Podcasts about Wiki

rWotD Episode 3232: Tom Tellmann Welcome to random Wiki of the Day, your journey through Wikipedia's vast and varied content, one random article at a time.The random article for Tuesday, 10 March 2026, is Tom Tellmann.Thomas John Tellmann (born March 29, 1954) is an American former professional baseball pitcher. He played in Major League Baseball (MLB) for the San Diego Padres from 1979 to 1980, the Milwaukee Brewers from 1983 to 1984, and the Oakland Athletics in 1985.In 227 innings pitched over 112 appearances, Tellmann accepted 65 total chances (15 putouts, 50 assists) without an error for a perfect 1.000 fielding percentage in his major league career.This recording reflects the Wikipedia text as of 00:14 UTC on Tuesday, 10 March 2026.For the full current version of the article, see Tom Tellmann on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm neural Kimberly.

fWotD Episode 3231: Duckport Canal Welcome to featured Wiki of the Day, your daily dose of knowledge from Wikipedia's finest articles.The featured article for Tuesday, 10 March 2026, is Duckport Canal.The Duckport Canal was an unsuccessful military venture by Union forces during the Vicksburg campaign of the American Civil War. Ordered built in late March 1863 by Major General Ulysses S. Grant, the canal stretched from the Mississippi River near Duckport, Louisiana, to New Carthage, Louisiana, and utilized a series of swampy bayous for much of its path. It was intended to provide a water-based supply route for a southward movement against the Confederate-held city of Vicksburg, Mississippi, as high water levels made overland travel difficult. Manual digging was provided by 3,500 soldiers from Grant's army and was finished on April 12. The next day, the levee separating the canal cut and the Mississippi River was breached, and water flowed into the canal. Trees that had grown up in the bayous and falling water levels that reached as shallow as 6 inches (15 cm) at one point hampered the use of the canal, and the project was abandoned on May 4. Grant moved men and supplies through the overland route, which had been made more accessible by the same falling water levels that doomed the canal. After some inland maneuvering and a lengthy siege, Vicksburg surrendered on July 4, marking a significant turning point in the war.This recording reflects the Wikipedia text as of 01:02 UTC on Tuesday, 10 March 2026.For the full current version of the article, see Duckport Canal on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm neural Gregory.

pWotD Episode 3233: Mojtaba Khamenei Welcome to popular Wiki of the Day, spotlighting Wikipedia's most visited pages, giving you a peek into what the world is curious about today.With 518,042 views on Monday, 9 March 2026 our article of the day is Mojtaba Khamenei.Mojtaba Hosseini Khamenei (born 8 September 1969) is an Iranian politician and Shia cleric who has been the third supreme leader of Iran since 8 March 2026. He is a member of the Khamenei family and the second son of the second supreme leader Ali Khamenei.Born in Mashhad, a city in northeastern Iran, into the Azeri–Persian Khamenei family, he was nine when his father emerged as a leading figure in the Iranian Revolution. He received early education in Sardasht and Mahabad, and graduated high school from Tehran, after which he studied Islamic theology under the guidance of his father and Mahmoud Hashemi Shahroudi. He joined the Islamic Revolutionary Guard Corps (IRGC) in 1987 and served in the Iran–Iraq War. In 1999, he continued his studies in Qom to become a cleric, and joined the Qom Seminary as a theological teacher afterwards. He took control of the Basij paramilitary volunteer militia in 2009.Following the assassination of his father in the 2026 Iran war, Mojtaba was elected as his successor by Iran's Assembly of Experts. He has previously been sanctioned by the United States Department of the Treasury in 2019 as part of their policy of sanctioning individuals linked to Ali Khamenei.In political ideology and jurisprudence, he is considered to be among the most hardline of the Iranian principlists, and has close ties to some of the "most ideologically extremist clerics" per a report from Atlantic Council. Analysts have generally seen him as more favorable to developing an Iranian nuclear weapons program than his father, supporting a reinterpretation of Ali Khamenei's fatwa against nuclear weapons.This recording reflects the Wikipedia text as of 03:23 UTC on Tuesday, 10 March 2026.For the full current version of the article, see Mojtaba Khamenei on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm standard Kendra.

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rWotD Episode 3231: Kirstie Marshall Welcome to random Wiki of the Day, your journey through Wikipedia's vast and varied content, one random article at a time.The random article for Monday, 9 March 2026, is Kirstie Marshall.Kirstie Claire Marshall (born 21 April 1969) is an Australian aerial skier and Victorian state politician.Marshall was an ex-gymnast who became an aerial skier at Mount Buller, Victoria. During her skiing career Marshall won over 40 World Cup medals, including 17 World Cup gold medals. Marshall competed in aerial skiing as a demonstration sport at the 1992 Albertville Winter Olympics, and as a medal event at the 1994 Lillehammer and 1998 Nagano games, where she came sixth and fourteenth, respectively.In December 2002, aged 33, Marshall was elected as a Member of Parliament in the Victorian Legislative Assembly for the Labor Party.On 26 February 2003, she was ejected from the Lower House chamber for breastfeeding her 11-day-old baby, Charlotte Louise. A section of the Parliamentary rules, namely Standing Order 30, states: "Unless by order of the House, no Member of this House shall presume to bring any stranger into any part of the House appropriated to the Members of this House while the House, or a Committee of the whole House, is sitting." As there is no age limit to 'strangers in the House' (non-elected persons), only MPs and certain parliamentary staff are allowed in the House during sitting times.Subsequently, the Speaker of the House set aside a room in which female MPs can feed their children without violating the Standing Orders.This recording reflects the Wikipedia text as of 01:19 UTC on Monday, 9 March 2026.For the full current version of the article, see Kirstie Marshall on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm neural Matthew.

fWotD Episode 3230: Zungeni Mountain skirmish Welcome to featured Wiki of the Day, your daily dose of knowledge from Wikipedia's finest articles.The featured article for Monday, 9 March 2026, is Zungeni Mountain skirmish.The Zungeni Mountain skirmish took place on 5 June 1879 between British and Zulu forces during the second invasion of Zululand, in what is now South Africa, during the later stages of the Anglo-Zulu War. British irregular horse commanded by Colonel Redvers Buller discovered a force of 300 Zulus at the settlement of eZulaneni near Zungeni Mountain. The horsemen charged towards and scattered the Zulus before burning the settlement. Buller's men withdrew after coming under fire from Zulus who threatened to surround them.Buller's men were joined by more irregulars and a force of British regular cavalry, the latter under the command of Major-General Frederick Marshall. Two squadrons of the 17th (The Duke of Cambridge's Own) Lancers, led by Colonel Drury Drury-Lowe, approached the Zulu position. They could not close with the Zulus, who were in an area of long grass and bushes, and Zulu fire killed the 17th Lancers' adjutant, Lieutenant Frederick John Cokayne Frith. Drury-Lowe ordered some of his men to dismount and return fire. When the Zulus threatened to outflank the British, Marshall ordered a withdrawal. Aside from Frith, British casualties included two irregulars wounded; two months after the battle, the remains of 25 Zulus were discovered on the battlefield. After the skirmish, the British paused to fortify their camp before proceeding further into Zululand. They then decisively defeated the Zulu in the 4 July Battle of Ulundi.This recording reflects the Wikipedia text as of 00:20 UTC on Monday, 9 March 2026.For the full current version of the article, see Zungeni Mountain skirmish on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm neural Jasmine.

pWotD Episode 3232: Men's T20 World Cup Welcome to popular Wiki of the Day, spotlighting Wikipedia's most visited pages, giving you a peek into what the world is curious about today.With 408,441 views on Sunday, 8 March 2026 our article of the day is Men's T20 World Cup.The ICC Men's T20 World Cup, formerly the ICC World Twenty20, is a biennial world cup for cricket in Twenty20 International (T20I) format, organised by the International Cricket Council (ICC). It was held in every odd year from 2007 to 2009, and since 2010 has been held in every even year with the exception of 2018 and 2020. In 2018, the tournament was rebranded from the World Twenty20 to the Men's T20 World Cup.The 2011 edition of the tournament was brought forward to 2010 to replace the ICC Champions Trophy. In May 2016, the ICC put forward the idea of having a tournament in 2018, with South Africa being the possible host country, but later dropped the idea due to multiple bilateral series taking place that year. The 2020 edition of the tournament was scheduled to take place in Australia but due to the COVID-19 pandemic, the tournament was postponed until 2021, with the intended host changed to India. The 2021 Men's T20 World Cup was later relocated to the United Arab Emirates and Oman due to problems relating to the COVID-19 pandemic in India, taking place five years after the previous (2016) iteration.As of 2026, ten editions have been played, a total of 24 teams have competed and six national teams have won the T20 World Cup so far. India hold the most victories, (2007, 2024, 2026) as they have won the competition three times. West Indies (2012, 2016) and England (2010, 2022) have won it twice, and Pakistan (2009), Sri Lanka (2014), and Australia (2021) have one title each. A total of 15 countries have hosted the tournament (including 6 island nations of the West Indies).This recording reflects the Wikipedia text as of 03:19 UTC on Monday, 9 March 2026.For the full current version of the article, see Men's T20 World Cup on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm neural Emma.

It's been a huge weekend and Rosetta and Milly are back in the studio to give you the rundown! E whai ake nei, coming up on your Monday Breakfast; Whakataukī o Tēnei Wiki with Arini, This Is How We Brew It with Christian, Rosetta spoke with Hone Kouka about Waiora Te Ūkaipō, Nate is up for Loose Reads, and Casual Swim is your bosom selecta today! Whakarongo mai nei.  Thanks to eighthirty coffee roasters!

Arini joins us for another whakataukī to take into our week! Whakarongo mai nei.  Kia kotahi kī // just say one thing.

rWotD Episode 3230: Mukhtar Ablyazov Welcome to random Wiki of the Day, your journey through Wikipedia's vast and varied content, one random article at a time.The random article for Sunday, 8 March 2026, is Mukhtar Ablyazov.Mukhtar Qabyluly Ablyazov (Kazakh: Мұхтар Қабылұлы Әблязов, Mūhtar Qabylūly Äbliazov; born 16 May 1963) is a Kazakh opposition leader, businessman and political activist who served as chairman of Bank Turan Alem (BTA Bank), and is a co-founder and a leader of the unregistered political party Democratic Choice of Kazakhstan (QDT). He was also the former head of the state-owned Kazakhstan Electricity Grid Operating Company (KEGOC) as well as briefly holding the position of Minister for Energy, Industry, and Trade under Balgimbayev's cabinet before resigning from and joining the opposition against President Nursultan Nazarbayev. In November 2001, he, along with other former Kazakh government officials founded the Democratic Choice of Kazakhstan (QDT). As result, Ablyazov was imprisoned in March 2002 over accusations of financial fraud and political abuse until being pardoned by Nazarbayev in 2003. After being released from prison, he ceased his formal political activities with the opposition.Ablyazov has been accused of embezzling $6 billion from BTA Bank while serving as chairman. In 2015, a French court in Lyon issued an extradition order. However, in December 2016, France's highest administrative court, the Conseil d'Etat, canceled the extradition order, on the ground that Russia had a political motive in making the extradition request. Ablyazov was subsequently released from the Fleury-Mérogis Prison and was believed to reside in Paris. The UK High Court of Justice has twice issued arrest warrants on Ablyazov; most recently on 25 July 2019, and extended to 22 months a court-ordered detention originating in 2012 for a contempt of court judgement. In September 2020, Ablyazov obtained the status of political refugee in France.On December 9, 2022, CNDA finally deprived Ablyazov of political asylum in France. In July 2023, Ablyazov posted on his Facebook that he was ordered to leave French territory within 30 days.This recording reflects the Wikipedia text as of 00:38 UTC on Sunday, 8 March 2026.For the full current version of the article, see Mukhtar Ablyazov on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm generative Stephen.

fWotD Episode 3229: Luisa Capetillo Welcome to featured Wiki of the Day, your daily dose of knowledge from Wikipedia's finest articles.The featured article for Sunday, 8 March 2026, is Luisa Capetillo.Luisa Capetillo Perón (October 28, 1882 – April 10, 1922) was a Puerto Rican labor organizer, writer, journalist, and cigar factory reader. She organized workers in Puerto Rico, the Republic of Cuba, the Dominican Republic, and the United States. She also published four books in her lifetime, covering a wide variety of forms, genres, and topics. As an anarcha-feminist and social anarchist, she advocated for free love, universal education, women's liberation, and collective ownership of scientific advances while opposing state control.Capetillo was born in Arecibo, in the Captaincy General of Puerto Rico. Her father taught her to read and write, and she read extensively, including works by influential political thinkers. In 1897, she took Manuel Ledesma, the son of a wealthy aristocrat, as her lover. The two separated in 1900, but their relationship influenced her later feminist works. She began writing for the local newspaper in 1904, and in 1905, she became involved with the Free Federation of Workers (Spanish: Federación Libre de Trabajadores de Puerto Rico, FLT), an anarcho-syndicalist union, helping organize an agricultural strike in Arecibo. In 1906, she became a reader at a cigar factory, reading to the cigar makers as they worked and meeting members of the FLT-affiliated Federation of Tobacco Rollers (Spanish: Federación de Torcedores de Tabaco, FTT) and becoming a leader in the FLT over time. Starting in 1912, she moved across the Atlantic and Caribbean, writing and organizing workers. In 1915, she was arrested in Cuba for wearing trousers and deported back to Puerto Rico. She continued to travel and organize until her death in 1922.Interest in Capetillo's life surged in 1990 with the publication of the biography Luisa Capetillo, historia de una mujer proscrita (transl. Luisa Capetillo, History of an Outlawed Woman) by journalist Norma Valle Ferrer. After this, Capetillo became the subject of a docudrama series, and in 2014, the Legislative Assembly of Puerto Rico honored her with a plaque in the Plaza in Honor of Puerto Rican Women in San Juan. She is considered one of Puerto Rico's first feminists and, according to historian Jorrell A. Meléndez-Badillo, she was an influential node in an anarchist "counter-republic of letters": a network of writers who used their writing as a form of political struggle.This recording reflects the Wikipedia text as of 00:45 UTC on Sunday, 8 March 2026.For the full current version of the article, see Luisa Capetillo on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm generative Ruth.

pWotD Episode 3231: Soham murders Welcome to popular Wiki of the Day, spotlighting Wikipedia's most visited pages, giving you a peek into what the world is curious about today.With 701,993 views on Saturday, 7 March 2026 our article of the day is Soham murders.The Soham murders were a double child murder committed in Soham, Cambridgeshire, England, on 4 August 2002. The victims were two 10-year-old girls, Holly Marie Wells and Jessica Amiee Chapman, who were lured into the home of a local resident and school caretaker, Ian Huntley, who murdered them – likely via asphyxiation – and disposed of their bodies in an irrigation ditch close to RAF Lakenheath in Suffolk. The bodies were discovered on 17 August 2002.Huntley was convicted of the murder of both girls on 17 December 2003 and sentenced to two terms of life imprisonment, with the High Court later imposing a minimum term of 40 years. His girlfriend, Maxine Ann Carr – the girls' teaching assistant – had knowingly provided Huntley with a false alibi. She received a three-and-a-half-year prison sentence for conspiring with Huntley to pervert the course of justice. Huntley died in March 2026, after sustaining severe head injuries inflicted in an attack by another inmate at HMP Frankland.The search for Holly and Jessica in the thirteen days of their disappearance has been described as one of the most intense and extensive in British criminal history.This recording reflects the Wikipedia text as of 03:00 UTC on Sunday, 8 March 2026.For the full current version of the article, see Soham murders on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm standard Russell.

It's been a huge weekend and Rosetta and Milly are back in the studio to give you the rundown! E whai ake nei, coming up on your Monday Breakfast; Whakataukī o Tēnei Wiki with Arini, This Is How We Brew It with Christian, Rosetta spoke with Hone Kouka about Waiora Te Ūkaipō, Nate is up for Loose Reads, and Casual Swim is your bosom selecta today! Whakarongo mai nei.  Thanks to eighthirty coffee roasters!

rWotD Episode 3229: Crypsitricha roseata Welcome to random Wiki of the Day, your journey through Wikipedia's vast and varied content, one random article at a time.The random article for Saturday, 7 March 2026, is Crypsitricha roseata.Crypsitricha roseata is a species of moth in the family Tineidae. It was described by Edward Meyrick in 1913. This species is endemic to New Zealand. The type locality of this species is the suburb of Wadestown, in Wellington.The wingspan is about 12 mm. The forewings are light rosy-purple-brownish with about eight small blackish costal marks and an irregular brown mark on the fold towards the base, terminated by a few blackish scales, and edged with some whitish suffusion. There is a narrow oblique brown fascia from before the middle of the costa to beyond the middle of the dorsum, partially edged with blackish posteriorly. A streak of brown suffusion runs from the middle of the disc to the middle of the termen, including a line of black scales, and edged above posteriorly by a fine white streak. The hindwings are grey.This recording reflects the Wikipedia text as of 01:05 UTC on Saturday, 7 March 2026.For the full current version of the article, see Crypsitricha roseata on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm long-form Gregory.

fWotD Episode 3228: Mean (song) Welcome to featured Wiki of the Day, your daily dose of knowledge from Wikipedia's finest articles.The featured article for Saturday, 7 March 2026, is Mean (song)."Mean" is a song written and recorded by the American singer-songwriter Taylor Swift for her third studio album, Speak Now (2010). Big Machine Records released it to country radio in the United States as the album's third single on March 7, 2011. Produced by Swift and Nathan Chapman, "Mean" is a six-string banjo-led country, country pop, and bluegrass track that incorporates fiddles, mandolins, hand claps, and multitracked vocals. In the lyrics, Swift addresses her detractors, recognizes her shortcomings, and strives to overcome the criticism and achieve success.Some music critics deemed "Mean" an anti-bullying anthem and praised the production as airy and catchy, while others considered the narrative ineffective. Several media publications have retrospectively listed it as one of the best country songs. "Mean" won Best Country Song and Best Country Solo Performance at the 2012 Grammy Awards, and also received other industry awards and nominations. The track reached the national charts of Australia, Canada, and the United States, peaking at number two on the country music charts in the latter two countries. It received certifications in Australia, Brazil, Canada, New Zealand, the United Kingdom, and the United States.The music video for "Mean" was directed by Declan Whitebloom, who wrote its treatment with Swift. Featuring themes of self-empowerment and anti-bullying, the video received a mixed response from critics, who generally criticized its viewpoint on bullying as stereotypical and deemed its concept confusing. The video received nominations at the MTV Video Music Awards, the Academy of Country Music Awards, and the Country Music Association Awards. Swift included "Mean" in the set lists of the Speak Now World Tour (2011–2012) and the Red Tour (2013–2014), and performed it on some dates of her later tours.Following a 2019 dispute regarding the ownership of her back catalog, Swift re-recorded the song as "Mean (Taylor's Version)" for her third re-recorded album, Speak Now (Taylor's Version) (2023). The track peaked at number 33 on the Billboard Global 200 and reached the national charts of Australia, Canada, New Zealand, the Philippines, Singapore, and the United States.This recording reflects the Wikipedia text as of 01:00 UTC on Saturday, 7 March 2026.For the full current version of the article, see Mean (song) on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm standard Aditi.

pWotD Episode 3230: Kristi Noem Welcome to popular Wiki of the Day, spotlighting Wikipedia's most visited pages, giving you a peek into what the world is curious about today.With 702,642 views on Friday, 6 March 2026 our article of the day is Kristi Noem.Kristi Lynn Arnold Noem ( NOHM; née Arnold; born November 30, 1971) is an American politician who has served as the 8th United States secretary of homeland security since January 2025. A member of the Republican Party, she served as the 33rd governor of South Dakota from 2019 to 2025 and represented South Dakota's at-large congressional district in the U. S. House of Representatives from 2011 to 2019.Born in Watertown, South Dakota, Noem began her political career in the South Dakota House of Representatives, serving from 2007 to 2011. Noem was elected as the first female governor of South Dakota in 2018 with the endorsement of President Donald Trump. She gained national attention during the COVID-19 pandemic for opposing statewide mask mandates in favor of voluntary measures. Noem has conservative positions on most domestic issues, particularly gun rights, abortion, and immigration. During her tenure as Secretary of Homeland Security, her immigration policies generated significant controversy.Noem is a farmer, rancher, and member of the Civil Air Patrol. She has published two autobiographies, Not My First Rodeo: Lessons from the Heartland (2022) and No Going Back (2024), the latter of which sparked controversy for its account of her killing a young family dog and false claims about meeting with foreign leaders. Donald Trump nominated her as his secretary of homeland security. She was confirmed by a Senate vote of 59–34, and was officially sworn in on January 25, 2025.Following the killings of Renée Good and Alex Pretti in January 2026, Noem faced questions of judgment from some lawmakers, including calls for her resignation and possible impeachment. After reports of her relationship with the political operative Corey Lewandowski and her use of government funding on television advertisements and private luxury jets emerged, criticism mounted from Congress and Trump. In March, Noem appeared before the Senate Committee on the Judiciary. Noem's testimony before the committee intensified Trump's dissatisfaction with her. That month, Trump fired Noem and announced that Oklahoma senator Markwayne Mullin would succeed her, nominating him to the position. Noem is set to leave her position on March 31.This recording reflects the Wikipedia text as of 02:18 UTC on Saturday, 7 March 2026.For the full current version of the article, see Kristi Noem on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm standard Russell.

Sonia Grego speaks with us about a topic no one likes to talk about, but could be used to monitor personal dietary health and widespread disease outbreaks. Toilets! Sonia leads Duke University's Smart Toilet Lab and the spin out Coprata which makes the Microbiome Activity Tracker.  As discussed in the show, when developing a project far from where it will be deployed, there are many common issues. The Lessons Learned chapter of Sonia's recent book gives an excellent introduction to the unexpected environment far from the comfort of desks. The book is (free online!) Engineering Field Testing of Non-Sewered Sanitation Systems: Compendium of Lessons Learned,  See Sonia Grego at  Duke Electrical & Computer Engineering, the Smart Toilet Lab  as well as Sonia's company Coprata.  Some terms that came up in the show: Coprolite: fossilized excrement (Wikipedia entry has great(?) pics!) Scatological: material, humor, or language that relates directly to bodily waste Helminths: parasitic worms including roundworms, tapeworms, and fluke (oh look. more great images on Wiki entry) Poop jokes Transcript

Episode 307: Jay & Ray learn about a few old odds and ends.scuttlebuttodds and endsExpressway (To Your Heart)Subscribe, review, and come find us on BlueSky, Insta, &  FB.Big Science Music is an award-winning original music and sound boutique. Providing scoring, sound design, radio, podcast, and all audio-post production services for the advertising, film, and video industries. Grab a taste of the groove salad at bigsciencemusic.comsome of the W9Y sources include phrase finder uk, word wizard forums, etymology online, the OED, American Dictionary of Idioms, Wiki, newspapers.com, stackexchange, worldwidewords.

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rWotD Episode 3228: Nick House Welcome to random Wiki of the Day, your journey through Wikipedia's vast and varied content, one random article at a time.The random article for Friday, 6 March 2026, is Nick House.Nicholas Martin House (born September 1974), known as Nick House, is a British entrepreneur and nightclub owner. Based in London, his venues cater to celebrities and royals.He is the co-owner of Mahiki (a Polynesian-themed nightclub and cocktail bar), Whiskey Mist, Bodo's Schloss and Mayfair's The Punch Bowl, owned with Guy Ritchie and Madonna. He co-founded Steam & Rye with Kelly Brook in the City of London.This recording reflects the Wikipedia text as of 01:05 UTC on Friday, 6 March 2026.For the full current version of the article, see Nick House on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm standard Kimberly.

fWotD Episode 3227: Paradises Lost Welcome to featured Wiki of the Day, your daily dose of knowledge from Wikipedia's finest articles.The featured article for Friday, 6 March 2026, is Paradises Lost.Paradises Lost is a science fiction novella by American author Ursula K. Le Guin. It was first published in 2002 as a part of the collection The Birthday of the World. It is set during a multigenerational voyage from Earth to a potentially habitable planet. The protagonists, Liu Hsing and Nova Luis, are members of the fifth generation born on the ship. The story follows them as they deal with members of a religious cult who believe the ship ought not to stop at its intended destination. They also face a crisis brought on by a drastic change in the ship's schedule. The novella has since been anthologized as well as adapted into an opera of the same name.The novella explores the isolation brought on by space travel, as well as themes of religion and utopia. It contains elements of ecocriticism, or a critique of the idea that human beings are altogether separate from their natural environment. The novella and the collections in which it was published received high praise from commentators. For its generation ship setting and examination of utopia, critics compared it to other Le Guin works such as "Newton's Sleep", and The Telling, as well as to the works of Gene Wolfe and Molly Gloss. Scholar Max Haiven described the novella as "a chastening lesson in both the potential and the perils of freedom", while author Margaret Atwood said that it "shows us our own natural world as a freshly discovered Paradise Regained, a realm of wonder".This recording reflects the Wikipedia text as of 00:56 UTC on Friday, 6 March 2026.For the full current version of the article, see Paradises Lost on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm standard Emma.

pWotD Episode 3229: Markwayne Mullin Welcome to popular Wiki of the Day, spotlighting Wikipedia's most visited pages, giving you a peek into what the world is curious about today.With 1,089,123 views on Thursday, 5 March 2026 our article of the day is Markwayne Mullin.Markwayne Mullin (born July 26, 1977) is an American politician and businessman who has served as the junior United States senator from Oklahoma since 2023. A member of the Republican Party, he was elected in a special election in 2022 to serve the remainder of Jim Inhofe's term.A member of the Cherokee Nation, Mullin is the first Native American U. S. senator since Ben Nighthorse Campbell retired in 2005. He is also the second Cherokee citizen elected to the Senate since 1925. From 2013 to 2023, Mullin served as the U. S. representative for Oklahoma's 2nd congressional district. In March 2026, President Donald Trump nominated Mullin as the secretary of homeland security, to replace Kristi Noem.This recording reflects the Wikipedia text as of 02:28 UTC on Friday, 6 March 2026.For the full current version of the article, see Markwayne Mullin on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm standard Brian.

The reception to our recent post on Code Reviews has been strong. Catch up!Amid a maelstrom of discussion on whether or not AI is killing SaaS, one of the top publicly listed SaaS companies in the world has just reported record revenues, clearing well over $1.1B in ARR for the first time with a 28% margin. As we comment on the pod, Aaron Levie is the rare public company CEO equally at home in both worlds of Silicon Valley and Wall Street/Main Street, by day helping 70% of the Fortune 500 with their Enterprise Advanced Suite, and yet by night is often found in the basements of early startups and tweeting viral insights about the future of agents.Now that both Cursor, Cloudflare, Perplexity, Anthropic and more have made Filesystems and Sandboxes and various forms of “Just Give the Agent a Box” cool (not just cool; it is now one of the single hottest areas in AI infrastructure growing 100% MoM), we find it a delightfully appropriate time to do the episode with the OG CEO who has been giving humans and computers Boxes since he was a college dropout pitching VCs at a Michael Arrington house party.Enjoy our special pod, with fan favorite returning guest/guest cohost Jeff Huber!Note: We didn't directly discuss the AI vs SaaS debate - Aaron has done many, many, many other podcasts on that, and you should read his definitive essay on it. Most commentators do not understand SaaS businesses because they have never scaled one themselves, and deeply reflected on what the true value proposition of SaaS is.We also discuss Your Company is a Filesystem:We also shoutout CTO Ben Kus' and the AI team, who talked about the technical architecture and will return for AIE WF 2026.Full Video EpisodeTimestamps* 00:00 Adapting Work for Agents* 01:29 Why Every Agent Needs a Box* 04:38 Agent Governance and Identity* 11:28 Why Coding Agents Took Off First* 21:42 Context Engineering and Search Limits* 31:29 Inside Agent Evals* 33:23 Industries and Datasets* 35:22 Building the Agent Team* 38:50 Read Write Agent Workflows* 41:54 Docs Graphs and Founder Mode* 55:38 Token FOMO Culture* 56:31 Production Function Secrets* 01:01:08 Film Roots to Box* 01:03:38 AI Future of Movies* 01:06:47 Media DevRel and EngineeringTranscriptAdapting Work for AgentsAaron Levie: Like you don't write code, you talk to an agent and it goes and does it for you, and you may be at best review it. That's even probably like, like largely not even what you're doing. What's happening is we are changing our work to make the agents effective. In that model, the agent didn't really adapt to how we work.We basically adapted to how the agent works. All of the economy has to go through that exact same evolution. Right now, it's a huge asset and an advantage for the teams that do it early and that are kinda wired into doing this ‘cause you'll see compounding returns. But that's just gonna take a while for most companies to actually go and get this deployed.swyx: Welcome to the Lane Space Pod. We're back in the chroma studio with uh, chroma, CEO, Jeff Hoover. Welcome returning guest now guest host.Aaron Levie: It's a pleasure. Wow. How'd you get upgraded to, uh, to that?swyx: Because he's like the perfect guy to be guest those for you.Aaron Levie: That makes sense actually, for We love context. We, we both really love context le we really do.We really do.swyx: Uh, and we're here with, uh, Aaron Levy. Welcome.Aaron Levie: Thank you. Good to, uh, good to be [00:01:00] here.swyx: Uh, yeah. So we've all met offline and like chatted a little bit, but like, it's always nice to get these things in person and conversation. Yeah. You just started off with so much energy. You're, you're super excited about agents.I loveAaron Levie: agents.swyx: Yeah. Open claw. Just got by, got bought by OpenAI. No, not bought, but you know, you know what I mean?Aaron Levie: Some, some, you know, acquihire. Executiveswyx: hire.Aaron Levie: Executive hire. Okay. Executive hire. Say,swyx: hey, that's my term. Okay. Um, what are you pounding the table on on agents? You have so many insightful tweets.Why Every Agent Needs a BoxAaron Levie: Well, the thing that, that we get super excited by that I think is probably, you know, should be relatively obvious is we've, we've built a platform to help enterprises manage their files and their, their corporate files and the permissions of who has access to those files and the sharing collaboration of those files.All of those files contain really, really important information for the enterprise. It might have your contracts, it might have your research materials, it might have marketing information, it might have your memos. All that data obviously has, you know, predominantly been used by humans. [00:02:00] But there's been one really interesting problem, which is that, you know, humans only really work with their files during an active engagement with them, and they kind of go away and you don't really see them for a long time.And all of a sudden, uh, with the power of AI and AI agents, all of that data becomes extremely relevant as this ongoing source of, of answers to new questions of data that will transform into, into something else that, that produces value in your organization. It, it contains the answer to the new employee that's onboarding, that needs to ramp up on a project.Um, it contains the answer to the right thing to sell a customer when you're having a conversation to them, with them contains the roadmap information that's gonna produce the next feature. So all that data. That previously we've been just sort of storing and, and you know, occasionally forgetting about, ‘cause we're only working on the new active stuff.All of that information becomes valuable to the enterprise and it's gonna become extremely valuable to end users because now they can have agents go find what they're looking for and produce new, new [00:03:00] value and new data on that information. And it's gonna become incredibly valuable to agents because agents can roam around and do a bunch of work and they're gonna need access to that data as well.And um, and you know, sometimes that will be an agent that is sort of working on behalf of, of, of you and, and effectively as you as and, and they are kind of accessing all of the same information that you have access to and, and operating as you in the system. And then sometimes there's gonna be agents that are just.Effectively autonomous and kind of run on their own and, and you're gonna collaborate and work with them kind of like you did another person. Open Claw being the most recent and maybe first real sort of, you know, kind of, you know, up updating everybody's, you know, views of this landscape version of, of what that could look like, which is, okay, I have an agent.It's on its own system, it's on its own computer, it has access to its own tools. I probably don't give it access to my entire life. I probably communicate with it like I would an assistant or a colleague and then it, it sort of has this sandbox environment. So all of that has massive implications for a platform that manage that [00:04:00] enterprise data.We think it's gonna just transform how we work with all of the enterprise content that we work with, and we just have to make sure we're building the right platform to support that.swyx: The sort of shorthand I put it is as people build agents, everybody's just realizing that every agent needs a box. Yes.And it's nice to be called box and just give everyone a box.Aaron Levie: Hey, I if I, you know, if we can make that go viral, uh, like I, I think that that terminology, I, that's theswyx: tagline. Every agentAaron Levie: needs a box. Every agent needs a box. If we can make that the headline of this, I'm fine with this. And that's the billboard I wanna like Yeah, exactly.Every agent needs a box. Um, I like it. Can we ship this? Like,swyx: okay, let's do it. Yeah.Aaron Levie: Uh, my work here is done and I got the value I needed outta this podcast Drinks.swyx: Yeah.Agent Governance and IdentityAaron Levie: But, but, um, but, but, you know, so the thing that we, we kind of think about is, um, is, you know, whether you think the number 10 x or a hundred x or whatever the number is, we're gonna have some order of magnitude more agents than people.That's inevitable. It has to happen. So then the question is, what is the infrastructure that's needed to make all those agents effective in the enterprise? Make sure that they are well governed. Make sure they're only doing [00:05:00] safe things on your information. Make sure that they're not getting exposed. The data that they shouldn't have access to.There's gonna be just incredibly spectacularly crazy security incidents that will happen with agents because you'll prompt, inject an agent and sort of find your way through the CRM system and pull out data that you shouldn't have access to. Oh, weJeff Huber: have God,Aaron Levie: right? I mean, that's just gonna happen all over the place, right?So, so then the thing is, is how do you make sure you have the right security, the permissions, the access controls, the data governance. Um, we actually don't yet exactly know in many cases how we're gonna regulate some of these agents, right? If you think about an agent in financial services, does it have the exact same financial sort of, uh, requirements that a human did?Or is it, is the risk fully on the human that was interacting or created the agent? All open questions, but no matter what, there's gonna need to be a layer that manages the, the data they have access to, the workflows that they're involved in, pulling up data from multiple systems. This is the new infrastructure opportunity in the era of agents.swyx: You have a piece on agent identities, [00:06:00] which I think was today, um, which I think a lot of breaking news, the security, security people are talking about, right? Like you basically, I, I always think of this as like, well you need the human you and then there you need the agent. YouAaron Levie: Yes.swyx: And uh, well, I don't know if it's that simple, but is box going to have an opinion on that or you're just gonna be like, well we're just the sort of the, the source layer.Yeah. Let's Okta of zero handle that.Aaron Levie: I think we're gonna have an opinion and we will work with generally wherever the contours of the market end up. Um, and the reason that we're gonna have an opinion more than other topics probably is because one of the biggest use cases for why your agent might need it, an identity is for file system access.So thus we have to kind of think about this pretty deeply. And I think, uh, unless you're like in our world thinking about this particular problem all day long, it might be, you know, like, why is this such a big deal? And the reason why it's a really big deal is because sometimes sort of say, well just give the agent an, an account on the system and it just treats, treat it like every other type of user on the system.The [00:07:00] problem is, is that I as Aaron don't really have any responsibility over anybody else's box account in our organization. I can't see the box account of any other employee that I work with. I am not liable for anything that they do. And they have, I have, I have, you know, strict privacy requirements on everything that they're able to, you know, that, that, that they work on.Agents don't have that, you know, don't have those properties. The person who creates the agent probably is gonna, for the foreseeable future, take on a lot of the liability of what that agent does. That agent doesn't deserve any privacy because, because it's, you know, it can't fully be autonomously operated and it doesn't have any legal, you know, kind of, you know, responsibility.So thus you can't just be like, oh, well I'll just create a bunch of accounts and then I'll, I'll kind of work with that agent and I'll talk to it occasionally. Like you need oversight of that. And so then the question is, how do you have a world where the agent, sometimes you have oversight of, but what if that agent goes and works with other people?That person over there is collaborating with the agent on something you shouldn't have [00:08:00] access to what they're doing. So we have all of these new boundaries that we're gonna have to figure out of, of, you know, it's really, really easy. So far we've been in, in easy mode. We've hit the easy button with ai, which is the agent just is you.And when you're in quad code and you're in cursor, and you're in Codex, you're just, the agent is you. You're offing into your services. It can do everything you can do. That's the easy mode. The hard mode is agents are kind of running on their own. People check in with them occasionally, they're doing things autonomously.How do you give them access to resources in the enterprise and not dramatically increased the security risk and the risk that you might expose the wrong thing to somebody. These are all the new problems that we have to get solved. I like the identity layer and, and identity vendors as being a solution to that, but we'll, we'll need some opinions as well because so many of the use cases are these collaborative file system use cases, which is how do I give it an agent, a subset of my data?Give it its own workspace as well. ‘cause it's gonna need to store off its own information that would be relevant for it. And how do I have the right oversight into that? [00:09:00]Jeff Huber: One thing, which, um, I think is kind interesting, think about is that you know, how humans work, right? Like I may not also just like give you access to the whole file.I might like sit next to you and like scroll to this like one part of the file and just show you that like one part and like, you know,swyx: partial file access.Jeff Huber: I'm just saying I think like our, like RA does seem to be dead, right? Like you wanna say something is dead uhhuh probably RA is dead. And uh, like the auth story to me seems like incredibly unsolved and unaddressed by like the existing state of like AI vendors.ButAaron Levie: yeah, I think, um, we're, I mean you're taking obviously really to level limit that we probably need to solve for. Yeah. And we built an access control system that was, was kind of like, you know, its own little world for, for a long time. And um, and the idea was this, it's a many to many collaboration system where I can give you any part of the file system.And it's a waterfall model. So if I give you higher up in the, in the, in the system, you get everything below. And that, that kind of created immense flexibility because I can kind of point you to any layer in the, in the tree, but then you're gonna get access to everything kind of below it. And that [00:10:00] mostly is, is working in this, in this world.But you do have to manage this issue, which is how do I create an agent that has access to some of my stuff and somebody else's stuff as well. Mm-hmm. And which parts do I get to look at as the creator of the agent? And, and these are just brand new problems? Yeah. Crazy. And humans, when there was a human there that was really easy to do.Like, like if the three of us were all sharing, there'd be a Venn diagram where we'd have an overlapping set of things we've shared, but then we'd have our own ways that we shared with each other. In an agent world, somebody needs to take responsibility for what that agent has access to and what they're working on.These are like the, some of the most probably, you know, boring problems for 98% of people on, on the internet, but they will be the problems that are the difference between can you actually have autonomous agents in an enterprise contextswyx: Yeah.Aaron Levie: That are not leaking your data constantly.swyx: No. Like, I mean, you know, I run a very, very small company for my conference and like we already have data sensitivity issues.Yes. And some of my team members cannot see Yes. Uh, the others and like, I can't imagine what it's like to run a Fortune 500 and like, you have to [00:11:00] worry about this. I'm just kinda curious, like you, you talked to a lot like, like 70, 80% of your cus uh, of the Fortune 500, your customers.Aaron Levie: Yep. 67%. Just so we're being verySEswyx: precise.So Yeah. I'm notAaron Levie: Okay. Okay.swyx: Something I'm rounding up. Yes. Round up. I'm projecting to, forAaron Levie: the government.swyx: I'm projecting to the end of the year.Aaron Levie: Okay.swyx: There you go.Aaron Levie: You do make it sound like, like we, we, well we've gotta be on this. Like we're, we're taking way too long to get to 80%. Well,swyx: no, I mean, so like. How are they approaching it?Right? Because you're, you don't have a, you don't have a final answer yet.Why Coding Agents Took Off FirstAaron Levie: Well, okay, so, so this is actually, this is the stark reality that like, unfortunately is the kinda like pouring the water on the party a little bit.swyx: Yes.Aaron Levie: We all in Silicon Valley are like, have the absolute best conditions possible for AI ever.And I think we all saw the dke, you know, kind of Dario podcast and this idea of AI coding. Why is that taken off? And, and we're not yet fully seeing it everywhere else. Well, look, if you just like enumerated the list of properties that AI coding has and then compared it to other [00:12:00] knowledge work, let's just, let's just go through a few of them.Generally speaking, you bring on a new engineer, they have access to a large swath of the code base. Like, there's like very, like you, just, like new engineer comes on, they can just go and find the, the, the stuff that they, they need to work with. It's a fully text in text out. Medium. It's only, it's just gonna be text at the end of the day.So it's like really great from a, from just a, uh, you know, kinda what the agent can work with. Obviously the models are super trained on that dataset. The labs themselves have a really strong, kind of self-reinforcing positive flywheel of why they need to do, you know, agent coding deeply. So then you get just better tooling, better services.The actual developers of the AI are daily users of the, of the thing that they're we're working on versus like the, you know, probably there's only like seven Claude Cowork legal plugin users at Anthropic any given day, but there's like a couple thousand Claude code and you know, users every single day.So just like, think about which one are they getting more feedback on. All day long. So you just go through this list. You have a, you know, everybody who's a [00:13:00] developer by definition is technical so they can go install the latest thing. We're all generally online, or at least, you know, kinda the weird ones are, and we're all talking to each other, sharing best practices, like that's like already eight differences.Versus the rest of the economy. Every other part of the economy has like, like six to seven headwinds relative to that list. You go into a company, you're a banker in financial services, you have access to like a, a tiny little subset of the total data that's gonna be relevant to do your job. And you're have to start to go and talk to a bunch of people to get the right data to do your job because Sally didn't add you to that deal room, you know, folder.And that that, you know, the information is actually in a completely different organization that you now have to go in and, and sort of run into. And it's like you have this endless list of access controls and security. As, as you talked about, you have a medium, which is not, it's not just text, right? You have, you have a zoom call that, that you're getting all of the requirements from the customer.You have a lot of in-person conversations and you're doing in-person sales and like how do you ever [00:14:00] digitize all of that information? Um, you know, I think a lot of people got upset with this idea that the code base has all the context, um, that I don't know if you follow, you know, did you follow some of that conversation that that went viral?Is like, you know, it's not that simple that, that the code base doesn't have all the knowledge, but like it's a lot, you're a lot better off than you are with other areas of knowledge work. Like you, we like, we like have documentation practices, you write specifications. Those things don't exist for like 80% of work that happens in the enterprise.That's the divide that we have, which is, which is AI coding has, has just fully, you know, where we've reached escape velocity of how powerful this stuff is, and then we're gonna have to find a way to bring that same energy and momentum, but to all these other areas of knowledge work. Where the tools aren't there, the data's not set up to be there.The access controls don't make it that easy. The context engineering is an incredibly hard problem because again, you have access control challenges, you have different data formats. You have end users that are gonna need to kind of be kind of trained through this as opposed to their adopting [00:15:00] these tools in their free time.That's where the Fortune 500 is. And so we, I think, you know, have to be prepared as an industry where we are gonna be on a multi-year march to, to be able to bring agents to the enterprise for these workflows. And I think probably the, the thing that we've learned most in coding that, that the rest of the world is not yet, I think ready for, I mean, we're, they'll, they'll have to be ready for it because it's just gonna inevitably happen is I think in coding.What, what's interesting is if you think about the practice of coding today versus two years ago. It's probably the most changed workflow in maybe the history of time from the amount of time it's changed, right? Yeah. Like, like has any, has any workflow in the entire economy changed that quickly in terms of the amount of change?I just, you know, at least in any knowledge worker workflow, there's like very rarely been an event where one piece of technology and work practice has so fundamentally, you know, changed, changed what you do. Like you don't write code, you talk to an agent and it goes and [00:16:00] does it for you, and you may be at best review it.And even that's even probably like, like largely not even what you're doing. What's happening is we are changing our work to make the agents effective. In that model, the agent didn't really adapt to how we work. We basically adapted to how the agent works. Mm-hmm. All of the economy has to go through that exact same evolution.The rest of the economy is gonna have to update its workflows to make agents effective. And to give agents the context that they need and to actually figure out what kind of prompting works and to figure out how do you ensure that the agent has the right access to information to be able to execute on its work.I, you know, this is not the panacea that people were hoping for, of the agent drops in, just automates your life. Like you have to basically re-engineer your workflow to get the most out of agents and, uh, and that, that's just gonna take, you know, multiple years across the economy. Right now it's a huge asset and an advantage for the teams that do it early and that are kinda wired into doing this.‘cause [00:17:00] you'll see compounding returns, but that's just gonna take a while for most companies to actually go and get this deployed.swyx: I love, I love pushing back. I think that. That is what a lot of technology consultants love to hear this sort of thing, right? Yeah, yeah, yeah. First to, to embrace the ai. Yes. To get to the promised land, you must pay me so much money to a hundred percent to adopt the prescribed way of, uh, conforming to the agents.Yes. And I worry that you will be eclipsed by someone else who says, no, come as you are.Aaron Levie: Yeah.swyx: And we'll meet you where you are.Aaron Levie: And, and, and and what was the thing that went viral a week ago? OpenAI probably, uh, is hiring F Dees. Yeah. Uh, to go into the enterprise. Yeah. Yeah. And then philanthropic is embedded at Goldman Sachs.Yeah. So if the labs are having to do this, if, if the labs have decided that they need to hire FDE and professional services, then I think that's a pretty clear indication that this, there's no easy mode of workflow transformation. Yeah. Yeah. So, so to your point, I think actually this is a market opportunity for, you know, new professional services and consulting [00:18:00] firms that are like Agent Build and they, and they kind of, you know, go into organizations and they figure out how to re-engineer your workflows to make them more agent ready and get your data into the right format and, you know, reconstruct your business process.So you're, you're not doing most of the work. You're telling agents how to do the work and then you're reviewing it. But I haven't seen the thing that can just drop in and, and kinda let you not go through those changes.swyx: I don't know how that kind of sales pitch goes over. Yeah. You know, you're, you're saying things like, well, in my sort of nice beautiful walled garden, here's, there's, uh, because here's this, here's this beautiful box account that has everything.Yes. And I'm like, well, most, most real life is extremely messy. Sure. And like, poorly named and there duplicate this outdated s**tAaron Levie: a hundred percent. And so No, no, a hundred percent. And so this is actually No. So, so this is, I mean, we agree that, that getting to the beautiful garden is gonna be tough.swyx: Yeah.Aaron Levie: There's also the other end of the spectrum where I, I just like, it's a technical impossibility to solve. The agent is, is truly cannot get enough context to make the right decision in, in the, in the incredibly messy land. Like there's [00:19:00] no a GI that will solve that. So, so we're gonna have to kind of land in somewhere in between, which is like we all collectively get better at.Documentation practices and, and having authoritative relatively up-to-date information and putting it in the right place like agents will, will certainly cause us to be much better organized around how we work with our information, simply because the severity of the agent pulling the wrong data will be too high and the productivity gain of that you'll miss out on by not doing this will be too high as well, that you, that your competition will just do it and they'll just have higher velocity.So, uh, and, and we, we see this a lot firsthand. So we, we build a series of agents internally that they can kind of have access to your full box account and go off and you give it a task and it can go find whatever information you're looking for and work with. And, you know, thank God for the model progress, but like, if, if you gave that task to an agent.Nine months ago, you're just gonna get lots of bogus answers because it's gonna, it's gonna say, Hey, here's, here are fi [00:20:00] five, you know, documents that all kind of smell like the right thing. And I'm gonna, but I, but you're, you're putting me on the clock. ‘cause my assistant prompt says like, you know, be pretty smart, but also try and respond to the user and it's gonna respond.And it's like, ah, it got the wrong document. And then you do that once or twice as a knowledge worker and you're just neverswyx: again,Aaron Levie: never again. You're just like done with the system.swyx: Yeah. It doesn't work.Aaron Levie: It doesn't work. And so, you know, Opus four six and Gemini three one Pro and you know, whatever the latest five 3G BT will be, like, those things are getting better and better and it's using better judgment.And this sort of like the, all of these updates to the agentic tool and search systems are, are, we're seeing, we're seeing very real progress where the agent. Kind of can, can almost smell some things a little bit fishy when it's getting, you know, we, we have this process where we, we have it go fan out, do a bunch of searches, pull up a bunch of data, and then it has to sort of do its own ranking of, you know, what are the right documents that, that it should be working with.And again, like, you know, the intelligence level of a model six months ago, [00:21:00] it'd be just throwing a dart at like, I'm just, I'm gonna grab these seven files and I, I pray, I hope that that's the right answer. And something like an opus first four five, and now four six is like, oh, it's like, no, that one doesn't seem right relative to this question because I'm seeing some signal that is making that, you know, that's contradicting the document where it would normally be in the tree and who should have access.Like it's doing all of that kind of work for you. But like, it still doesn't work if you just have a total wasteland of data. Like, it's just not, it's just not possible. Partly ‘cause a human wouldn't even be able to do it. So basically if a, if a really, really smart human. Could not do that task in five or 10 minutes for a search retrieval type task.Look, you know, your agent's not gonna be able to do it any better. You see this all day long. SoContext Engineering and Search Limitsswyx: this touches on a thing that just passionate about it was just context engineering. I, I'm just gonna let you ramble or riff on, on context engineering. If, if, if there's anything like he, he did really good work on context fraud, which has really taken over as like the term that people use and the referenceAaron Levie: a hundred percent.We, we all we think about is, is the context rob problem. [00:22:00]Jeff Huber: Yeah, there's certainly a lot of like ranking considerations. Gentech surgery think is incredibly promising. Um, yeah, I was trying to generate a question though. I think I have a question right now. Swyx.Aaron Levie: Yeah, no, but like, like I think there was this moment, um, you know, like, I don't know, two years ago before, before we knew like where the, the gotchas were gonna be in ai and I think someone was like, was like, well, infinite context windows will just solve all of these problems and ‘cause you'll just, you'll just give the context window like all the data and.It's just like, okay, I mean, maybe in 2035, like this is a viable solution. First of all, it, it would just, it would just simply cost too much. Like we just can't give the model like the 5,000 documents that might be relevant and it's gonna read them all. And I've seen enough to, to start believing in crazy stuff.So like, I'm willing to just say, sure. Like in, in 10 years from now,swyx: never say, never, never.Aaron Levie: In, in 10 years from now, we'll have infinite context windows at, at a thousandth of the price of today. Like, let's just like believe that that's possible, but Right. We're in reality today. So today we have a context engineering [00:23:00] problem, which is, I got, I got, you know, 200,000 tokens that I can work with, or prob, I don't even know what the latest graph is before, like massive degradation.16. Okay. I have 60,000 tokens that I get to work with where I'm gonna get accurate information. That's not a lot of tokens for a corpus of 10 million documents that a knowledge worker might have across all of the teams and all the projects and all the people they work with. I have, I have 10 million documents.Which, you know, maybe is times five pages per document or something like that. I'm at 50 million pages of information and I have 60,000 tokens. Like, holy s**t. Yeah. This is like, how do I bridge the 50 million pages of information with, you know, the couple hundred that I get to work with in that, in that token window.Yeah. This is like, this is like such an interesting problem and that's why actually so much work is actually like, just like search systems and the databases and that layer has to just get so locked in, but models getting better and importantly [00:24:00] knowing when they've done a search, they found the wrong thing, they go back, they check their work, they, they find a way to balance sort of appeasing the user versus double checking.We have this one, we have this one test case where we ask the agent to go find. 10 pieces of information.swyx: Is this the complex work eval?Aaron Levie: Uh, this is actually not in the eval. This is, this is sort of just like we have a bunch of different, we have a bunch of internal benchmark kind of scenarios. Every time we, we update our agent, we have one, which is, I ask it to find all of our office addresses, and I give it the list of 10 offices that we have.And there's not one document that has this, maybe there should be, that would be a great example of the kind of thing that like maybe over time companies start to, you know, have these sort of like, what are the canonical, you know, kind of key areas of knowledge that we need to have. We don't seem to have this one document that says, here are all of our offices.We have a bunch of documents that have like, here's the New York office and whatever. So you task this agent and you, you get, you say, I need the addresses for these 10 offices. Okay. And by the way, if you do this on any, you know, [00:25:00] public chat model, the same outcome is gonna happen. But for a different kind of query, you give it, you say, I need these 10 addresses.How many times should the agent go and do its search before it decides whether or not, there's just no answer to this question. Often, and especially the, the, let's say lower tier models, it'll come back and it'll give you six of the 10 addresses. And it'll, and I'll just say I couldn't find the otherswyx: four.It, it doesn't know what It doesn't know. ItAaron Levie: doesn't know what It doesn't know. Yeah. So the model is just like, like when should it stop? When should it stop doing? Like should it, should it do that task for literally an hour and just keep cranking through? Maybe I actually made up an office location and it doesn't know that I made it up and I didn't even know that I made it up.Like, should it just keep, re should it read every single file in your entire box account until it, until it should exhaust every single piece of information.swyx: Expensive.Aaron Levie: These are the new problems that we have. So, you know, something like, let's say a new opus model is sort of like, okay, I'm gonna try these types of queries.I didn't get exactly what I wanted. I'm gonna try again. I'm gonna, at [00:26:00] some point I'm gonna stop searching. ‘cause I've determined that that no amount of searching is gonna solve this problem. I'm just not able to do it. And that judgment is like a really new thing that the model needs to be able to have.It's like, when should it give up on a task? ‘cause, ‘cause you just don't, it's a can't find the thing. That's the real world of knowledge, work problems. And this is the stuff that the coding agents don't have to deal with. Because they, it just doesn't like, like you're not usually asking it about, you're, you're always creating net new information coming right outta the model for the most part.Obviously it has to know about your code base and your specs and your documentation, but, but when you deploy an agent on all of your data that now you have all of these new problems that you're dealing withJeff Huber: our, uh, follow follow-up research to context ride is actually on a genetic search. Ah. Um, and we've like right, sort of stress tested like frontier models and their ability to search.Um, and they're not actually that good at searching. Right. Uh, so you're sort of highlighting this like explore, exploit.swyx: You're just say, Debbie, Donna say everything doesn't work. Like,Aaron Levie: well,Jeff Huber: somebody has to be,Aaron Levie: um, can I just throw out one more thing? Yeah. That is different from coding and, and the rest [00:27:00] of the knowledge work that I, I failed to mention.So one other kind of key point is, is that, you know, at the end of the day. Whether you believe we're in a slop apocalypse or, or whatever. At the end of the day, if you, if you build a working product at the end of, if you, if you've built a working solution that is ultimately what the customer is paying for, like whether I have a lot of slop, a little slop or whatever, I'm sure there's lots of code bases we could go into in enterprise software companies where it's like just crazy slop that humans did over a 20 year period, but the end customer just gets this little interface.They can, they can type into it, it does its thing. Knowledge work, uh, doesn't have that property. If I have an AI model, go generate a contract and I generate a contract 20 times and, you know, all 20 times it's just 3% different and like that I, that, that kind of lop introduces all new kinds of risk for my organization that the code version of that LOP didn't, didn't introduce.These are, and so like, so how do you constrain these models to just the part that you want [00:28:00] them to work on and just do the thing that you want them to do? And, and, you know, in engineering, we don't, you can't be disbarred as an engineer, but you could be disbarred as a lawyer. Like you can do the wrong medical thing In healthcare, you, there's no, there's no equivalent to that of engineering.Like, doswyx: you want there to be, because I've considered softwareJeff Huber: engineer. What's that? Civil engineering there is, right? NotAaron Levie: software civil engineer. Sure. Oh yeah, for sure. But like in any of our companies, you like, you know, you'll be forgiven if you took down the site and, and we, we will do a rollback and you'll, you'll be in a meeting, but you have not been disbarred as an engineer.We don't, we don't change your, you know, your computer science, uh, blameJeff Huber: degree, this postmortem.Aaron Levie: Yeah, exactly. Exactly. So, so, uh, now maybe we collectively as an industry need to figure out like, what are you liable for? Not legally, but like in a, in a management sense, uh, of these agents. All sorts of interesting problems that, that, that, uh, that have to come out.But in knowledge work, that's the real hostile environments that we're operating in. Hmm.swyx: I do think like, uh, a lot of the last year's, 2025 story was the rise of coding agents and I think [00:29:00] 2026 story is definitely knowledge work agents. Yes. A hundredAaron Levie: percent.swyx: Right. Like that would, and I think open claw core work are just the beginning.Yes. Like it's, the next one's gonna just gonna be absolute craziness.Aaron Levie: It it is. And, and, uh, and it's gonna be, I mean, again, like this is gonna be this, this wave where we, we are gonna try and bring as many of the practices from coding because that, that will clearly be the forefront, which is tell an agent to go do something and has an access to a set of resources.You need to be responsible for reviewing it at the end of the process. That to me is the, is the kind of template that I just think goes across knowledge, work and odd. Cowork is a great example. Open Closet's a great example. You can kind of, sort of see what Codex could become over time. These are some, some really interesting kind of platforms that are emerging.swyx: Okay. Um, I wanted to, we touched on evals a little bit. You had, you had the report that you're gonna go bring up and then I was gonna go into like, uh, boxes, evals, but uh, go ahead. Talk about your genetic search thing.Jeff Huber: Yeah. Mostly I think kinda a few of the insights. It's like number one frontier model is not good at search.Humans have this [00:30:00] natural explore, exploit trade off where we kinda understand like when to stop doing something. Also, humans are pretty good at like forgetting actually, and like pruning their own context, whereas agents are not, and actually an agent in their kind of context history, if they knew something was bad and they even, you could see in the trace the reason you trace, Hey, that probably wasn't a good idea.If it's still in the trace, still in the context, they'll still do it again. Uhhuh. Uh, and so like, I think pruning is also gonna be like, really, it's already becoming a thing, right? But like, letting self prune the con windowsswyx: be a big deal. Yeah. So, so don't leave the mistake. Don't leave the mistake in there.Cut out the mistake but tell it that you made a mistake in the past and so it doesn't repeat it.Jeff Huber: Yeah. But like cut it out so it doesn't get like distracted by it again. ‘cause really, you know, what is so, so it will repeat its mistake just because it's been, it's inswyx: theJeff Huber: context. It'sAaron Levie: in the context so much.That's a few shot example. Even if it, yeah.Jeff Huber: It's like oh thisAaron Levie: is a great thing to go try even ifJeff Huber: it didn't work.Aaron Levie: Yeah,Jeff Huber: exactly.Aaron Levie: SoJeff Huber: there's like a bunch of stuff there. JustAaron Levie: Groundhogs Day inside these models. Yeah. I'm gonna go keep doing the same wrongJeff Huber: thing. Covering sense. I feel like, you know, some creator analogy you're trying like fit a manifold in latent space, which kind is doing break program synthesis, which is kinda one we think about we're doing right.Like, you know, certain [00:31:00] facts might be like sort of overly pitting it. There are certain, you know, sec sectors of latent space and so like plug clean space. Yeah. And, uh, andswyx: so we have a bell, our editor as a bell every time you say that. SoJeff Huber: you have, you have to like remove those, likeswyx: you shoulda a gong like TPN or something.IfJeff Huber: we gong, you either remove those links to like kinda give it the freedom, kind of do what you need to do. So, but yeah. We'll, we'll release more soon. That'sAaron Levie: awesome.Jeff Huber: That'll, that'll be cool.swyx: We're a cerebral podcast that people listen to us and, and sort of think really deep. So yeah, we try to keep it subtle.Okay. We try to keep it.Aaron Levie: Okay, fine.Inside Agent Evalsswyx: Um, you, you guys do, you guys do have EVs, you talked about your, your office thing, but, uh, you've been also promoting APEX agents and complex work. Uh, yeah, whatever you, wherever you wanna take this just Yeah. How youAaron Levie: Apex is, is obviously me, core's, uh, uh, kind of, um, agent eval.We, we supported that by sort of. Opening up some data for them around how we kind of see these, um, data workspaces in, in the, you know, kind of regular economy. So how do lawyers have a workspace? How do investment bankers have a workspace? What kind of data goes into those? And so we, [00:32:00] we partner with them on their, their apex eval.Our own, um, eval is, it's actually relatively straightforward. We have a, a set of, of documents in a, in a range of industries. We give the agent previously did this as a one shot test of just purely the model. And then we just realized we, we need to, based on where everything's going, it's just gotta be more agentic.So now it's a bit more of a test of both our harness and the model. And we have a rubric of a set of things that has to get right and we score it. Um, and you're just seeing, you know, these incredible jumps in almost every single model in its own family of, you know, opus four, um, you know, sonnet four six versus sonnet four five.swyx: Yeah. We have this up on screen.Aaron Levie: Okay, cool. So some, you're seeing it somewhere like. I, I forget the to, it was like 15 point jump, I think on the main, on the overall,swyx: yes.Aaron Levie: And it's just like, you know, these incredible leaps that, that are starting to happen. Um,swyx: and OP doesn't know any, like any, it's completely held out from op.Aaron Levie: This is not in any, there's no public data which has, you know, Ben benefits and this is just a private eval that we [00:33:00] do, and then we just happen to show it to, to the world. Hmm. So you can't, you can't train against it. And I think it's just as representative of. It's obviously reasoning capabilities, what it's doing at, at, you know, kind of test time, compute capabilities, thinking levels, all like the context rot issues.So many interesting, you know, kind of, uh, uh, capabilities that are, that are now improvingswyx: one sector that you have. That's interesting.Industries and Datasetsswyx: Uh, people are roughly familiar with healthcare and legal, but you have public sector in there.Aaron Levie: Yeah.swyx: Uh, what's that? Like, what, what, what is that?Aaron Levie: Yeah, and, and we actually test against, I dunno, maybe 10 industries.We, we end up usually just cutting a few that we think have interesting gains. All extras, won a lot of like government type documents. Um,swyx: what is that? What is it? Government type documents?Aaron Levie: Government filings. Like a taxswyx: return, likeAaron Levie: a probably not tax returns. It would be more of what would go the government be using, uh, as data.So, okay. Um, so think about research that, that type of, of, of data sets. And then we have financial services for things like data rooms and what would be in an investment prospectus. Uhhuh,swyx: that one you can dog food.Aaron Levie: Yeah, exactly. Exactly. Yes. Yes. [00:34:00] So, uh, so we, we run the models, um, in now, you know, more of an agent mode, but, but still with, with kinda limited capacity and just try and see like on a, like, for like basis, what are the improvements?And, and again, we just continue to be blown away by. How, how good these models are getting.swyx: Yeah, I mean, I think every serious AI company needs something like that where like, well, this is the work we do. Here's our company eval. Yeah. And if you don't have it, well, you're not a serious AI company.Aaron Levie: There's two dimensions, right?So there's, there's like, how are the models improving? And so which models should you either recommend a customer use, which one should you adopt? But then every single day, we're making changes to our agents. And you need to knowswyx: if you regressed,Aaron Levie: if you know. Yeah. You know, I've been fully convinced that the whole agent observability and eval space is gonna be a massive space.Um, super excited for what Braintrust is doing, excited for, you know, Lang Smith, all the things. And I think what you're going to, I mean, this is like every enter like literally every enterprise right now. It's like the AI companies are the customers of these tools. Every enterprise will have this. Yeah, you'll just [00:35:00] have to have an eval.Of all of your work and like, we'll, you'll have an eval of your RFP generation, you'll have an eval of your sales material creation. You'll have an eval of your, uh, invoice processing. And, and as you, you know, buy or use new agentic systems, you are gonna need to know like, what's the quality of your, of your pipeline.swyx: Yeah.Aaron Levie: Um, so huge, huge market with agent evals.swyx: Yeah.Building the Agent Teamswyx: And, and you know, I'm gonna shout out your, your team a bit, uh, your CTO, Ben, uh, did a great talk with us last year. Awesome. And he's gonna come back again. Oh, cool. For World's Fair.Aaron Levie: Yep.swyx: Just talk about your team, like brag a little bit. I think I, I think people take these eval numbers in pretty charts for granted, but No, there, I mean, there's, there's lots of really smart people at work during all this.Aaron Levie: Biggest shout out, uh, is we have a, we have a couple folks at Dya, uh, Sidarth, uh, that, that kind of run this. They're like a, you know, kind of tag tag team duo on our evals, Ben, our CTO, heavily involved Yasha, head of ai, uh, you know, a bunch of folks. And, um, evals is one part of the story. And then just like the full, you know, kind of AI.An agent team [00:36:00] is, uh, is a, is a pretty, you know, is core to this whole effort. So there's probably, I don't know, like maybe a few dozen people that are like the epicenter. And then you just have like layers and layers of, of kind of concentric circles of okay, then there's a search team that supports them and an infrastructure team that supports them.And it's starting to ripple through the entire company. But there's that kind of core agent team, um, that's a pretty, pretty close, uh, close knit group.swyx: The search team is separate from the infra team.Aaron Levie: I mean, we have like every, every layer of the stack we have to kind of do, except for just pure public cloud.Um, but um, you know, we, we store, I don't even know what our public numbers are in, you know, but like, you can just think about it as like a lot of data is, is stored in box. And so we have, and you have every layer of the, of the stack of, you know, how do you manage the data, the file system, the metadata system, the search system, just all of those components.And then they all are having to understand that now you've got this new customer. Which is the agent, and they've been building for two types of customers in the past. They've been building for users and they've been building for like applications. [00:37:00] And now you've got this new agent user, and it comes in with a difference of it, of property sometimes, like, hey, maybe sometimes we should do embeddings, an embedding based, you know, kind of search versus, you know, your, your typical semantic search.Like, it's just like you have to build the, the capabilities to support all of this. And we're testing stuff, throwing things away, something doesn't work and, and not relevant. It's like just, you know, total chaos. But all of those teams are supporting the agent team that is kind of coming up with its requirements of what, what do we need?swyx: Yeah. No, uh, we just came from, uh, fireside chat where you did, and you, you talked about how you're doing this. It's, it's kind of like an internal startup. Yeah. Within the broader company. The broader company's like 3000 people. Yeah. But you know, there's, there's a, this is a core team of like, well, here's the innovation center.Aaron Levie: Yeah.swyx: And like that every company kind of is run this way.Aaron Levie: Yeah. I wanna be sensitive. I don't call it the innovation center. Yeah. Only because I think everybody has to do innovation. Um, there, there's a part of the, the, the company that is, is sort of do or die for the agent wave.swyx: Yeah.Aaron Levie: And it only happens to be more of my focus simply because it's existential that [00:38:00] we get it right.swyx: Yeah.Aaron Levie: All of the supporting systems are necessary. All of the surrounding adjacent capabilities are necessary. Like the only reason we get to be a platform where you'd run an agent is because we have a security feature or a compliance feature, or a governance feature that, that some team is working on.But that's not gonna be the make or break of, of whether we get agents right. Like that already exists and we need to keep innovating there. I don't know what the right, exact precise number is, but it's not a thousand people and it's not 10 people. There's a number of people that are like the, the kind of like, you know, startup within the company that are the make or break on everything related to AI agents, you know, leveraging our platform and letting you work with your data.And that's where I spend a lot of my time, and Ben and Yosh and Diego and Teri, you know, these are just, you know, people that, that, you know, kind of across the team. Are working.swyx: Yeah. Amazing.Read Write Agent WorkflowsJeff Huber: How do you, how do you think about, I mean, you talked a lot about like kinda read workflows over your box data. Yep.Right. You know, gen search questions, queries, et cetera. But like, what about like, write or like authoring workflows?Aaron Levie: Yes. I've [00:39:00] already probably revealed too much actually now that I think about it. So, um, I've talked about whatever,Jeff Huber: whatever you can.Aaron Levie: Okay. It's just us. It's just us. Yeah. Okay. Of course, of course.So I, I guess I would just, uh, I'll make it a little bit conceptual, uh, because again, I've already, I've already said things that are not even ga but, but we've, we've kinda like danced around it publicly, so I, yeah, yeah. Okay. Just like, hopefully nobody watches this, um, episode. No.swyx: It's tidbits for the Heidi engaged to go figure out like what exactly, um, you know, is, is your sort of line of thinking.Sure. They can connect the dots.Aaron Levie: Yeah. So, so I would say that, that, uh, we, you know, as a, as a place where you have your enterprise content, there's a use case where I want to, you know, have an agent read that data and answer questions for me. And then there's a use case where I want the agent to create something.And use the file system to create something or store off data that it's working on, or be able to have, you know, various files that it's writing to about the work it's doing. So we do see it as a total read write. The harder problem has so far been the read only because, because again, you have that kind of like 10 [00:40:00] million to one ratio problem, whereas rights are a lot of, that's just gonna come from the model and, and we just like, we'll just put it in the file system and kinda use it.So it's a little bit of a technically easier problem, but the only part that's like, not necessarily technically hard, it is just like it's not yet perfected in the state of the ecosystem is, you know, building a beautiful PowerPoint presentation. It's still a hard problem for these models. Like, like we still, you know, like, like these formats are just, we're not built for.They'reswyx: working on it.Aaron Levie: They're, they're working on it. Everybody's working on it.swyx: Every launch is like, well, we do PowerPoint now.Aaron Levie: We're getting, yeah, getting a lot, getting a lot of better each time. But then you'll do this thing where you'll ask the update one slide and all of a sudden, like the fonts will be just like a little bit different, you know, on two of the slides, or it moved, you know, some shape over to the left a little bit.And again, these are the kind of things that, like in code, obviously you could really care about if you really care about, you know, how beautiful is the code, but at the end, user doesn't notice all those problems and file creation, the end user instantly sees it. You're [00:41:00] like, ah, like paragraph three, like, you literally just changed the font on me.Like it's a totally different font and like midway through the document. Mm-hmm. Those are the kind of things that you run into a lot of in the, in the content creation side. So, mm-hmm. We are gonna have native agents. That do all of those things, they'll be powered by the leading kind of models and labs.But the thing that I think is, is probably gonna be a much bigger idea over time is any agent on any system, again, using Box as a file system for its work, and in that kind of scenario, we don't necessarily care what it's putting in the file system. It could put its memory files, it could put its, you know, specification, you know, documents.It could put, you know, whatever its markdown files are, or it could, you know, generate PDFs. It's just like, it's a workspace that is, is sort of sandboxed off for its work. People can collaborate into it, it can share with other people. And, and so we, we were thinking a lot about what's the right, you know, kind of way to, to deliver that at scale.Docs Graphs and Founder Modeswyx: I wanted to come into sort of the sort of AI transformation or AI sort of, uh, operations things. [00:42:00] Um, one of the tweets that you, that you wanted to talk about, this is just me going through your tweets, by the way. Oh, okay. I mean, like, this is, you readAaron Levie: one by one,swyx: you're the, you're the easiest guest to prep for because you, you already have like, this is the, this is what I'm interested in.I'm like, okay, well, areAaron Levie: we gonna get to like, like February, January or something? Where are we in the, in the timelines? How far back are we going?swyx: Can you, can you describe boxes? A set of skills? Right? Like that, that's like, that's like one of the extremes of like, well if you, you just turn everything into a markdown file.Yeah. Then your agent can run your company. Uh, like you just have to write, find the right sequence of words toAaron Levie: Yes.swyx: To do it.Aaron Levie: Sorry, isthatswyx: the question? So I think the question is like, what if we documented everything? Yes. The way that you exactly said like,Aaron Levie: yes.swyx: Um, let's get all the Fortune five hundreds, uh, prepared for agents.Yes. And like, you know, everything's in golden and, and nicely filed away and everything. Yes. What's missing? Like, what's left, right? LikeAaron Levie: Yeah.swyx: You've, you've run your company for a decade. LikeAaron Levie: Yeah. I think the challenge is that, that that information changes a week later. And because something happened in the market for that [00:43:00] customer, or us as a company that now has to go get updated, and so these systems are living and breathing and they have to experience reality and updates to reality, which right now is probably gonna be humans, you know, kinda giving those, giving them the updates.And, you know, there is this piece about context graphs as as, uh, that kinda went very viral. Yeah. And I, I, I was like a, i, I, I thought it was super provocative. I agreed with many parts of it. I disagree with a few parts around. You know, it's not gonna be as easy as as just if we just had the agent traces, then we can finally do that work because there's just like, there's so much more other stuff that that's happening that, that we haven't been able to capture and digitize.And I think they actually represented that in the piece to be clear. But like there's just a lot of work, you know, that that has to, you just can't have only skills files, you know, for your company because it's just gonna be like, there's gonna be a lot of other stuff that happens. Yeah. Change over time.Yeah. Most companies are practically apprenticeships.swyx: Most companies are practically apprenticeships. LikeJeff Huber: every new employee who joins the team, [00:44:00] like you span one to three months. Like ramping them up.Aaron Levie: Yes. AllJeff Huber: that tat knowledgeAaron Levie: isJeff Huber: not written down.Aaron Levie: Yes.Jeff Huber: But like, it would have to be if you wanted to like give it to an Asian.Right. And so like that seems to me like to beAaron Levie: one is I think you're gonna see again a premium on companies that can document this. Mm-hmm. Much. There'll be a huge premium on that because, because you know, can you shorten that three month ramp cycle to a two week ramp cycle? That's an instant productivity gain.Can you re dramatically reduce rework in the organization because you've documented where all the stuff is and where the answers are. Can you make your average employee as good as your 90th percentile employee because you've captured the knowledge that's sort of in the heads of, of those top employees and make that available.So like you can see some very clear productivity benefits. Mm-hmm. If you had a company culture of making sure you know your information was captured, digitized, put in a format that was agent ready and then made available to agents to work with, and then you just, again, have this reality of like add a 10,000 person [00:45:00] company.Mapping that to the, you know, access structure of the company is just a hard problem. Is like, is like, yeah, well, you just, not every piece of information that's digitized can be shared to everybody. And so now you have to organize that in a way that actually works. There was a pretty good piece, um, this, this, uh, this piece called your company as a file is a file system.I, did you see that one?swyx: Nope.Aaron Levie: Uh, yes. You saw it. Yeah. And, and, uh, I actually be curious your thoughts on it. Um, like, like an interesting kind of like, we, we agree with it because, because that's how we see the world and, uh,swyx: okay. We, we have it up on screen. Oh,Aaron Levie: okay. Yeah. But, but it's all about basically like, you know, we've already, we, we, we already organized in this kind of like, you know, permission structure way.Uh, and, and these are the kind of, you know, natural ways that, that agents can now work with data. So it's kind of like this, this, you know, kind of interesting metaphor, but I do think companies will have to start to think about how they start to digitize more, more of that data. What was your take?Jeff Huber: Yeah, I mean, like the company's probably like an acid compliant file system.Aaron Levie: Uh,Jeff Huber: yeah. Which I'm guessing boxes, right? So, yeah. Yes.swyx: Yeah. [00:46:00]Jeff Huber: Which you have a great piece on, but,swyx: uh, yeah. Well, uh, I, I, my, my, my direction is a little bit like, I wanna rewind a little bit to the graph word you said that there, that's a magic trigger word for us. I always ask what's your take on knowledge graphs?Yeah. Uh, ‘cause every, especially at every data database person, I just wanna see what they think. There's been knowledge graphs, hype cycles, and you've seen it all. So.Aaron Levie: Hmm. I actually am not the expert in knowledge graphs, so, so that you might need toswyx: research, you don't need to be an expert. Yeah. I think it's just like, well, how, how seriously do people take it?Yeah. Like, is is, is there a lot of potential in the, in the HOVI?Aaron Levie: Uh, well, can I, can I, uh, understand first if it's, um, is this a loaded question in the sense of are you super pro, super con, super anti medium? Iswyx: see pro, I see pros and cons. Okay. Uh, but I, I think your opinion should be independent of mine.Aaron Levie: Yeah. No, no, totally. Yeah. I just want to see what I'm stepping into.swyx: No, I know. It's a, and it's a huge trigger word for a lot of people out Yeah. In our audience. And they're, they're trying to figure out why is that? Because whyAaron Levie: is this such aswyx: hot item for them? Because a lot of people get graph religion.And they're like, everything's a graph. Of course you have to represent it as a graph. Well, [00:47:00] how do you solve your knowledge? Um, changing over time? Well, it's a graph.Aaron Levie: Yeah.swyx: And, and I think there, there's that line of work and then there's, there's a lot of people who are like, well, you don't need it. And both are right.Aaron Levie: Yeah. And what do the people who say you don't need it, what are theyswyx: arguing for Mark down files. Oh, sure, sure. Simplicity.Aaron Levie: Yeah.swyx: Versus it's, it's structure versus less structure. Right. That's, that's all what it is. I do.Aaron Levie: I think the tricky thing is, um, is, is again, when this gets met with real humans, they're just going to their computer.They're just working with some people on Slack or teams. They're just sharing some data through a collaborative file system and Google Docs or Box or whatever. I certainly like the vision of most, most knowledge graph, you know, kind of futuristic kind of ways of thinking about it. Uh, it's just like, you know, it's 2026.We haven't seen it yet. Kind of play out as as, I mean, I remember. Do you remember the, um, in like, actually I don't, I don't even know how old you guys are, but I'll for, for to show my age. I remember 17 years ago, everybody thought enterprises would just run on [00:48:00] Wikis. Yeah. And, uh, confluence and, and not even, I mean, confluence actually took off for engineering for sure.Like unquestionably. But like, this was like everything would be in the w. And I think based on our, uh, our, uh, general style of, of, of what we were building, like we were just like, I don't know, people just like wanna workspace. They're gonna collaborate with other people.swyx: Exactly. Yeah. So you were, you were anti-knowledge graph.Aaron Levie: Not anti, not anti. Soswyx: not nonAaron Levie: I'm not, I'm not anti. ‘cause I think, I think your search system, I just think these are two systems that probably, but like, I'm, I'm not in any religious war. I don't want to be in anybody's YouTube comments on this. There's not a fight for me.swyx: We, we love YouTube comments. We're, we're, we're get into comments.Aaron Levie: Okay. Uh, but like, but I, I, it's mostly just a virtue of what we built. Yeah. And we just continued down that path. Yeah.swyx: Yeah.Aaron Levie: And, um, and that, that was what we pursued. But I'm not, this is not a, you know, kind of, this is not a, uh, it'sswyx: not existential for you. Great.Aaron Levie: We're happy to plug into somebody else's graph.We're happy to feed data into it. We're happy for [00:49:00] agents to, to talk to multiple systems. Not, not our fight.swyx: Yeah.Aaron Levie: But I need your answer. Yeah. Graphs or nerd Snipes is very effective nerd.swyx: See this is, this is one, one opinion and then I've,Jeff Huber: and I think that the actual graph structure is emergent in the mind of the agent.Ah, in the same way it is in the mind of the human. And that's a more powerful graph ‘cause it actually involved over time.swyx: So don't tell me how to graph. I'll, I'll figure it out myself. Exactly. Okay. All right. AndJeff Huber: what's yours?swyx: I like the, the Wiki approach. Uh, my, I'm actually

rWotD Episode 3227: Mutualism (biology) Welcome to random Wiki of the Day, your journey through Wikipedia's vast and varied content, one random article at a time.The random article for Thursday, 5 March 2026, is Mutualism (biology).Mutualism describes the ecological interaction between two or more species where each species has a net benefit. Mutualism is a common type of ecological interaction. Prominent examples are:the nutrient exchange between vascular plants and mycorrhizal fungi,the fertilization of flowering plants by pollinators,the ways plants use fruits and edible seeds to encourage animal aid in seed dispersal, andthe way corals become photosynthetic with the help of the microorganism zooxanthellae.Mutualism can be contrasted with interspecific competition, in which each species experiences reduced fitness, and exploitation, and with parasitism, in which one species benefits at the expense of the other. However, mutualism may evolve from interactions that began with imbalanced benefits, such as parasitism.The term mutualism was introduced by Pierre-Joseph van Beneden in his 1876 book Animal Parasites and Messmates to mean "mutual aid among species".Mutualism is often conflated with two other types of ecological phenomena: cooperation and symbiosis. Cooperation most commonly refers to increases in fitness through within-species (intraspecific) interactions, although it has been used (especially in the past) to refer to mutualistic interactions, and it is sometimes used to refer to mutualistic interactions that are not obligate. Symbiosis involves two species living in close physical contact over a long period of their existence and may be mutualistic, parasitic, or commensal, so symbiotic relationships are not always mutualistic, and mutualistic interactions are not always symbiotic. Despite a different definition between mutualism and symbiosis, they have been largely used interchangeably in the past, and confusion on their use has persisted.Mutualism plays a key part in ecology and evolution. For example, mutualistic interactions are vital for terrestrial ecosystem function as:about 80% of land plants species rely on mycorrhizal relationships with fungi to provide them with inorganic compounds and trace elements.estimates of tropical rainforest plants with seed dispersal mutualisms with animals range at least from 70% to 93.5%. In addition, mutualism is thought to have driven the evolution of much of the biological diversity we see, such as flower forms (important for pollination mutualisms) and co-evolution between groups of species.A prominent example of pollination mutualism is with bees and flowering plants. Bees use these plants as their food source with pollen and nectar. In turn, they transfer pollen to other nearby flowers, inadvertently allowing for cross-pollination. Cross-pollination has become essential in plant reproduction and fruit/seed production. The bees get their nutrients from the plants, and allow for successful fertilization of plants, demonstrating a mutualistic relationship between two seemingly-unlike species.Mutualism has also been linked to major evolutionary events, such as the evolution of the eukaryotic cell (symbiogenesis) and the colonization of land by plants in association with mycorrhizal fungi.This recording reflects the Wikipedia text as of 00:45 UTC on Thursday, 5 March 2026.For the full current version of the article, see Mutualism (biology) on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm neural Kimberly.

fWotD Episode 3226: Elizabeth Lyon (criminal) Welcome to featured Wiki of the Day, your daily dose of knowledge from Wikipedia's finest articles.The featured article for Thursday, 5 March 2026, is Elizabeth Lyon (criminal).Elizabeth Lyon (fl. c. 1722–1726), nicknamed Edgworth Bess or Edgware Bess, was an English thief, a prostitute, and the partner of the criminal Jack Sheppard. Little is known about her background or her early life, but it is known that she was working as a prostitute at the Black Lyon alehouse in London by 1722 or 1723. Here she met Sheppard—at the time an apprentice carpenter—and the two began a relationship.At Lyon's instigation, Sheppard soon began his career in crime, first stealing from places where he worked before moving into housebreaking; Lyon and his brother became his accomplices. Sheppard was arrested for his crimes on several occasions, invariably breaking out soon after incarceration, normally assisted by Lyon. In May 1724, she was arrested when visiting him in prison, so together the pair broke out of New Prison into the adjoining Clerkenwell Bridewell prison, then out of that to freedom.After Sheppard's execution in November 1724, Lyon entered into relationships with other men who were, or became, involved in housebreaking, sometimes accompanying them to assist in perpetrating the crimes. She was arrested in March 1726 and transported to the Province of Maryland—then a British colony in North America—in October; her name does not appear in official sources after that date. Lyon's notoriety is based on her connection to Sheppard, and in the years following his execution, novels were published and plays performed that retold his and Lyon's story.This recording reflects the Wikipedia text as of 01:24 UTC on Thursday, 5 March 2026.For the full current version of the article, see Elizabeth Lyon (criminal) on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm generative Joanna.

pWotD Episode 3228: James Talarico Welcome to popular Wiki of the Day, spotlighting Wikipedia's most visited pages, giving you a peek into what the world is curious about today.With 415,813 views on Wednesday, 4 March 2026 our article of the day is James Talarico.James Dell Talarico ( TAL-uh-REE-koh; né Causey, born May 17, 1989) is an American politician, Presbyterian seminarian, and former educator who has served as a member of the Texas House of Representatives since 2018. Talarico is the Democratic nominee for U. S. Senate in 2026 and has been called a "rising star" among Texas Democrats.Born in Round Rock, Texas, Talarico graduated from the University of Texas at Austin with a Bachelor of Arts in government. He later joined Teach For America, where he taught sixth-grade English language arts in San Antonio. Afterward, he served as the Central Texas executive director for Reasoning Mind, a nonprofit focused on bringing technology to low-income classrooms. He later graduated from Harvard University with a Master of Education degree in education policy.Talarico serves as vice chair of two bodies in the Texas House: the Trade, Workforce, and Economic Development Committee, and the Subcommittee on Academic and Career-Oriented Education under the Public Education Committee. He also serves on the Public Education Committee and the House Administration Committee.In September 2025, Talarico announced his candidacy for the 2026 U. S. Senate race. He defeated U. S. Representative Jasmine Crockett in the primary and will face John Cornyn or Ken Paxton in the general election.This recording reflects the Wikipedia text as of 19:02 UTC on Thursday, 5 March 2026.For the full current version of the article, see James Talarico on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm standard Matthew.

pWotD Episode 3225: 2026 Iran war Welcome to popular Wiki of the Day, spotlighting Wikipedia's most visited pages, giving you a peek into what the world is curious about today.With 1,060,165 views on Sunday, 1 March 2026 our article of the day is 2026 Iran war.Beginning on 28 February 2026, Israel and the United States engaged in coordinated joint attacks on various sites in Iran. Codenamed Operation Roaring Lion by Israel and Operation Epic Fury by the United States, the attacks have targeted key Iranian officials, military commanders, and facilities. Iran's response has been named Operation True Promise IV.The operation began with joint strikes by Israel and the US in the cities of Tehran, Isfahan, Qom, Karaj, and Kermanshah. The attacks included the assassination of Supreme Leader Ali Khamenei, whose compound was destroyed; Ali Shamkhani, former secretary of Iran's Supreme National Security Council; and several other Iranian officials. In retaliation, Iran launched dozens of its drones and ballistic missiles throughout the Persian Gulf at targets in Israel and US military bases in Jordan, Kuwait, Bahrain, Qatar, Iraq, Saudi Arabia, and the United Arab Emirates. Iran launched strikes on civilian airports and shipping ports in Kuwait, the UAE, and Oman. Britain's Akrotiri and Dhekelia military base in Cyprus was struck. Iran closed the Strait of Hormuz, disrupting global oil and gas shipments. Hezbollah in Lebanon joined the war after the killing of Khamenei.US officials, including President Donald Trump, have offered various and shifting reasons for launching the war on Iran, including to ward off an imminent threat, destroy missile and military capabilities, prevent it from obtaining nuclear weapons, and ultimately to achieve regime change by bringing the Iranian opposition to power. Iran rejected US claims that it was preparing an attack. After the strikes, the International Atomic Energy Agency said that Iran does not have a nuclear weapons program. The United Nations and several uninvolved countries condemned the initial strikes for undermining the stability of the Middle East; others condemned Iran's retaliatory strikes on US allies in the region. Critics of the operation described it as illegal under US law, an act of imperialism, and a violation of Iran's sovereignty under international law.This recording reflects the Wikipedia text as of 16:02 UTC on Thursday, 5 March 2026.For the full current version of the article, see 2026 Iran war on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm generative Stephen.

pWotD Episode 3226: Iran Welcome to popular Wiki of the Day, spotlighting Wikipedia's most visited pages, giving you a peek into what the world is curious about today.With 410,897 views on Monday, 2 March 2026 our article of the day is Iran.Iran, officially the Islamic Republic of Iran, and also known as Persia, is a country in West Asia. It borders Iraq to the west, Turkey, Azerbaijan, and Armenia to the northwest, the Caspian Sea to the north, Turkmenistan to the northeast, Afghanistan to the east, Pakistan to the southeast, and the Gulf of Oman and the Persian Gulf to the south. With a population of over 92 million, Iran ranks 17th globally in both geographic size and population and is the sixth-largest country in Asia. It is divided into five regions with 31 provinces. Tehran is the nation's capital, largest city, and financial center.Home to one of the world's oldest continuous major civilizations, most of Iran was first united as a nation by the Medes under Cyaxares in the 7th century BC and reached its territorial height in the 6th century BC, when Cyrus the Great founded the Achaemenid Empire. Alexander the Great conquered the empire in the 4th century BC. An Iranian rebellion in the 3rd century BC established the Parthian Empire, which later liberated the country. In the 3rd century AD, the Parthians were succeeded by the Sasanian Empire, which oversaw a golden age in the history of Iranian civilization. During this period, ancient Iran saw some of the earliest developments of writing, agriculture, urbanization, religion, and administration. Once a center for Zoroastrianism, Iran underwent Islamization following the 7th century AD Muslim conquest. Innovations in literature, philosophy, mathematics, medicine, astronomy and art were renewed during the Islamic Golden Age and Iranian Intermezzo, a period during which Iranian Muslim dynasties ended Arab rule and revived the Persian language. This era was followed by Seljuk and Khwarazmian rule, Mongol conquests and the Timurid Renaissance from the 11th to 14th centuries.In the 16th century, the native Safavid dynasty re-established a unified Iranian state with Twelver Shia Islam as the official religion, laying the framework for the modern state of Iran. During the Afsharid Empire in the 18th century, Iran was a leading world power, but it lost this status after the Qajars took power in the 1790s. The early 20th century saw the Persian Constitutional Revolution and the establishment of the Pahlavi dynasty by Reza Shah, who ousted the last Qajar Shah in 1925. Following the Anglo-Soviet invasion of Iran in 1941, his son Mohammad Reza Pahlavi rose to power. Attempts by Mohammad Mosaddegh to nationalize the oil industry led to the Anglo-American coup in 1953. The Iranian Revolution in 1979 overthrew the monarchy, and the Islamic Republic of Iran was established by Ruhollah Khomeini, the country's first supreme leader. In 1980, Iraq invaded Iran, sparking the eight-year-long Iran–Iraq War, which ended in a stalemate. Iran has since been involved in proxy wars with Israel and Saudi Arabia; in June 2025, Israeli strikes on Iran escalated tensions into the Twelve-Day War. Following the war and amid a growing economic crisis, potentially the largest protests since 1979 erupted in late December 2025. The United States and Israel launched a major attack on Iran with the stated goal of regime change in late February 2026.Iran's government is an Islamic theocracy governed by elected and unelected institutions, with ultimate authority vested in the supreme leader. While it holds elections, key offices, including the head of state and military, are not subject to public vote. The Iranian government is an authoritarian regime which has been widely criticized internationally due to its poor human rights record, including restrictions on freedom of assembly, expression, and the press, as well as its treatment of women, ethnic minorities, and political dissidents. International observers have raised concerns over the fairness of its electoral processes, especially the vetting of candidates by unelected bodies such as the Guardian Council. Iran maintains a centrally planned economy with significant state ownership in key sectors, though private enterprise exists alongside this. It is a middle power, due to its large reserves of fossil fuels (including the world's second largest natural gas supply and third largest proven oil reserves), its geopolitically significant location, and its role as the world's focal point of Shia Islam. Iran is a threshold state with one of the most scrutinized nuclear programs, which it claims is solely for civilian purposes; however, the IAEA, a United Nations (UN) agency tasked with monitoring the production of nuclear weapons, has on two occasions found Iran to be non-compliant with its safeguards obligations. It is a founding member of the UN and a member state of numerous international organisations. Iran has 29 UNESCO World Heritage Sites (the 10th-highest in the world) and ranks 4th in intangible cultural heritage or human treasures.This recording reflects the Wikipedia text as of 16:11 UTC on Thursday, 5 March 2026.For the full current version of the article, see Iran on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm neural Danielle.

pWotD Episode 3227: March 2026 lunar eclipse Welcome to popular Wiki of the Day, spotlighting Wikipedia's most visited pages, giving you a peek into what the world is curious about today.With 202,721 views on Tuesday, 3 March 2026 our article of the day is March 2026 lunar eclipse.A total lunar eclipse occurred at the Moon’s descending node of orbit on Tuesday, March 3, 2026, with an umbral magnitude of 1.1507. A lunar eclipse occurs when the Moon moves into the Earth's shadow, causing the Moon to be darkened. A total lunar eclipse occurs when the Moon's near side entirely passes into the Earth's umbral shadow. Unlike a solar eclipse, which can only be viewed from a relatively small area of the world, a lunar eclipse may be viewed from anywhere on the night side of Earth. A total lunar eclipse can last up to nearly two hours, while a total solar eclipse lasts only a few minutes at any given place, because the Moon's shadow is smaller. The Moon's apparent diameter was near the average, as it occured 6.7 days after perigee (on February 24, 2026, at 18:15 UTC) and 6.9 days before apogee (on March 10, 2026, at 09:45 UTC).This lunar eclipse was the third of an almost tetrad, with the others being on March 14, 2025 (total); September 8, 2025 (total); and August 28, 2026 (partial).During the eclipse, the Moon occulted NGC 3423 over North America. Deep-sky objects are rarely occulted during a total eclipse from any given spot on Earth. This eclipse fell on the Lantern Festival, the first since February 11, 2017.This recording reflects the Wikipedia text as of 18:08 UTC on Thursday, 5 March 2026.For the full current version of the article, see March 2026 lunar eclipse on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm long-form Patrick.

030426 Second Hour Susan on Wiki Black Vs White Hats and Manipulation; Iran by Kate Dalley

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Join us as we recap and chat about Bob's Burgers Season 11 Episode 11 Romancing the Beef and Season 11 Episode 12 Die Card, or Card TryingDid you know while operating as URGE, the Burger of the Day board in the restaurant says SPLURGE AT URGE (still cheaper than Pesto's Pizzeria), in reference to Pesto charging $20.00 for only an appetizer?Wiki page for the episode:Romancing the BeefDie Card, or Card TryingLinks, articles, and videos mentioned in this episode:Join our Book Club and get access to exclusive content on PatreonFollow us on InstagramFollow us on TiktokFollow us on Bluesky

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fWotD Episode 3225: Montana-class battleship Welcome to featured Wiki of the Day, your daily dose of knowledge from Wikipedia's finest articles.The featured article for Wednesday, 4 March 2026, is Montana-class battleship.The Montana class was a planned set of battleships for the United States Navy. Intended as the successor to the Iowa class, the Montanas were to be slower but larger, better armored, and better armed. Five ships were approved for construction during World War II, but changes in wartime building priorities resulted in their cancellation in favor of continuing production of Essex-class aircraft carriers and Iowa-class battleships.Armament would have been twelve 16-inch (406 mm) Mark 7 guns in four 3-gun turrets, up from the nine Mark 7 guns in three turrets used by the Iowa class. Unlike the three preceding classes of battleships, the Montana class was designed without any restrictions from treaty limitations. With increased anti-aircraft capability and substantially thicker armor in all areas, the Montanas would have been the largest, best-protected, and most heavily armed US battleships ever, and the only ones to rival Japan's Yamato-class battleships in terms of displacement.Preliminary design work for the Montana class began before the US entry into World War II. The first two vessels were approved by Congress in 1939 following the passage of the Naval Act of 1938. The Japanese attack on Pearl Harbor delayed the construction of the Montana class. The success of carrier combat at the Battle of the Coral Sea, and to a greater extent the Battle of Midway, diminished the perceived value of the battleship. Consequently, the US Navy first delayed, and later cancelled, the Montana class in favor of more urgently needed aircraft carriers as well as amphibious and anti-submarine vessels.This recording reflects the Wikipedia text as of 03:05 UTC on Wednesday, 4 March 2026.For the full current version of the article, see Montana-class battleship on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm neural Salli.

rWotD Episode 3226: Cristián de la Fuente Welcome to random Wiki of the Day, your journey through Wikipedia's vast and varied content, one random article at a time.The random article for Wednesday, 4 March 2026, is Cristián de la Fuente.Cristián Andrés de la Fuente Sabarots (Spanish pronunciation: [kɾisˈtjan de la ˈfwente]; born March 10, 1974) is a Chilean actor, presenter, model and producer. He began his career appearing in the Chilean telenovelas before moving to United States for starring in television series Family Law (1999–2001) and Hidden Hills (2002–2003). De la Fuente later made his big screen debut in the 2001 action film Driven and later appeared in films Vampires: Los Muertos (2002) and Basic (2003). In later years he was a regular cast member in In Plain Sight (2008–12) and Devious Maids (2015). De la Fuente also acted in a number of Spanish-language telenovelas.This recording reflects the Wikipedia text as of 00:48 UTC on Wednesday, 4 March 2026.For the full current version of the article, see Cristián de la Fuente on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm standard Aditi.

fWotD Episode 3224: John Tonkin Welcome to featured Wiki of the Day, your daily dose of knowledge from Wikipedia's finest articles.The featured article for Tuesday, 3 March 2026, is John Tonkin.John Trezise Tonkin (2 February 1902 – 20 October 1995) was an Australian politician who was the premier of Western Australia from 3 March 1971 to 8 April 1974. A member of the Labor Party, Tonkin was a minister in the Willcock, Wise and Hawke governments. He was a member of the Western Australian Legislative Assembly from 1933 to 1977, making him the longest-serving member of the Parliament of Western Australia as of 2021.Tonkin was born in the Goldfields town of Boulder, Western Australia, the eldest of three children. His family moved several times before returning to Boulder, where he attended Boulder City Central School and Eastern Goldfields High School. After several successive jobs, he graduated from Claremont Teachers College and became a teacher, mainly working in small schools in rural areas. After several unsuccessful attempts to enter state parliament, Tonkin was elected as the member for North-East Fremantle in the 1933 state election.Tonkin first served as a minister from 1943 to 1947. He held several portfolios during this time, the most important being that of Minister for Education. Labor lost the 1947 state election, which resulted in Tonkin losing his portfolios. He transferred to the electoral district of Melville when North-East Fremantle was abolished at the 1950 state election. After the resignation of Wise as Labor leader in 1951, Hawke became leader and Tonkin became deputy leader. When Labor won the 1953 state election, he reassumed his role as a minister, including as the minister for works and minister for water supplies. In 1955, he became the first deputy premier of Western Australia following an act of Parliament to formally create the position. Labor lost the 1959 state election, causing Tonkin to lose his portfolios again.Following the resignation of Hawke in 1966, Tonkin became the leader of the Labor Party in Western Australia. After its longest period in opposition ever, the Labor Party won the 1971 state election to defeat David Brand and make Tonkin premier. Labor's one-seat majority meant that any by-election had a chance of defeating the Tonkin government. By-elections occurred in 1971 and in 1973, each of which was narrowly won by Labor. The Tonkin government's achievements included reforms in industrial relations and employment, and the passing of the Aboriginal Heritage Act 1972. After three years in government, Labor was defeated in the 1974 state election. Tonkin was succeeded as premier by Charles Court. Tonkin was appointed Companion of the Order of Australia in 1977, and has been honoured with the Tonkin Highway and John Tonkin College being named after him.This recording reflects the Wikipedia text as of 00:43 UTC on Tuesday, 3 March 2026.For the full current version of the article, see John Tonkin on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm generative Joanna.

rWotD Episode 3225: Breckinridge County, Kentucky Welcome to random Wiki of the Day, your journey through Wikipedia's vast and varied content, one random article at a time.The random article for Tuesday, 3 March 2026, is Breckinridge County, Kentucky.Breckinridge County is a county located in the Commonwealth of Kentucky. As of the 2020 census, the population was 20,432. Its county seat is Hardinsburg, Kentucky. The county was named for John Breckinridge (1760–1806), a Kentucky Attorney General, state legislator, United States Senator, and United States Attorney General. It was the 38th Kentucky county in order of formation. Breckinridge County is now a wet county, following a local-option election on January 29, 2013, but it had been a dry county for the previous 105 years.This recording reflects the Wikipedia text as of 00:16 UTC on Tuesday, 3 March 2026.For the full current version of the article, see Breckinridge County, Kentucky on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm long-form Ruth.

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Join us as we recap and chat about Once Upon a Time Episode 5x21 "Last Rites"Did you know all of Hades' backstory is laid out in the pages of the book he had ripped out in earlier episodes?Wiki page for the episode: https://onceuponatime.fandom.com/wiki/Last_RitesJoin our Book Club and get access to exclusive content on PatreonFollow us on InstagramFollow us on TiktokFollow us on Bluesky

rWotD Episode 3224: Daqin Railway (company) Welcome to random Wiki of the Day, your journey through Wikipedia's vast and varied content, one random article at a time.The random article for Monday, 2 March 2026, is Daqin Railway (company).Daqin Railway Co., Ltd. is a Chinese company that operates several railways with a total length of 1000 km, including the Daqin Railway and most assets on railway transportation of CR Taiyuan. The company is based in Datong, Shanxi. It was listed on the Shanghai Stock Exchange in 2006 with IPO capital raising of $1.9 billion US dollars.Daqin Railway is a component of SSE 50 Index. The parent company of Daqin Railway was CR Taiyuan, a state-owned enterprise that the China Railway acted as its only shareholder.This recording reflects the Wikipedia text as of 01:15 UTC on Monday, 2 March 2026.For the full current version of the article, see Daqin Railway (company) on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm generative Kajal.

fWotD Episode 3223: Galaxy Welcome to featured Wiki of the Day, your daily dose of knowledge from Wikipedia's finest articles.The featured article for Monday, 2 March 2026, is Galaxy.A galaxy is a system of stars, stellar remnants, interstellar gas, dust, and dark matter bound together by gravity. The word is derived from the Greek galaxias (γαλαξίας), literally 'milky', a reference to the Milky Way galaxy that contains the Solar System. Galaxies, averaging an estimated 100 million stars, range in size from dwarfs with less than a thousand stars, to the largest galaxies known – supergiants with one hundred trillion stars, each orbiting its galaxy's centre of mass. Most of the mass in a typical galaxy is in the form of dark matter, with only a few percent of that mass visible in the form of stars and nebulae. Supermassive black holes are a common feature at the centres of galaxies.Galaxies are categorised according to their visual morphology as elliptical, spiral, or irregular. The Milky Way is an example of a spiral galaxy. In addition to shape, galaxies may be notable due to special properties, such as interacting with another galaxy, producing stars at an unusual rate, or having an active galactic nucleus. It is estimated that there are between 200 billion (2×1011) and 2 trillion galaxies in the observable universe. Most galaxies are 1,000 to 100,000 parsecs in diameter (approximately 3,000 to 300,000 light years) and are separated by distances in the order of millions of parsecs (or megaparsecs). For comparison, the Milky Way has a diameter of at least 26,800 parsecs (87,400 ly). Its nearest large neighbour, the Andromeda Galaxy, is just over 750,000 parsecs (2.4 million ly) away.Most galaxies are gravitationally organised into groups, clusters and superclusters. The Milky Way is part of the Local Group, which it dominates along with the Andromeda Galaxy. The group is part of the Virgo Supercluster. At the largest scale, these associations are generally arranged into sheets and filaments surrounded by immense voids. While there are good models describing the formation of stars from gravitational condensation of dense clouds of gas, galaxy formation is less well understood. The process operates on the scale of a billion years. Galaxies occasionally collide during their lifetime.This recording reflects the Wikipedia text as of 01:31 UTC on Monday, 2 March 2026.For the full current version of the article, see Galaxy on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm generative Matthew.

rWotD Episode 3223: Peel Sound Welcome to random Wiki of the Day, your journey through Wikipedia's vast and varied content, one random article at a time.The random article for Sunday, 1 March 2026, is Peel Sound.Peel Sound is an Arctic waterway in the Qikiqtaaluk, Nunavut, Canada. It separates Somerset Island on the east from Prince of Wales Island on the west. To the north it opens onto Parry Channel while its southern end merges with Franklin Strait.There are several named islands within the sound, including: Lock, Vivian, Prescott, Pandora, Otrick, Barth, De la Roquette, and Gibson.Sir John Franklin passed through the strait in 1846 during an unseasonably warm summer, since typically Peel Sound is frozen. Its east side was traced by James Clark Ross in 1849. In 1858 Francis Leopold McClintock tried to penetrate it and was blocked by ice.This recording reflects the Wikipedia text as of 00:16 UTC on Sunday, 1 March 2026.For the full current version of the article, see Peel Sound on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm standard Kendra.

fWotD Episode 3222: High and Low (1963 film) Welcome to featured Wiki of the Day, your daily dose of knowledge from Wikipedia's finest articles.The featured article for Sunday, 1 March 2026, is High and Low (1963 film).High and Low (Japanese: 天国と地獄, Hepburn: Tengoku to Jigoku; lit. 'Heaven and Hell') is a 1963 Japanese police procedural film directed by Akira Kurosawa. It was written by Kurosawa, Hideo Oguni, Eijirō Hisaita, and Ryūzō Kikushima as a loose adaptation of the 1959 novel King's Ransom by Evan Hunter. Starring Toshirō Mifune, Tatsuya Nakadai, Kyōko Kagawa and Tatsuya Mihashi, it tells the story of Japanese businessman Kingo Gondō (Mifune) struggling for control of the major shoe company at which he is a board member. He plans a leveraged buyout of the company with his life savings, when a kidnapper mistakenly abducts his chauffeur's son to ransom him for ¥30 million. The film is viewed as influential on police procedural cinema, and has been remade multiple times internationally.The film was produced by Toho, who bought the rights to Hunter's novel in 1961 for $5,000. Working on a production budget of ¥230 million, filming on High and Low began on 2 September 1962, taking place on location at Yokohama and on set at Toho Studios. Only one attempt could be made to film the ransom exchange. The shoot required multiple cameramen, leading to all other film productions being shut down for the day. Filming ended on 30 January 1963. Kurosawa worked with Masaru Satō to score the film in their eighth collaboration together; the film's soundtrack contains a variety of influences, including mambo, classical, and modern popular music. Post-production took just under a month and, after test screenings in mid-February 1963, the film received a wide distribution.High and Low was released in Japan on 1 March 1963 and became the highest-grossing film at the Japanese box office for that year. The film received generally positive reviews both domestically and abroad. In September 1963, the film was entered into the Official Selection for the Venice Film Festival. The limited American release of the film in late November coincided with the assassination of President John F. Kennedy, an event that led to a depression in initial box office takings. High and Low was nominated for Best Foreign Film at the Golden Globe Awards for 1964. Critical opinion of the film has remained high, with analyses of the film focusing on Kurosawa's humanism in tackling the issue of a growing class divide, the growth of an international consumer culture, and the film's use of structure to interrogate morality and social division.This recording reflects the Wikipedia text as of 00:04 UTC on Sunday, 1 March 2026.For the full current version of the article, see High and Low (1963 film) on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm standard Brian.

Episode 306: Jay & Ray get chilly with a few old idioms.full borearcticSubscribe, review, and come find us on BlueSky, Insta, &  FB.Big Science Music is an award-winning original music and sound boutique. Providing scoring, sound design, radio, podcast, and all audio-post production services for the advertising, film, and video industries. Grab a taste of the groove salad at bigsciencemusic.comsome of the W9Y sources include phrase finder uk, word wizard forums, etymology online, the OED, American Dictionary of Idioms, Wiki, newspapers.com, stackexchange, worldwidewords.

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One of the things that always amazed me about the songs that became big hits in the pop era between the 1950s and the 1990s was the sheer variety of musical styles that topped the charts. When doing research for this show I went into a bit of a spiral looking at Billboard charts for the late 1970s, and I got stuck on the summer of 1978, with the amazing wealth of songs that were in the top 20 at the time. Some of the songs, of course have fallen into the mists of time because they don’t get the oldies airplay anymore, and that’s a shame because there’s still some very good stuff there. This was the Top 20 chart for the week ending August 19, 1978 according to The Real American Top 40 Wiki page: Commodores – Three Times A Lady (↔) Frankie Valli – Grease (↔) Donna Summer – Last Dance (↔) Rolling Stones – Miss You (↔) Foreigner – Hot Blooded (↔) A Taste Of Honey – Boogie Oogie Oogie (↔) Pablo Cruise – Love Will Find A Way (↔) Barry Manilow – Copacabana (↔) Walter Egan – Magnet And Steel (↔) Andy Gibb – An Everlasting Love (↔) Olivia Newton-John – Hopelessly Devoted To You (↔) Joe Walsh – Life’s Been Good (↔) Toby Beau – My Angel Baby (↔) Atlanta Rhythm Section – I’m Not Gonna Let It Bother Me Tonight (↔) Evelyn King – Shame (↑4) Exile – Kiss You All Over (↑2) Steve Martin – King Tut (↔) Chris Rea – Fool (If You Think It’s Over) (↑3) Earth Wind & Fire – Got To Get You Into My Life (↑10) Jackson Browne – The Load-Out/Stay (↔) Pretty much all of these songs, I wouldn’t mind listening to again. And that’s not always the case with songs at this level of the charts. (“Having My Baby,” anyone?) We have here a mix of disco, R&B, retro pop, ballads, a live track, a novelty song, a soundtrack title,  about a half-dozen well-established acts and three one-hit wonders (I’m not counting Steve Martin there because it’s a novelty). And they’re all at varying levels of “good”! All of this is prologue to the fact that Stevie Nicks was in the thick of the music industry for a three-year period, either as a writer, a lead or a backup singer. All of which cemented her in the musical firmament. If she’d done nothing else after 1979, she’d still be fondly remembered. But in addition to being all the things above, she was also a muse for several artists, much of which we’re covering in this episode. I bumped into some problems with regard to quantifying songs about her, but I did my best and I’d be curious to know what you may think I’ve overlooked, because there were a few I did research on and got nowhere.

Support the show by joining our Patreon:https://www.patreon.com/OccultistsAnonymousGet your own Occultists Anonymous, Rookery, and Into The West Merch:http://occultanon.threadless.comFollow us on Twitter:https://twitter.com/OccultistsAJoin us on Discord:http://www.yeetointo.spaceGet the Book. Play the Game:https://www.drivethrurpg.com/product/181754/Mage-the-Awakening-2nd-Edition?affiliate_id=723048View the Chronicle's Wiki:https://kanka.io/en-US/campaign/54701Character Art by Brenna Goche: https://twitter.com/CloudBoundCorgiTheodosia Character Design by Elijah Vardo:https://linktr.ee/elijahvardoMusic: LuIzA - Chrono Trigger "...And in Her Self-Loathing and Despair, She Found Wrath" https://ocremix.org/remix/OCR01989

This episode features Virginia Pratt, who brings decades of expertise to the discussion, including five years on the FamilySearch Research Wiki Team and a bachelor's degree in Family History from BYU. Virginia joins Nicole and Diana to discuss the essential, free online genealogical resource: the FamilySearch Research Wiki. Nicole asks Virginia to explain the Wiki's purpose, access methods, and how to navigate the main page, noting that it should be used for finding information on localities and record types, not individuals. Diana then asks Virginia to explain locality pages, where Virginia details what researchers can find at the country, state, and county levels regarding record keeping and jurisdictions. The conversation transitions to topic pages, where Virginia explains how they help genealogists find background information on records for specific areas, including online resources, historical context, and language-specific terms. Finally, Nicole asks Virginia to walk listeners through a practical example of using the Wiki for research in the John J. Hoover case study, demonstrating how to find information about records available in Henry County, Indiana, and Otero County, Colorado. Listeners will learn how to use the FamilySearch Research Wiki to find explanations of record types, develop effective research strategies, and successfully locate records for their ancestors worldwide. This summary was generated by Google Gemini. Links Research Like a Pro Institute Courses including Merging and Separating Identities - https://familylocket.com/product-category/institute-course/ Virginia's four-part series on the Wiki at Family Locket: Using the FamilySearch Research Wiki – Part 1: Getting Startedhttps://familylocket.com/using-the-familysearch-research-wiki-part-1-getting-started/ Using the FamilySearch Research Wiki – Part 2: Understanding Locality Pageshttps://familylocket.com/using-the-familysearch-research-wiki-part-2-understanding-locality-pages/ Using the FamilySearch Research Wiki – Part 3: Understanding Topic Pageshttps://familylocket.com/using-the-familysearch-research-wiki-part-3-understanding-topic-pages/ Understanding the FamilySearch Research Wiki – Part 4: Case Studyhttps://familylocket.com/understanding-the-familysearch-research-wiki-part-4-case-study/ Sponsor – Newspapers.com For listeners of this podcast, Newspapers.com is offering new subscribers 20% off a Publisher Extra subscription so you can start exploring today. Just use the code "FamilyLocket" at checkout.  Research Like a Pro Resources Airtable Universe - Nicole's Airtable Templates - https://www.airtable.com/universe/creator/usrsBSDhwHyLNnP4O/nicole-dyer Airtable Research Logs Quick Reference - by Nicole Dyer - https://familylocket.com/product-tag/airtable/ Research Like a Pro: A Genealogist's Guide book by Diana Elder with Nicole Dyer on Amazon.com - https://amzn.to/2x0ku3d 14-Day Research Like a Pro Challenge Workbook - digital - https://familylocket.com/product/14-day-research-like-a-pro-challenge-workbook-digital-only/ and spiral bound - https://familylocket.com/product/14-day-research-like-a-pro-challenge-workbook-spiral-bound/ Research Like a Pro Webinar Series - monthly case study webinars including documentary evidence and many with DNA evidence - https://familylocket.com/product-category/webinars/ Research Like a Pro eCourse - independent study course -  https://familylocket.com/product/research-like-a-pro-e-course/ RLP Study Group - upcoming group and email notification list - https://familylocket.com/services/research-like-a-pro-study-group/ Research Like a Pro with DNA Resources Research Like a Pro with DNA: A Genealogist's Guide to Finding and Confirming Ancestors with DNA Evidence book by Diana Elder, Nicole Dyer, and Robin Wirthlin - https://amzn.to/3gn0hKx Research Like a Pro with DNA eCourse - independent study course -  https://familylocket.com/product/research-like-a-pro-with-dna-ecourse/ RLP with DNA Study Group - upcoming group and email notification list - https://familylocket.com/services/research-like-a-pro-with-dna-study-group/ Thank you Thanks for listening! We hope that you will share your thoughts about our podcast and help us out by doing the following: Write a review on iTunes or Apple Podcasts. If you leave a review, we will read it on the podcast and answer any questions that you bring up in your review. Thank you! Leave a comment in the comment or question in the comment section below. Share the episode on Twitter, Facebook, or Pinterest. Subscribe on iTunes or your favorite podcast app. Sign up for our newsletter to receive notifications of new episodes - https://familylocket.com/sign-up/ Check out this list of genealogy podcasts from Feedspot: Best Genealogy Podcasts - https://blog.feedspot.com/genealogy_podcasts/

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