Podcasts about JPEG

2-hours of live improvised experimental radio sound-art broadcast live from the Chakra Chimp Research Kitchens of Northern California-land.  Netcast on DFM Radio TV International (www.dfm.nu)  DFM RTV INT  21 DECEMBER 2025....This item belongs to: audio/ubradio_salon.This item has files of the following types: AIFF, Archive BitTorrent, Columbia Peaks, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

Another day, another malicious JPEG https://isc.sans.edu/diary/Another%20day%2C%20another%20malicious%20JPEG/32738 Calibre Path Traversal Leading to Arbitrary File Write and Potentially Code Execution CVE-2026-26064 CVE-2026-26065 https://github.com/kovidgoyal/calibre/security/advisories/GHSA-72ch-3hqc-pgmp https://github.com/kovidgoyal/calibre/security/advisories/GHSA-vmfh-7mr7-pp2w CVE-2026-25755: PDF Object Injection in jsPDF (addJS Method) https://github.com/ZeroXJacks/CVEs/blob/main/2026/CVE-2026-25755.md Roundcube Webmail Exploited CVE-2025-49113 https://roundcube.net/news/2025/06/01/security-updates-1.6.11-and-1.5.10 https://www.openwall.com/lists/oss-security/2025/06/02/3

AGENDA:  Intro Resultados torneos del fin Carlos Alcaraz vence a Arthur Fils 6-2, 6-1 para ganar el ATP 500 de Doha por primera vez. Título 26 para el español. Tomas Etcheverry vence a Alejandro Tabilo 3-6, 7-6, 6-4 para ganar el ATP 500 de Rio y el primer título de su carrera Sebastian Korda vence a Tommy Paul 6-4, 6-3 para ganar el ATP 250 de Delray Beach y el tercer título de su carrera Jessica Pegula vence a Elina Svitolina 6-2, 6-4 para ganar el WTA 1000 de Dubai y el 10mo título de su carrera. Torneos esta semana ATP 500 Acapulco ATP 500 Dubai ATP 250 Santiago WTA 500 Mérida WTA 250 Austin Top 10's Y más ... Instagram: @TennisPiochas Twitter: @TennisPiochas TikTok: @tennis.piochas Distribuido por Genuina Media Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hosts Snarfdude and Daffodil bring you Cheezy Music, on the road in a van in a 30 min version of the show in series 2.0  The show is still in production as of this writing. Details at www.cheezepleeze.com  PLAYLIST FOR THIS SHOW: Song List Star Wars (Main Title)-Electric Moog Orchestra Cantina Ban....This item has files of the following types: Archive BitTorrent, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

Hosts Snarfdude and Daffodil bring you Cheezy Music, on the road in a van in a 30 min version of the show in series 2.0  The show is still in production as of this writing. Details at www.cheezepleeze.com  PLAYLIST FOR THIS SHOW: Alternating Current-Ferrante and Teicher Liza-Ferrante and Teicher C....This item has files of the following types: Archive BitTorrent, Columbia Peaks, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

2-hours of live improvised experimental radio sound-art broadcast live from the Chakra Chimp Research Kitchens of Northern California-land.  Netcast on DFM Radio TV International (www.dfm.nu)  DFM RTV INT  1 FEB 2026....This item belongs to: audio/ubradio_salon.This item has files of the following types: AIFF, Archive BitTorrent, Columbia Peaks, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

2-hours of live improvised experimental radio sound-art broadcast live from the Chakra Chimp Research Kitchens of Northern California-land.  Netcast on DFM Radio TV International (www.dfm.nu)  DFM RTV INT  8 FEB 2026....This item belongs to: audio/ubradio_salon.This item has files of the following types: AIFF, Archive BitTorrent, Columbia Peaks, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

2-hours of live improvised experimental radio sound-art broadcast live from the Chakra Chimp Research Kitchens of Northern California-land.  Netcast on DFM Radio TV International (www.dfm.nu)  DFM RTV INT  15 FEB 2026....This item belongs to: audio/ubradio_salon.This item has files of the following types: AIFF, Archive BitTorrent, Columbia Peaks, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

2-hours of live improvised experimental radio sound-art broadcast live from the Chakra Chimp Research Kitchens of Northern California-land.  Netcast on DFM Radio TV International (www.dfm.nu)  DFM RTV INT  25 JAN 2026....This item belongs to: audio/ubradio_salon.This item has files of the following types: AIFF, Archive BitTorrent, Columbia Peaks, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

In this masterclass episode, Favour Obasi-ike, MBA, MS delivers an in-depth exploration of web sales optimization (CRO - conversation rate optimization) through strategic search engine marketing (SEM). The episode focuses on the critical relationship between website speed and conversion rates, revealing how technical optimization directly impacts sales performance. Favour emphasizes that web sales are fundamentally a result of web speed, explaining that websites loading slower than 3 seconds can decrease conversion rates by at least 7%, with compounding effects reaching 20% for sites taking 10 seconds to load.The discussion covers comprehensive website optimization strategies, including image optimization (recommending WebP format over JPEG/PNG), structured data implementation with schema markup, and the importance of optimizing every website element from headers and footers to file names and internal linking structures. Favour introduces the concept of treating URLs like seeds that need time to grow, recommending a 2-3 month planning horizon for content strategy.The masterclass also explores collection pages, category optimization, and the strategic use of content hubs to create pathways for user navigation. Favour shares practical tools and resources for keyword research and competitive analysis, while emphasizing the importance of submitting websites to Google Search Console and Bing Webmaster Tools for maximum visibility. The episode concludes with actionable advice on implementing these strategies either independently or through professional SEO consultation.Book SEO Services | Quick Links for Social Business>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Book SEO Services with Favour Obasi-ike⁠>> Visit Work and PLAY Entertainment website to learn about our digital marketing services>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our exclusive SEO Marketing community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Read SEO Articles>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to the We Don't PLAY Podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Purchase Flaev Beatz Beats Online>> Favour Obasi-ike Quick Links

Hosts Snarfdude and Daffodil bring you Cheezy Music, on the road in a van in a 30 min version of the show in series 2.0  The show is still in production as of this writing. Details at www.cheezepleeze.com  PLAYLIST FOR THIS SHOW: UFO Special Blast Off-Jimmie Haskell Music To Watch Space Girls by-L....This item has files of the following types: Archive BitTorrent, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

Hosts Snarfdude and Daffodil bring you Cheezy Music, on the road in a van in a 30 min version of the show in series 2.0  The show is still in production as of this writing. Details at www.cheezepleeze.com  PLAYLIST FOR THIS SHOW: Want To Hold Your Hand-Enoch Light & Orchestra Come Together & Hey J....This item has files of the following types: Archive BitTorrent, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

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

Calling all art and design enthusiasts! The Children's Alliance of South Texas is holding a T-shirt design contest. Participants are encouraged to make a cool T-shirt design using 1-2 colors for Child Abuse Prevention and Awareness Month in April. The contest winner will receive a free T-shirt with their design on it. Entries must be submitted in JPEG format by Feb. 20, to development@castac.org.Article Link

Show Notes Tarek Matar, founder of Scalar AI, explains the tool's purpose. He describes Scalar AI as an AI engine designed for consultants to build McKinsey level, end-to-end slides and presentations. The tool is differentiated from general AI tools like ChatGPT and GPT-3 by focusing on consulting-grade presentations. The founders include a research scientist from Google Brain and two other experienced professionals. Features and Functionality of Scalar AI Scalar AI automates the entire research, analysis, structure, and visualization process for consultants. The tool can create single slides or entire decks based on user prompts.It offers various modes: AI generation, text to slide, and sketch to slide, allowing flexibility in input methods. The tool includes a custom brand identity feature, allowing users to upload and customize their firm's PowerPoint templates. A Scalar.AI Demonstration Tarek demonstrates the tool by creating a slide and a deck.  Adding Prompts   Adding custom brand identity Tarek creates a waterfall slide showing the top five countries by international tourist arrivals.  Detailed data and insights The tool generates a visually appealing slide with detailed data and insights. Tarek explains the process of editing and refining the generated slides to meet specific needs. The Text to Slide Mode Tarek demonstrates the text to slide mode by pasting a long text about key success factors for post-merger integration in banking.  Data generation  The tool summarizes the text into a concise slide with bullet points and icons. They also show the sketch to slide mode by uploading a hand-drawn image, which the tool converts into a PowerPoint slide. The tool supports various image formats, including JPEG, PNG, and PDF. The Custom Brand Identity Feature Tarek explains the custom brand identity feature, which allows users to upload their firm's PowerPoint templates. The tool can save and apply custom colors, fonts, and slide masters. A prompting guide and video tutorials are available to help users effectively use the tool. Tarek mentions the importance of proper prompting to get the best results from the AI. Pricing and Subscription Details Tarek talks about the pricing and mentions discounts available for annual subscriptions and partnerships. The tool is designed for B2B clients, including consulting firms and independent consultants. Tarek discusses the possibility of working with freelancers and organizations like Umbrex to offer special pricing. The tool is integrated with PowerPoint, making it easy for users to access and use. Security and Data Privacy Tarek addresses concerns about data security and privacy when using Scalar AI. The tool uses enterprise LLMs and follows strict data retention policies, ensuring data is encrypted and anonymized. The tool generates slides on the user's device, not on Scalar AI's servers, maintaining data privacy. Tarek mentions that the tool is compliant with GDPR and can meet the security requirements of government entities. The Genesis Story of Scalar.AI Tarek shares the background of Scalar AI, including his experience as a consultant and his co-founders' technical expertise. The idea for the tool came from the need to automate workflows and create professional slides for consulting clients. The founders spent a significant amount of time in stealth mode, refining and testing the product. The tool is now entering the commercialization stage, with plans to expand its user base and features. Scalar.AI and the Consulting Industry Tarek discusses the potential impact of Scalar AI on the consulting industry. Tarek emphasizes the tool's ability to save time and improve productivity for consultants. They plan to continue refining the tool and exploring partnerships with organizations like Umbrex. Timestamps: 02:21: Features and Functionality of Scalar AI  02:37: Demonstration of Scalar AI's Capabilities  04:11: Text to Slide and Sketch to Slide Modes  22:15: Custom Brand Identity and Prompting Guide  22:36: Pricing and Subscription Details 31:08: Security and Data Privacy  36:14: Backstory and Development of Scalar AI  Links: Website: getscalar.ai  This episode on Umbrex: https://umbrex.com/wp-admin/post-new.php?post_type=unleashed#:~:text=https%3A//umbrex.com/unleashed/240677/   Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com. *AI generated timestamps and show notes.  

Hosts Snarfdude and Daffodil bring you Cheezy Music, on the road in a van in a 30 min version of the show in series 2.0  The show is still in production as of this writing. Details at www.cheezepleeze.com  PLAYLIST FOR THIS SHOW: My Cherie Amour-The Organ Masters The Thrill Is Gone-The Organ Maste....This item has files of the following types: Archive BitTorrent, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

Hosts Snarfdude and Daffodil bring you Cheezy Music, on the road in a van in a 30 min version of the show in series 2.0  The show is still in production as of this writing. Details at www.cheezepleeze.com  PLAYLIST FOR THIS SHOW: Tiger Rag-Marty Gold & His Doowackadoodlers Doo Wacka Doo-Marty Gold....This item has files of the following types: Archive BitTorrent, Columbia Peaks, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

2-hours of live improvised experimental radio sound-art broadcast live from the Chakra Chimp Research Kitchens of Northern California-land.  Netcast on DFM Radio TV International (www.dfm.nu)  DFM RTV INT  18 JAN 2026....This item belongs to: audio/ubradio_salon.This item has files of the following types: AIFF, Archive BitTorrent, Columbia Peaks, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

durée : 00:39:12 - La Terre au carré - par : Mathieu Vidard - Mondialement reconnu pour ses travaux sur les ondelettes, il est entre autres l'inventeur du format JPEG 2000, Stéphane Mallat a reçu la médaille d'or du CNRS en décembre dernier pour l'ensemble de ses recherches en mathématiques à la fois théoriques et appliquées. Vous aimez ce podcast ? Pour écouter tous les autres épisodes sans limite, rendez-vous sur Radio France.

2-hours of live improvised experimental radio sound-art broadcast live from the Chakra Chimp Research Kitchens of Northern California-land.  Netcast on DFM Radio TV International (www.dfm.nu)  DFM RTV INT  11 JAN 2026....This item belongs to: audio/ubradio_salon.This item has files of the following types: AIFF, Archive BitTorrent, Columbia Peaks, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

Hosts Snarfdude and Daffodil bring you Cheezy Music, on the road in a van in a 30 min version of the show in series 2.0  The show is still in production as of this writing. Details at www.cheezepleeze.com  PLAYLIST FOR THIS SHOW: Our Pig Special! Oink Oink Mambo- Chuy Reyes & His Orch My Piggie'....This item has files of the following types: Archive BitTorrent, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

Hosts Snarfdude and Daffodil bring you Cheezy Music, on the road in a van in a 30 min version of the show in series 2.0  The show is still in production as of this writing. Details at www.cheezepleeze.com  PLAYLIST FOR THIS SHOW: Candy Man-The Brass Explosion Orch & Chorus Brandy-The Brass Explosi....This item has files of the following types: Archive BitTorrent, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

2-hours of live improvised experimental radio sound-art broadcast live from the Chakra Chimp Research Kitchens of Northern California-land.  Netcast on DFM Radio TV International (www.dfm.nu)  DFM RTV INT  4 JAN 2026....This item belongs to: audio/ubradio_salon.This item has files of the following types: AIFF, Archive BitTorrent, Columbia Peaks, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

2-hours of live improvised experimental radio sound-art broadcast live from the Chakra Chimp Research Kitchens of Northern California-land.  Netcast on DFM Radio TV International (www.dfm.nu)  DFM RTV INT  28 DEC 2025....This item belongs to: audio/ubradio_salon.This item has files of the following types: AIFF, Archive BitTorrent, Columbia Peaks, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

Thank you to our sponsor, Mantle!Mantle is launching the Global Hackathon 2025 to accelerate the future of Real-World Assets. With a $150k prize pool, backing from a $4B treasury, and direct access to Bybit's 7M+ users, this is the ultimate ecosystem for builders. Sign up here! In this year-end Bits + Bips roundtable, hosts Austin Campbell and Chris Perkins are joined by John D'Agostino, Head of Strategy at Coinbase Institutional, for a wide-ranging and often contentious look at what 2026 may hold for crypto. They debate whether a major global brand will launch its own stablecoin, whether altcoins are structurally doomed—or secretly set up for a Wall Street–driven resurgence—and whether a major crypto hack is coming. The conversation also explores how tokens accrue value and whether there will be a new M&A trend that'll reshape the industry as we know it. Plus: don't miss what they have to say about NFTs, financial nihilism, and whether we'll see all-time highs for bitcoin in 2026. Hosts: Ram Ahluwalia, CFA, CEO and Founder of Lumida Austin Campbell, NYU Stern professor and Founder of Zero Knowledge Consulting Christopher Perkins, Managing Partner and President of CoinFund Guest: John D'Agostino, Head of Strategy for Coinbase Institutional Timestamps

Thank you to our sponsor, Mantle!Mantle is launching the Global Hackathon 2025 to accelerate the future of Real-World Assets. With a $150k prize pool, backing from a $4B treasury, and direct access to Bybit's 7M+ users, this is the ultimate ecosystem for builders. Sign up here! In this year-end Bits + Bips roundtable, hosts Austin Campbell and Chris Perkins are joined by John D'Agostino, Head of Strategy at Coinbase Institutional, for a wide-ranging and often contentious look at what 2026 may hold for crypto. They debate whether a major global brand will launch its own stablecoin, whether altcoins are structurally doomed—or secretly set up for a Wall Street–driven resurgence—and whether a major crypto hack is coming. The conversation also explores how tokens accrue value and whether there will be a new M&A trend that'll reshape the industry as we know it. Plus: don't miss what they have to say about NFTs, financial nihilism, and whether we'll see all-time highs for bitcoin in 2026. Hosts: Ram Ahluwalia, CFA, CEO and Founder of Lumida Austin Campbell, NYU Stern professor and Founder of Zero Knowledge Consulting Christopher Perkins, Managing Partner and President of CoinFund Guest: John D'Agostino, Head of Strategy for Coinbase Institutional Timestamps

Hosts Snarfdude and Daffodil bring you Cheezy Music, on the road in a van in a 30 min version of the show in series 2.0  The show is still in production as of this writing. Details at www.cheezepleeze.com  PLAYLIST FOR THIS SHOW: The Angry Desert-The Sound Offs Brontosaurus Stomp-The Piltdown Men ....This item has files of the following types: Archive BitTorrent, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

This is a recap of the top 10 posts on Hacker News on December 23, 2025. This podcast was generated by wondercraft.ai (00:30): Inside CECOT – 60 Minutes [video]Original post: https://news.ycombinator.com/item?id=46361024&utm_source=wondercraft_ai(01:51): Fabrice Bellard Releases MicroQuickJSOriginal post: https://news.ycombinator.com/item?id=46367224&utm_source=wondercraft_ai(03:13): Meta is using the Linux scheduler designed for Valve's Steam Deck on its serversOriginal post: https://news.ycombinator.com/item?id=46366998&utm_source=wondercraft_ai(04:35): Instant database clones with PostgreSQL 18Original post: https://news.ycombinator.com/item?id=46363360&utm_source=wondercraft_ai(05:57): Ask HN: What are the best engineering blogs with real-world depth?Original post: https://news.ycombinator.com/item?id=46363921&utm_source=wondercraft_ai(07:18): We replaced H.264 streaming with JPEG screenshots (and it worked better)Original post: https://news.ycombinator.com/item?id=46367475&utm_source=wondercraft_ai(08:40): Snitch – A friendlier ss/netstatOriginal post: https://news.ycombinator.com/item?id=46361229&utm_source=wondercraft_ai(10:02): X-ray: a Python library for finding bad redactions in PDF documentsOriginal post: https://news.ycombinator.com/item?id=46369923&utm_source=wondercraft_ai(11:24): Show HN: CineCLI – Browse and torrent movies directly from your terminalOriginal post: https://news.ycombinator.com/item?id=46362655&utm_source=wondercraft_ai(12:45): 10 years bootstrapped: €6.5M revenue with a team of 13Original post: https://news.ycombinator.com/item?id=46363319&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Welcome to Turtle Jump--the video game podcast of your heart and mine (and mine)! We're back in your life with another TJ, just in time for Christmas! (We're dropping this one a day early since tomorrow is Christmas Eve!) This week, Paul and Allen finally share their thoughts on the legend that is Metroid Prime 4: Beyond. And by "legendary," I literally mean it could've been nothing more than a legend... But it's here, it's Metroid, and it's def not just a JPEG from 2017... We hope you have a very Merry Christmas, and we'll see you in January!

The thought exercise was to consider what you would like to change or enhance on your current motorcycle and does that push you to another motorcycle? If so, then what? This could have also been used for the cover art :) Ducati Multistrada V4 RS https://media24.fireside.fm/file/fireside-uploads-2024/images/e/e9f8d2eb-9140-4ae8-8ade-c38bd0c47027/uMokpXpK.JPEG

Send us a text

Brian Alaska 1 https://media24.fireside.fm/file/fireside-uploads-2024/images/e/e9f8d2eb-9140-4ae8-8ade-c38bd0c47027/qP-DlW1E.JPEG Brian Alaska 2 https://media24.fireside.fm/file/fireside-uploads-2024/images/e/e9f8d2eb-9140-4ae8-8ade-c38bd0c47027/z-NPSfqb.JPEG Brian Alaska 3 https://media24.fireside.fm/file/fireside-uploads-2024/images/e/e9f8d2eb-9140-4ae8-8ade-c38bd0c47027/nCQgmr7a.JPEG Fighter Jet Motorcycle AKA BMW R1300GSA https://media24.fireside.fm/file/fireside-uploads-2024/images/e/e9f8d2eb-9140-4ae8-8ade-c38bd0c47027/sASYK_dS.JPEG

Join the Imagen Community on Facebook to continue the discussions between episodes.Ready to rethink your photography workflow? Whether you're passionate about crafting conceptual images or streamlining your edits, this episode of Workflows offers a fresh look at how photographers manage their files, from camera to cloud.Aaron Nace is the founder of PHLEARN - an online education platform for photographers and digital artists. Through his love of teaching, he has reached millions of people via YouTube , where you can catch weekly free videos teaching photography, retouching, compositing and more.Aaron shares his personal approach to photography, compares shooting in RAW versus JPEG, and reveals why he swears by cloud storage for his entire creative process. Discover the pros, the pitfalls, and the best practices you can implement right away to gain more time for what you love: making photos.“You capture as much information as possible as the camera can possibly capture, and then you can decide later what to do with that information.” - Aaron NaceResourcesDropboxGoogle DriveProton DriveWhy You Should ListenLearn how industry pros streamline and safeguard their photography files.Decide whether RAW or JPEG fits best into your workflow, backed by real-world experience.Get actionable strategies for storage and organization that will reduce clutter and stress.Discover how to collaborate seamlessly with remote editors or team members.Gain insight on embracing a minimalist approach for more freedom and less distraction.Subscribe to Workflows and never miss an episode packed with tips for editing, organizing, and delivering exceptional imagery with less hassle.(00:00) - Aaron Nace (01:42) - RAW vs JPEG Debate (05:55) - Black and White Photography Insights (10:44) - AI in Photo Editing (12:33) - Cloud Storage for Photographers (20:13) - Managing Local and Cloud Storage with Dropbox (20:55) - Organizing Files for Efficient Storage (22:33) - Challenges with Lightroom Catalogs (25:06) - A Minimalist Approach to Digital Storage (29:09) - Choosing the Right Cloud Storage Solution

1) Stéphane Mallat et la poésie des maths Stéphane Mallat, lauréat de la médaille d'or du CNRS, révolutionne les mathématiques appliquées avec son algorithme, à lʹorigine du format JPEG. Ses travaux sur les ondelettes et l'IA dévoilent la poésie des maths, essentielles pour comprendre le monde. 2) L'influence de la ménopause sur le cerveau La ménopause, ainsi que la période qui la précède, influence le cerveau, causant parfois un "brouillard mental" et des troubles de l'humeur. Lors d'une rencontre à Lausanne, des femmes ont partagé leurs expériences, soulignant un manque de données scientifiques. 3) Félicette, la seule et unique chatte spationaute Cʹest une petite chatte noire et blanche qui a été sélectionnée par le programme spatial Français, pour monter à bord dʹune fusée en 1963. Voilà Felicette en route pour lʹespace afin que les chercheurs et chercheuses puissent constater comment un cerveau se conduit en apesanteur!

Lords * Alexander * Kev Topics: * "Floaty movement" in games * The power to give people clutter that they can't throw away * https://www.lancaster.ac.uk/users/yorkdoom/palweb/week12/palwk12.htm * https://www.anabuzzalino.com/wp-content/uploads/2015/04/book-of-kells.jpg * Dunning Kruger got debunked! * https://economicsfromthetopdown.com/2022/04/08/the-dunning-kruger-effect-is-autocorrelation/ * With or Without Witnesses, by Hasbro, Inc. * https://scryfall.com/card/jmp/457/alloy-myr * Esper says: "A lot of the lore and worldbuilding of Magic is conveyed through flavor text, but there's also about a novel's worth of writing for each set distributed as webfiction and a few dozen novels throughout the game's history. It's good stuff, usually!" * The instinct to not swing a heavy object at someone, even when you're supposed to * Japanese curry Microtopics: * The Humble Screw. * What it might take to be good at hammering nails. * Fingerbashing. * An inclined plane that goes around in a circle. * Metric Rules Football. * Whether a state machine is the same kind of thing as a wedge. * Clockwork Maiden. * Speedrunning, cleaning and dating. * Floaty movement. * The JPEG artifact of physics simulation. * How to Unit Test a video game. * Using MIDI knobs to tune your game's physics. * Game Oriented Assembly Lisp. * Hot Reloading Workflow. * Why Naughty Dog stopped using Lisp. * Just going about your life when suddenly you have to use Perl for something. * Peak Node.JS/SAAS years. * Walk/run/sprint transition speed. * Getting an special drink for your guys. * Medieval style calligraphy. * Gifting someone with a burden for the rest of their lives. * Pouring rocks into your dad's pocket because that's where you keep your rocks. * The kitten going to sleep behind your laptop because it's warm back there. * Here's how someone in the 14th century would've written these words. * How to say "fuck around and find out" in medieval Latin. * Writing a meme in 14th century Blackletter Hand. * Sans-serif calligraphy. * The ancient Greeks trying to invent ASCII art but they couldn't close the deal. * Letters placed inside other letters. * Autocorrelation. * Figures 8 and 9. * Coming out of discussion the topic with a better understanding of the topic. * Using a bird feather to write. * Knowledge that we used to have and now have again. * Learning to Topic Lords while doing home improvement stuff. * Scrapping your fragile human body to merge with the machines. * Rainbows in Space. * Suns going around in circles regardless of whether anyone's looking. * A semi-conscious little robot dude. * Calling the Qualia Function. * A real messed-up looking machine cat. * Communicating the story solely via flavor text on mana dorks. * A squadron of little kids waving wooden swords at each other. * How do you turn sword fighting into something you can do in a video game? * East Bay Rat Motorcycle Club Flight Nights. * Situations where they ask if you want a mouth guard. * Asking the ref at the boxing match what the etiquette is for tapping out. * The crowd roaring when they realize you're a southpaw. * Punching a guy you just met three seconds ago. * Did you end up cutting that girl's head off? * Renaissance MMA. * That Awkward Sparring Feeling. * A simulacrum of kicking each other in the face. * Getting a part-time job at your favorite curry shop so you can learn the recipe. * What part of curry is the curry? * The curry you eat so you don't die. * Emperor Riding a Dragon to the Forbidden Palace. * The advantage of a burger as a fast food staple. * How to make curry convenient. * Rendang. * Dry curry. * Joining a discord and asking "what are all these users for??" * Which user is which lord on which episode?

You've heard it before: Whenever possible, always create RAW files rather than JPEGs. The reason given is that camera RAWs give you much more data to work with, which in turn means that you can do a lot more in the digital darkroom. The same is true if you look back at film photography. One of the reasons that JPEGs and digital images were so exciting to the first digital photographers is that you can do so much more with a JPEG than with film. To put it all into perspective, I'll show you some of the limits that early film photographers have to work with. You'll also see how RAW files represent a leap in technology at least as great as the leap from film to digital... Podcast Notes: https://www.moneymakerphotography.com/pros-shoot-raw/ Photography Clips Podcast: https://www.moneymakerphotography.com/podcast/ Follow me: https://www.facebook.com/Will.Moneymaker  #PhotographyClips #WillMoneymaker #Photography

Last week on REKT Vision, Mando, Rekt co-founder and author of the Mando Minutes newsletter, is joined by JPEG enthusiast (as he calls himself) gmoney. They discuss the biggest narratives and themes driving cryptocurrencies right now, including the Federal Reserve' interest rate cut, BNB and HYPE rallies, Base exploring issuing a token, new crypto ETFs, and much more.

We're explaining why trying to filter Bitcoin is a fool's errand. We dive into the “filter debate,” dissecting why some Bitcoin purists are demanding JPEG‑free blocks and why their efforts are futile, but harmful to the bitcoin network. Fee economics, block‑size limits, real‑world examples, and the clash between censorship resistance and arbitrary data. Subscribe to the newsletter! https://newsletter.blockspacemedia.com Notes:     Block size capped ~4 MB (~250 GB/yr)     $600 M+ spent on ordinal fees     Knots rose from 5 % to 18 %     30/25 000 nodes filtered in early test     100 % filtered nodes still ineffective     Fee market drives transaction inclusion Timestamps: 00:00 Start 02:54 Letter Analogy 05:40 Nations censoring transactions 07:16 Spam 16:16 JPEGS 18:03 Block size 19:12 Death to JPEGS 20:32 IBD (initial block download) 26:11 But we are filtering X transactions! 27:24 First principles 34:36 Oh Luke... so disappointing.. -

AI 正在「杀死」网络！这是过去一段时间外网媒体在讨论的一个话题。它们为何会有这样的讨论，AI 在「杀死」的究竟是什么？这对我们普通人会有影响吗？ 如果你有留意过谷歌或者小红书等网络应用上新出现的「AI 摘要」，那你正在见证一种变化。实际上，通过改变人们的搜索、点击行为，AI 正在改变传统的网络流量分配机制，以及建立在此基础上的「广告+免费内容」商业模式。 这期节目，我们和老朋友方可成老师一起，从「AI 正在杀死万维网」的讨论出发，聊聊旧模式的坍塌、创作者的生存困境，以及在 AI 正在制造的内容荒漠中，是否还有新的商业模式和内容生态可以被想象。 本期人物 徐涛，声动活泼联合创始人 方可成，香港中文大学新闻与传播学院副教授 主要话题 [00:58] 为什么说 AI 正在杀死万维网 (Web) [09:06] AI 摘要正在如何影响我们的搜索行为和流量分配 [19:53] 广告+免费内容的商业模式：皆大欢喜还是互联网原罪 [33:46] 我们有可能在 AI 环境下创造出一个更健康的商业模式吗 [56:12] AI 可能导向的内容荒漠以及创作者生存手册 给声东击西投稿 「声东击西」一直在寻找来自不同社会和群体的真实声音。我们曾经采访过为特朗普竞选生产 MAGA 帽子的中国制造商、记录过七位在美国大选中经历起伏的华人个体，也讲述了签证突然被取消的在美留学生的故事。 如果你也有一些特别的经历、观察或想法，不论是亲身体验的故事，还是你在某个行业、社区中的所见所闻，都欢迎你向我们投稿。 你的声音可能出现在未来的节目当中，我们非常期待你的分享！ 投稿入口 (https://eg76rdcl6g.feishu.cn/share/base/form/shrcne1CGVaSeJwtBriW6yNT2dg) 你也可以直接通过邮箱直接联系节目组：kexuan@shengfm.cn 青少年节目「Knock Knock 世界」 Untitled https://media24.fireside.fm/file/fireside-uploads-2024/images/8/8dd8a56f-9636-415a-8c00-f9ca6778e511/go4MB7KJ.JPEG 今年 3 月，我们推出了一档专为青少年制作的播客节目：每期从一个青少年感兴趣的现象谈起，涉及商业、科技、社会和文化，解读表象背后的深层逻辑，启发青少年提出自己的好奇。每期 10 分钟，每周一三五更新。 前 3 期节目可以免费试听，可在各大平台搜索「Knock Knock 世界」收听； 小宇宙听友请点这里 (https://sourl.cn/sJfRsk) Apple Podcast 听友请点这里 (https://sourl.cn/Nckucx) 加入我们 声动活泼目前开放节目运营、社群运营、内容营销这三个市场部门岗位，以及 bd 经理和HR 行政助理、人才发展伙伴岗，详情点击招聘入口，加入声动活泼（在招职位速览） (加入声动活泼（在招职位速览）)，点击相应链接即可查看岗位详情及投递指南。 幕后制作 监制：可宣 后期：赛德 运营：George 设计：饭团 商务合作 声动活泼商业化小队，点击链接可直达商务会客厅（商务会客厅链接：https://sourl.cn/QDhnEc），也可发送邮件至 business@shengfm.cn 联系我们。 关于声动活泼 「用声音碰撞世界」，声动活泼致力于为人们提供源源不断的思考养料。 我们还有这些播客：不止金钱（2024 全新发布） (https://www.xiaoyuzhoufm.com/podcast/65a625966d045a7f5e0b5640)、跳进兔子洞第三季（2024 全新发布） (https://www.xiaoyuzhoufm.com/podcast/666c0ad1c26e396a36c6ee2a)、声东击西 (https://etw.fm/episodes)、声动早咖啡 (https://sheng-espresso.fireside.fm/)、What's Next｜科技早知道 (https://guiguzaozhidao.fireside.fm/episodes)、反潮流俱乐部 (https://fanchaoliuclub.fireside.fm/)、泡腾 VC (https://popvc.fireside.fm/)、商业WHY酱 (https://msbussinesswhy.fireside.fm/) 欢迎在即刻 (https://okjk.co/Qd43ia)、微博等社交媒体上与我们互动，搜索 声动活泼 即可找到我们。 也欢迎你写邮件和我们联系，邮箱地址是：ting@sheng.fm 获取更多和声动活泼有关的讯息，你也可以扫码添加声小音，在节目之外和我们保持联系！ 声小音 https://files.fireside.fm/file/fireside-uploads/images/8/8dd8a56f-9636-415a-8c00-f9ca6778e511/hdvzQQ2r.png Special Guest: 方可成.

本期节目由【蚂蚁保·长钱保】赞助播出 美国的债务和赤字问题如何影响了现今美国的政治走势，而大洋彼岸的这一问题为何甚至会影响不同地区的个人？ 根据美联社的最新报道 (https://apnews.com/article/treasury-debt-spending-trump-obbb-6f807c4aae78dcc96f29ff07a3c926f4)，美国的国债已经突破了 37 万亿。而从特朗普的减税、加征关税，到马斯克批评「大而美法案」，再到美联储和白宫的拉锯，近期美国社会的一系列政治与经济波动，其实也都与这笔债务相关。 这期节目，我们从达里奥的新书《国家为什么会破产》出发，梳理过去和现在，发生在美国和全球其他国家的债务困境，它们为何发生、结局如何？国家的债务究竟能不能「放任生长」？最后，我们也回到个人：在一个高度不确定的年代，我们可以如何为自己做长期的准备？ 本期人物 徐涛，声动活泼联合创始人 周玖洲 Aaron，资深投资人 &「不止金钱」主播 主要话题 [02:04] 36 万亿的美国国债意味着什么 [10:29] 全球哪个国家背的债最多 [19:17] 如果美国用印美钞来还债会发生什么 [32:11] 美国债务问题的未来有哪些可能性 [45:38] 长期主义想要解决的问题是什么 [50:59] 大债务周期下的普通人可以怎么做 蚂蚁保·长钱保 在不确定的世界里，每个人都可以尽早做4笔钱的规划，来应对周期波动。 1笔活钱（占比 10%）用于3-6个月短期生活支出，1笔保命钱（占比 20%）应对健康风险，1笔进取钱（占比 30%）博取高收益，1笔长钱（占比 40%）长期增值为未来几十年的生活保驾护航。 其中，40% 的长钱尤为重要，其特质为资金安全、收益稳定，可以实现较长期的升值。 想做长钱规划的朋友，可以上支付宝搜【长钱保】，它是蚂蚁保（蚂蚁集团旗下的保险优选平台）联合多家知名保司推出的储蓄型保险品牌，也是普通家庭做长期资金规划的更好选择，它可以用每月缴的方式给未来做好储备，用低门槛锁定长期收益，收益较稳定且能写进合同，希望每个人都可以轻松做长钱规划。 注：市场有风险，投资需谨慎。本期内容不构成任何投资建议。 Untitled https://media24.fireside.fm/file/fireside-uploads-2024/images/8/8dd8a56f-9636-415a-8c00-f9ca6778e511/u6Ep40mk.jpeg 给声东击西投稿 「声东击西」一直在寻找来自不同社会和群体的真实声音。我们曾经采访过为特朗普竞选生产 MAGA 帽子的中国制造商、记录过七位在美国大选中经历起伏的华人个体，也讲述了签证突然被取消的在美留学生的故事。 如果你也有一些特别的经历、观察或想法，不论是亲身体验的故事，还是你在某个行业、社区中的所见所闻，都欢迎你向我们投稿。 你的声音可能出现在未来的节目当中，我们非常期待你的分享！ 投稿入口 (https://eg76rdcl6g.feishu.cn/share/base/form/shrcne1CGVaSeJwtBriW6yNT2dg) 你也可以直接通过邮箱直接联系节目组：kexuan@shengfm.cn 延伸阅读 《国家为什么会破产》 (https://book.douban.com/subject/37370552/) 青少年节目「Knock Knock 世界」 Untitled https://media24.fireside.fm/file/fireside-uploads-2024/images/8/8dd8a56f-9636-415a-8c00-f9ca6778e511/go4MB7KJ.JPEG 今年 3 月，我们推出了一档专为青少年制作的播客节目：每期从一个青少年感兴趣的现象谈起，涉及商业、科技、社会和文化，解读表象背后的深层逻辑，启发青少年提出自己的好奇。每期 10 分钟，每周一三五更新。 前 3 期节目可以免费试听，可在各大平台搜索「Knock Knock 世界」收听； 小宇宙听友请点这里 (https://sourl.cn/sJfRsk) Apple Podcast 听友请点这里 (https://sourl.cn/Nckucx) 加入我们 声动活泼目前开放节目运营、社群运营、内容营销这三个市场部门岗位，以及 bd 经理和HR 行政助理、人才发展伙伴岗，详情点击招聘入口，加入声动活泼（在招职位速览） (加入声动活泼（在招职位速览）)，点击相应链接即可查看岗位详情及投递指南。 幕后制作 监制：可宣 内容实习生：飞扬 后期：赛德 运营：George 设计：饭团 商务合作 声动活泼商业化小队，点击链接可直达商务会客厅（商务会客厅链接：https://sourl.cn/QDhnEc ），也可发送邮件至 business@shengfm.cn 联系我们。 关于声动活泼 「用声音碰撞世界」，声动活泼致力于为人们提供源源不断的思考养料。 我们还有这些播客：不止金钱（2024 全新发布） (https://www.xiaoyuzhoufm.com/podcast/65a625966d045a7f5e0b5640)、跳进兔子洞第三季（2024 全新发布） (https://www.xiaoyuzhoufm.com/podcast/666c0ad1c26e396a36c6ee2a)、声东击西 (https://etw.fm/episodes)、声动早咖啡 (https://sheng-espresso.fireside.fm/)、What's Next｜科技早知道 (https://guiguzaozhidao.fireside.fm/episodes)、反潮流俱乐部 (https://fanchaoliuclub.fireside.fm/)、泡腾 VC (https://popvc.fireside.fm/)、商业WHY酱 (https://msbussinesswhy.fireside.fm/) 欢迎在即刻 (https://okjk.co/Qd43ia)、微博等社交媒体上与我们互动，搜索 声动活泼 即可找到我们。 也欢迎你写邮件和我们联系，邮箱地址是：ting@sheng.fm 获取更多和声动活泼有关的讯息，你也可以扫码添加声小音，在节目之外和我们保持联系！ 声小音 https://files.fireside.fm/file/fireside-uploads/images/8/8dd8a56f-9636-415a-8c00-f9ca6778e511/hdvzQQ2r.png Special Guest: 周玖洲 Aaron.

We're pleased to welcome Dr. Jimi Jones and Dr. Marek Jancovic, authors of The Future of Memory: A History of Lossless Format Standards in the Moving Image Archive (U of Illinois Press, 2025), to the New Books Network. In this book, Jimi Jones and Marek Jancovic document the development and adoption of JPEG 2000, FFV1, MXF, and Matroska while investigating the social and material aspects of their design and the forces driving their journeys from niche to ubiquity. Drawing on interviews with archivists and developers, Jones and Jancovic reveal the archive as a dynamic space where deeply entrenched social practices produce disagreements but also resourceful collaborations. They contrast the unprecedented rise of archivist-driven standardization and controversies around non-standard technology with the historical dominance of the film and broadcast industries. Throughout, the authors clarify the role of tech companies, software developers, film pirates, hackers, and other players with poorly understood roles in the process. A timely look at the state of audiovisual preservation, The Future of Memory provides a history of recent innovations alongside a snapshot of a field in the midst of profound technological change. Your host is Dr. Adam Kriesberg, Associate Professor at the Simmons University School of Library and Information Science. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

While Brian frolics somewhere in the Pacific Northwest, Jason brings in cyber-sleuth Dave Bittner for a jam-packed episode covering everything from Gen X's slow descent into obscurity to furries, feds, and face-scanning your way into porn. The guys start with a salute to the late, great Tom Lehrer—a math nerd with a piano and zero tolerance for BS—before diving into the avalanche of cyber screwups plaguing today's digital circus.The biggest spill? The so-called “safe” dating app Tea just doxxed its entire user base—because who needs privacy when you've got bad Firebase settings from 2017? Meanwhile, teens are befriending chatbots, Microsoft is issuing pink slips via PowerPoint, and Meta might be training its AI on stolen porn. Add in farmers installing turnstiles in the Dolomites to keep influencers off their grass, age verification laws that Norman Reedus can bypass with a JPEG, and Tesla diners turning into 24/7 neighbor hellscapes, and yeah—it's just another week on the internet.If you're a Gen Xer feeling invisible, underpaid, and over it, congrats—you're not alone. This episode is a full buffet of schadenfreude, digital paranoia, and good old-fashioned grump. Pour a cup of whatever's not boiling, and tune in for the roast. Tom Lehrer would've approved.Sponsors:DeleteMe - Head over to JoinDeleteMe.com/GOG and use the code "GOG" for 20% off.Private Internet Access - Go to GOG.Show/vpn and sign up today. For a limited time only, you can get OUR favorite VPN for as little as $2.03 a month.SetApp - With a single monthly subscription you get 240+ apps for your Mac. Go to SetApp and get started today!!!1Password - Get a great deal on the only password manager recommended by Grumpy Old Geeks! gog.show/1passwordShow notes at https://gog.show/708FOLLOW UPWhy Gen X is the real loser generationTeens say they are turning to AI for friendshipIN THE NEWSHackers steal images from women's dating safety app that vets menHackers leak 13,000 user photos and IDs from the Tea app, designed as a women's safe spaceTea dating app disables direct messaging as it investigates data breachThe Tea App Data Breach: What Was Exposed and What We Know About the Class Action LawsuitTea App's Second Breach: 1.1 Million Private Messages Exposed in ...The Tea App Breach: A Catastrophic Privacy Failure in the Quest for ...Tea App Leak: What's Going on With the 4chan Tea App Data ...Tea app hacked: 13,000 photos leaked after 4chan call to actionThe Tea app hack explained – how a data breach spilled thousands of photos from the top free US app, and what to doWomen are reporting bad men on this app. Here's the legal tea on the app called TeaMajor Security Breach at Tea App Exposes Sensitive User DataThe dating app that doxxed 72,000 women... - YouTubeTea app fallout worsens as leaked selfies used in rating site, online ...Two data breaches in one week on social media site TeaDating safety app Tea suspends messaging after hack - BBCFirst Came Tea. Then Came the Male Rage.The Tea App Data Breach: What Was Exposed and What We Know ...How Tea's data breach became a brand momentTea app takes messaging system offline after security breachTea app hacked as women's photos, IDs & even DMs leaked onlineMicrosoft Releases List of Jobs Most and Least Likely to Be Replaced by AICopyright Lawsuit Accuses Meta of Pirating Adult Films for AI TrainingFed-up Italian farmers set up mountain turnstiles to charge access to Instagram hot spotsGrumpy Old Geeks recommend Private Internet AccessThe Age-Gated Internet Is HereSocial media age verification laws in the United States - WikipediaAll the loopholes people are using to get past the Online Safety ActAge Verification Laws Send VPN Use Soaring—and Threaten the Open InternetThe UK's new age-gating rules are easy to bypass - The VergeHow Minors Bypass Age Verification: 6 Common Methods to Watch ...Age Verification in the United States: Insights from the Open ...Age-Verification Evasion in 2025: How Minors Outsmart ... - Shufti ProExploring Privacy-Preserving Age Verification: A Close Look at Zero-Knowledge ProofsWhat to know about online age verification laws | AP NewsUS State age verification laws for adult content – AVPAAge verification tools on adult websites bypassed in secondsAge Verification - The Heritage FoundationAge Verification Bill Tracker - Free Speech CoalitionOnline Pornography Age Verification Laws by US State - KindbridgeOnline Age Verification Laws Could Do More Harm Than GoodUK probes 34 porn sites under new age-check rulesHow to Bypass US Porn Ban and Age Verification Laws - CybernewsWhy I Emphatically Oppose Online Age Verification MandatesReady or not, age verification is rolling out across the internetTesla partly liable in Florida Autopilot trial, jury awards $200M punitive damagesChatGPT users shocked to learn their chats were in Google search resultsLiving Next To Tesla Diner Is 'Absolute Hell,' Neighbors SaySongs and Lyrics by Tom LehrerTHE DARK SIDE WITH DAVEDave BittnerThe CyberWireHacking HumansCaveatControl LoopOnly Malware in the BuildingFurries and SecurityTom Lehrer was the face of the real 1950sTom Lehrer Full Copenhagen PerformanceThe delightful story of a prank Tom Leher played on the NSAPeter SchickeleInsta360 X5The History of Hollywood's Large Format Film Cameras!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

#584 In this episode of the podcast, I explore the idea that your photos are not broken just because they aren't technically perfect or heavily edited. I share my own experiences as a photographer, reflecting on how the rise of digital editing and AI has made many of us feel like every photo needs to be “fixed” to be worth keeping or sharing. I encourage you to let go of those expectations and to rediscover what made you pick up a camera in the first place—whether it was to slow down, see the world differently, or simply enjoy making images.KEY TOPICS COVEREDThe Pressure of Perfection and Editing in Modern Photography - Raymond discusses how digital tools, social media, and AI have shifted expectations toward perfection. He challenges the presumption that photos must always be edited, and recalls the joy of simple, unedited film photography. The key takeaway is to let go of perfection and rediscover the fun in photography.Imperfection as Art and Authenticity - The episode illustrates, with anecdotes from Raymond's wedding photography, the value of candid moments (like a flower girl picking her nose), emphasizing that these “flaws” make photos memorable and human. He connects this to the broader theme of authenticity, suggesting that imperfection is what sets human-made images apart from AI-generated ones.Practical Approach: Enjoyment and Mindset Shift (Plork) - Raymond introduces “plork” (play + work). He encourages listeners to shoot for enjoyment, intentionally practicing and experimenting with their cameras rather than working only to “fix” images later. Actionable advice includes shooting JPEGs, skipping editing, and focusing on moments that feel right rather than those that look perfect.IMPORTANT DEFINITIONS & CONCEPTSPlork: A blend of play and work, meaning practicing a craft with a playful, no-pressure attitude. In photography, “plorking” means shooting for enjoyment while still improving your skills—the core mindset Raymond urges listeners to adopt.DISCUSSION & REFLECTION QUESTIONSWhen do you feel most pressure to edit your photos, and how does it affect your enjoyment of photography?Can you think of a photo you love because of, not in spite of, its imperfections? Why does it resonate with you?What habits can you adopt to “plork” more during your photography sessions?RESOURCES:Check out the Headway App for book summaries - https://makeheadway.com/Book: "The Creative Act" by Rick RubinBook: "The Dude and the Zen Master" by Jeff BridgesSign up for your free CloudSpot Account today at www.DeliverPhotos.comConnect with Raymond! Join the free Beginner Photography Podcast Community at https://beginnerphotopod.com/group Get your Photo Questions Answered on the show - https://beginnerphotopod.com/qa Grab your free camera setting cheatsheet - https://perfectcamerasettings.com/ Thanks for listening & keep shooting!

#580 In Today's Episode of the podcast I chat with YOU as I tackle listener-submitted questions for our monthly Photo Q&A session. We cover a variety of challenges that many beginner photographers run into—from understanding the difference between RAW and JPEG files, managing noise in corporate event shots, to building a strong portfolio through strategic “model calls.” I'll also share practical insights on why your photos might look different across devices or editing programs, when to use settings like aperture priority or manual mode, and how to set up and communicate a successful portfolio shoot. Plus, we'll get into deeper topics like how to price your work as a new photographer and whether AI editing tools are changing the value of getting things right in-camera. KEY TOPICS COVEREDRAW vs. JPEG & Display Differences - Raymond explains why photos often appear different on camera screens or phones compared to Lightroom. He outlines the technical reasons, including how cameras display a JPEG preview even for RAW shots, and how monitor quality and calibration affect perceived color and contrast. He demystifies RAW editing and encourages beginners to experiment with JPEGs if extensive editing isn't required.Shooting Busy Events & Managing Noise - Responding to a question on noisy corporate event shots, Hatfield stresses the relationship between noise, ISO, and light quality. He recommends using manual mode for full ISO control and explains the importance of understanding light “quality vs. quantity” rather than relying solely on semi-automatic modes like aperture priority.Model Calls & Portfolio Building - Practical guidance is given for running a “model call” to expand one's portfolio. Raymond highlights where and how to recruit volunteer models (or clients), setting clear expectations, the value exchange, and strategies to ensure you're showcasing desired styles or filling portfolio gaps.IMPORTANT DEFINITIONS & CONCEPTSRAW (Image Format): An unprocessed file that retains all data captured, offering maximum flexibility for editing, though often appearing flat until processed.Model Call: A public invitation (often via social media) for volunteers to participate in portfolio shoots, typically in exchange for free or discounted images.DISCUSSION & REFLECTION QUESTIONSConsidering your current portfolio, what “gaps” could you fill with targeted model calls?How does understanding RAW vs. JPEG workflows shape your approach to in-camera settings and post-processing?Reflect on your pricing strategy: What personal factors (time, opportunity cost) might you consider before setting rates?Sign up for your free CloudSpot Account today at www.DeliverPhotos.comConnect with Raymond! Join the free Beginner Photography Podcast Community at https://beginnerphotopod.com/group Get your Photo Questions Answered on the show - https://beginnerphotopod.com/qa Grab your free camera setting cheatsheet - https://perfectcamerasettings.com/ Thanks for listening & keep shooting!

Extracting Data From JPEGs Didier shows how to efficiently extract data from JPEGs using his tool jpegdump.py https://isc.sans.edu/diary/A%20JPEG%20With%20A%20Payload/32048 Windows Recall Export in Europe In its latest insider build for Windows 11, Microsoft is testing an export feature for data stored by Recall. The feature is limited to European users and requires that you note an encryption key that will be displayed only once as Recall is enabled. https://blogs.windows.com/windows-insider/2025/06/13/announcing-windows-11-insider-preview-build-26120-4441-beta-channel/ Anubis Ransomware Now Wipes Data The Anubis ransomware, usually known for standard double extortion, is now also wiping data preventing any recovery even if you pay the ransom. https://www.trendmicro.com/en_us/research/25/f/anubis-a-closer-look-at-an-emerging-ransomware.html Mitel Vulnerabilities CVE-2025-47188 Mitel this week patched a critical path traversal vulnerability (sadly, no CVE), and Infoguard Labs published a PoC exploit for an older file upload vulnerability. https://labs.infoguard.ch/posts/cve-2025-47188_mitel_phone_unauthenticated_rce/ https://www.mitel.com/support/mitel-product-security-advisory-misa-2025-0007

International law enforcement takes down a darknet drug marketplace. The Washington Post is investigating a cyberattack targeting several journalists' email accounts. Anubis ransomware adds destructive capabilities. The GrayAlpha threat group uses fake browser update pages to deliver advanced malware. Researchers uncover a stealthy malware campaign that hides a malicious payload in a JPEG image. Tenable patches three high-severity vulnerabilities in Nessus Agent. Attackers can disable Secure Boot on many Windows devices by exploiting a firmware flaw. Lawmakers introduce a bipartisan bill to strengthen coordination between CISA and HHS. Harry Coker reflects on his tenure as National Cyber Director. Maria Varmazis checks in with Brandon Karpf on agentic AI. When online chatbots overshare, it's no laughing Meta.  CyberWire Guest Joining us today to discuss Agentic AI and it relates to cybersecurity and space with T-Minus Space Daily host Maria Varmazis is Brandon Karpf, friend of the show, founder of T-Minus Space Daily, and cybersecurity expert. Selected Reading Police seizes Archetyp Market drug marketplace, arrests admin (Bleeping Computer) Washington Post investigating cyberattack on journalists' email accounts, source says (Reuters) Anubis Ransomware Packs a Wiper to Permanently Delete Files (SecurityWeek) GrayAlpha Hacker Group Weaponizes Browser Updates to Deploy PowerNet Loader and NetSupport RAT (Cyber Security News) Malicious Payload Uncovered in JPEG Image Using Steganography and Base64 Obfuscation (Cyber Security News) Tenable Fixes Three High-Severity Flaws in Vulnerability Scanner Nessus (Infosecurity Magazine) Microsoft-Signed Firmware Module Bypasses Secure Boot (Gov Infosecurity) Bipartisan bill aims to create CISA-HHS liaison for hospital cyberattacks (The Record) Coker: We can't have economic prosperity or national security without cybersecurity (The Record) The Meta AI app is a privacy disaster (TechCrunch) Audience Survey Complete our annual audience survey before August 31. Want to hear your company in the show? You too can reach the most influential leaders and operators in the industry. Here's our media kit. Contact us at cyberwire@n2k.com to request more info. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Katz Stealer in JPG Xavier found some multistage malware that uses an Excel Spreadsheet and an HTA file to load an image that includes embeded a copy of Katz stealer. https://isc.sans.edu/diary/More+Steganography/32044 https://unit42.paloaltonetworks.com/malicious-javascript-using-jsfiretruck-as-obfuscation/ JavaScript obfuscated with JSF*CK is being used on over 200,000 websites to direct victims to malware Expired Discord Invite Links Used for Malware Distribution Expired discord invite links are revived as vanity links to direct victims to malware sites https://research.checkpoint.com/2025/from-trust-to-threat-hijacked-discord-invites-used-for-multi-stage-malware-delivery/

Do our bodies impact our emotions or do our emotions impact our bodies? We are revisiting some essential episodes of the Ancient Health Podcast. In this pivotal episode, Dr. Motley dives into the complex system that is our fascia to explain how exactly our emotions can be tied to our health. In TCM, healthy organ systems help you process emotions. Learn about the organ clock and how emotional and neurological connections are tied to our bodies and organs. Show notes⬇️ Show notes: Organ Clock → TCM Organ Clock.JPEG , https://shorturl.at/m771L Organs and their Corresponding Emotions → Organs_Corresponding_Emotions.pdf https://shorturl.at/sPdXH ------  Follow Doctor Motley Instagram Twitter/ Facebook Website ------  *Head to Zona.com and use code DRMOTLEY for $100 off a Zona Plus device for better heart health naturally & effectively! *Do you have a ton more in-depth questions for Doctor Motley? Check out his course on emotions and the body in his membership. You'll find other courses full of his expertise and clinical wisdom, plus bring all your questions to his weekly lives! To try risk-free for 15 days click here: https://www.doctormotley.com/15