Podcasts about Two Sigma

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

Story of the Week (DR):CEOs are finding their blowhard whistles?Jamie Dimon is done being ‘binary': On Trump's ‘economic disaster' credit card plan, foreign policy, and NATOJamie Dimon issues rare CEO criticism of Trump's immigration policy: 'I don't like what I'm seeing'JPMorgan CEO Jamie Dimon said Trump's proposed 10% cap on credit card rates would be an 'economic disaster'Jamie Dimon issues rare CEO criticism of Trump's immigration policy: 'I don't like what I'mOf course… Trump sues ‘woke' JP Morgan for $5bn over debanking Nestlé chief blames Trump for company going quiet on sustainabilityAmazon CEO Jassy says Trump's tariffs have started to 'creep' into prices Ryanair CEO rips Trump as a 'liar' who is 'historically wrong'Of course… Minneapolis ICE Standoff Has Become the Political Issue CEOs Can't IgnoreEmployees in Minnesota are afraid to show up to workTarget in Your Town: How We're Showing Up in Communities from Coast to CoastLast "statement:" Target Statement on Texas Floods (July 8, 2025)And two new dudes on the board:John Hoke, former Chief Innovation Officer at NIKESteve Bratspies, former CEO of HanesBrandsSome stakeholder wins?Trump administration drops appeal over anti-DEI funding threat to schools and colleges Trump administration concedes DOGE team may have misused Social Security dataJamie Dimon tells Davos: ‘You didn't do a particularly good job making the world a better place'Jamie Dimon says government should have power to intervene in AI-driven mass layoffsRollout of AI may need to be slowed to ‘save society', says JP Morgan bossSalesforce's Benioff calls for AI regulation, says models have become 'suicide coaches'BlackRock's billionaire CEO warns AI could be capitalism's next big failure after 30 years of unsustainable inequality after the Cold WarBlackRock CEO says capitalism isn't spreading the wealth – and AI might not eitherBrett Kavanaugh says letting Trump fire Lisa Cook ‘would weaken, if not shatter, the independence of the Federal Reserve'A majority of millionaires say extreme wealth is a threat to democracyAmazon Joins Microsoft In Pledge To Self-Fund Power Grids, While CEO Andy Jassy Questions OpenAI's 'Ambitious' SpendingThe board matters??Lululemon founder Chip Wilson blames board for 'total operational failure' in Get Low launch [more later]Early 2026 season proxy indicators MMApple: 1 SHPNational Center for Public Policy Research: China Entanglement AuditExcluded: National Legal and Policy Center: Financial Impact of Renewable Energy ImplementationDisney: 4 SHPsBowyer Research: How the Employee Gift-Matching Program May Impact Risks Related to Religious Discrimination Against EmployeesNational Center for Public Policy Research: Return on Investment from Climate CommitmentsNational Legal and Policy Center: Cumulative Voting for Board ElectionsErik G. Paul: Accessibility and Disability Inclusion PracticesQualcomm: 2 SHPsJohn Chevedden: Shareholder Ability to Call for a Special Shareholder MeetingBowyer Research: Risk of China ExposureGoodliest of the Week (MM/DR):DR: America could ‘lose the AI race' because of too much ‘pessimism,' White House AI czar David Sacks saysMM: Elon Musk Says 'They Will Eventually Apply the Wealth Tax to Everyone' —Just Like How Income Tax Started As A 'Temporary' Tax For Top 1%This is a great ideaMM: AOC and Paris Hilton team up on a bill targeting AI deepfake pornWhat a teamAssholiest of the Week (MM):Governance asshole: Chip Wilson DRLululemon's founder is blasting the company for selling sheer leggings, calling it a 'new low'Lululemon founder Chip Wilson blames board for 'total operational failure' in Get Low launch“In 2013, Lululemon recalled 17% of all its pants for being too sheer. At that point, the company blamed the manufacturing error on an incomplete testing protocol”Wilson owned 29.22% of the stock at the timeSAME BOARD MEMBERS THAT CHIP WILSON PICKED:Martha Morfitt (2008)David Mussafer (2014)Michael Casey (2007)Emily White (2011)40% of the board IS CHIP WILSON'S HAND PICKED PEOPLELast week: Lululemon founder Chip Wilson launches proxy fight for board shakeupWilson has nominated three independent director candidates to be elected at the 2026 annual meeting and submitted a proposal to "declassify" the board so that all members must stand for election annuallyHE CLASSIFIED THE BOARD - sucks to be on the outside looking inCapitalist assholes: DavosBlackRock CEO says capitalism isn't spreading the wealth and AI might not eitherBlackRock's $40 billion deal highlights the unstoppable AI data center gold rush, as CEO Larry Fink pushes back on AI bubble fearsJamie Dimon tells Davos: ‘You didn't do a particularly good job making the world a better place'As he attends every year without irony - and this: How Wall Street Turned Its Back on Climate ChangeBillionaire Marc Benioff challenges the AI sector: ‘What's more important to us, growth or our kids?'Salesforce CEO Marc Benioff says he cut 4,000 support roles because of AISo not THEIR kids obviously“Antimicrobial resistance pandemic will kill more people than cancer by 2050 and no one at Davos is talking about it" – leading scientists speak out at Frontiers Science HouseThe anti-education uber-wealthy tech bros:Nvidia's Jensen Huang says it's a good time to be a plumber; and not just because it's an AI-proof jobPalantir CEO says AI ‘will destroy' humanities jobs but there will be ‘more than enough jobs' for people with vocational trainingHeadliniest of the WeekDR: Ryanair launches 'Great Idiots' seat sale 'especially for Elon' as feud escalatesDR: Palantir CEO Alex Karp says humanities jobs are doomed in the age of AI: 'Hopefully you have some other skill'62% of bachelor's degrees in the humanities were earned by women; 63% of mastersMM: Nestlé chief blames Trump for company going quiet on sustainability Uh… you… run… the… company?MM: How anti-doomscrolling influencers are combating social media addictionAlcoholics typically use alcohol to get over their addiction to alcoholWho Won the Week?DR: ani-China right wing blowhardsMM: Private jets: Business Insider tracked at least 157 private jets that arrived near Davos, using data from ADS-B Exchange and JetSpy. They included airplanes belonging to Salesforce CEO Marc Benioff and former Google CEO Eric Schmidt. Jets from companies like Aramco, BlackRock, Blackstone, Citigroup, Google, HP, JPMorgan Chase, Lockheed Martin, and the quantitative hedge fund Two Sigma also arrived in the area.PredictionsDR: Target soft-launches brown-colored oranges to see if America is ready to care about race againMM: Jamie Dimon officially declares himself as “non binary” and requests the media address him as “they” whenever quoting him. They then contacts Fortune after reading this headline about himself - Jamie Dimon says he'd have no issue paying higher taxes if it actually went to people who need it—right now it just goes to the Washington ‘swamp' - and demands an edit to “Jamie Dimon says they'd have generally some but not none issue paying higher or lower taxes if it actually went to poor or rich people, but now it goes to the Washington swamp or everglade or desert, either way it's delightful but also could be terrible.

Carter Lyons '97 of Two Sigma Investments shares his perspective on AI in the investment industry.Blue book examsWatch the 2025 Gruhn lecture featuring Carter Lyons '97Learn more about McKay Price and his researchLLM use case in finance from Two SigmaLearn more about Two Sigma

In this episode, I speak with Chris Carrano, Vice President of Strategic Research at Venn by Two Sigma.Chris has had a rare vantage point in the world of factors — spanning smart beta, long/short hedge funds, and risk modeling — and that experience has shaped a thoughtful view of what factors really are and how they can be practically used.We dive into the philosophy and design behind Venn: why it uses just 18 orthogonalized factors, how it blends Lasso and OLS to reduce overfitting, and why it prioritizes interpretability over complexity.We also tackle messy real-world challenges: how to analyze private markets with sparse data, how to trust synthetic return streams, and where to draw the line when using monthly snapshots that embed structural portfolio shifts.Finally, we explore what it means to make factor results actionable—whether through stress testing, residual interpretation, or portfolio diagnostics.Please enjoy my conversation with Chris Carrano.

Today, we're joined by Ben Wellington, deputy head of feature forecasting at Two Sigma. We dig into the team's end-to-end approach to leveraging AI in equities feature forecasting, covering how they identify and create features, collect and quantify historical data, and build predictive models to forecast market behavior and asset prices for trading and investment. We explore the firm's platform-centric approach to managing an extensive portfolio of features and models, the impact of multimodal LLMs on accelerating the process of extracting novel features, the importance of strict data timestamping to prevent temporal leakage, and the way they consider build vs. buy decisions in a rapidly evolving landscape. Lastly, Ben also shares insights on leveraging open-source models and the future of agentic AI in quantitative finance. The complete show notes for this episode can be found at https://twimlai.com/go/736.

Follow me @samirkaji for my thoughts on the venture market, with a focus on the continued evolution of the VC landscape.Welcome to another episode of Venture Unlocked. In this episode, I had the pleasure of welcoming Villi Iltchev, founder and managing partner of Category Ventures. Villi has had a long history in tech, both in operating roles at companies like Box and Lifelock, as well as investing roles at August Capital and Two Sigma, where he departed in 2024 to launch Category Ventures.We covered a lot of ground in our conversation, including his inspiration for starting a new firm and the experiences that informed his true north. We also spoke about the fragmentation of the market and what it means to win in early-stage investing in a heavily crowded market of dedicated seed funds & larger funds who are active in see and Series A. I really enjoyed the authenticity of the conversation and hope you do as well.About Villi IltchevVilli Iltchev is the Founder and Managing Partner of Category Ventures, an early-stage venture firm focused on backing category-defining enterprise software companies. With over two decades of experience as both an operator and investor, Villi has held leadership roles at Box, LifeLock, and Salesforce, where he led investments and acquisitions in companies like HubSpot, MuleSoft, Gusto, and Zapier. As a General Partner at August Capital and later at Two Sigma Ventures, he backed standout startups like GitLab—turning a $20M investment into over $900M in returns. Originally from Bulgaria, Villi brings a global perspective and a founder-first mindset to every partnership.Category Ventures is an early-stage venture firm founded in 2024 by veteran investor Villi Iltchev, focused on backing category-defining enterprise software startups. With a $160M debut fund, the firm invests in pre-seed and seed-stage companies across infrastructure, dev tools, AI, and applications. Drawing on Iltchev's track record—including early investments in GitLab, Zapier, and Gusto—Category Ventures brings deep technical and go-to-market expertise to help founders build enduring businesses. Their approach centers on hands-on support and founder-first partnership to shape the future of enterprise software.In this episode, we discuss:* Villi's Background and Journey (1:50)* Lessons from Venture Capital Firms (5:35)* Market Fragmentation in Venture Capital (8:47)* Flexible Investment Strategy (12:24)* Challenges with Traditional VC Models (13:26)* Product Market Fit and Founder Support (17:35)* Counterpoints on Large VC Firms (21:40)* Winning in Venture Capital (24:07)* Kindness and Community (26:24)* Components of Success (30:00)* Decision-Making Process (33:21)* Intellectual Honesty in Investments (36:16)* The Role of Fresh Perspectives (40:08)* Acting on Great Ideas and Final Thoughts (42:27)I'd love to know what you took away from this conversation with Villi. Follow me @SamirKaji and give me your insights and questions with the hashtag #ventureunlocked. If you'd like to be considered as a guest or have someone you'd like to hear from (GP or LP), drop me a direct message on X. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit ventureunlocked.substack.com

What can ancient languages and epic poems teach us about building resilient tech and future-proof teams?Host Andrew Zigler welcomes Matt Greenwood, Chief Innovation Officer of Two Sigma, for a conversation that uniquely blends their shared background in classical languages with over two decades of Matt's experience at the forefront of financial technology. Matt unveils how this classical training has profoundly shaped his approach to building enduring innovation, fostering a resilient company culture, and leading with empathy.Matt explains his vision for building and evolving innovation via platforms, alongside his strategies for effective goal-setting and fostering deep conceptual understanding in his team. He offers invaluable lessons on the importance of forgiving the decisions of our past selves, adapting processes for evolving needs, and thoughtfully engaging with AI to augment human creativity. This conversation is a masterclass in strategic foresight, cultivating a constant hunger for learning, and building an adaptable organization ready for whatever the future holds.Check out:Beyond Copilot: What's Next for AI in Software DevelopmentSurvey: Discover Your AI Collaboration StyleFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Website: twosigma.comLinkedIn: Matt GreenwoodReferenced in today's show:Google I/OMicrosoft BuildCode with ClaudeJava at 30: The Genius Behind the Code That Changed TechSupport the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever

What if you could predict consumer behavior—without running a single survey or focus group?  In this episode, host Rebecca Kalogeris sits down with Avi Yashchin, founder and CEO of Subconscious AI, to explore how synthetic respondents and causal AI are transforming the world of market research.   Avi shares how his background in high-frequency trading, behavioral economics, and product management at IBM Watson and Two Sigma led him to reimagine how businesses gain customer insights.  Subconscious AI's platform delivers what Avi calls “SimCity for Market Research”—a behavioral simulation that replaces traditional research with faster, more ethical, and scientifically grounded modeling. From shrinking the say-do gap to exploring causation (not just correlation), Avi outlines a powerful vision for the next era of insight.  For detailed takeaways, show notes, and more, visit: www.pragmaticinstitute.com/resources/podcasts  Pragmatic Institute is the global leader in Product, Data, and Design training and certification programs for working professionals. Learn more at www.pragmaticinstitute.com. 

Mr. Kenny Lam: Two Sigma APAC CEO, ex-McKinsey Global Partner, UPenn Wharton School Finance Alumni & Asia Executive Board Chairman, Oxford Law Graduate, Author of “Choices,” Project Melo Co-Founder, SmarTone & Bank of East Asia Independent Board Member林國灃先生: Two Sigma 亞洲首席執行官、麥肯錫前全球高級合夥人、數碼通及東亞銀行中國獨立董事、美國賓夕法尼亞大學沃頓商學院校友、牛津大學法律校友、

David Weisberger,  Co-Founder Emeritus of CoinRoutes, has over 40 years' experience as a capital markets executive focused on equity market structure, quantitative finance, and trading automation.  He has worked either directly or alongside all aspects of the investment ecosystem from asset owners through investment managers, investment banks as well as exchanges and other market centers.   This included development of portfolio trading and transition management businesses, impact and trading cost modelling, statistical and index arbitrage strategies, institutional and retail market making, smart order router and algorithm optimization, and the development of statistically valid measurement for analyzing trading effectiveness.   Mr. Weisberger started his career at Morgan Stanley, in technology, where he built their first program and electronic trading systems before moving to the sales and trading business. He subsequently started the non-dollar program trading desk for Salomon Brothers, was the global architect of the firm's equity trading platform where he designed their first trading algorithm and quantitatively managed central risk book before becoming responsible for the statistical arbitrage trading business.  He later ran their smart order routing business before joining Two Sigma to develop their wholesale market making business, which he ran for several years.  Next, Mr. Weisberger was the global head of equity market structure and quantitative equity products for IHS Markit befores co-founding CoinRoutes Inc, which is a market leading provider of algorithmic trading products for institutional crypto market makers, funds and trading firms.

David Weisberger,  Co-Founder Emeritus of CoinRoutes, has over 40 years' experience as a capital markets executive focused on equity market structure, quantitative finance, and trading automation.  He has worked either directly or alongside all aspects of the investment ecosystem from asset owners through investment managers, investment banks as well as exchanges and other market centers.   This included development of portfolio trading and transition management businesses, impact and trading cost modelling, statistical and index arbitrage strategies, institutional and retail market making, smart order router and algorithm optimization, and the development of statistically valid measurement for analyzing trading effectiveness.   Mr. Weisberger started his career at Morgan Stanley, in technology, where he built their first program and electronic trading systems before moving to the sales and trading business. He subsequently started the non-dollar program trading desk for Salomon Brothers, was the global architect of the firm's equity trading platform where he designed their first trading algorithm and quantitatively managed central risk book before becoming responsible for the statistical arbitrage trading business.  He later ran their smart order routing business before joining Two Sigma to develop their wholesale market making business, which he ran for several years.  Next, Mr. Weisberger was the global head of equity market structure and quantitative equity products for IHS Markit befores co-founding CoinRoutes Inc, which is a market leading provider of algorithmic trading products for institutional crypto market makers, funds and trading firms.

How can AI improve clinical trials and accelerate drug development?  In this episode of Data in Biotech, Ross Katz sits down with Patrick Leung, CTO of Faro Health, to explore how AI-driven tools are reshaping clinical trial design. Patrick shares insights into document generation, patient burden analysis, and AI governance in biotech.  Learn how Faro Health is developing clinical protocols and leveraging AI to optimize trial success while ensuring regulatory compliance. Whether you're in biotech or healthcare, this conversation offers valuable takeaways on the future of AI in increasing clinical trial efficiency.  What You'll Learn in This Episode: How AI is used to generate clinical trial protocols and reduce inefficiencies.The role of AI in assessing patient burden and optimizing trial designs.How data model development enables specialized biomedical AI workflows.How large language models (LLMs) support clinical trial automation.Future trends in AI-driven clinical trial optimization. Links: Find out more about Faro Health: https://www.farohealth.comConnect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ Connect with Patrick Leung on LinkedIn: https://www.linkedin.com/in/puiwah/ Meet Our Guest: Patrick Leung is the Chief Technology Officer at Faro Health, where he leads AI-driven innovations in clinical trial design. With a background in data science and software engineering from companies like Google and Two Sigma, Patrick brings a fresh perspective to life sciences, focusing on optimizing clinical trials through AI and structured data models. About the Host: Ross Katz is the Principal and Data Science Lead at CorrDyn, specializing in applying data science to biotech and healthcare. As the host of Data in Biotech, Ross explores the latest trends and innovations shaping the industry. Enjoying the Show? Visit Faro Health to learn more about AI-driven clinical trial optimization. Don't forget to rate and review Data in Biotech on Apple Podcasts! Sponsored by CorrDyn This episode is brought to you by CorrDyn, a leader in data-driven solutions for biotech and healthcare. Learn more at CorrDyn.

Alex Botte, CFA, CAIA is a Partner focused on investor relations and research at Hack VC, a crypto-native venture capital firm. Previously, she was the Head of Client and Portfolio Solutions at Runa Digital Assets, a liquid token investment manager. Prior to Runa, she spent eight years in quantitative investment management, holding product specialist and business development roles at Two Sigma and AQR. She started her career in prime services at Barclays. Alex holds a BS from Cornell University.

Alex Botte, CFA, CAIA is a Partner focused on investor relations and research at Hack VC, a crypto-native venture capital firm. Previously, she was the Head of Client and Portfolio Solutions at Runa Digital Assets, a liquid token investment manager. Prior to Runa, she spent eight years in quantitative investment management, holding product specialist and business development roles at Two Sigma and AQR. She started her career in prime services at Barclays. Alex holds a BS from Cornell University.

Katie and Matt discuss a Vanguard tax oopsie, buffered Bitcoin ETFs, crypto utility, and a hedge fund model calibration oopsie.See omnystudio.com/listener for privacy information.

Predictive Modeling (4:15) Human judgement and processes (14:06)Imperfection in models (21:40)BioClaudia Perlich is Managing Director and Head of Strategic Data Science for Investment Management at Two Sigma, where she has worked for seven years. In this role, Claudia is responsible for developing innovative alpha strategies at the intersection of alternative data, thematic hypotheses and machine learning in public markets. Claudia joined Two Sigma from Dstillery, an AI ad targeting company, where she worked as Chief Scientist. Claudia began her career in data science at the IBM Watson Research Center, concentrating on research in data analytics and machine learning for complex real-world domains and applications.Since 2011, Claudia has served as an adjunct professor teaching Data Mining in the M.B.A. program at New York University's Stern School of Business. Claudia received a Ph.D. in Information Systems from Stern School of Business, New York University, holds an M.S. of Computer Science from Colorado University and a B.S. in Computer Science from Technical University Darmstadt, Germany. Connect with ClaudiaClaudia Perlich on LinkedinConnect with UsMargot Gerritsen on LinkedInFollow WiDS on LinkedIn (@Women in Data Science (WiDS) Worldwide), Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Dr. Mike Schuster is the head of the AI Core team at Two Sigma, where he leads engineers and quantitative researchers in advancing AI technologies across the firm's investment strategies and internal efficiencies. With over 25 years of expertise in machine learning and deep learning, Mike has been at the forefront of AI trends in tech and finance. Prior to Two Sigma, he spent 12 years at Google, contributing to transformative projects like Google Translate as part of the Google Brain team. Dr. Schuster holds a PhD in Electrical Engineering from the Nara Institute of Science and Technology in Japan and is recognized as a pioneer whose work has significantly shaped the AI landscape.In this conversation, we discuss:The challenges and importance of building collaborative teams for complex AI systems in finance.Key differences between developing AI technologies in tech companies like Google versus finance firms like Two Sigma.The evolution of neural networks and their transformative impact on applications like Google Translate.The ethical considerations and risks of using AI in finance compared to other industries.Insights into data quality challenges and strategies for addressing bias in financial modeling.Predictions for the future of AI, focusing on efficiency, data quality, and practical advancements over the next five years.Resources:Subscribe to the AI & The Future of Work NewsletterConnect with Mike SchusterAI fun fact articleEpisode on how AI is diagnosing and treating sleep disorders  

Camille Fournier is the author of The Manager's Path, which many consider the definitive guide for navigating one's career path in tech. Camille was previously the CTO of Rent the Runway, VP of Technology at Goldman Sachs, Head of Platform Engineering at Two Sigma, and Global Head of Engineering and Architecture at JPMorgan Chase. She is about to release new newest book, Platform Engineering: A Guide for Technical, Product, and People Leaders. In our conversation, we discuss:• What product managers do that annoys engineers• Why major rewrites are a trap• Why you should have fewer one-on-ones• Strategies for organizing and working with platform teams• Tips for new managers• Advice for transitioning from individual contributor to manager• Much more—Brought to you by:• DX—A platform for measuring and improving developer productivity• CommandBar—AI-powered user assistance for modern products and impatient users• Coda—The all-in-one collaborative workspace—Find the transcript and show notes at: https://www.lennysnewsletter.com/p/engineering-leadership-camille-fournier—Where to find Camille Fournier:• LinkedIn: https://www.linkedin.com/in/camille-fournier-9011812/• Website: https://skamille.medium.com/—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) Camille's background(02:17) Common annoyances between PMs and engineers(07:09) Avoiding the telephone game(08:05) Hoarding ideas and over-engineering(09:55) The importance of involving engineers in ideation(11:37) The middle-person dilemma(14:21) Rewriting systems: a big trap?(20:40) Engineering leadership lessons(36:02) Moving from IC to management(40:32) One-on-one meetings(45:10) Pushing beyond comfort zones(45:27) Building a balanced work culture(48:01) Effective time management strategies(54:15) Advice for platform team success(01:02:42) Platform team responsibilities(01:04:43) When to form a platform team(01:07:02) Thriving on a platform team(01:12:48) AI corner(01:17:03) Lightning round and final thoughts—Referenced:• Platform Engineering: A Guide for Technical, Product, and People Leaders: https://www.amazon.com/Platform-Engineering-Technical-Product-Leaders/dp/1098153642/• The Manager's Path: A Guide for Tech Leaders Navigating Growth and Change: https://www.amazon.com/Managers-Path-Leaders-Navigating-Growth/dp/1491973897• 97 Things Every Engineering Manager Should Know: Collective Wisdom from the Experts: https://www.amazon.com/Things-Every-Engineering-Manager-Should/dp/1492050903• Avoiding the Rewrite Trap: https://skamille.medium.com/avoiding-the-rewrite-trap-b1283b8dd39e• Levelsio on X: https://x.com/levelsio• Pieter Levels on the Lex Fridman Podcast: https://www.youtube.com/watch?v=oFtjKbXKqbg• GraphQL: https://graphql.org/• New Blue Sun by André 3000 on Spotify: https://open.spotify.com/album/33Ek6daAL3oXyQIV1uoItD• Musk's 5 Steps to Cut Internal Bureaucracy at Tesla and SpaceX: https://icecreates.com/insight/musk-s-5-steps-to-cut-internal-bureaucracy-at-tesla-and-spacex-you-may-say-it-s-his-algorithm/• Ian Nowland on LinkedIn: https://www.linkedin.com/in/inowland/• Studio Pulls ‘Megalopolis' Trailer Using Fake Quotes from Famed Movie Critics: https://www.huffpost.com/entry/studio-pulls-megalopolis-trailer-using-fake-quotes-from-famed-movie-critics_n_66c74046e4b0f1ca469413c7• Claude 2: https://www.anthropic.com/news/claude-2• What Got You Here Won't Get You There: How Successful People Become Even More Successful: https://www.amazon.com/What-Got-Here-Wont-There/dp/1401301304• When Things Fall Apart: Heart Advice for Difficult Times: https://www.amazon.com/When-Things-Fall-Apart-Difficult/dp/1611803438• Alien: Romulus: https://www.imdb.com/title/tt18412256/• Whoop: https://www.whoop.com—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. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Plus: The Treasury Department completes regulations to address gaps in U.S. anti-money-laundering rules. The Israeli military launches a major ground-and-air operation across a large area of the West Bank to prevent terrorist attacks. J.R. Whalen reports. Sign up for the WSJ's free What's News newsletter.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Marcelo Lebre is the President and Co-founder of Remote, a Global HR Platform that helps companies hire, manage, and pay their entire team — and more effectively compete in the modern global economy through a comprehensive set of core solutions including, HRIS, payroll, international employment, contractor management, and more. Remote has raised to date $500 million from notable investors such as Accel, Sequoia, Index Ventures, Two Sigma, General Catalyst and Day One VenturesIn 2022, Remote surpassed a $1 billion valuation after closing a $300 million Series C round led by Accel. Where to find Marcelo Lebre:• Website: Global HR Solutions & Employment Tools for Distributed Teams | Remote• LinkedIn (9) Marcelo Lebre | LinkedInWhere to find Hadi Radwan:• Newsletter: Principles Friday | Hadi Radwan | Substack• LinkedIn: Hadi Radwan | LinkedInIf you like our podcast, please don't forget to subscribe and support us on your favorite podcast players. We also would appreciate your feedback and rating to reach more people.We recently launched our new newsletter, Principles Friday, where I share one principle that can help you in your life or business, one thought-provoking question, and one call to action toward that principle. Please subscribe Here.It is Free and Short (2min).

Are LLMs useful for enterprises? Well, what is the use of a large language model that is trained on trillions of tokens but knows little to nothing about your business. To make LLMs actually useful for enterprises, it is important for them to retrieve company's data effectively. LlamaIndex has been at the forefront of providing such solutions and frameworks to augment LLMs. In this episode, Jerry Liu, Co-founder and CEO of LlamaIndex, joins Raja Iqbal, CEO and Chief Data Scientist at Data Science Dojo, for a deep dive into the intersection of generative AI, data. and entrepreneurship. Jerry walks us through the cutting-edge technologies reshaping the generative AI landscape such as LlamaIndex. He also explores Retrieval Augmented Generation (RAG) and fine-tuning in detail, discussing their benefits, trade-offs, use cases, and enterprise adoption, making these complex tools and topics not just easily understandable but also fascinating. Jerry further ventures into the heart of entrepreneurship, sharing valuable lessons and insights learned along his journey, from navigating his corporate career at tech giants like Apple, Quora, Two Sigma, and Uber, to starting as a founder in the data and AI landscape. Amidst the excitement of innovation, Raja and Jerry also address the potential risks and considerations with generative AI. They raise thought-provoking questions about its impact on society, for instance, whether we're trading critical thinking for convenience. Whether you're a generative AI enthusiast, seasoned entrepreneur, or simply curious about the future, this podcast promises plenty of knowledge and insights for you.

In episode 115 of The Gradient Podcast, Daniel Bashir speaks to Ben Wellington.Ben is the Deputy Head of Feature Forecasting at Two Sigma, a financial sciences company. Ben has been at Two Sigma for more than 15 years, and currently leads efforts focused on natural language processing and feature forecasting. He is also the author of data science blog I Quant NY, which has influenced local government policy, including changes in NYC street infrastructure and the design of NYC subway vending machines. Ben is a Visiting Assistant Professor in the Urban and Community Planning program at the Pratt Institute in Brooklyn where he teaches statistics using urban open data. He holds a Ph.D. in Computer Science from New York University.Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pubSubscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (01:30) Ben's background* (04:30) Why Ben was interested in NLP* (05:48) Ben's work on translational equivalence, dominant techniques* (10:14) Scaling, large datasets at Two Sigma* (12:50) Applying ML techniques to quantitative finance, features in financial ML systems* (17:27) Baselines and time-dependence in constructing features, human knowledge* (19:23) Black box models in finance* (24:00) Two Sigma's presence in the AI research community* (26:55) Short- and long-term research initiatives at Two Sigma* (30:42) How ML fits into Two Sigma's investment strategy* (34:05) Alpha and competition in investing* (36:13) Temporality in data* (40:38) Challenges for finance/AI and beating the market* (44:36) Reproducibility* (49:47) I Quant NY and storytelling with data* (56:43) Descriptive statistics and stories* (1:01:05) Benefits of simple methods* (1:07:11) OutroLinks:* Ben's work on translational equivalence and scalable discriminative learning* Two Sigma Insights* Storytelling with data and I Quant NY Get full access to The Gradient at thegradientpub.substack.com/subscribe

Are you ready for a deep dive into quantitative investing, the private credit trend, and the Canada Pension Plan (CPP)? Then this episode is for you! Joining us today is Robin Wigglesworth, The Financial Times' global finance correspondent, and author of Trillions: How a Band of Wall Street Renegades Invented the Index Fund and Changed Finance Forever, a groundbreaking book about the past, present, and future of passive investing. We talk with Robin about quantitative investing and the ideas he lays out in his article ‘A Quant Winter's Tale', before hearing his insights on the private credit trend and his intriguing new book titled Bonds, all about the history of the bond market. Today's episode also features our Mark to Market segment, where Mark McGrath joins us to talk about the Canada Pension Plan (CPP), providing a comprehensive overview of its inner workings, his response to the criticisms levelled against it, and why he believes it's of huge benefit to a great many Canadians. Next, we take a look back at our conversation with Alexandra Macqueen on annuities before sharing our thoughts on its relevance to today's discussion and why it's worth revisiting. Be sure to stay tuned for our after-show segment where we share our book, blog, and viewing recommendations, plus our favourite reviews, followed by a sneak peek of some of the exciting guests we have coming up. Press play now for a deep dive into quant investing, the hype around private credit, saving for retirement, and a whole lot more!   Key Points From This Episode:   An introduction to today's guest, Robin Wigglesworth, followed by his breakdown of quantitative investing. (0:04:05) Theories on what happened to factor investing between 2018 and 2020; what is meant by the quant winter and why we are now in a quant summer. (0:09:59) How investor sentiment regarding factor investing changed after the quant winter and how the algorithm aversion phenomenon impacted it. (0:15:13) The collapse of value; the impact of the COVID-19 pandemic (plus its role in the quant winter), and where we are right now. (0:20:14) An overview of quant investing products, and why many of them are too expensive. (0:23:24) Breaking down the noisy-ness in factor data and Robin's predictions for where factor investing will go from here. (0:25:51) Unpacking the hype around private credit: indications that it's in a bubble, how it could impact broader trends, and who stands to benefit most.  (0:36:36) We hear about the fascinating book that Robin is currently working on about the history of the bond market. (0:40:22) Our Mark to Market segment on the complicated (and divisive) Canada Pension Plan (CPP); how it works, its profound benefits, and responding to the criticism it has received. (0:41:50) A look back at our conversation with Alexandra Macqueen on annuities and how it links in with today's discussion. (01:01:31) Our after-show section: an update on the Money Scope Podcast, reading recommendations, reviews from our listeners, and some of the incredible guests we have coming up! (01:04:33) Links From Today's Episode: Robin Wigglesworth — https://robinwigglesworth.com/ Robin Wigglesworth on LinkedIn — https://www.linkedin.com/in/robin-wigglesworth-17101722 Financial Times — https://www.ft.com/ Trillions: How a Band of Wall Street Renegades Invented the Index Fund and Changed Finance Forever — https://www.amazon.com/Trillions-Renegades-Invented-Changed-Finance/dp/0593087682 ‘Quant Winter's Tale' — https://www.ft.com/content/e0f98278-432e-4ece-b170-2c40e40d2835 Episode 184: Robin Wigglesworth — https://rationalreminder.ca/podcast/184 Episode 93: Cliff Asness from AQR — https://rationalreminder.ca/podcast/93 Cliff Asness — https://www.aqr.com/About-Us/OurFirm/Cliff-Asness-Bio AQR — https://www.aqr.com/ Two Sigma — https://www.twosigma.com/ D.E Shaw — https://www.deshaw.com/ CPP Investments — https://www.cppinvestments.com/ StatsCan — https://www.statcan.gc.ca/en/start Financial Planning for Canadian Business Owners Episode 116: True Cost of CPP with Aravind Sithamparapillai — https://jasonpereira.ca/all-content-jason-pereira-toronto/true-cost-of-cpp-with-aravind-sithamparapillai-e116 Episode 59: Alexandra Macqueen — https://rationalreminder.ca/podcast/59 Pensionize Your Nest Egg — https://www.amazon.com/Pensionize-Your-Nest-Egg-Allocation/dp/1119025257 Griselda Blanco — https://www.imdb.com/title/tt15837600/ Cocaine Cowboys — https://www.imdb.com/title/tt0380268/ Queen of the South — https://www.imdb.com/title/tt1064899/ Fortune's Children: The Fall of the House of Vanderbilt — https://www.amazon.com/Fortunes-Children-Fall-House-Vanderbilt/dp/0062224069 Farnam Street — https://fs.blog/ 24 in 24 Reading Challenge — https://rationalreminder.ca/24in24 The Money Scope Podcast — https://moneyscope.ca/ The Money Scope Podcast on YouTube — https://www.youtube.com/@moneyscopepod Rational Reminder on iTunes — https://itunes.apple.com/ca/podcast/the-rational-reminder-podcast/id1426530582. Rational Reminder Website — https://rationalreminder.ca/  Rational Reminder on Instagram — https://www.instagram.com/rationalreminder/ Rational Reminder on X — https://twitter.com/RationalRemind Rational Reminder on YouTube — https://www.youtube.com/channel/ Rational Reminder Email — info@rationalreminder.ca Benjamin Felix — https://www.pwlcapital.com/author/benjamin-felix/  Benjamin on X — https://twitter.com/benjaminwfelix Benjamin on LinkedIn — https://www.linkedin.com/in/benjaminwfelix/ Cameron Passmore — https://www.pwlcapital.com/profile/cameron-passmore/ Cameron on X — https://twitter.com/CameronPassmore Cameron on LinkedIn — https://www.linkedin.com/in/cameronpassmore/ Mark McGrath on LinkedIn — https://www.linkedin.com/in/markmcgrathcfp/ Mark McGrath on X — https://twitter.com/MarkMcGrathCFP

S3 Ep8 | What does Artificial Intelligence (AI) have to do with financial activism? And what should investors know as the field continues to evolve? This episode Renegade is joined by Katy Knight, President & Executive Director, Siegel Family Endowment and Deepti Doshi, Co-Director, New_Public to learn more about AI and innovation in this field. Our guests share how leveraging technology for the public good is a critical part of building inclusive access to information, education, and wealth-building opportunities.   About Deepti.Deepti Doshi is a community organizer who has been working at the intersection of social change, social media, and leadership development across the private, non-profit and public sectors. As a Director at Meta, Deepti helped establish the New Product Experimentation team and created the Community Partnerships team. She founded Haiyya, India's largest community organizing platform; Escuela Nueva India, an education company that serves the urban poor; the Fellows Program at Acumen Fund to build leaders for the social enterprise sector. About Katy.Katy Knight is President and Executive Director of Siegel Family Endowment, a foundation focused on the nexus of technology and society. Beginning as Deputy Executive Director in 2017, she has pioneered an inquiry-driven approach to philanthropy, grounded in the scientific method and centered on reframing big questions and learning alongside grantees. Katy has been recognized for her leadership in Crain's Notable Black Leaders in 2022, and City & State's 40 Under 40 Rising Stars in 2015. She previously led Community Engagement at Two Sigma and held positions on Google's Communications and Public Affairs teams, where she acted as a liaison to stakeholders in local government, communities, and nonprofits.Renegade Capital Tools & TipsA renegade not only listens but acts. We've consolidated a few tips from this episode to help you prepare for the future of AI and investing. Be patient. Whether you are a funder, investor, or everyday activist, this work requires thoughtfulness and patience from everyone involved. That means patient capital, taking time to collect the right input, and thinking long term. Embrace duality. The future of technology can feel both exciting and terrifying, and we have to get comfortable with the fact that there are many possibilities. Find the reasons to be optimistic AND ask the hard questions that will push change for the better.Find ways to use and support tech for public good. Deepti and Katy shared several resources for our listeners to use technology in informed and inclusive ways. New_Public Digital Spaces DirectorySiegel Family Endowment: TechCongressSiegel Family Endowment: Public Interest Technology Case StudiesSupport the showLove the podcast? Subscribe and follow to never miss an episode.Linkedin | Twitter | Facebook | Instagram | Join our mailing list

Matt Greenwood, Chief Innovation Officer & Head of Investment Management Engineering @ Two Sigma shares some of the most unique and valuable cultural practices behind how the engineering org operates at Two Sigma. We discuss strategies that prepare you for scaling (like intentional relationship-building with your front-line managers); examples of how Two Sigma successfully deployed the “Inverse Conway Maneuver,” how to reinforce a consensus-driven culture from early-days to 1000+, how to navigate both large & small reorgs; and why Two Sigma made the intentional decision to rebrand their R&D org as M&E (modeling & engineering)! Plus, Matt's approach to full-bodied problem-solving.ABOUT MATT GREENWOODMatt is Chief Innovation Officer and Head of Investment Management Engineering at Two Sigma. He joined Two Sigma in 2003 and since then has led a number of company-wide efforts in both engineering and modeling. Matt is also an Advisor at Two Sigma Ventures and works closely with the business' portfolio companies as a board member and advisor.Matt began his career at Bell Labs and later moved to IBM Research, where he was responsible for a number of early efforts in tablet computing and distributed computing. In 2000, Matt was lead developer and manager for Entrisphere, Inc., where he helped create a product providing access equipment for broadband service providers. Matt earned a BA and MA in Math from Oxford University, and a Master's degree in Theoretical Physics from the Weizmann Institute of Science in Israel. He also holds a PhD in Mathematics from Columbia University, where he taught for many years."We came to New York in 2003, nothing was happening in New York. Silicon Alley, as they called it back then was just kaput. Then one day, I was browsing Craigslist, because that's what you did in 2003, and there was a little ad, ‘Hedge fund, looking for excellent engineers.' So I was like, 'All right, maybe.' I said to my wife, 'This is either the sketchiest thing ever or the best decision of my life. It's one of those two things.' On Craigslist, there's no other way you can be, right? And it was probably the best decision of my life.”- Matt Greenwood   This episode is brought to you by incident.ioincident.io is trusted by hundreds of tech-led companies across the globe, including Etsy, monday.com, Skyscanner and more to seamlessly orchestrate incident response from start to finish. Intuitively designed, and with powerful and flexible built-in workflow automation, companies use incident.io to supercharge incident response and up-level the entire organization.Learn more about how you can better identify, learn from, and respond to incidents at incident.ioInterested in joining an ELC Peer Group?ELCs Peer Groups provide a virtual, curated, and ongoing peer learning opportunity to help you navigate the unknown, uncover solutions and accelerate your learning with a small group of trusted peers.Apply to join a peer group HERE: sfelc.com/peerGroupsSHOW NOTES:Matt's eng leadership journey & discovering Two Sigma on Craigslist (3:34)Key moments of Two Sigma's evolution as an org that sparked excitement (7:26)Lessons learned on keeping your work exciting by focusing on “human problems” (10:25)Create a culture of investing in people's growth across longer timelines (12:22)How Sigma Two intentionally structures its R&D org (15:18)An unexpected way to prepare for scaling your org - intentional relationship-building strategies for your first-line managers (18:10)Frameworks for deploying the inverse Conway maneuver (20:56)The right people / conversations for small & large reorgs (23:30)Consensus-driven culture at 1000+, approaches to create buy-in & ownership with organizational change (26:02)Two Sigma's approach to full-bodied problem solving (30:26)Rapid fire questions (34:06)LINKS AND RESOURCESWhalefall - A scientifically accurate thriller from Daniel Kraus about a scuba diver who's been swallowed by an eighty-foot, sixty-ton sperm whale and has only one hour to escape before his oxygen runs out.This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/

Rishi Ganti argues there is a meaningful alternative to investing in tradeable markets — but it's not easy. In this episode of the podcast, Julie Segal talks to Rishi Ganti, a veteran of J.P. Morgan and Two Sigma and co-founder of Orthogon Partners, which invests in untraded or esoteric assets. Or what might be even better described as investments that require discovery, a process Rishi calls magical  — it's finance essentialism, bringing ideas and money together. Julie and Rishi also talk about the seemingly endless stream of college graduates who make their way to Wall Street every year to work in markets that are designed to eliminate exactly what they came for — alpha. People choose to forget what they learned in Economics 101: the law of competition. Rishi, who chose instead to invest in untraded assets, talks about the challenges of finding these opportunities, building structures so they can be invested in, and convincing allocators of the rewards. For more on Orthogon and the problems with modern finance, read ⁠II's story⁠ from 2020.

Ever been on an executive cruise? This episode is brought to you by UnCruise Adventures Small Ship Cruising that connects guests with nature and wildlife while exploring some of the most remote and scenic destinations on the West Coast of the Americas including Alaska, Baja, and Hawaii. Get the latest deals benleads.com/cruise-------This is where teams fail.Brian Finn, COO of Circle of Care, has achieved monumental results in the military, insurance, tech, and now healthcare using a powerful daily execution framework. Success hinges on more than meticulous planning and strategy; ultimately, it thrives on the relentless execution of daily actions.This is an especially painful recognition when you see how much leaders spend on planning but pay little heed to the “get it done” process that should be prioritized. Brian leads one of the largest pediatric therapy providers in the state of Texas with a proven track record in leading organizational transformation and implementing cutting-edge technologies in the healthcare sector.Previously he was COO at Two Sigma, the financial sciences company that combines advanced technology and data science with rigorous human inquiry to solve the toughest challenges in finance.LinkedIn Profile http://www.linkedin.com/in/brian-finn-33745aaCompany Link: https://www.twosigma.com/What You'll Discover in this Episode:What Inspired Brian's Leadership PerspectiveThe Turning Point for his LegacyThe 15-Minute Meeting that Keeps his Team FocusedThe Vital Difference Between Setting a Goal vs IntentionAvoiding the Damage of Task SaturationInstilling the Habit of Daily ExecutionWhat Motocross Teaches You about Leadership-----Connect with the Host, #1 bestselling author Ben FanningSpeaking and Training inquiresSubscribe to my Youtube channelLinkedInInstagramTwitter

Steve Francia is a highly accomplished technology executive and entrepreneur. Steve is a Managing Director at Two Sigma, serving as the Product Lead for the Investment Management Platform. Two Sigma is a technology-driven investment firm based in New York City. Steve is widely known for his involvement in MongoDB, Hugo, and the Go community. In this episode, Steve takes us through his journey in the tech industry discussing various projects and companies he participated in so far in his career. 00:00 Introduction01:07 What is Steve Doing Today?05:00 First Memories of a Computer15:00 Interests Leading to College24:00 Entering College 33:50 First Tech Jobs in/after College58:45 Discovering MongoDB1:17:00 Creation of Hugo1:22:48 Working at Docker1:28:15 Joining the Go Team1:42:00 Expectations of the Job1:54:20 Contact InfoConnect with Steve: Twitter:  https://twitter.com/spf13?lang=enLinkedIn: https://www.linkedin.com/in/stevefranciaMentioned in today's episode:Docker: https://www.docker.com/MongoDB: https://www.mongodb.com/Two Sigma: https://www.twosigma.com/Hugo: https://gohugo.io/ Want more from Ardan Labs? You can learn Go, Kubernetes, Docker & more through our video training, live events, or through our blog!Online Courses : https://ardanlabs.com/education/ Live Events : https://www.ardanlabs.com/live-training-events/ Blog : https://www.ardanlabs.com/blog Github : https://github.com/ardanlabs

In this episode, we speak with Lorien Gabel, Co-founder and CEO of Figment, the leading provider of blockchain infrastructure. The company has raised over $150 million and is backed by Thoma Bravo, B Capital, Two Sigma, and other notable investors. Figment provides comprehensive staking solutions for over 250 institutional clients. The world's largest asset managers, custodians, exchanges, foundations, and token holders leverage Figment's infrastructure and APIs to earn staking rewards. Staking is a process in which token holders can earn rewards by helping to secure a blockchain network. Before Co-Founding Figment, Lorien was both a serial entrepreneur and corporate executive.  Along with his brother, Lorien founded three companies - 1) pingg.com, which was acquired by 1800-Flowers, 2) Bird on a Wire Networks, which was acquired by AT&T Canada and 3) Interlog, one of Canada's first commercial ISPs, which was acquired by a large multinational telecommunications provider. Lorien also spearheaded the exponential U.S. growth of UK-based tech company MessageLabs as Global VP Business Development, ran a division of Fortune 500 company Micron, and has served on the advisory board of a number of start-ups. Lorien supports The Murph Challenge. To learn more about the annual event click here. I am your host RJ Lumba.  We hope you enjoy the show.  If you like the episode, click to subscribe.

Want to help define the AI Engineer stack? >800 folks have weighed in on the top tools, communities and builders for the first State of AI Engineering survey, which we will present for the first time at next week's AI Engineer Summit. Join us online!This post had robust discussion on HN and Twitter.In October 2022, Robust Intelligence hosted an internal hackathon to play around with LLMs which led to the creation of two of the most important AI Engineering tools: LangChain

Thoughts on Sir Humphrey Gilbert and his motto "Quid Non" and Benjamin Bloom and his famous "two sigma problem." --- Nintil on Bloom's Two Sigma Problem Learn more about your ad choices. Visit megaphone.fm/adchoices

Our guest this week is Jeremy Schwartz. Jeremy is global chief investment officer for WisdomTree Investments. In that role, Jeremy leads the firm's investment team and is responsible for constructing its equity indexes, quantitative active strategies, and model portfolios. Jeremy joined WisdomTree in May 2005 and previously held several posts at the firm, including serving as its global head of research. Prior to joining WisdomTree, Jeremy served as a research assistant for Professor Jeremy Siegel and is credited as Siegel's co-author of the sixth edition of the book, Stocks for the Long Run. Jeremy received his bachelor's degree from the Wharton School and is a CFA charterholder.BackgroundBioStocks for the Long Run: The Definitive Guide to Financial Market Returns & Long-Term Investment Strategies, Sixth Edition, by Jeremy Siegel, with Jeremy SchwartzThe Future for Investors: Why the Tried and True Triumphs Over the Bold and the New, by Jeremy SiegelDo-Nothing Strategies“What Beat the S&P 500 Over the Past Three Decades? Doing Nothing,” by Jeffrey Ptak, Morningstar.com, April 17, 2023.Creative Destruction: Why Companies That Are Built to Last Underperform the Market—and How to Successfully Transform Them, by Richard Foster and Sarah Kaplan“Wise Words in Warren's Recent Letter,” by Jeremy Schwartz, wisdomtree.com, March 8, 2023.“Long-Term Returns on the Original S&P 500 Companies,” by Jeremy Siegel and Jeremy Schwartz, jstor.org, 2006.Market Outlook“The Most Important Charts for 2023,” by Jeremy Schwartz and Brian Manby, caia.org, May 8, 2023.Risk and Return“A Surprise Influence in S&P 500 Earnings? The Dollar,” by Jeremy Schwartz, wisdomtree.com, May 31, 2023.Fama and French Three Factor Model Definition: Formula and Interpretation, by Adam Hayes, www.investopedia.com, May 31, 2022.“A Deep Dive Into Growth & Momentum,” by Jeremy Schwartz, wisdomtree.com, July 12, 2021.“A Surprising Rebalance Season for S&P Style Indexes,” by Jeremy Schwartz, wisdomtree.com, Jan. 5, 2023.WisdomTree Index Performance AttributionInflation and Dividends“A New Regime for Commodities: An Update,” by Jeremy Schwartz, wisdomtree.com, May 31, 2022.“To Reveal Value, Start With Dividends,” Focus Points video series with Jeremy Schwartz, wisdomtree.com, Jan. 11, 2023.“Quality Dividend Growth Performance Viewed Through Venn by Two Sigma,” by Jeremy Schwartz and Christopher Carrano, wisdomtree.com, May 15, 2023.Foreign Stocks“Lowering Your China Risk,” by Jeremy Schwartz and Matt Wagner, wisdomtree.com, May 19, 2023.“Values Strong Run in Emerging Markets,” by Aneeka Gupta and Jeremy Schwartz, wisdomtree.com, May 5, 2023.“Don't Layer Currency Risk on Top of Equity Exposure,” by Jeremy Schwartz and Christopher Gannatti, wisdomtree.com, 2019.ESG“Revisiting the WisdomTree ESG Model Portfolios,” by Scott Welch, wisdomtree.com, Oct. 14, 2022.“Aswath Damodaran: A Valuation Expert's Take on Inflation, Stock Buybacks, ESG, and More,” The Long View podcast, Morningstar.com, May 16, 2023.

In this episode, we are joined by Chris Carrano, Vice President of Strategic Research at Venn by Two Sigma, to discuss the work that Venn is doing in the digital assets space, the Venn platform, and how returns-based analytics can help investors better understand their risk.  Listen in as Chris discusses: Venn by Two Sigma's … Read More Read More

Rember when you needed a full tech team to launch a simple brochureware website? Now you can just jump on Wix or Squarespace and spin up a well designed, responsive website in seconds. The rise of the no-code movement has helped increase the pace and usefulness of technology and it has transcended into lots of other applications and use cases. It's a win - win, as business users can create applications or make changes on the fly, while the engineering team gets to spend their time working on more complex and sophisticated problems. Candu is right in the strikezone of this no-code movement, as the company is building a better way to activate users and release new features. The simple, drag-and-drop UI Editor empowers product and customer teams to create UI themselves, saving developer time and trimming technical debt. Candu is venture backed by Two Sigma, CRV, Angular Ventures, and Haystack. In this episode of our podcast, we cover: * Advice for improving the activation rate for SaaS products and whether or not those post activation email campaigns are effective. * Jonathan's background story and how his career accelerated while at InsightSquared, plus the details on wayOUT, a non-profit he co-founded. * How Candu came to fruition and the evolution of the platform to its current state including sample use cases for product growth teams and its value. * The vibe around raising venture capital in the current market and how entrepreneurs need to think about today's market reality. * Advice on how to have a successful launch on Product Hunt. * Business ideas from spending one year writing down a new startup idea every day. * And so much more. If you like the show, please remember to subscribe and review us on iTunes, Soundcloud, Spotify, Stitcher, or Google Play.

In this episode of the [i3] Podcast, we look at machine learning and artificial intelligence. Our guests are Kaggle Founder Anthony Goldbloom and Stanford CS Professor Chris Manning, who are both involved in venture capital firm AIX Ventures, as investment directors. We speak about the state of play in machine learning and artificial intelligence, the most interesting applications, including ChatGPT, and opportunities for investors in this space, covering smart sensors, travel agents and personal assistants. Overview of the podcast: Anthony Goldbloom: 04:00 The idea for Kaggle came from a conference competition 06:00 Looking for the most accurate algorithms 07:30 Before Kaggle, every academic discipline had their own set of machine learning techniques 08:00 One technique won problem after problem 09:00 Rise of neural networks: 2012 is often called the annus mirabilis for machine learning 10:30 I'm mind blown by what you can do with ChatGPT 11:30 Using summarization through ChatGPT 12:30 We are in a world right now where the capabilities of these models run far ahead of the applications. People haven't really build companies around these models yet 14:50 The rise of chat-powered travel agents? Adding databases to ChatGPT 17:00 Why was Google interested in Kaggle? 19:00 Tweaking the value estimation algorithm for US real estate website Zillow 21:00 Surpassing physicians on diagnosing lung cancer 22:30 Two Sigma and Optiver also used Kaggle to solve problems 23:00 Hedge funds who crowdsourced investment problems 24:00 Being a one person band is hard in investing: you need to not only find the alpha signal, but also implement the trade in a way that doesn't move the market 27:00 Does machine learning work in time series? Yes, but it requires more babysitting if your algorithm works in an adversarial setting 32:00 What I bring to AIX Ventures is the understanding of where the gaps are in the tools for machine learning 33:00 Examples of companies we invested in 35:00 Embedding ultra light machine learning into appliances Chris Manning: 37:24 I was interested in how people learn languages, while I was always playing around with computers. Then I became interested in Ross Quinlan's ID3 algorithm for natural language processing 40:00 I started to work with large digital language databases slightly before the world wide web really kicked off 43:00 The combination of neutral networks and predictive text led to the revolutionary breakthroughs we see now with ChatGPT 45:30 You can use ChatGPT for text analysis, such as sentiment analysis or summarization of specific information 46:00 These models are just wonderful, but of course there are still problems. On occasion these models tend to hallucinate. They are just as confident producing made up stuff. And at times they lack consistency in thinking. They will say things that contradicts what they said previously 47:00 Human learning is still far more efficient in getting signal from data than machine learning 50:00 The majority of businesses are conducted through human language, whether it is sales or support. These models can help people work fast and better. 52:00 The case of Google and zero shot translation 57:00 Facebook experimented with two systems talking to each other, but they found the systems would not stick to English, but developed a more efficient symbol system 1:00:00 Interesting businesses we've invested in: weather prediction 1:02 A lot of computing that was previously done in the cloud is now done on the device, which is much quicker 1:04 Can NPL read the sentiment of a market by consuming just a lot of text? Well, a lot of mob mentality is expressed in language rather than numbers 1:07 The start of AIX Ventures and the two Australians 1:11 What might be the next big thing in NPL? 1:13 Future applications of language models might potentially look at video and personal assistants

Ramiro Berrelleza is Founder & CEO of Okteto, the Kubernetes development platform that allows developers to spin up production-like dev environments in the cloud. Okteto's open source project, also called Okteto, allows users to spin up a development container, which is configured like the user's production Kubernetes deployment. Today, it has 2.8K start on GitHub. Okteto has raised $18M from investors including Root VC and Two Sigma. In this episode, we discuss the challenges of building with kubernetes, figuring out market timing, how to position for your specific users & more!

In this episode, we speak with Warren Valdmanis, Partner at Two Sigma Impact, the impact investing business of Two Sigma exclusively focused on workforce impact. Two Sigma Impact combines active, principled ownership and data science with the goal of achieving superior returns and positive social outcomes. It invests in industries that rely heavily on human capital such as education and training, healthcare, consumer, and business services. They believe improving employee engagement and creating more good jobs can improve business performance and create a model for other companies to emulate. Previously, Warren was a Managing Director at Bain Capital, where he helped launch the firm's first dedicated social impact fund and develop the firm's strategic approach to social impact investing.  Warren worked at Bain Capital from 2005 to 2019. I am your host RJ Lumba. We hope you enjoy the show.

Unless you were already in the sector, topics within the manufacturing and supply chain industry were probably not on the radar for most people. As consumers, when you went to the store, you bought what you needed and that was that. However, people became very aware of the importance that these industries play in our lives during the pandemic when everything changed. Supply chain and manufacturing issues were the topics of the nightly news as shelves were bare with lots of essential items missing or purchasing a new car was almost impossible due to inventory shortages. Even though technology has helped these industries evolve over time, there is still a massive opportunity for disruption within the manufacturing and supply chain industries. It is a topic that Colin is thinking deeply about in terms of making investments and we start out our conversation with a discussion around the trends and opportunities for technology to make an impact to these sectors. Two Sigma Ventures is an early stage venture firm that was started in 2012 under the Two Sigma umbrella. The firm has made over 100 investments across many industries. In this episode of our podcast, we cover: * Colin's professional background including how he gained experience in the tech industry and then as an Investment Banker at Lehman Brothers. * What led him down the path of early stage investing and starting Two Sigma Ventures. * An overview of the firm today including portfolio examples. * His decision making criteria for making new investments. * How the tech scene has evolved in NYC. * And so much more. If you like the show, please remember to subscribe and review us on iTunes, Soundcloud, Spotify, Stitcher, or Google Play.

On this episode of Numerically Speaking: The Anaconda Podcast, host Peter Wang welcomes pandas maintainer Jeff Reback, Managing Director at Two Sigma.   Jeff began his career on Wall Street in the 1990's and used Perl for a long time. He developed an interest in Python in the 2000's. He was then quickly drawn to pandas and began to spend his hour-long ferry commutes contributing to its open-source code. His contributions over the years have been significant, to say the least.   When it comes to open source, says Peter, “my flame isn't diminished by lighting your candle.” Cloning a copy of pandas, for example, does not make the original copy any less valuable. In fact, source code actually increases in value as it circulates.   Until recently, only volunteers worked on pandas—but as of 2022, three full-time maintainers are paid to contribute, review code, and triage issues.   Jeff's advice for anybody interested in contributing to open source? Find a community and just help out.   Click https://www.youtube.com/watch?v=7JHqxODJG9k to check out “Two Sigma Presents Pandas at a Crossroads the Past Present and Future with Jeff Reback” on YouTube.   You can find a human-verified transcript of this episode here  - https://know.anaconda.com/rs/387-XNW-688/images/ANACON_Jeff%20Reback_V1.docx.pdf Resources: Peter Wang LinkedIn - https://www.linkedin.com/in/pzwang/ Jeff Reback LinkedIn - https://www.linkedin.com/in/jeff-reback-3a20876/ Two Sigma LinkedIn - https://www.linkedin.com/company/two-sigma-investments/   If you enjoyed today's show, please leave a 5-star review. For more information, visit https://anaconda.com/podcast.

Vinay Iyengar is a Principal at Two Sigma Ventures, focusing on early-stage investments in enterprise software, machine learning, marketplaces, and infrastructure. Prior to Two Sigma Ventures, Vinay was an Investor at Bessemer Venture Partners, having been an angel or co-invested with Bessemer in the likes of Canva, Discord, Zapier among others. Before Bessemer, Vinay started his career as a Machine Learning Engineer at Intel Corporation before moving to Quantitative Trading at Goldman Sachs. Vinay also holds a bachelor's degree in computer science and economics from Harvard. Download the Callin app for iOS and Android to listen to this podcast live, call in, and more! Also available at callin.com

“There are things that are much more important about investing in technology companies than technology,” says Colin Beirne, Founder of Two Sigma Ventures. TSV has invested in around 100 start-ups over the last 10 years, and funded 10 unicorns. They're part of Two Sigma, a hedge fund with more than $60 billion under management. Colin is surrounded by data scientists and programmers, but doesn't have a background in programming. Listen to this episode to hear how Colin went from a liberal arts college to becoming one of the world's leading deep tech investors. Learning notes from this episode: “The winning company is not always the one with the best technology. Tech can be a differentiator, but usually it's only temporary. The job of a venture capitalist is not to figure out which company has the best tech. It's to figure out which company has the best business that can ultimately be the biggest impact,” says Colin. Data science and knowing how to analyse data to spot trends is domain agnostic. This is why you often see data scientists changing jobs from completely different fields, such as going from insurance to social media.  Companies across industries can and do use data analysis to make better decisions at scale. Media companies do this when serving us content and advertising, and investment firms do this to decide which companies to invest in. To decide whether joining a start-up is a good idea, evaluate the founders: do you think they have the ability to grow a successful business? Resources mentioned in this episode: Tech for Non-Techies podcast: Lessons from the Netflix C Suite  Tech for Non-Techies podcast: The Business of AI with Harvard Business School Prof Marco Iansiti Book: Competing in the Age of AI Strategy and Leadership When Algorithms and Networks Run the World  Book: How Generalists Triumph in a Specialized World   Get your FREE guide to the top 10 concepts non-technical leaders need to work with developers, designers and data scientists.  ----- If you like learning about how tech products and profits get made, you'll like our newsletter. It's funny too. Sign up here. ----- There are 2 ways to apply this work to your goals: For individuals, APPLY FOR A CONSULTATION CALL for Tech For Non-Techies membership. For companies: If you want to increase productivity, innovation and diversity, then your non-technical teams need to learn how to collaborate with the techies.  BOOK A CALL to discuss bespoke training & consulting. We love hearing from our readers and listeners. So if you have questions about the content or working with us, just get in touch on info@techfornontechies.co   Say hi to Sophia on Twitter and follow her on LinkedIn. Following us on Facebook, Instagram and TikTok will make you smarter. 

About swyxswyx has worked on React and serverless JavaScript at Two Sigma, Netlify and AWS, and now serves as Head of Developer Experience at Airbyte. He has started and run communities for hundreds of thousands of developers, like Svelte Society, /r/reactjs, and the React TypeScript Cheatsheet. His nontechnical writing was recently published in the Coding Career Handbook for Junior to Senior developers.Links Referenced: “Learning Gears” blog post: https://www.swyx.io/learning-gears The Coding Career Handbook: https://learninpublic.org Personal Website: https://swyx.io Twitter: https://twitter.com/swyx TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: This episode is sponsored in part by our friend EnterpriseDB. EnterpriseDB has been powering enterprise applications with PostgreSQL for 15 years. And now EnterpriseDB has you covered wherever you deploy PostgreSQL on-premises, private cloud, and they just announced a fully-managed service on AWS and Azure called BigAnimal, all one word. Don't leave managing your database to your cloud vendor because they're too busy launching another half-dozen managed databases to focus on any one of them that they didn't build themselves. Instead, work with the experts over at EnterpriseDB. They can save you time and money, they can even help you migrate legacy applications—including Oracle—to the cloud. To learn more, try BigAnimal for free. Go to biganimal.com/snark, and tell them Corey sent you.Corey: Let's face it, on-call firefighting at 2am is stressful! So there's good news and there's bad news. The bad news is that you probably can't prevent incidents from happening, but the good news is that incident.io makes incidents less stressful and a lot more valuable. incident.io is a Slack-native incident management platform that allows you to automate incident processes, focus on fixing the issues and learn from incident insights to improve site reliability and fix your vulnerabilities. Try incident.io, recover faster and sleep more.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn. Some folks are really easy to introduce when I have them on the show because, “My name is, insert name here. I built thing X, and my job is Y at company Z.” Then we have people like today's guest.swyx is currently—and recently—the head of developer experience at Airbyte, but he's also been so much more than that in so many different capacities that you're very difficult to describe. First off, thank you for joining me. And secondly, what's the deal with you?swyx: [laugh]. I have professional ADD, just like you. Thanks for having me, Corey. I'm a—Corey: It works out.swyx: a big fan. Longtime listener, first time caller. Love saying that. [laugh].Corey: You have done a lot of stuff. You have a business and finance background, which… okay, guilty; it's probably why I feel some sense of affinity for a lot of your work. And then you went into some interesting directions. You were working on React and serverless YahvehScript—which is, of course, how I insist on pronouncing it—at Two Sigma, Netlify, AWS—a subject near and dear to my heart—and most recently temporal.io.And now you're at Airbyte. So, you've been focusing on a lot of, I won't say the same things, but your area of emphasis has definitely consistently rhymed with itself. What is it that drives you?swyx: So, I have been recently asking myself a lot of this question because I had to interview to get my new role. And when you have multiple offers—because the job market is very hot for DevRel managers—you have to really think about it. And so, what I like to say is: number one, working with great people; number two, working on great products; number three, making a lot of money.Corey: There's entire school of thought that, “Oh, that's gauche. You shouldn't mention trying to make money.” Like, “Why do you want to work here because I want to make money.” It's always true—swyx: [crosstalk 00:03:46]—Corey: —and for some reason, we're supposed to pretend otherwise. I have a lot of respect for people who can cut to the chase on that. It's always been something that has driven me nuts about the advice that we give a new folks to the industry and peop—and even students figuring out their career path of, “Oh, do something you love and the money will follow.” Well, that's not necessarily true. There are ways to pivot something you'd love into something lucrative and there are ways to wind up more or less borderline starving to death. And again, I'm not saying money is everything, but for a number of us, it's hard to get to where we want to be without it.swyx: Yeah, yeah. I think I've been cast with the kind of judgmental label of being very financially motivated—that's what people have called me—for simply talking about it. And I'm like, “No. You know, it's number three on my priority list.” Like, I will leave positions where I have a lot of money on the table because I don't enjoy the people or the products, but having it up there and talking openly about it somehow makes you [laugh] makes you sort of greedy or something. And I don't think that's right. I tried to set an example for the people that I talk to or people who follow me.Corey: One of the things I've always appreciated about, I guess, your online presence, which has remained remarkably consistent as you've been working through a bunch of different, I guess, stages of life and your career, is you have always talked in significant depth about an area of tech that I am relatively… well, relatively crap at, let's be perfectly honest. And that is the wide world of most things front-end. Every time I see a take about someone saying, “Oh, front-end is junior or front-end is somehow less than,” I'd like to know what the hell it is they know because every time I try and work with it, I wind up more confused than I was when I started. And what I really appreciate is that you have always normalized the fact that this stuff is hard. As of the time that we're recording this a day or so ago, you had a fantastic tweet thread about a friend of yours spun up a Create React App and imported the library to fetch from an endpoint and immediately got stuck. And then you pasted this ridiculous error message.He's a senior staff engineer, ex-Google, ex-Twitter; he can solve complex distributed systems problems and unable to fetch from a REST endpoint without JavaScript specialist help. And I talk about this a lot in other contexts, where the reason I care so much about developer experience is that a bad developer experience does not lead people to the conclusion of, “Oh, this is a bad interface.” It leads people to the conclusion, “Oh, I'm bad at this and I didn't realize it.” No. I still fall into that trap myself.I was under the impression that there was just this magic stuff that JS people know. And your tweet did so much to help normalize from my perspective, the fact that no, no, this is very challenging. I recently went on a Go exploration. Now, I'm starting to get into JavaScript slash TypeScript, which I think are the same thing but I'm not entirely certain of that. Like, oh, well, one of them is statically typed, or strongly typed. It's like, “Well, I have a loud mechanical keyboard. Everything I do is typing strongly, so what's your point?”And even then we're talking past each other in these things. I don't understand a lot of the ecosystem that you live your career in, but I have always had a tremendous and abiding respect for your ability to make it accessible, understandable, and I guess for lack of a better term, to send the elevator back down.swyx: Oh, I definitely think about that strongly, especially that last bit. I think it's a form of personal growth. So, I think a lot of people, when they talk about this sending the elevator back down, they do it as a form of charity, like I'm giving back to the community. But honestly, you actually learn a lot by trying to explain it to others because that's the only way that you truly know if you've learned something. And if you ever get anything wrong, you'll—people will never let you forget it because it is the internet and people will crawl over broken glass to remind you that you're wrong.And once you've got it wrong, you will—you know, you've been so embarrassed that you'll never forget it. So, I think it's just a really good way to learn in public. And that's kind of the motto that I'm kind of known for. Yeah, we can take the direction anywhere you want to go in JavaScript land. Happy to talk about it all day. [laugh].Corey: Well, I want to start by something you just said where you're doing the learning in public thing. And something I've noticed is that there are really two positions you can take—in the general sense—when you set out to make a bit of a reputation for yourself in a particular technical space. You can either do the, “I'm a beginner here, same as the rest of you, and I'm learning in public,” or you can position yourself as something of an expert. And there are drawbacks and advantages to both. I think that if you don't look as wildly over-represented as I do, both of them are more fraught in different ways, where it's, “Oh, you're learning in public. Ah, look at the new person, she's dumb.”Or if you're presenting yourself as an expert, you get nibbled to death by ducks on a lot of the deep technical nuances and well, actually'ed to death. And my position has always been and this is going to be a radical concept for some folks, is that I'm genuinely honest. I tend to learn in public about the things that I don't know, but the things that I am something of a subject matter expert in—like, I don't know, cloud billing—I don't think that false modesty necessarily serves me particularly well. It's yeah, I know exactly what I'm talking about here. Pretending otherwise it's just being disingenuous.swyx: I try to think of it as having different gears of learning in public. So, I've called this “Learning Gears” in a previous blog post of mine, where you try to fit your mode of learning to the terrain that you're on, your domain expertise, and you should never over-represent the amount that you know because I think people are very rightly upset when there are a lot of people—let's say on Twitter, or YouTube, or Udemy even—who present themselves as experts who are actually—they just read the docs the previous night. So, you should try not to over-represent your expertise.But at the same time, don't let your imposter syndrome stop you from sharing what you are currently learning and taking corrections when you're wrong. And I think that's the tricky balance to get which is constantly trying to put yourself out there while accepting that you might be wrong and not getting offended when or personally attacked when someone corrects you, inevitably. And sometimes people will—especially if you have a lot of followers, people will try to say—you know, someone of your following—you know, it's—I kind of call this follower shaming, like, you should act, uh—invulnerable, or run every tweet through committee before you tweet after a certain sort of following size. So, I try to not do that and try to balance responsibility with authenticity.Corey: I think that there's something incredibly important about that, where there's this idea that you either become invulnerable and get defensive and you yell at people, and down that path lies disaster because, believe it or not, we all get it wrong from time to time, and doubling down and doubling down and doubling down again, suddenly, you're on an island all by yourself and no one respectable is going to be able to get there to help you. And the other side of it is going too far in the other direction, where you implicitly take any form of criticism whatsoever as being de facto correct. And I think that both paths don't lead to super great places. I think it's a matter of finding our own voices and doing a little bit of work as far as the validity of accepting a given piece of feedback goes. But other than that, I'm a big fan of being able to just more or less be as authentic as possible.And I get that I live in a very privileged position where I have paths open to me that are not open to most folks. But in many respects so to you are one of the—easily—first five people I would think of if someone said, “Hey if I need to learn JavaScript for someone, who should I talk to first?” You're on that list. And you've done a lot of things in this area, but you've never—you alluded to it a few minutes ago, but I'm going to call it out a little more pointedly—without naming names, let's be clear—and that you're never presented as a grifter, which is sort of the best way I can think of it of, “Well, I just learned this new technology stack yesterday and now I'm writing a book that I'm going to sell to people on how to be an expert at this thing.” And I want to be clear, this is very distinct from gatekeeping because I think that, “Oh, well, you have to be at least this much of an expert—” No, but I think that holding yourself out as I'm going to write a book on how to be proud of how to become a software engineer.Okay, you were a software engineer for six months, and more to the point, knowing how to do a thing and knowing how to teach a thing are orthogonal skill sets, and I think that is not well understood. If I ever write a book or put something—or some sort of info product out there, I'm going to have to be very careful not to fall into that trap because I don't want to pretend to be an expert in things that I'm not. I barely think I'm an expert in things that I provable am.swyx: there are many ways to answer that. So, I have been accused a couple of times of that. And it's never fun, but also, if you defend yourself well, you can actually turn a critic into a fan, which I love doing.Corey: Mm-hm.swyx: [laugh].Corey: Oh yes.swyx: what I fall back to, so I have a side interest in philosophy, based on one of my high school teachers giving us, like, a lecture in philosophy. I love him, he changed my life. [Lino Barnard 00:13:20], in case—in the off chance that he's listening. So, there's a theory of knowledge of, like, how do you know what you know, right? And if you can base your knowledge on truth—facts and not opinions, then people are arguing with the facts and not the opinions.And so, getting as close to ground truth as possible and having certainty in your collection of facts, I think is the basis of not arguing based on identity of, like, “Okay, I have ten years experience; you have two years experience. I am more correct than you in every single opinion.” That's also not, like, the best way to engage in the battlefield of ideas. It's more about, do you have the right amount of evidence to support the conclusions that you're trying to make? And oftentimes, I think, you know, that is the basis, if you don't have that ability.Another thing that I've also done is to collect the opinions of others who have more expertise and present them and curate them in a way that I think adds value without taking away from the individual original sources. So, I think there's a very academic way [laugh] you can kind of approach this, but that defends your intellectual integrity while helping you learn faster than the typical learning rate. Which is kind of something I do think about a lot, which is, you know, why do we judge people by the number of years experience? It's because that's usually the only metric that we have available that is quantifiable. Everything else is kind of fuzzy.But I definitely think that, you know, better algorithms for learning let you progress much faster than the median rate, and I think people who apply themselves can really get up there in terms of the speed of learning with that. So, I spend a lot of time thinking about this stuff. [laugh].Corey: It's a hard thing to solve for. There's no way around it. It's, what is it that people should be focusing on? How should they be internalizing these things? I think a lot of it starts to with an awareness, even if not in public, just to yourself of, “I would like advice on some random topic.” Do you really? Are you actually looking for advice or are you looking—swyx: right.Corey: For validation? Because those are not the same thing, and you are likely to respond very differently when you receive advice, depending on which side of that you're coming from.swyx: Yeah. And so, one way to do that is to lay out both sides, to actually demonstrate what you're split on, and ask for feedback on specific tiebreakers that would help your decision swing one way or another. Yeah, I mean, there are definitely people who ask questions that are just engagement bait or just looking for validation. And while you can't really fix that, I think it's futile to try to change others' behavior online. You just have to be the best version of yourself you can be. [laugh].Corey: DoorDash had a problem. As their cloud-native environment scaled and developers delivered new features, their monitoring system kept breaking down. In an organization where data is used to make better decisions about technology and about the business, losing observability means the entire company loses their competitive edge. With Chronosphere, DoorDash is no longer losing visibility into their applications suite. The key? Chronosphere is an open source compatible, scalable, and reliable observability solution that gives the observability lead at DoorDash business, confidence, and peace of mind. Read the full success story at snark.cloud/chronosphere. That's snark.cloud slash C-H-R-O-N-O-S-P-H-E-R-E.Corey: So, you wrote a book that is available at learninpublic.org, called The Coding Career Handbook. And to be clear, I have not read this myself because at this point, if I start reading a book like that, and you know, the employees that I have see me reading a book like that, they're going to have some serious questions about where this company is going to be going soon. But scrolling through the site and the social proof, the testimonials from various people who have read it, more or less read like a who's-who of people that I respect, who have been on this show themselves.Emma Bostian is fantastic at explaining a lot of these things. Forrest Brazeal is consistently a source to me of professional envy. I wish I had half his musical talent; my God. And your going down—it explains, more or less, the things that a lot of folks people are all expected to know but no one teaches them about every career stage, ranging from newcomer to the industry to senior. And there's a lot that—there's a lot of gatekeeping around this and I don't even know that it's intentional, but it has to do with the idea that people assume that folks, quote-unquote, “Just know” the answer to some things.Oh, people should just know how to handle a technical interview, despite the fact that the skill set is completely orthogonal to the day-to-day work you'll be doing. People should just know how to handle a performance review, or should just know how to negotiate for a raise, or should just know how to figure out is this technology that I'm working on no longer the direction the industry is going in, and eventually I'm going to wind up, more or less, waiting for the phone to ring because there's only three companies in the world left who use it. Like, how do you keep—how do you pay attention to what's going on around you? And it's the missing manual that I really wish that people would have pointed out to me back when I was getting started. Would have made life a lot easier.swyx: Oh, wow. That's high praise. I actually didn't know we're going to be talking about the book that much. What I will say is—Corey: That's the problem with doing too much. You never know what people have found out about you and what they're going to say when they drag you on to a podcast.swyx: got you, got you. Okay. I know, I know, I know where this is going. Okay. So, one thing that I really definitely believe is that—and this happened to me in my first job as well, which is most people get the mentors that they're assigned at work, and sometimes you have a bad roll the dice. [laugh].And you're supposed to pick up all the stuff they don't teach you in school at work or among your friend group, and sometimes you just don't have the right network at work or among your friend group to tell you the right things to help you progress your career. And I think a lot of this advice is written down in maybe some Hacker News posts, some Reddit posts, some Twitter posts, and there's not really a place you to send people to point to, that consolidates that advice, particularly focused at the junior to senior stage, which is the stage that I went through before writing the book. And so, I think that basically what I was going for is targeting the biggest gap that I saw, which is, there a lot of interview prep type books like Crack the Coding Career, which is kind of—Crack the Coding Interview, which is kind of the book title that I was going after. But once you got the job, no one really tells you what to do after you got that first job. And how do you level up to the senior that everyone wants to hire, right? There's—Corey: “Well, I've mastered cracking the coding interview. Now, I'm really trying to wrap my head around the problem of cracking the showing up at work on time in the morning.” Like, the baseline stuff. And I had so many challenges with that early in my career. Not specifically punctuality, but just the baseline expectation that it's just assumed that by the time you're in the workplace earning a certain amount of money, it's just assumed that you have—because in any other field, you would—you have several years of experience in the workplace and know how these things should play out.No, the reason that I'm sometimes considered useful as far as giving great advice on career advancement and the rest is not because I'm some wizard from the future, it's because I screwed it all up myself and got censured and fired and rejected for all of it. And it's, yeah, I'm not smart enough to learn from other people's mistakes; I got to make them myself. So, there's something to be said for turning your own missteps into guidance so that the next person coming up has an easier time than you did. And that is a theme that, from what I have seen, runs through basically everything that you do.swyx: I tried to do a lot of research, for sure. And so, one way to—you know, I—hopefully, I try not to make mistakes that others have learned, have made, so I tried to pick from, I think I include 1500 quotes and sources and blog posts and tweets to build up that level of expertise all in one place. So hopefully, it gives people something to bootstrap your experience off of. So, you're obviously going to make some mistakes on your own, but at least you have the ability to learn from others, and I think this is my—you know, I'm very proud of the work that I did. And I think people have really appreciated it.Because it's a very long book, and nobody reads books these days, so what am I doing [laugh] writing a book? I think it's only the people that really need this kind of advice, that they find themselves not having the right mentorship that reach out to me. And, you know, it's good enough to support a steady stream of sales. But more importantly, like, you know, I am able to mentor them at various levels from read my book, to read my free tweets, to read the free chapters, or join the pay community where we have weekly sessions going through every chapter and I give feedback on what people are doing. Sometimes I've helped people negotiate their jobs and get that bump up to senior staff—senior engineer, and I think more than doubled their salary, which was very personal proud moment for me.But yeah, anyway, I think basically, it's kind of like a third place between the family and work that you could go to the talk about career stuff. And I feel like, you know, maybe people are not that open on Twitter, but maybe they can be open in a small community like ours.Corey: There's a lot to be said for a sense of professional safety and personal safety around being—having those communities. I mean, mine, when I was coming up was the freenode IRC network. And that was great; it's pseudo-anonymous, but again, I was Corey and network staff at the time, which was odd, but it was great to be able to reach out and figure out am I thinking about this the wrong way, just getting guidance. And sure, there are some channels that basically thrived on insulting people. I admittedly was really into that back in the early-two-thousand-nothings.And, like, it was always fun to go to the Debian channel. It's like, “Yeah, can you explain to me how to do this or should I just go screw myself in advance?” Yeah, it's always the second one. Like, community is a hard thing to get right and it took me a while to realize this isn't the energy I want in the world. I like being able to help people come up and learn different things.I'm curious, given your focus on learning in public and effectively teaching folks as well as becoming a better engineer yourself along the way, you've been focusing for a while now on management. Tell me more about that.swyx: I wouldn't say it's been, actually, a while. Started dabbling in it with the Temporal job, and then now fully in it with Airbyte.Corey: You have to know, it has been pandemic time; it has stood still. Anything is—swyx: exactly.Corey: —a while it given that these are the interminable—this is the decade of Zoom meetings.swyx: [laugh]. I'll say I have about a year-and-a-half of it. And I'm interested in it partially because I've really been enjoying the mentoring side with the coding career community. And also, I think, some of the more effective parts of what I do have to be achieved in the planning stages with getting the right resources rather than doing the individual contributor work. And so, I'm interested in that.I'm very wary of the fact that I don't love meetings myself. Meetings are a means to an end for me and meetings are most of the job in management time. So, I think for what's important to me there, it is that we get stuff done. And we do whatever it takes to own the outcomes that we want to achieve and try to manage people's—try to not screw up people's careers along the way. [laugh]. Better put, I want people to be proud of what they get done with me by the time they're done with me. [laugh].Corey: So, I know you've talked to me about this very briefly, but I don't know that as of the time of this recording, you've made any significant public statements about it. You are now over at Airbytes, which I confess is a company I had not heard of before. What do y'all do over there?swyx: [laugh]. “What is it we do here?” So Airbyte—Corey: Exactly. Consultants want to know.swyx: Airbyte's a data integration company, which means different things based on your background. So, a lot of the data engineering patterns in, sort of, the modern data stack is extracting from multiple sources and loading everything into a data warehouse like a Snowflake or a Redshift, and then performing analysis with tools like dbt or business intelligence tools out there. We like to use MetaBase, but there's a whole there's a whole bunch of these stacks and they're all sort of advancing at different rates of progress. And what Airbyte would really like to own is the data integration part, the part where you load a bunch of sources, every data source in the world.What really drew me to this was two things. One, I really liked the vision of data freedom, which is, you have—you know, as—when you run a company, like, a typical company, I think at Temporal, we had, like, 100, different, like, you know, small little SaaS vendors, all of them vying to be the sources of truth for their thing, or a system of record for the thing. Like, you know, Salesforce wants to be a source of truth for customers, and Google Analytics want to be source of truth for website traffic, and so on and so forth. Like, and it's really hard to do analysis across all of them unless you dump all of them in one place.So one, is the mission of data freedom really resonates with me. Like, your data should be put in put somewhere where you can actually make something out of it, and step one is getting it into a format in a place that is amenable for analysis. And data warehouse pattern has really taken hold of the data engineering discipline. And I find, I think that's a multi-decade trend that I can really get behind. That's the first thing.Corey: I will say that historically, I'm bad at data. All jokes about using DNS as a database aside, one of the reasons behind that is when you work on stateless things like web servers and you blow trunks and one of them, oops. We all laugh, we take an outage, so maybe we're not laughing that hard, but we can reprovision web servers and things are mostly fine. With data and that going away, there are serious problems that could theoretically pose existential risk to the business. Now, I was a sysadmin and a, at least mediocre one, which means that after the first time I lost data, I was diligent about doing backups.Even now, the data work that we do have deep analysis on our customers' AWS bills, which doesn't sound like a big data problem, but I assure you it is, becomes something where, “Okay, step one. We don't operate on it in place.” We copy it into our own secured environment and then we begin the manipulations. We also have backups installed on these things so that in the event that I accidentally the data, it doesn't wind up causing horrifying problems for our customers. And lastly, I wind up also—this is going to surprise people—I might have securing the access to that data by not permitting writes.Turns out it's really hard—though apparently not impossible—to delete data with read-only calls.swyx: [crosstalk 00:28:12].Corey: It tends to be something of just building guardrails against myself. But the data structures, the understanding the analysis of certain things, I would have gotten into Go way sooner than I did if the introduction to Go tutorial on how to use it wasn't just a bunch of math problems talking about this is how you do it. And great, but here in the year of our lord 2022, I mostly want a programming language to smack a couple of JSON objects together and ideally come out with something resembling an answer. I'm not doing a whole lot of, you know, calculating prime numbers in the course of my week. And that is something that took a while for me to realize that, no, no, it's just another example of not being a great way of explaining something that otherwise could be incredibly accessible to folks who have real problems like this.I think the entire field right now of machine learning and the big data side of the universe struggles with this. It's, “Oh, yeah. If you have all your data, that's going to absolutely change the world for you.” “Cool. Can you explain how?” “No. Not effectively anyway.” Like, “Well, thanks for wasting everyone's time. It's appreciated.”swyx: Yeah, startup is sitting on a mountain of data that they don't use and I think everyone kind of feels guilty about it because everyone who is, like, a speaker, they're always talking about, like, “Oh, we used our data to inform this presidential campaign and look at how amazing we are.” And then you listen to the podcasts where the data scientists, you know, talk amongst themselves and they're like, “Yeah, it's bullshit.” Like, [laugh], “We're making it up as we go along, just like everyone else.” But, you know, I definitely think, like, some of the better engineering practices are arising under this. And it's professionalizing just like front-end professionalized maybe ten years ago, DevOps professionalized also, roughly in that timeframe, I think data is emerging as a field that is just a standalone discipline with its own tooling and potentially a lot of money running through it, especially if you look at the Snowflake ecosystem.So, that's why I'm interested in it. You know, I will say there's also—I talked to you about the sort of API replication use case, but also there's database replication, which is kind of like the big use case, which, for example, if you have a transactional sort of SQL database and you want to replicate that to an analytical database for queries, that's a very common one. So, I think basically data mobility from place to place, reshaping it and transferring it in as flexible manner as possible, I think, is the mission, and I think there's a lot of tooling that starts from there and builds up with it. So, Airbyte integrates pretty well with Airflow, Dexter, and all the other orchestration tools, and then, you know, you can use dbt, and everything else in that data stack to run with it. So, I just really liked that composition of tools because basically when I was a hedge fund analyst, we were doing the ETL job without knowing the name for it or having any tooling for it.I just ran a Python script manually on a cron job and whenever it failed, I would have to get up in the middle of night to go kick it again. It's, [laugh] it was that bad in 2014, '15. So, I really feel the pain. And, you know, the more data that we have to play around with, the more analysis we can do.Corey: I'm looking forward to seeing what becomes of this field as folks like you get further and further into it. And by, “Well, what do you mean, folks like me?” Well, I'm glad you asked, or we're about to as I put words in your mouth. I will tell you. People who have a demonstrated ability not just to understand the technology—which is hard—but then have this almost unicorn gift of being able to articulate and explain it to folks who do not have that level of technical depth in a way that is both accessible and inviting. And that is no small thing.If you were to ask me to draw a big circle around all the stuff that you've done in your career and define it, that's how I would do it. You are a storyteller who is conversant with the relevant elements of the story in a first-person perspective. Which is probably a really wordy way to put it. We should get a storyteller to workshop that, but you see the point.swyx: I try to call it, like, accessibly smart. So, it's a balance that you want to make, where you don't want to talk down to your audience because I think there are a lot of educators out there who very much stay at the basics and never leave that. You want to be slightly aspirational and slightly—like, push people to the bounds of their knowledge, but then not to go too far and be inaccessible. And that's my sort of polite way of saying that I dumb things down as service. [laugh].Corey: But I like that approach. The term dumbing it down is never a phrase to use, as it turns out, when you're explaining it to someone. It's like, “Let me dumb that down for you.” It's like, yeah, I always find the best way to teach someone is to first reach them and get their attention. I use humor, but instead we're going to just insult them. That'll get their attention all right.swyx: No. Yeah. It does offend some people who insist on precision and jargon. And I'm quite against that, but it's a constant fight because obviously there is a place at time for jargon.Corey: “Can you explain it to me using completely different words?” If the answer is, “No,” the question then is, “Do you actually understand it or are you just repeating it by rote?”swyx: right.Corey: There's—people learn in different ways and reaching them is important. [sigh].swyx: Exactly.Corey: Yeah. I really want to thank you for being so generous with your time. If people want to learn more about all the various things you're up to, where's the best place to find you?swyx: Sure, they can find me at my website swyx.io, or I'm mostly on Twitter at @swyx.Corey: And we will include links to both of those in the [show notes 00:33:37]. Thank you so much for your time. I really appreciate it.swyx: Thanks so much for having me, Corey. It was a blast.Corey: swyx, head of developer experience at Airbyte, and oh, so much more. I'm Cloud Economist Corey Quinn, and this is Screaming in the Cloud. If you've enjoyed this podcast, please leave a five-star review on your podcast platform of choice or if it's on the YouTubes thumbs up and subscribe, whereas if you've hated this podcast, same thing, five-star review wherever you want, hit the buttons on the YouTubes, but also leaving insulting comment that is hawking your book: Why this Episode was Terrible that you're now selling as a legitimate subject matter expert in this space.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business and we get to the point. Visit duckbillgroup.com to get started.Announcer: This has been a HumblePod production. Stay humble.

Warren Valdmanis, partner at Two Sigma Impact, speaks with Penta senior writer Abby Schultz about why companies should focus more on creating good jobs and how to do it.

Andy Kangpan, a Principal at Two Sigma Ventures, shares his experience supporting founders building enterprise software, cybersecurity, and crypto startups. Andy talks about the growth of decentralized finance (DeFi) and gives real-life risk management tips for entrepreneurs and investors wishing to venture into DeFi. In this episode, you'll learn:4:15 Risk-taking for venture capital investors means thoughtfully making calculated bets on ideas that have a clear path to sustainability.8:30 Ideas for finding unique insights into quickly growing nascent spaces12:13 Decentralized finance represents a big paradigm shift, but are we there yet?16:33 How risky is decentralized finance as an asset class? What should founders know before venturing into this field?The non-profit organization that Andy is passionate about: NYU Entrepreneurship InstituteAbout Guest SpeakerAndy Kangpan is a Principal at Two Sigma Ventures. He currently focuses on investments in enterprise software, cybersecurity, and crypto. Andy has been an operator, advisor and investor. Prior to joining Two Sigma, he was an investor with ff Venture Capital where he invested in early-stage companies building innovative technologies in areas such as robotics and machine learning.Fun fact: In his spare time, Andy enjoys skateboarding, gaming, devouring science fiction, and going to concerts around the city.About Two Sigma VenturesTwo Sigma Ventures is a New York City-based venture capital firm investing in transformative companies harnessing information growth and computing power to change the world. The firm supports data- and computing-driven transformation in every industry - from infrastructure tools and healthcare to real estate and consumer hardware. Its portfolio companies include Anomalo, Brella, Carbon, Enveda, Firedome, GitLab, Odeko, Remote, Radar among others.Next Week's EpisodeComing up next week in Episode 87, we welcome Winter Mead, the founder and CEO of Coolwater Capital. Coolwater is a new organization that is changing the way venture capital investors build new firms. Winter will take us behind the scenes to show us how venture capital works, how LPs think about the industry and what Coolwater is doing to revolutionize the sector.Subscribe to our podcast and stay tuned for our next episode that will drop next Tuesday. Follow Us:  Twitter | Linkedin | Instagram | Facebook

In this episode I speak to Vera Shulgina of SigTech, a software platform for quantitative investors. Vera has recently joined SigTech having previously enjoyed a successful career on the operations side of hedge funds including Two Sigma and Citadel.In our conversation, Vera and I talk about the recent trend of buysiders moving across into product companies, SigTech's offering, and some of the challenges Vera faces in creating a new US office. Hosted on Acast. See acast.com/privacy for more information.

Susan Soh, is Head of Capital Formation and Business Development at GrowthCurve Capital, a private equity firm focused on building world-class businesses by leveraging data, analytics, and machine learning, combined with a comprehensive approach to human capital, to accelerate growth and drive value creation.  At the time of the podcast recording she was Chief of Strategy and Capital Development for the private investment activities at Two Sigma, a $60B financial sciences firm with businesses in investment management, insurance, private equity and venture capital.  She was previously a founding partner and Global Head of Marketing and Client Services at Perella Weinberg Partners and prior to that held a series of roles in business development at a series of hedge funds and private equity firms. She originally trained as a lawyer and M&A banker. She is a Board Member of AAAIM since 2014 – Association of Asian American Investment Managers.Our conversation traces Susan's childhood and the expectations of her Chinese American family, as well as her cycle through her expected career, and then an unexpected one.  This naturally led us to discuss what Susan describes as "AQ" - adaptive intelligence, and how being able to pivot and adapt to life's surprises is so key.  We return to the topic of diversity time and time again - in particular the unique position of Asian Americans and why setting the standard is so important for role models of all kinds. This podcast is brought to you with the kind support of Pluscios Capital, a women-owned, WBENC certified investment management firm based in Evanston, IL. With over 60+ years of combined investment management experience, co-founders Constance Teska and Kelly Chesney are committed to the development of bespoke investment solutions on behalf of institutions and intermediaries. In addition to broadly diversified core and catalyst solutions, Pluscios provides hands on product development support and custom solutions with a focus on diversity-led and emerging managers.

This week we had on Vinay Iyengar (@VinIyengar) of Two Sigma Ventures. Two Sigma is a multi-strategy fund that invests from seed to Series B across a number of industries. Vinay helps lead up the San Francisco office, and he focuses on early-stage investments in enterprise software, machine learning, marketplaces, and infrastructure. In this talk, we discuss: Why now is the golden age to start a business Implications and second order effects of software becoming commoditized Ways to demystify the job search process

In this episode, we talk with Jehron Petty from the Class of 2020, founder and CEO of ColorStack, a nonprofit on a mission to increase the number of Black, Latinx, and Native American college students in computing through community building and career development. While studying Computer Science at Cornell, he interned at companies like Two Sigma and Google as both a software engineer and product manager. Jehron turned down the Google APM program and instead raised over $500K to seed ColorStack.

With over 1,000 data scientists and engineers and 70 petabytes of data, Two Sigma is uniquely positioned to seek to bring data science to impact investing. Warren Valdmanis co-founded the Two Sigma Impact team after 14 years at Bain Capital, including a role in creating Bain Double Impact. Warren is focused on solving two threats to our economy: companies and executives prioritizing short term profits over long term growth and and the hollowing out of the middle-class workforce.  Both topics have social impact metrics where private capital can play a role in helping to shape the future.

In Season 2, Episode 1 of Lead Time Chats, Jean Hsu, VP of Engineering at Range, talks to Camille Fournier, Managing Director of Two Sigma and author of The Manager's Path, about boring stuff — specifically building boring tech and making boring plans. Camille and Jean discuss: Limited innovation tokens and how to decide where to spend them (hint: it's not on cool tech)! What boring plans actually look like, especially when you do need to upgrade to more interesting tech so your team doesn't fall behind. Rolling out company-wide platform updates as rigorously as you would plan for an external launch How to manage up and build credibility that you're delivery value when undertaking long-running technical projectsAdditional Resources: The Manager's Path: A Guide for Tech Leaders Navigating Growth and Change  Make Boring Plans by Camille FournierChoose Boring Technology by Dan McKinley