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  7 JUNE 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

Start building your own agent with memory and MD files → https://value.8figureagency.co/hermesstartMetaSwarm (open source) — Dave's agentic harness → https://github.com/dsifry/metaswarm18 default agents, each with definitions and rubrics, plus defined workflows for software development. Three ways to use it: build agentic systems with it, point it at your own SOPs to analyze them, or study how it does agentic decomposition. (Fun fact from the episode: search "metaswarm" and it ranks right behind Meta itself.)Dave Sifry has started nine companies. Now he's building a company that builds companies.In this one, he hands agency owners the framework for the thing almost everyone is getting wrong with AI: memory and context. If your agency runs 50, 100, even 600 clients and information keeps falling through the cracks — stakeholders forgotten, context lost, the same questions re-answered — this is the episode.No fluff. Dave breaks down what actually deserves to be remembered, the exact MD files that run an agent (soul.md, agents.md, heartbeat), and how to wire a team of agents that report to each other and improve themselves every single day.What You'll LearnNot all data is information. A JPEG is a mountain of data and almost no information. One sentence from the CEO saying "go do this" carries more than a thousand video keyframes. Store the signal, not the noise.Memory is a compression problem. Save the process — who you talked to, how the deal got done, where it went off the rails — not every artifact you produced.Timeliness is value. Information from 30 days ago usually beats information from 7 years ago. Decide what goes into deep storage and what stays live.Treat every AI agent like a new employee who always needs onboarding. What's the minimum they need to do the job well? That's your context window.Three layers of control: policy, guidelines, gates. Policy = "be nice to the client, don't cuss." Guidelines = your stop-word list. Gates = a deterministic wall, like a credit card with exactly $2,000 on it that rejects $2,001.Run an analysis phase first. Find the 5–6 things a role does 80–90% of the day before you build anything. (An account manager is a farmer, not a hunter.)The MD-file stack that runs an agent:soul.md — who am I, why am I different from every other agent, what I do and what I don't do.agents.md — the bootstrap: read your soul, read your heartbeat, and here are your SOPs as links (loaded only when the situation calls for it, so you never pollute the context window).heartbeat — the recurring loop. The five tasks every hour, the three tasks every four hours. Keep it light or it eats you alive.Great agent systems are an org-design problem, not a super-intelligence problem. You don't need one all-knowing brain. You need agents that each know their job and who to talk to. It works like an anthill.Build a COO agent whose only job is writing and revising SOPs. Feed it a daily retrospective and you've built a self-learning organization

2-hours of live improvised experimental radio sound-art broadcast live on location in Yosemite, CA-USA. Netcast on DFM Radio TV International (www.dfm.nu)  DFM RTV INT  31 MAY 2026. UB Radio Salon 959 - uB Tales From Beyond: Making Connections......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

This show has been flagged as Clean by the host. This series is dedicated to exploring little-known—and occasionally useful—trinkets lurking in the dusty corners of UNIX-like operating systems. In UNIX Curio #4 ( HPR episode 4617 ), I teased the subject of file compression. Today I'm circling back to that. The history of data compression goes back at least to the 1970s, and in contexts outside UNIX and computers, probably even earlier. Somehow, it is refreshing to learn that humans have always struggled to have enough storage space to keep all the data they want to hang on to. One way around this limitation is to use some form of compression. I am only going to dive into lossless compression for this episode—that is, a compression method that can be reversed and will spit out the original data bit for bit. Lossy compression methods also have their places: you might be familiar with their use for audio (such as Ogg Vorbis or MP3); it's also used for images (such as JPEG). Lossy compression allows some of the original data to be thrown away, resulting in a smaller file than is possible with lossless compression, but the intent is for the result to still sound or look "good enough" to a human observer. Also, I am going to limit my discussion to generic methods used for many types of data; while FLAC does lossless compression, it is specifically designed just for audio. I should make clear that I have never studied computer science or information theory, so this episode will not get into the science behind various types of compression algorithms and how they differ. But in general, these methods take advantage of the fact that many types of data have recurring patterns. English text mostly consists of words that often re-appear many times—source code similarly has keywords and variable names that recur. Compression is accomplished by representing a piece of data that occurs multiple times with a symbol that is shorter in length. The first compression program in the UNIX world I could find is called pack , from 1978 1 . It was shortly followed in 1979 by a similar program called compact 2 . Both of these used a technique called Huffman coding, but with some differences between them. Files compressed with pack were given a .z extension and compact gave filenames a .C extension. Roughly every five or ten years after this, a new program would come along and achieve lasting popularity. There were, and still are, two opposing forces facing any new form of compression. Working in favor was the advantages it provided—first among these was achieving a better compression ratio, but performance improvements such as speed or reduced memory usage could also be compelling. The force against any new method was the fact that it was not yet widely supported—it doesn't much help to have a smaller file if the people you share it with cannot decompress it. The next major advance in compression arose out of three scientific papers: two in 1977 and 1978 by Abraham Lempel and Jacob Ziv (called LZ77 and LZ78), and one by Terry Welch in 1984 which built on LZ78. This last method is typically referred to as LZW. Our UNIX Curio for today is a program called compress 3 that implements the LZW method. Files compressed this way are named with the extension .Z . I had always assumed that this was to honor Jacob Ziv, but now that I've researched the history, it seems more likely to be a follow-on from how files compressed by pack were named. Since pack did not use any of the Lempel-Ziv methods, I would guess that it used .z because that wasn't already taken by anything else, but that's pure speculation. I do recall encountering .Z files in the wild, but feel certain that hasn't happened in the last 25 years, maybe longer. If you need to expand one of these, uncompress 4 is the program to use ( GNU's gunzip can also handle them 5 ). However, there was a serious problem that arose with the LZ78 and LZW compression methods. Both of them were patented, and the owner became aggressive in seeking payment from developers and users. The compress utility was developed within two months of the publication of Welch's 1984 paper and was included in Bell Laboratories' Eighth Edition UNIX before these shakedowns started. The paper did not disclose that a patent had been filed, and apparently Spencer Thomas and the other developers of compress were unaware of it. The utility became popular for a while, and was even standardized by POSIX, but people moved away from LZW once the legal threats started. Another important advance came in 1991 and was called the DEFLATE compression method. It combined the un-patented LZ77 method with Huffman coding to achieve a similar level of compression as LZW (actually, often better) without the legal trouble. DEFLATE was developed for PKZIP and was soon adopted by the GNU project's gzip compressor. While Phil Katz (the "PK" in PKZIP ) patented one way of implementing the DEFLATE method, it was possible to write a compressor and decompressor without infringing 6 ; also, he apparently never tried to enforce the patent 7 . As I mentioned in UNIX Curio #4, .zip is both an archive and a compression format. Each archive member can be compressed with one of several possible methods (or stored without compression). Unlike a tar file where compression can be applied to the entire archive, in .zip each archive member is compressed individually. This often means a .zip file will be slightly bigger than a tar file with the same contents compressed with gzip , because the .zip format cannot take advantage of duplication that occurs among more than one member of the archive. The vast majority of .zip files use only the DEFLATE and uncompressed storage methods and these are the only options if you want to follow the profile standardized in ISO/IEC 21320-1. Actually, since they both use DEFLATE, gzip is able to extract a .zip file in the special case where it only holds one member compressed with that method. From the 1990s onward, people paid significant attention to avoiding patent landmines, so only methods that didn't have that problem became broadly popular. While the patents on LZ78 and LZW have since expired, I feel like their most successful legacy was in discouraging people from using those methods, leading to DEFLATE taking the popularity crown. The next step came in 1996 and 1997 with the development of bzip and bzip2 by Julian Seward. The original method was quickly followed by bzip2 , which was the version that achieved true popularity. They use the Burrows-Wheeler transform, which does not itself compress data but re-arranges it to make it more compressible; this is combined with other techniques 8 . (At least, that's my understanding. I told you, I'm not up on information theory.) This provides a significant reduction in the compressed size of the data compared to earlier methods—however, it is slower than DEFLATE both during compression and decompression. Separate projects have developed parallel versions of gzip and bzip2 that can take advantage of multi-processor machines, but the original utilities run single-threaded. Another five years later, in 2001, Igor Pavlov added the Lempel-Ziv-Markov chain algorithm (LZMA), an enhancement to LZ77, to his 7-Zip compression tool. This was followed a few years later by LZMA2, a container format that allowed for LZMA compression to be split between multiple threads. Broad LZMA2 support came to the UNIX world in 2009 with the xz utility 9 . It offers roughly similar compression ratios to bzip2 , though it can be better or worse depending on the data to be compressed. While compression generally takes even longer than bzip2 , decompression is significantly faster (though still not as fast as gzip ). The Linux kernel relatively quickly supported booting from xz-compressed images 10 because it was a good match for that use case—compression, the time-consuming activity, only has to be done once while the more frequent decompression during boot happens relatively fast. The last method I will cover is Zstandard 11 , often written as zstd . This came about in 2015, and is another variation on LZ77 that uses finite-state entropy (which means nothing to me, but you might understand it). It performs about as well as DEFLATE in terms of compression ratios, but is much faster both when compressing and decompressing data. I should say that these statements are true with the typical default settings—depending on the compression level selected, it can compress more slowly, but compress the data smaller. However, decompression is always speedier than DEFLATE. This makes it attractive for some uses, and it is heavily promoted by Meta/Facebook, where Yann Collet developed it. For example, shipping large amounts of actively-used data between machines in a data center can go more quickly when the size is reduced; however, if the compression and decompression steps take too long that benefit is lost. A speedy method can be valuable even if it doesn't result in the greatest reduction in size. This use case stands in contrast to, say, a compressed backup file which might only be accessed in a disaster recovery scenario or never accessed at all, making size more important than speed. Both the xz and zstd utilities have some built-in support for multi-threading, but the default is to run in a single thread. While xz can use multiple threads for decompression (but only if the file was compressed in multi-thread mode), the reference zstd utility can only use more than one thread for compression, not decompression. There are many other methods of lossless compression that have been developed over the decades, but I believe these are the ones you are most likely to encounter in the world of UNIX-like systems. This is a personal opinion, and others might choose a different set. As mentioned, it can be tough for a new method to gain popularity and 35-year-old DEFLATE is still probably the most commonly used despite not being the fastest or offering the greatest reduction in size. Even systems like FreeBSD, NetBSD, and OpenBSD that do not like to include GNU tools supported it by developing their own version of gzip based on the permissively-licensed zlib library. Technically, the LZW method used by the compress utility is still standardized by POSIX, so one might expect it to have the widest support. However, aggressive patent enforcement discouraged adoption, especially by Free and Open Source Software systems—even though the patent has expired, it is still out of favor compared to DEFLATE. For this reason, I feel justified in calling it a curio. References: Eighth Edition UNIX pack.c https://www.tuhs.org/cgi-bin/utree.pl?file=V8/usr/src/cmd/pack/pack.c 2.9BSD compact.c https://www.tuhs.org/cgi-bin/utree.pl?file=2.9BSD/usr/src/ucb/compact/compact.c Compress specification https://pubs.opengroup.org/onlinepubs/009695399/utilities/compress.html Uncompress specification https://pubs.opengroup.org/onlinepubs/009695399/utilities/uncompress.html GNU Gzip manual https://www.gnu.org/software/gzip/manual/gzip.html RFC 1951: DEFLATE Compressed Data Format Specification version 1.3 https://tools.ietf.org/html/rfc1951 History of Lossless Data Compression Algorithms: The Rise of Deflate https://ethw.org/History_of_Lossless_Data_Compression_Algorithms#The_Rise_of_Deflate bzip2 https://en.wikipedia.org/wiki/Bzip2 XZ Utils https://en.wikipedia.org/wiki/XZ_Utils 2.6.38 merge window part 2 https://lwn.net/Articles/423541/ zstd https://en.wikipedia.org/wiki/Zstd Appendix The table below demonstrates the results of compressing different types of data using tools described in this episode. While not totally rigorous, I did run each compression and decompression multiple times to ensure I was getting consistent results. The laptop I used has an Intel Core i5-6200U CPU running at 2.30GHz, and the system had at least 5 GB of free memory for each run. While this processor has two cores and can run four simultaneous threads, all utilities were run single-threaded. The term "best" means the highest level of compression available (the exact level used is shown). For bzip2 , the default is the best. For zstd , "best" is -19, which is the highest "normal" level, but "ultra" levels that are even higher also exist. Ratios are the percentage of the original size that the file was reduced to (other sources might instead express the compression ratio as the reduction in size achieved). In all results, smaller numbers are better. ┌────────────────────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐ │ │ gzip │ gzip │ bzip2 │ xz │ xz │ zstd │ zstd │ │ │(default -6) │ (best -9) │ (-9) │(default -6) │ (best -9) │(default -3) │ (best -19) │ ├──────────────┬─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │Size (ratio) │ 22,036,508 │ 21,891,623 │ 15,795,698 │ 13,487,768 │ 12,938,464 │ 20,454,657 │ 13,709,078 │ │ │ │ (24%) │ (24%) │ (17%) │ (15%) │ (14%) │ (23%) │ (15%) │ │English Text ├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │(90,532,092 │Compression │ 4.8s │ 7.6s │ 8.5s │ 49.8s │ 58.8s │ 0.6s │ 65.2s │ │bytes │time │ │ │ │ │ │ │ │ │uncompressed) ├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │Decompression│ 0.7s │ 0.8s │ 3.7s │ 1.2s │ 1.2s │ 0.4s │ 0.4s │ │ │time │ │ │ │ │ │ │ │ ├──────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │Size (ratio) │ 125,291,122 │ 124,189,544 │ 98,016,512 │ 84,882,492 │ 81,954,344 │ 120,604,855 │ 87,298,645 │ │ │ │ (21%) │ (21%) │ (17%) │ (14%) │ (14%) │ (20%) │ (15%) │ │Source Code ├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │(590,008,320 │Compression │ 22.0s │ 39.3s │ 54.8s │ 241s │ 298s │ 3.7s │ 348s │ │bytes │time │ │ │ │ │ │ │ │ │uncompressed) ├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │Decompression│ 5.1s │ 5.1s │ 20.3s │ 8.1s │ 7.8s │ 2.4s │ 2.4s │ │ │time │ │ │ │ │ │ │ │ ├──────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │Size (ratio) │ 32,830,905 │ 32,371,241 │ 26,856,579 │ 20,717,288 │ 20,352,880 │ 28,538,810 │ 23,154,582 │ │ │ │ (19%) │ (19%) │ (16%) │ (12%) │ (12%) │ (17%) │ (13%) │ │Binary Program├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │(171,972,264 │Compression │ 6.4s │ 22.4s │ 18.6s │ 62.2s │ 67.8s │ 0.8s │ 111s │ │bytes │time │ │ │ │ │ │ │ │ │uncompressed) ├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │Decompression│ 1.5s │ 1.5s │ 5.6s │ 2.3s │ 2.3s │ 0.7s │ 0.7s │ │ │time │ │ │ │ │ │ │ │ ├──────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │Size (ratio) │ 146,397,772 │ 146,397,757 │ 144,485,451 │ 131,950,232 │ 130,926,780 │ 147,154,979 │ 145,703,840 │ │ │ │ (89%) │ (89%) │ (88%) │ (80%) │ (80%) │ (90%) │ (89%) │ │WAVE Audio ├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │(164,396,302 │Compression │ 9.2s │ 9.2s │ 25.1s │ 70.4s │ 97.7s │ 0.7s │ 58.3s │ │bytes │time │ │ │ │ │ │ │ │ │uncompressed) ├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │Decompression│ 2.0s │ 2.0s │ 13.5s │ 12.2s │ 12.1s │ 0.6s │ 0.8s │ │ │time │ │ │ │ │ │ │ │ ├──────────────┴─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │ gzip │ gzip │ bzip2 │ xz │ xz │ zstd │ zstd │ │ │(default -6) │ (best -9) │ (-9) │(default -6) │ (best -9) │(default -3) │ (best -19) │ └────────────────────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘ English text consists of Titles 1 through 10 of the 2020 U.S. Code of Federal Regulations . Source code consists of a tar file containing the Linux kernel source, version 4.0. Binary program consists of an ELF-format executable of the pandoc application, version 2.17.1.1 found on Debian 12. Audio consists of a 24-bit Signed Integer PCM WAVE file with 2 channels at 44.1kHz, about 10:21 in length. For comparison, the audio-specific flac lossless compression utility reduced this file to 97,962,711 bytes (60%) in 2.6 seconds at the default (-5) level and to 97,714,876 bytes (59%) in 5.4 seconds at the highest (-8) level. Provide feedback on this episode.

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  24 MAY 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  17 MAY 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

¿Alguna vez has vuelto de una sesión emocionado por lo que viste en la pantalla de tu cámara, solo para descubrir en el ordenador que la foto está totalmente arruinada? En el episodio de hoy, desglosamos por qué la clave para hacer "fotones" no está en comprar un objetivo nuevo, sino en evitar fallos técnicos y de hábito que cometemos con las prisas.Lo que aprenderás en este episodio:La mentira de la pantalla: Por qué el brillo externo te engaña y cómo el histograma es tu único aliado real para una exposición "niquelada".Composición intencionada: La diferencia entre un aficionado y un profesional suele ser un simple paso a un lado para eliminar objetos que distraen, como una silla o un cubo de basura.El poder del trípode: Más allá de ser un "trasto", te explicamos cómo abre la puerta a la creatividad extrema: largas exposiciones, estrellas y nitidez absoluta.El chequeo de 5 segundos: El hábito vital de revisar ISO y formato (RAW vs JPEG) antes de empezar para no lamentar la pérdida de calidad en tus mejores recuerdos.No importa si llevas dos días o 20 años en la fotografía; estos consejos básicos transformarán tu flujo de trabajo.¡Dale al play y empieza a aplicar estos cambios hoy mismo!

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  10 MAY 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 the Mapcreator booth at NAB in Las Vegas, Julia Schellekens provides an update on their service that delivers detailed maps for news and other organizations who want to clearly show what is happening where. With a wide array of export and animation capabilities to help drive home the information, it is almost certain that you have seen their product in use no matter where in the world you are.  Show Notes: Chapters: 00:03 MacVoices at NAB 202600:08 Chuck Joiner opens from NAB in Las Vegas00:12 Returning to the Mapcreator booth00:24 Julia joins Chuck to show what is new00:37 What Mapcreator is and how it works00:43 Creating static, interactive, and animated maps00:47 Mapping tools for journalists and reporters01:37 Helping news agencies focus on the story02:16 What's new with MapCreator this year02:26 Newsroom system and Adobe workflow integrations02:52 Keeping users in their preferred design tools03:29 Maps and visuals as storytelling tools03:41 Creating animations quickly from GPX files04:05 Matching map creation to fast-moving news04:12 Export options for interactive maps04:26 Static map exports including PNG, JPEG, PDF, and SVG04:34 Animated exports including MOV, MP4, WebM, and image sequences05:27 Why Mapcreator is more than a quick coded tool05:34 Reducing manual work and saving production time05:49 Mapcreator website: Mapcreator.io Support:      Become a MacVoices Patron on Patreon     http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:     http://macvoices.com      Twitter:     http://www.twitter.com/chuckjoiner     http://www.twitter.com/macvoices      Mastodon:     https://mastodon.cloud/@chuckjoiner      Facebook:     http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:     http://www.facebook.com/macvoices/      MacVoices Group on Facebook:     http://www.facebook.com/groups/macvoice      LinkedIn:     https://www.linkedin.com/in/chuckjoiner/      Instagram:     https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes     Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

In the Mapcreator booth at NAB in Las Vegas, Julia Schellekens provides an update on their service that delivers detailed maps for news and other organizations who want to clearly show what is happening where. With a wide array of export and animation capabilities to help drive home the information, it is almost certain that you have seen their product in use no matter where in the world you are.  Show Notes: Chapters: 00:03 MacVoices at NAB 2026 00:08 Chuck Joiner opens from NAB in Las Vegas 00:12 Returning to the Mapcreator booth 00:24 Julia joins Chuck to show what is new 00:37 What Mapcreator is and how it works00:43 Creating static, interactive, and animated maps 00:47 Mapping tools for journalists and reporters 01:37 Helping news agencies focus on the story 02:16 What's new with MapCreator this year 02:26 Newsroom system and Adobe workflow integrations 02:52 Keeping users in their preferred design tools 03:29 Maps and visuals as storytelling tools 03:41 Creating animations quickly from GPX files 04:05 Matching map creation to fast-moving news 04:12 Export options for interactive maps 04:26 Static map exports including PNG, JPEG, PDF, and SVG 04:34 Animated exports including MOV, MP4, WebM, and image sequences 05:27 Why Mapcreator is more than a quick coded tool 05:34 Reducing manual work and saving production time 05:49 Mapcreator website: Mapcreator.io Support:      Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:      http://macvoices.com      Twitter:      http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices      Mastodon:      https://mastodon.cloud/@chuckjoiner      Facebook:      http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:      http://www.facebook.com/macvoices/      MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice      LinkedIn:      https://www.linkedin.com/in/chuckjoiner/      Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

Beeple didn't turn an 18-year daily JPEG habit into a $69 million Christie's sale by waiting for permission. He posted, missed, learned, repeated, and inadvertently walked straight into the moment NFTs forced the art world to take digital work seriously. In this episode, Reid Hoffman talks with Mike Winkelmann (aka Beeple) about the real story behind the sale, why deadlines beat inspiration, how satire lets artists ask dangerous questions without preaching, and why AI is not a soul, a friend, or a shortcut. It is a tool that can—and should—make humans do more. From robot dogs with billionaire faces to AI-built sculptures shaped by strangers, Beeple argues the future is going to get much weirder, the bar for originality is rising fast, and artists who opt out may not like what happens next. For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/ 

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  3 MAY 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 on location in Yosemite, CA-USA. Netcast on DFM Radio TV International (www.dfm.nu)  DFM RTV INT  26 APRIL, 2026. UB Radio Salon 954: Silver Sailer - uB Tales from Beyond......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

Join host Walter Sterling as he sits down with Marine Corps combat veteran and intelligence expert Edward Jones to unpack the shocking, yet surprisingly absurd, security breach at the recent Washington Correspondents' Dinner. They dissect how an armed would-be assassin casually commuted via an Amtrak train to the event, exploiting a shockingly lax ticketing system where unverified JPEG images were simply swapped for nameless paper tickets. Amidst the serious security analysis, Sterling and Jones share hilarious behind-the-scenes observations from the chaos—including elite guests refusing to abandon their free salads and a Ukrainian ambassador famously swiping a bottle of champagne on her way out. To wrap up, Jones delivers a rapid-fire intelligence update on the current geopolitical standstill in Iran. Learn more about your ad choices. Visit megaphone.fm/adchoices

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 Good The Bad and The Ugly Theme-Sir Julian Gould You Only Live T....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: Our Divorce Song Special Divorce Court-The Five Du Tones Divorce Dec....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  19 APRIL 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 solo deep dive, I walk you through the exact camera setup I use before every shoot, every trip, and sometimes even check between battery or memory card swaps. These aren't just preferences—they're the important, yet often-overlooked settings that can make or break your ability to capture fleeting moments in nature photography. We get into the philosophy behind shooting in manual with auto ISO, why I prioritize speed and readiness for wildlife over anything else, and how I customize my camera to react as fast as the scene unfolding in front of me. From autofocus strategies and drive modes to white balance and RAW vs. JPEG, this episode is all about removing friction so you can focus on what really matters: making great photographs.Whether you've just unboxed a new camera or you want to fine-tune your current setup, think of this as your field-ready blueprint for getting your gear working for you, not against you.Not all things do I check and reset daily, but here is a quick guide for those that I do look at near-daily:Double-check RAW is still activeConfirm drive mode it back to high-speedRe-check autofocus settings for single point / single shotVerify Manual + Auto ISO is setCheck exposure settings (and go back to an even exposure)Image stabilization is turned on, both in-camera and on lensesCourt's WebsitesCheck out my photo portfolio here: shop.courtwhelan.comSign up for my photo and conservation blog at www.courtwhelan.comFollow me on YouTube (@courtwhelan) for more photography tipsView my camera kit and recommended camera gearSponsors and Promo Codes:MPB.com - Buy, Sell, or Trade Camera GearArtStorefronts.com - Mention this podcast for free photo website designBayPhoto.com - 25% off your first order (code: TWP25) ArtHelper.com - a photo community to learn, share and be inspiredArthelper.Ai - Smart tools to promo and showcase your art.LensRentals.com - WildPhoto15 for 15% off

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  12 APRIL 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: Five Foot Two Eyes Of Blue-Mrs Mills with Geoff Love & Orch....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  5 APRIL 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: Whispering Hope-Authentic Carousel Music The Woodpecker Song-Svend A....This item has files of the following types: Archive BitTorrent, Item Tile, JPEG, JPEG Thumb, Metadata, PNG, Spectrogram, VBR MP3

https://erickimphotography.com/infinite-capital-2/Buy Bitcoin & the Ricoh monochrome & chill.BITCOIN & THE RICOH GR MONOCHROME, two perfect products on the planet:Bitcoin is so beautiful I'm about to cry!So a lot of people are kind of confused on why I'm so into bitcoin, and how and why I got into the whole crypto game etc.So long story short, grew up super super poor, and then, I became self-employed, and the whole time during my journey and my philosophical endeavors… Has always been about money, life, everything in between.What's kind of eerie is truth be told… bitcoin is starting to become like a pseudo religion to me. Michael Saylor is like the high priest, the St. Paul,,, once denouncing Jesus (Satoshi),,, and now, becoming one of the strongest and ardent advocates. I think, lotta people… Maybe like 99.9% of their issues in life is typically rooted about money finances economics etc. period much of the social illness society like poverty crime theft, gambling whatever… Or even the low birth rate, or the interest in dogs or children, this typically I think maybe primarily an economical financial issue. Like if you think that positive economic future, which is typically the feeling that Americans get.... of course, nobody is going to save money, have kids, set roots,,, etc. Bitcoin solves all of this. And it's like as exciting as investing in Apple in the 1980s even as exciting as the advent of digital photography.What kind of interesting is many of my followers work in tech, are millennials like myself, I'm born in 1988 currently 38 years old ,,,, they get tech but they don't get bitcoin? It's like, telling people as photographers, imagine if, you had to write a paper check every time you went to the grocery store, ordered something on Amazon, and then I could promise you a credit card or Apple Pay or visa instead ,,,, what would you choose? Of course the digital payment solution!Even if these world, one of my happiest new uncoveries is,,, DoorDash with meat from Costco and super King, meat delivered,,, on tap,,, beef ribs only $5.99 a pound? To me this is amazing. It's like the digital transformation of food! So in other random news, I think I've had a fair amount of time now to play with the new Ricoh GR monochrome, now will probably give it a perfect 10 out of 10, the perfect camera ever created, ever since I started digital photography at the age of 18.  so I guess 20 years in the game. The first, the size. That's kind of shocking is… I think it actually may be smaller than my original Canon SD power shot 600 that I got as a high school graduation present when I was 18 years old. The reason why this matters is because… In some ways, it actually feels more portable and more lightweight and better balanced than even an iPhone Pro? Second, the monochrome only feature. It's totally is the bees knees, .. and the red filter is actually insanely shocking on what's different it does make. It's almost like the new flash, because… It brightens human faces and even blooming yellow red and orange flowers, maybe even orange Lamborghinis ,,, which means, you don't need a flash anymore, ... this is kind of an insanely big deal.Also… You could literally shoot it out like 1 million ISO with practically normal noise, so there is not really ever going to be a situation in which it is too dark to take photos.Third, the macro feature. I'm shocked, it's been so long since I've had the opportunity to shoot in macro mode, wow it's amazing. Like 1 trillion more things more opened up to you.Fourth, a level of contrast you could get out of camera in JPEG high contrast monochrome mode blows my mind. And also what's kind of surprising and shocking too is the new grainy monochrome feature really does look like Neopan 400 film pushed to 1600. In my eyes it looks like 99.99% film. So there's literally zero reason anymore to shoot black-and-white film anymore. 

2-hours of live improvised experimental radio sound-art broadcast live on location in Yosemite, CA-USA. Netcast on DFM Radio TV International (www.dfm.nu)  DFM RTV INT  29 MARCH, 2026. UB Radio Salon 950: SquircUBe Chronicles: LIVE! From The Yose-Mite Forest Moon......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

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Video zur Episode Text-/Audio-/Videokommentar einreichen HS-Hörer:innen im Slack treffen #hsfeedback Nachtrag zu japanischen/chinesischen Schriftzeichen von Stefan: Besuch im Leica Museum Titel für einen neuen Workshop: LichtGestalten von Paul: Fotorucksack, der in ein Flugzeug Handgepäck passt Vergrößerungsfaktor in Lightroom von Arne: Vermisster Workshop von Manuel: Fotolabore von Erik: Abspeichern als RAW und JPEG ohne Hintertürchen? von … „#938 – Lizenzgebamsel“ weiterlesen

Ingrid Daubechies (1954) is Belgisch-Amerikaanse wiskundige en natuurkundige, verbonden aan Duke University in North Carolina. Ze ontwikkelde de Daubechies-wavelets — de wiskundige basis voor JPEG 2000-beeldcompressie — en de New York Times noemde haar the godmother of the digital image. Ze was de eerste vrouwelijke voorzitter van de Internationale Wiskundige Unie en in 2014 werd ze benoemd tot barones door koning Albert II.Ze werd geboren in Houthalen, studeerde aan de Vrije Universiteit Brussel en verhuisde in 1987 naar de Verenigde Staten. Ze is James B. Duke Distinguished Professor Emeritus of Mathematics aan Duke University.We hadden afgesproken bij Urgent, de studentenradio van de universiteit, in de studio op de onderste verdieping van bibliotheek De Krook. In ons gesprek legt ze uit wat die wavelets kunnen en vertelt ze over de dag dat ze haar uitvinding deed. Het gaat over voetbalverslaggeving en de restauratie van het Lam Gods, over de Belgen in haar Amerikaanse boekenkast, over haar chronische depressie, over de reactie van haar moeder toen ze barones werd.Alle boeken en auteurs uit deze aflevering vind je⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠in de shownotes op wimoosterlinck.be⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Wil je de nieuwsbrief in je mailbox? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠wimoosterlinck.substack.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Wil je de podcast steunen? Bestel je boeken dan steeds via⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠de ⁠⁠link op wimoosterlinck.be⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠!⁠⁠ Merci.De drie boeken van Ingrid Daubechies zijn:1. Ursula Vernon: Digger2. Mary Doria Russell: The Sparrow3. Harry Mulisch: De ontdekking van de hemelLuister ook naar de drie boeken van: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Stefan Hertmans⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Eva Mouton⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Nicci French⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Josse De Pauw⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ish Ait Hamou⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Murielle Scherre⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠,  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Michèle Cuvelier⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠Françoise Chombar⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠en ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠vele anderen⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Wil je het boek '103 boeken die je gelezen moet hebben' bestellen - het boek van de podcast? Dat kan op ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠wimoosterlinck.be.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ik schrijf er met plezier iets in voor jou of voor de persoon aan wie je het boek cadeau wil doen.

What happens in Miami... is up for full and frank discussion! Such as: why did Coco Gauff wear a hat? Also, in this bombastic episode, host Lizzy Hoo reveals that Monte Carlo — the place — is actually, a sh*thole. Hot take, Lizzy! We marvel at the tennis-themed wizardry of Jannik Sinner, and dive headfirst into the murky world of 'ball daddies' (fully grown ball kids). What we find will shock you. We also discuss Dr Rafael Nadal's impressive cloak, and chat in-studio to American gun, Jessica 'the JPEG' Pegula. Cancel your plans, stay in, and listen to this! You can use the skip forward 15 seconds feature to avoid Mike's Anagrams — he understands. AusOpen.comiHeartApple PodcastsSpotifyYouTubeSee omnystudio.com/listener for privacy information.

We shall journey to the very edges of the JPEG!   We encourage you to check out our Patreon and/or Ko-Fi, as they've got sweet sweet benefits and also you can help support your favorite show. AND Our Store is a thing, with all your t-shirts, tote bags, stickers and more!   Background music and sound effects: 60s Computer Lab, Interrogation Room, and Dungeon II Mechanical Tabletop Audio https://tabletopaudio.com   Desolate Underground City Ambience The Hollywood Edge https://hollywoodedge.com   Comedy Spilt Paint Ovani Sound   Catoptricon, Sepulcrum, Torus, Outriders, and Under the Hill Battle Theme Zak   Email us at PodAgainsttheMachine@gmail.com Remember to check out https://podagainstthemachine.com for show transcripts, player biographies, and more. Stop by our Discord server to talk about the show: https://discord.gg/TVv9xnqbeW Follow @podvsmachine on Bluesky Find us on Reddit, Instagram, and Facebook as well.  

On this week's episode of Hands-On Tech, Mikah helps Wayne understand why iPhone photos appear invisible or fail to copy when transferred to a Windows PC, and walks through the best methods to fix it. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

On this week's episode of Hands-On Tech, Mikah helps Wayne understand why iPhone photos appear invisible or fail to copy when transferred to a Windows PC, and walks through the best methods to fix it. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

On this week's episode of Hands-On Tech, Mikah helps Wayne understand why iPhone photos appear invisible or fail to copy when transferred to a Windows PC, and walks through the best methods to fix it. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

On this week's episode of Hands-On Tech, Mikah helps Wayne understand why iPhone photos appear invisible or fail to copy when transferred to a Windows PC, and walks through the best methods to fix it. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

On this week's episode of Hands-On Tech, Mikah helps Wayne understand why iPhone photos appear invisible or fail to copy when transferred to a Windows PC, and walks through the best methods to fix it. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

On this week's episode of Hands-On Tech, Mikah helps Wayne understand why iPhone photos appear invisible or fail to copy when transferred to a Windows PC, and walks through the best methods to fix it. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

On this week's episode of Hands-On Tech, Mikah helps Wayne understand why iPhone photos appear invisible or fail to copy when transferred to a Windows PC, and walks through the best methods to fix it. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

On this week's episode of Hands-On Tech, Mikah helps Wayne understand why iPhone photos appear invisible or fail to copy when transferred to a Windows PC, and walks through the best methods to fix it. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

On this week's episode of Hands-On Tech, Mikah helps Wayne understand why iPhone photos appear invisible or fail to copy when transferred to a Windows PC, and walks through the best methods to fix it. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

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.

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

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

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.  

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.

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

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

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.

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.