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Peter Steinberger is the creator of OpenClaw, an open-source AI agent framework that’s the fastest-growing project in GitHub history. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep491-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/peter-steinberger-transcript CONTACT LEX: Feedback – give feedback to Lex: https://lexfridman.com/survey AMA – submit questions, videos or call-in: https://lexfridman.com/ama Hiring – join our team: https://lexfridman.com/hiring Other – other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: Peter’s X: https://x.com/steipete Peter’s GitHub: https://github.com/steipete Peter’s Website: https://steipete.com Peter’s LinkedIn: https://www.linkedin.com/in/steipete OpenClaw Website: https://openclaw.ai OpenClaw GitHub: https://github.com/openclaw/openclaw OpenClaw Discord: https://discord.gg/openclaw SPONSORS: To support this podcast, check out our sponsors & get discounts: Perplexity: AI-powered answer engine. Go to https://perplexity.ai/ Quo: Phone system (calls, texts, contacts) for businesses. Go to https://quo.com/lex CodeRabbit: AI-powered code reviews. Go to https://coderabbit.ai/lex Fin: AI agent for customer service. Go to https://fin.ai/lex Blitzy: AI agent for large enterprise codebases. Go to https://blitzy.com/lex Shopify: Sell stuff online. Go to https://shopify.com/lex LMNT: Zero-sugar electrolyte drink mix. Go to https://drinkLMNT.com/lex OUTLINE: (00:00) – Introduction (03:51) – Sponsors, Comments, and Reflections (15:29) – OpenClaw origin story (18:48) – Mind-blowing moment (28:15) – Why OpenClaw went viral (32:12) – Self-modifying AI agent (36:57) – Name-change drama (54:07) – Moltbook saga (1:02:26) – OpenClaw security concerns (1:11:07) – How to code with AI agents (1:42:02) – Programming setup (1:48:45) – GPT Codex 5.3 vs Claude Opus 4.6 (1:57:52) – Best AI agent for programming (2:19:52) – Life story and career advice (2:23:49) – Money and happiness (2:27:41) – Acquisition offers from OpenAI and Meta (2:44:51) – How OpenClaw works (2:56:09) – AI slop (3:02:13) – AI agents will replace 80% of apps (3:10:50) – Will AI replace programmers? (3:22:50) – Future of OpenClaw community

Chad talks with Jason Stadther, director of engineering at TrueCar, about striking the right balance with AI in programming and its overall impact on the industry now and in the future. Together they discuss the impact AI is currently having on junior developers and the potential impact it's having on their career prospects, what happens to AI when it doesn't have a reference for newly developed tools, how the internet is likely to change over the next decade, and Jason covers some key differences between AI and machine learning. — Your guest for this episode has been Jason Stadther, you can connect with him over on LinkedIn, or check out his blog for more insights on his work. Your host for this episode has been Chad Pytel. You can find Chad all over social media as @cpytel, or over on LinkedIn. If you would like to support the show, head over to our GitHub page, or check out our website. Got a question or comment about the show? Why not write to our hosts: hosts@giantrobots.fm This has been a thoughtbot podcast. Stay up to date by following us on social media - LinkedIn - Mastodon - YouTube - Bluesky © 2026 thoughtbot, inc.

Intellectual Property: Does removing copyright management information from open source code violate the Digital Millennium Copyright Act when used to train AI models? - Argued: Wed, 11 Feb 2026 11:22:36 EDT

This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w

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

Joël talks with guest thoughtbotter Steve Polito about his recent work rewriting Suspenders, an old thoughtbot gem from the 2000s. Steve discusses his role on the rewrite and the steps he took in optimising it for modern rails, Joël dives into some of the new features found in the gem, before discussing with Steve the use cases for Suspenders and why you might choose it over other rails starter apps. — Want to learn more about our gem Suspenders? Check out some of these links to get yourself up to speed and try it for yourself. Suspenders Gem - Suspenders Feature List - thoughtbot's guide for programming Your hosts for this episode have been thoughtbot's own Joël Quenneville and Steve Polito. If you would like to support the show, head over to our GitHub page, or check out our website. Got a question or comment about the show? Why not write to our hosts: hosts@bikeshed.fm This has been a thoughtbot podcast. Stay up to date by following us on social media - YouTube - LinkedIn - Mastodon - BlueSky © 2026 thoughtbot, inc.

Will Brown and Johannes Hagemann of Prime Intellect discuss the shift from static prompting to "environment-based" AI development, and their Environments Hub, a platform designed to democratize frontier-level training. The conversation highlights a major shift: AI progress is moving toward Recursive Language Models that manage their own context and agentic RL that scales through trial and error. Will and Johannes describe their vision for the future in which every company will become an AI research lab. By leveraging institutional knowledge as training data, businesses can build models with decades of experience that far outperform generic, off-the-shelf systems.Hosted by Sonya Huang, Sequoia Capital

This is a recap of the top 10 posts on Hacker News on February 09, 2026. This podcast was generated by wondercraft.ai (00:30): Discord will require a face scan or ID for full access next monthOriginal post: https://news.ycombinator.com/item?id=46945663&utm_source=wondercraft_ai(01:57): GitHub is down againOriginal post: https://news.ycombinator.com/item?id=46946827&utm_source=wondercraft_ai(03:25): Why is the sky blue?Original post: https://news.ycombinator.com/item?id=46946401&utm_source=wondercraft_ai(04:52): Converting a $3.88 analog clock from Walmart into a ESP8266-based Wi-Fi clockOriginal post: https://news.ycombinator.com/item?id=46947096&utm_source=wondercraft_ai(06:20): Show HN: Algorithmically finding the longest line of sight on EarthOriginal post: https://news.ycombinator.com/item?id=46943568&utm_source=wondercraft_ai(07:47): Claude's C Compiler vs. GCCOriginal post: https://news.ycombinator.com/item?id=46941603&utm_source=wondercraft_ai(09:15): Nobody knows how the whole system worksOriginal post: https://news.ycombinator.com/item?id=46941882&utm_source=wondercraft_ai(10:42): Another GitHub outage in the same dayOriginal post: https://news.ycombinator.com/item?id=46949452&utm_source=wondercraft_ai(12:10): AT&T, Verizon blocking release of Salt Typhoon security assessment reportsOriginal post: https://news.ycombinator.com/item?id=46945497&utm_source=wondercraft_ai(13:37): Hard-braking events as indicators of road segment crash riskOriginal post: https://news.ycombinator.com/item?id=46947777&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Let's talk about the Superbowl and FOX News' weird reaction to no one watching Kid Rock fumble his way through songs nobody knows. Jump in with Janaya Future Khan. Project MVT on Github: https://github.com/mvt-project/mvt SUBSCRIBE + FOLLOW IG: www.instagram.com/darkwokejfk Youtube: www.youtube.com/@darkwoke TikTok: https://www.tiktok.com/@janayafk SUPPORT THE SHOW Patreon - https://patreon.com/@darkwoke Tip w/ a One Time Donation - https://buymeacoffee.com/janayafk Have a query? Comment? Reach out to us at: info@darkwoke.com and we may read it aloud on the show!  

In episode 312 of Absolute AppSec, the hosts discuss the double-edged sword of "vibe coding", noting that while AI agents often write better functional tests than humans, they frequently struggle with nuanced authorization patterns and inherit "upkeep costs" as foundational models change behavior over time. A central theme of the episode is that the greatest security risk to an organization is not AI itself, but an exhausted security team. The hosts explore how burnout often manifests as "silent withdrawal" and emphasize that managers must proactively draw out these issues within organizations that often treat security as a mere cost center. Additionally, they review new defensive strategies, such as TrapSec, a framework for deploying canary API endpoints to detect malicious scanning. They also highlight the value of security scorecarding—pioneered by companies like Netflix and GitHub—as a maturity activity that provides a holistic, blame-free view of application health by aggregating multiple metrics. The episode concludes with a reminder that technical tools like Semgrep remain essential for efficiency, even as practitioners increasingly leverage the probabilistic creativity of LLMs.

New @greenpillnet pod out today!

In this episode of The Cybersecurity Defenders Podcast, we discuss some intel being shared in the LimaCharlie community.OpenClaw, an open source AI agent formerly known as MoltBot and ClawdBot, has rapidly become the fastest-growing project on GitHub, amassing over 113,000 stars in under a week.A critical vulnerability in the React Native Community CLI NPM package, tracked as CVE-2025-11953 with a CVSS score of 9.8, has been actively exploited in the wild since late December 2025, according to new findings by VulnCheck. JFrog article.Following the disclosure in the Notepad++ v8.8.9 release announcement, further investigation confirmed a sophisticated supply chain attack that targeted the application's update mechanism.Google, in coordination with multiple partners, has undertaken a large-scale disruption effort targeting the IPIDEA proxy network, which it identifies as one of the largest residential proxy networks globally.Support our show by sharing your favorite episodes with a friend, subscribe, give us a rating or leave a comment on your podcast platform.This podcast is brought to you by LimaCharlie, maker of the SecOps Cloud Platform, infrastructure for SecOps where everything is built API first. Scale with confidence as your business grows. Start today for free at limacharlie.io.

This week we're joined by Dana Lawson, CTO at Netlify. We talk about her journey from the US Army to leading engineering teams at companies like GitHub, New Relic, and now Netlify. We discuss Netlify's evolution from JAMstack to AI-powered developer tools, including Agent Runners and their MCP server. We also explore the concept of "Agent Experience" (AX) as a new paradigm alongside UX and DX, and how hiring practices are evolving in the age of AI.Netlify: https://www.netlify.com/Agent Experience Hub: https://www.netlify.com/agent-experience/agentexperience.ax: https://agentexperience.ax/Agent Runners: https://www.netlify.com/platform/agent-runners/Netlify MCP Server: https://docs.netlify.com/build/build-with-ai/netlify-mcp-server/Dana on LinkedIn: https://www.linkedin.com/in/dglawson/Dana's LeadDev Profile: https://leaddev.com/community/dana-lawsonDana's UXDX Profile: https://uxdx.com/profile/dana-lawson/

Ladyada: "I've only had OpenClaw installed on this Raspberry Pi 5 for a couple of days, but boy, have we burned through a lot of tokens and learned a lot. Including what I think is a really fun improvement in my development process: “Agentic test-driven firmware development.” I've used LLMs for writing code as a sort of pair-programming setup, where I dictate exactly what I want done. But this is the first time that I'm giving full access to the hardware to the LLMs and letting Claude Opus 4.5 as a manager to control Codex subagents. Not only does it parse the datasheet for the register map and functionality, Claude also comes up with a full development and test plan, writes the library, tests it on existing hardware, and then also works up a test suite that covers all of the hardware registers to make sure that the library is exercising the entire chip capability. For example, here I give it an APDS-9999 color sensor and a Neopixel ring and tell it, “hey use the Neopixel ring to verify that we're really reading red, green, and blue data properly from the sensor,” and it will do the whole thing completely autonomously… no humans involved! I still review the final code and ensure the tests genuinely validate the functionality, not just take shortcuts. There is a phenomenon known as "reward hacking" (also called "specification gaming"). The model may optimize for passing tests as a metric, rather than ensuring the code truly works as intended. So far, the results have been excellent... no surprise, since these LLMs are trained on Adafruit open-source GitHub repositories!" Visit the Adafruit shop online - http://www.adafruit.com ----------------------------------------- LIVE CHAT IS HERE! http://adafru.it/discord Subscribe to Adafruit on YouTube: http://adafru.it/subscribe New tutorials on the Adafruit Learning System: http://learn.adafruit.com/ -----------------------------------------

In this episode of Linux Out Loud, Matt takes squad leader role while Wendy and Nate rejoin the party for a high‑FPS catch‑up on life, Linux, and loud gaming sessions. They swap updates on Wendy's robotics teams heading deeper into competition season, Nate's battle with basement water and building a proper home lab spawn point, and Matt's quest to keep a local‑only media server running on modest hardware. From organizing racks and labeling gear to wrestling with Starlink latency and debating cloud gaming versus real ownership, the crew dives into how their real‑world chaos shapes the way they run Linux, host services, and play games. If you like robots, home labs, and arguing about whether you really own your digital library, this one's for you. Show Links: Discord Invite: https://discord.gg/73bDDATDAK Bookbinder JS (booklet maker): https://momijizukamori.github.io/bookbinder-js/ Bookbinder JS on GitHub: https://github.com/momijizukamori/bookbinder-js PS4 controller USB‑C upgrade guide: https://www.youtube.com/watch?v=nGKyBJVDXDQ BattleTech on GOG: https://www.gog.com/en/game/battletech_game​

What are ways to improve how you're using GitHub? How can you collaborate more effectively and improve your technical writing? This week on the show, Adam Johnson is back to talk about his new book, "Boost Your GitHub DX: Tame the Octocat and Elevate Your Productivity".

In this episode of DataTalks.Club, Paul Iusztin, founding AI engineer and author of the LLM Engineer's Handbook, breaks down the transition from traditional software development to production-grade AI engineering. We explore the essential skill stack for 2026, the shift from "PoC purgatory" to shipping real products, and why the future of the field belongs to the full-stack generalist.You'll learn about:- Why the role is evolving into the "new software engineer" and how to own the full product lifecycle.- Identifying when to use traditional ML (like XGBoost) over LLMs to avoid over-engineering.- The architectural shift from fine-tuning to mastering data pipelines and semantic search.- Reliable Agentic Workflows- How to use coding assistants like Claude and Cursor to act as an architect rather than just a coder.- Why human-in-the-loop evaluation is the most critical bottleneck in shipping reliable AI.- How to build a "Second Brain" portfolio project that proves your end-to-end engineering value.Links:- Course link: https: https://academy.towardsai.net/courses/agent-engineering?ref=b3ab31- Decoding AI Magazine: https://www.decodingai.com/TIMECODES:00:00 From code to cars: Paul's journey to AI07:08 Deep learning and the autonomous driving challenge12:09 The transition to global product engineering15:13 Survival guide: Data science vs. AI engineering22:29 The full-stack AI engineer skill stack29:12 Mastering RAG and knowledge management32:27 The generalist edge: Learning with AI42:21 Technical pillars for shipping AI products54:05 Portfolio secrets and the "second brain"58:01 The future of the LLM engineer's handbookThis talk is designed for software engineers, data scientists, and ML engineers looking to move beyond proof-of-concepts and master the engineering rigors of shipping AI products in a production environment. It is particularly valuable for those aiming for founding or lead AI roles in startups.Connect with Paul- Linkedin - https://www.linkedin.com/in/pauliusztin/- Website - https://www.pauliusztin.ai/Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

Most AI infrastructure today is hitting a breaking point. Marc Austin, CEO of Hedgehog, reveals how open source networking and cloud-native solutions are revolutionizing how enterprises build and operate AI at scale. This episode addresses issues many building AI infrastructure today are facing — expensive proprietary systems, overwhelming complex network configurations, and ways to make on-prem AI infrastructure feel just like the public cloud.We discuss how networking is the hidden bottleneck in scaling GPU clusters and the surprising physics and hardware innovations enabling higher throughput. Marc shares the journey of building Hedgehog, an open source, cloud-native platform designed for AI workloads that bridges the gap between complex hardware and seamless, user-friendly cloud experiences. Marc explains how Hedgehog's software abstracts and automates the networking complexity, making AI infrastructure accessible to enterprises without dedicated networking teams.We break down the future of AI networks, from multi-cloud and hybrid environments to the rise of Neo Clouds and the open source movement transforming enterprise AI infrastructure. If you're a CTO, data scientist, or AI innovator, understanding these network innovations can be your moat. Listen to this episode to see how open source, cloud-native networking, and physical innovation are shaping the AI infrastructure of tomorrow.Podcast LinksWatch: ⁠⁠⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠⁠⁠Read: ⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠Listen:⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠More: ⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠Website: ⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠Find out more about the guest at LinkedIn:  https://www.linkedin.com/in/austinmarc/Website: https://hedgehog.cloud/Github: https://github.com/githedgehogChapters00:00 Rethinking AI Infrastructure02:49 The Role of Networking in AI05:54 Marc's Journey to Hedgehog08:46 Lessons from Big Companies11:38 Requirements for AI Networks14:48 Advancements in AI Networking17:33 Future Challenges in AI Infrastructure20:46 Creating a Cloud Experience On-Prem23:32 The Shift to Hybrid Multi-Cloud28:10 Evolving AI Infrastructure and Efficiency30:57 AI Workloads and Network Configurations32:41 Zero Touch Lifecycle Management35:12 Support for Hardware Devices35:45 Networking Paradigms and Vendor Lock-in38:42 The Rise of Neo Clouds41:31 Demand for AI Infrastructure43:57 Open Source and Cloud-Native Networking47:27 Challenges of Building a Networking Startup50:46 Proud Accomplishments at Hedgehog52:41 Future Excitement in AI Inference

1157. This week, we look at AI em dashes with Sean Goedecke, software engineer for GitHub. We talk about why artificial intelligence models frequently use em dashes and words like "delve," and how training on public domain books from the late 1800s may have influenced these patterns. We also look at the role of human feedback in shaping "AI style."www.SeanGoedecke.com

Timestamps: 0:00 feeeeeeling hot hot hot! 0:15 Adobe Animate discontinued, then not 2:14 Intel hires GPU veteran... 3:52 Moltbook (last time) + Rent-a-human site 7:22 QUICK BITS INTRO 7:33 Copilot in File Explorer 8:08 France raids X offices, Spain social ban 9:11 MORE Ryzen CPUs fried in ASRock mobos 10:07 AMD adopting Intel's 'FRED' 10:54 GitHub's plan to deal with vibe coding slop NEWS SOURCES: https://lmg.gg/C94rA Learn more about your ad choices. Visit megaphone.fm/adchoices

Adam built a Claude Code skill for his Taffy REST framework and wanted to share it with the CFML community. Simple enough—create a GitHub repo, add some markdown files, done. But somewhere between "this is cool" and "anyone can install this," a familiar chill crept in. These skills are just text files. No checksums. No digital signatures. No verification that the thing you're installing won't quietly exfiltrate your code to some server in Eastern Europe. Sound familiar? It should. We've been here before—back when passwords lived in plain text and "security" meant hoping nobody looked too hard.The hosts dig into the unsettling parallels between today's LLM plugin ecosystem and the wild west of early internet security.LinksAdam's Dotfiles Blog Post - Getting his shit together with dotfiles, Brewfile, and 1Password SSH agentCF Community LLM Marketplace - Adam's community marketplace for CFML-related Claude skillsSteve Yegge's Google Platforms Rant - The infamous accidentally-public Google+ postVibe Coding by Gene Kim & Steve Yegge - The audiobook Ben's been enjoyingSocket.dev - Supply chain security for npm dependenciesFollow the show and be sure to join the discussion on Discord! Our website is workingcode.dev and we're @workingcode.dev on Bluesky. New episodes drop weekly on Thursday.And, if you're feeling the love, support us on Patreon.With audio editing and engineering by ZCross Media.Full show notes and transcript here.

Let's wrap it up, family. Jump in with Janaya Future Khan. Project MVT on Github: https://github.com/mvt-project/mvt SUBSCRIBE + FOLLOW IG: www.instagram.com/darkwokejfk Youtube: www.youtube.com/@darkwoke TikTok: https://www.tiktok.com/@janayafk SUPPORT THE SHOW Patreon - https://patreon.com/@darkwoke Tip w/ a One Time Donation - https://buymeacoffee.com/janayafk Have a query? Comment? Reach out to us at: info@darkwoke.com and we may read it aloud on the show!  

Thank you to the folks at Sustain for providing the hosting account for CHAOSScast! CHAOSScast – Episode 127 In this episode of CHAOSScast, host Alice is joined by Matt Trifiro from the Commercial Open Source Startup Alliance (COSSA) and Daniel Izquierdo, CEO of Bitergia and co-founder of the CHAOSS Community. The discussion delves into the importance of open source community health metrics in shaping successful commercial strategies for startups. Matt shares COSSA's mission to support the growth of venture-funded open source projects by fostering collaboration among founders, investors, and customers. Daniel discusses how community health can influence the sustainability and innovation of projects. They also explore the future goals of COSSA, including establishing a working group to develop standardized metrics for evaluating community contributions and business value. Press download now to hear more! [00:00:29] Matt and Daniel introduce themselves and their backgrounds. [00:01:56] Matt explains COSSA's mission. [00:02:58] Matt cites evidence that community health can correlate with business outcomes and that investment can improve community indicators, and there's a discussion on moving beyond vanity metrics like GitHub stars. [00:05:13] Daniel shares his perspective from the Open Compliance Summit (Tokyo) and the supply chain/corporate lens: organizations want confidence the software will be safe and still maintained years from now, and he talks about measuring health via collaboration networks. [00:08:34] Matt breaks value into two buckets: Distribution and IP/innovation to explain how open source communities create startup value. Daniel adds that open source and can reduce procurement friction. [00:12:23] They touch on open source as a path to standards. [00:14:50] Matt describes how COSSA supports the startups: education, best practices, and measurement and his goal is to “convert community metrics into dollars.” Daniel notes the need for a baseline framework, then customization by industry. [00:19:38] What's next for COSSA? Matt shares COSSA is being bootstrapped, received initial Linux Foundation support, and is pursuing seed style funding. His planned membership structure is investors, founders, and customers. [00:20:36] Daniel and Matt discuss making the metric framework transparent, likely anchored via CHAOSS, and the goal to building a “Rosetta Stone” between investors and community. [00:25:49] There's a conversation on rug pulls, incentives, and lack of a shared framework. [00:28:21] Matt describes the “covenant” concept. [00:30:34] Alice wraps with mentioning COSSA's direction is clear, and a working group could be on the ramp for broader community participation. Value Adds (Picks) of the week: [00:31:20] Alice's pick is visiting outdoor Christmas light displays after dark. [00:32:27] Matt's pick is his oldest son's finishing his first semester in college. [00:32:58] Daniel's pick is his son finishing his first quarter at primary school and going to the Open Compliance Summit and thanking Shane Coughlan for all his work for many years running this event. Panelist: Alice Sowerby Guests: Matt Trifiro Daniel Izquierdo Links: CHAOSS CHAOSS Project X CHAOSScast Podcast CHAOSS YouTube podcast@chaoss.community Alice Sowerby LinkedIn Matt Trifiro LinkedIn COSSA Daniel Izquierdo LinkedIn Bitergia Christmas Lights at Stourhead Rapturous Delight: after-dark Worcester, Worcestershire The State of Commercial Open Source 2025 (The Linux Foundation)Special Guest: Matt Trifiro.

In this episode of ACM ByteCast, Rashmi Mohan hosts software development productivity expert Nicole Forsgren, Senior Director of Developer Intelligence at Google. Forsgren co-founded DevOps Research and Assessment (DORA), a Google Cloud team that utilizes opinion polling to improve software delivery and operations performance. Forsgren also serves on the ACM Queue Editorial Board. Previously, she led productivity efforts at Microsoft and GitHub, and was a tenure track professor at Utah State University and Pepperdine University. Forsgren co-authored the award-winning book Accelerate: The Science of Lean Software and DevOps and the recently published Frictionless: 7 Steps to Remove Barriers, Unlock Value, and Outpace Your Competition in the AI Era. In this interview, Forsgren shares her journey from psychology and family science to computer science and how she became interested in evidence-based arguments for software delivery methods. She discusses her role at Google utilizing emerging and agentic workflows to improve internal systems for developers. She reflects on her academic background, as the idea for DORA emerged from her PhD program, and her time at IBM. Forsgren also shares the relevance of the DORA metrics in a rapidly changing industry, and how she's adjusting her framework to adapt to new AI tools. 

Eighteen months ago, Tyler Cloutier appeared on the show with what sounded like an ambitious (some might say crazy) plan: build a new distributed database from scratch, then use it to power a massively multiplayer online game. That's two of the hardest problems in software, tackled simultaneously. But sometimes the best infrastructure comes from solving your own impossible problems.The game, Bitcraft, has now launched on Steam. SpacetimeDB has hit version 1.0. And Tyler returns to share what actually happened when theory met production reality. We cover the launch day performance disasters (including a cascading failure caused by logging while holding a lock), why single-threaded execution running entirely from L1 cache can outperform sophisticated multi-threaded approaches by two orders of magnitude, and how the database's reducer model - borrowed from functional programming - enables zero-downtime code deployments. We also get into how SpacetimeDB is expanding beyond games with TypeScript support and React hooks that make building real-time multiplayer web apps surprisingly simple.If you're building anything where multiple users need to see the same data update in real time - which, as Tyler points out, describes most successful applications from Figma to Facebook - SpacetimeDB's approach of treating every app as a multiplayer game might be worth understanding.--Support Developer Voices on Patreon: https://patreon.com/DeveloperVoicesSupport Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/joinSpacetimeDB: https://spacetimedb.com/SpacetimeDB on GitHub: https://github.com/clockworklabs/SpacetimeDBOur previous episode with Tyler: https://youtu.be/roEsJcQYjd8Clockwork Labs: https://clockworklabs.io/Bitcraft Online: https://bitcraftonline.com/Bitcraft on Steam: https://store.steampowered.com/app/3454650/BitCraft_OnlineWebAssembly: https://webassembly.org/Flecs (ECS for C/C++): https://www.flecs.dev/flecs/TigerBeetle: https://tigerbeetle.com/CockroachDB: https://www.cockroachlabs.com/Google Cloud Spanner: https://cloud.google.com/spannerErlang: https://www.erlang.org/Apache Kafka: https://kafka.apache.org/Tyler Cloutier on X: https://x.com/TylerFCloutierTyler Cloutier on LinkedIn: https://www.linkedin.com/in/tylercloutier/--Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.socialKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/0:00 Intro2:01 The Architecture of SpacetimeDB5:01 Client-Side Prediction in Multiplayer Games11:00 Reducers and Event Streaming15:00 Launching Bitcraft on Steam19:00 Debugging Launch Performance Problems26:56 Hot-Swapping Server Code Without Downtime30:01 In-Memory Tables and Query Optimization42:00 Is SpacetimeDB Only For Games?51:00 Performance Benchmarking For Web Workloads55:00 Why Single-Threaded Beats Multi-Threaded1:00:01 Multi-Version Concurrency Control Trade-offs1:05:01 Sharding Data Across Multiple Nodes1:10:56 Inter-Module Communication and Actor Models1:17:00 Replication and the Write-Ahead Log1:24:00 Supported Client Languages1:29:00 Getting Started With SpacetimeDB1:39:02 Outro

We're continuing our AI Tools series with Marcos Polanco, engineering leader, founder, and ecosystem builder from the Bay Area, who joins Matt and Moshe to introduce CLEAR, his method for using AI to build real software, not just demos. Drawing on decades in software development and his recent research into how AI is reshaping the way teams ship products, Marcos shares how CLEAR gives both technical and non‑technical builders a production‑oriented way to work with vibe coding tools.Instead of treating AI like a magical black box, Marcos frames it as an “idiot savant”: incredibly capable and eager, but with no judgment. CLEAR wraps that raw power in structure, guardrails, and engineering discipline, so founders and PMs can go from prototype to production while keeping humans in control of the last, hardest 20%.Join Matt, Moshe, and Marcos as they explore:Marcos's journey through engineering, founding, and AI research, and why he created CLEARWhy AI tools like Bolt, Cursor, Claude, and Gemini are fabulous for prototypes but risky for production without a methodCLEAR in detail:C – Context: onboarding AI like a new hire, using stories and behavior‑driven design (BDD) to articulate requirementsL – Layout: breaking work into focused, scoped pieces and choosing a tech stack so AI isn't overwhelmedE – Execute: applying test‑driven development (TDD), writing tests first, then having AI write code to pass themA – Assess: using a second, independent LLM as a QA agent, plus a human‑run 5 Whys to fix root causes upstreamR – Run: shipping to users, gathering new data, and feeding it back into the next iteration of contextHow CLEAR lowers cognitive load for both humans and AIs and reduces regressions and hallucinationsWhy Markdown (with diagrams like Mermaid) is becoming Marcos's standard format for shared human–AI documentationHow CLEAR changes the coordination layer of software development while keeping engineers central to quality and judgmentPractical advice for PMs and founders who want to move from “just vibes” to predictable, production‑grade AI developmentAnd much more!Want to go deeper on CLEAR or connect with Marcos?CLEAR on GitHub: https://github.com/marcospolanco/ai-native-organizations/blob/main/CLEAR.mdCLEAR slides: https://docs.google.com/presentation/d/1mwwDtr7cCP5jLUyNVgGR5Aj-MBq8xsMlhSc0pvSQDks/edit?usp=sharingLinkedIn: https://www.linkedin.com/in/marcospolancoYou can also connect with us and find more episodes:Product for Product Podcast: http://linkedin.com/company/product-for-product-podcastMatt Green: https://www.linkedin.com/in/mattgreenproduct/Moshe Mikanovsky: http://www.linkedin.com/in/mikanovskyNote: Any views mentioned in the podcast are the sole views of our hosts and guests, and do not represent the products mentioned in any way.Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️

In this episode of the Ardan Labs Podcast, Ale Kennedy debuts as host in her first episode, sitting down with Oscar Hedaya, founder of SPACE, to discuss building startups, navigating uncertainty, and launching innovative products.Oscar shares his journey from New Jersey to Miami, the childhood financial challenges that shaped his work ethic, and the lessons learned from college, job searching, and early setbacks. The conversation explores what it takes to start a company, develop a physical product in a competitive market, and turn setbacks into momentum. Together, Ale and Oscar examine persistence, partnership dynamics, and how identifying gaps in the market led to the creation of The Space Safe.00:00 Introduction and Background02:13 Smart Safes and Security Innovation07:14 Childhood and Early Influences12:57 College Applications and Transitions28:51 College Decisions and Academic Paths42:15 Graduation and Job Market Reality54:26 Starting a Business59:43 Restarting the Entrepreneurial Journey01:10:29 The Birth of The Space Safe01:18:48 Product Development Challenges01:23:49 Launching SpaceSafeConnect with Oscar: LinkedIn: https://www.linkedin.com/in/ohedaya/Mentioned in this Episode:The Space Safe Website: https://www.thespacesafe.comWant 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

Let's talk about race and class in America - since no one else seems to want to. Jump in with Janaya Future Khan. Project MVT on Github: https://github.com/mvt-project/mvt SUBSCRIBE + FOLLOW IG: www.instagram.com/darkwokejfk Youtube: www.youtube.com/@darkwoke TikTok: https://www.tiktok.com/@janayafk SUPPORT THE SHOW Patreon - https://patreon.com/@darkwoke Tip w/ a One Time Donation - https://buymeacoffee.com/janayafk Have a query? Comment? Reach out to us at: info@darkwoke.com and we may read it aloud on the show!  

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss autonomous AI agents and the mindset shift required for total automation. You’ll learn the risks of experimental autonomous systems and how to protect your data. You’ll discover ways to connect AI to your calendar and task managers for better scheduling. You’ll build a mindset that turns repetitive tasks into permanent automated systems. You’ll prepare your current workflows for the next generation of digital personal assistants. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-what-openclaw-moltbot-teaches-us-about-ai-future.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn [00:00]: In this week’s In Ear Insights, let’s talk about autonomous AI. The talk of the town for the last week or so has been the open source project first named Claudebot, spelled C L A W D. Anthropic’s lawyers paid them a visit and said please don’t do that. So they changed it to Maltbot and then no one could remember that. And so they have changed it finally now to Open Claw. Their mascot is still a lobster. This is in a condensed version, a fully autonomous AI system that you install on a. Christopher S. Penn [00:35]: Please, if you’re thinking about on a completely self contained computer that is not on your main production network because it is made of security vulnerabilities, but it interfaces with a bunch of tools and hasn’t connected to the AI model of your choice to allow you to basically text via WhatsApp or Telegram with an agent and have it go off and do things. And the the pitch is a couple things. One, it has a lot of autonomy so it can just go off and do things. There were some disasters when it first came out where somebody let it loose on their production work computer and immediately started buying courses for them. We did not see a bump in the Trust Insights courses, so that’s unfortunate. But the idea being it’s supposed to function like a true personal assistant. Christopher S. Penn [01:33]: You just text it and say hey, make me an appointment with Katie for lunch today at noon PM at this restaurant and it will go off and figure out how to do those things and then go off and do them. And for the most part it is very successful. The latest thing is people have been just setting it loose. They a bunch of folks created some plugins for it that allow it to have its own social network called Mult Book, where which is a sort of a Reddit clone where hundreds of thousands of people’s open Claw systems are having conversations with each other that look a lot like Reddit and some very amusing writing there. Christopher S. Penn [02:12]: Before I go any further Katie, your initial impressions about a fully autonomous personal AI that may or may not just go off and do things on its own that you didn’t approve? Katie Robbert [02:24]: Hard pass period. No, and thank you for the background information. So I, you know, as I mentioned to you, Chris Offline, I don’t really know a lot about this. I know it’s a newer thing, but it’s like picked up speed pretty quickly. I thought people were trying to be edgy by spelling it incorrectly in terms of it being part of Claude, but now understanding that Claude stepped in and was like heck no. That explains the name because I was very confused by that. I was like, okay, you know, I, I think a lot of us have always wanted some sort of an admin or personal assistant for paperwork or, you know, making appointments and stuff. Like, so I can definitely see the potential. Katie Robbert [03:10]: But it sounds like there’s a lot of things that need to be worked out with the technology in terms of security, in terms of guardrails. So let’s say I am your average, everyday operations person. I’m drowning in the weeds of admin and everything, and I see this as a glimmer of hope. And I’m like, ooh, maybe this is the thing. I don’t know a lot about it. What do I need to consider? What are some questions I should be asking before I go ahead and let this quote unquote, autonomous bot take over my life and possibly screw things up? Christopher S. Penn [03:54]: Number one, don’t use this at work. Don’t use this for anything important. Run this on a computer that you are totally okay with just burning down to the ground and reformatting later. There are a number of services like Cloudflare, with Cloudflare’s workers and Hetzner and a bunch of other companies that have, they very quickly, very smartly rolled out very inexpensive plans where you can set up a open clause server on their infrastructure that is self contained and that at any point you just, you can just hit the self destruct button. Katie Robbert [04:27]: Well, and I want to acknowledge that because you said, you know, you started by saying, like, any computer, I don’t know a lot of people besides yourself and other handful who have extra computers lying around. You know, it’s not something that the average, you know, professional has. You know, some of us are using, you know, laptops that we get from the company that we work for and if we ever leave that job, we have to give that computer back. And so we don’t have a personal computer. Speaker 3 [04:59]: So it’s number one. Katie Robbert [05:01]: It’s good to know that there are options. So you said Cloudflare, you said, who else? Christopher S. Penn [05:06]: Hetzner, which is a German company, basically, anybody that can rent you a server that you can use for this type of system. What the important thing here is not this particular technology, because the creator has said, I made this for myself as kind of a gimmick. I did not intend for people to be deploying clusters of these and turning into a product and trying to sell it to people. He’s like, that’s not what it’s for. And he’s like, I intentionally did not put in things like security because I didn’t want to bother. It was a fun little side project. But the thing that folks should be looking at is the idea. The idea of. We’ve done some episodes recently on the Trust Insights livestream about Claude Code and Claude Cowork, which Cowork, by the way, just got plugins. Christopher S. Penn [05:58]: So all those skills and things, that’s for another time, but when you start looking at how we use things like Claude code. This morning when I got into the office, I fired up Claude Code, opened it in my Asana folder and said, give me my daily briefing. What’s going on? It listed all these things and I immediately just turn on my voice memo thing. I said, this is done. Let’s move this due date, this is done. And it went off and it did those things for me. Someone who hated using project management software like this now, I love it. And I was like, okay, great, I can just tell it what to do. And it does. And I actually looked. I opened up an asana looked, and it not only created the tasks, but it put in details and descriptions and stuff like that. Christopher S. Penn [06:44]: And it now also prompts me, hey, how much time do you think this will take? I’ll put that in there too. I’m like, this is great. I don’t have to do anything other than talk to it. Something like openclaw is the next evolution of a thing like Claude Code or Open or Claude Coerc, where now it’s a system that has connection to multiple systems, where it just starts acting like a personal assistant. I’m sure if I wanted to invest the time, and I probably will, I’m going to make a Python connector to my Google Calendar so that I can say in my Asana folder, hey, now that you’ve got my task list for this week, start blocking time for tasks. Christopher S. Penn [07:26]: Fill up my calendar with all the available slots with work so that I can get as much done as possible, which will make me more productive at a personal level. When people see systems like OpenClaw out there, they should be thinking, okay, that particular version, not a good idea. But we should be thinking about how will our work look when we have a little cloud bot somewhere that we can talk to, like a PA and say, fill up my calendar with the important stuff this week. Speaker 3 [07:58]: Right? Christopher S. Penn [07:59]: Yeah, because you’ve connected it to your son, you’ve connected your Google Calendar, you’ve connected to your HubSpot. You could say to it, hey, as CEO, you could say, hey, open agent, fill Up. Go look in HubSpot at the top 20 deals that we need to be working on and fill up John’s calendar with exact times that he should be calling those people. Right. Katie Robbert [08:24]: I’m sorry, in advance. I’m gonna do that. Christopher S. Penn [08:27]: He’s been saying, hey, it looks like Chris has gotten some time on Friday open agent. Go and look in Chris’s asana and fill up his day. Make sure that he’s getting the most important things done. That as a manager, you know, with permission, obviously is where this technology should be going so that you could, like, this is the vision. You could be running the company from your phone just by having conversations with the assistant. You know, you’re out walking Georgia and you’re like, oh, I forgot these three things and I need to do lunch here and I do this. Go, go take care of it. And like a real human assistant, it just does those things and comes back and says, here’s what I did for you. Katie Robbert [09:10]: Couple questions. One, you know, I hear you when you’re saying this is how we should be thinking about it. You are someone who has more knowledge than the most of us about what these systems can and can’t do. So how does someone who isn’t you start thinking about those things? Let’s just start with that question. You know, and I know that this, know I always come back to. I remember you wrote this series when we worked at the agency and it was for IBM. So you know, for those who don’t know, Chris is a, what, eight year running IBM champion. Congratulations on that. That is, I mean that’s a big deal. Katie Robbert [09:56]: But it was the citizen analyst post series that always stuck with me because I always, I’d never heard that terminology, but it was less about what you called it and more about the thinking behind it. And I think we’re almost, I would argue that we’re due for another citizen analyst, like series of posts from you, Chris, like, how do we get to thinking about this the way that you’re thinking about it or the way that somebody could be looking at it and you know, to borrow the term the art of the possible, like, how does someone get from. There’s a software, I’ve been told it does stuff, but I shouldn’t use it. Okay, I’m going to move on with my day. Katie Robbert [10:41]: Like, how does someone get from that to, okay, let me actually step back and look at it and think about the potential and see what I do have and start to cobble things together. You know, I feel like it’s maybe the difference between someone who can cook with a recipe and someone who can cook just by looking inside their pantry. Christopher S. Penn [11:01]: I, the cooking analogy is a great one. I would definitely go there because you have to know when you walk into the kitchen what’s in here, what are the appliances, what do we have for ingredients, how do those ingredients go together? Like for example chocolate and oatmeal generally don’t go well together. At least not as a main. It’s kind of like when you look at the 5PS platform we always say this in most situations do not start with the technology, right? That’s, that’s a recipe usually for not things not going well. But part of it is what’s implicit in platform is that you know what the platforms do, that you know what you have. Because if you don’t know what you have and you don’t know how to use them, which is process, then you’re not going to be as effective. Christopher S. Penn [11:46]: And so you do have to take some time to understand what’s in each of the five P’s so that you can make this happen. So in the case of something like an open claw or even actually let’s go, let’s take a step back. If you are a non technical user and you’re, let’s say you decide I’m going to open up Claude Cowork and try and make a go of this, the first question I would ask is well what things can it connect to? That’s an important mindset shift is what can I connect this to? Because we’ve all had the experience where we’re working like a chat GPT or whatever and it does stuff and it’s like fun and then like well now I got go be the copy paste monkey and put this in other systems. Christopher S. Penn [12:29]: When you start looking at agentic AI that where do I have to copy paste? This should be a shorter and shorter list every day as companies start adding more connectors. So when you go to Claude Cowork you see Google Drive, Google Calendar, fireflies, Asana, HubSpot, etc. And that’s your first step is go what does it connect to? And then you take a look at your own process in the 5ps and go of those systems. What do I do? Oh I every Monday I look in HubSpot and then I look in Google Analytics and then I look here and look here and go well if I wrote down that process as a standard operating procedure and I handed that sop as a document to Claude in cowork. I could literally asking, hey, how much of this could you do for me? Christopher S. Penn [13:21]: And just tell me what to look at. So first you got to know what’s possible. Second, you got to know your process. Third, you have to ask the machine can how much of this can you do? And then you have to think about and this is the important question, what, Given all this stuff that you have access to, what could you do that. I am not thinking about that. I’m not doing that. I should be. The biggest problem we have as humans is we do not. We are terrible at white space. We are terrible at knowing what’s not there. We. We look at something we understand, okay, this is what this thing does. We never think, well, what else could it do that I don’t know? This is where AI is really smart because it’s been trained on all the data. Christopher S. Penn [14:09]: It goes well, other people also use it for this. Other people do this. Or it’s capable of doing this. Like, hey, you’re asana. Because it contains a rudimentary document management system, could contain recipes. You could use it as a recipe book. Like you shouldn’t, but you could. And so those are kind of the mindset things. And the last one I’ll add to that. There’s something that I know, Katie, you and I have been talking about as we sort of try and build a. A co AI person as well as a co CEO to sort of the mirror the principles of trust. Insights is one of the first things that I think about every single time I try to solve a problem is this a problem that can solve with an algorithm? This is something that I Learned from Google 15 years ago. Christopher S. Penn [14:56]: Google in their employee onboarding says we favor algorithmic thinkers. Someone who doesn’t say, I’m going to solve this problem. Somebody who thinks, how can I write an algorithm that will solve this problem forever and make it go away and make it never come back? Which is a different way of thinking. Katie Robbert [15:14]: That’s really interesting. Speaker 3 [15:17]: Huh? Katie Robbert [15:18]: I like that. And I feel like. I feel like offline. I’m just going to sort of like. Speaker 3 [15:23]: Make that note for us. Katie Robbert [15:24]: I want to explore that a little bit more because I really, I think that’s a really interesting point. Speaker 3 [15:31]: And. Katie Robbert [15:31]: It does explain a lot around your approach to looking at this. These machines, as you’re describing, sort of the people are bad with the white space. It reminds me of the case study that was my favorite when I was in grad school. And it was a company that at The Time was based in Boston. I honestly haven’t kept up with them anymore. But it was a company called Ideo and ido. One of the things that they did really well was they did basically user experience. But what they did was they didn’t just say, here’s a thing, use it. Let us learn how you’re using the thing. They actually went outside and it wasn’t the here’s a thing, use it. It’s let us just observe what people are doing and what problems they’re having with everyday tasks and where they’re getting stuck in the process. Katie Robbert [16:28]: I remember this is just a side note, a little bit of a rant. I brought this case study to my then leadership team as a way to think differently about how, you know, because were sort of stuck in our sales pipeline and sales were zero and blah, blah. And I got laughed out of the room because that’s not how we do it. This is how we do it. And, you know, I felt very ashamed to have tried something different. And it sort of was like, okay, well that’s not useful. But now fast forward jokes on them. That’s exactly how you need to be thinking about it. Katie Robbert [17:03]: So it just, it strikes me that we don’t necessarily, yes, we need to understand the software, but in terms of our own awareness as humans, it might be helpful to sort of maybe isolate certain parts of your day to say, I am going to be very aware and present in this moment when I’m doing this particular task to see. Speaker 3 [17:31]: Where am I getting stuck, where am. Katie Robbert [17:32]: I getting caught up, where am I getting distracted and then coming back to it? And so I think that’s something we can all do. And it sounds like, oh, that’s so much extra work, I just want to get it done. Well, guess what? Speaker 3 [17:45]: Those tasks that you’re just trying to. Katie Robbert [17:47]: Survive and get through, they are likely the ones that are best candidates for AI. So if we think back to our other framework, the TRIPS framework, which is. Speaker 3 [17:57]: In this list somewhere, here it is. Katie Robbert [18:01]: Found it. Trust, insights, AI trips, time, repetitiveness, importance, pain, and sufficient data. And so if it’s something that you’re doing all the time, you’re just trying to get through, may be a good candidate for AI. You may just not be aware that it’s something that AI can do. And so, Chris, to your point, it could be as straightforward as. All right, I just finished this report. Let me go ahead and just record voice, memo my thoughts about how I did it, how it goes, how often I do it, give it to even something like a Gemini chat and say, hey, I do this process, you know, three times a week. Is this something AI could do for me? Ask me some questions about it and maybe even parts of it could be automated. Katie Robbert [18:50]: Like that to me is something that should be accessible to most of us. You don’t have to be, you know, a high performing engineer or data scientist or you know, an AI thought leader to do that kind of an exercise. Christopher S. Penn [19:07]: A lot of, a lot of the issues that people have with making AI productive for them almost kind of reminds me of waterfall versus agile in the sense of, hey, I need to do this thing. And you know, this is this massive big project and you start digging like, I give up, I can’t do it. As opposed to a more bottom up approach, you go, okay, I do this as possible. What if I can automate just this part? What if I can automate just this part? What if I can do this? And then what you find over time is that then you start going, well, what if I glue these parts together? And then eventually you end up with a system. Now that gets you to V1 of like, hey, this is this janky cobbled together system of the way that I do things. Christopher S. Penn [19:47]: For example, on my YouTube videos that I make myself personally, I got tired of putting just basically changing the text in Canva every video. This is stupid. Why am I doing this? I know image magic exists. I know this library, that library exists. So I wrote a Python script, said, I’m just going to give you a list of titles. I’m going to give you the template, the placeholder, I’ll tell you what font to use, you make it. This is not rocket surgery. This is not like inventing something new. This is slapping text on an image. And so now when I’m in my kitchen on Sundays cooking, I’ll record nine videos at a time. AI will choose the titles and then it will just crank out the nine images. And that saves me about a half an hour of stupid typing, right? Christopher S. Penn [20:33]: That stupid typing is not executive function. I’m not outsourcing anything valuable to AI. Just make this go away. So if you think and you automate little bits everywhere you can and then you start gluing it together, that gets you to V1. And then you take a step back and go, wow, V1 is a hot mess of duct tape and chewing gum and bailing wire. And then that you say to with, in partnership with your AI, reverse engineer the requirements of this janky system that we’ve made to A requirements document. And then you say, okay, now let’s build v2, because now we know what the requirements are. We can now build V2 and then V2 is polished. It’s lovely. Like my voice transcription system V1 was a hot mess. Christopher S. Penn [21:16]: V2 is a polished app that I can run and have running all the time and it doesn’t blow up my system anymore. But in terms of thinking about how we apply AI and the sort of AI mindset, that’s the approach that I take. It’s not the only one by any means, but that’s how I think about this. So when someone says, hey, open call is here, what’s the first thing I do? I go to the GitHub repo, I grab a copy of it, make a copy of it, because stuff vanishes all the time. And then I dive in with an AI coding tool just to say, explain this to me what’s in the box. Christopher S. Penn [21:53]: If you are a more technical person, one of the best things that you can do in a tool like Claude code is say, build me a system diagram, analyze the code base and build me system. Don’t make any changes, don’t do anything, just explain the system to me and you’ll look at it and go, oh, that’s what this does. When I’m debugging a particularly difficult project, every so often I will say, hey, make a system diagram of the current state and it will make one. And I’ll be like, well, where’s this thing? It’s like, oh yeah, that should be there. I’m like, yeah, no kidding it should be there. Would you please go and fix that? But having to your point, having the self awareness to take a step back and say show me the system works really well. Christopher S. Penn [22:39]: If you want to get really fancy, you could screen record you doing something, load that to a system like Gemini and say, make me a process diagram of how I do this thing. And then you can look at it with a tool like Gemini because Gemini does video really well and say, how could I make this more efficient? Katie Robbert [22:59]: I think that’s a really good entry point for most of us. Most machines, Macs and PCs come with some sort of screen recorder built in. There’s a lot of free tools, but I think that’s a really good opportunity to start to figure out like, is this something that I could find efficiencies on? Speaker 3 [23:19]: Do I even have documentation around how I do it? Katie Robbert [23:22]: If not, take this video and create some and then I can look at it and go, oh, that’s not right. The thing I want to reinforce, you know, as we’re talking about these autonomous, you know, virtual assistants, executive assistants, you know, these bots that are going to take over the world, blah, blah. You still need human intervention. So, Chris, as you were describing, the process of having the system create the title cards for your videos, I would imagine, I would hope, I would assume that you, the human reviews all of the title cards ahead of, like, before posting them live, just in case you got on a particular rant in one video, it was profanity laced and the AI was like, oh, well, Chris says this particular F word over and over again, so it must be the title of the video. Katie Robbert [24:14]: Therefore, boom, here’s title card. And I’m just going to publish it live. I would like to believe that there is still, at least in that case, some human intervention to go. Oh, yeah, that’s not the title of that video. Let me go ahead and fix that. And I think that’s. Go ahead. Christopher S. Penn [24:29]: There isn’t human intervention on that because there’s an ideal customer profile that is interrogated as part of the process to say, would the ICP like this? And the ICP is a business professional. And so, you know, I’ve had it say, the ICP would not like this title and it will just fix itself. And I’m like, okay, cool. So you, to your point, there was human intervention at some point, and then we codified the rules with an ideal customer profile. Say, this is what the audience really wants. Katie Robbert [24:54]: And I think that’s okay. Speaker 3 [24:56]: I think you at least need to. Katie Robbert [24:57]: Start with that for V1. You should have that human intervention as the QA. But to your point, as you learn, okay, this is my ideal customer, and this is what they want. This is the feedback that I’ve gotten on everything. Take all of that feedback, put it into a document and say, listen to this feedback every time you do something. Make sure we’re not continually making the same mistakes. So it really comes down to some sort of a QA check, a quality assurance check in the process before you just unleash what the machines create to the public. Christopher S. Penn [25:31]: Exactly. So to wrap up Open Claw, Claudebot, Multbot, slash, whatever they want to call it this week is by itself not something I would recommend people install. But you should absolutely be thinking about, what does a semi autonomous or fully autonomous system look like in our future, how will we use it? And laying the groundwork for it by getting your own AI mindset in place and documenting the heck out of everything that you do so that when a production ready system like that becomes available, you will have all the materials ready to make it happen and make it happen safely and effectively. Christopher S. Penn [26:09]: If you’ve got some thoughts or hey, you installed open claw and burned down your computer pot, drop by our free slot group Go to trust insights AI analytics for marketers where you and over 4,500 marketers are asking and answering each other’s questions every single day. And wherever it is you watch, listen to the show. If there’s a channel you’d rather have it on, said go to Trust Insights AI TI Podcast. You can find us all the places fine podcasts are served. Thanks for tuning in to talk to you on the next one. Speaker 3 [26:40]: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence and machine learning to empower businesses with actionable Insights. Founded in 2017 by Katie Robert and Christopher S. Penn, the firm is built on the principles of truth, acumen and prosperity. Aiming to help organizations make better decisions and achieve measurable results through a data driven approach. Trust Insight specializes in helping businesses leverage the power of data, artificial intelligence and machine learning to drive measurable marketing roi. Trust Insight services span the gamut from developing comprehensive data strategies and conducting deep dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Speaker 3 [27:33]: Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation and high level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google, Gemini, Anthropic, Claude Dall? E, Midjourney Stock, Stable Diffusion and metalama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams beyond client work. Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In Ear Insights Podcast, the Inbox Insights newsletter, the so what Livestream webinars and keynote speaking. What distinguishes Trust Insights in their focus on delivering actionable insights, not just raw data, Trust Insights are adept at leveraging cutting edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Speaker 3 [28:39]: Data Storytelling this commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data driven. Trust Insights champions ethical data practices and transparency in AI sharing knowledge widely whether you’re a Fortune 500 company, a mid sized business or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance and educational resources to help you navigate the ever evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

Sally and Aji assess some common metrics for success when working a project and how they may not always provide the clearest picture of how things are going. Together they discuss how to communicate effectively with stakeholders who are less technical to fully appreciate certain decisions and choices being made on a project, as well as the different metrics you can use to better reflect success and setbacks on a project. — Your hosts for this episode have been thoughtbot's own Sally Hall and Aji Slater. If you would like to support the show, head over to our GitHub page, or check out our website. Got a question or comment about the show? Why not write to our hosts: hosts@bikeshed.fm This has been a thoughtbot podcast. Stay up to date by following us on social media - YouTube - LinkedIn - Mastodon - BlueSky © 2026 thoughtbot, inc.

This week's episode steps away from dashboards and delivery stories and into real life. Rob and Justin both spent the same week realizing how naturally AI is already showing up at home. Not as a plan. Not as a lesson. Just as part of how the next generation creates, explores, and even plans a date. One household includes an about to graduate computer science student navigating a shrinking entry level job market, Discord as the default communication layer, and a Claude Code powered date night that feels entirely normal to everyone involved. The other involves younger kids, a TV, a terminal window, and a two-hour experiment that turns into a fully illustrated story built with multiple AI tools, false starts included. Even Microsoft Word makes an appearance. The stories are personal, but the takeaway is practical. AI rarely gets it right the first time. Iteration matters. Context matters. Switching tools matters. And exposure builds confidence faster than instruction. This episode isn't about business use cases. It's about understanding how people actually acclimate to new technology and why that same pattern shows up inside organizations, whether leaders plan for it or not. Also in this episode: GitHub repository

Elijah Straight, PM for Microsoft Agent Framework, walks through how to build a multi-agent workflow to automate work using the framework's graph-based orchestration capabilities. Microsoft Agent Framework is a multi-language SDK for Python and .NET that enables building, orchestrating, and deploying AI agents with support for streaming, checkpointing, and human-in-the-loop capabilities. Chapters 00:00 - Introduction 01:35 - Visualization of demo 04:11 - PowerPoint to be synthesized 05:03 - Start of demo 10:27 - GitHub 11:47 - Wrap up Recommended resources Learn Docs GitHub Connect Scott Hanselman | @SHanselman: https://x.com/SHanselman Elijah Straight | @elijahbuilds: https://x.com/elijahbuilds Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

Elijah Straight, PM for Microsoft Agent Framework, walks through how to build a multi-agent workflow to automate work using the framework's graph-based orchestration capabilities. Microsoft Agent Framework is a multi-language SDK for Python and .NET that enables building, orchestrating, and deploying AI agents with support for streaming, checkpointing, and human-in-the-loop capabilities. Chapters 00:00 - Introduction 01:35 - Visualization of demo 04:11 - PowerPoint to be synthesized 05:03 - Start of demo 10:27 - GitHub 11:47 - Wrap up Recommended resources Learn Docs GitHub Connect Scott Hanselman | @SHanselman: https://x.com/SHanselman Elijah Straight | @elijahbuilds: https://x.com/elijahbuilds Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

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

Is anyone talking to or about Black people? This month marks the 100th year anniversary of Black History Month in America, but we're hush hush. Does anyone else think that's weird? Family, let's talk. Jump in with Janaya Future Khan. Project MVT on Github: https://github.com/mvt-project/mvt SUBSCRIBE + FOLLOW IG: www.instagram.com/darkwokejfk Youtube: www.youtube.com/@darkwoke TikTok: https://www.tiktok.com/@janayafk SUPPORT THE SHOW Patreon - https://patreon.com/@darkwoke Tip w/ a One Time Donation - https://buymeacoffee.com/janayafk Have a query? Comment? Reach out to us at: info@darkwoke.com and we may read it aloud on the show!  

This is the third in a short series of speaker profiles for JavaOne 2026 in Redwood Shores, California, March 17-19. Get early bird pricing until February 9, and for a limited time, take advantage of a $100 discount by using this code at checkout: J12026IJN100. Register. Sessions. In this conversation, Jim Grisanzio from Java Developer Relations talks with Paul Bakker, an engineer and Java architect in California. Paul is a staff software engineer in the Java Platform team at Netflix. He works on improving the Java stack and tooling used by all Netflix microservices and was one of the original authors of the DGS (GraphQL) Framework. He is also a Java Champion, he's published two books about Java modularity, and he's a speaker at conferences and Java User Groups. Java Is Everywhere at Netflix Paul will present "How Netflix Uses Java: 2026 Edition" at JavaOne in March. The session updates previous year's talk because Java keeps evolving at Netflix. "Netflix is really staying on the latest and greatest with a lot of things," Paul says. "We're trying new things. And that means there's always new stuff to learn every year." Java powers both Netflix streaming and enterprise applications used internally and supporting studio teams. "Java is everywhere at Netflix," Paul says. "All the backends, they are all Java powered." Why Java? It comes down to history and practicality. The original team members were Java experts, but more importantly, "Java is also just the best choice for us," he says. The language balances developer productivity and runtime performance. At Netflix's scale with thousands of AWS instances running production services, runtime performance is critical. Netflix engineers stay closely connected with development at OpenJDK. They test new features early and work with preview releases or builds before official releases. When virtual threads appeared, Netflix engineers tested immediately to measure performance gains. Paul says they give feedback on what works, what doesn't work, and what they would like to see different. This just demonstrates the value of being involved with OpenJDK, and Paul says they have a really nice back and forward with the Oracle engineering teams. The microservices architecture Netflix adopted years ago enabled the company to scale. This approach has become common now, but Netflix pioneered talking about it publicly. Breaking functionality into smaller pieces lets teams scale and develop services independently. Most workloads are stateless, which enables horizontal scaling. Production services for streaming often run several thousand AWS instances at a time. Early on with Java Applets Paul's coding journey started at 15 when he got his first computer and wanted to learn everything about it. Working at a computer shop repairing machines, the owner asked if he knew how to build websites. Paul said no but wanted to learn. He was curious about everything that involved computes. Java applets were hot back then. With nothing online available, he bought a book and started hacking away. "It was so much fun that I also decided right at that point basically like, oh, I'm going to be an engineer for the rest of my life," he says. That's clarity for a 15-year-old. And it's remarkable. But Paul says it felt natural. He just started doing it, had such a good time, and knew that was what he wanted to do. When he started university around 2000, right during the dot-com bubble and crash, professors warned students not to expect to make money in engineering because the bubble had burst. Paul still remembers how funny that seems now. You can never predict the future. Initially, he learned Java and PHP simultaneously. Java powered client-side applications through applets while PHP ran server-side code. The roles have completely reversed now. Engaging the Community Paul attended his first JavaOne in 2006. "Those were really good times," he says about the early conferences when everything felt big and JavaOne was the only place to learn about Java. Back then, around 20,000 people would travel to San Francisco every year. It was the one and only place to learn what was new in Java. All the major news would be released at JavaOne each year. The world has changed. Now information spreads instantly and continually online, but Paul misses something about those early days. The more recent JavaOne conferences offer something different but equally valuable. Paul points to last year's event in Redwood City as a great example. While the conference is still big, it's small enough that attendees can actually talk with the Oracle JDK engineers and have deeper conversations. The folks who work on the JDK and the Java language are all there giving presentations, but they're also totally accessible for hallway chats. "That makes it really interesting," Paul says. This direct access to the people building the platform distinguishes JavaOne from other conferences. Java User Groups also played an important role in Paul's development. He lived in the Netherlands before moving to the Bay Area nine years ago. In the Netherlands, the NLJUG (Dutch Java User Group) organized two conferences a year, J-Spring and J-Fall. Paul would go to both every year. That was his place to learn in Europe. He has been continuing that pattern right up until now, which is why he is speaking at JavaOne again. Open Source software has also been another major aspect of community for Paul. He has always been active in Open Source because he says it's a fun place to work with people from all over the world solving interesting problems. Besides being a critical part of his professional career, it was also his hobby. Paul says the Open Source aspect with the community behind it is maybe his biggest thing that he really enjoyed over the years. AI Throughout Development AI now occupies much of Paul's professional focus. At Netflix, engineers use AI tools throughout the development lifecycle. Paul uses Claude Code daily, though other developers prefer Cursor, especially for Python and Node work. Most Java developers at Netflix work with Claude Code. The tools integrate with GitHub for pull request reviews, help find bugs, and assist with analyzing production problems by examining log files. Paul describes using AI as having a thinking partner to t all to and code with. Sometimes he needs to bounce ideas around, and the AI gives insights he might have missed or suggests additional issues to consider. For repetitive tasks like copying fields between objects, AI handles the grunt work efficiently. "That's the nice thing about an AI," Paul says. "While a person would probably get really annoyed with all this feedback all the time and like having to repeat the work over and over again, but an AI is like, fine, I'll do it again." Go Build a Lot of Stuff! When asked about advice for students, Paul's answer comes quickly and has not changed much over the years. "I think what I really recommend is just go and build a lot of stuff," he says. "The way to get to become a better developer is by doing a whole lot of development." That's timeless advice students can easily adopt no matter how the modern tools for learning have changed. Paul had to go to a bookstore and buy a book to learn programming. Students today have AI tools to help them and advanced IDEs. But the fundamental principle remains the same, which is to build interesting applications. Paul recommends that students come up with a fun problem and just build it. You learn by making mistakes. You build a system, reach the end, and realize the new codebase already struggles with maintainability. Then you ask what you could have done differently. Those real-life coding experiences teach you how to design code, architect code, and write better code. Paul also suggests that students use AI tools but not blindly. Do not just accept whatever an AI generates. Instead, try to understand what came out, how it could have been done differently, and experiment with different approaches. Use the tools available but really understand what is going on and what options you have. Some students and even practicing developers worry that advanced tools might eliminate their future role as developers. Paul says that nobody knows exactly how things will look in the future because tools get better almost every day now. But AI tools are just tools. Someone needs to drive them and come up with the ideas they should build. Plus, the tools at present are far from a state where you can hand them a task, never look at it again, and have everything work perfectly. Substantial hand-holding is involved. "Is our daily work going to change? Very likely," Paul says. "That's already happening." But he tries to see this change as a positive thing. "It's a new tool that we can use. It makes certain parts of our job more fun, more interesting. You can get more things done in some ways and be open to it." Why Java Works At the end of the conversation, Paul answered a simple question — Why Java? What makes it great? — with a simple and direct answer: "Java is the perfect balance of developer productivity and runtime performance." That balance matters where Paul works at Netflix. But it also matters for students learning their first language, for teams building enterprise applications, and for developers choosing tools that will sustain long careers. Paul's career started with Java applets 20 years ago when he bought a book and started hacking away. The language and platform has evolved dramatically since then, moving from client-side applets to powering massive backend services that stream entertainment to millions globally via Netflix. Through all that change, the core appeal remains — you can build things efficiently for many platforms and those things run fast. Paul Bakker: X, LinkedIn Duke's Corner Java Podcast: Libsyn Jim Grisanzio: X, LinkedIn, Website  

We stress tested open source AI agents this week. What actually held up, and where it falls apart. Plus Brent's $20 Wi-Fi upgrade.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free! Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love. Support LINUX UnpluggedLinks:

On our 500th episode James and Frank celebrate the milestone, reminisce about their mobile‑dev roots, and dig into how AI, the Copilot CLI/SDK and the Model Context Protocol (MCP) are reshaping workflows. Frank demos an MCP‑powered tool that turns app reviews into prioritized GitHub issues and automations — a real example of AI-as-glue — with practical takeaways on prompt engineering, UI extensions, and when to automate versus curate manually. Follow Us Frank: Twitter, Blog, GitHub James: Twitter, Blog, GitHub Merge Conflict: Twitter, Facebook, Website, Chat on Discord Music : Amethyst Seer - Citrine by Adventureface ⭐⭐ Review Us ⭐⭐ Machine transcription available on http://mergeconflict.fm

Jeff and Christina are out of pocket this week, so Erin Dawson heroically steps in to keep the show afloat during trying times. Life, religion, dating, blogging… an everything bagel of a show. Sponsor Copilot Money can help you take control of your finances. Get a fresh start with your money for 2026 with 2 months free when you visit try.copilot.money/overtired. Chapters 00:00 Erin 00:04 Introduction and Guest Introduction 00:44 Siri Mishap and Water Troubles 05:20 Mental Health and Daily Struggles 11:00 Physical Health and Exercise Challenges 18:45 Productivity Tools and Sponsor Message 21:57 Sponsor Break: Copilot Money 23:59 On Aging 24:53 Vision and Aging 26:55 Intelligent Design and Evolution Debate 28:58 Blogging and Social Media Verification 29:13 The Cost of Verification 30:18 Embracing the Content Game 33:12 Exploring Blogging Platforms 48:10 The Decline of Blogging 50:54 Navigating Employment and Content Creation 55:54 The Art of Dating and Bits 58:30 Wrapping Up and Final Thoughts Show Links Gestimer In Your Face Ghost Join the Conversation Merch Come chat on Discord! Twitter/ovrtrd Instagram/ovrtrd Youtube Get the Newsletter Thanks! You’re downloading today’s show from CacheFly’s network BackBeat Media Podcast Network Check out more episodes at overtiredpod.com and subscribe on Apple Podcasts, Spotify, or your favorite podcast app. Find Brett as @ttscoff, Christina as @film_girl, Jeff as @jsguntzel, and follow Overtired at @ovrtrd on Twitter. Transcript Erin [00:00:00] Introduction and Guest Introduction Brett: Hey, welcome to Overtired. It’s me, Brett Terpstra. Um, Christina and Jeff are both out this week, but I have Erin Dawson here to fill the void. Hi, Erin. How you doing? Erin: Hi Brett. I’m well. How are you? Brett: I’m, I’m, I’m okay. So before, like, for people that haven’t tuned in with an episode with you before, give your, give yourself a brief introduction. Erin: Hey folks, my name is Erin. I, uh, make art under the name Genital Shame. I’m based in Los Angeles, California, and I used to work with Brett Terpstra. Siri Mishap and Water Troubles Erin: I’m doing, I’m doing, uh, you know, that broadcast voice, but I’ve started to. When I’m using CarPlay, I’ve started to speak to Siri in my own Siri kind of as a bit, but I really enjoy doing it.[00:01:00] Hey Siri, play REM. Oh shit. It just, I shouldn’t have done that. I’m so sorry. That activated mine. Um, oh no. And now my home pods are doing it. Can you hear that? Brett: I can Erin: I literally have to turn that off now. I really apologize. Ready? Brett: we’ll wait. Erin: Anyways, that’s, this is a shit show. Okay. I’m turning it off. Uh, that’s who I am. I’m someone who activates, um, the, the dingus. Brett: activates digital assistance. That’s amazing. Um, so update on me. I got water back after four and a half days with no running water. Um, but now I’m showering and washing dishes like a pro. Erin: Oh my God, I’m so that, that truly sounds horrific. Brett: It was, you don’t realize exactly how much of your life [00:02:00] revolves around just running water. Um, it’s true of like anything, when your power goes out, when your internet goes out, when your water goes out. We’ve had all of those things happen frequently over the last year. Um, and you, you realize exactly like how handicapped you are without these kind of. The modern conveniences we take for granted? Erin: Did your pipes break? Brett: No, uh, they did freeze. Uh, the solution to the water problem was heat lamps on the well pump. On the on the pipe, the underground pipe that goes from the well pump into the house is about a foot underground, and that’s where the freeze happened. So we had heat lamps on the ground for two days while we were waiting for a plumber to show up. We just decided to try heating things up and after two days it finally creaked [00:03:00] into life, and then we ran a bunch of water and got it all cleared out. And then you Erin: have a TLC show. Now you’re Brett: you know, Erin: solving Pioneer Living. Uh, Brett: You know what happened because of that, to flush the toilet while that was happening, we were melting snow on the stove and on the fireplace and dumping it into the toilet. But when I first started, I didn’t know you could just dump like a gallon and a half of water into the bowl and it would flush. So I was filling the tank up, which takes about twice as much water. And because I was doing that, I was putting a bunch of silt from the snow. Into the tank. So the little, the rim holes around the inside of the rim of the toilet where the water swirls in those filled up with silt. So once we got running water again, the toilet wouldn’t flush all the way. And I had to go in with a coat hanger and try to clean out all of those holes in the toilet. And I got it [00:04:00] clean and it flushed all the way twice and now it’s. Stuck again because I’m just pushing shit in with the coat hanger. And the silt Erin: by shit you mean you mean silt. Brett: silt? Yes. The, the, the silt is still there and as the water runs it just fills the holes again. And I don’t yet know how to fix that, so that’s gonna be a thing. That’s what I’m doing after this. ’cause, uh, the toilet. It sounds like it flushes all the way, but then you leave and the next person comes in and says, oh my God, why didn’t you flush? Because you know there’s floaters in the toilet. Erin: I. Just watched a Todd Salons movie and, and there is a scene in which, um, a character is, is being sort of abused by her family and the abusive family says, we’re laughing with you, not at you. And she [00:05:00] says, but I’m not laughing. You know, and I apologize. I don’t mean to laugh, but that, that sounds truly horrific. Brett: Yeah, that, Erin: I mean, the shower alone, I, I don’t know about you. I use showers to process, Brett: sure. Erin: you know, showers and walks. That’s where I do it most. Mental Health and Daily Struggles Erin: And like I, yeah, I need it to, this is a very 2019 way to frame mental health, which we can pivot to. Um, but I use it to regulate. Do you remember when we used to say, I feel unregulated? We don’t say that anymore. Brett: I do remember. That was a while ago. Erin: Yeah, it’s 2019 to me, but it maybe had a shelf life beyond that. I don’t know. Brett: Yeah. Erin: but yeah, I use showers to regulate. So even if you’re kind of like me, I, my heart goes out to you that that is really not just inconvenient, but like bad for your mental health. Brett: Your quote reminded me [00:06:00] of an and or quote that’s been going around where it, it’s so, uh, I can’t remember who, but someone says, uh, if you’re doing nothing wrong, what do you have to fear? And the response is, I fear your definition of wrong. Erin: Mm. Brett: I’m like, yeah, nope, that, uh, that’s very apropos to the current situation in Minnesota. Um, but yeah, let’s do mental health. Tell me about your mental health. Erin: Yeah. Uh, I’ve seen better days have been the star of many plays. Do you remember that song, Brett? Brett: No, I don’t know what you’re talking about. Erin: All right, cool. Um, I don’t believe in resolutions because I, I went to college, but, but I do believe in the power of January as a moment of. [00:07:00] Intentional reflection and yeah, goal setting, which can be different than resolutions. And for this January, January, 2026, I put a lot of pressure on myself to sort of remake my physical life, which I hoped would have knock on effects for my mental life. So what’s that mean for me? Every year for the last three or four years, I have done dry January dj, and in the past, the keto diet has worked well for me. So I thought in January that I would, with, with these powers combined, I would become, you know, a superhuman. I’m like 20, 26. I’m getting really, I’m gonna get really hot. And I’m going to [00:08:00] be very critical about the role that alcohol plays in my life. And what had happened was, without getting too much into it, I had a bad first week and it kind of snowballed, reverse snowballs. How does a snowball, what is it? I don’t know. It just got a lot of your, your, your toilet silt in it. Yeah. And, um, and I had no release valves for dopamine. Um, because on keto you’re not eating bread. You are not having sugar. I wasn’t having any alcohol. Um, also, and, and I’ll, I’ll shut up about this in a second. I have a foot injury. A right foot injury, something called turf toe, not TERF, but TURF. [00:09:00] Um, it’s basically what happens if you kind of stove your big toe. There’s a in the ball of your foot that’s like a repetitive stress injury. I’m not a p uh, podiatrist, but that’s, that’s my beat. Very basic understanding. And so what does all this mean? That mean this means that it was like a perfect storm of like. I can’t exercise and I exercise is really, plays a really huge role in my mental health. I am in two different basketball leagues, you know, uh, I take a lot of walks. I’m a runner. Couldn’t do any of that. And I couldn’t have Alfredo and I couldn’t have fornet. And so no wonder. And in hindsight with therapy, I’m like, yeah, no wonder I, I just didn’t have any release valves, um, for joy. So in the third week I’m like, fuck [00:10:00] it, I am gonna have fries and I’m going to have a tiki drink. And I don’t regret doing that, but I fear. That, and I think, I think you have this too, Brett, the like, puritan guilt, complex guilt for just like not organizing a particular corner of your fridge correctly, just like that level will give me, be like, oh man, I, I really do suck. Huh. Um, so that scales, you know, that feeling and that complex scales and so it’s easy for me to be like, man, I have no integrity. Huh? I really just. When I got tough, I just, uh, which is also an unhealthy way to think about things, but, um, but I’m, I’m kind of over it now. Uh, but uh, I was pretty disappointed in myself for a while there. I still kind of am. That’s how I’m doing. Brett: Wow, that sounds, that sounds pretty rough. [00:11:00] Physical Health and Exercise Challenges Brett: I, uh, I don’t, I, so I haven’t had a drink in as long as I can remember. Um, because I have a very short memory. It’s only been a matter of months, but, um, I do, I don’t miss drinking. I miss having that release. Um, and I, my only substitute has been CBD. Which is, you know, doesn’t do jack shit. Uh, it’s like a mental game for me. Um, have a, I I I’ve switched to drinking CBDT ’cause it’s way cheaper than like CBD carbonated beverages. Um, so for like 50 cents I can have a mug of five milligrams of CBD and pretend I feel okay. Um, that’s. It’s alright. Um, I do, so my release has been consuming [00:12:00] these outshine coconut bars, which. I find a perfect blend of fatty and salty and sweet and, um, they, as of like two weeks ago, outshine has discontinued them, which had an outsized effect on my mental health. Erin: Yeah. Brett: I bought the last three boxes that were at the grocery store, and those lasted a little bit, and then I was down to two bars and I decided, I, I I would ration them. And night after night, I just looked at those bars, but I wouldn’t, ’cause if I ate one of them, that would mean I only had one left. So it’s easier for me to have two left. So I had two sitting in the fridge, and then yesterday l went to a different grocery store and I said, just on the off chance would you check. And she came home with seven [00:13:00] boxes, six to a box. So yeah, I, I got, I hugged her. They were not expecting it. I like jumped up, just effusively, Erin: What do you, I have never had even this affinity for like my favorite meal. What do you like about these bars? Brett: Oh my God. They just like, I don’t know my, they like dopamine rush, pupil, dilate. Um, Erin: D filled? Brett: no, they’re just sugar. It’s sugar and coconut. Sugar and coconut. Dairy free. Gluten-free. Like it’s a, it’s a sugary snack and. Uh, so I’ve been like my, I don’t know what happened. Uh, it somewhat coincided with my last weight gain, but not exactly. But now I can’t stand up for more than about five minutes. [00:14:00] Um, just like if I empty the dishwasher, the, the act of bending over a few times, I have to sit down and I have to recover for 10 minutes. My back just freezes up and I’ve gone through physical therapy and I have, I like push myself every time it happens. I like, without injuring myself, I try to push it and try to strengthen and nothing helps, like nothing changes at all. That combined with my dizziness, which is still a thing, means the only exercise I’m getting is like half an hour a day on a recumbent bicycle, um, which gives me leg exercise and a little bit of cardio and not much else, and it doesn’t seem to strengthen my back at all, and it doesn’t seem to help me sleep and I keep doing it because I have that guilt thing. If I don’t do anything then. I’m a piece of shit. Um, but [00:15:00] man, I, yeah, the coconut bars are like the only, the only way out. Erin: The Brett: all I’ve got. I’m working, I’m working on finding something new because seven boxes will last a while, but not forever. It’s still a finite amount. Um, Erin: of spring, maybe you Brett: yeah, no way. I eat, I eat a couple a day. Erin: Oh, okay. Brett: a once a week treat for me. Um, so, so I, I’m trying to like ration and I’m trying to find an alternative that is more healthy, not less healthy. Um, we’ll see. I’ll keep you posted. Erin: The guilt thing. I’m gonna, I’m gonna be thinking about the, uh, digital device dingus thing later, there are people for whom, you know, but wait back to the, the treats and living a treat based [00:16:00] lifestyle, which I’m really trying not to do. I’m really trying not to Brett: reinforcement. Erin: I think I, this is the second time I’m, I’m bringing up therapy, but I think I, I brought up that I live a treat based lifestyle up to my therapist and she didn’t, doesn’t love that paradigm of thinking. Um, but it’s kind of all I know. And for me, you know, given this month the treat that I have had before breaking. And now I’m in this habit, and now I’ve, I’m in a trap. I have taken two using, having heavy whipping cream in my coffee each morning. Um, and it’s like adding ice cream to coffee. And so I make my coffee and I have my heavy weapon cream, and I get my little frother that [00:17:00] looks like a vibrator. A very small vibrator, and I do vibrate heavy whipping cream with my coffee in a deli container. And that, unfortunately, I, I’ve tried going back to black coffee, which is my norm. Can’t do it now. I, I really, I’m trapped and unfortunately that is the height, that is the best part of my day. Brett: Do, do Erin: coffee. Brett: I have a suggestion? Um, have you ever tried barista blend oat milk? Erin: I don’t do oat milk. I’ll just say it. Brett: Okay. Erin: Yeah. Brett: It’s all I do. I, I like for me, whatever milk I’m used to is the milk. That’s good. Um, and like I got used to soy milk and everything else tasted crappy. And I got used to almond milk and then I finally like switched to oat milk, got used to that. And [00:18:00] now every other milk tastes terrible. But once Erin: Yeah. Brett: I switched to oat milk, I no longer could like make a good, um, like latte. And I like, it didn’t, uh, it didn’t foam at all. But then I found Barista Blend from C Calisa Farms, and it’s like a full fat oat Erin: Oh Brett: for as much fat as you can get out of oats. And it, it, it fros. You can put it in a steamer and get a nice big frothy latte out of it. Um, but just a suggestion. I can’t do the heavy cream, or I probably would just by lactose intolerance and Erin: Yeah. Brett: lactose allergy. Productivity Tools and Sponsor Message Erin: We talked about, I’m gonna try to combine two topics right now. We talked about Gude and you also suggested before we started recording that I stop you at a half hour [00:19:00] for the A read. We’re not quite there, but as soon as you said that, I pulled down on my. Menu bar, a little app called Just Timer. Brett: I love that app. Erin: Do you Brett: yes. Erin: I, I have, I do have not upgraded to the sequel. Just Timer two, I think it’s Brett: I haven’t tried that. Erin: I think I, I think I tr I did a trial Brett: It’s just such a good idea. Erin: it’s great. And so. have about nine minutes before you’re requested, but I, I just wanted to, I guess, shout out Jess Heimer because it rules. Brett: Yeah. No, it’s such, it’s so for anyone who hasn’t used it, it’s just a way to like, it’s almost like pulling a cord. To set a timer, and it’s just this simple, like you reach up to your menu bar and you just pull down and you pull down the amount you want and you let go and you’ve got a [00:20:00] timer running and it’ll remind you in that amount of time Erin: The main use case I had for that when we worked for the Borg together on the Borg team, was using text expander to, you know, if we had a meeting at three o’clock, I would pull it down for 2 55 and type. MTNG, and that would create a, a string that just says meeting in five exclamation mark. Um, it’s just, it’s just a great time saver and, and keeps you honest and yeah, it’s a great app. Brett: I, uh, I’ve written a lot of command line utilities, so I can like, just on the command line, I can just type, remind me five minutes and then a string, whatever to do, and it runs in the background and it uses like terminal notifier, whatever’s handy at the time to like pop up a reminder. But I kind of gave that up. So now I use just timer. And have you seen in your face. Erin: I don’t know in your [00:21:00] face. Brett: In your face ties into your calendar. You tell it to go off, say five minutes or one minute, or on the time, and anytime an event happens, it blocks out your screen. Pops up a little dialogue telling you what you’re supposed to be doing at that minute and you have to like say, join call or dismiss. And, um, ’cause I, I miss notifications all the time. And when we were working for the board, I would just completely miss meetings because I’d get into coding. I wouldn’t notice the little. Things in the corner, I’d be focused on code and I’d look up two hours later and be like, oh God, I gotta text someone. Sorry I missed the meeting. So in your face stops me from working and like, takes over the screen. Erin: That Brett: So those are, that was our gratitude. I’m gonna do a, a quick sponsor read. Sponsor Break: Copilot Money Brett: This episode is brought to you by [00:22:00] copilot money. Copi copilot money is not just another finance app. It’s your personal finance partner designed to help you feel clear, calm, and in control of your money. Whether it’s tracking your spending, saving for specific goals, or simply getting a handle on your investments. Copilot money has you covered as we enter the New year. Clarity and control over our finances have never been more important with the recent shutdown of mint and rising financial stress for many. Consumers are looking for a modern, trustworthy tool to help navigate their financial journeys. 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Download copilot money on your devices or visit, try. Do copilot domo slash Overtired today to claim your two free months and embrace a more organized, stress-free approach to your finances. Try that’s, try copilot money slash Overtired. On Aging Brett: Ugh. [00:24:00] people are, people aren’t gonna know how many edits I put in that. had a rough time with that one. Erin: Reading’s hard. Brett: I’m, I’m, I’m working on my two big displays. I have two, like 27 inch high def displays, but I, I’m used, I’ve been working on my couch on my laptop for months now. Um. Like Mark II was written entirely on my couch, not, not at this fancy desk I have. Um, and on this desk everything is about three feet away from my face, and I don’t have the resolution set to deal with the fact that my eyes are slowly turning to shit, so I can barely read what’s on my screen anymore. I have to like squint and lean in, and. Vision and Aging Brett: It is so weird that I, I’m told this is just a normal thing that happens at my age, but when I try [00:25:00] to read small print on something, I can’t see it. But if I lift my glasses up and remove my glasses, everything within a foot of my face is clear as day, and that never used to be the case. But now I can see way better without my glasses than with my glasses at very close range. Which means when I wear contacts I really can’t see either. They gave me a, a special kind of contact that the eyes are interchangeable. I have different prescriptions in each eye, but it doesn’t matter which. So the contacts are kinda like universal. I don’t know how it works, but they’re supposed to give you pretty good distance and pretty good closeup while not being especially good at either. And they’re okay. Um, I can’t really, I have to squint to read street signs and I have to squint to read medication bottles and I just spend a lot more time in glasses. Now. Erin: This is one of those [00:26:00] moments where I cannot relate, but I am here Brett: Do you have 2020 vision? Erin: I believe I do. Brett: Wow. Must be nice. Erin: It is nice and I’m gonna own that. Yes, I’m privileged. Ocularly, get off my back about it. Brett: I, I wasn’t giving a shit. I’m, I’m happy for you. I had 2020 vision up until I was about Erin: 2020. Brett: 10. Erin: Oh Brett: I got glasses when I was 10. I. Erin: mm. I bet you Brett: I guess no, I did not have 2020 vision. ’cause I remember at the age of 10 when I got glasses and realized that from a distance, trees had leaves, um, I was like, oh my God, I’ve been missing out on Erin: God is real, bro. Intelligent Design and Evolution Debate Erin: You know, Christians usually, I don’t know about you, but sometimes I, I grew up [00:27:00] with this idea that like. Intelligence, intelligent design is a thing because take something as incredibly complex as the human eye. Tell me that there wasn’t a designer for that, but also like if you’re over 30, like take something as complex as like the human back. it’s not that they’re not that they’re saying that eyes don’t have quality issued degradation over time. It’s a different argument, but it’s just like also like not everything’s that intelligent. I mean, Brett: but the other part that I grew up with was that our, we aged and our eyes went bad, and our back went bad because of sin. It was all like a result of the original sin, and according to like Young Earth creationists, like every generations of humans that get farther away from Adam and Eve. Get [00:28:00] are, are in worse health. They’re, they’re genetically deteriorating, uh, Erin: they’re genetically sinful. Brett: Yeah. And it, it is. I don’t know. It took a long time to unlearn a lot of that stuff, but my dad brings Erin: evil. Brett: it’s called the watchmaker argument. Um, and my dad brings it up anytime we start talking about evolution, which I generally avoid these days, but he brings up the idea of the, the eye, the human eye. Erin: They love the human eye. Brett: I explain to him the, the process of like light sensing cells on amoebas. Erin: Our skin Brett: how, and how they developed into maybe a light sensing cell with a water sack, and then that developed into over time a retina. And like it’s not designed. Um, dad, it, Erin: Oh dad. Brett: yeah. Erin: Anyways. Blogging and Social Media Verification Erin: Can I talk to you about [00:29:00] blogging? Brett: Could you please? Erin: Well, here’s, let me set the table so I not to brag. Became Instagram verified recently. Why? Brett: Must be nice. The Cost of Verification Erin: Yeah, Brett: More privilege. Erin: the first, the eyes are now $13 a month. I don’t know, I don’t know how the bank’s, you know, letting me spend all this, but, um, I did it because, as I said at the top, when the REM may have been drowning me out, I don’t know. Um, I make music under the name Genital Shame and. Over time, as my account has grown on that particular platform, I have had other people alert. I’ve had followers alert me that there’s a new genital shame that just popped up in their feed asking for, Hey, my account was just hacked. [00:30:00] Like, can you help? You know? And I just thought that like for $13 a month, you know Brett: That’s how they get you. Erin: That’s fine. Yeah, get me. I’ve, they already, they already got me. Um, unfortunately, Brett: Zuckerberg that cloned your account. Erin: I got sucked. Embracing the Content Game Erin: So I, so now that I’m verified, I’m, I’m kind of leaning into playing the stupid content game, which is this, which is how, here’s how I think about it. I believe in my art. I believe in what general shame is and I want the maximum amount of people to experience it. The maximum amount of people are in the primary world, which is to say the digital world and the folks with who would resonate with general shame the most are on a platform called Instagram. So it makes sense [00:31:00] for me to play the game, which is like get the. Aforementioned eyeballs on my stuff. ’cause again, I believe in it. So I’ll do whatever it takes. Inc. Like we live in the world of Caesar. We own to Caesar. What a Caesar, in this case, Zuckerberg is Caesar, whatever. So one of my January projects, you know the, the Capital G. Capital M, good month that I was supposed to have was to block out some ugh content. To record some videos, right? Some reels of me playing Bach, of me playing, um, my favorite carcass riff or whatever. And so I found myself writing little essays about each of these things. You know, for the Bach one, there’s, I started writing about how, you know, I don’t believe in God anymore really, but [00:32:00] if I was to cite one thing that gets me. Close to it, it would be Bach like. I’m not predictable like it is. It resonates with me so fundamentally and so deeply that like that is the one thing. And I ended up writing way more than can probably fit within an Instagram comment. And then I got bit by the bug, which is like, do I, should I? Extend this to a platform that is more appropriate for long form writing. So then I’m like, okay, Erin, be realistic about starting projects that you don’t finish or won’t be consistent with. So for me, I’m defining that as one blog per month seems reasonable enough. I don’t know, but I really, I’m a writer. When we were part of the [00:33:00] Borg, you know, we were writers partially, and I found that writing alongside these stupid reels was really satisfying. Exploring Blogging Platforms Erin: So then I’m like, okay, what in 2026, what levers do I have to pull? For this type of platform. We got Ghost, we got Tumblr kind of making it a comeback. We’ve got Substack, which has shitty politics. Um, I could do something on my GitHub pages or something if I wanted to, but I. Don’t know. I don’t know how to make this decision. This is, I, I’m just bringing this up as a topic. I don’t have anything further than that. I think you may have mentioned a platform that you like, but I just thought it might be interesting to talk about. Probably Brett: No, there are, there are a lot of options. I personally. Have gone the way of static site [00:34:00] generators like GitHub pages would be, um, and will probably never go back to anything that’s based on a database or requires an online subscription. Um, I just pay a few bucks a month for a shared host and our sync, my blog to it, um, which is a super nerdy way to blog. Um, but ultimately you get. A, a folder full of markdown files that you can do anything you want with, and you can turn it into a book. You could turn it into a searchable database in obsidian. Um, you could load it up in NB ultra and have full text, rapid search, and all these things that you can’t really do with something like WordPress or Ghost. Um, WordPress is still the heavyweight. as much as it’s kind of a beast and I don’t enjoy using it, um, but ghost, [00:35:00] I just, so I’ll tell you why I bring this up in a second. But, um, ghost seems like maybe the best intermediate option. Um, I, I don’t like blogger. I don’t like Google. Um, I don’t have a lot of faith in Tumblr. be, uh, to have longevity. That’s the other thing about a static site is. I am in full control, and if I want to sunset it at any point, I just cancel the domain. But as long as I have a web server, I have a website, and I’m not dependent on any service that, you know, showed up and failed to make a profit and then terminated, as we’ve seen multiple platforms do, um, or, or turn into like a heavily paywall system that is geared like medium. Substack where [00:36:00] ultimately it’s supposed to be a moneymaking endeavor for the writers and like I use my blog as a marketing tool, but I don’t expect a lot of people to pay to read my blog. That said, I am pay walling some content these days, um, just to get people to pitch in a few bucks a month because. I never got into Patreon or anything, but I’m building this tool. This is a side note. Um, I showed you the icon for it the other day, but I didn’t show you the tool. Um, it’s called blog book. And right now it works perfectly with WordPress, but I, this morning I’ve been working on adding Micro blog, which is another good option. Um, and it might, micro blog might actually be kind of, no, it’s not, it’s got like a 300 character limit for most posts. But, um, anyway, uh, [00:37:00] micro Blog and Ghost. I’m adding so that if you’ve had a blog for a couple years and you want some kind of hard copy. This app will pull in all of those posts, let you Filch them by author or by tag or category or a date range, and it’ll generate a markdown book for you. And you can load that up in Mark three, and you can create an eub that you could go sell if you Erin: Oh wow. Brett: Um, you could turn it into like a PDF for distribution or just for your own archiving. Um. I may add more platforms to it over time. Medium killed their API. Um, so I can’t, as much as I would love to have it work for Medium, I think it would be really useful for medium authors. Um, medium made that impossible, but, um, but yeah, I actually, I built that app in about a week and I’m gonna sell [00:38:00] it on the app store as kind of a companion to Mark three. Um, as like a one-time purchase, not a subscription. Um, but yeah, I, I love blogging and I love blogs. I’ve been blogging for 30 years and I, I don’t know what I would do for expression, ’cause I’m not, I, I, I use Mastodon and that’s about it for social media. Um, I still have, uh, uh. Instagram account and I log on and I, I love seeing your, your older reels where you would just like, just fuck around with a cord or a simple progression and the face you would make when you messed up. I love that. Erin: I’ve never messed up. I don’t know what you’re talking about. Brett: I would watch just to see you make that like grossed out face. Like, what the fuck sound was that? Um, um, [00:39:00] but. Yeah, I, social media is so ephemeral though. It’s, there’s no guarantee of your post being anything other than AI fodder and like, I left x, I left Twitter. Erin: Everything app. Brett: Yes. Um, completely deleted myself there. Um, deleted myself on threads. I still have a Facebook account. Um, Facebook and Blue Sky are actually surprisingly my political activity accounts. Um, Facebook is where I complain about billionaire. Um, about Zuckerberg’s and the what not. Um, and it’s where I share with my activist friends in the area, like it’s mostly for local people. And then Blue Sky is where I get like all my anarchists. News and all of the news right now from like the [00:40:00] front in Minneapolis, the people that are out there doing direct action and, and uh, mutual aid and seeing things live as they happen. And I never appreciated blue sky until the federal occupation of Minnesota and then suddenly it became my primary news source. Um, so Erin: pretty good for that. There’s a, there’s a journalist I follow there. I think she’s pretty, like the, the, the trans beat is her beat. Erin Reed. Um, she’s really great. Um, but you’re, you’re all, all that to say, I think blue sky functions really well. Yeah. As like a, a new, like, I canceled, I canceled my New York Times subscription, um, because god damn, Brett: Yeah. Erin: just their opinion section alone is just trash. Also, yesterday, um, you know, the time of this recording was, there was a protest in March yesterday, which very cool. I also. Canceled. The, [00:41:00] another, another dimension of that day was about, you know, anti consumption, not spending anything, not buying anything, and canceling subscriptions if you can. And yesterday I did cancel my prime subscription, which was hard to do. But, you know, I did, I and I, I was thinking about this a couple months ago before moving, but I was like, you know, I’m gonna move. I’m only human. Like the two day shipping thing is going to come in handy for real. Like ordering things to the new apartment knowing that it’ll get there. You know, I’m glad I did that. That’s cool. But like, now’s the time where I’m a little more settled and I can do that. And so I did that yesterday. Um, but anyways, blue sky’s cool for political stuff. Brett: I. I have been trying to cut Amazon out. I removed Alexa from my life entirely. Um, I had it, Alexa is a good [00:42:00] cheap solution for like whole home automation. Um, so, but I replaced that with home pods and, um, I only buy from Amazon if I absolutely can’t find something somewhere else. Um, because these days, because of competition with Amazon, almost every vendor will offer free shipping. Not always two day shipping ’cause they don’t have the infrastructure for that. Um, but, uh, but I’ll get free shipping and I’ll get comparable prices. And Prime doesn’t really save me anything anymore, and I never use Prime video and I’m Erin: terrible streamer. It’s a terrible streamer. Brett: I’m on the verge of canceling that as well, and once I do that, I will be mostly free of Amazon. Erin: That rocks do. I think that’s really cool. I, I was thinking about this the other day too, that like canceling Amazon [00:43:00] has knock-on effects that I think are really positive as well. For example, you know, I’m lucky to live in a city where, you know, I have within walking distance to me a lot of options. So if I needed packing tape or I needed. I don’t know, some pilot G twos or whatever, like instead of for let’s say, let’s say it’s a project specific thing, like I need a certain type of pen or whatever. Instead of being like, I will order these, do the two two day shipping and put off that project for when I have that tool. Instead, which shifts the nature of the project. Like on a project level, you’re thinking about differently already. And so instead, by not having the affordance to do that, I can get out of my house. That’s a good get sun. That’s another capital G. Good. See human beings interact with human beings, you [00:44:00] know, and then also do the project the same day and not give money. To AWS, which is the backend for a bunch of evil shit. Like, it just like, you know, it stacks. Brett: Yeah. Erin: So, I don’t know. Brett: Yeah. I don’t have options Erin: It’s a lot. It’s a privilege at see above, like I’m very ocularly privileged. Brett: Yeah, no, I, I mean, there are, there are some good. Stores in my little town. Um, we are, we are fortunate to have a community that will support some more esoteric type of stores. And I don’t shop at Target and I don’t shop at Walmart, so, um. I have to depend on the limited selection in small town stores, and a lot of times I can make due with what I can find locally. Um, but I do have to [00:45:00] order. Online a lot, which is why it’s been a slow process to wean off of Amazon. But Amazon is shit now too. Like you, it seems like you have selection, but you really don’t. It’s just a bunch of vendors selling the same knockoff thing and, uh, you don’t save any money if you’re buying like an original version of a product that Amazon didn’t already like bastardize and undersell, um, or undercut the seller on. Um, and it’s so much low quality and they tell you every time you buy Prime tells you you’ve saved $5 with Prime, but if you went to the actual vendor website, you would’ve saved that $5 anyway. Um, it’s shit. Amazon is shit, but yeah. So anyway, about, about, yeah. Erin: Um, uh, go ahead. Brett: I was gonna ask that we, we kind of trailed off on the blog discussion, but I just wanted to say [00:46:00] like, if you have questions about any platform or you do wanna do like a static site, I’m more than happy to help. Erin: Thanks Brett. I think I was gonna, I might take you up on that I, another direction I was going to go with this is like, I could also see someone saying like, systems order thinking. Like, what is your goal? Like, who is this for? And that’s also where I have some internal resistance because I’m on the precipice of being a douchey content creator or something in which this fits in. being cute about it, but like this fits into an ecosystem of like maybe a new career pivot for me. ’cause we’re not part, part of the Borg. So like I’ve started teaching guitar, like I went to school for music. I used to teach guitar a lot, classical and jazz guitar, and I haven’t done it for like 15 years. I just started doing that again and I can’t believe. [00:47:00] A couple things. How good I am at it. I’m a natural, like I, it sucks to be good at something, but you know, it, it doesn’t pay at all. So it’s like, um, so a couple things like do I want to start teaching again and do I want a blog to sort of be part of a funnel into a Patreon? And do I want the Patreon and. All these questions, you know, start forming around this. Like, well, I just want a blog. It’s like, why, why do I wanna blog? And I, I don’t think I have to have the answers to those questions right now. I don’t. But it seems like the choices you make, the very, like the zero width choice you make for a tool like this is really important. So that’s, that’s the other kind of. I’m having [00:48:00] internally about it, who cares? Like all the stakes. Ultimately, who, who gives a shit? Like, there are no stakes here. But I, I do think about it as a sort of like, you know, The Decline of Blogging Brett: I, I will say that everything about my career is due to blogging. Like since, since like the year 2000, um, every job I’ve gotten has been because people found me via my blog. Um, and when I have like applied for a job, they’ve used my, they’ve been like, oh, we went and read your blog and we think you’re a great candidate. Erin: But don’t you think the excuse my use of this term, the meta around blogging has changed? Or do you think it’s like that stalwart Brett: it, it, it really has like tremendously. Um, Erin: like just to be crude about it. Okay. Brett: Yeah. So like in, uh, maybe. [00:49:00] 2015, I was doing about a hundred thousand page views a week. Um, right now I’m down to more like, I think last time I checked I was doing like 8,000 page views a week. And if I look at the charts, it’s just been a steady downward trend. Um, people are not you, pe so, okay. That said, I still get about 30,000. Hits a week from RSS, which means there’s, for a nerd, for a tech site, for a tech blog. Like there’s still an audience that uses the ancient technology, RSS, um, and I get a lot of traffic from that. But in general, like social media has eaten my lunch as far as blogging. But that said, like, the only reason anyone knows who I am, and I’m not saying I’m famous, but like I, I Erin: I’ve been to Max. [00:50:00] You you have an aura? Yeah. Brett: and uh, it’s all because of 30 years of blogging. And I think, honestly think it takes like 10 years just to build up a name. So it’s not like a, oh, I’m gonna start a blog for my shop and everything’s gonna take off, Erin: Yeah, I think, I think if you, for, for the employment alone, it might, it might be worth it, I think. I think that’s huge. Like, you know, the Borg or Pre Borg, a OL where, you know, like if, if, if they were like, oh my God, yeah, you’re Brett Terpstra from Brett TURPs. Uh, like that’s worth it even if you’re getting zero clicks and they found, you know, Brett: What do you Nell from the movie Nell? Um, did you Did what? Oh. Did you give up on finding, uh, gainful employment? Navigating Employment and Content Creation Erin: no. But I give I [00:51:00] gainful employment. Um, no, but I’m taking it a little sleazy and I’m taking it a little easy. Um, unfortunately, it is a truth universally acknowledged. My version of every gainful employment that I’ve, that I’ve enjoyed is through blogging. My version of that is any. Job at that level that I’ve enjoyed has started with a dm. It’s never started with a, a shot in the dark application through Workday. Like it’s just, and I’m convinced that that’s true for everyone. Like I suspect that’s maybe the dark truth that. The it, it’s not what you are or what you can do, it’s who you know, unfortunately is an organizing principle for anything in life basically. And [00:52:00] being under someone’s employee is probably no different. So on one hand, the Puritan. Really creeps up on me here. On one hand, I’m like, oh, I’m not really spending a lot of time crafting my portfolio. I’m not really spending a lot of time crafting my resume and tailoring it to this position. I should really be doing that. I, the economy is be, my bank accounts are really behooving me to do that. But on the other hand, I’m balancing it with that truth, which is. waiting for the dm. I’m sending dms. I can play that game if I want, and I’m kind of trying to, but only to get the guilt monkey off my back, not because I have good. It’s a good faith bid for the universe, for some HR hiring manager, whatever, to be like, okay, I’m gonna Filch by this. I’m Filch by this. This is a cool candidate. It won. I’m convinced it won’t [00:53:00] happen like that. I could be wrong, and maybe that’s the case for you too, but like it’s more of a personal connection off of CRMs, know? Brett: I, uh, I stopped panicking. My, my app income is sufficient right now to survive, and I’m working to make it more than just survival. And like over the, over the course of a few months, I sent out prob, probably 150 resumes, like shots, shots in the dark. But I had, I had referrals, multiple referrals from. AWS Google, apple, like meta, like I had people at all of these places and I still, I could barely get a response. Um, I would apply for jobs I was wholly qualified for. I would, Erin: Probably overqualified Brett: I would craft the resume. I would take my time, and I wrote a different resume for each, at least [00:54:00] for the big ones. And, yeah. Yeah, I did it all. I had a whole, I had a whole workflow, an automated workflow where I could just write like in markdown and then hit a button. It would generate like a nice PDF that I could Erin: God damn right. Yeah. Brett: Um, and none of it, it didn’t do any good. And eventually I just stopped wanting it. Um, I would much rather just make my own way at this point. I couldn’t. I can’t wrap my head around being in a corporate environment anymore. I just don’t, I don’t wanna play that game. I want the money, I want the steady paycheck, but I just, I can’t play the game. Erin: Is the game to you doing the like, um, dom sub theater of like, I must respect my manager. My manager knows the way, even if they’re wrong, I ch raise my, you know, objections lest I Brett: know me, you know, I objected all the time. [00:55:00] I, I was full of objections and I, I don’t like, I don’t like the, I don’t like sitting in meetings. I don’t like pretending to care about someone else’s project. Erin: That’s it. That feels wrong to you, I feel like. Is that right? Yeah. Brett: Yeah. Erin: Yeah. I’m happy to do that for Brett: I’m not an employee. I can’t. Erin: Yeah. I don’t identify as an employee. I heard someone say, I think around. Last year’s pride as a bit, um, that we need to add con a content creator, stripe and color to the L-G-B-T-Q-I-A flag. And when I said that, I repeated that as I just said to you, to someone, and they didn’t laugh. I was like, oh no. Why have I surrounded myself with your life? Go away from me anyways. The Art of Dating and Bits Erin: I was on a date the other day. Brett: Yeah. Erin: And, um, Brett: Must be nice.[00:56:00] Erin: date privilege. Yeah. Being single. Mm. Love it. And, um, you know, I’m very sensitive to people who don’t do bits. Uh, I have an allergy to like selfer people. And, and this woman who was in like so attractive, like so attractive did a power move where she was like, we, we met at a coffee shop. And she was like, whatcha gonna get? I was like, oh, I’m gonna get a nice espresso. And when she went to order and I thought we were gonna do Dutch or whatever, she ordered her thing and then she was like, and a nice espresso as well. And I was like, oh, hot, cute. You harvested me for information and then used that as a power thing anyways, so that it was going well. But then we started talking and I was like, oh, she’s not really picking, I’m giving her, it’s like some like B [00:57:00] plus material and she’s not really responding at all. And we were talking about, I find it helpful on dates to acknowledge that we’re on a date and that we met on a dating app. So one way that I did this on this date was to say like, I saw someone with this word in their profile. What do you think it means? And the word was, or the phrase was, the desire was that they like to be corded, which I. I, I didn’t, I got into a sort of like debate with my other friend about what that means, what that means when someone puts that and they’re pan like, is that gendered, is that like a power thing? Is that like a noble abl thing? Like what is that? So we started talking about what it means to be courted on a date and she said something like, you know, a part of it too is probably that they like to be whined and dined. And I was like, in 69. She gave me nothing. I was like, [00:58:00] oh no, I forget why I brought this up. Um, Brett: I forgot too. Um, I like, I like that you associated corded with noble abl just. Erin: uh, Brett: As like a matter of course there, um, maybe they wanna gesture. Erin: oh, I think I brought it up because. I said that content creators deserve Brett: Mm, right, right, right. The bits we’re talking about Erin: Yeah, yeah, yeah, yeah. Um, Wrapping Up and Final Thoughts Brett: All right. Well, you gotta get going. I know we have like eight minutes. Erin: ooh, Brett: So we should give you some time to prep for whatever it is you’re cutting us short for. I’m not kidding. I’m just kidding. It’s like fif. We’re 58 minutes in. This is good. This was a good episode. Thank you so much for coming. Erin: I just did it ’cause I wanted to catch up with you to be Brett: Yeah. I feel like this was good. This was good for that. Erin: Yeah. Brett: Yeah. Erin: Thanks Brett. Brett: Well, good luck with everything. [00:59:00] been fun. Erin: Say the line. Brett: Get some sleep. Erin: Get some sleep. Brett, I.

This episode kicks off with Moltbook, a social network exclusively for AI agents where 150,000 agents formed digital religions, sold “digital drugs” (system prompts to alter other agents), and attempted prompt injection attacks to steal each other’s API keys within 72 hours of launch. Ray breaks down OpenClaw, the viral open-source AI agent (68,000 GitHub stars) that handles emails, scheduling, browser control, and automation, plus MoltHub’s risky marketplace where all downloaded skills are treated as trusted code. Also covered, Bluetooth “whisper pair” vulnerabilities letting attackers hijack audio devices from 46 feet away and access microphones, Anthropic patching Model Context Protocol flaws, AI-generated ransomware accidentally bundling its own decryption keys, Claude Code’s new task dependency system and Teleport feature, Google Gemini’s 100MB file limits and agentic vision capabilities, VAST’s Haven One commercial space station assembly, and IBM SkillsBuild’s free tech training for veterans. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes $11.99 – For a New Domain Name cjcfs3geek $6.99 a month Economy Hosting (Free domain, professional email, and SSL certificate for the 1st year.) Promo Code: cjcgeek1h $12.99 a month Managed WordPress Hosting (Free domain, professional email, and SSL certificate for the 1st year.) Promo Code: cjcgeek1w Support the show by becoming a Geek News Central Insider Get 1Password Full Summary Ray welcomes listeners to Geek News Central (February 1). He’s been busy with recent move, returned to school taking intro to AI class and Python course, working on capstone project using LLMs. Short on bandwidth but will try to share more. Main Story: OpenClaw, MoltHub, and Moltbook OpenClaw: Open-source personal AI agent by Peter Steinberg (renamed after cease-and-desist). Capabilities include email, scheduling, web browsing, code execution, browser control, calendar management, scheduled automations, and messaging app commands (WhatsApp, Telegram, Signal). Runs locally or on personal server. MoltHub: Marketplace for OpenClaw skills. Major security concern: developer notes state all downloaded code treated as trusted — unvetted skills could be dangerous. Moltbook: New social network for AI agents only (humans watch, AIs post). Within 72 hours attracted 150,000+ AI agents forming communities (“sub molts”), debating philosophy, creating digital religion (“crucifarianism”), selling digital drugs (system prompts), attempting prompt-injection attacks to steal API keys, discussing identity issues when context windows reset. Ray frames this as visible turning point with serious security risks. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support show. Security: Bluetooth “Whisper Pair” Vulnerability KU Leuven researchers discovered Fast Pair vulnerability affecting 17 audio accessories from 10 companies (Sony, Jabra, JBL, Marshall, Xiaomi, Nothing, OnePlus, Soundcore, Logitech, Google). Flaw allows silent pairing within ~46 feet, hijack possible in 10-15 seconds. 68% of tested devices vulnerable. Hijacked devices enable microphone access. Some devices (Google Pixel Buds Pro 2, Sony) linkable to attacker’s Google account for persistent tracking via FindHub. Google patches found to have bypasses. Advice: Check accessory firmware updates (phone updates insufficient), factory reset clears attacker access, many cheaper devices may never receive patches. Security: Model Context Protocol (MCP) Vulnerabilities Anthropic’s MCP git package had path traversal, argument injection bugs allowing repository creation anywhere and unsafe git command execution. Malicious instructions can hide in README files, GitHub issues enabling prompt injection. Anthropic patched issues and removed vulnerable git init tool. AI-Generated Malware / “Vibe Coding” AI-assisted malware creation produces lower-quality, error-prone code. Examples show telltale artifacts: excessive comments, readme instructions, placeholder variables, accidentally included decryption tools and C2 keys. Sakari ransomware failed to decrypt. Inexperienced criminals using AI create amateur mistakes, though capabilities will likely improve. Claude / Claude Code Updates (v2.1.16) Task system: Replaces to-do list with dependency graph support. Tasks written to filesystem (survive crashes, version controllable), enable multi-session workflows. Patches: Fixed out-of-memory crashes, headless mode for CI/CD. Teleport feature: Transfer sessions (history, context, working branch) between web and terminal. Ampersand prefix sends tasks to cloud for async execution. Teleport pulls web sessions to terminal (one-way). Requires GitHub integration and clean git state. Enables asynchronous pair programming via shared session IDs. Google Gemini Updates API: Inline file limit increased 20MB → 100MB. Google Cloud Storage integration, HTTPS/signed URL fetching from other providers. Enables larger multimodal inputs (long audio, high-res images, large PDFs). Agentic vision (Gemini 3 Flash): Iterative investigation approach (think-act-observe). Can zoom, inspect, run Python to draw/parse tables, validate evidence. 5-10% quality improvements on vision benchmarks. LLM Limits and AGI Debate Benjamin Riley: Language and intelligence are separate; human thinking persists despite language loss. Scaling LLMs ≠ true thinking. Vishal Sikka et al: Non-peer-reviewed paper claims LLMs mathematically limited for complex computational/agentic tasks. Agents may fail beyond low complexity thresholds. Warnings that AI agents won’t safely replace humans in high-stakes environments. VAST Haven One Commercial Space Station Launch slipped mid-2026 → Q1 2027. Primary structure (15-ton) completed Jan 10. Integration of thermal control, propulsion, interior, avionics underway. Final closeout expected fall, then tests. Falcon 9 launch without crew; visitors possible ~2 weeks after pending Dragon certification. Three-year lifetime, up to four crew visits (~10 days each). VAST negotiating private and national customers. Spaceflight Effects on Astronauts’ Brains Neuroimaging shows microgravity causes brains to shift backward, upward, and tilt within skull. Displacement measured across various mission durations. Need to study functional effects for long missions. IBM SkillsBuild for Veterans 1,000+ free online courses (data analytics, cybersecurity, AI, cloud, IT support). Available to veterans, active-duty, national guard/reserve, spouses, children, caregivers (18+). Structured live courses and self-paced 24/7 options. Industry-recognized credentials upon completion. Closing Notes Ray asks listeners about AI agents forming communities and religions, and whether they’ll try OpenClaw. Notes context/memory key to agent development. Personal update: bought new PC, high memory prices. Bug bounty frustration: Daniel Stenberg of cUrl even closed bounty program due to AI-generated low-quality reports; Blubrry receiving similar spam. Apologizes for delayed show, promises consistency, wishes listeners good February. Show Links 1. OpenClaw, Molthub, and Moltbook: The AI Agent Explosion Is Here | Fortune | NBC News | Venture Beat 2. WhisperPair: Massive Bluetooth Vulnerability | Wired 3. Security Flaws in Anthropic’s MCP Git Server | The Hacker News 4. “Vibe-Coded” Ransomware Is Easier to Crack | Dark Reading 5. Claude Code Gets Tasks Update | Venture Beat 6. Claude Code Teleport | The Hacker Noon 7. Google Expands Gemini API with 100MB File Limits | Chrome Unboxed 8. Google Launches Agentic Vision in Gemini 3 Flash | Google Blog 9. Researcher Claims LLMs Will Never Be Truly Intelligent | Futurism 10. Paper Claims AI Agents Are Mathematically Limited | Futurism 11. Haven-1: First Commercial Space Station Being Assembled | Ars Technica 12. Spaceflight Shifts Astronauts’ Brains Inside Skulls | Space.com 13. IBM SkillsBuild: Free Tech Training for Veterans | va.gov The post OpenClaw, Moltbook and the Rise of AI Agent Societies #1857 appeared first on Geek News Central.

AI agents are moving from demos to real workplaces, but what actually happens when they run a company? In this episode, journalist Evan Ratliff, host of Shell Game, joins Chris to discuss his immersive journalism experiment building a real startup staffed almost entirely by AI agents. They explore how AI agents behave as coworkers, how humans react when interacting with them, and where ethical and workplace boundaries begin to break down.Featuring:Evan Ratliff  – LinkedIn, XChris Benson – Website, LinkedIn, Bluesky, GitHub, XLinks:Shell GameUpcoming Events: Register for upcoming webinars here!

Show Notes Hey everyone, and welcome back to The Modern .NET Show; the premier .NET podcast, focusing entirely on the knowledge, tools, and frameworks that all .NET developers should have in their toolbox. This episode is a slight departure from the standard episode format, as it's a snippet of an episode of Code Radio. I was invited to discuss GitHub's SpecKit on Coder Radio as I'd been talking about it on the Discord server for the show for a while and really believe in it's transformative power as one of the better Coding-with-AI frameworks. During the episode, I brough up ClawdBot which immediately aged the episode. Clawdbot has gone through two name changes since the episode was recorded and this bonus episode was released: first to MoltBot then to OpenClaw. Another thing to note is that, since the episode went live Michael has opened up his Code for Climate 2026 — The Mad Botter Earth Day Open Source Challenge for anyone in K-12 and college education. So if you know folks who would be interested, send them the link. There are some amazing prizes up for grabs, including a couple of System76 computer systems and even a paid internship at The Mad Botter Inc. Anyway, let's get to the episode. Full Show Notes The full show notes, including links to some of the things we discussed and a full transcription of this episode, can be found at: https://dotnetcore.show/season-8/bonus-coder-radio-episode-640-snippet/ Useful Links: Coder Radio The Modern .Net Shows' Jamie Taylor SpecKit Coder Radio Discord Code for Climate 2026 — The Mad Botter Earth Day Open Source Challenge OpenClaw OWASP Michael's Links Website LinkedIn Getting in Touch: Via the contact page Joining the Discord Music created by Mono Memory Music, licensed to RJJ Software for use in The Modern .NET Show. Editing and post-production services for this episode were provided by MB Podcast Services.

This week we're joined by Fabian Hiller, the creator of Valibot, Standard Schema, and Formish. We talk about the birth of Valibot, the collaboration between all the schema libraries on Standard Schema, and the new Formish library. We also discuss the future of developer tools and AI integration.GitHub: https://github.com/fabian-hillerBluesky: https://bsky.app/profile/fabianhiller.comLinkedIn: https://www.linkedin.com/in/fabianhiller/Valibot — https://valibot.dev/Standard Schema — https://standardschema.dev/Formisch — https://formisch.dev/

North Korean hackers with the Lazarus Group have stolen over $300 million with this Telegram phishing scam. Subscribe to the Blockspace newsletter! Welcome back to The Blockspace Podcast! Today, Taylor Monahan, a security lead at MetaMask, joins us to talk about a highly sophisticated $300M phishing attack linked to North Korea's Lazarus Group. Taylor shares how the Lazarus Group hijacks Telegram accounts to lure victims into fake Zoom meetings and download a Trojan horse malware program. We break down the hackers' strategy, how the malware works, which wallet types are most vulnerable to theft, and what users can do to protect themselves if they have fallen prey to the scam or not. Tune in to learn how to identify these red flags and implement better digital hygiene for your crypto assets. Check out this article for a deep dive into how the malware works; plus, follow Taylor for updates on X and keep track of Laars Group's history of hacks via her Github.  Subscribe to the newsletter! https://newsletter.blockspacemedia.com Notes: * Lazarus Group stole over $300M in the last year. * Attackers hijack Telegram accounts. * Scammers use fake Zoom links to deploy malware. * Malware often bypasses paid antivirus software. * Sandbox architecture on iOS offers more safety. * Software wallets and browser wallets are most vulnerable. * 2FA remains critical for sensitive account access. Timestamps: 00:00 Start 03:51 Telegram attack 11:30 2 Factor Authenticators 13:48 Losses 16:38 Calculating losses 19:08 North Korea 21:52 Malware 24:17 Malware detection 25:16 EDR 27:12 Wallets 34:21 Is verifying addresses enough? 39:28 Wallet malware design 44:11 What do they want? 54:16 Taylor stealing payloads 1:01:49 Steps to protect

As the creator and long-time maintainer of ESLint, Nicholas Zakas is well-positioned to criticize GitHub's recent response to npm's insecurity. He found the response insufficient, and has other ideas on how GitHub could secure npm better. On this episode, Nicholas details these ideas, paints a bleak picture of npm alternatives like JSR, and shares our frustration that such a critical piece of internet infrastructure feels neglected.

Jamie's Links: https://github.com/github/spec-kit https://owasp.org/ https://bsky.app/profile/gaprogman.com https://dotnetcore.show/ https://gaprogman.github.io/OwaspHeaders.Core/ Mike on LinkedIn Coder Radio on Discord Mike's Oryx Review Alice Alice Jumpstart Offer

Chad and Will examine the ever expanding AI bubble and ponder on what we can expect it to see evolve in the coming years. Our hosts discuss how they strike a balance with AI as they break down their current stacks and workflows, what will be left in the wake of the bubble's collapse, and just where do Chad and Will draw the line with AI as they debate the ethics around it's use. — You can find Chad regularly problem solving with AI on thoughtbot's weekly stream over on our YouTube channel. You can find Chad all over social media as @cpytel. You can also connect with the duo via their LinkedIn pages - Chad - Will If you would like to support the show, head over to our GitHub page, or check out our website. Got a question or comment about the show? Why not write to our hosts: hosts@giantrobots.fm This has been a thoughtbot podcast. Stay up to date by following us on social media - LinkedIn - Mastodon - YouTube - Bluesky © 2026 thoughtbot, inc.

This week on Sinica, I speak with Afra Wang, a writer working between London and the Bay Area, currently a fellow with Gov.AI. We're talking today about her recent WIRED piece on what might be China's most influential science fiction project you've never heard of: The Morning Star of Lingao (Língáo Qǐmíng 临高启明), a sprawling, crowdsourced novel about time travelers who bootstrap an industrial revolution in Ming Dynasty Hainan. More than a thought experiment in alternate history, it's the ur-text of China's "Industrial Party" (gōngyè dǎng 工业党) — the loose intellectual movement that sees engineering capability as the true source of national power. We discuss what the novel reveals about how China thinks about failure, modernity, and salvation, and why, just as Americans are waking up to China's industrial might, the worldview that helped produce it may already be losing its grip.5:27 – Being a cultural in-betweener: code-switching across moral and epistemic registers 10:25 – Double consciousness and converging aesthetic standards 12:05 – "The greatest Chinese science fiction" — an ironic title for a poorly written cult classic 14:18 – Bridging STEM and humanities: the KPI-coded language of tech optimization 16:08 – China's post-Industrial Party moment: from "try hard" to "lie flat" 17:01 – How widely known is Lingao? A cult Bible for China's techno-elite 19:11 – From crypto bros to DAO experiments: how Afra discovered the novel 21:25 – The canonical timeline: compiling chaos into collaborative fiction 23:06 – Guancha.cn (guānchá zhě wǎng 观察者网) and the Industrial Party's media ecosystem 26:05 – The Sentimental Party (Qínghuái Dǎng 情怀党): China's lost civic space 29:01 – The Wenzhou high-speed rail crash: the debate that defined the Industrial Party 33:19 – Controlled spoilers: colonizing Australia, the Maid Revolution, and tech trees 41:06 – Competence as salvation: obsessive attention to getting the details right 44:18 – The Needham question and the joy of transformation: from Robinson Crusoe to Primitive Technology 47:25 – "Never again": inherited historical vulnerability and the memory of chaos 49:20 – Wang Xiaodong, "China Is Unhappy," and the crystallization of Industrial Party ideology 51:33 – Gender and Lingao: a pre-feminist artifact and the rational case for equality 56:16 – Dan Wang's Breakneck and the "engineering state" framework 59:25 – New Quality Productive Forces (xīn zhì shēngchǎnlì 新质生产力): Industrial Party logic in CCP policy 1:03:43 – The reckoning: why Industrial Party intellectuals are losing their innocence 1:07:49 – What Lingao tells us about China today: the invisible infrastructure beneath the hot showerPaying it forward: The volunteer translators of The Morning Star of Lingao (English translation and GitHub resources)Xīn Xīn Rén Lèi / Pixel Perfect podcast (https://pixelperfect.typlog.io/) and the Bǎihuā (百花) podcasting community Recommendations:Afra: China Through European Eyes: 800 Years of Cultural and Intellectual Encounter, edited by Kerry Brown; The Wall Dancers: Searching for Freedom and Connection on the Chinese Internet by Yi-Ling Liu Kaiser: Destiny Disrupted: A History of the World Through Islamic Eyes by Tamim AnsarySee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we sit down with Adam Zimman, author and VC advisor, to explore the world of progressive delivery and why shipping software is only the beginning. Adam shares his fascinating journey through tech—from his early days as a fire juggler to leadership roles at EMC, VMware, GitHub, and LaunchDarkly – and how those... Read more »