also in focus: all the earnings names you should be watching here, from Astrazeneca to Datadog to Spotify - and David's new reporting on Paramount's enhanced offer to buy Warner Brothers Discovery.  Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

APAC stocks were mostly higher as the region took impetus from the gains on Wall Street, where the S&P 500 approached closer towards its record levels, and the Nasdaq outperformed as the tech rebound persisted.US President Trump and Chinese President Xi's summit is reportedly set for the first week of April, POLITICO reported, but the White House later clarified that the Trump-Xi meeting has not been finalised.The EU is reportedly readying options to give Ukraine gradual membership rights and is preparing a series of options to embed Ukraine's membership in a future peace deal.UK PM Starmer told Labour MPs that he is "not prepared to walk away" from power or "plunge us into chaos" as previous prime ministers have done.European equity futures indicate a slightly lower cash market open with Euro Stoxx 50 futures down 0.1% after the cash market closed with gains of 1.0% on Monday.Looking ahead, highlights include Norwegian CPI (Jan), US NFIB (Jan), Weekly ADP, ECI (Q4), Retail Sales (Dec) & EIA STEO. Speakers include Fed's Hammack & Logan, Supply from the Netherlands, UK, Germany & US. Earnings from Coca-Cola, S&P, Gilead, Robinhood, Welltower, Duke Energy, Datadog, Ford, AIG, Xylem, Spotify, AstraZeneca, BP, Barclays, Ferrari and Mediobanca.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

European bourses are mostly firmer, US equity futures are flat/incrementally higher.DXY is flat awaiting Retail Sales/ECI, JPY bid alongside JGB stabilisation whilst NOK gains post-inflation.Fixed rebounds from Monday's pressure into data & supply; Gilts outperform as PM Starmer pushed back on calls to resign.WTI and Brent mildly lower, XAU remains above USD 5k/oz; Copper muted heading into Chinese festive period.Looking ahead, highlights include US NFIB (Jan), Weekly ADP, ECI (Q4), Retail Sales (Dec) & EIA STEO. Speakers include Fed's Hammack & Logan, Supply from the US. Earnings from Coca-Cola, S&P, Gilead, Robinhood, Welltower, Datadog, Ford, AIG, Xylem.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

MrBeast Industries kauft die Gen-Z-Banking-App Step Mobile – ist das die Zukunft der Influencer-Monetarisierung? Amazon baut einen KI-Content-Marketplace, um Publisher am Leben zu halten. Die EU warnt Meta: WhatsApp darf keine Chatbot-Konkurrenten mehr blockieren. OpenAI feiert 10% Wachstum im Januar. Der Software-Ausverkauf trennt Spreu vom Weizen: Monday.com verliert 20%, Datadog gewinnt 15%. Spotify überrascht mit starken Zahlen. Frank Thelen behauptet in einem Podcast, er hätte "oft 1000x" mit Investments gemacht. Die USA wollen MAGA-nahe Think Tanks und NGOs in Europa finanzieren. Die Trump-Familie hat bereits 1,4 Milliarden Dollar aus World Liberty Financial gezogen. Und Taiwan-Chips werden von US-Zöllen ausgenommen. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Intro (00:03:06) MrBeast kauft Step Mobile Banking App (00:06:04) Influencer-Monetarisierung: Infrastruktur statt Merch (00:11:56) Amazon baut KI-Content-Marketplace (00:14:49) EU warnt Meta: WhatsApp blockiert Chatbots (00:18:02) OpenAI feiert 10% Januar-Wachstum (00:21:06) Monday.com Earnings (00:38:13) Datadog Earnings (00:39:14) Spotify Earnings (00:42:06) Frank Thelen: Die 1000x-Lüge im Faktencheck (00:54:11) USA finanzieren MAGA-NGOs in Europa (01:00:42) Eric Schmidt datet Söder-Tochter (01:05:20) Krypto: Tether & World Liberty Financial (01:16:30) Taiwan-Chips von US-Zöllen ausgenommen Shownotes MrBeast's Beast Industries to Buy Gen Z–Focused Banking App - theinformation.com Amazon Discusses AI Content Marketplace With Publishers - theinformation.com Update: ChatGPT & Google dropped Grokipedia. - linkedin.com Sam Altman lobt ChatGPTs Wachstum bei OpenAI's $100 Milliarden Finanzierung. - cnbc.com Meta von EU aufgefordert, WhatsApp für Rivalen zu öffnen. - bloomberg.com Monday.com drops 21% as AI disruption fears mount in software - cnbc.com Datadog ist heute Spitzenreiter im S&P 500. - barrons.com Spotify pops 16% on strong user growth, earnings beat - cnbc.com {ungeskriptet} Frank Thelen Podcast - open.spotify.com US government to fund Maga-aligned think-tanks and charities in Europe - ft.com Krypto-Riese Tether half Türkei bei Milliardenschlag gegen Betrug. - bloomberg.com Eine Generation regiert, die nächste profitiert von Krypto. - wsj.com US Tarifs chips Ausnahme - ft.com

Tim Berglund talks to Richie Artoul (WarpStream/Confluent) about his career in data infrastructure. Richie's first job: working at Howie's Game Shack, a walk‑in LAN gaming cafe. His challenge: working at Datadog on a new log storage system.SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo

#335: Observability tools have exploded in recent years, but most come with a familiar tradeoff: either pay steep cloud vendor markups or spend weeks building custom dashboards from scratch. Coroot takes a different path as a self-hosted, open source observability platform that prioritizes simplicity over flexibility. Using eBPF technology, Coroot automatically instruments applications without requiring code changes or complex configuration, delivering what co-founder Peter Zaitsev calls opinionated observability—a philosophy of less is more that aims to reduce cognitive overload rather than drowning users in endless metrics and dashboards. The conversation explores how Coroot differentiates itself in a crowded market with over a hundred observability vendors. Rather than competing head-to-head with cloud giants like Datadog and Dynatrace, Coroot focuses on developers who need answers fast without building elaborate monitoring systems. The platform combines systematic root cause analysis with AI-powered recommendations, using deterministic methods to trace how errors propagate through microservices before handing off to LLMs for actionable fix suggestions. Darin and Viktor dig into Coroot's business model with Peter, examining why the company chose Apache 2.0 licensing instead of more restrictive options, and how staying bootstrapped with minimal angel funding allows them to play the long game without pressure to chase every hype cycle.   Peter's contact information: X: https://x.com/PeterZaitsev Bluesky: https://bsky.app/profile/peterzaitsev.bsky.social LinkedIn: https://www.linkedin.com/in/peterzaitsev/   YouTube channel: https://youtube.com/devopsparadox   Review the podcast on Apple Podcasts: https://www.devopsparadox.com/review-podcast/   Slack: https://www.devopsparadox.com/slack/   Connect with us at: https://www.devopsparadox.com/contact/

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

When your website stops working at 3 AM, you need to answer one question fast: Is it my code or is a big cloud provider having problems? Omri Sass from Datadog explains updog.ai, a tool that monitors whether major services like AWS, CloudFlare, and others are actually working. Instead of asking people to report problems like Down Detector does, updog uses real data from thousands of computers to detect when services go down. Omri shares why this took 6 years to build, how they process massive amounts of data with machine learning, and why cloud providers have been strangely upset about these tools existing.About Omri: Omri Sass is a Director of Product Management at Datadog, where he leads and supports a team of 25+ product managers driving initiatives across Bits AI SRE, Data Observability, Service Management, and most recently, the launch of updog.ai. Outside of work, Omri is an avid sci-fi reader, a dedicated yoga practitioner, and happily outmatched by his cat.Show Highlights:(02:12) What is Updog and How Does It Work(03:38) Why Knowing If It's a Global Problem Matters(04:01) The Problem With Testing Every Endpoint Yourself(05:52) How Datadog Discovered EC2 Outages From Their Own Systems(10:38) When AWS Regions Go Down and Cascade Failures(13:13) What Happens When Services Rebuild Completely(16:29) The Most Important Learning During a 3 AM Incident(20:11) Why This Took So Long to Build(23:40) When Datadog Going Down Isn't Critical Path(25:22) How They Picked Which AWS Services to Monitor(27:07) What Comes Next for Updog(30:11) Where to Find Omri and UpdogLinks: Datadog: datadoghq.comOmir's LinkedIn: https://www.linkedin.com/in/omri-sass-65632a14/Sponsored by: duckbillhq.com

Wall Street startet freundlich in den Tag: S&P, Nasdaq und Dow liegen vorbörslich im Plus, der VIX fällt – der Markt wirkt auffallend sorglos. Im Fokus stehen heute EZB-Protokoll, US-Erstanträge (200k erwartet) und später Einkommen & Ausgaben. Breaking: Spirit Airlines verhandelt mit Castlelake über eine mögliche Übernahme als Ausweg aus Chapter 11. Bei den Zahlen: PG und Abbott zeigen ein zähes Umsatzumfeld, GE Aerospace punktet mit Orders und Cashflow – aber Margen bleiben das Thema. Analysten treiben den Tape: Upgrades für Vertex und Datadog, höhere Kursziele u.a. für ASML und Lam Research – während Crocs abgestuft wird. Abonniere den Podcast, um keine Folge zu verpassen! ____ Folge uns, um auf dem Laufenden zu bleiben: • X: http://fal.cn/SQtwitter • LinkedIn: http://fal.cn/SQlinkedin • Instagram: http://fal.cn/SQInstagram

In a market dominated by AI narratives and software-driven growth, it's easy to lump technology companies into a single bucket. But a closer look often reveals very different stories beneath the surface. Two companies that highlight this contrast particularly well are Datadog (DDOG) and CoreWeave (CRWV)—both high-quality software businesses, yet operating in entirely different domains and exhibiting sharply different market behavior.

In this finance-focused discussion, we examine three companies that often appear together in conversations about cloud computing and data—but operate in meaningfully different segments of the market: Oracle, Snowflake, and Datadog.

Ce vendredi x mois année, Liste INVITES et FONCTiON, se sont penchés sur les titres (....à lister... ), dans On achète ou on vend ? dans l'émission C'est Votre Argent présentée par Marc Fiorentino. C'est Votre Argent est à voir ou écouter le vendredi sur B

Steve Koenig goes inside out on the software sector, highlighting growing job opportunities and picking some favorite stocks. He likes Autodesk (ADSK), Zscaler (ZS), and Datadog (DDOG), giving each of them Outperform ratings. George Tsilis brings an example options trade for Datadog. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

From Berkeley robotics and OpenAI's 2017 Dota-era internship to shipping RL breakthroughs on GPT-4o, o1, and o3, and now leading model development at Cursor, Ashvin Nair has done it all. We caught up with Ashvin at NeurIPS 2025 to dig into the inside story of OpenAI's reasoning team (spoiler: it went from a dozen people to 300+), why IOI Gold felt reachable in 2022 but somehow didn't change the world when o1 actually achieved it, how RL doesn't generalize beyond the training distribution (and why that means you need to bring economically useful tasks into distribution by co-designing products and models), the deeper lessons from the RL research era (2017–2022) and why most of it didn't pan out because the community overfitted to benchmarks, how Cursor is uniquely positioned to do continual learning at scale with policy updates every two hours and product-model co-design that keeps engineers in the loop instead of context-switching into ADHD hell, and his bet that the next paradigm shift is continual learning with infinite memory—where models experience something once (a bug, a mistake, a user pattern) and never forget it, storing millions of deployment tokens in weights without overloading capacity.We discuss:* Ashvin's path: Berkeley robotics PhD → OpenAI 2017 intern (Dota era) → o1/o3 reasoning team → Cursor ML lead in three months* Why robotics people are the most grounded at NeurIPS (they work with the real world) and simulation people are the most unhinged (Lex Fridman's take)* The IOI Gold paradox: “If you told me we'd achieve IOI Gold in 2022, I'd assume we could all go on vacation—AI solved, no point working anymore. But life is still the same.”* The RL research era (2017–2022) and why most of it didn't pan out: overfitting to benchmarks, too many implicit knobs to tune, and the community rewarding complex ideas over simple ones that generalize* Inside the o1 origin story: a dozen people, conviction from Ilya and Jakob Pachocki that RL would work, small-scale prototypes producing “surprisingly accurate reasoning traces” on math, and first-principles belief that scaled* The reasoning team grew from ~12 to 300+ people as o1 became a product and safety, tooling, and deployment scaled up* Why Cursor is uniquely positioned for continual learning: policy updates every two hours (online RL on tab), product and ML sitting next to each other, and the entire software engineering workflow (code, logs, debugging, DataDog) living in the product* Composer as the start of product-model co-design: smart enough to use, fast enough to stay in the loop, and built by a 20–25 person ML team with high-taste co-founders who code daily* The next paradigm shift: continual learning with infinite memory—models that experience something once (a bug, a user mistake) and store it in weights forever, learning from millions of deployment tokens without overloading capacity (trillions of pretraining tokens = plenty of room)* Why off-policy RL is unstable (Ashvin's favorite interview question) and why Cursor does two-day work trials instead of whiteboard interviews* The vision: automate software engineering as a process (not just answering prompts), co-design products so the entire workflow (write code, check logs, debug, iterate) is in-distribution for RL, and make models that never make the same mistake twice—Ashvin Nair* Cursor: https://cursor.com* X: https://x.com/ashvinnair_Full Video EpisodeTimestamps00:00:00 Introduction: From Robotics to Cursor via OpenAI00:01:58 The Robotics to LLM Agent Transition: Why Code Won00:09:11 RL Research Winter and Academic Overfitting00:11:45 The Scaling Era and Moving Goalposts: IOI Gold Doesn't Mean AGI00:21:30 OpenAI's Reasoning Journey: From Codex to O100:20:03 The Blip: Thanksgiving 2023 and OpenAI Governance00:22:39 RL for Reasoning: The O-Series Conviction and Scaling00:25:47 O1 to O3: Smooth Internal Progress vs External Hype Cycles00:33:07 Why Cursor: Co-Designing Products and Models for Real Work00:34:14 Composer and the Future: Online Learning Every Two Hours00:35:15 Continual Learning: The Missing Paradigm Shift00:44:00 Hiring at Cursor and Why Off-Policy RL is Unstable Get full access to Latent.Space at www.latent.space/subscribe

From Berkeley robotics and OpenAI's 2017 Dota-era internship to shipping RL breakthroughs on GPT-4o, o1, and o3, and now leading model development at Cursor, Ashvin Nair has done it all. We caught up with Ashvin at NeurIPS 2025 to dig into the inside story of OpenAI's reasoning team (spoiler: it went from a dozen people to 300+), why IOI Gold felt reachable in 2022 but somehow didn't change the world when o1 actually achieved it, how RL doesn't generalize beyond the training distribution (and why that means you need to bring economically useful tasks into distribution by co-designing products and models), the deeper lessons from the RL research era (2017–2022) and why most of it didn't pan out because the community overfitted to benchmarks, how Cursor is uniquely positioned to do continual learning at scale with policy updates every two hours and product-model co-design that keeps engineers in the loop instead of context-switching into ADHD hell, and his bet that the next paradigm shift is continual learning with infinite memory—where models experience something once (a bug, a mistake, a user pattern) and never forget it, storing millions of deployment tokens in weights without overloading capacity. We discuss: Ashvin's path: Berkeley robotics PhD → OpenAI 2017 intern (Dota era) → o1/o3 reasoning team → Cursor ML lead in three months Why robotics people are the most grounded at NeurIPS (they work with the real world) and simulation people are the most unhinged (Lex Fridman's take) The IOI Gold paradox: "If you told me we'd achieve IOI Gold in 2022, I'd assume we could all go on vacation—AI solved, no point working anymore. But life is still the same." The RL research era (2017–2022) and why most of it didn't pan out: overfitting to benchmarks, too many implicit knobs to tune, and the community rewarding complex ideas over simple ones that generalize Inside the o1 origin story: a dozen people, conviction from Ilya and Jakob Pachocki that RL would work, small-scale prototypes producing "surprisingly accurate reasoning traces" on math, and first-principles belief that scaled The reasoning team grew from ~12 to 300+ people as o1 became a product and safety, tooling, and deployment scaled up Why Cursor is uniquely positioned for continual learning: policy updates every two hours (online RL on tab), product and ML sitting next to each other, and the entire software engineering workflow (code, logs, debugging, DataDog) living in the product Composer as the start of product-model co-design: smart enough to use, fast enough to stay in the loop, and built by a 20–25 person ML team with high-taste co-founders who code daily The next paradigm shift: continual learning with infinite memory—models that experience something once (a bug, a user mistake) and store it in weights forever, learning from millions of deployment tokens without overloading capacity (trillions of pretraining tokens = plenty of room) Why off-policy RL is unstable (Ashvin's favorite interview question) and why Cursor does two-day work trials instead of whiteboard interviews The vision: automate software engineering as a process (not just answering prompts), co-design products so the entire workflow (write code, check logs, debug, iterate) is in-distribution for RL, and make models that never make the same mistake twice — Ashvin Nair Cursor: https://cursor.com X: https://x.com/ashvinnair_ Chapters 00:00:00 Introduction: From Robotics to Cursor via OpenAI 00:01:58 The Robotics to LLM Agent Transition: Why Code Won 00:09:11 RL Research Winter and Academic Overfitting 00:11:45 The Scaling Era and Moving Goalposts: IOI Gold Doesn't Mean AGI 00:21:30 OpenAI's Reasoning Journey: From Codex to O1 00:20:03 The Blip: Thanksgiving 2023 and OpenAI Governance 00:22:39 RL for Reasoning: The O-Series Conviction and Scaling 00:25:47 O1 to O3: Smooth Internal Progress vs External Hype Cycles 00:33:07 Why Cursor: Co-Designing Products and Models for Real Work 00:34:14 Composer and the Future: Online Learning Every Two Hours 00:35:15 Continual Learning: The Missing Paradigm Shift 00:44:00 Hiring at Cursor and Why Off-Policy RL is Unstable

All government agencies face the challenge of achieving ambitious IT modernization goals while juggling limited resources and seemingly endless needs.  This week on Feds At the Edge, experts explore practical strategies to make modernization both achievable and cost-effective.  Christine Maii Sakuda, State Chief Information Officer (CIO) and administrator of the Office of Enterprise Technology Services for Hawaii, shares how a dedicated change management advocate and early practitioner engagement can transform digital initiatives, emphasizing that investing in people upfront leads to smoother, more efficient transitions. And Abe Rosloff, Senior Sales Engineer at Datadog, adds that not every system needs to be included in a transition. Understanding priorities, cataloging applications, and involving the team early are key steps to achieving cost-effective modernization.   Tune in on your favorite podcast platform to hear actionable insights that can help your agency modernize IT without breaking the budget.               

Alexander Embiricos leads product on Codex, OpenAI's powerful coding agent, which has grown 20x since August and now serves trillions of tokens weekly. Before joining OpenAI, Alexander spent five years building a pair programming product for engineers. He now works at the frontier of AI-led software development, building what he describes as a software engineering teammate—an AI agent designed to participate across the entire development lifecycle.We discuss:1. Why Codex has grown 20x since launch and what product decisions unlocked this growth2. How OpenAI built the Sora Android app in just 18 days using Codex3. Why the real bottleneck to AGI-level productivity isn't model capability—it's human typing speed4. The vision of AI as a proactive teammate, not just a tool you prompt5. The bottleneck shifting from building to reviewing AI-generated work6. Why coding will be a core competency for every AI agent—because writing code is how agents use computers best—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs: https://workos.com/lennyFin—The #1 AI agent for customer service: https://fin.ai/lennyJira Product Discovery—Confidence to build the right thing: https://atlassian.com/lenny/?utm_source=lennypodcast&utm_medium=paid-audio&utm_campaign=fy24q1-jpd-imc—Transcript: https://www.lennysnewsletter.com/p/why-humans-are-ais-biggest-bottleneck—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/180365355/my-biggest-takeaways-from-this-conversation—Where to find Alexander Embiricos:• X: https://x.com/embirico• LinkedIn: https://www.linkedin.com/in/embirico—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Alexander Embiricos (05:13) The speed and ambition at OpenAI(11:34) Codex: OpenAI's coding agent(15:43) Codex's explosive growth(24:59) The future of AI and coding agents(33:11) The impact of AI on engineering(44:08) How Codex has impacted the way PMs operate(45:40) Throwaway code and ubiquitous coding(47:10) Shipping the Sora Android app(49:01) Building the Atlas browser(53:34) Codex's impact on productivity(55:35) Measuring progress on Codex(58:09) Why they are building a web browser(01:01:58) Non-engineering use cases for Codex(01:02:53) Codex's capabilities(01:04:49) Tips for getting started with Codex(01:05:37) Skills to lean into in the AI age(01:10:36) How far are we from a human version of AI?(01:13:31) Hiring and team growth at Codex(01:15:47) Lightning round and final thoughts—Referenced:• OpenAI: https://openai.com• Codex: https://openai.com/codex• Inside ChatGPT: The fastest-growing product in history | Nick Turley (Head of ChatGPT at OpenAI): https://www.lennysnewsletter.com/p/inside-chatgpt-nick-turley• Dropbox: http://dropbox.com• Datadog: https://www.datadoghq.com• Andrej Karpathy on X: https://x.com/karpathy• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Atlas: https://openai.com/index/introducing-chatgpt-atlas• How Block is becoming the most AI-native enterprise in the world | Dhanji R. Prasanna: https://www.lennysnewsletter.com/p/how-block-is-becoming-the-most-ai-native• Goose: https://block.xyz/inside/block-open-source-introduces-codename-goose• Lessons on building product sense, navigating AI, optimizing the first mile, and making it through the messy middle | Scott Belsky (Adobe, Behance): https://www.lennysnewsletter.com/p/lessons-on-building-product-sense• Sora Android app: https://play.google.com/store/apps/details?id=com.openai.sora&hl=en_US&pli=1• The OpenAI Podcast—ChatGPT Atlas and the next era of web browsing: https://www.youtube.com/watch?v=WdbgNC80PMw&list=PLOXw6I10VTv9GAOCZjUAAkSVyW2cDXs4u&index=2• How to measure AI developer productivity in 2025 | Nicole Forsgren: https://www.lennysnewsletter.com/p/how-to-measure-ai-developer-productivity• Compiling: https://3d.xkcd.com/303• Jujutsu Kaisen on Netflix: https://www.netflix.com/title/81278456• Tesla: https://www.tesla.com• Radical Candor: From theory to practice with author Kim Scott: https://www.lennysnewsletter.com/p/radical-candor-from-theory-to-practice• Andreas Embirikos: https://en.wikipedia.org/wiki/Andreas_Embirikos• George Embiricos: https://en.wikipedia.org/wiki/George_Embiricos: https://en.wikipedia.org/wiki/George_Embiricos—Recommended books:• Culture series: https://www.amazon.com/dp/B07WLZZ9WV• The Lord of the Rings: https://www.amazon.com/Lord-Rings-J-R-R-Tolkien/dp/0544003411• A Fire Upon the Deep (Zones of Thought series Book 1): https://www.amazon.com/Fire-Upon-Deep-Zones-Thought/dp/1250237750• Radical Candor: Be a Kick-Ass Boss Without Losing Your Humanity: https://www.amazon.com/Radical-Candor-Kick-Ass-Without-Humanity/dp/1250103509—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

SQLite is embedded everywhere - phones, browsers, IoT devices. It's reliable, battle-tested, and feature-rich. But what if you want concurrent writes? Or CDC for streaming changes? Or vector indexes for AI workloads? The SQLite codebase isn't accepting new contributors, and the test suite that makes it so reliable is proprietary. So how do you evolve an embedded database that's effectively frozen?Glauber Costa spent a decade contributing to the Linux kernel at Red Hat, then helped build Scylla, a high-performance rewrite of Cassandra. Now he's applying those lessons to SQLite. After initially forking SQLite (which produced a working business but failed to attract contributors), his team is taking the bolder path: a complete rewrite in Rust called Turso. The project already has features SQLite lacks - vector search, CDC, browser-native async operation - and is using deterministic simulation testing (inspired by TigerBeetle) to match SQLite's legendary reliability without access to its test suite.The conversation covers why rewrites attract contributors where forks don't, how the Linux kernel maintains quality with thousands of contributors, why Pekka's "pet project" jumped from 32 to 64 contributors in a month, and what it takes to build concurrent writes into an embedded database from scratch.--Support Developer Voices on Patreon: https://patreon.com/DeveloperVoicesSupport Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/joinTurso: https://turso.tech/Turso GitHub: https://github.com/tursodatabase/tursolibSQL (SQLite fork): https://github.com/tursodatabase/libsqlSQLite: https://www.sqlite.org/Rust: https://rust-lang.org/ScyllaDB (Cassandra rewrite): https://www.scylladb.com/Apache Cassandra: https://cassandra.apache.org/DuckDB (analytical embedded database): https://duckdb.org/MotherDuck (DuckDB cloud): https://motherduck.com/dqlite (Canonical distributed SQLite): https://canonical.com/dqliteTigerBeetle (deterministic simulation testing): https://tigerbeetle.com/Redpanda (Kafka alternative): https://www.redpanda.com/Linux Kernel: https://kernel.org/Datadog: https://www.datadoghq.com/Glauber Costa on X: https://x.com/glcstGlauber Costa on GitHub: https://github.com/glommerKris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.socialKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/--0:00 Intro3:16 Ten Years Contributing to the Linux Kernel15:17 From Linux to Startups: OSv and Scylla26:23 Lessons from Scylla: The Power of Ecosystem Compatibility33:00 Why SQLite Needs More37:41 Open Source But Not Open Contribution48:04 Why a Rewrite Attracted Contributors When a Fork Didn't57:22 How Deterministic Simulation Testing Works1:06:17 70% of SQLite in Six Months1:12:12 Features Beyond SQLite: Vector Search, CDC, and Browser Support1:19:15 The Challenge of Adding Concurrent Writes1:25:05 Building a Self-Sustaining Open Source Community1:30:09 Where Does Turso Fit Against DuckDB?1:41:00 Could Turso Compete with Postgres?1:46:21 How Do You Avoid a Toxic Community Culture?1:50:32 Outro

Is it time to look past the AI bubble and focus on the infrastructure actually securing it? Today, we're pivoting to a top secular growth trend: Cybersecurity.With the industry projected to grow 12% annually and hit $215 billion in spending by 2025, Palo Alto Networks (PANW) is making aggressive moves to dominate the landscape. We discuss their M&A strategy—including the purchase of Chronosphere and the pending CyberArk deal—and what this means for their entry into the cloud observability market against competitors like Datadog and Dynatrace.In this video, we cover:-- AI-Native Security: Why AI agents and cloud workloads are driving the next wave of IT spending.--The Financials: a breakdown of PANW's cash pile, revenue acceleration, and rising stock-based compensation.-- Valuation Check: With the stock trading around 30-33x Free Cash Flow, is Palo Alto Networks a buy, a hold, or just fair value?.We analyze whether this cybersecurity giant can execute on its "platformization" strategy and if the recent sell-off offers a prime entry point for investors.Tickers mentioned: PANW,CYBR,DT,DDOG#PaloAltoNetworks #Cybersecurity #StockMarket #Investing #PANW #CloudSecurity #AIStocksJoin us on Discord with Semiconductor Insider, sign up on our website: www.chipstockinvestor.com/membershipCharts & Data provided by fiscal.ai. Get 25% off any paid plan (Nov 26 - Dec 1) using our link: https://fiscal.ai/csi/Sign Up For Our Newsletter: https://mailchi.mp/b1228c12f284/sign-up-landing-page-short-formIf you found this video useful, please make sure to like and subscribe!*********************************************************Affiliate links that are sprinkled in throughout this video. If something catches your eye and you decide to buy it, we might earn a little coffee money. Thanks for helping us (Kasey) fuel our caffeine addiction!Content in this video is for general information or entertainment only and is not specific or individual investment advice. Forecasts and information presented may not develop as predicted and there is no guarantee any strategies presented will be successful. All investing involves risk, and you could lose some or all of your principal.Nick and Kasey own shares of Palo Alto Networks

Jyoti Bansal is the co-founder and CEO of Harness, the software delivery platform used by thousands of engineering teams, and previously founded AppDynamics, which he led from inception to a multibillion-dollar acquisition by Cisco. In this episode, Jyoti unpacks what it really takes to move from mid-market to enterprise, why he thinks in terms of “product-market-sales fit,” and how he structures Harness as a collection of “startups within a startup” to launch multiple “best-of-breed” products. In today's episode, we discuss: Why companies get stuck in the mid-market and struggle to move up into enterprise Why Jyoti deliberately lost Netflix as their customer The difference between product-market-sales fit, and product-market-fit How to build a scalable, capacity-driven go-to-market machine (instead of chasing deals) Diagnosing whether you have a product problem or a distribution problem How to hire and evaluate your first head of sales and top sales leaders Why Jyoti sold AppDynamics three days before IPO The “binary differentiator” rule for launching new products into crowded markets Why Harness runs 16 product lines under one roof Where to find Jyoti: LinkedIn: https://www.linkedin.com/in/jyotibansal/ Twitter/X: https://x.com/jyotibansalsf Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast References: Amazon: https://www.amazon.com/ AppDynamics: https://www.appdynamics.com/ Barclays: https://home.barclays/ BIG Labs: https://www.biglabs.com/ Carlos Delatorre: https://www.linkedin.com/in/cadelatorre/ Charles Schwab: https://www.schwab.com/ Cisco: https://www.cisco.com/ Citi: https://www.citi.com/ Cloudability: https://www.apptio.com/products/cloudability/ Datadog: https://www.datadoghq.com/ Dynatrace: https://www.dynatrace.com/ Harness: https://www.harness.io/ Jeff Bezos: https://x.com/JeffBezos Microsoft: https://www.microsoft.com/ Nasdaq: https://www.nasdaq.com/ Netflix: https://www.netflix.com/ New Relic: https://newrelic.com/ Salesforce: https://www.salesforce.com/ Splunk: https://www.splunk.com/ Traceable: https://www.traceable.ai/ Unusual Ventures: https://www.unusual.vc/ VMware: https://www.vmware.com/ Timestamps: (01:48) Why do companies get stuck in the mid-market? (05:09) Designing a product for enterprise and mid-market (07:19) Why Jyoti lost Netflix as a customer - on purpose (10:18) Becoming a scalable GTM organization (12:32) The real signs of product-market fit (14:04) Have you delivered the value? (15:46) How to hire your first sales team (19:59) The four signs of excellent sales leaders (23:16) How to interview a sales leader (27:51) Where Jyoti developed his commercial taste (29:37) Why early founders need to learn sales (32:02) How AppDynamics began (36:36) Why Jyoti sold three days pre-IPO (41:55) What does a healthy board look like? (44:23) How Jyoti perceives competition (46:18) Why you need a binary differentiator (49:53) How to launch multiple products (52:00) “We need to be best of breed” (57:38) Why PMs are like mini-entrepreneurs (1:00:20) The startup within a startup (1:02:45) A culture of continuous improvement

Andrew Lock is a staff software engineer at Datadog and educator whose contributions to the .NET ecosystem have shaped how developers approach modern web applications.  Located in the UK, Andrew is a Microsoft MVP, Author of ASP.NET Core in Action, and has an active blog all about his experience working with .NET and ASP.NET Core.   Topics of Discussion: [2:56] Andrew talks about appreciating the joy of coding and the minutiae of figuring out the correct way to do things. [3:28] Andrew discusses the various testing frameworks available for .NET, including MS Test, NUnit, XUnit, and TUnit. He explains the history and evolution of these frameworks, noting that XUnit has become the de facto default version. [7:41] Andrew explains his interest in TUnit, a newer testing library that addresses some of the limitations of XUnit. [9:29] TUnit is designed to be fast, supporting parallel execution and native AOT for better performance. [12:16] Is there a way to radically speed up the execution of big test suites? [15:39] Andrew explains the importance of each type of test in providing confidence that the software works as intended. [21:26] Andrew notes that full system tests can provide strong confidence by exercising critical pathways in the application. [29:44] Andrew mentions that tools like Octopus Deploy can be used to automate smoke tests as part of the deployment process. [30:26] Advice to new developers regarding automated testing, and the importance of writing code that is easy to test, and thinking about testing when writing code.   Mentioned in this Episode: Clear Measure Way Architect Forum Software Engineer Forum Andrew Lock "Andrew Lock: Containers in .NET8 - Ep 281" "Andrew Lock: Web Applications in .NET6 - Ep 198" "Updates to Docker images in .NET8"   Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.  

Head of Global ABM & Campaigns at Datadog, Kevin Driscoll leads a global team driving pipeline through integrated, data-led programs. With experience at IBM and Anaplan, he blends demand generation, growth marketing, and competitive strategy to unite sales and marketing around impact. His focus on scalable personalization, creative testing, and bridging PLG and SLG motions has made him a leading voice in account-based growth.Watch this episode and learn:How Datadog evolved from PLG to a focused, account-based growth model.Why a “two-hat” ABM structure strengthens GTM alignment.What B2C-style creativity can teach B2B marketers about engagement.How AI enhances research and personalization while keeping ABM human-led.

AGENDA: 04:22 Sequoia's Leadership Transition 09:46 Michael Burry's Big Short on Nvidia and Palantir 17:41 Gamma Raises $100M at a $2BN Valuation 32:34 Does Defensibility Exist Today When Copying is Easy 40:31 Should All Funds Be Way More Diversified 47:12 How to Run a Fundraising Process & What Not To Do 57:57 Datadog Surges 20% and Duolingo Crashes: What Happened    

For years, observability sat quietly in the background of enterprise technology, an operational tool for engineers, something to keep the lights on and costs down. As systems became more intelligent and automated, observability has stepped into a far more strategic role. It now acts as the connective tissue between business intent and technical execution, helping organizations understand not only what is happening inside their systems, but why it's happening and what it means.This shift forms the core of a recent Tech Transformed podcast episode between host Dana Gardner and Pejman Tabassomi, Field CTO for EMEA at Datadog. Together, they explore how observability has changed into what Tabassomi calls the “nervous system of AI”, a framework that allows enterprises to translate complexity into clarity and automation into measurable outcomes.Building AI LiteracyAI models make decisions that can affect everything from customer experiences to financial forecasting. It's important to understand that without observability, those decisions remain obscure.“Visibility into how models behave is crucial,” Tabassomi notes. True observability allows teams to see beyond outputs and into the reasoning of their systems, even if a model is drifting, automation is adapting effectively, and results align with strategic goals. This transparency builds trust. It also ensures accountability, giving organizations the confidence to scale AI responsibly without losing sight of the outcomes that matter most.Observability Observability is not merely about monitoring; it is about decision-making. It provides the insight required to manage complex systems, optimize outcomes, and act with agility. For organizations relying on AI and automation, observability becomes the differentiator between being merely efficient and achieving a sustainable competitive edge. In short, observability is no longer optional; it is central to translating technology into strategy and strategy into advantage.For more insights follow Datadog:X: @datadoghq Instagram: @datadoghq Facebook: facebook.com/datadoghq facebook.comLinkedIn: linkedin.com/company/datadogTakeawaysObservability has evolved from cost efficiency to a strategic role in...

In this segment, Dan Fougere breaks down how Product-Led Growth (PLG) fundamentally changes the traditional sales playbook. Drawing from his experience at Datadog and advising startups, he explains that PLG companies must rethink how they engage prospects—especially when users begin interacting with the product before any formal sales conversation.Dan emphasizes the importance of usage signals—such as downloading the product or reading documentation—as triggers for sales outreach. He also discusses the risk of force-fitting old playbooks into new environments and advocates for a first principles approach: understanding how users buy, how they use the product, and what commercial conversations are relevant at each stage.On this Veterans Day Week, check out one of the charities that's important to Dan.https://www.nplboutdoors.org/The No Person Left Behind Outdoors charity works with combat veterans to provide outdoor experiences to foster camaraderie, promote wellness, and celebrate resilience. They do everything from hiking trips to Kilimanjaro to turkey hunts. Support their important work.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of Defender Fridays, LimaCharlie Founder Maxime Lamothe-Brassard talks to Julie Agnes Sparks, Security Engineer at Datadog, about how to maximize logging visibility for effective detection engineering.Julie has a passion for continuous learning, proactively detecting significant security events, and responding effectively. Interests include: diversity & inclusion, privacy, and making technology more accessible.Join the Defender Fridays community, live every Friday, to discuss the dynamic world of information security in a collaborative space with seasoned professionals.Support our show by sharing your favorite episodes with a friend, subscribe, give us a rating or leave a comment on your podcast platform. This podcast is brought to you by LimaCharlie, maker of the SecOps Cloud Platform, infrastructure for SecOps where everything is built API first. Scale with confidence as your business grows. Start today for free at limacharlie.io.

In der heutigen Folge sprechen die Finanzjournalisten Lea Oetjen und Holger Zschäpitz über Erschöpfungstendenzen bei Tech-Titeln, eine krasse Rallye bei DHL und eine nächtliche Warnung für die Autoindustrie. Außerdem geht es um JPMorgan, Goldman Sachs, Rocket Lab, Robinhood, Opendoor, Palantir, Qualcomm, Nvidia, Amazon, Meta, DoorDash, Apple, Alphabet, Take Two Interactive, Airbnb, Expedia, Monster Beverage, Datadog, Fastly, Heidelberg Materials, Commerzbank, Zalando, Deutsche Börse, Hochtief, Lanxess, Peloton, Tesla, Nexperia, Bosch, ZF, Nissan, Hyundai, Virtune Stablecoin Index ETP (WKN: A4AQH5). Wir freuen uns über Feedback an aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts und AAA-Newsletter.[ Hier bei WELT.](https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html.) [Hier] (https://open.spotify.com/playlist/6zxjyJpTMunyYCY6F7vHK1?si=8f6cTnkEQnmSrlMU8Vo6uQ) findest Du die Samstagsfolgen Klassiker-Playlist auf Spotify! Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? [**Hier findest du alle Infos & Rabatte!**](https://linktr.ee/alles_auf_aktien) Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

Mehr Infos zum Kreditangebot von unserem Partner Scalable Capital findet ihr hier: https://de.scalable.capital/credit. Zalando und DHL liefern, Börse jubelt. Datadog überzeugt. Eli Lilly und Novo Nordisk haben Deal mit Trump. Duolingo will wachsen und nicht profitabel werden, Börse schimpft. Marvell wurde fast gekauft, HelloFresh hat Short-Attacke und Take-Two verschiebt GTA VI wieder. Lemonade (WKN: A2P7Z1) will das OpenAI der Versicherungswelt werden. Klappt das? Quantencomputing-Aktien waren an der Börse in den letzten Monaten Highflyer. Wie funktioniert die Technologie, wie funktioniert der Markt und wie erkennt man vertrauensvolle Firmen? Daniel Volz von Kipu Quantum klärt auf. Diesen Podcast vom 07.11.2025, 3:00 Uhr stellt dir die Podstars GmbH (Noah Leidinger) zur Verfügung.

MRKT Matrix - Thursday, November 6th Dow slides 400 points as AI stocks resume their decline, Nasdaq falls 2% (CNBC) Stocks making the biggest moves midday: Brighthouse Financial, Duolingo, Datadog, Snap & more (CNBC) US Companies Announce Most October Job Cuts in Over 20 Years (Bloomberg) Homebuilders Bet on 1% Mortgage Rates to Wake Up US Buyers (Bloomberg) Builders Are Offering Mortgage-Rate Discounts. Home Buyers Aren't Biting. (WSJ) Flight-Cancellation Plans Prompt Scramble Across Travel Industry (WSJ) Why Thanksgiving turkey prices might give shoppers a shock this season (CNBC) Lilly, Novo to Lower Obesity Drug Prices in Deal With Trump (Bloomberg) Sam Altman says OpenAI will top $20 billion in annualized revenue this year, hundreds of billions by 2030 (CNBC) Ford Considers Scrapping Electric Version of F-150 Truck (WSJ) --- Subscribe to our newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://riskreversalmedia.beehiiv.com/subscribe⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ MRKT Matrix by RiskReversal Media is a daily AI powered podcast bringing you the top stories moving financial markets Story curation by RiskReversal, scripts by Perplexity Pro, voice by ElevenLabs

In this episode of the Revenue Builders Podcast, our hosts John Kaplan and John McMahon are joined by Dan Fougere, a venture partner at Index Ventures and former CRO of Datadog. Dan shares insights from his extensive sales career, emphasizing the importance of developing adaptive and context-specific sales playbooks. He discusses the evolution of PLG (Product-Led Growth) strategies, the integration of AI in sales processes, and the critical need for continuous learning and adaptability. The episode also touches on Dan's philanthropic efforts, including his involvement with Homes for Our Troops and other charitable initiatives.ADDITIONAL RESOURCESConnect and learn more from Dan Fougere.Connect with Dan on LinkedIn: https://www.linkedin.com/in/danfougere/Support Homes For Our Troops: https://www.hfotusa.orgSupport Imagine Reading: https://imaginereading.com/Support No Person Left Behind Outdoors: https://www.nplboutdoors.orgRead the Guide on Six Critical Priorities for Revenue Leadership in 2026: https://hubs.li/Q03JN74V0Enjoying the podcast? Sign up to receive new episodes straight to your inbox: https://hubs.li/Q02R10xN0HERE ARE SOME KEY SECTIONS TO CHECK OUT[00:02:24] Advice for New Sales Leaders[00:02:52] Adapting Sales Playbooks[00:03:27] The Importance of Flexibility in Sales Strategies[00:03:54] Understanding Product-Led Growth (PLG)[00:06:44] Case Study: Datadog's Sales Evolution[00:07:57] Challenges in Scaling Sales Strategies[00:08:51] Building a Sales Organization for the Future[00:12:14] The Role of a CRO in Modern Sales[00:14:48] Adapting to Market Changes[00:26:23] Traits of Effective Sales Leaders[00:34:03] The Tip of the Spear: Leading from the Front[00:34:16] Medallia: Building a Sales Process from Scratch[00:36:58] Profile of a Successful Sales Leader[00:37:47] Recruiting and Building a High-Performance Team[00:39:25] The Importance of High Standards in Hiring[00:52:41] AI's Impact on Sales and Forecasting[01:02:07] Giving Back: Charitable EndeavorsHIGHLIGHT QUOTES[00:03:21] “A big mistake is trying to force fit a playbook from a previous company into a new company.”[00:06:01] “Approach it with a beginner's mind… it's actually an advantage you only get once.”[00:10:55] “Build your outbound before you need it, because at some point you're going to need it.”[00:13:33] “98.5% of companies realize, ‘I wish I had a great sales organization to go with this great PLG motion.'”[00:19:07] “The thing that tops people out is the inability to adapt and collaborate—they become too rigid.”[00:22:25] “If you know in your heart your team is mediocre, you're never going to be great. Raise those standards.”[00:31:36] “Don't just assume you can get rid of BDRs and have AI do it. I don't see anybody telling me that's working yet." Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Spending on AI infrastructure continues at a breakneck pace. Will this growth continue? • Learn more at thriventfunds.com • Follow us on LinkedIn • Share feedback and questions with us at podcast@thriventfunds.com • Thrivent Distributors, LLC is a member of FINRA and a subsidiary of Thrivent, the marketing name for Thrivent Financial for Lutherans. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of Planning Aces, host Jack Sweeney and resident thought leader Brett Knowles explore how finance leaders are approaching AI's early returns—balancing efficiency, experimentation, and human judgment. CFO Craig Foster of Pax8 discusses how AI enablement is driving measurable productivity gains. CFO David Obstler of Datadog reflects on finding ROI amid rapid innovation and market demand. And CFO Ben Gammell of Brex shares why forecasting still requires human intuition despite data-driven progress. Together, their insights reveal a spectrum of FP&A strategies defining the modern CFO's mindset toward AI adoption and business transformation.Brett Knowles' Key TakeawaysBrett Knowles observes that finance leaders are positioning themselves along a broad continuum—from bold experimentation to cautious skepticism—when it comes to AI in planning. He notes a shift in tone: CFOs are now openly discussing productivity gains and cost efficiency rather than avoiding them. Knowles cautions against overreliance on ROI metrics, emphasizing instead disciplined cost management, pragmatic experimentation, and the evolving role of finance in navigating technology-driven transformation.

AI-powered cyberattacks are rapidly evolving, prompting a significant shift in cybersecurity strategies. According to a recent Gartner report, IT leaders are expected to allocate over half of their cybersecurity budgets to preemptive defense measures by 2030. This change is driven by the inadequacy of traditional detection and response tools in the face of sophisticated cyber threats, particularly those enhanced by artificial intelligence. Experts warn that while preemptive measures can mitigate risks, organizations may encounter challenges in integrating these new systems and overcoming cultural inertia.Datadog's 2025 State of Cloud Security Report highlights a growing trend among organizations adopting data perimeters to combat credential theft, with 40% of organizations implementing this advanced security practice. Additionally, 86% of organizations are utilizing multi-account setups within AWS, which allows for better enforcement of security protocols. Meanwhile, OpenAI's report reveals that cybercriminals are increasingly leveraging AI for malicious activities, including phishing and surveillance, showcasing the urgent need for enhanced cybersecurity measures.In response to market pressures, Synology has reversed its policy on drive restrictions for its network-attached storage models, allowing the use of non-validated third-party drives. This decision comes after user feedback indicated dissatisfaction with the previous requirement for proprietary drives, which were often more expensive. For managed service providers (MSPs), this change offers greater flexibility and cost-effectiveness, making Synology's products more appealing once again.Pax8 has launched the Pax8 Agent Store, a platform designed to help MSPs adopt and offer AI-driven tools to small and medium-sized businesses. This marketplace aims to facilitate the integration and monetization of intelligent automation solutions, with early access set for December 2025. Additionally, SolarWinds has introduced an AI agent to enhance operational resilience for IT teams, while Barracuda Networks has launched Barracuda Research, a centralized resource for threat intelligence. Both initiatives aim to empower organizations in managing cybersecurity threats more effectively. Four things to know today00:00 Gartner, OpenAI, Datadog, and DHS Paint a Stark Cyber Future: AI Attacks Surge, Budgets Shift, and Defenses Fracture06:01 New Pax8 Platform Targets Repeatable AI Services, Sets Early Access for December08:03 Synology Reverses Course on Pricey Drives — Because You Stopped Buying09:53 SolarWinds and Barracuda Push AI to Ease IT Burdens—But Can They Deliver Real Value? This is the Business of Tech.     Supported by:  Comet, Scalepad Webinar:  https://bit.ly/msprmail

Our featured guest this week is Greg Foss, Manager - Threat Detection @ Datadog, interviewed by Frank Victory. News from Echostar, Space Command, DenAI Summit, CU Boulder, Webroot, Red Canary, Zvelo, Optiv, Ping Identity, and a lot more! You can find Greg and Frank at the following events if you'd like to see them in purpose:Greg: Lunch keynote at the CSA Fall Summit 2025 October 29th Frank: BSides Colorado Springs - "Pyramid of Pain - Defenders Edition" October 25th SnowFROC 2026 - March 26 and 27 University of Michigan CyberSecurity Symposium - Challenges of Training the Next Generation of Cybersecurity Professionals - October 30th Come join us on the Colorado = Security Slack channel to meet old and new friends. Sign up for our mailing list on the main site to receive weekly updates - https://www.colorado-security.com/. If you have any questions or comments, or any organizations or events we should highlight, contact Alex and Robb at info@colorado-security.com This week's news: EchoStar unloads wireless spectrum to Musk's SpaceX for $17 billion Will Colorado lose 30,000 jobs when Space Command moves to Alabama? CU Boulder ranked No. 1 for launching startups based on university discoveries Guarding your family against the latest online threats Node problem: Tracking recent npm package compromises SaaS Risk Management for Vendors in the Age of AI Cybersecurity Capabilities for Maturing Your TPRM Programs Complying with NIST SP 800-63-4 Standards: Identity as the Roadmap Redefining incident response in the age of AI Upcoming Events: Check out the full calendar ISSA Denver - Denver ISSA Chapter Meeting at Secure World: How I Got Caught: A Deep Dive Into a 800K Fraud - 10/9 ISACA Denver - October Chapter Meeting - 10/16 ASIS Denver - ASIS ROCKY MOUNTAIN TRADE SHOW and NETWORKING - 10/21 ISSA Pikes Peak - Chapter Meeting - 10/22 CSA - CSA Fall Summit - 10/29 View our events page for a full list of upcoming events * Thanks to CJ Adams for our intro and exit! If you need any voiceover work, you can contact him here at carrrladams@gmail.com. Check out his other voice work here. * Intro and exit song: "The Language of Blame" by The Agrarians is licensed under CC BY 2.0

Are you looking to save time, make money, and start winning with less risk? Then head to https://www.ovtlyr.com.SoFi stock just crashed hard, dropping over 10% in a single session, and the big question is: what do you do if you're holding shares? In this video we break down the warning signs that could have saved traders from heavy losses, the technical red flags flashing across the chart, and why sticking to proven signals with OVTLYR helps you avoid getting caught in a free fall.We start by analyzing SoFi's chart and the sell signals that fired off before the collapse. Order blocks showed clear resistance overhead while no support was in sight. On top of that, the 10 EMA and 20 EMA weren't just broken, they were crushed, confirming that the short-term and intermediate trends had flipped bearish. Add in the 50/80 rule—where 50% of the time a stock like this can fall 80%—and the risk was obvious to anyone following the data.➡️ Learn how order blocks reveal resistance zones and why lack of support signals danger➡️ See why SoFi's moving average breakdown was a massive red flag➡️ Understand how the 50/80 rule plays out in real-world trades➡️ Discover why buying dips in crashing stocks is a losing strategy➡️ Get the simple trend template OVTLYR traders use to confirm entries and exitsThis episode also dives into trading psychology. Many investors convince themselves that “the market is wrong” when a stock like SoFi drops. But the market is never wrong. Price is truth, and your job is to follow the wave, not fight it. That's why patience and discipline matter—waiting for stocks that are crashing up instead of catching falling knives that destroy your account.We also compare SoFi to names like AMD, Tesla, and others that show what happens when you ignore exit signals. These examples prove that even the strongest rallies can evaporate quickly, and the only way to protect your gains is by having a plan. Averaging down might feel like a strategy, but it ties up your money for months with no guarantee of recovery. Averaging up when the trend is confirmed is where the real profits are made.Beyond SoFi, we look at opportunities across the market using OVTLYR's plans: Plan M, Plan A, and Plan ETF. With the S&P and Nasdaq showing mixed signals, sometimes the smartest move is simply sitting in cash. Other times, ETF strategies shine by capturing broader uptrends with lower stress. And when the signals line up, individual names like Intel, Zscaler, and Datadog show traders exactly how to time their moves with confidence.If you've been burned holding stocks through massive drops, this video is for you. You'll see why trusting signals, following the trend template, and using OVTLYR to confirm setups is the smarter way to trade. Stop guessing, stop hoping, and start trading with rules that protect your capital.Gain instant access to the AI-powered tools and behavioral insights top traders use to spot big moves before the crowd. Start trading smarter today

When David Obstler joined Datadog in 2018, the company's co-founders had already built momentum with a product that observed modern cloud workloads. What struck Obstler was the alignment with a powerful long-term trend—the shift from legacy, on-premise systems to modern cloud applications. “It was a product that had a lot of product market fit in a really strong growing market,” he tells us.From that foundation, Datadog scaled rapidly. Today, the platform serves more than 3,100 customers worldwide, including Samsung, Nasdaq, Shell, Autodesk, and Toyota. The company recently entered the S&P 500 after reporting more than $820 million in second-quarter revenue—a 28% year-over-year increase—alongside $200 million in free cash flow, Obstler tells us.The CFO attributes the growth to Datadog's unwavering commitment to product-led innovation. The company began in infrastructure monitoring and quickly expanded into logs, application monitoring, and security. “The company invests R&D at very high and consistent levels to continue to maintain and grow the platform,” Obstler tells us.His own role centers on scaling the infrastructure needed to support expansion. That includes building global go-to-market operations and strengthening his team across financial planning, predictability, and business operations. “We've been investing behind this growth opportunity and doing it in a strong, prioritized way,” he tells us.With new investments in AI, Datadog is preparing for its next chapter. For Obstler, disciplined prioritization and product-driven growth remain at the heart of how finance can fuel scale.

Today I'm joined by Olivier Pomel, cofounder/CEO of Datadog. We trace his path from French open-source tinkerer to NYC founder, the dev-vs-ops friction that sparked Datadog, finding product-market fit through integrations, and the choice to stay independent en route to a 2019 IPO and S&P 500. Olivier shares scaling war stories, culture and GTM lessons, and what observability means in an AI era. If you build software—or companies—this one's packed with playbooks, from hiring to pricing to platform bets that work. Learn more about your ad choices. Visit megaphone.fm/adchoices

LA County is joining the fight against the feds over personal data of SNAP recipients. Orange County Republicans want to make sure there are no more DOGS casting votes. A housing project moves forward at Big Bear Lake despite concerns from bald eagle watchers. Plus, more.Support The L.A. Report by donating at LAist.com/join and by visiting https://laist.comVisit www.preppi.com/LAist to receive a FREE Preppi Emergency Kit (with any purchase over $100) and be prepared for the next wildfire, earthquake or emergency! Support the show: https://laist.com

AGENDA: 00:04 – Was GPT-5 the Biggest AI Letdown Yet? 00:17 – Is OpenAI's Real Target Anthropic's $6B Revenue? 00:22 – Why Anthropic Might Secretly Be Worried 00:28 – The Hidden Business Strategy Behind OpenAI's “Underwhelming” Launch 00:32 – Should Perplexity Really Try to Buy Chrome for $34.5B? 00:35 – The $3B N8N Deal: Genius Bet or Bubble FOMO? 00:38 – Why Datadog's Best Quarter Ever Still Tanked the Stock 00:44 – Palantir's 50% Growth at Scale – Can It Last? Is Palantir Overpriced? 00:53 – Shopify's Ruthless Path to 91% Revenue Growth With 30% Fewer Staff 01:01 – Are Seed and Series A Valuations Now at Dangerous Highs? 01:06 – What Does The Highest Levels of Capital Concentration Mean For Early Stage Founders? 01:15 – Could Palantir Hit a $2 Trillion Market Cap by 2030?    

Martin Mignot is a Partner at Index Ventures, the best-performing fund in the world right now. In the last three months, they have sold Wiz for $ 32 billion, sold Scale for $14.9 billion, and IPO'd Figma as the largest investor. In addition to this, they are the largest or second-largest shareholders in Roblox, Revolut, Adyen and Datadog.  Agenda for Today: 00:00 – Why Gross Margin is the Biggest Sin in the Early Days 04:50 – Why Most People Shouldn't Become VCs 07:40 – Why it is BS to Suggest the Future of VC is Boutique vs Mega Fund 09:10 – Do Multi-Stage Funds Really Give a S*** About Seed 13:50 – The Founder Trait That Trumps Market Size Every Time 18:45 – How Spotify Still Haunts Index Ventures & What They Learn From It? 28:50 – The Brutal Truth About European vs. U.S. Founders 34:20 – The Case for a European AI Giant (and Who Might Build It) 40:50 – The Return of the 7-Day Founder Work Week 52:10 – Biggest Lessons from Leading Revolut's Series A 56:40 – Betting Against Nick Storonsky? Don't. 1:03:10 – The One Competitor Index Ventures Admires    

Austan Goolsbee is one of Tyler Cowen's favorite economists—not because they always agree, but because Goolsbee embodies what it means to think like an economist. Whether he's analyzing productivity slowdowns in the construction sector, exploring the impact of taxes on digital commerce, or poking holes in overconfident macro narratives, Goolsbee is consistently sharp, skeptical, and curious. A longtime professor at the University of Chicago's Booth School and former chair of the Council of Economic Advisers under President Obama, Goolsbee now brings that intellectual discipline—and a healthy dose of humor—to his role as president of the Federal Reserve Bank of Chicago. Tyler and Austan explore what theoretical frameworks Goolsbee uses for understanding inflation, why he's skeptical of monetary policy rules, whether post-pandemic inflation was mostly from the demand or supply side, the proliferation of stablecoins and shadow banking, housing prices and construction productivity, how microeconomic principles apply to managing a regional Fed bank, whether the structure of the Federal Reserve system should change, AI's role in banking supervision and economic forecasting, stablecoins and CBDCs, AI's productivity potential over the coming decades, his secret to beating Ted Cruz in college debates, and more. Read a full transcript enhanced with helpful links, or watch the full video on the new dedicated Conversations with Tyler channel. Recorded March 3rd, 2025. Help keep the show ad free by donating today! Other ways to connect Follow us on X and Instagram Follow Tyler on X Follow Austan on X Sign up for our newsletter Join our Discord Email us: cowenconvos@mercatus.gmu.edu Learn more about Conversations with Tyler and other Mercatus Center podcasts here.