Podcasts about Identified

Identity doesn't come from effort it comes from life that God gives for His glory. Join us as we wrap up our student ministry weekend with our guest preach Joe Yarborough. Ezekiel 37

03:29 Illinois sued over race reparations program13:46 Canada school shooter who murdered 5 students and faculty identified33:34 Officer fatally shoots fleeing suspect after picking up firearm42:43 City reaches $29M lawsuit for the death of civilianLEO Round Table (law enforcement talk show)Season 11, Episode 032 (2,621) filmed on 02/13/20261. https://www.lawofficer.com/race-based-payments/2. https://www.rvmnews.com/2026/02/online-posts-of-transgender-teen-surface-after-canada-school-shooting-wanting-to-be-petit-watch/https://nypost.com/2026/02/11/world-news/transgender-canadian-school-shooter-who-slaughtered-8-including-mom-and-stepbrother-seen-in-first-photos/https://www.usatoday.com/story/news/nation/2026/02/11/jesse-van-rootselaar-tumbler-ridge-canada-shooter/88631889007/3. https://rumble.com/v75m1a4-plainclothes-miami-officer-fatally-shot-armed-suspect-near-miami-edison-sen.html?e9s=src_v1_upp_a4. https://globalordnancenews.com/2026/02/12/seattle-reaches-29m-settlement-with-family-of-woman-struck-and-killed-by-officer-en-route-to-emergency-call-2/Show Panelists and Personalities:Chip DeBlock (Host and retired police detective)Chief Joel F. Shults, Ed.D. (retired chief and author)Ralph Ornelas (former chief of the Westminster Police Department and commander at the LA County Sheriff's Department)Related Events, Organizations and Books:Retired DEA Agent Robert Mazur's works:Interview of Bryan Cranston about him playing Agent Robert Mazur in THE INFILTRATOR filmhttps://vimeo.com/channels/1021727Trailer for the new book, THE BETRAYALhttps://www.robertmazur.com/wp-content/uploads/2023/05/The-Betrayal-trailer-reMix2.mp4Everything on Robert Mazurhttps://www.robertmazur.com/The Wounded Blue - Lt. Randy Sutton's charityhttps://thewoundedblue.org/Rescuing 911: The Fight For America's Safety - by Lt. Randy Sutton (Pre-Order)https://rescuing911.org/Books by panelist and retired Lt. Randy Sutton:https://www.amazon.com/Randy-Sutton/e/B001IR1MQU%3Fref=dbs_a_mng_rwt_scns_shareThey're Lying: The Media, The Left, and The Death of George Floyd - by Liz Collin (Lt. Bob Kroll's wife)https://thelieexposed.com/Lt. Col. Dave Grossman - Books, Newsletter, Presentations, Shop, Sheepdogshttps://grossmanontruth.com/Sheriff David Clarke - Videos, Commentary, Podcast, Shop, Newsletterhttps://americassheriff.com/Content Partners:Red Voice Media - Real News, Real Reportinghttps://www.redvoicemedia.com/shows/leo/ThisIsButter - One of the BEST law enforcement video channelshttps://rumble.com/user/ThisIsButterThe Free Press - LEO Round Table is in their Cops and Crimes section 5 days a weekhttps://www.tampafp.com/https://www.tampafp.com/category/cops-and-crime/Video Show Schedule On All Outlets:http://leoroundtable.com/home/syndication/Syndicated Radio Schedule:http://leoroundtable.com/radio/syndicated-radio-stations/Sponsors:Galls - Proud to serve America's public safety professionalshttps://www.galls.com/leoCompliant Technologies - Cutting-edge non-lethal tools to empower and protect those who servehttps://www.complianttechnologies.net/The International Firearm Specialist Academy - The New Standard for Firearm Knowledgehttps://www.gunlearn.com/MyMedicare.live - save money in Medicare insurance options from the expertshttp://www.mymedicare.live/

FOLLOW RICHARD Website: https://www.strangeplanet.ca YouTube: @strangeplanetradio Instagram: @richardsyrettstrangeplanet TikTok: @therealstrangeplanet EP. #1320 Epstein's Secret Project: Genetics, Power, and the Story They Tried to Bury For years, we were told the Epstein case was closed. But newly surfaced documents, buried emails, and emerging witnesses suggest a far darker dimension—one centered on genetics, eugenics, and engineered human life. Did Epstein's ambitions extend beyond trafficking and blackmail into the realm of biotech and elite reproduction? Richard Syrett sits down with retired U.S. Air Force Lt. Col. and Epstein investigator Peter Schinn to examine the evidence, the silencing of key voices, and the questions mainstream media still won't touch. Was Epstein building something far more ambitious—and disturbing—than we were led to believe? And who inside powerful institutions knew exactly what he was trying to create? GUEST: Peter Schinn is Associate Director of Epstein Justice and a retired U.S. Air Force Lieutenant Colonel who has spent more than a decade investigating the Jeffrey Epstein network. Identified early as a “problem” by figures within Epstein's orbit, Schinn has examined newly released documents, witness testimony, and institutional connections surrounding Epstein's operations and alleged scientific ambitions. His work focuses on accountability, transparency, and uncovering unanswered questions about Epstein's funding, influence, and long-term objectives. Schinn brings a disciplined investigative background and insider perspective to one of the most controversial and unresolved stories of our time. WEBSITE: https://epsteinjustice.com SUPPORT OUR SPONSORS!!! QUINCE Luxury, European linen that gets softer with every wash! Turn up the luxury when you turn in with Quince. Go to Quince dot com slash RSSP for free shipping on your order and 365-day returns. Now available in Canada, too ⁠ BECOME A PREMIUM SUBSCRIBER!!!⁠ ⁠https://strangeplanet.supportingcast.fm⁠ Three monthly subscriptions to choose from. Commercial Free Listening, Bonus Episodes and a Subscription to my monthly newsletter, InnerSanctum. Visit ⁠https://strangeplanet.supportingcast.fm⁠ Use the discount code "Planet" to receive $5 OFF off any subscription. We and our partners use cookies to personalize your experience, to show you ads based on your interests, and for measurement and analytics purposes. By using our website and services, you agree to our use of cookies as described in our Cookie Policy. Learn more about your ad choices. Visit ⁠megaphone.fm/adchoices Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://strangeplanet.supportingcast.fm/

In this company update, we are joined by Mike Spreadborough, Executive Co-Chairman, and Kas De Luca, General Manager of Exploration at Novo Resources (TSX: NVO | ASX: NVO | OTCQX: NSRPF). Following a flurry of activity across their portfolio, the team discusses the significant expansion of exploration targets at the Belltopper Project in Victoria, Australia. Key Discussion Points: Exploration Target Upgrade: The company recently announced a 40% increase in estimated ounces and a 48% increase in tonnage at the Belltopper Project, with the high-case exploration target now exceeding 880,000 ounces of gold. Geological Confidence: Kas De Luca explains how a year of mapping, 3D modeling of historical mine data, and modern drilling has expanded the target to eight distinct high-grade reefs, including the discovery of additional structures like the O'Connor's Reef. Proximity to World-Class Mines: The team highlights Belltopper's strategic location within the Bendigo Tectonic Zone, situated just 60km south of the high-grade Fosterville mine, in a province that has produced over 60 million ounces of gold. Upcoming Work Programs: Mike Spreadborough outlines the roadmap for the second half of 2026, which includes a major diamond and RC drilling campaign aimed at converting these exploration targets into a formal inferred resource. Portfolio-Wide Momentum: Beyond Victoria, the conversation touches on near-term drilling plans for projects in the Pilbara, supported by a solid cash position of approximately $8 million as of the start of the year.   Please email me with any follow up questions for Mike and the team at Novo Resources. My email address is Fleck@kereport.com.     Click here to visit the Novo Resources website to learn more about all the projects and exploration programs.    ----------------- For more market commentary & interview summaries, subscribe to our Substacks:  The KE Report: https://kereport.substack.com/  Shad's resource market commentary: https://excelsiorprosperity.substack.com/ Investment Disclaimer: This content is for informational and educational purposes only and does not constitute investment advice, an offer, or a solicitation to buy or sell any security or investment product. Investing in equities, commodities, really everything involves risk, including the possible loss of principal. Do your own research and consult a licensed financial advisor before making any investment decisions. Guests and hosts may own shares in companies mentioned.

Guest: Craig Unger. Unger explains how Trump's 1980 Commodore Hotel deal involved purchasing TVs from a KGBfront. This transaction reportedly initiated contact with Russian intelligence, who identified Trump's vanity and greed as ideal traits for recruitment.1936 HERALD SQUARE

America's economy added 130,000 jobs in January, almost double the number that analysts had been expecting, indicating that the labour market might have picked up after months of apparent stagnation. Hosted on Acast. See acast.com/privacy for more information.

Canadian police have identified the person who carried out a school massacre as 18-year-old Jesse Van Rootselaar, who they said had mental health issues, but they did not give a motive for one of the worst mass shootings in Canada's history. We get the latest on this with Kim Bolan, Journalist with the Vancouver Sun.

RCMP officials in British Columbia have released more details about the tragic Tumbler Ridge mass shooting that claimed nine lives. We get the latest updates from CTV B.C. Bureau Chief Andrew Johnson. We also hear from Peace River South MLA Larry Neufeld and B.C. Deputy Premier Nikki Sharma. On today's show: American author and former Washington Post columnist Philip Bump reacts to the U.S. Congress decision to vote against the Canadian-targeted Trump tariffs. Talk Science To Me with CTV Science and Technology specialist Dan Riskin: Why A.M. treatments could extend cancer survival by a year. The Daily Debrief Panel - featuring Rob Benzie, Laura Stone, and Mike LeCouteur. Former Conservative MP Bill Casey explains how the Clarity Act would impact Alberta separation.

Canadian police have identified the person who carried out a school massacre as 18-year-old Jesse Van Rootselaar, who they said had mental health issues, but they did not give a motive for one of the worst mass shootings in Canada's history. We get the latest on this with Kim Bolan, Journalist with the Vancouver Sun.

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Firestorm Erupts After DOJ's Epstein Coverup, Experts Raise Alarm AI Now “Building Itself” & Will Change EVERYTHING, Senate Republicans Sabotage SAVE Act That Requires Citizenship To Vote! Plus, Canadian School Shooter Identified As Transgender

AP correspondent Jennifer King has the latest on a deadly school shooting in the Canadian Rockies.

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In this episode of Gangland Wire, host Gary Jenkins takes listeners deep into one of the most chilling and revealing moments in Chicago mob history—a secretive 1967 party for Mob stalwart, Fi Fi Buccieri. It was held at the legendary Edgewater Beach Hotel. What appeared to be a lavish celebration was, in reality, a tightly controlled gathering of roughly 300 mobsters, political figures, and underworld insiders. The occasion marked the 40th birthday of feared Chicago Outfit enforcer Fiore “Fifi” Buccieri, a man whose reputation for violence made him one of the most dangerous figures in the city. Despite not being invited, veteran journalist Bob Wiedrich managed to infiltrate the event, raising serious questions about security, secrecy, and the gathering’s true purpose. This was no ordinary party. Federal surveillance later revealed that the Federal Bureau of Investigation had the room bugged, capturing disturbing conversations—including laughter and casual recollections of torture and murder by Buccieri and his associates. Central to this episode is Buccieri's alleged role in the brutal torture and murder of William “Action” Jackson, a crime that horrified even seasoned law-enforcement agents. These wiretap recordings provide rare insight into the mindset of mob enforcers and the normalization of extreme violence within the Chicago Outfit during the 1960s. The timing of the party was critical. Chicago boss Sam Giancana had recently been released from prison, and rumors swirled that major power moves were underway. Evidence suggests this birthday celebration doubled as a covert mob summit, where leadership issues, alliances, and strategic decisions were quietly discussed away from public view. This party was a who's who of the Chicago Outfit. Men like Mike Glitta, Teets Battalgia, Ceaser DiVarco, Ross Prio, Larry The Hood Bounaguidi, Irvin Weiner, Dominic DiBello, Wee Willie Messino, Joseph Cortino ( former chief of police in Forest Park and several others. You will learn how Anthony Accardo and his driver Jackie Cerone avoided the scene when the cops started taking pictures and writing down names. I also explore the role of the Santa Fe Saddle and Gun Club, an organization tied to questionable fundraising activities that blurred the lines between organized crime, business interests, and local politics. These raffles and social events weren't just about money—they were about influence, access, and control. Throughout the episode, I break down the cast of characters who attended this gathering: loan sharks, enforcers, racketeers, and political fixers. Their interconnected stories reveal a dense web of loyalty, fear, and ambition that defined the Chicago mob scene at its peak.   This episode uses the Edgewater Beach Hotel as more than a setting—it becomes a symbol of mob glamour masking ruthless criminal reality. It's a reminder of how deeply organized crime once penetrated American society, and why these stories continue to fascinate, disturb, and resonate today. 0:04 Chicago Mob Tales 1:39 Fifi Buccieri ‘s Infamy 3:19 Giancana’s Absence 4:22 The Santa Fe Saddle and Gun Club 5:36 Edgewater Beach Hotel 8:36 Police Intelligence Operation 12:22 The Notorious Players 16:02 Entertainment at the Banquet 18:54 Reflections on the Meeting Hit me up on Venmo for a cup of coffee or a shot and a beer @ganglandwire Click here to “buy me a cup of coffee” Subscribe to the website for weekly notifications about updates and other Mob information. To go to the store or make a donation or rent Ballot Theft: Burglary, Murder, Coverup, click here To rent ‘Brothers against Brothers’ or ‘Gangland Wire,’ the documentaries click here.  To purchase one of my books, click here. Transcript [0:00] Well, hey, all you wiretappers out there in gangland, wireland, [0:03] especially you guys up in Chicago. Yeah, I’ve done several stories on Chicago. I’m on a Chicago trip right now, I guess. I’m going to do one more with our friend, Mr. Cooley, Bob Cooley. We just haven’t set up a time yet, but I’m going to do one more with him for sure. But I’m going to keep some of these Chicago stories up. I got such a great reaction. You know, you guys, you know, like and share these, as they say, on the apps and on YouTube. But anyhow, let’s go back to March of 1967. [0:36] There was a real well-known reporter named Bob Wendrick at the time. He really covered the mob in Chicago. I mean, he might as well have been a member of the mob in Chicago. He was so close to so many people up there. And he had some really good sources and some inside tracks. And he went to a party, but he wasn’t invited to that party. You know, they never really were going to invite Bob Weindrich to a party. It was $25 a plate. There was about 300 outfit mobsters and their associates attended this party. Some of their political associates even. They called a chief of police and I think a mayor of a suburban city. It was at the Edgewater Hotel. It was sponsored by the Santa Fe Saddle and Gun Club. It was to honor the birthday of outfit enforcer, killer, and loan shark Fiore Fifi Bussieri. Fifi was a vicious killer, man. I mean, he was bad. Straight out of the Capone days. [1:36] And he was kind of best known in more modern times. It happened not too long before this party, I believe, or around this time, maybe right after. [1:48] He took part in the multi-day, I believe, three-day torture and murder of a bookie, a great big fat bookie named William Action Jackson. There’s some images, some pictures, a picture of him in his trunk was showing a lot of the torture that they did to him out there. I’ve seen it on the Internet. They kind of cut back on those pictures and try to keep those from getting circulated around on Facebook and some of the social media apps. I assume it’s still out there. Um, but anyhow, the Bureau had a, had a hidden microphone in a guy’s house, Jackie, the lackey Saron, who was, uh, uh, a Cardo’s driver at the time had a, had a hidden microphone in there and Jackie Saron and a couple others. And one of them was Fifi Sierra, Bussieri. I don’t remember who else it was. We’re laughing about Lacks and Jackson’s reactions to the cattle prod and some of the other gruesome details. [2:45] They thought he was talking to the hated FBI agent Bill Romer at the time, but in fact, he was not. He wasn’t talking to anybody. I did find one blurb where he was thought to be a child molester. So, you know, I don’t know. And I’m thinking it was a child of one of his girlfriends or something like that. I’m not sure. But anyhow, they tortured the heck out of him for about three days. Fifi came out of the 42 gang. If you remember, it was Alibaba and the 40 Thieves, so that meant there was 41 in Alibaba’s gang, and they wanted to have one more [3:17] than Alibaba, so they named themselves the 42 Gang. This party happened just as Sam Giancana was getting out of jail. [3:25] He didn’t attend, and he left for Mexico about that time to avoid further grand jury appearances. He’d been in jail about a year, I think, because they give him the old give you immunity and you have to testify. If you don’t, then they find you in contempt of court and send you to penitentiary or a jail for a year or so for the length of grand jury. And so he left town right after that and went down to Mexico for several years. Some speculate this meeting was really to get everybody together in one place and have some private meetings off the side without law enforcement really knowing what was going on, where Ricardo and Paul the Waiter Rica would name Joey Doves Iupa as the new boss in place of Gen Cona and make some other personnel shifts. You know, a few years later, when Giancana comes back, there’ll be a whole string of murders around the time he’s murdered because of some of his people that were always loyal to Giancana. [4:22] This Santa Fe Saddling Gun Club, anybody ever heard of that? I had not heard of this before. It was a registered club. The president was Joseph Scaramuza, who owned a gun store at Halstead & Taylor, which is, I believe that’s right down there in the middle of Mobland. There was an informant in the jfk files as i was researching scaramusa there was an informant that claimed that scaramusa knew jack ruby well and as they checked into scaramusa over that they found found that this halstead gun store that he owned had sold three pistols that were recovered after some puerto rican terrorists shot up the house of representative a few years before now you know what all that means i don’t know but uh and i remember that when i was a little kid these puerto Puerto Ricans, uh, now, uh, they tried to, they were trying to assassinate Harry Truman, who was staying out of the white house and the Blair house, uh, which is, I think maybe that’s where the vice president stays. Sometimes I’m not sure. Anyhow, he was not in the white house and they, they had a plan to assassinate him. They also went into the house of representatives and shot it up. They wanted complete freedom from the United States at the time. Now there’s not been any Puerto Rican freedom movement since that I know of. Anyhow, um. [5:36] The Edgewater Beach was a faded but once grand dom of hotels along Lake Michigan. They had their own beach for a while. Then something moved in between them and the beach. And it was about to declare bankruptcy. It was located a few guys that live in Chicago. It was 5555 North Sheridan. [5:56] And now members of the Chicago Police Intelligence Unit had found out about that themselves. It was like Weindrich had. Maybe they hip Weindrich to it. That all works, all that little undercover stuff. You have an employee at the Edgewater who knows somebody who knows somebody, and the work starts leaking out. When you have something this big, you have 300 people there, and it was really to make some money too, charged $25 a plate, and they did another little fundraiser. They’ve been selling raffle tickets all over Chicago and all, like down in northwestern Indiana. And in Indiana, anywhere that the outfit had some kind of influence and businesses that they could hold up. It’s like policemen. We used to go out and sell circus tickets. They were like $2 a ticket, but it wasn’t really for a ticket. It was like a support the police circus, which then gave a piece of the money to some police or widows and orphans fund. I don’t remember exactly. This is when I was brand new. and you were given like a handful of circus tickets and you’re supposed to go out to your local businessmen and sell them. Of course, they always bought them. All you had to do was go in and say, you know, I got some police tickets or circus tickets and they’d buy them. And they weren’t exactly even a ticket. They were a coupon and then they helped go buy a ticket. But, you know, that’s what they were doing, and that’s where they were. [7:23] Intelligence unit was milling around the hotel. They were, you know, I think what they were trying to do was waiting to see if the operators of this banquet, as this thing got going, if somebody actually, you know, drew, made a drawing or really raffled off a new car, which is what supposedly the raffle tickets were for, which would give them an excuse then to raid this place, saying it was an illegal lottery and then start really identifying the participants you know all of them that were there make them air everybody give you id and all that and then they had they were really loaded for bear they had 65 cops waiting close by it’s something called the foster avenue beach so it was it was a hell of an operation now the outfit during this time learned that the cops were going to be there and someone called Tony Accardo and Paula Guadarica, who were, you know, supposed to be there. They were like the headliners. They were the big ducks at that show. And really, if it was about having some meetings to realign personnel and name, maybe they’re going to have a making ceremony, but I doubt that. [8:30] But maybe they were going to name Joy Iupa as the new boss because he was the next boss. Somebody warned him not to come. And, of course, Jackie Lackey’s Roan didn’t show up either because he was a Cardo’s driver. [8:47] Cops, I’m going to tell you about some of the people the cops did find there and identify. Ross Prio, his north side loan shark and enforcer who had been Gen Conn’s second command and was reportedly consulted on all outfit murders. Now, Ross Prio, he’d been around. I can’t remember. I think he was out of the 42 gang himself. He had been around since the Capone days and a well-respected guy, had a lot of guys under him. And he was a bad dude. He was a bad actor. He was dangerous as hell and could take part in torturing the whole nine yards. They saw Irving Weiner there. He was a mob-connected bail bondsman. He was a guy who ended up a few years later walking with Alan Dorfman when somebody came up behind Dorfman and shot and killed him. Dorfman was their big guy in the Teamsters. Dorfman had helped him get those loans out of the Teamsters pension fund and loaned to people that wanted to buy Las Vegas casinos. Then everybody would get a kickback from those casinos. So he was integral. He was being investigated as an official of the Twin Cities. [9:54] Food products company and he had my he had partners felix milwaukee phil aldoricio and sam teach battaglia and marshall caifano i mean this guy is erb wiener he was he was a money man for the mob well known as a money man and and he was he was involved with with lombardo joe lombardo and tony splatter and some others and they got a loan for a guy named from the teamsters fund but for a guy named danny seifert they thought danny seifert had started a company with a lot of this money, and he was going to testify about how he got this Teamsters loan is my understanding. And I believe Lombardo and probably Frank Suisse showed up and killed him one day. He never spent a night in jail. Weiner never spent a night in jail. Go figure that. He’s kind of like, almost like Tony Accardo, huh? I saw a guy named Mike Glitta. He was an outfit member who had B-Girl bars, had these kind of hustling bars, and was involved, heavily involved in the porn business now. Um. [10:54] There was a lot of porn shops in Chicago, and Gletta was really, he was the guy on the porn shops. Chicago Crime Commission published something that said he supervised all pornography operations in an area that went from the near north side clear to the Wisconsin state line. So everything from, say, Rush Street on north was his. I guess he wasn’t down in, I think, Old Town is where Redwood met and some porn shops down there. and Frank Suisse was extorting money from some of them. Mob watchers claimed that Glitter always reported directly to Vincent Solano, who was a labor union leader and a capo, and the guy that probably had Tokyo Joe, Joe Ido killed. He was a racket boss on the north side and all the way up to the north suburbs. Identified a guy called Larry the Hood, who I’d seen that name before. It’s a really hard name to pronounce. was a Bonaguiti. [11:54] He was a mob wannabe at the time. As I researched into him, he was really just a wannabe. Hung around the Rush Street bars and he was associated with Mike Glitta. And he’ll eventually get an opportunity when Ross Prio dies and Mike Glitta has a heart attack and he moves on up real quick because he’s always in there around and he knows the porn business and the B-Girl bars on that near north side. And he’s the one that goes around and collects after after Glitter has a heart attack. [12:23] Another Northside vice boss named Joe Caesar Joseph DeVarco, he was dropped off by an underling driver. He came out of the 42 gang himself and is a well-known gangster on the Rush Street area. Dominic DiBello was a Northside gambling operator. He was seen with a friend of his and a fellow gambling operator named Bill Gold, or called Bill Gold. He had a longer name than that, and I don’t know him. If you guys make comments down below, if you know who this Bill Gold was and what the story was with him, he probably just ran a sports book or something or helped with the off-track betting outlets. And they arrived just before a guy named Joseph Cortino, according to the newspaper report. He was a former Forest Park chief of police. He was suspected of protecting gambling operations and leaking law enforcement information to the mob. A guy you hear mentioned, I’ve not really seen much on in detail, Willie Massino, and they called him Wee Willie because he was little, but he was supposedly really, really a bad character. [13:26] Here’s a guy when I believe it was Mario Raginone was invited to go on some kind of a crime, and he saw Willie Massino and somebody else in the area. And he said, uh-oh, if those guys are anywhere in the area where I am and they’ve got me kind of isolated like this, you know, going to do a crime so I’m not telling anybody where I’m going and what I’m doing and who I’m with, you know, they’re going to hit me. And he went in after that. That’s how feared Wee Willie Messino was. He had been a loan shark collector and enforcer for Tony Cardo and a guy named Joseph Gagliano, who I don’t know must have faded off into the woodwork by the 70s. 1970 he went to prison for kidnapping and beating a couple of contractors who owed money to the mob, George and Jack Chiagoris. [14:19] Sounds like they’re maybe Greek, huh? After he got out of the penitentiary, he went to work as an advisor with Marco D’Amico, who was, you know, remember Marco D’Amico had a gambling operation, and that’s who Bob Cooley worked with a lot. And he also did some work for Jackie Cerrone. [14:37] So Turk Torello, James Turk Torello, he was confronted by the cops as he was unloading sound equipment out of his, wherever his car. He yelled at him as they walked up. He said, hey, he said, I got machine guns in these boxes. You want to come and see? He was kind of a wise-ass, you know. He was a capo of the 26th Street crew and directly under Fifi Busseri. One time, he had been sent by an angry mob boss named Sam Giancana, who we all know, Mobo. And he was going to partner up with Jackie Cerrone to kill an outfit member named Frankie Esposito down in Florida. But the Bureau had recorded Giancana’s conversation and warned Esposito. and he came right back around. He didn’t help the Bureau. You know, you go out and you warn a guy and then you try to bring him in and make him a snitch or make him a cooperating witness in the end because they’re trying to kill him. They don’t all come in. And he ended up coming back to Chicago and settled his dispute with Giancana and that hit was canceled. According to the tape recordings, Torello and his killers were going to murder Esposito and cut him up in small pieces and feed him to the sharks off the Florida coast. You know, they had houses down in Florida. That’s where they, that was Jackie Cerrone’s Florida house where they overheard him and Fifi talking about the murdering and torturing Action Jackson. [16:03] Now, I mentioned bringing in the sound equipment. They had entertainment. Vic Dimone was the entertainment that night. Now, Vic Dimone has long-held connections to the Chicago outfit and I believe the Genovese family. I didn’t really go way in deep into him. I’ve got a bunch of notes. I’ll probably do a story just about Vic Dimone. [16:26] Maybe he was the character in The Singer and The Godfather, that kind of a blend of Frank Sinatra and Vic Dimone. As a singer in the Godfather movie. Guys named a couple brothers, Joseph and Donald Grieco, were there. Well, they had been in business with Vic Damone in the Vic Damone Frozen Pizza Company. Paul Rica and Fifi Boussieri had brought the famous singer Vic Damone into the outfits world and got him to lend his name to this frozen pizza business. And what they did, the Grieco brothers, They use it as a cover for their loan shark activities, but, you know, they sold pizzas, too, although I’ve never heard of. I don’t ever remember seeing a Vic DeMone frozen pizza. Vic DeMone had even taken his show to Giancana’s joint, the Armory. And if you’ve ever been by the Armory, it’s just like a neighborhood bar. A neighborhood joint is not a place. But Vic DeMone was big. You know, he would be playing Madison Square Garden maybe at the time or the big clubs, the Copacabana in New York. And they got him to bring his show out to. [17:33] Gincana’s Joint the Armory kind of like at his Villa Venice he got Sinatra, Dean Martin and Sammy Davis to bring their show there and it was not exactly it was not the Copacabana they tried to make it into the Copacabana of Chicago but it never really got there another guy they saw was an outfit bookmaker and a tough guy out of Cicero who will get killed here in a little bit Sam Sambos Cesario Yeah. [17:59] He was a longtime workhorse. He’s well-liked throughout the whole Chicago underworld, but he made a mistake. He ended up marrying a girlfriend slash mistress, the Gomar of Milwaukee Field Aldericio, while he was in the penitentiary. Two guys showed up with this woman. He marries her. They’re sitting out in front of their house. It was like a brownstone. It was a hot summer night. They’re sitting out in lawn chairs out in front of their house, and two guys pull up and run up and kill him. They say Harry Ailman was the guy that did that. They call that. I’ve had some kickback on this when I said this one time before a few years ago. I didn’t really investigate into it. But, you know, the popular story is that it’s a hit from beyond the grave because Aldericio had already died in prison [18:50] between the time he gave that order and this actual murder. So that is a story of the big meeting at the Edgewater Beach Hotel in Chicago. [19:02] It wasn’t exactly like Appalachian or some of the other famous mob meetings, and it was just Chicago only. They didn’t identify that they named anybody from out of town at this thing. Seemed like it was a big moneymaker, maybe a meeting that you could hire some other little meetings in, get people in there that you didn’t really want to be seen with in public. This article, they talked about other politicians and businessmen that were there, but they didn’t really name them. I guess they didn’t want to get sued or whatever, but it was a, it was definitely, it was a fundraiser. He charged 25 bucks a plate and then have that, uh, that lottery for that car. And, and, you know, they never gave that car to anybody. And you know how much money you can raise with, with, you got, you know, a hundred guys or so going out, mob guys going out and raising money, selling lottery tickets at five bucks, 10 bucks each. You can raise a lot of money like that. So maybe it’s just one more big Chicago scam and honored Fifi Boussieri at the time. I don’t know. But anyhow, thanks a lot, guys. I thought it was an interesting story, and I thought you would find it interesting. And some of the people that they named that were there, I wish I’d have been there, but writing down license numbers and taking pictures and all that stuff. So keep coming back. Like and subscribe, as they say. And we’re just going to keep doing this and doing this. [20:24] I’ve gotten some you know I’ve got some things up that are like non-fiction books that are based on mob stuff, I don’t know if that’s okay or not, but I kind of like mixing that up. There’s only so many mob stories out there. You know, I don’t want a lot of these that have already been told. I don’t remember seeing any. I kind of looked around in the other podcast having this story. So I try to find them. You know, give me any tips, your comments that you can. I’ll try to look it up. And if I can find enough information, I’ll do the story on it. So thanks a lot. And adieu to you guys out in Chicago. I bet it’s colder up there than it is down here. Thanks, guys.

Stupid News 2-6-2026 8am …The Fastest Elevator in the World …Authorities in Wisconsin have identified the Serial Defecator …The Fatty Your Junk the Further You Can Fly

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

In which author, music executive and host of the Identified podcast Nabil Ayers discusses selling used CDs, The Terminator, The Drum Doctor and more. Certificate #48391.

Update: MAGA trolls mock Rep. Maxwell Frost after he was attacked by a racist Trumper at Sundance. Update: Man Who Fatally Shot Black Woman Gets Sentenced to 21 Years to Life in Prison. Alex Pretti's killer's Identified. Host: Dr. Rashad Richey (@IndisputableTYT) Guest Host: Senator Nina Turner (@ninaturner) *** SUBSCRIBE on ⁠⁠⁠YOUTUBE⁠⁠⁠  ☞ ⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/IndisputableTYT⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ FOLLOW US ON: ⁠⁠⁠FACEBOOK⁠⁠⁠  ☞ ⁠⁠⁠  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/IndisputableTYT⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠TWITTER⁠⁠⁠  ☞     ⁠⁠⁠  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.twitter.com/IndisputableTYT⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠INSTAGRAM⁠⁠⁠ ☞ ⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/IndisputableTYT⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit podcastchoices.com/adchoices

The sound of regional Australia. News and analysis from the ABC's network of regional reporters.

In today’s episode, Ben O’Shea reveals why SA police are treating the disappearance of Gus Lamont as a “major crime” with a suspect identified. Plus, the alleged Invasion Day bomber charged with terrorism.See omnystudio.com/listener for privacy information.

See omnystudio.com/listener for privacy information.

It's not often that it happens, Slushies, but it's always a treat when it does. We're switching to fiction for the day with “Colfax,” a flash story from Patricia Q. Bidar, author of the short fiction collection Pardon Me for Moonwalking. Spoiler alert: read the story first in the show notes or listen to the story in full at 41:50 before our discussion ruins it for you. Something about the story's theme and concision reminds Sam of Louise Glück's prose poems in her late collection, A Faithful and Virtuous Night. Sam also appreciates how the story allows a female character the same kind of recklessness found in Denis Johnson's Jesus' Son. Jason shares his surprising childhood connection to Vacaville, CA, one of the story's locales. And in his role as bad cop, Jason raises a question about uncanny children. Tune in to find out what he means by that. While we're all bracing for winter storms, we're happy to dwell, for a moment, in California Central Valley's humid and fertile atmosphere. As always, thanks for listening! At the table: Tobi Kassim, Samantha Neugebauer, Jason Schneiderman, Kathleen Volk Miller, Lisa Zerkle, and Lillie Volpe (sound engineer) Bio:        Patricia Q. Bidar is a western writer and Port of Los Angeles native. Her novelette, Wild Plums (ELJ Editions), was published in 2024 and collection of flash fiction, Pardon Me for Moonwalking (Unsolicited Press), in 2025. Patricia's work has appeared in Waxwing, Wigleaf, SmokeLong Quarterly, The Pinch, and Another Chicago Magazine; in the Wigleaf Top 50, and in many anthologies including Flash Fiction America (W.W. Norton), Best Microfiction, and Best Small Fictions. Visit patriciaqbidar.com   Website www.patriciaqbidar.com   Facebook         https://www.facebook.com/patriciaqbidar Instagram        https://www.instagram.com/patriciaqbidar/ Bluesky              patriciaqbidar.bsky.social     Colfax Cristina swallows the last of the loose pills from Julian's glove box. Within a few minutes, fresh energy blooms and fizzes within her; the sensation is of tumbling backward into space.  Julian: a drug dealer so giant and peevish the floor mats on the driver's side are bunched and ruined. Underneath his criminal veneer, Julian is just a mundane mammal who's driven Cristina, an animal woman, to flight.  Half an hour later, she's reached Colfax. In this heat, this fecund place. The car has mashed against the gas station's cashier hut. Years ago, when Cristina was growing up here, this was a drive-in theatre, with a massive image of a vaquero on a rearing steed. Sweltering nights, Cristina would watch movies with her lonely mother, car windows open wide, clasped in the smell of tomatoes, melons, and insecticide.  Rain begins to pepper the hood. Cristina rises into vegetal air. She doesn't recall opening the door.  The window to the hut is dirty and rain spattered. She peers between cupped hands at the empty stool inside, the bank of cigarette packs. Lightning cracks; after a few seconds, thunder rumbles. Cristina presses her hand over her heart. Is she alarmed? Are the pills goosing her pulse? But she feels calm. The sky is a tight lid. It was a mistake, stealing Julian's car. Julian, who took her in. Identified and claimed her after Cristina finished her time and was so adrift and alone.  Cristina was working as a server in a West Sacramento brewery. Her last customer on a slow Tuesday night was a black-haired guy in a cowboy hat. Stiff-looking jeans and a pearl-buttoned shirt. A face that seemed not to match the hair. “Lady,” he said so low she had to incline her head. “You think no one sees you. I do. I do.” She joined Julian that very night on one of his quests. He was what her mother would have called a peeping tom. He wanted her to wear nylon hose, like he did. Why not? No one was getting hurt. It was simply watching. Watching women. Women when they were themselves and unaware they were being observed. In a word: seen. Julian was no Rawhead, no Slenderman. Not one of those serial killers roving California freeways in the nineteen-seventies, the ones Cristina's mother had been obsessed with. Now she imagines someone peering in through the car door and seeing her, Cristina, slumped behind the wheel. People idealize farmland, farm girls as wholesome. Green, yellow, and blue.  The sky is cobalt now. Fifty feet away is a bus shelter, sagging and white. A small form is hunched inside. Lightning again, and then, immediately following, that bass sky-rumble. Cristina runs. Inside, a child of about nine swings its legs. Windbreaker, hood up.  "Hello there?" Cristina ventures. "I'm studying these ants," the kid returns. A girl. "Would you like a churro?" Cristina cannot see the girl's face but is struck by the way she sits. A bell buried deep inside of her tolls. "Is this the bus stop for town?" Cristina asks. The churros smell nice; hot grease and cinnamon. Cristina used to make them for her little sisters. She thought she might become a baker one day. At least, when anyone asked, this was what she had answered. She should be hungry. "That's my car, in case you were wondering,” Cristina says. Nothing. She crouches down beside the girl. “Dead at the service station. Lucky, I guess.” The child considers this. "Well, not really." She speaks patiently, the way Cristina used to speak to adults at her age. As if they were her younger sisters or the kids in the slow class at school, or the witless ladies in the school office. “On second thought, I'll take one of those churros." Cristina says. But the girl has returned to her task: surveilling a line of ants. Cristina's mind unspools the types. Velvet ants. Pharaoh ants. Argentine ants. Thief ants. The odorous house ants, and then — wasn't there a sugar ant?  The smell of water-heavy crops and soil and chemical fertilizer thickens the air. All of the choices Cristina has made in life have led her to this place. "There's nothing left," she says aloud. "It depends on how you see it," the girl returns, pushing her eyeglasses up into place with a forefinger. Cristina squints at the obscured face. Then the girl daintily lifts and lowers her hood. And bares the side of her left pinky finger. The small oval scar is exactly like Cristina's.  “Did your mother tell you that people with six fingers and toes are giants sired by angels and human women? Something apart from God,” Cristina said. Those surgeries when she was four.  “She says I'm a monkey.” Cristina remembers a long-ago birthday party, her ninth, attended by zero children.  She feels the sky drawing her up, then. At the same time, the inverted bowl of sky pushes down. It is like that optical illusion where you can't tell if the black horse is headed toward you or walking away. Hail pounds the roof of the shelter. The discs of ice flash under the bright lights of the gas pump island. The girl returns to dropping pinches of dough onto the ants. Obeying their internal imperative: a perpetuation of their kind.  Cristina sees Julian preparing for bed. Applying his eye cream. Clapping twice to extinguish the bedside light. He refers to himself as cerebral. But what is so deep about dealing painkillers during the afternoon shift at the One Stop Spy Shop in Vacaville? Life with Julian had amounted to a slow and downhill slide, and that was for sure. “We live our lives with our ancestors as witness,” the girl says at last. Her words hang in the air like wet almond blossoms.  Cristina has to ask. “Am I that? Am I alive?” And a roar consumes the sky. A silver bus is careening toward them from behind blue oaks. And a metal monster slips from the asphalt. Rolls end over end. Sky-blotting. Deafening. Images rise and blend and collapse. The blanched face of the driver. The silhouettes of passengers. One of whom is standing. Julian? Something blooms and expands in Cristina's head. But there is no bus. No careening crash. Only a fecund silence. And the girl tears a piece of the churro, nudging Cristina's lips with the sugar and cinnamon confection. It is absolutely delectable and somehow still warm. Like the corner of a golden kitchen in bygone evenings. A humming mother, changing her dressings. An iron stove and a gray kitten, satisfied and warm.  Cristina really, finally, is free. She has made it back to the beginning.  Apart from time, the girl and Cristina stand in the little windbreak like gingerbread children or figures in a Frida Kahlo painting. The girl takes her hand. And then it is she and Cristina and the animal female chain, extending into and past the vanishing point: Girl Girl Girl Girl Girl Girl Girl.

What does it mean to raise yourself across cultures—and carry tradition forward through food? In this episode of Identified, chef and author Camille Becerra sits down with host Nabil Ayers for a heartfelt conversation about family, identity, and the life-shaping power of memory. Born in Puerto Rico to a Puerto Rican mother and Cuban father, Camille moved to New Jersey at just one year old and grew up between cultures, languages, and generations. She reflects on growing up with a single mother, navigating a distant relationship with her father, and finding connection through her Puerto Rican family’s rituals—like traveling alone as a child to visit cousins, or waking early to buy bread from the local bakery. Food became her anchor and a bridge to memory and belonging. Camille opens up about defining family on her own terms, the blurred lines between relatives and chosen loved ones, and what it means to pass down tradition through flavor. Guest: Camille Becerra Host: Nabil Ayers Executive Producer: Kieron Banerji Production by Palm Tree Island See omnystudio.com/listener for privacy information.

Jeff Woolwine is an investigator and researcher best known for his detailed analysis of The Phoenix Lights Investigated and Identified, one of the most famous mass UFO sighting events in modern history. Woolwine approaches the 1997 Phoenix Lights case with a methodical, evidence-based mindset, examining eyewitness testimony, video footage, flight data, military activity, and alternative explanations. His work focuses on separating speculation from verifiable facts, offering a grounded reassessment of what thousands of witnesses observed over Arizona skies and contributing to a clearer understanding of how extraordinary aerial events are interpreted, explained, or misidentified.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-x-zone-radio-tv-show--1078348/support.Please note that all XZBN radio and/or television shows are Copyright © REL-MAR McConnell Meda Company, Niagara, Ontario, Canada – www.rel-mar.com. For more Episodes of this show and all shows produced, broadcasted and syndicated from REL-MAR McConell Media Company and The 'X' Zone Broadcast Network and the 'X' Zone TV Channell, visit www.xzbn.net. For programming, distribution, and syndication inquiries, email programming@xzbn.net.We are proud to announce the we have launched TWATNews.com, launched in August 2025.TWATNews.com is an independent online news platform dedicated to uncovering the truth about Donald Trump and his ongoing influence in politics, business, and society. Unlike mainstream outlets that often sanitize, soften, or ignore stories that challenge Trump and his allies, TWATNews digs deeper to deliver hard-hitting articles, investigative features, and sharp commentary that mainstream media won't touch.These are stories and articles that you will not read anywhere else.Our mission is simple: to expose corruption, lies, and authoritarian tendencies while giving voice to the perspectives and evidence that are often marginalized or buried by corporate-controlled media

A decades-old killing in the Midwest remains unresolved, with questions lingering years later Federal officials open a civil rights probe into a fatal police shooting in Minnesota Two teens are arrested after a shooting disrupts a community parade in Louisiana All eight inmates who escaped a Louisiana parish jail have now been recaptured See omnystudio.com/listener for privacy information.

This Day in Maine for Tuesday, February 3, 2026.

Clark County Public Health has identified an additional measles exposure site in Ridgefield connected to a confirmed case announced Jan. 23, with potential exposure at a local medical clinic and earlier exposure at Ridgefield High School, while officials continue to monitor for symptoms and report no additional confirmed cases to date. https://www.clarkcountytoday.com/news/additional-measles-exposure-site-identified-in-ridgefield/ #ClarkCounty #Ridgefield #Measles #PublicHealth #HealthAlert

A cyber security company says they've identified the person responsible for hacking into the Manage My Health portal and now they want to see justice served. Finn Blackwell reports.

One of the victims of the deadly Mount Maunganui landslide has been formally identified as Max Furse-Kee. RNZ Reporter in Mount Maunganui, Lauren Crimp spoke to Corin Dann.

A man fatally shot by ICE officers in Minneapolis has now been identified as 37-year-old Alex Peretti; a dangerous polar vortex passing through Canada is pushing homeless shelters beyond their limits; residents in Northwest Territories are struggling with homelessness as public housing units fall to disrepair; and more.

MeidasTouch host Ben Meiselas reports on new information surfacing from the horrific shooting in Minnesota today including the identity of the victim Alex Jeffrey Pretti who was an ICU nurse at the VA and who had legal permit for a firearm and Meiselas discusses Trump's awful response as he tries to cover up the awful shooting. Remember to subscribe to ALL the MeidasTouch Network Podcasts: MeidasTouch: https://www.meidastouch.com/tag/meidastouch-podcast Legal AF: https://www.meidastouch.com/tag/legal-af MissTrial: https://meidasnews.com/tag/miss-trial The PoliticsGirl Podcast: https://www.meidastouch.com/tag/the-politicsgirl-podcast Cult Conversations: The Influence Continuum with Dr. Steve Hassan: https://www.meidastouch.com/tag/the-influence-continuum-with-dr-steven-hassan The Weekend Show: https://www.meidastouch.com/tag/the-weekend-show Burn the Boats: https://www.meidastouch.com/tag/burn-the-boats Majority 54: https://www.meidastouch.com/tag/majority-54 On Democracy with FP Wellman: https://www.meidastouch.com/tag/on-democracy-with-fpwellman Uncovered: https://www.meidastouch.com/tag/maga-uncovered Learn more about your ad choices. Visit megaphone.fm/adchoices

It’s the multi-billion-dollar industry that’s preying on the insecurities of young men with dangerous consequences. The MANOSPHERE is fuelled by social media and is now playing out in the real world, with major impacts on mental health, behaviour, and relationships. In this episode of The Briefing, Natarsha Belling is joined by extremism researcher Vivian Gerrard to explain what the Manosphere is and the solutions we all need to fight for. Headlines: A major manhunt continues for 37-year-old Julian Ingram after three people were shot dead in the central west of NSW, the Prime Minister claims the Liberal Party has undermined its first female leader, and a number of NSW beaches are set to reopen this weekend after four shark attacks this week. See omnystudio.com/listener for privacy information.

What is the church really? Pastor Heath Lambert explains that the church isn't a building or a place - it's the blood-bought people of Jesus, both local and global, identified with Christ and accomplishing His purposes in the world. This foundational teaching clarifies what most Christians misunderstand.Timestamps0:00 - Introduction and the question0:40 - Most Christians would stumble on this answer1:46 - The short answer begins with Jesus1:56 - Titus 2:14: Jesus purified a people for Himself2:42 - The church is the people of Jesus3:27 - The church as an organic group, not a building3:49 - 1 Corinthians 1:2: Two ways to talk about the church4:35 - The local church in a specific place4:59 - The global church in every place5:56 - The church is identified with Jesus6:12 - Acts 9:4: Persecuting the church is persecuting Jesus7:24 - The church as the body of Christ7:46 - Other metaphors: Bride, vine and branches8:24 - The church accomplishes Jesus's purposes8:42 - Ephesians 3:10: Making God's wisdom known9:16 - 1 Timothy 3:15: Pillar and buttress of truthKey Topics Covered- Common Misunderstandings - Why most Christians can't properly define the church- Jesus as Starting Point - Titus 2:14 and the foundation of the church- Blood-Bought People - Those redeemed from lawlessness who trust in Christ- Not a Building - Why the church is people, not places or structures- Two Dimensions - Local assemblies and the global body of believers- Local Church - Believers gathering in a specific geographic location- Global Church - All believers across the world in every place- Identified with Christ - Acts 9:4 showing Jesus's unity with His people- Body of Christ - The church as Christ's organism in the world- Bride of Christ - The marriage metaphor showing intimate unity- Vine and Branches - Jesus as source, church dependent on Him- Accomplishing His Purposes - How Jesus works through the church today- Pillar of Truth - The church as God's means of proclaiming truth to the worldScripture ReferencesTitus 2:14 - Jesus purified a people for His own possession1 Corinthians 1:2 - The church locally and globallyActs 9:4 - Saul persecuting Jesus by persecuting the churchEphesians 3:10 - God's wisdom made known through the church1 Timothy 3:15 - The church as pillar and buttress of truthAbout The Ten Commandments BookHeath Lambert's new book "The Ten Commandments: A Short Book for Normal People" is now available. This accessible guide explains how God's commands apply to modern life without requiring theological education. Perfect for personal study, evangelism, or gifts to friends, neighbors, and family.Order now and download a free chapter at fbcjax.com/tencommandmentsLike this episode? Subscribe for more biblical teaching from Marked by Grace. Share your thoughts in the comments below about how understanding the church as people rather than a place changes your perspective.Have a question you'd like answered? Send it to markedbygrace@fbcjax.com

Top headlines for Friday, January 16, 2026In this episode, we discuss escalating tensions in Minneapolis following the fatal shooting of Renee Nicole Good by an ICE agent, leading to widespread protests and calls for accountability. President Donald Trump has threatened to invoke the Insurrection Act to suppress unrest, a move that has sparked significant debate. Additionally, Iran's temporary closure of its airspace amid U.S. tensions and the emotional response of Portland's Police Chief to recent shootings are examined.00:11 Iran shuts airspace; US Qatar base personnel asked to evacuate01:04 Trump threatens to use Insurrection Act amid Minneapolis unrest01:53 Porn star Lily Phillips responds to critics of her faith, baptism02:42 Police chief cries, confirms Tren de Aragua ties in CBP shooting03:31 Evangelical leader criticizes Trump for flipping off auto worker04:19 Oklahoma city rejects proposal to build 15-acre Islamic center05:06 Angel Studios to partner with Neal McDonough's production companySubscribe to this PodcastApple PodcastsSpotifyOvercastFollow Us on Social Media@ChristianPost on XChristian Post on Facebook@ChristianPostIntl on InstagramSubscribe on YouTubeGet the Edifi AppDownload for iPhoneDownload for AndroidSubscribe to Our NewsletterSubscribe to the Freedom Post, delivered every Monday and ThursdayClick here to get the top headlines delivered to your inbox every morning!Links to the NewsIran shuts airspace; US Qatar base personnel asked to evacuate | U.S.Trump threatens to use Insurrection Act amid Minneapolis unrest | PoliticsPorn star Lily Phillips responds to critics of her faith, baptism | EntertainmentPolice chief cries, confirms Tren de Aragua ties in CBP shooting | U.S.Evangelical leader criticizes Trump for flipping off auto worker | U.S.Oklahoma city rejects proposal to build 15-acre Islamic center | U.S.Angel Studios to partner with Neal McDonough's production company | Entertainment

Is Blood Really Family? In this episode of Identified, host Nabil Ayers sits down with Traci Thomas, creator and host of The Stacks Podcast, to explore the deeply personal terrain of identity, family, and legacy. Traci opens up about growing up mixed-race with older parents, navigating loss at a young age, and the impact of her Black and Jewish heritage on how she views belonging. Their conversation weaves through themes of grief, motherhood, and emotional inheritance, as Traci reflects on what it meant to lose her father in her twenties—and how that absence continues to shape her relationship to parenting and selfhood. She shares insights on the expansive nature of chosen family, the cultural roots of inclusion in Black and Jewish communities, and the quiet power of forging your own path in the absence of clear models. Traci’s honesty about not feeling naturally inclined to motherhood—while fully embracing the role—offers a fresh and nuanced view of modern parenting. This episode challenges traditional definitions of family, makes space for contradiction, and honors the complexity of claiming one’s story. Guest: Traci Thomas Host: Nabil Ayers Executive Producer: Kieron Banerji Production by: Palm Tree IslandSee omnystudio.com/listener for privacy information.

WDAY First News anchors Lisa Budeau, Scott Engen and Robert Poynter break down your regional news and weather for Wednesday, January 14. InForum Minute is produced by Forum Communications and brought to you by reporters from The Forum of Fargo-Moorhead and WDAY TV. Visit https://www.inforum.com/subscribe to subscribe.

Today on another episode of the Rarified Heir Podcast, we are talking to Nabil Ayers, son of musician Roy Ayers, a jazz/funk/soul giant most famous for his song "Everybody Loves the Sunshine" and is likely one of the most sampled artists of all time. A vibraphonist, singer and composer, Ayers songs have been sampled by everyone from Dr. Dre, Mary J. Blige and Snoop Dogg as well as had  collaborations with Alicia Keyes, The Roots and Tyler, The Creator among others. As you will soon hear, Nabil's story is unlike anything we have heard before on the podcast. Imagine growing up knowing who your father was but only meeting him occasionally. By design. Sometimes it was a planned meeting that lasted just long enough to ask, "Do you want some Tempura?" and others were times that were literally a chance meeting on the street at a music store. As you will soon hear, we discuss this and much more around his book, 2022's My Life in the Sunshine that explains all this and much more. Nabil himself was open, engaging, honest and ready to discuss everything. What it was like growing up with a Jewish/Baha'i Faith mother who really only wanted a child at the age of 20, his relationship with his uncle Alan, a jazz musician himself who really was the masculine figure Nabil looked up to the most and how he finally had lunch with his dad well into his 30s when things seemed to not be making as much sense as they did earlier in his life. Currently a record executive, he's the President of the Beggars Group of labels, a group of well respected, independent US and UK labels, Nabil has also played in bands, owned his own record store, has his own podcast on both family and identity, called Identified, has written articles for The Guardian, the New York Times and others, has his own Substack page and much more. Accomplished, talented and versatile, Nabil opened up to us about pretty much everything we asked about. His story is about as unique take on celebrity and growing up the child of a celebrity as we could imagine. This is the Rarified Heir Podcast and everyone has a story. But none of them are like the one you are going to hear, right now.

Plantd is reinventing engineered lumber by replacing trees with rapidly renewable biomass, scaling manufacturing technology that costs 100x less than traditional OSB production. With customers including DR Horton and growing demand across furniture, RV, and international markets, Plantd has attracted partnerships throughout the building materials industry. In this episode of BUILDERS, I sat down with Nathan Silvernail, Co-Founder & CEO at Plantd, to explore how his decade at SpaceX shaped his approach to building a capital-intensive hardware company that could transform the $65 billion engineered lumber market.   Topics Discussed Building continuous OSB production systems versus $500M batch presses used by incumbents Securing DR Horton, furniture manufacturers, and building material companies as early customers Managing the bifurcation between OPEX-intensive manual processes and CAPEX transitions to AI robotic vision systems Designing machines for 400,000 panels/year output with sub-one-year payback at scale Navigating opinion-based building inspection processes where "no two blocks in this entire country build a house the same way" The strategic calculus of positioning away from climate tech to avoid green premium assumptions Scaling from pilot production to deploying 25-30 machines to meet current demand pipeline Achieving 70-layer panel construction versus 6-8 layers in timber-based OSB //   Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co   // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

They Were FINALLY IDENTIFIEDBecome a supporter of this podcast: https://www.spreaker.com/podcast/missing-persons-mysteries--5624803/support.

THE BATTLE FOR RAFAH AND THE FUTURE OF HAMAS Colleague Seth Frantzman. The discussion focuses on the strategic importance of Rafah and the Philadelphi Corridor along the Egyptian border, identified as the primary artery for Hamas's weapons smuggling. Frantzman argues that Israel must control this border to prevent Hamas from rearming, noting Egypt's failure to secure the area effectively. Regarding Hamas leadership, Frantzman speculates that Yahya Sinwar remains in Khan Yunis, refusing to leave his hometown. While Hamas's organized battalions have been significantly degraded, Frantzman warns that without a comprehensive political strategy, the group could transition back to an insurgency, similar to the Viet Cong. OCTOBER 7 WAR BY SETH FRANTZMAN NUMBER 41891 NAZARETH

The U.S. Immigration and Customs Enforcement officer who shot and killed 37-year-old Renee Good on Wednesday in Minneapolis is Jonathan Ross, according to court documents and accounts from federal officials.Ross shot into Good's car while she was driving away from the site of an ICE operation near Portland Avenue and East 34th Street. U.S. government officials have labeled Good as a “domestic terrorist” who attempted to strike and kill ICE officers.During Thursday's White House press briefing, Vice President JD Vance said Ross “might have been a little nervous” because of an incident last summer in which he was dragged by a vehicle and needed upwards of 30 stitches.Vance's description of Ross' injuries matches with an encounter that took place in Bloomington on June 17.Court documents detail the injuries an ICE agent suffered in the course of attempting to stop Roberto Carlos Muñoz-Guatemala, who prosecutors say was in the country without legal immigration status and had a previous conviction of fourth-degree criminal sexual conduct.An exhibit list describes photos of the injuries as belonging to “Officer Ross.” A jury trial notice lists “Jonathan Ross” as the federal government's first witness who testified in that trial.During the stop, Ross ordered Muñoz-Guatemala to get out of the car or he would break the window, a criminal complaint states. When Muñoz-Guatemala didn't comply, Ross broke the rear driver's side window and attempted to reach in and unlock the front driver's side door. Muñoz-Guatemala put the car in drive while Ross' arm was in the broken window, and he was dragged “approximately 100 yards” down the street.According to court documents, one cut to Ross' right arm required 20 stitches; he received 13 more stitches for a gash on his left hand; and he suffered scrapes to his knee, elbows and face. A case brief filed five months after the initial indictment stated he needed 50 or more stitches for treatment.Last month, a jury convicted Muñoz-Guatemala of one count of assault on a federal officer with a dangerous and deadly weapon. He has yet to be sentencedOne law enforcement official confirmed to ABC News that Ross has 10 years of experience and is a Minneapolis-based member of ICE's special response team, which is deployed for high-profile and tactical situations.“He's an experienced officer that has served a number of years, and we recognize he acted according to his training, and we expect that all of the policies and procedures of review will be exactly that he acted appropriately to protect his life and the life of his colleagues and fellow law enforcement officers that were there,” Homeland Security Secretary Kristi Noem said of Ross during a news conference held Thursday morning in New York.Noem and DHS officials say the officer was taken to the hospital because he was hit by Good's vehicle. However, bystander video of the shooting does not appear to show any serious contact, and the shooter was up and walking the entire time.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

An unassuming fossilized slab in the basement of a museum in Brazil turned out to be 110-million-year-old dinosaur vomit, and inside that vomit were the bones of two strange, seagull-sized pterosaurs.PLUS:Loss of fresh groundwater is now the leading driver of sea level riseHow doubting your self-doubt makes you doubt lessA huge black hole in a peculiar galaxy may date from the universe's earliest moments Shining a light on where viruses hide out in our bodies, and how they make us sick

The U.S. Immigration and Customs Enforcement officer who shot and killed 37-year-old Renee Good on Wednesday in Minneapolis is Jonathan Ross, according to court documents and accounts from federal officials.Ross shot into Good's car while she was driving away from the site of an ICE operation near Portland Avenue and East 34th Street. U.S. government officials have labeled Good as a “domestic terrorist” who attempted to strike and kill ICE officers.During Thursday's White House press briefing, Vice President JD Vance said Ross “might have been a little nervous” because of an incident last summer in which he was dragged by a vehicle and needed upwards of 30 stitches.Vance's description of Ross' injuries matches with an encounter that took place in Bloomington on June 17.Court documents detail the injuries an ICE agent suffered in the course of attempting to stop Roberto Carlos Muñoz-Guatemala, who prosecutors say was in the country without legal immigration status and had a previous conviction of fourth-degree criminal sexual conduct.An exhibit list describes photos of the injuries as belonging to “Officer Ross.” A jury trial notice lists “Jonathan Ross” as the federal government's first witness who testified in that trial.During the stop, Ross ordered Muñoz-Guatemala to get out of the car or he would break the window, a criminal complaint states. When Muñoz-Guatemala didn't comply, Ross broke the rear driver's side window and attempted to reach in and unlock the front driver's side door. Muñoz-Guatemala put the car in drive while Ross' arm was in the broken window, and he was dragged “approximately 100 yards” down the street.According to court documents, one cut to Ross' right arm required 20 stitches; he received 13 more stitches for a gash on his left hand; and he suffered scrapes to his knee, elbows and face. A case brief filed five months after the initial indictment stated he needed 50 or more stitches for treatment.Last month, a jury convicted Muñoz-Guatemala of one count of assault on a federal officer with a dangerous and deadly weapon. He has yet to be sentencedOne law enforcement official confirmed to ABC News that Ross has 10 years of experience and is a Minneapolis-based member of ICE's special response team, which is deployed for high-profile and tactical situations.“He's an experienced officer that has served a number of years, and we recognize he acted according to his training, and we expect that all of the policies and procedures of review will be exactly that he acted appropriately to protect his life and the life of his colleagues and fellow law enforcement officers that were there,” Homeland Security Secretary Kristi Noem said of Ross during a news conference held Thursday morning in New York.Noem and DHS officials say the officer was taken to the hospital because he was hit by Good's vehicle. However, bystander video of the shooting does not appear to show any serious contact, and the shooter was up and walking the entire time.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

GENERAL BUFORD'S CRUCIAL DECISIONS ON JULY 1ST Colleague Colonel Jeff McCausland. Colonel Jeff McCausland discusses Union General John Buford's crucial decisions on July 1st. Buford identified key ridge terrain and chose to delay superior Confederate infantry using dismounted cavalry. By trading space for time, Buford screened the arriving Union army and secured advantageous ground for the coming battle. NUMBER 1

LA could be facing its wettest New Year's Day in more than 90 years. One of the hikers who died at Mount Baldy has been identified, and neighbors are in shock. The Rose Parade is getting wet and wild. Plus, more from Morning Edition. Support The L.A. Report by donating at LAist.com/join and by visiting https://laist.com Visit www.preppi.com/LAist to receive a FREE Preppi Emergency Kit (with any purchase over $100) and be prepared for the next wildfire, earthquake or emergency!Support the show: https://laist.com

DALLAS THE DOG TEAMS UP WITH MAGPIES TO FIGHT COCKATOOS Colleague Jeremy Zakis. Zakisrecounts how his dog, Dallas, successfully chased destructive cockatoos off their property. While usually friendly, Dallasidentified the birds as enemies, aided by territorial magpies that swooped in to drive the cockatoos away. Although cockatoos are often considered pests that raid trash bins and damage homes in New South Wales, Dallas's vigilance has protected Zakis's yard, forcing the birds to target the neighbor's roof instead.

Special guest Kyle Seraphin! https://x.com/KyleSeraphin ----- Support Viva: GET MERCH! www.vivafrei.com BUY A BOOK! https://amzn.to/4qBXikS SEND ME SOMETHING! David Freiheit 20423 SR 7 Ste F6319 Boca Raton 33498 TIP WITH CTYPTO! bc1qt0umnqna63pyw5j8uesphsfz0dyrtmqcq5ugwm THAT IS ALL!

The Brown University shooter has been identified as a 48 year-old Portuguese National and a former Brown student who suffered a self-inflicted gunshot wound. The suspect was found after being exposed on Reddit. Ben Shapiro UNLEASHES On Candace Owens and Tucker Carlson at TPUSA's Americafest.  A megachurch's Christmas show features “Frosty the Snowman” sung to the tune of AC/DCs “Thunderstruck”.Netflix cancels 'Boots' Queer military dramedy after just one season. Two Haitians are arrested & charged in an alleged scheme to fraudulently obtain millions in SNAP benefits in Massachusetts. John Launius from Shihan Wellness joins us to break down how aromatherapy has assisted veterans suffering from PTSD and supported a network that gets our warriors back to good.Australian PM Albanese says hundreds of thousands of firearms will be collected & destroyed. Minnesota and New York settled with Hyundai and Kia over their cars not being protected enough from theft. Texas Congressional Candidate Brandon Herrera joins us to react to President Trump's endorsement of his primary opponent and explain why his opponent's platform is fringing on your constitutional rights..Thank you for supporting our sponsors that make The Dana Show possible…HumanNhttps://HumanN.comNow's the perfect time to try them—get $5 off Humann's Turmeric Chews at Sam's Club through December 29.Byrnahttps://Byrna.comMake 2026 the year you protect your family with solid options—Get the Byrna today.Patriot Mobilehttps://PatriotMobile.com/Dana  OR CALL 972-PATRIOTWhat are you waiting for? Switch today during the Red, White, and Blue sale and get a free smartphone with code DANA. PreBornhttps://Preborn.com/DANAThis Christmas, for just $28 you can help save a life.. Dial #250 and say “Baby,” or give securely online. Make your gift today.AmmoSquaredhttps://AmmoSquared.comDon't get caught without ammo and be sure to tell them you heard about Ammo Squared on this show. Webroothttps://Webroot.com/DanaMake sure your family stays secure online with WebRoot.  Get 60% off Webroot Total Protection today.Subscribe today and stay in the loop on all things news with The Dana Show. Follow us here for more daily clips, updates, and commentary:YoutubeFacebookInstagramXMore Info

Good evening, everyone! This episode of Police Off The Cuff provides a crucial update on the Brown University shooting, where a potential break in the case has emerged in rhode island. We discuss the ongoing efforts of law enforcement and the meticulous police procedure involved in identifying a person of interest john reese. Retired NYPD Sergeant Bill Cannon offers his expert commentary on this critical criminal investigation as authorities search for the shooter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.