Podcasts about Bugs

We have had many media panics about swarms and hordes, about infestations and plagues of dangerous insects and arachnids that promise to rain destruction down on the defenseless American public. My guest today is comedian Akilah Hughes, host of the podcast How Is This Better? We are talking about our American fear around sensationalized bugs, from killer bees to infected mosquitos to floating spiders to the kissing bug. We'll discuss what these panics can tell us about the language of our politics and the way mass hysteria can create monstrous problems from perceived threats, no matter how tiny. Get more of Akilah's work: Website / How Is This Better? / YouTube / Instagram Become a Patron⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to support our show and get early ad-free episodes and bonus content Or subscribe to American Hysteria on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Get some of our new merch at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠americanhysteria.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, all profits go to The Sameer Project, a Palestinian-led mutual aid group who are on the ground in Gaza delivering food and supplies to displaced families. Leave us a message on the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Urban Legends Hotline⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Thank You To Our Sponsor: Go to ⁠https://surfshark.com/chelsey⁠ or use code CHELSEY at checkout to get 4 extra months of Surfshark VPN! Producer and Editor: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Miranda Zickler⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Associate Producer: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Riley Swedelius-Smith⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Additional editing by ⁠⁠⁠⁠⁠Kaylee Jasperson⁠⁠⁠⁠⁠ Hosted by Chelsey Weber-Smith Learn more about your ad choices. Visit megaphone.fm/adchoices

Jonny, and Joel discuss some minor changes in the latest Minecraft snapshot, speculate on the next Minecraft Live date, and discuss Minecraft bugs… The creepy crawly kind.Show notes for The Spawn Chunks are here:https://thespawnchunks.com/2026/03/02/the-spawn-chunks-391-what-bugs-you-about-minecraft/Join The Spawn Chunks Discord community!https://Patreon.com/TheSpawnChunksThe Spawn Chunks YouTube:https://youtube.com/thespawnchunks Hosted on Acast. See acast.com/privacy for more information.

Join us in this episode as we explore the inspiring journey of Dr. Louise Lynch-O'Brien, a dedicated entomologist, educator, and lifelong learner. From her early days in New York to her impactful work at the University of Nebraska-Lincoln, Louise shares her unique perspective on teaching, community engagement, and the importance of building relationships with insects and people alike. This conversation highlights her innovative approach to science communication, qualitative research methods, and her love for continuous learning. In this episode: Louise's path from childhood curiosity to professional entomology The role of nature in fostering wonder and scientific inquiry How she integrates qualitative research into insect outreach and education Strategies for shifting public perceptions of insects and arthropods Balancing teaching, extension, and research in higher education The value of experiential learning courses for students Using storytelling and soft skills to enhance science communication Insights on managing student mentorship and fostering growth mindset The impact of community science and long-term outreach programs like Bugfest Her favorite MasterClass courses and continuous professional development To get to know Louise: UNL Directory: https://entomology.unl.edu/person/dr-louise-i-lynch-obrien/ Faculty Spotlight: https://ianrnews.unl.edu/faculty-spotlight-louise-lynch-obrien Facebook: https://www.facebook.com/civicentolab/ Instagram: https://www.instagram.com/drlynchobrien/ UNL Online M.S. in Entomology: https://entomology.unl.edu/academic-programs/online-master-science-entomology/ Get the show through Apple Podcast, Spotify, or your favorite podcatching app! Older episodes can be accessed through Archive.org. If you can spare a moment, we appreciate when you subscribe to the show on those apps or when you take time to leave a review! Thank you so much for listening!

Stormy seas battered the hatches of the SS Sparks & Recreation as news of a shuffle bug hits the Realms! Join the crew as they talk through this surprising discovery and what it means for players.The storm doesn't stop there – more bug talk follows as the crew discusses the latest update before breaking down which balance changes actually landed.Finally, a breakthrough has been made on Hero Helper development, and Tuff brings the latest from behind the scenes. All that and more on this episode, thanks for listening! WWYD: 7:08Bugs: 21:34Balance: 46:59(Hero-helper) Breakthrough: 1:41:56Community Round-up: 1:47:15Taps, Scrap, and Good-byes: 2:04:10Hero Realms is a fantasy-themed expandable deckbuilding game from Wise Wizard Games.Hosts: Chris "DblDubz" Walberg, Cooper "Filtrophobe" Fitzpatrick, and John "Tuff" LabellaProducer: Chris WalbergHero Helper: https://hero-helper.com/Realms Rising: https://www.realmsrising.comYou can find the WWYD screenshots for this episode here: https://www.realmsrising.com/podcast/sparks-and-recreation-96-bugs-balance-and-breakthroughsPatreon: https://patreon.com/sparksandrecHyperGeometric Calculator: https://aetherhub.com/Apps/HyperGeometricCommunity Tournaments & Events Primer (+ signup links): https://www.realmsrising.com/community-events/Realms Rising Discord: https://discord.gg/8pTxKqzFDcContact S&R: contact@sparks-and-recreation.comSupport Sparks & Rec: https://hero-helper.com/support-usSparks & Recreation Website: https://www.realmsrising.com/sparks-and-recreation/Thank you so much to Level 12 Hero Sarah T., Warden Slayer, as well as Level 7 Hero Nudeltulpe!Specific songs used in this episode were:Intro/Outro Music: "Uplifting Orchestra Pack" by GoodBunny. (Under the Music Standard License)Licensed under Creative Commons BY Attribution 4.0 License Hosted on Acast. See acast.com/privacy for more information.

Flies, ticks, and parasites don't just annoy cattle—they steal gain and profit. Recorded live at the Central Oklahoma Cattle Conference in Stillwater, OK, this episode features Dr. Jonathan Cammack (OSU Extension livestock entomology & parasitology) breaking down what producers should know about common pests like horn flies, how researchers test control tools, and why day-to-day management matters more than most folks think. The team also tackles two headline issues: New World screwworm and the invasive Asian longhorned tick. Dr. Cammack explains why screwworm is such a serious wound pest, how sterile insect technique works, and why animal movement can spread risk faster than the fly ever could. Then they pivot east—where Asian longhorned ticks have been detected in Oklahoma—and discuss why explosive tick populations and tick-borne disease threats are a growing concern across the region. Top 10 takeaways for producers Pests “steal” performance quietly—stress and blood-feeding divert energy away from gain. Screwworm isn't a nuisance fly: it targets living tissue in wounds and can escalate fast. Time matters: screwworm eggs can hatch in 12–24 hours, so delayed checks can get costly. Animal movement beats fly movement—trailers move risk hundreds of miles in a day. Sterile insect technique works because females mate once; scale and logistics are the challenge during outbreaks. Asian longhorned tick can explode in numbers because it can reproduce without mating (parthenogenesis). High tick loads can cause real blood loss, and tick-vectored disease is a growing regional concern. Feedlots are a special concern due to animal density and the difficulty of visually monitoring every animal. Good management beats extremes: not “once a year,” not necessarily “daily,” but consistent eyes-on and quick response. Research behind the scenes is constant—colonies, susceptible/resistant strains, and field tests inform what works on your operation.   Detailed timestamped rundown 00:00–01:06 Dave Deken tees up Episode 507: flies, ticks, parasites; guest Dr. Jonathan Cammack; recorded at the Central Oklahoma Cattle Conference in Stillwater.01:06–02:42 “Trip around the table” intros: Brian Arnall and Josh Lofton; setting the scene at the Payne County Expo Center.02:42–06:56 Cammack's role: OSU Extension livestock entomology/parasitology; what he covers across livestock species; why they keep fly colonies (houseflies, blowflies) for research and pesticide trials.06:56–10:51 Colony realities: genetic bottlenecks, refreshing genetics from field populations; why “susceptible” vs “resistant” strains matter for chemical testing.10:51–14:54 How trials work: planning population numbers; counting flies on cattle with visual estimates + photos; students doing image-based counts; “2000+” becomes the practical ceiling.14:54–20:01 Screwworm basics: obligate parasite of living tissue; eggs hatch fast (12–24 hours); damage can be severe; regulatory questions around response/harvest are still evolving.20:01–27:44 Control strategy: sterile insect technique; females mate once; sterile males overwhelm wild males; program history and why scaling facilities matters as the “front” widens northward.27:44–30:40 Beyond cattle: wildlife, pets, and people can be affected; reminder that wildlife movement can complicate containment; key deer example in Florida Keys (2016–2017) discussed.30:40–33:36 Other big concern: Asian longhorned tick found in northeast Oklahoma (summer 2024); parthenogenetic reproduction; potential for heavy infestations and disease-vector risk.33:36–35:27 Wrap-up: “safe from the west (for now)” tone; thanks to guest; where to find resources (reddirtagronomy.com). RedDirtAgronomy.com

As things start to warm up, what does that mean for the bugs we start seeing more of? And how can we keep them out of our homes and yards? Aaron Ashbrook, assistant professor of urban/peri-urban entomology at LSU, joins us.

* As things start to warm up, what does that mean for the bugs we start seeing more of? And how can we keep them out of our homes and yards? * We'll spend some time with Jay Morris, the COO and co-owner of Juan's Flying Burrito, about the challenges of navigating repeated boil water advisories.

We’re starting our read of Cavan Scott’s Path of Vengeance, the final novel in Phase II of the High Republic. We talk about ghosts, unraveling the Mother’s identity, and what Marda Ro has been up to (spoiler alert: it’s cult shit). For a list of Black-owned bookstores to order from, now and always, click here. […]

Thanks for listening!

i ain't interpreting all that. i'm happy for you tho. or sorry that happenedToday we're talking about Eclipsium by Housefire! A game about walking around and unspeakable things happening to your hand.Get Eclipsium on Steam!!! Follow Housefire's work on their website!Discussed in the episode:AJ on The Worst Community ReportAJ's 2025 Game of the Year video on YouTubeEclipsium (Original Game Soundtrack) by Hudson Bikichky on BandcampAJ's playthrough of the beginning of Eclipsium on YouTubeFinding Emilie by Radiolab---Support us on Ko-fi!Visit our website!Follow us on YouTube!Follow the show on Bluesky!Check out The Worst Garbage Online!---Art by Tara CrawfordTheme music by _amaranthine and check out his new project Foster Hope!Additional sounds by BoqehProduced and edited by AJ Fillari---Timecodes:(00:00) - Listen to AJ on The Worst Garbage Community Report :-) (01:45) - Welcome to the game (03:31) - What is Eclipsium? (04:58) - What'd we think (08:30) - Bugs? (10:00) - The music!!!!!!!!!!!!!!!! The sound!!!!!!!!!! (13:10) - The hand and the things done to the hand (15:42) - Heading to spoilers (16:11) - Chase wants to talk about the images (20:01) - Kim wants to talk about judgment (22:48) - Robin wants to talk about it literally (25:16) - AJ wants to talk about the opting-in (27:24) - What death means and religious imagery (34:21) - Going to the panopticon (47:27) - They can't ALL be PT!!! (50:03) - An actually interesting series of hallways (56:47) - Big Takeaways (56:59) - AJ's Big Takeaway (59:42) - Chase's Big Takeaway (01:04:08) - Kim's Big Takeaway (01:06:19) - Robin's Big Takeaway (01:09:39) - The end of the game hot damn (01:09:57) - Thanks for listening! ★ Support this podcast ★

In this special guest episode, Andrew sits down with Russell Watkins, co-founder of Sempai. Andrew first met Russell at the Gemba Summit in Belfast, where Russell delivered a keynote titled “10 Lightbulb Moments from Working with Toyota Japan and UK.” After cornering him at lunch with a notebook full of questions, Andrew knew this had to become a podcast conversation.They explore:What Russell learned apprenticing under a direct student of Taiichi Ohno and why he was told to “stop reading and start doing”Why you don't learn lean from books alone (but why books still matter)How to actually observe work on the Gemba, and why empty workstations don't tell the full storyThe danger of “putting lipstick on a pig” by optimizing rework instead of eliminating the need for itWhy “Fix What Bugs You” works and where it falls short without strategic directionA practical introduction to Hoshin Kanri (policy deployment) for small manufacturersHow to connect shop-floor improvements to real business needsThe power of visual defect analysis—even without formal data systemsFour simple questions that reveal the strength (or weakness) of your SOPsHow to handle the 20-70-10 dynamic when rolling out lean initiativesWhy humility and “opening the kimono” as a leader builds trust and cultural momentumThis conversation bridges the gap between the Two Second Lean community and traditional Toyota Production System thinking, offering practical insight for small and mid-sized manufacturers who want to move beyond local optimization and align improvement with long-term business survival.Links:The explainer on Hoshin Kanri/policy deployment that Russell mentioned

Saturday Mornings Show host Glenn van Zutphen and co-host Neil Humphreys step into a world where insects tower over us. Joining us in the studio are Honor Harger, Vice President of the ArtScience Museum, and Foo Maosheng, Curator of the Cryogenic Collection and Insecta Senior Scientific Officer at the Lee Kong Chian Natural History Museum. They take us inside Insects: "Microsculptures Magnified", ArtScience Museum’s first major exhibition of the year and the Southeast Asian debut of award‑winning photographer Levon Biss. Thirty seven magnification portraits created in collaboration with the American Museum of Natural History, the exhibition transform beetles, flies, wasps, and other tiny creatures into monumental artworks up to seven feet tall. Colours, textures, and anatomical structures invisible to the naked eye are revealed in astonishing detail. Beyond the art, the exhibition invites visitors to dig deeper into the natural world through interactive displays, real specimens, and behind‑the‑scenes insights into Biss’ meticulous photographic process. Maosheng shares how Singapore’s own insect biodiversity—often misunderstood or dismissed as “pests”—plays essential roles in our ecosystems, and how public education can help shift perceptions and even overcome fears.See omnystudio.com/listener for privacy information.

When temperatures drop in the fall, suddenly the outdoors becomes insect-free. That doesn't feel like much of a mystery. But when temperatures rise in the spring and suddenly insects emerge, it raises questions, like how do these tiny creatures survive in subzero conditions? Entomologist Laura Iles demystifies this phenomenon. Later, horticulturist Aaron Steil joins the conversation to answer listeners' questions. To further grow your gardening knowledge, sign up for our Garden Variety newsletter. And check out all the episodes of Garden Variety, the horticulture podcast for all the things you'd like to grow or grow better.

A few days of sunny weather in late winter or early spring, and the bugs are immediately back. It raises questions like, how do these tiny creatures survive in subzero conditions? We explore that question with Laura Iles, director of the North Central Integrated Pest Management Center.

Find our Matrix review here: https://www.podomatic.com/podcasts/alostplot/episodes/2026-01-30T16_36_07-08_00 In this episode, Maverick and Andrew delve into 'The Matrix Resurrections', exploring its themes, character arcs, and overall execution. They discuss their initial ratings, the film's opening scene, and the reintroduction of Neo and Trinity. The conversation highlights the film's strengths and weaknesses, particularly in character development and the portrayal of villains like Agent Smith and The Analyst. Ultimately, they critique the film's failure to resonate emotionally and its undermining of the original trilogy's legacy. -----------Highlights:0:00 'The Matrix Resurrections' Introductions4:55 Opening Scene8:15 The New Matrix13:28 When Good Premises Go Awry17:11 Neo22:05 Agent Smith & The Analyst31:13 Trinity44:10 Bugs47:00 Character Arcs & Themes49:15 Lasting Impact#thematrix #matrixresurrections #alostplot 

Time to take down the Christmas lights… ack! They’re flying!

"The internet loves lists. The click bait ones often choose to list the worst of something and choose the best of it just to upset the audience for engagement. I can usually ignore these but this one really bugged me for some reason. I'll tell you the list and debunk it and offer some of mine."

What did you think of Bad Bunny's halftime performance in that game that wasn't played in a bowl and wasn't all that super? There was no slowdown to the showdown between the Bad Bunny lovers and haters.Your super hosts didn't fall for the hype until there was hype to be fallen for. Today, we bowl through the controversy and reveal what's really important: Our thoughts on the extravaganza!Then we shoehorn Breaking Bad into the title of this round!Round 303!Love what you're hearing on Beer Thursday? Show your support on our Beer Thursday Patreon page! Your contributions help us keep the beer flowing and the stories coming.At the $10 level, the next 18 Great Human Beings will get access to the Beer Thursday Facebook group.~~~~~~~We'd love to hear what you think and see Jay's brilliant beertography at @BeerThursdayShow on Instagram! Your feedback is not just appreciated, it's integral to our growth. Join the conversation, share your thoughts, and be a part of our growing community! Your voice matters to us, and we value your contributions to our discussions.~~~~~~~Never miss an episode, and help us take you to the top by subscribing and leaving a 5-star review on your favorite podcasting app.Here's what our house elf, Artie (not Archie), says about this round: This week on Beer Thursday, Shayne and Jay hop straight into the most “controversial” Super Bowl halftime show since… well, since the last time people got mad about something they didn't understand. That's right — we're talking Bad Bunny, the Puerto Rican superstar who brought dancing bushes, Mickey Mouse gloves, and a whole lot of Spanish to America's favorite football‑and‑pharmaceutical‑commercial event.Was the outrage real?Was it fake?Was it just people yelling at clouds again?Was it all worth it in the end? Your fearless purveyors of truth, justice, and the American way break down the performance, the politics, the poking‑the‑bear moments, and why Kid Rock is the last person who should headline anything involving the word “family.”Along the way, they cover:Why Bad Bunny's climbing a telephone pole actually meant somethingWhy Prince still holds the halftime crown foreverAnd why Shayne keeps calling him Big Bunny (senior moment? Guinness moment? both?)Plus: a toast to all bunnies — Bugs, Big, Bad, and Easter.Grab a cold one and hop in.00:00 – The Bunny Begins Shayne and Jay dive into the Bad Bunny halftime controversy and why people were mad before the show even started.01:00 – Fake Outrage & Real Opinions The guys unpack the political noise, the “he's not American” nonsense, and the art of being mad online.03:00 – The NFL Wants the World: Why the league wants global fans — and why Bad Bunny was the obvious choice.05:00 – Bunny Lore & Fun Facts Shayne shares Bad Bunny trivia: SoundCloud beginnings, church choir days, and F1 shoutouts.10:00 – The Halftime Show Review From Mickey Mouse gloves to dancing bushes, the guys break down the performance.13:00 – Symbolism & Power Lines: Why Bad Bunny's climbing a telephone pole was more than a stunt.15:00 – Surprise Guests & Real Weddings Lady Gaga, Ricky Martin, and a couple who actually got married on the field.17:00 – America, the Americas, and the Argument A surprisingly thoughtful moment about identity, geography, and why everyone argues too much.19:00 – Kid Rock and The Other Show Jay's story about the worst opening act ever and why the “alternative halftime show” was… something.21:00 – Lyrics, Lines & Limits The guys talk about the “offensive” lyrics and why the outrage feels selective.22:00 – Final Thoughts & Final Sips: A toast to bunnies everywhere and a reminder to join the Beer Thursday Patreon.~~~~~~~Disclosure: I don't really have a house elf. Aritie is AI. Get it? Aritie-ficial Intelligence!

Send a textValentine's Day, 1929 — Chicago.While the city spent thousands on candy and romance, seven men stood lined up inside a North Side garage.Two “police officers” walked in. Then two more men followed. Seventy rounds later, the garage floor was covered in blood.The targets were associates of George Clarence "Bugs" Moran — rivals of Al Capone during the Prohibition.Ballistics expert Calvin Goddard was brought in for his firearms examination skills.And Moran? He was on his way to the garage… saw what he thought was a routine raid… and turned back — unknowingly escaping death.The St. Valentine's Day Massacre changed organized crime forever.Support the showInstagram @vintagehomicidepodcastFacebook Vintage Homicide Podcasthttps://www.buymeacoffee.com/lachesis19vemail vintagehomicidepodcast@gmail.comwebsite https://vintagehomicide.buzzsprout.com

Three stories, one conversation: we review The Lincoln Lawyer and Good Luck, Have Fun, Don't Die, then tackle the thorny ethics of generative AI in a documentary about Lucy Letby. The Lincoln Lawyer and The Investigation of Lucy Letby are streaming on Netflix. Good Luck, Have Fun, Don’t Die is now showing in Malaysian cinemas. Thank you for checking out The Goggler Podcast, if you have any thoughts or questions, just email us on podcast@goggler.my, or reach out to us via Instagram. You can also WhatsApp us on The Goggler Hotline, on +60125245208 RSS: https://goggler.my/feed/podcast/

Fake Incident Report Used in Phishing Campaign https://isc.sans.edu/diary/Fake%20Incident%20Report%20Used%20in%20Phishing%20Campaign/32722 Divide and conquer: how the new Keenadu backdoor exposed links between major Android botnets https://securelist.com/keenadu-android-backdoor/118913/ CVE-2026-25903: Apache NiFi: Missing Authorization of Restricted Permissions for Component Updates https://seclists.org/oss-sec/2026/q1/166 The Next Frontier of Runtime Assembly Attacks: Leveraging LLMs to Generate Phishing JavaScript in Real Time https://unit42.paloaltonetworks.com/real-time-malicious-javascript-through-llms/ Encrypted RCS in iOS/iPadOS https://developer.apple.com/documentation/ios-ipados-release-notes/ios-ipados-26_4-release-notes

It's a party in Pewaukee! Super Greats Stud and Sonya are back with a school days story so good, it's nun approved! There's also meaty croissants (quaso!), lotsa lost pasta, a fresh batch of Bugs or Not Bothered and Hot Girl Trends. And don't miss our new POPs.Find us on Instagram:https://www.instagram.com/notsupergreatpodcastEat the delicious Bad Johnny's Pizza at The Longroom:https://www.badjohnnys.com/Go to the places we record at:The Wolfhoundhttps://wolfhoundchicago.com/Web Pubhttps://www.webpubbucktown.com/The Longroomhttps://www.longroomchicago.com/Wrigleyville Northhttps://www.WrigleyvilleNorthChicago.com/

Dr Jenny Jandt is a Senior Lecturer in Zoology at the University of Otago – Ōtākou Whakaihu Waka and joins Emile Donovan to answer this question.

Chullin #8- Rov With Miut Hamatzui, Bedikas HaReiah, Checking for Bugs (5786)

In this episode of Talk Nerdy, Cara is joined by science writer and media producer, Mindy Weisberger. They discuss her 2025 book, Rise of the Zombie Bugs: The Surprising Science of Parasitic Mind-Control. Follow Mindy: @laminda

Most preppers understand that gardening is a cornerstone of long-term self-reliance, yet far too many operate under a dangerous assumption: that growing food is simply a matter of seeds, soil, and water. The reality is that every garden faces an inevitable adversary—pests that can decimate weeks of careful cultivation in a matter of days. If your preparedness plan includes food production but lacks a reliable, organic solution for pest control, you've built your garden on a foundation that could collapse when you need it most. In this episode, Todd shares a time-tested neem oil recipe he's used successfully in his own garden, along with the critical details that make the difference between an effective treatment and wasted effort. You'll learn why this particular approach to organic gardening matters for preppers who refuse to compromise their soil with harsh chemicals, how to properly mix and apply the solution for maximum effectiveness against garden pests, and the storage considerations that most people overlook—mistakes that render even the best organic bug recipe useless. Todd also addresses the broader preparedness implications of having this capability when supply chains fail and garden stores are no longer an option. For those serious about genuine food independence, understanding how to protect your harvest isn't optional—it's fundamental knowledge. Whether you're currently maintaining a productive garden or building toward that goal, these are the practical, organic pest control skills that separate those who can actually feed themselves from those who merely think they can. Episode Page on EP.888 Of Interest Get One Preparedness Tip in Your Email Weekly! For more about Todd and RYF Join the Exclusive Email Group The Christian Prepper Podcast Buy Me a Coffee: https://www.buymeacoffee.com/prepperwebsiteSee omnystudio.com/listener for privacy information.

Assume Bugs Abstract Enrico and Fred discuss the idea that no design or development project is perfect. Key Points Join Enrico and Fred as they discuss the need to have an approach and a positive mindset to identify and resolve defects and bugs. Topics include: Assuming the design is perfect is foolish Encourage a positive […]

Bridget and Brookes react to the new trailer for the second season of Fallout, and decide that scorpions is, in fact, bugs.-Join our Discord community, subscribe to our Twitch channel, follow us on social media, and more! ⁨Fallout⁩ Season Two Trailer - YouTuber/WouldYouRather - Reddit@pyromaniu on TikTok@quick_question00 on TikTok-Fighting With Friends is a member of the Eclectic Cult Media Network.Support this show http://supporter.acast.com/fighting-with-friends-1. Hosted on Acast. See acast.com/privacy for more information.

Thank you to Ridge & Huel for sponsoring this episode! #ad - Limited Time Offer – Get Huel today with my exclusive offer of 15% OFF online with my code POGCAST at https://huel.com/pogcast - New Customers Only. Thank you to Huel for partnering and supporting our show! - Upgrade your wallet today! Get 10% Off @Ridge with code POGCAST at https://www.Ridge.com/pogcast #Ridgepod CHECK OUT THE PATREON! - https://www.patreon.com/ThePogcastPod On this episode of the Pogcast we talk about Mewgenics which is the recently released roguelike made by the same maker of The Binding of Isaac. We also talk through some of the crazy exploits in Arc Raiders recently, Marathons open beta & the recently announced king of the hill game called Wardogs. Check it out! Timestamps 00:00:00 - Intro Banter 00:13:06 - Mewgenics 00:29:24 - Ridge! 00:34:07 - Arc Raiders Exploits 00:57:14 - Arrival The Movie & React Content 01:07:30 - Huel 01:11:48 - Marathon Open Beta 01:13:06 - Wardogs 01:41:45 - John Wick Game & Random Other Games Check out JesseKazam Twitch: http://Twitch.tv/jessekazam YouTube: https://www.youtube.com/jessekazam Twitter: http://Twitter.com/jessekazam Discord: https://discord.gg/jessekazam Check out Veritas Twitch: http://Twitch.tv/Veritas YouTube: https://www.youtube.com/@VeritasGames Twitter: http://twitter.com/veriitasgames Spotify: https://open.spotify.com/artist/2S6iwClVoSNnpOcCzyMeUj Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode of Water Prairie Chronicles, Tonya returns after a two-month hiatus due to illness and discusses season five with a new audio-only format. The episode includes personal updates, such as coping with the flu and helping a son move to a new college. Tonya also shares insights on upcoming changes related to digital accessibility laws and teases a new comprehensive digital training tool aimed at helping businesses and community groups be more inclusive. Listeners are encouraged to provide feedback and subscribe for updates.

If I remember correctly, cicadas used to be quite a bit more common in the Auckland Summer Months. Yep they changed from year to year and occasionally almost completely quiet, but that was rare, to be frank. In Christchurch they've been a lot less noisy – especially the past 4 years or so. But early 2026 it started with a few choruses and now the “clappers” are also occupying the sound-scape. Male cicadas have so-called Timpany, which are little drum cavities on the underside of the bellies. They look a little bit like bent and shaped flaps. The timpany are really good at amplifying the sounds they make to lure females closer and closer – Party time! Females are known to aim for the noisiest male on the block. Egg-laying is happening from now on, at this time of the year. When the female has a good number of fertilised eggs to get rid of, she climbs into a suitable host tree. Her Ovipositor is a pretty useful tool to lay eggs inside the wood of a branch; a dozen or two are laid in an elegant pattern in the bark, where the eggs develop into very small larvae; these will emerge late autumn or early winter. Gardeners are often quite good at finding these herring-bone pattern because the damage in the twigs often causes weak-spots, leading to broken branches; Fruit growers are not keen on having many damaged branches in the orchard. Life Cycle: The eggs hatch in a few months and the tiny “nymphs” drop off the branch or twig in which they were born... drop to the ground and start digging. They create a tunnel and a cell around a tree root (or shrub root – or even grass roots) and suck the sweet phloem juices out of the root system – sugar is turned into protein and the body grows. They shed their skin 4, 5, 6 times and a few years later (up to 5 or 6 years in the soil!) they climb to the top layers of the soil... waiting for a perfect time to emerge at night in late spring or summer At night the nymphs come out of the soil, climb up a tree trunk and grasp the bark Their skin splits and out comes a fully winged adult cicada; it pumps up its wings and is ready for some R&R... singing and dancing Threats to larval cicadas: When they are in the top layers of the soil late winter/early spring, they are in easy reach of the probing bills of kiwi. Yep – cicada nymphs are the spring-time bulk food of Northland Brown Kiwi.See omnystudio.com/listener for privacy information.

"The Rockin 1000 is a project that started in Italy as gag to create a video of 1000 musicians playing Learn to Fly in order to get the Foo Fighters to come and put on a concert. It has since grown into full scale concerts across Europe. On January 31 the Rockin 1000 played their first concert in America, in New Orleans, and I was part of the band. Let me tell you the story."

Heute sprechen wir ein wenig über das Wetter, die TB303 und ein paar Mac Apps sowie den Launch von EuroSky. Ralf berichtet von seinen Erfahrungen, Windows-Games auf dem Mac laufen zu lassen und dann sprechen wir über die Bugs, die Apple so liebt, dass sie uns davon nicht befreit. Dem folgt eine vorübergehende Bewertung der 26er Betriebssystem-Releases von Apple, die uns nur so begrenzt glücklich machen. Einen großen Block widmen wir Linux, da Ralf sich mal einem Selbsttest unterzogen hat. Ist 2026 nun endlich das Jahr von Linux auf dem Desktop? Zum Schluß schauen wir auf das Datenmassaker OpenClaw und dem Social Network Moltboook, in dem AI Agents sich gegenseitig aufhetzen, Religionen und Casinos gründen.

¿Sigue teniendo la música un poder de concienciación? Invertimos nuestro tiempo de radio en una figura que se ha convertido en fenómeno de internet. Jesse Welles, de 33 años, llevaba más de una década dedicado a la música con diferentes proyectos. Pero fue en 2024 cuando, con una propuesta de folk rock y canción propuesta, comenzó a hacerse viral. Procedente de una pequeña población de Arkansas, con melena desaliñada y voz rasposa, este trovador y su guitarra le cantan a las noticias de actualidad, abordando temas como el conflicto de Gaza, los abusos de poder del ICE, la problemática del fentanilo o el asesinado de un director ejecutivo de una compañía de seguros sanitarios.Desde las redes ha saltado a grandes escenarios, a programas televisivos, a conseguir cuatro nominaciones en los Grammy o a que Joan Baez colabore en uno de los 5 álbumes que ha lanzado en menos de dos años. Su estilo bebe sin tapujos de gigantes como Bob Dylan, Phil Ochs o John Prine, pasando por Neil Young, Tom Petty o John Fogerty. El tiempo dirá hasta dónde puede llegar su música.Playlist;JESSE WELLES “The poor”JESSE WELLES “War isn’t murder”JESSE WELLES “United health”JESSE WELLES “Join ICE”JESSE WELLES feat JOAN BAEZ “No kings”JESSE WELLES “War is a God”JESSE WELLES “Horses”JESSE WELLES “It don’t come easy”JESSE WELLES “Anything but me”JESSE WELLES “Certain”JESSE WELLES “Whistle boeing”JESSE WELLES “Bugs”JESSE WELLES “Life is good”JESSE WELLES “That can’t be right”JESSE WELLES “Red”Escuchar audio

Join Jonathan and Michael as they return to the pocket universe of Pokémon to revisit the various arthropods you could catch there. This one has some cultural explorations of video games and gamer identities as well as conversation about why Pokémon might appeal specifically to the entomologists of the world.   Show Notes https://www.reddit.com/r/pokemon/comments/ckenhi/a_barely_scientific_cladogram_of_arthropod_pokemon/#lightbox  https://academic.oup.com/ae/article/64/3/159/5098346 Entomology Today interview on last article https://entomologytoday.org/2018/10/22/how-pokemon-opens-door-entomology-education/ The Entomological Diversity of Pokemon https://jgeekstudies.org/2018/10/12/entomological-diversity-of-pokemon/ The Phylogeny of Pokemon https://www.improbable.com/airchives/paperair/volume18/v18i4/Phylogeny-Pokemon.pdf   Get the show through Apple Podcast, Spotify, or your favorite podcatching app! Older episodes can be accessed through Archive.org. If you can spare a moment, we appreciate when you subscribe to the show on those apps or when you take time to leave a review! Thank you so much for listening!

In October 2025, CyberPress reported a critical security vulnerability in the Redis Server, an open-source in-memory database that allowed authenticated attackers to achieve remote code execution through a use-after-free flaw in the Lua scripting engine. In 2024, another prominent temporal memory safety flaw was found in the Netfilter subsystem in the Linux kernel: CVE-2024-1086. Bugs related to temporal memory safety, such as use-after-free and double-free vulnerabilities, are challenging issues in C and C++ code. In this podcast from the Carnegie Mellon University Software Engineering Institute (SEI), Lori Flynn, a senior software security researcher in the SEI's CERT Division, and David Svoboda, a senior software engineer, also in CERT, sit down with Tim Chick, technical manager of CERT's Applied Systems Group, to discuss recent updates to the Pointer Ownership Model for C, a modeling framework designed to improve the ability of developers to statically analyze C programs for errors involving temporal memory.  

In this finale, the gang has already taken out one of the human burglars, Larry. But Frank is made of different stuff. Apparently, he's come prepared and the sight of little folk attacking him doesn't seem to phase him too much. So what happens when 4 tiny beings take on a human burglar hellbent on either killing them or catching them? Find Out! The Cast: - Slone as Phil: https://www.tiktok.com/@slonerunning - Blackwell as Brabrex: https://linktr.ee/EveryMan95 - Spencer as Ekxander: https://linktr.ee/spenceydm - Taylor as Carl/Cam: https://linktr.ee/soyouwannabeadm Music is provided by Epidemic Sound Use our link for a 30-day Free Trial! https://share.epidemicsound.com/spf7rg/?playlist=pqqzqnqauplu95iie858kw85v7xnqexx Content Warnings: Violence, Bodily harm, Explicit Language, Drug and Alcohol use, Harm to animals (but 95% of animals are fully sentient people in this world), Claustrophobia, Bugs, Spiders. If we've missed anything, please let us know so that we can update our list. And remember to give us 5 Stars ⭐️⭐️⭐️⭐️⭐️ AND a positive review! This helps us so much, so tell your friends!  We have VIDEO now! Subscribe to our YouTube to see the podcast! And other content! --------------------------- And CLICK HERE to find links to our Patreon, Discord, YouTube, Twitch, TikTok, Instagram, and more! Email us at Soyouwannabeadm@gmail.com WE HAVE MERCH! Click the link above!

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

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Show DescriptionWe're passing over another milestone episode and answering your Q's with our A's while we do it: Dave goes 3D printing, should CSS be inside a web component, Chris is trying to build web component for popovers, why isn't Vue used or talked about more, finding bugs in blocks in the new CodePen, and we're grateful for 700 episodes. Listen on WebsiteWatch on YouTubeLinks Vue.js - The Progressive JavaScript Framework | Vue.js vuejs/petite-vue Syntax: Hacking Pizza Ordering For Fun And Profit - YouTube Theo - Twitch SponsorsAxe-ConAxe-con - the world's largest digital accessibility conference is from the makers of Axe-core and Axe DevTools Browser Extension. Taking place online on February 24-25. Registration is free and also gets you access to the on-demand recordings. Axe-con has a specific Development Track for dev content - some top speakers are Ire Aderinokun (front-end developer and Google developer expert), Jesse Beach (Software Engineering Manager at Meta), and other prominent folks from orgs like Coinbase, Zendesk, Red Hat, Atlassian, and more.

Agile Failed Us After 18 Months - Here we go...On month eighteen, our average lead time crossed 27 days. Production defects doubled. A supposedly minor release missed its window by three weeks.Nothing had “broken.” Velocity charts still looked healthy. Every ceremony was running on time. But releases slowed, confidence eroded, and engineers stopped believing what the board said.This hurt because customers felt it immediately. Bugs lived longer, features arrived stale, and every delay came with an explanation no one trusted anymore.How to connect with AgileDad:- [website] ⁠https://www.agiledad.com/⁠- [instagram] ⁠https://www.instagram.com/agile_coach/⁠- [facebook] ⁠https://www.facebook.com/RealAgileDad/⁠- [Linkedin] ⁠https://www.linkedin.com/in/leehenson/

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Linktree: ⁠https://linktr.ee/Analytic⁠Join The Normandy For Additional Bonus Audio And Visual Content For All Things Nme+! Join Here: ⁠https://ow.ly/msoH50WCu0K⁠ In the Notorious Mass Effect segment, Analytic Dreamz explores Noah Kahan's highly anticipated return with the new single "The Great Divide", released January 30, 2026, as the lead track and title song for his fourth studio album of the same name, arriving April 24, 2026.Following the massive success of Stick Season (2022)—which spawned viral hits, expanded editions in 2023, a Best New Artist Grammy nomination in 2024, and stadium-level touring—the 29-year-old Vermont native delivers his first new music in over a year. "The Great Divide" delves into emotional distance from rapid fame, reflecting on the gap between past and present self, strained ties with hometown friends and family, unsaid words, and unresolved conversations among those who grew up together but drifted apart.The narrative-driven music video—Kahan's first heavily story-focused—co-produced with Mastercard, premieres during the brand's commercial slot in the 2026 Grammy Awards broadcast (Sunday, February 1, 8:00 p.m. ET on CBS/Paramount+). Shot at a single gas station, it uses aging characters to symbolize evolving friendships, group conflicts, time's passage, and growing isolation. Full video streams at priceless.com/noahkahan, with a Mastercard sweepstakes launching post-premiere for Easter egg hunts, offering prizes like listening parties and Priceless Experiences.Recorded across Nashville (piano), Guilford VT (pond-side), Upstate NY (legendary studio), and Only, TN (farm with firetower), the album reunites Kahan with Stick Season producer Gabe Simon and adds Aaron Dessner. Themes center on childhood reflection, family, old friends, Vermont roots, regret, personal growth, and fame's isolating effects—described by Kahan as "the words I would say if I could" and "the fears I dance with before I drift off to sleep."Teased via TikTok's "The Last of the Bugs" account (nodding to "Everywhere, Everything" lyrics) since December 2025, with snippets of "The Great Divide" and possible "Porch Light." An upcoming documentary from Live Nation Productions, Federal Films, Polygram Entertainment, and RadicalMedia traces his arc from Busyhead and I Was / I Am to post-Stick Season stardom.Analytic Dreamz breaks down why this marks Kahan's cinematic, introspective next chapter, amplified by Grammy visibility, brand partnership, and fan-driven momentum—solidifying his shift to album-driven storytelling.Support this podcast at — https://redcircle.com/analytic-dreamz-notorious-mass-effect/donationsPrivacy & Opt-Out: https://redcircle.com/privacy

Lil AI productivity secret: we've become the duct tape for AI.

For a throwback, we're looking back at one of our all-time favorite episodes—our science episode from 2020. We chat with flavor chemist Dr. Arielle Johnson about how to eat a tree, how insects use flavor molecules to communicate and the science of taste and smell. Plus, Meathead teaches us how to grill perfect steaks; J. Kenji López-Alt investigates food expiration dates; and we make a no-fuss, all-flavor Spanish Almond Cake.Get the recipe for Spanish Almond Cake here.Listen to Milk Street Radio on: Apple Podcasts | Spotify

Comedians Jensen & Mike Figs join Big Jay Oakerson, Luis J. Gomez & Dave Smith to discuss Jordan being in Bradley Cooper's new movie, and unravel the saga of Luis's new neighbor's disdain for a certain tree in his backyard. Plus sportscaster mishaps, and Michael Rapaport announces that he's running for mayor. All This and More, ONLY on The Most Offensive Podcast on Earth, The LEGION OF SKANKS!!!Original Air Date: 01/13/26Support our sponsors!Visit BodyBrainCoffee.com and use code LOS25 for a limited time to get 25% off your order! #BodyBrainPodSupport the show & get 20% off your 1st Sheath order with code SKANKS20 at https://www.sheathunderwear.com #SheathPodNew customers get 50% off with code SKANKS at http://GLD.com $GLDpodVisit https://prizepicks.onelink.me/LME0/SKANKS & use code SKANKS to get $50 in lineups when you play your first $5 lineup! #PrizePicksPod---------------