Podcasts about open sourcing

In this episode, Fintan Costello, Jon Bruford, and returning guest Mark Flores Martin of XGENIA explore the transformative potential of open-sourcing AI platforms in the iGaming industry. They discuss innovative developments, the future of AI-driven game creation, and how transparency and collaboration can reshape gambling technology.This episode is brought to you by our sponsors: Optimove, GLI, and World Gaming, all of which are utter, utter legends. Scroll down for more information on their market-defining wares.Things we talk about, but in a list: The shift toward open-source AI platforms in the gambling industryHow community collaboration accelerates innovation in iGaming softwareMark Flores Martin's vision for Xgenia's open-source, community-supported AI ecosystemThe advantages of open source over proprietary models for security and adaptabilityThe role of AI in hyper-customization and game developmentFuture trends: AI-generated games, hyper-customization, and industry evolution over five yearsThe impact of AI on responsible gaming and player safetyHow open-source AI can lower barriers for startups and small developersMarketplace for plugins, integrations, and community contributionsThe importance of transparency, safety, and open standards in gaming AI developmentChoice quotes: Fintan: "True happiness is jumping out of bed in the morning, excited to go to work, and at the end of the day, you're excited to go home to your family."Mark Flores Martin: "We've pushed the fully agentic platform out, which means you ask for something, and AI just does it for you in the background."Jon: "One of my worries about AI development in games is that someone is going to make a super addictive slot that optimizes for danger."Chapters and all that, but add 30 seconds or so because of all the razzamatazz: 00:00 - Welcome and episode overview 01:29 - Pleased vs dissatisfied: the internal debate 02:01 - Aging, philosophy, and podcast journey 03:49 - Mark Flores Martin's recent developments and platform open-sourcing 04:42 - The significance of open-source AI in gambling 05:40 - GLI sponsorship and industry standards 06:27 - Mark's work on the fully agentic AI platform 07:25 - The role of open-source in fostering ecosystems 08:06 - Building community-driven marketplaces for AI plugins and tools 09:04 - Future of hyper-customized AI-generated games 10:48 - Support for community contributions and open development 11:15 - Headless AI creation and automation in game design 12:42 - The importance of transparency and trust in open platforms 14:15 - AI as a moat for the industry and developing features efficiently 15:12 - Business models based on open source and community engagement 16:23 - The evolution of open-source development with AI assistance 17:28 - Balancing security, innovation, and industry standards 19:21 - Building complex applications like raffles with AI tools 20:03 - Accessibility of AI platforms for startups and small teams 21:02 - AI-driven code understanding and secure development 22:41 - Marketplace for features, plugins, and integrations 24:52 - Industry evolution, AI-driven new game types, and market share 26:26 - The future of AI in responsible gaming and player protection 31:26 - Practical steps to launch gambling platforms using AI 32:20 - Building apps and integrations with AI support 33:24 - Payment integration and third-party tools 34:28 - Robotics, hardware, and accessible AI tech 35:56 - Industry outlook in five years: AI-driven innovation 38:29 - Risks of highly addictive AI-enhanced games 41:30 - AI education and user protection through AI 45:00 - AI's role in personal data, user experience, and safety 48:14 - Accessing XGENIA codebase and community collaboration 48:55 - Future industry landscape and closing thoughtsResources & Links:Mark on LinkedIn - https://www.linkedin.com/in/markfm/As ever, we thank all of our sponsors for their vibrant and excellent support that makes all of this… magic… possible.Optimove, who turn customer data into something special, with tools that make businesses just plain work better. Optimove, your support helps us to keep creating content for an industry that probably thinks we disappeared years ago.Then of course there is Clarion Gaming, no hang on World Gaming, providers of the magnificent ICE expo and iGB Live! in London. There is simply nobody better at what they do.And the new-ish members of the family, the excellent Gaming Laboratories International. GLI is a world-class Testing, Inspections and Certification company committed to delivering the highest quality land-based, lottery, and iGaming testing and assessment services, working in more than 710 jurisdictions.For more information, visit gaminglabs.com.The Gambling Files podcast delves into the business side of the betting world. Each week, join Jon Bruford and Fintan Costello as they discuss current hot topics with world-leading gambling experts.Website: https://www.thegamblingfiles.com/Subscribe on Apple Podcasts: https://apple.co/3A57jkRSubscribe on Spotify: https://spoti.fi/4cs6ReF Subscribe on YouTube: https://www.youtube.com/@TheGamblingFilesPodcast Fintan Costello on LinkedIn: https://www.linkedin.com/in/fintancostello/ Jon Bruford on LinkedIn: https://www.linkedin.com/in/jon-bruford-84346636/ Follow the podcast on LinkedIn: https://www.linkedin.com/company/the-gambling-files-podcast/ Sponsorship enquiries: https://www.thegamblingfiles.com/contact/ Get our newsletter: https://thegamblingfilestldr.substack.com/

In this episode, Tyler and eco hold down the fort while Skot is away and dive deep into the frontier of Bitcoin-powered heating and open-source mining. They walk through a new Home Assistant + Venstar-based dashboard built for a customer that tracks miner-delivered BTUs vs. natural gas, stage changes, outdoor temps, sats earned, and economics—proving a single 5kW miner can carry a 3,000+ sq ft home through shoulder season. We unpack heat pumps versus combustion heat, why furnaces are oversized, the sovereignty trade-offs of remote monitoring, and the promise of “buddy systems” that pair hashrate heat with legacy boilers or even wood-fired hydronic setups. We also discuss policy shifts in Denver County, energy resilience at altitude and in extreme cold, and the real-world business models for small-town installers versus metro markets. Then we shift to the 256 Foundation's roadmap. They outline funding realities post-Telehash and the near-term plan to keep four core open-source projects moving: Ember One hash boards (next rev targeting Intel BZM2), LibreBoard control board (v3 on deck and designed to orchestrate multiple boards, relays, and sensors), HydraPool (one-click, self-hostable pool with gamified dashboard and future Lightning/eCash payouts, Start9/Umbral packaging, and plugin architecture), and Mujina firmware (a Linux-like, no-dev-fee, open standard that can be flashed onto legacy S19-class hardware and, ultimately, ship on flagship miners). We talk market dynamics, why open source beats closed aftermarket firmware in the long run, and how Ember One serves as a reference platform for builders even if efficiency lags cutting-edge ASICs today. We wrap with community updates, forum plans for better knowledge sharing, shoutouts to our HydraPool supporters, and details on our “Open Sourcing the Bitcoin Mining Ecosystem” panel in Las Vegas on Monday, April 27.

Example of OpenErrata nitting the Sequences I just published OpenErrata on GitHub, a browser extension that investigates the posts you read using your OpenAI API key and underlines any factual claims that are sourceably incorrect. Once finished, it caches the results for anybody else reading the same articles so that they get them on immediate visit. If you don't have an OpenAI key, you can still view the corrections on posts other people have viewed, but it doesn't start new investigations. I've noticed lately that while people do this sort of thing by pasting everything you read into ChatGPT, A. They don't have the time to do that, B. It duplicates work, and C. It takes around ~5 minutes to get a really good sourced response for most mid-length posts. I figure most of LessWrong is reading the same stuff, so if a good portion of the community begins using this or an extension like it, we can avoid these problems. Here is OpenErrata at work with some recent LessWrong & Substack articles, published within the last week. I consider myself a cynical person, but I'm a little surprised at what a high percentage of the articles I read make [...] --- First published: February 24th, 2026 Source: https://www.lesswrong.com/posts/iMw7qhtZGNFxMRD4H/open-sourcing-a-browser-extension-that-tells-you-when-people --- Narrated by TYPE III AUDIO. ---Images from the article:

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

In our latest episode, co-hosts Robby and Tim talk with Robert Brennan, Co-Founder & CEO of OpenHands - the open platform for cloud coding agents. Their open source project, also called OpenHands, has 67K starts on GitHub and provides a software agent SDK, CLI, and local GUI. They also have OpenHands cloud - their paid, hosted version of the OpenHands GUI. This episode traces the rise of OpenDevin - now OpenHands - as an open-source alternative to closed AI coding agents like Devin. Open to anyone from day one, it attracted highly technical developers, academics, and eventually large enterprises that valued flexibility, privacy, and lack of model lock-in. Launched amid the 2024 surge of excitement around autonomous coding agents, OpenHands quickly built a massive community and differentiated itself by rejecting the idea of replacing engineers, instead focusing on empowering them through transparent, human-in-the-loop tooling.The discussion also covers the fragmented AI dev-tool landscape and why open source may define future standards. While many tools compete in the individual “inner loop” of coding, OpenHands emphasizes the collaborative “outer loop,” safety, and running agents at scale. Its organic growth, community-driven roadmap, and focus on real developer pain points highlight a future where AI accelerates software creation without removing human accountability.

In this episode, the 256 Foundation crew and developer d++ go deep on HydraPool, our open‑source Bitcoin mining pool stack, and the new HashDash and upcoming TeleDash dashboards powering the Telehash fundraiser stream. We unpack how HydraPool fits into the broader plan to open‑source the entire mining stack (hashboard, control board, firmware, and pool), its Rust-based design inspired by CKPool and P2Pool v2, and flexible payout models (solo, PPLNS, and multi-address coinbase). We also talk user experience tweaks for Telehash, like smoothing hash rate visualization, displaying best shares, units for difficulty, leaderboard ideas, and integrating Nostr npubs for social profiles. D++ walks through the HashDash visualizer and plans for TeleDash: real-time overlays for stream viewers and a separate jumbotron view showing total hash rate, active workers, funds raised (on-chain and Lightning), block height, BTC price, donation messages, odds, leaderboards, and instructions to point hash rate. We discuss stress-testing the pool to 10,000 workers, Prometheus data, and potential features like miner-type fingerprinting via user agents. We also touch on industry rumors around Bitmain's S23 air-cooled units, shifting manufacturer focus to hydro/data-center gear, hand‑me‑down hardware implications, and why open source is crucial as proprietary vendors change course. Finally, we preview Telehash (join at pool.256foundation.org:33303 with a valid BTC address), celebrate contributions to Samourai dev families, and tease hardware progress on Ember One, Mujina firmware, water-cooled blocks, Heat Punk Summit plans, and more; all with an open-source-first ethos to dismantle the closed mining monopoly.

In the world of Free and Open Source Software, you don't owe any subscription fees, your work isn't fed into an AI, and your money can go where it matters - to your team and your games. But is anything ever TRULY free? Well, it turns out, sometimes yes. In this presentation, we'll go over some incredible products in the FOSS ecosystem that you can and should adopt into your tech stack, what the expectations are for being a part of an open source community, and what risks and benefits are involved with abandoning big tech subscriptions in favor of open source.

What if Google had the solution to save 1 billion people... and just threw it away? Let's find out! Google spent 4 YEARS secretly developing a device that pulls safe drinking water from thin air using only sunlight. They proved it worked. They proved it could help over 1 BILLION people without clean water. Then they just... quit and open-sourced everything in 2021.

Scott and Wes are joined by Erich Gamma, creator of VS Code, and Kai Maetzel, Copilot Lead, to share some big news about the future of VS Code and Copilot. They discuss what it means for developers, how AI is shaping the future of coding, and why staying open to the community is key. Show Notes 00:00 Welcome to Syntax! 01:00 The inception of VS Code. 02:49 VS Code adoption. 04:31 Brought to you by Sentry.io. 04:55 Syntax Denver Meetup! 05:19 The big announcement. 06:25 The current state of Copilot and VS Code. 08:31 The challenges with LLMs running outside of the codebase. 09:31 How to make a business case for AI. 10:47 The maturing of the AI landscape. 13:01 The limitations of extensions. 14:06 Open source vs closed source. 14:49 Copilot's context is public. 19:23 Is context language-specific? 21:23 How does this affect paid Copilot features? 23:27 Secrets of Copilot's server-side. 28:36 What will be open and what will not? 29:03 Is Copilot's UI influenced by VS Code forks? 31:31 Maintaining VS Code identity in forks. 33:07 What does open-sourcing GitHub Copilot mean for Cursor and Windsurf? 38:42 Were you surprised to see VS Code forks? 40:03 Are other extensions able to tap into the AI offerings? 43:20 There's work to be done. 44:13 The timeline. 45:39 Simulation Tests (S Tests). 48:07 How to test LLMs. 49:10 The future of software development with AI. 52:47 What's your favorite model? Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Pyrefly is a faster, open-source Python type checker written in Rust, succeeding Pyre. But what prompted the rewrite and what besides the language choice ended up making it faster? Host Pascal talks to Maggie, Rebecca and returning guest Neil about the unexpected complexities of building an incremental type checker that scales to mono repositories in episode 75. Got feedback? Send it to us on Threads (https://threads.net/@metatechpod), Instagram (https://instagram.com/metatechpod) and don't forget to follow our host Pascal (https://mastodon.social/@passy, https://threads.net/@passy_). Fancy working with us? Check out https://www.metacareers.com/. Links Pyrefly: https://pyrefly.org/ Pyre: https://pyre-check.org/  Ruff: https://github.com/astral-sh/ruff  PEP 484: https://peps.python.org/pep-0484/  Timestamps Intro    0:06 Rebecca Introduction    1:45 Maggie Introduction    2:45 Neil (Re-)Introduction    3:12 Team Mission    3:56 History of Typing in Python    4:29 The State of Typed Python at Meta    5:32 fbcode    6:02 Original Motivation for building Pyre    6:19 Justifying the Rewrite    7:48 Pyrefly vs the Rest    9:41 Why Rust?    10:45 Fearless Concurrency    12:02 Why is it faster?    12:37 Python community and Rust    14:57 Pyrefly wasm crate    15:46 Upgrade experience    17:34 Type checking differences    19:12 IDE experience    21:31 State of Pyrefly at Meta    22:27 Being open-source-first    23:36 Open-source challenges    25:06 Unexpected challenges    26:39 Outro    31:05  

Filip Baturan is the CEO of Tanari – a decentralized finance application suite that's built on top of Citrea. With it, bitcoiners can enjoy the features and user experience from banking applications such as Revolut... but within a self-custodial and sovereign environment, where the user sets the rules. The discussion centers around Tanari's vision to make Bitcoin more useful and replace the current financial system, contrasting it with projects that merely embed Bitcoin into existing structures. Baturan explains that Tanari aims to deliver on Bitcoin's promise of self-custody and financial system replacement, drawing inspiration from the ideal financial system rather than specific products like Revolut. We talk about Tanari's features: including secure Bitcoin storage, spending capabilities, and integration with the Bitcoin ecosystem (Citrea, Bitcoin mainnet, Lightning Network). The platform prioritizes ease of use, employing Face ID/fingerprint authentication and social recovery methods. The interview also covers the potential for financial primitives like lending, borrowing, and Bitcoin-backed stablecoins, all within a transparent framework. The discussion also addresses the challenges of balancing user-friendliness with privacy and security, particularly regarding usernames and potential transaction traceability. Filip Baturan emphasizes Tanari's commitment to open-source principles, sustainable business models, and integration with other Citrea applications. The interview concludes with Filip sharing his background in ZK rollups on Ethereum and his enthusiasm for building on Bitcoin with Citrea, highlighting the unique opportunities presented by this technology. ––––––––––––––––––––––––––––– Time stamps: 00:00:53 - Introducing Filip Baturan 00:02:04 - Tanari's Origin and Citrea 00:05:30 - Ease of Use and Recovery Methods 00:08:20 - Tanari's Features 00:11:04 - Open Sourcing and Fees 00:12:19 - Citrea Integration and Usernames 00:15:24 - On-Chain Interoperability 00:17:19 - Lightning Network Integration 00:19:02 - Business Solutions and Competitive Advantages 00:21:22 - Custody and Decentralization 00:24:48 - Reputation and Social Media Integration 00:29:22 - Filip's Background 00:37:53 - Scalability and Settlement Time 00:41:44 - Trade-offs and Self-Ownership 00:48:00 - Swapping Services 00:51:46 - Future of Bitcoin and Tanari 01:02:10 - Tanari's Name and Meaning

KeywordsBitcoin, mining, open source, 256 Foundation, Texas Energy and Mining Summit, Bitcoin Park, Econo Alchemist, decentralized mining, technology, innovationSummaryIn this conversation, Rob Warren and Eco discuss the upcoming Texas Energy and Mining Summit, the mission of the 256 Foundation, and the importance of open-source solutions in Bitcoin mining. They delve into the challenges posed by closed-source mining technologies, the future of Bitcoin mining, and the four key projects being developed to promote innovation and decentralization in the mining ecosystem. The discussion emphasizes the need for community support and collaboration to drive the Bitcoin mining industry forward.TakeawaysBitcoin mining is facing challenges that require innovative solutions.The 256 Foundation aims to dismantle proprietary mining technologies.Open source is crucial for fostering innovation in Bitcoin mining.Bitmain's control over the mining market stifles competition.Community involvement is essential for the success of open-source projects.The upcoming Texas Energy and Mining Summit will highlight these initiatives.Four key projects are being developed to open source the mining stack.Standardization in mining hardware can lead to better efficiency.The importance of transparency in mining operations cannot be overstated.Support for open-source solutions is vital for the future of Bitcoin mining.Chapters00:00 Introduction to Bitcoin Park and Atlanta BitLab01:45 Texas Energy and Mining Summit Overview03:47 The 256 Foundation and Its Mission09:39 The Importance of Open Source in Bitcoin Mining20:45 Projects of the 256 Foundation31:43 Final Thoughts and Call to Action

What if you could grow health-boosting compounds—not in fields, but in bioreactors—using the power of plant cells? In this episode, Karl and Erum sit down with Ilan Sobel, CEO of BioHarvest, to explore how the company is reshaping biotech, nutraceuticals, and biomanufacturing. From leveraging botanical synthesis to create highly bioavailable compounds like piceid resveratrol, to scaling sustainable DTC operations that rival software margins, Ilan reveals how BioHarvest is closing the gap between nature, science, and commerce. Hear how this Coca-Cola veteran is building a biological infrastructure poised to disrupt pharma, food, and beyond—one cell at a time.Grow Everything brings the bioeconomy to life. Hosts Karl Schmieder and Erum Azeez Khan share stories and interview the leaders and influencers changing the world by growing everything. Biology is the oldest technology. And it can be engineered. What are we growing?Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠www.messaginglab.com/groweverything⁠⁠⁠⁠⁠⁠⁠⁠⁠ Chapters:00:00:00 - Kicking Off with Biotech That Prints Money00:00:21 - $9 Trillion Vanishes: Markets, Mayhem, and Monday Mood00:00:36 - Brooklyn Birthdays, Bio Buzz & Citizen Scientists Unite00:02:24 - Tariffs, Pharma, and the Future of Drug Supply Chains00:04:20 - Reddit Rabbit Holes and Underrated Science That Could Change Everything00:06:49 - Pharma's Dirty Secret: Why Cures Don't Always Win00:09:39 - Meet Ilan Sobel: From Coca-Cola Exec to Biotech World-Changer00:19:13 - What the Heck Is Botanical Synthesis? (And Why It's a Game-Changer)00:26:46 - Tiny Footprint, Massive Impact: Economics That Would Make Silicon Valley Jealous00:29:02 - How a Grape Cell Became the King of Blood Flow Supplements00:35:09 - Why Longevity Starts with Blood, Not Kale00:37:32 - The Secret Sauce Behind Supplements That Actually Work00:38:55 - How BioHarvest Orchestrated a Biotech Masterpiece00:40:20 - From Plant Cells to Profit: Scaling Biotech Like a Pro00:43:10 - Open Sourcing the Future: CDMO Deals with Global Impact00:48:47 - Making Sweeteners Smarter (And Actually Tasty)00:51:35 - Building the Tesla of Biomanufacturing—Globally00:57:39 - What's Next for BioHarvest? Legacy, Longevity, and Global ScaleLinks and Resources:BioHarvestVinia.comPwC's US Tariff Industry Analysis – Pharmaceutical, Life Science, and Medical DeviceReddit: Scientific Ideas that haven't gotten enough tractionCommon Side Effects (tv series)Genesis Machine (book)Topics Covered: biomanufacturing, botanical cellular synthesis, plant biology, phytonutrients, sweet proteins,  distributed biomanufacturing, direct to consumer biotechHave a question or comment? Message us here:Text or Call (804) 505-5553 ⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠  / ⁠⁠⁠⁠⁠⁠⁠⁠⁠Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠ / ⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠ / ⁠⁠⁠⁠⁠⁠⁠⁠⁠Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠ / ⁠⁠⁠⁠⁠⁠⁠⁠⁠Grow Everything⁠⁠⁠⁠⁠⁠⁠Email: groweverything@messaginglab.comMusic by: NihiloreProduction by: Amplafy Media

KeywordsBitcoin mining, Adit board, modular systems, open source, community collaboration, heat reuse, project management, market insights, future of mining, Bitcoin, market trends, investment strategies, privacy, KYC, regulatory challenges, Bitcoin mining, community support, educationSummaryThis conversation explores the intersection of fitness technology and Bitcoin mining, discussing personal anecdotes, the AddIt board, modular mining systems, and the importance of open-source standards. The participants share insights on community collaboration, project management, and the future of mining, emphasizing the potential for innovation and growth in the industry. In this conversation, the speakers delve into the cyclical nature of Bitcoin trends, sharing personal experiences with market volatility and investment strategies. They discuss the importance of privacy and anonymity in Bitcoin transactions, the implications of KYC regulations, and the future of privacy tools. The conversation also touches on regulatory challenges facing Bitcoin services, community support for the Samurai Wallet case, innovative uses of Bitcoin mining, and the critical role of communication and education in Bitcoin adoption.TakeawaysThe AddIt board is a USB adapter for Antminer hash boards, enhancing modularity.Modular mining systems allow for customization and flexibility in setups.Open-source standards are crucial for the future of Bitcoin mining.Community collaboration can drive innovation in mining projects.Heat reuse in mining can create new business opportunities.Effective project management is key to successful outcomes in tech projects.Market insights reveal the current state of Bitcoin mining and its challenges.The conversation emphasizes the importance of resilience in the face of market fluctuations. The Bitcoin market is cyclical, with trends changing over time.Personal experiences in the market can shape investment strategies.Long-term investment strategies include automated dollar-cost averaging.Privacy in Bitcoin transactions is crucial to protect user identity.KYC regulations pose challenges for Bitcoin users seeking anonymity.There are always options for those who have used KYC services.The future of privacy tools in Bitcoin is uncertain but necessary.Regulatory challenges can impact the development of Bitcoin services.Community support is vital for projects like Samurai Wallet.Effective communication is essential for educating non-technical users about Bitcoin.Chapters00:00 Introduction to Fitness and Technology03:05 Bar Fight Stories and Personal Anecdotes06:02 Understanding the AddIt Board09:10 Modular Mining Systems and Open Source Standards11:53 The Future of Bitcoin Mining and Heat Reuse15:05 Project Management and Team Dynamics17:58 Community Engagement and Project Updates20:46 Closing Thoughts and Future Directions37:13 Innovative Entrepreneurship in the Bitcoin Era43:22 The Future of Bitcoin Mining and Energy Solutions49:01 Navigating Bitcoin's Price Cycles and Market Psychology55:18 Privacy and Anonymity in the Bitcoin Ecosystem01:08:23 Innovative Heating Solutions for Winter01:10:17 Creative Deck Heating Ideas01:11:57 The Legend of the Massachusetts Teacher01:14:10 Support for the 256 Foundation01:16:40 The Importance of Strategic Communication01:28:39 Bridging the Gap in Understanding Bitcoin01:32:33 Exciting Developments at the 256 Foundation

KeywordsBitcoin, mining, hashrate, Africa, hardware, open source, renewable energy, mega mining, cryptocurrencySummaryThis conversation explores the evolving landscape of Bitcoin mining, focusing on the recent hashrate explosion and its implications for the industry. The panel discusses the shift from hobbyist mining to large-scale operations, particularly in Africa, and the impact of hardware advancements. They also delve into the reasons behind the current hashrate boom despite low hash prices, and speculate on the future of mining dynamics, including the role of home mining and the importance of open-source development.TakeawaysBitcoin mining is transitioning from hobbyist to large-scale operations.The hashrate explosion is significantly impacting Africa's energy landscape.Hardware competition is crucial for the future of Bitcoin mining.Despite low hash prices, the hashrate continues to grow.Sovereign mining is a hidden factor in the hashrate increase.The US remains a leader in Bitcoin mining, but competition is rising globally.Home mining is fading but not entirely dead, as recent successes show.Open-source development is essential for the future of mining technology.Demand response strategies are becoming vital for miners' profitability.The future of mining will involve a mix of large and small operations.Chapters00:00 Introduction to Bitcoin Mining and Hashrate Explosion03:09 The Shift from Hobby to Mega Mining06:06 Impact of Hashrate Growth in Africa08:58 Hardware Evolution and Manufacturer Competition11:55 Understanding the Hashrate Boom Despite Low Prices15:06 The Future of Mining: Sovereign Mining and Global Trends17:48 Home Mining: Is It Dead or Alive?21:03 Open Sourcing and Community Development

A Daily Chronicle of AI Innovations on December 02nd 2024

In Episode 8 of Season 10 of the Virtual Coffee Podcast, hosts Bekah and Dan delve into the intricate process of open sourcing a private repository. Drawing from their experience with Virtual Coffee's community docs, they discuss considerations like verifying the safety of repository content, ensuring appropriate permissions, and maintaining the privacy of sensitive discussions. They also touch on the importance of updating READEMEs, licensing, and providing clear guidelines for new contributors.Episode Sponsor!We're grateful to be sponsored by LevelUP Financial Planning, who understands the importance of finding balance between having an awesome life today, and being confident and excited about your future possibilities. If you want to take your financial confidence to the next level, check out levelupfinancialplanning.com.Sponsor Virtual Coffee! Your support is incredibly valuable to us. Direct financial support will help us to continue serving the Virtual Coffee community. Please visit our sponsorship page on GitHub for more information - you can even sponsor an episode of the podcast! Virtual Coffee: Virtual Coffee: virtualcoffee.io Podcast Contact: podcast@virtualcoffee.io Bekah: dev.to/bekahhw, Twitter: https://twitter.com/bekahhw, Instagram: bekahhw Dan: dtott.com, Twitter: @danieltott

In today's digital age, we're inundated with a constant stream of information, making it challenging to navigate and make sense of what's important. And now, in the midst of increasingly-capable AI, the very concept of importance is coming into question. Could open source be the solution to managing our impending sensemaking crisis? In this episode of the Win-Win Podcast, we're joined by Peter Wang, a physicist, computer scientist, and founder of Anaconda, one of the most widely used open source platforms for Python development. Peter leads Anaconda's AI Incubator, which focuses on advancing core Python technologies and developing new frontiers in open-source AI and machine learning, especially in the areas of edge computing, data privacy, and decentralized computing. We dig in with Peter to discuss the history and politics of the open source movement, and the security concerns around open sourcing AI models. And we attempt to understand how open source software can enhance transparency and collaboration between players, and how these technologies can be harnessed to better navigate the complexities of our information-rich environment. Chapters: 00:00:00 - What is Open Source Software 00:10:29 - Peter's History with The Open Source Movement 00:35:06 - Security and State Interests in Open Source 00:37:16 - Open Science and The Commons of Knowledge 00:39:40 - The Central Problem of Coordination 00:43:46 - The Solutions That Markets Solve and The Problems They Create 01:04:40 - Synchronous Attention As A Scarce Resource 01:09:23 - The Liminal Act of Modelling The World 01:19:58 - Virtuality and Colorful Dystopias 01:22:03 - Is Technology Values-Neutral? 01:32:30 - Moloch Invades The Tech Stack 01:35:57 - Psychosecurity and The Dangers of Attention-Renting Software 01:42:00 - Is The Global Community Actually Excelling in Science? 01:43:51 - Our Cosmic Scale and The Instruments To Probe It 01:53:18 - The Stagnation of Physics 01:56:06 - The Civilizational Perspective on AI Safety 02:05:23 - The Benefits of Open Source To Society 02:27:26 - Will AI Accelerate A Global Security Crisis? Credits: ♾️ Hosted and Produced by Liv Boeree ♾️ Edited and Mixed by Ryan Kessler Links: ♾️ Peter's Twitter: ⁠https://x.com/pwang?lang=en⁠ ♾️ Anaconda: ⁠https://www.anaconda.com/⁠ ♾️ Peter's Blog: ⁠https://medium.com/@pwang⁠ The Win-Win Podcast: Poker champion Liv Boeree takes to the interview chair to tease apart the complexities of one of the most fundamental parts of human nature: competition. Liv is joined by top philosophers, gamers, artists, technologists, CEOs, scientists, athletes and more to understand how competition manifests in their world, and how to change seemingly win-lose games into Win-Wins. #WinWinPodcast #Moloch #AI #Python

Elizabeth Seger is the Director of Technology Policy at Demos, a cross-party UK think tank with a program on trustworthy AI. You can find links and a transcript at www.hearthisidea.com/episodes/seger   In this episode we talked about open source the risks and benefits of open source AI models. We talk about: What ‘open source' really means What is (and isn't) open about ‘open source' AI models How open source weights and code are useful for AI safety research How and when the costs of open sourcing frontier model weights might outweigh the benefits Analogies to ‘open sourcing nuclear designs' and the open science movement You can get in touch through our website or on Twitter. Consider leaving us an honest review wherever you're listening to this — it's the best free way to support the show. Thanks for listening! Note that this episode was recorded before the release of Meta's Llama 3.1 family of models. Note also that in the episode Elizabeth referenced an older version of the definition maintained by OSI (roughly version 0.0.3). The current OSI definition (0.0.8) now does a much better job of delineating between different model components.

Sometimes, you have to take a step back to leap forward. Over the past couple of years, Meta, Amazon, Block and Shopify are among the growth companies to have made efficiency cuts following the pandemic. Gary Robinson, an investor in Baillie Gifford's US Equity Team, says that's made them more agile and resilient – qualities that will let them take advantage of artificial intelligence and other opportunities to drive long-term growth. Background: Gary Robinson is joint manager of the Baillie Gifford US Growth Trust, a manager of the American Fund and a partner in our firm. In this episode of Short Briefings on Long Term Thinking, he explores how four leading internet-focused firms have streamlined their operations and reallocated resources to become more adaptable during a period of rapid change.Robinson draws a parallel with companies that made cutbacks after the global financial crisis to suggest that the markets may have underestimated how much growth can be unlocked by leaders taking a hard look at their firm's spending, organisational structure and business priorities.Robinson suggests that recent efficiency drives will help Shopify, Meta and Amazon pursue AI-related opportunities that could meaningfully increase their earnings. And at Block, efforts to bring two products closer together could help the firm challenge Visa, Mastercard and American Express. Resources: Behind The Tech: Tobi Lütke: CEO and Founder, ShopifyDwarkesh Podcast: Mark Zuckerberg – Llama 3, Open Sourcing $10b Models & Caesar AugustusBent Flyvberg: How Big Things Get DoneCyril Northcote Parkinson: Parkinson's Law, and Other Studies in Administration More from Gary Robinson: Lessons from evolutionary biologyWhy companies should embrace chaos Companies mentioned include: AmazonBlockMetaNetflixShopify Timecodes: 00:00    Introduction01:40    A background in biochemistry02:55    The appeal of American companies03:30    Parallels with the global financial crisis04:40   Post-Covid efficiency efforts06:25    Addressing overhiring and patched-together processes07:40    Future-proofed businesses08:00    The potential of AI08:10    Shopify and the distraction of side quests10:45    Shopify's Sidekick assistant12:50    Engineering Shopify's internal operations14:20    The authority of founder-leaders16:00    Meta's ‘year of efficiency'18:00    How AI can drive further growth at Facebook and Instagram20:10    Business chatbots on WhatsApp and Messenger21:15    Investing in Block22:30    Capping employee numbers without compromising growth24:40    Square and Cash App's potential to rival Visa and Mastercard26:35    Meeting Jack Dorsey27:40    Discipline and focus at Amazon29:00    Amazon's fast-growing advertising business30:20    Generative AI's trillion-dollar opportunity for AWS31:25    Offloading routine tasks to artificial intelligence32:25    Book recommendation33:40    Outro

Uncover the power of TLDraw's make-real feature, transforming sketches into functional prototypes using AI. Explore the cutting-edge browser-based technologies and the future of visual communication with founder and CEO, Steve Ruiz.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Open Sourcing Metaculus, published by christian on June 25, 2024 on The Effective Altruism Forum. This is a linkpost for an announcement by Metaculus CEO, Deger Turan, originally published June 5th, 2024. In addition to spreading the word here, we are interested to gather feedback and ideas from the EA community. We make reasoning and coordination tools for the public benefit, and that requires the trust and participation of the wider forecasting community. That's why we're going open source. GitHub issues, pull requests, and community-sourced forecasts will dictate much of what we build and how. Ultimately, forecasters will benefit from better infrastructure, the Metaculus team will benefit from more idea diversity and greater capacity, and readers will get more accurate aggregates. What's next Metaculus will go open source in Q3. There will be opportunities to audit, critique, and improve on our efforts. Creating a better epistemic environment is a collective endeavor. As part of open-sourcing Metaculus, we'll help implement our best features into other open-source forecasting codebases. We'll make our roadmap and building process public, and we'll give more visibility into our decision-making criteria and discussions. Our codebase is far from perfect, but we can improve it better together out in the open. In a future post I'll write more about how others can contribute to this project, and specifications for how code can be modified, used, and distributed - as well as guidelines for coding standards and our submission process. We'll provide processes for pull requests, and how we'll implement submissions from the community. We want to be transparent that we may still choose to offer proprietary offerings targeting particular enterprise and commercial use cases: We believe that part of making forecasting more useful and more used is by productizing it and generating revenue. Why isn't Metaculus already open source? Before I joined as CEO I wondered why Metaculus wasn't already open source. I discovered the idea was being discussed, but that there were some concerns about platform security, about accessibility of the codebase and about the potential for exploits that would allow some forecasters to game the system and create an uneven playing field. Now that we've been able to observe the new scoring system at work for some time with no issues, we're confident the benefits outweigh the risks. While the codebase is nowhere near perfect, we believe making improvements on the platform is better done in the open, with feedback from the larger ecosystem guiding us explicitly. We're looking for Researchers who want to backtest their aggregation algorithms on our data UX specialists who see better ways to translate beliefs into forecasts Nonprofits who want to spin up their own internal forecasting engines built on our architecture Developers who can help add new features and enhance existing ones As our plan evolves I'll provide more details. Until then, let me know what you think: What resources and documentation will you want to see? What would make contributing easier? Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

with @jinglejamOP @eddylazzarin @rhhackettHello and welcome to web3 with a16z, a show about building the next era of the internet by the team at a16z crypto, that includes me, host Robert Hackett.Today's episode features Jing Wang, CEO and executive director of the Optimism Foundation, along with a16z crypto CTO Eddy Lazzarin. We discuss the peculiarities of open source software — including the incentives that bind contributors together, tradeoffs between the freedom to customize versus sticking to standards, and the challenges in setting up and running a foundationWe also cover the nuances of governance and accountability, the importance of vibes, the indispensability of shipping products (versus debating roadmaps), and, the vision behind the so-called “superchain”.As head of the Optimism Foundation, Wang helps stewards the Optimism collective — a band of decentralized companies, communities, contributors, and others who are using a suite of open source software – called the OP Stack — to scale the Ethereum blockchain network. The OP Stack also powers a number of popular "layer two" rollups — including Base, which we covered in last week's episode with its creator and lead, Coinbase's head of protocols Jesse Pollak.Be sure also to check out the a16z crypto YouTube channel for video podcast episodes, as well as talks from our recent startup accelerator programs CSX featuring Jing, Optimism co-founder Karl Floersch, and more.Resources for references in this episode:More on Optimism: open source code software licensesthe OP StackMore on the Optimism superchain collective, including:Coinbase's BaseRedstoneWorldcoin"Understanding Dencun, the biggest upgrade to Ethereum since The Merge" by Noah Citron and Valeria Nikolaenko (a16z crypto, March 2024)More on Ethereum upgrade EIP-4844 (Github)"Layer 2, rollups, and building onchain (with Base)" by Jesse Pollak, Eddy Lazzarin, and Robert Hackett (a16z crypto, May 2024)"Composability is to software as compound interest is to finance" by Chris Dixon (a16z crypto, October 2021)"The Nature of the Firm" by Ronald Coase (Economica, November 1937)"Weaknesses in the Articles of Confederation" [Intro 6.2 footnote] (Congress.gov)As a reminder none of the following should be taken as tax, business, legal, or investment advice. See a16zcrypto.com/disclosures for more important information, including a link to a list of our investments.

Winamp is finally opening the source! Nvidia decides to play nice with Wayland, official m.2 HAT for the RasPi 5, and benchmarking your SSD like a Windows user.

This episode dives into the latest trends in AI as seen at Coachella, where AI-generated visuals were a standout feature. We shift focus to the significant developments in AI technology, specifically discussing the launch of Llama 3. We cover the impact of open sourcing on AI innovation and outline the key elements required to develop next-generation AI models. Topics include the role of specialized chips, data center locations, and the overarching benefits of open-source contributions. Tune in to understand how these advancements are influencing both the tech and creative sectors.

Pour l'épisode #232 je recevais Matthias Le Brun. On en débrief avec Julien.**Découvrez Shopify : Votre Allié E-commerce** "Vous êtes développeur ou entrepreneur et cherchez à créer ou optimiser votre boutique en ligne ? Ne cherchez pas plus loin que Shopify. Cette plateforme de commerce tout-en-un vous offre les outils nécessaires pour lancer, gérer et développer votre entreprise avec aisance et efficacité. Que vous vendiez en personne ou en ligne, Shopify s'adapte à vos besoins et vous permet de personnaliser votre expérience e-commerce. Avec une interface intuitive et un large éventail d'outils de gestion puissants, Shopify transforme le processus de vente en une expérience fluide et agréable. Profitez maintenant d'une période d'essai à un euro par mois en vous inscrivant sur Shopify. Prenez le contrôle de votre aventure commerciale et faites passer votre marque au niveau supérieur avec Shopify."Archives | Site | Boutique | TikTok | Discord | Twitter | LinkedIn | Instagram | Youtube | Twitch | Job Board |

This week, Tristan and Tasia discuss the Humane Ai Pin's disappointing reviews, other AI devices coming soon, and AI model developments from OpenAI, Grok and Microsoft. Then we look at Meta's update to its Llama model and infusing its AI magic into Facebook, Instagram, WhatsApp and more. Join us as we grab some potion and some food and watch as another tech champion harnesses the forces of open source to battle for AI dominance.FOLLOWAI Named This Show Tristan & TasiaAI Named This Show podcastLLM UPDATESChatGPT update: OpenAI makes popular chatbot ‘more direct' and moreElon Musk's xAI previews Grok-1.5V, its first multimodal modelElon Musk's Grok keeps making up fake news based on X users' jokesAI DEVICESHumane AI Hands-On: My Life So Far With a Wearable AI PinHumane AI Pin review: not even closeMKBHD calls the Humane AI pin “the worst product I've ever reviewed.”See also: Limitless' $99 AI wearable to promises to remember your meetings and, well, everything elseSee also: Rabbit R1 just annihilated the Humane AI Pin in new video — but here's the weirdest partSee also: Rabbit R1 vs Humane AI Pin vs Limitless Pendant: AI wearables comparedChatGPT is coming to Nothing's earbudsMICROSOFT VASA-1 AI MODELCool or creepy? Microsoft's VASA-1 is a new AI model that turns photos into 'talking faces'VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real TimeMETA AI NEWSMeta rolls out an updated AI assistant, built with the long-awaited Llama 3Meta says Llama 3 beats most other models, including GeminiMeta's battle with ChatGPT begins nowMeta AIMark Zuckerberg - Llama 3, Open Sourcing $10b Models, & Caesar AugustusFacebook's AI Told Parents Group It Has a Gifted, Disabled Child Hosted on Acast. See acast.com/privacy for more information.

Mark Zuckerberg on:- Llama 3- open sourcing towards AGI- custom silicon, synthetic data, & energy constraints on scaling- Caesar Augustus, intelligence explosion, bioweapons, $10b models, & much moreEnjoy!Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Human edited transcript with helpful links here.Timestamps(00:00:00) - Llama 3(00:08:32) - Coding on path to AGI(00:25:24) - Energy bottlenecks(00:33:20) - Is AI the most important technology ever?(00:37:21) - Dangers of open source(00:53:57) - Caesar Augustus and metaverse(01:04:53) - Open sourcing the $10b model & custom silicon(01:15:19) - Zuck as CEO of Google+SponsorsIf you're interested in advertising on the podcast, fill out this form.* This episode is brought to you by Stripe, financial infrastructure for the internet. Millions of companies from Anthropic to Amazon use Stripe to accept payments, automate financial processes and grow their revenue. Learn more at stripe.com.* V7 Go is a tool to automate multimodal tasks using GenAI, reliably and at scale. Use code DWARKESH20 for 20% off on the pro plan. Learn more here.* CommandBar is an AI user assistant that any software product can embed to non-annoyingly assist, support, and unleash their users. Used by forward-thinking CX, product, growth, and marketing teams. Learn more at commandbar.com. Get full access to Dwarkesh Podcast at www.dwarkeshpatel.com/subscribe

► Thanks to Silverstone for sponsoring today's video! Check out their air coolers here: https://www.silverstonetek.com/en/product/info/coolers/arv140_argb/ & https://www.silverstonetek.com/en/product/info/coolers/hyd140_argb/ ► Check out today's hottest tech deals here: https://www.ufd.deals/ https://howl.me/clYqy4lDXen https://howl.me/clYqz451Qjl https://howl.me/clYqC4aBeRL 0:00 - Intro 00:19 - TSMC Pauses for Earthquake: https://tinyurl.com/229nd77b https://tinyurl.com/2b46omen 01:25 - Sponsor 03:06 - Facebook Changing Video Players: https://tinyurl.com/229u6hbt https://tinyurl.com/29cuzgg3 https://tinyurl.com/24jsadhd 03:45 - Net Neutrality Being Restored: https://tinyurl.com/2ajhhxub https://tinyurl.com/29vvnr78 04:42 - Intel Struggling But Hopeful: https://tinyurl.com/28hzt7ts https://tinyurl.com/2y7l93jc https://tinyurl.com/25lkz2v7 https://tinyurl.com/29x2mrqz 07:33 - UFD Deals: https://www.ufd.deals/ https://howl.me/clYqy4lDXen https://howl.me/clYqz451Qjl https://howl.me/clYqC4aBeRL 08:32 - Amazon Abandoning Self-Checkout: https://tinyurl.com/25qylxe5 10:25 - How Much To Keep Windows 10: https://tinyurl.com/25tnct6w 11:20 - AMD's Big Open Sourcing: https://tinyurl.com/2c7b94n6 https://tinyurl.com/2dpm5r84 12:53 - Comment Response ► Follow me on Twitch - http://www.twitch.tv/ufdisciple ► Join Our Discord: https://discord.gg/GduJmEM ► Support Us on Floatplane: https://www.floatplane.com/channel/ufdtech ► Support Us on Patreon: https://www.patreon.com/UFDTech ► Twitter - http://www.twitter.com/UFDTech ► Facebook - http://www.facebook.com/ufdtech ► Instagram - http://www.instagram.com/ufd_tech ► Reddit - https://www.reddit.com/r/UFDTech/ Presenter: Brett Sticklemonster Videographer: Brett Sticklemonster Editor: Rikus Strauss Thumbnail Designer: Reece Hill

Companies are employing AI agents and co-pilots to help their teams increase efficiency and accuracy, but developing apps that are trained properly can require a skill set many enterprise teams don't have. This week on No Priors, Sarah and Elad are joined by Harrison Chase, the CEO and co-founder of LangChain, an open-source framework and developer toolkit that helps developers build LLM applications. In this conversation they talk about the gaps in open source app development, what it will take to keep up with private companies, the importance of creating prompts that can be compatible with many API models, and why memory is so undeveloped in this space.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil |@hwchase17 Show Notes:  (0:00) Introduction to LangChain (1:45) Managing an open source environment (4:30) Developing useful AI agents (10:03) Sophistication and limitations of AI app development (14:17) Switching between model APIs (17:10) Context windows, fine-tuning and functionality (21:37) Evolution of AI open source environment (23:53) The next big breakthroughs

The wave of cannabis patents ARE COMING! Today we have a brand new guest Caleb from Copyleft Cultivars- he joins us with mutual friend Steve Raisner to talk about the implications of patenting cannabis genetics. Copyleft explains his mission of open sourcing as many genetics as possible, while also building an open to anyone cannabis genome galaxy. Caleb highlights the difference between his project and other similar projects in the past- and how he is looking to reshape patent law both inside AND outside of cannabis. This leads to a conversation about patent trolls- people who engage in lawsuits around budding new industries and use lawsuits over intellectual property as extortion to get paid off. These groups are incredibly harmful to a variety of industries and cannabis is RIPE for this type of legal attack- which is why it's so important to open source our genetics. Caleb and Steve wrap the show by showcasing their new Natural Farming AI Bot that will produce answers and solutions to garden problems using natural farming inputs. GrowCast Membership Weekly Live Streams - Personal Garden Advice- 100s of HOURS of Bonus Content  - MEMBERS ONLY DISCOUNTS! Join the greatest community in cannabis! GrowCast Seed Co If you are reading down this far... Go to Seed Co page, you may find a new drop live right now! *Rooted Leaf Carbon Based Nutrients - liquid organic nutrients with NO NEED to PH! Visit www.rootedleaf.com and use code GROWCAST for 20% off, just add to filtered water and watch the EXPLOSIVE growth!*

In this episode, I delve into Meta's significant stride with MusicGen, AudioGen, and EnCodec4, exploring the implications of open-sourcing advanced AI audio technologies. Invest in AI Box: https://Republic.com/ai-box Get on the AI Box Waitlist: https://AIBox.ai/ AI Facebook Community

In the podcast 'The Free Speech Solution' with Miljan Braticevic, we explore Nostr's role in free speech and its unique aspects. The discussion covers why Nostr is pivotal, its ecosystem, and Primal's user-focused approach, featuring an integrated wallet and open-source stack. Key topics include Zaps, reshaping content publishing, content moderation in open networks, Primal Wallet's innovations, and partnerships. Insights into Nostr's current technical challenges and entrepreneurial lessons, along with future prospects for both Nostr and Primal, are also highlighted. IN THIS EPISODE, YOU'LL LEARN: The Fundamentals of Nostr: Understand what Nostr is and why it's essential for free speech and decentralization. Unique Features of Nostr: Discover what sets Nostr apart from other protocols and why it's considered a game-changer. Primal's User-Centric Approach: Learn about Primal's focus on optimizing user experience, onboarding, and performance. Integration of Wallet in Primal: Insights into the integrated wallet feature and its significance for users. Open Sourcing in Primal: Understand Primal's commitment to open sourcing their entire stack and its impact. The Role of Zaps in Nostr: Explore how Zaps are fundamental to Nostr and their potential to reshape content publishing and the web. Challenges of Content Moderation: Delve into the complexities of moderating content on a radically open network like Nostr. Innovations in Primal Wallet: Learn about the unique features of the Primal Wallet, including its Bitcoin address system and partnership with Strike. Technical Hurdles in Nostr Development: Gain insight into the major technical challenges currently faced in the development of Nostr. Entrepreneurial Lessons and Future Visions: Hear about key learnings as an entrepreneur and the future roadmap for Nostr and Primal. Disclaimer: Slight discrepancies in the timestamps may occur due to podcast platform differences. BOOKS AND RESOURCES Try out the Primal App on Nostr. Miljan's Nostr Account. Preston's Nostr Account. NEW TO THE SHOW? Check out our We Study Billionaires Starter Packs. Browse through all our episodes (complete with transcripts) here. Try our tool for picking stock winners and managing our portfolios: TIP Finance Tool. Enjoy exclusive perks from our favorite Apps and Services. Stay up-to-date on financial markets and investing strategies through our daily newsletter, We Study Markets. Learn how to better start, manage, and grow your business with the best business podcasts. Check out all the books mentioned and discussed in our podcasts here.  SPONSORS Support our free podcast by supporting our sponsors: River Efani Salesforce Toyota Vanta Babbel Shopify Notion AI NetSuite Noble Gold Investments Ka'Chava Learn more about your ad choices. Visit megaphone.fm/adchoices

show notes at https://tuxdigital.com/dl352

On this episode of Destination Linux (352), we have another awesome interview lined up for you. Michael sits down with Randy Packer, from DreamWorks, to talk about their open-source projects. Let's get this show on the road toward Destination Linux! Download as MP3 (https://aphid.fireside.fm/d/1437767933/32f28071-0b08-4ea1-afcc-37af75bd83d6/12baac3a-64c2-4434-a75e-ea4ab2cb7cd3.mp3) Special Guests: Randy Packer Link: https://openmoonray.org/ Supported By: Namecheap = https://destinationlinux.net/namecheap LINBIT = https://destinationlinux.net/linbit Hosted by: Michael Tunnell = https://michaeltunnell.com Ryan (DasGeek) = https://dasgeekcommunity.com Jill Bryant = https://jilllinuxgirl.com Want to Support the Show? Become a Patron = https://tuxdigital.com/membership Store = https://tuxdigital.com/store Chapters: 0:00:00 Destination Linux 352 Intro 0:00:29 Community Feedback 0:09:46 NAMECHEAP [ link (https://destinationlinux.net/namecheap) ] 0:10:45 Interview with DreamWorks from the Ubuntu Summit 0:45:39 LINBIT [ link (https://destinationlinux.net/linbit) ] 0:46:57 Gaming: Printersim [ link (https://store.steampowered.com/app/1665200/Printersim/) ] 0:51:53 Software Spotlight: Coppwr [ link (https://github.com/dimtpap/coppwr) ] 0:55:17 Tips and Tricks: Gift Giving 1:01:00 Events 1:02:25 Close

In this jam-packed episode, hosts Frank and Andy delve into a wide range of topics, from the chaos of podcast scheduling and the allure of Cyber Week deals, to the behind-the-scenes world of data engineering and AI professionals. Join us as we journey through the challenges of podcasting, the important roles of data engineers, and the potential open sourcing of Dingo, an innovative blogging automation tool. Along the way, the hosts share personal anecdotes, discuss legislative impacts, and even touch on cult-followed gas stations. You won't want to miss this delightful, informative, and always data-driven episode!Show Notes00:00 Glamorous world of podcasting and Microsoft Bookings.13:48 Privacy laws are spreading globally, impacting data sovereignty.27:14 Funny moment at Dunkin' Donuts sparks creativity.32:27 Importance of data engineering in AI projects.49:38 Struggling with hearing loss, amplifiers magnify all sounds.01:02:45 Emotions on camera, times sidetrack, sarcastic leadership.01:07:32 Excited to hang out at the mall.01:21:04 Considering discontinuing blog after reaching 100 posts.01:25:18 Wants to shift focus to new projects.01:37:09 Transition from long-form to short-form content.01:49:50 Drove up to Jersey for Christmas, reminisced.01:58:48 Concerns about coastal development and zoning enforcement.Links01:02:45 Here's an example of early FWTV where I am at the mall and not happy about it: https://www.youtube.com/watch?v=f8S7ha9fZWo

What's it like to open source an internal project at a big tech company like LinkedIn? When should a company open source a project and what are the benefits and challenges that come along with it? If you want to open source an internal project, how should you go about advocating for it? Félix is a Principal Staff Engineer at LinkedIn where he works on the data infrastructure team that builds Venice. Venice is a distributed derived data store which LinkedIn open sourced in the fall of 2022. He joins the show to chat about his experiences leading the open source efforts for Venice, as well as his thoughts on balancing leadership with execution, delegating responsibility and fostering a culture of ownership, and growth within a team. --- Show Notes: Check out Venice: https://github.com/linkedin/venice Félix's linkedin: https://github.com/linkedin/venice --- Stay in Touch: ✉️ Subscribe to our newsletter: https://softwaremisadventures.com

As AI development races forward, a fierce debate has emerged over open source AI models. So what does it mean to open-source AI? Are we opening Pandora's box of catastrophic risks? Or is open-sourcing AI the only way we can democratize its benefits and dilute the power of big tech? Correction: When discussing the large language model Bloom, Elizabeth said it functions in 26 different languages. Bloom is actually able to generate text in 46 natural languages and 13 programming languages - and more are in the works. RECOMMENDED MEDIA Open-Sourcing Highly Capable Foundation ModelsThis report, co-authored by Elizabeth Seger, attempts to clarify open-source terminology and to offer a thorough analysis of risks and benefits from open-sourcing AIBadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13BThis paper, co-authored by Jeffrey Ladish, demonstrates that it's possible to effectively undo the safety fine-tuning from Llama 2-Chat 13B with less than $200 while retaining its general capabilitiesCentre for the Governance of AISupports governments, technology companies, and other key institutions by producing relevant research and guidance around how to respond to the challenges posed by AIAI: Futures and Responsibility (AI:FAR)Aims to shape the long-term impacts of AI in ways that are safe and beneficial for humanityPalisade ResearchStudies the offensive capabilities of AI systems today to better understand the risk of losing control to AI systems forever RECOMMENDED YUA EPISODESA First Step Toward AI Regulation with Tom WheelerNo One is Immune to AI Harms with Dr. Joy BuolamwiniMustafa Suleyman Says We Need to Contain AI. How Do We Do It?The AI DilemmaYour Undivided Attention is produced by the Center for Humane Technology. Follow us on Twitter: @HumaneTech_

Summary: FOSS4G NA 2023 speaker, Michael Terner, addresses the question of making money with FOSS4G (Free and Open Source Software for Geospatial) technology. He outlines three viable business models: the Red Hat model (service and support fees), integration of FOSS4G into larger solutions, and open-sourcing technology with a premium model. Terner emphasizes the evolving hybrid landscape where open source and proprietary technologies coexist, highlighting the importance of supporting and giving back to the FOSS4G community. Highlights:

Summary Josh Carlson discusses the transformation of farm assessments in Kendall County, Illinois, highlighting the transition from a cumbersome proprietary tool to an open-source, efficient, and accurate solution. The new approach ensures precision in calculating agricultural parcel values, overcoming issues like rounding errors and software dependencies. Highlights

Zach Roseman is the founder of Draftboard. Draftboard is where companies publicly post what referral bonus they'll pay for a new hire and Scouts (referrers) compete to earn that referral bonus by scouring their networks for the best talent available. ★ Support this podcast ★

This episode is sponsored by Oracle. AI is revolutionizing industries, but needs power without breaking the bank. Enter Oracle Cloud Infrastructure (OCI): the one-stop platform for all your AI needs, with 4-8x the bandwidth of other clouds. Train AI models faster and at half the cost. Be ahead like Uber and Cohere. If you want to do more and spend less like Uber, 8x8, and Databricks Mosaic - take a free test drive of OCI at https://oracle.com/eyeonai   Welcome to episode 150 of the ‘Eye on AI' podcast. In this episode, host Craig Smith sits down with Yann LeCun, a Turing Award winner who has been instrumental in advancing convolutional neural networks and whose work spans machine learning, computer vision, and more. Tune is as Craig and Yann explore the intricacies of AI, world models, and the challenges of continuous learning. In this episode, Yann delves deep into the concept of a "world model" - systems that can predict the world's future states, allowing agents to make informed decisions. The discussion transitions to the challenges of training these models, particularly when dealing with diverse data like text and images. We then discuss the computational demands of modern AI models, with Yann highlighting the nuances between generative models for videos and language.  He also touches upon the idea of the "Embodied Turing Tests" and how augmented language models can bridge the gap between human-like behavior and computational efficiency.The spotlight then shifts to pressing concerns surrounding the open-source nature of AI models, with Yann articulating the legal ramifications and the future of open-source AI. Drawing from global perspectives, including China's stance on open-source, Yann underscores the imperative for a collaborative approach in the AI space, ensuring it's reflective of diverse global needs.   Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI   (00:00) Preview, Oracle and Introduction (02:42) Decoding The World Model and Gaia 1  (07:43) Energy and Computational Demands of AI (08:06) Video vs. Text Processing & True AI Capabilities (11:17) Embodied Turing Test & Augmented LLMs (15:38) Is AI a Threat To Society? (25:04) Where is AI Development Headed? (31:06) Interplay of Neuroscience and AI** (33:33) Yann's Vision, JEPA, and Learning Challenges (39:05) Yann's Career, AI Progress, and Challenges (44:47) The Open Source Debate in AI (55:30) Oracle Cloud Infrastructure

The trust and safety conversation tends to focus on the huge platforms, and the millions of smaller websites (some still quite big!) get ignored. But those websites have trust and safety needs too, and they use a lot of different tools to meet them. Most of these tools are proprietary, but there's a growing push to build more open source tooling for the purpose, which was discussed by Derek Slater in a recent Atlantic Council report. This week, Derek joins us on the podcast to talk about the problems that open source trust and safety tools can solve.

This podcast is a commentary and does not contain any copyrighted material of the reference source. We strongly recommend accessing/buying the reference source at the same time. ■Reference Source https://www.ted.com/talks/massimo_banzi_how_arduino_is_open_sourcing_imagination ■Post on this topic (You can get FREE learning materials!) https://englist.me/163-academic-words-reference-from-massimo-banzi-how-arduino-is-open-sourcing-imagination-ted-talk/ ■Youtube Video https://youtu.be/QE9UMKi_tKQ (All Words) https://youtu.be/J87btj0Iwak (Advanced Words) https://youtu.be/nYQfcOVR-iE (Quick Look) ■Top Page for Further Materials https://englist.me/ ■SNS (Please follow!)

    Bitcoin Monthly 0028   In this episode of monthly we discus the topics listed bellow and more.    BlueWallet v6.4.6  - ADD: add new languages  - ADD: support scanning SeedQR backup (closes #4959)  -  - https://github.com/BlueWallet/BlueWallet/releases/tag/v6.4.6   BitGO Musig2  - https://www.nobsbitcoin.com/bitgo-added-taproot-musig2-on-bitcoin-hot-wallets/   Envoy 1.3.0  - redeem azte.co vouchers  - update within Envoy for Founder's Edition  - much more... https://github.com/Foundation-Devices/envoy/releases/tag/v1.3.0     Nunchuck Android v1.9.33      - Make TAPSIGNER and software keys compatible with BIP48  - Add the ability to export transaction in raw hex when in Ready-to-broadcast state  - Allow mobile to trigger adding Coldcard/Trezor/Ledger via USB on desktop  - Minor bugs and improvements  -  - https://github.com/nunchuk-io/nunchuk-android/releases/tag/android.1.9.33     SparrowWallet 1.7.8  - Add BIP322 message signing for singlesig addresses including P2TR  - Add zbar QR reader for all QR scans (wide, cropped and inverted)  - Add useZbar config variable to disable zbar scanning (enabled by default)  - Add Rename Wallet command to File menu  - Set initial fee for proposed RBF transaction to satisfy minimum relay requirements  -  - https://github.com/sparrowwallet/sparrow/releases/tag/1.7.8   PDK  -  - https://www.nobsbitcoin.com/pdk-a-payjoin-sdk/   HRF Bounties  - #1 Open-Sourcing the Design Guide  - #2 Serverless Payjoin  - #3 End-to-End Encrypted Nostr Group Chats  - #4 Silent payments  - #5 Human Readable Offers  - #6 Self-custodial Mobile Lightning address  - #7 Mobile Border Wallets  - #8 Easy Mobile Multisig  - #9 Frost Multisig Wallet  - #10 Cashu  - #11 BIP47 Expansion  -  - https://hrfbounties.org/?ref=bitcoin-2go.de     Kraken ordered to hand over data to IRS  - Name  - Date of birth  - Tax numbers  - Addresses  - Telephone numbers  - Email addresses  - other documents         Show Host: Max  https://twitter.com/MaxBitbuybit     Show Host: QNA @BitcoinQ_A   Show Host: Antomous  @antomousB           Ungovernable Misfits Socials https://www.ungovernablemisfits.com   Twitter  https://twitter.com/ungovernablemf     Show Sponsor - Foundation Devices   Foundation builds Bitcoin-centric tools that empower you to reclaim your digital sovereignty. As a sovereign computing company, Foundation is the antithesis of today's tech conglomerates. Returning to cypherpunk principles, they build open source technology that “can't be evil,”    Thank you Foundation Devices for sponsoring the show.  Use code BITBUYBIT at check out for $10 off your purchase.   https://foundationdevices.com       Show Sponsor: sx6.store   SECURE YOUR BITCOIN IN MARINE GRADE, 316L STAINLESS STEEL!         As always please feel free to reach out and ask me any questions.

This week The Candyman lays out a basic outline for getting the various nations of the world to come together for the first time ever in a Global Govornment. Also some ideas on Open Sourcing everything.The King is back for a special shout out to her work friends.Danny Demonic up to his regular routine.Macka is out partying somewhere, and could't make it this week.

Vote for me in the AccountingHigh bracket challengeRyan's Future Firm Accelerate communityThe Lazanis Files have been releasedThe ARC GPT study discussed - page 55 has the Captcha bitThe video version of this podcastJason's TwitterJason's LinkedInJason's YouTube

For episode 51, Pascal speaks with Neil and Marie, two of the engineers behind Buck2, our open source, large scale build system. Thousands of developers at Meta are already using Buck2 and performing millions of builds per day that on average complete in half the time of Buck1 builds. Marie and Neil discuss the design choices that make Buck2 so much faster and the various challenges they faced in engineering and open sourcing the build system. Got feedback? Send it to us on Twitter (https://twitter.com/metatechpod), Instagram (https://instagram.com/metatechpod) and don't forget to follow our host @passy (https://twitter.com/passy and https://mastodon.social/@passy). Fancy working with us? Check out https://www.metacareers.com/. Links Announcement blog post: https://engineering.fb.com/2023/04/06/open-source/buck2-open-source-large-scale-build-system/ Buck2: https://buck2.build/ Buck2 on GitHub: https://github.com/facebook/buck2 Build Systems à la Carte - https://www.microsoft.com/en-us/research/uploads/prod/2018/03/build-systems.pdf  Lexical YouTube clip: https://www.youtube.com/watch?v=Vpv0BYhhlak Lexical for iOS: https://github.com/facebook/lexical-ios Timestamps Intro 0:06 Intro Marie 1:30 Intro Neil 2:57 Why a custom build tool? 4:21 Rewriting Buck 6:49 Buck2 vs Bazel 8:49 Building language support 12:06 Buck2 as a developer 13:15 Upgrade from Buck1 to Buck2 15:05 How is Buck2 faster? 16:31 Rust and Profiling 18:44 From Python to Starlark 25:54 Open-Sourcing 28:18 Outro 32:15  

Supabase teamed up with Snaplet to build (and open source) postgres-wasm, a PostgreSQL server that runs inside a browser. Mark Burggraf join us to talk about how it all works. Links https://twitter.com/burggraf2 https://github.com/burggraf https://dev.to/burggraf https://supabase.com/blog/postgres-wasm Tell us what you think of PodRocket We want to hear from you! We want to know what you love and hate about the podcast. What do you want to hear more about? Who do you want to see on the show? Our producers want to know, and if you talk with us, we'll send you a $25 gift card! If you're interested, schedule a call with us (https://podrocket.logrocket.com/contact-us) or you can email producer Kate Trahan at kate@logrocket.com (mailto:kate@logrocket.com) Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket combines frontend monitoring, product analytics, and session replay to help software teams deliver the ideal product experience. Try LogRocket for free today. (https://logrocket.com/signup/?pdr) Special Guest: Mark Burggraf.

Microsoft makes available the video-creation tool 3D Movie Maker. The Mozilla Foundation shares its research regarding mental health apps and their privacy policies and protections. Google announces new hardware at its Google I/O developer conference. Meta's Mark Zuckerberg shares a short demo of the company's next AR/VR headset. First, Scott Hanselman of Microsoft stops by to share how he successfully published the archive of Microsoft 3D Movie Maker after a developer on Twitter "nerd-sniped" him. Self-proclaimed software necromancer, Foone, is now working to modernize the movie-making tool. Then, Misha Rykov of the Mozilla Foundation shares the foundation's work on "*Privacy Not Included," a guide that details the privacy practices and protections of smart home hardware, apps, and services. The Foundation's latest focus? Mental health apps. Then, Mikah recounts the exciting — and numerous — hardware announcements at Google I/O. The company revealed a new Pixel 6 smartphone, a Pixel Watch smartwatch, and true-wireless earbuds with active noise cancelation. Google also shared some details about the Pixel 7 & Pixel 7 Pro smartphones, a Pixel tablet, and augmented-reality glasses. Lastly, Mikah discusses a new reveal from Meta's Mark Zuckerberg. The company is working on a mixed-reality headset, currently called Project Cambria, that will use high-quality cameras to stream a person's environment and overlay augmented reality experiences. Host: Mikah Sargent Guests: Scott Hanselman and Misha Rykov Download or subscribe to this show at https://twit.tv/shows/tech-news-weekly. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: nureva.com NetFoundry.io/TWIT NewRelic.com/TNW