Podcasts about Tau

El paleontólogo Joao Zilhão detalla una investigación en la Cueva de los Aviones (Cartagena), la cual revela que hace 115.000 años los neandertales ya gestionaban los recursos marinos de forma estratégica. El estudio demuestra que consumían moluscos principalmente en los meses fríos para evitar la toxicidad de las mareas rojas de verano, un comportamiento que evidencia capacidades cognitivas similares a las del Homo sapiens. Además, se destaca el papel de la cueva como precursora en el descubrimiento de pensamiento simbólico y adornos personales en esta especie.El oncólogo Noel Blaya, del Hospital Morales Meseguer, presenta un caso extraordinario de quimerismo detectado durante un estudio genético de rutina. El paciente, tratado por cáncer de próstata, mostraba el ADN de un familiar que le había donado médula ósea años atrás. Lo excepcional del caso fue que el equipo logró diagnosticar un síndrome genético hereditario en el donante de forma indirecta, planteando retos éticos sobre la transmisión de información genética a personas que no han solicitado el estudio.La investigadora Rut Valdor, de la Universidad de Murcia, explica un prometedor descubrimiento: el fármaco P140, originalmente diseñado para el lupus, tiene el potencial de frenar el crecimiento del glioblastoma, un tumor cerebral muy agresivo. El fármaco actúa de forma dual sobre los pericitos (células que el tumor "parasita" para obtener nutrientes) y reactiva el sistema inmune. Un hallazgo clave es el uso de la proteína TAU como biomarcador para medir la eficacia del tratamiento. El equipo busca actualmente financiación para iniciar la fase clínica 1 en pacientes.

Morgens bin ich ein besserer Mensch. Aber das Gute, das mir morgens klar ist, ist mir mittags schon fraglich geworden und abends oft vergessen. „Eure Liebe ist wie der Tau, der bald vergeht“, sagt der Prophet Hosea. Von der Morgenliebe, die bis zum Abend geht, handelt der BetDenkzettel vom 7. Juni 2026 unter ⁠https://www.betdenkzettel.deFra' Georg Lengerke(Aus technischen Gründen ist die Aufnahme nicht optimal. Ich bitte um Entschuldigung)

NYUMBA Radio: June 2026 w/ MoIsh Show: NYUMBA RADIO Artist: MyDir Guest: MoIsh Air Date: 5 June 2026 Genre: House / Deep Tech / Melodic House / Deep House / Tech House Nyumba Radio is a monthly Afro House and Afro Tech radio show built around deep listening, long-form journeys, and intentional curation. Guided by a monthly theme, each episode opens with a Nyumba-curated hour before transitioning into an uninterrupted 60-minute guest mix. A home for sound with meaning. Tracklist: WeAreiDyll Records 1. Kutullo Nawa - Space & Peace 2. Ethiopian Chyld - In MY Space 3. Nuzu Deep - I Am 1 With The Universe (Rancido Remix) 4. Double Drop, Giluuu, Nwamachita - Summertime (Freddy Da Stupid Remix) 5. Warren Deep, Cilongo, Kusterr - The Unknown 6. DJ Kid, Idd Aziz - Dibala (Chaleee & Sammi Ferrer Remix) 7. Native P., Shredder SA - Horror 8. TechTonic'Tay - Shona 9. TAU, NAAK - Asoze 10. Shredder SA, Sadam Seguya - SOTKM 11. PATT, Nes Mburu - POTEA MoIsh 1. Da Capo-The Animal 2. Major Leaque-Come With Me(Bad Ape Remix) 3. ID - ID 4. Karyendasoul-Digital Analog x Masšh-Ilanga(Remix) 5. NAAK- SHO! 6. ID - ID 7. Chelsea Como, Jacko, Moish - No Ordinary Love 8. Major League, Thabza De Soul, Moish, Thatohatsi - Hlaya 9. Major League - Dinaledi (Remix) 10 Swedish House Mafia And The Weeknd - Moth To A Flame (Moish Remix) Originally broadcast on Data Transmission Radio. Listen live and explore the archive: https://radio.datatransmission.co

This episode explores how reproductive ageing and biological sex influence Alzheimer's disease risk. Rachel Buckley discusses menopause, hormonal changes, genetic factors such as the X chromosome, and emerging evidence showing differences in tau accumulation between women and men. Timestamps: 00:00:55:03 – Menopause and Alzheimer's disease 00:04:51:11 – X chromosome in Alzheimer's disease 00:08:29:01 – Tau accumulation in women 00:10:43:09 – Hormone replacement therapy 00:14:22:24 – The sweet spot with hormone therapy

What if amyloid is only the match, tau is the brush fire, and neuroinflammation is the wildfire that causes the most damage in Alzheimer's disease?In this episode of Happy & Healthy with Amy, Amy explains why researchers are paying closer attention to neuroinflammation, what may be keeping the brain's immune system stuck in the “on” position, and why midlife is such an important window for protecting your brain.You'll learn how sleep, blood sugar, chronic stress, infections, oral health, and social connection may all influence the conditions that make the brain more—or less—flammable.What to Listen For[00:00] Why amyloid may be the match—but neuroinflammation is the wildfire. [02:30] What the Cochrane review found about anti-amyloid drugs. [04:30] Why timing matters in Alzheimer's disease. [07:00] Is neuroinflammation a side effect—or a driver? [09:00] Why inflammation itself is not the villain. [11:00] Meet microglia: the brain's immune cells. [14:00] Why gum disease matters for Alzheimer's risk.[18:00] The shingles vaccine and dementia risk. [22:00] Blood sugar, insulin resistance, stress, and sleep. [29:00] How to make your brain less “flammable.” Neuroinflammation may be one of the most important pieces of the Alzheimer's prevention puzzle because it connects so many things we often treat separately: sleep, stress, blood sugar, oral health, infections, diet, and connection.Listen to the full episode to understand what may be making your brain more “flammable,” then download the free RESTORED Protocol so you can choose one simple, evidence-based next step for protecting your brain.Mentioned in The EpisodeDownload the RESTORED ProtocolDownload The First Steps Guide for supporting a parent after Alzheimer's diagnosisRelated EpisodesAlzheimer's Prevention: What the Cochrane Review MeansAlzheimer's Drugs: Why Amyloid Removal May Not Be EnoughGum Disease, Menopause & Your Alzheimer's RiskSourcesRESOURCES:Book a FREE Discovery Call with AmyDownload After Mom's Alzheimer's Diagnosis: The First 8 Things to Know and learn how to support her with more calm, clarity, and confidence.Download the RESTORED Protocol: Eight Essential Protective Factors to Build an Alzheimer's-Resistant BrainSchedule your Breakthrough Roadmap session with AmyFollow Amy on Instagram @amylangcoaching  and on Facebook @amylangcoachingSubscribe to Amy's YouTube channel @happyandhealthywithamy

Domingos Filho já foi presidente da Assembleia Legislativa do Ceará, vice-governador, conselheiro e presidente do Tribunal de Contas dos Municípios e está sempre presente nas discussões sobre a composição de chapas eleitorais no Ceará.  No papel de presidente estadual do PSD (de Gilberto Kassab), ele emplacou a vice-prefeita de Evandro Leitão (PT) em Fortaleza em 2024 e agora pleiteia espaço para o seu partido na chapa majoritária do governador Elmano de Freitas, nas eleições de 2026.  Com todo esse currículo - que inclui ainda ser marido da prefeita de Tauá, Patrícia Aguiar, e pai do deputado federal Domingos Neto e da vice-prefeita de Fortaleza, Gabriela Aguiar -, Domingos falou às Cunhãs sobre as posições divergentes entre membros de seu partido no plano nacional e no local (nacionalmente, o partido tem candidato próprio à presidência, Ronaldo Caiado, que já andou declarando apoio a Ciro Gomes no Ceará); sobre a lógica que rege a definição de quem ficará com cada cargo majoritário que entrará em disputa entre os aliados governistas; e lembrou de episódios que viveu no passado (alguns, que reverberam até hoje, como o rompimento dos irmãos Ferreira Gomes em 2022).  Foi uma conversa boa demais e com muitos bastidores da política. Bora ouvir?  Aproveitamos para te convidar para participar da Comunidade das Cunhãs no Whatsapp. Pense num espaço bacana de conversa boa, muitas informações exclusivas e atualizações quentinhas, com a participação direta das cunhãs. Para participar, entre no link apoia.se/ascunhascomunidade. Basta se cadastrar e contribuir com R$ 19 por mês. Bora lá, você não sabe o que está perdendo! Para participar da Comunidade das Cunhãs no Whatsapp: apoia.se/ascunhascomunidade; Se quiser mandar um PIX, só pra apoiar o podcast, envie para a chave ascunhaspodcast@gmail.com.Produção: Inês Aparecida, Hébely Rebouças e Kamila FernandesEstúdio de gravação: Pro ProduçõesApoio nas redes sociais: Ponto IndieTrilha sonora: Barruada Gagá (Breculê)

When pirates beam away Voyager's computer core and the Doctor's mobile emitter, **Janeway** ends up chasing them to an alien world where her holographic **Leonardo da Vinci** — now powered by the stolen emitter — believes he's been kidnapped to America. The man who has the computer core? Leonardo's new patron. His name is **Tau**. **Dom Bettinelli**, **Jimmy Akin**, and **Fr. Jason Tyler** go through "Concerning Flight," the 11th episode of Voyager Season 4, and the verdict is unanimous: this is a middle-of-the-road episode saved entirely by **John Rhys-Davies**. They dig into the behind-the-scenes story, in which the episode's writer wanted a Leonardo-centric adventure and was overruled — a decision that reportedly made the writer hate the final product. The plot holes are significant. Voyager's computer core was apparently unencrypted and unpassword-protected. There was no backup. And yet the ship somehow navigates vast distances of space for 10 days while "almost none of the ship's crucial systems work." The panel has thoughts. Beyond the plot holes, the conversation goes wide. There's a close read of the Doctor's characterization here (not good — he's more interested in ship gossip than the emergency). A look at Tuvok's stiff but effective attempt at small talk with Leonardo. The Requiem for Methuselah callback — Janeway's aside that James T. Kirk claimed to have met Leonardo da Vinci. And the parallel to the TNG Moriarty two-parter, where another beloved literary figure escapes the holodeck. The episode also sparks a long digression on the science of human skin pigmentation — why did melanin decrease as humans migrated to higher latitudes, how long did it take, and why this makes Tuvok the least plausible Scandinavian on the ship. One detail worth catching: the villain's name is Tau — the last letter of the Hebrew alphabet, as Jimmy notes — not the philosophical principle. The post Concerning Flight (VOY) appeared first on StarQuest Media.

What if the real obstacle to growing your practice isn't getting more calls, but what happens after the phone rings? In this episode, Dr. Len Tau unpacks the step-by-step system any dentist can use to turn a $500,000 practice into a $1 million powerhouse in just 12 months (without breaking the bank on marketing.) He dispels the biggest myth in dentistry: that growth is out of reach with limited resources, revealing that most offices are held back by overlooked “leaks” and conversion gaps, not a lack of opportunity. Michael and Dr. Tau explore why a marketing budget as low as $1,500/month is enough to drive 20+ new patients when coupled with consistent processes, sharp phone skills, and clear internal accountability.Through stories and practical tactics, Dr. Tau guides us through an initial audit to spot where practices are losing potential patients, from missed calls and ineffective front-desk handling to low case acceptance and patient attrition. He shares actionable tools, like listening to recorded calls, asking the right operational questions, and introducing comfort-driven patient forms to turn new patients into lifelong patients. From scripting insurance conversations and pre-visit rapport-building to optimizing treatment presentations and review management, Dr. Tau details how any practice can harness its existing patient flow for exponential growth, one clear step at a time.What You'll Learn in This Episode:How to audit your practice to plug costly “leaks” before increasing marketing spendThe critical phone skills that make or break conversion ratesTactical steps to build patient trust from the very first interactionWhy active listening and “disarming” patients improves show rates and reviewsThe exact operational data (new patients, attrition, case acceptance, etc.) to track for fast growthKey system changes to raise daily production from detailed patient forms to same-day dentistryHow to frame treatment and payment options to maximize acceptance (and avoid sticker shock)Smart methods to measure marketing effectiveness (no matter your ad budget)The truth about online reviews and how to build reputation resilienceReady to see where your practice might be leaking profitability? Let's find out with Dr. Len Tau!‍Sponsors:‍CallRail: Call tracking + AI that turns calls into campaigns that convert, quality patients, and cost savings. Click our link to start a free trial today! https://sta.mx/vz69Click here for a special offer!Guest: Dr. Len TauCheck out Len's Media:Website: https://www.drlentau.com/Phone: 215-292-2100Supercharge Your Dental Practice Event: https://superchargeyourdentalpractice.com/‍Mentions & Links:‍CallRail Article: 5 healthcare marketing trends shaping patient growth in 2026‍Host: Michael AriasJoin my newsletter: https://thedentalmarketer.lpages.co/newsletter/‍Join this podcast's Facebook Group: The Dental Marketer Society‍Love the Podcast? Follow on Your Favorite App! https://lnkfi.re/TDMPod

En Mediterráneo viajamos desde las cocinas marineras de la Costa Brava hasta las nuevas geografías sonoras del Sónar 2026. Conversamos con Ágata Albero sobre los talleres “Dones i Cuina Marinera” y la recuperación del pescado local como herramienta de convivencia, memoria y sostenibilidad. Observamos el recorrido del Etnomusic València, una mirada a la tradición oral mediterránea, y se abre también a Gaza y a las tensiones contemporáneas del mar compartido. Y hacemos una selección de algunas de las propuestas en la nueva etapa del Sonar 2026. Suena en Mediterráneo: Cocanha — “Que son aüros”Mathieu Saglio — “Ahlam”Gala i Ovidio — “Sí quiero”Jota de Santa Pola — “Grupo desconocido”Magalí Sare — “Mira la boca”Caïm Riba+Teresa Noguerón - La meva cambra a CadaquésSBTRKT — “Wildfire”Mina & Bryte — “Loko”TAYHANA — “Contacto”Carlita — “Bon Trip”Arp Frique & The Perpetual Singers — “Father Father”WhoMadeWho — “Silence & Secrets”Νέγρος Του Μοριά — “GreeKs”Escuchar audio

OPEN HEAVENSMATALA LE LAGI MO LE ASO GAFUA 18 ME 2026(tusia e Pastor EA Adeboye) Manatu Autu: Tuliese le lē faatuatua (Banish unbelief) Tauloto Tusi Paia: Mataio 21:21“A ‘ua tali atu Iesu, ‘ua fa‘apea atu ‘iā te i latou, “E moni, ‘ou te fai atu ‘iā te ‘outou, ‘Āfai ‘ua ‘iā te ‘outou le fa‘atuatua, ma ‘outou lē māsalosalo, ‘ona faia lea e ‘outou e lē na o le mea ‘ua faia i le mati, ‘ae ‘āfai tou te fai atu i le mauga lenei, ‘‘Ia si‘itia oe, ma lafoina i le sami,' ‘ona faia lava lea.”Faitauga - Tusi Paia: Mataio 17:19-20 Fai mai le Eperu 4:12 o le Upu a le Atua, o lo‘o matuā galue fo‘i, o lo‘o sili fo‘i lona ma‘ai i pelu uma e fa‘atau ma‘ai itū. Fai mai foi le Ioane 11:3 na faia mea uma i le Upu a le Atua. E ui e malosi le mana i le Upu a le Atua, peitai a folafola pe faaaogaina e aunoa ma le faatuatua, e le mafai ona maua se taunuuga. I le 1 Ioane 4:4 o loo tusia e sili o ia ‘ua ‘iā te ‘outou, i lē ‘ua i le lalolagi. O loo soifua le Atua i totonu ia te oe, ma e sili ma maualuga atu o ia i lo soo se faafitauli poo se luitau e togi atu i ou ala. O le mea moni lea i lou tulaga, e te maualuga atu i le mativa ma le faaumatia aua o le mana o le Atua o loo afio i totonu ia te oe, ma e mafai ona e faaaogaina e faatoilaloina soo se tulaga faaletonu i lou olaga. E ui i lea, o le lē faatuatua e mafai ona faalavelave i le mana o le Atua mai le tafe atu e ala i lana Upu i lou fofoga. E mafai ona avea oe o se kerisiano ae sauaina pea e le agaga o le masalosalo ma le lē faatuatua. O le tulaga e faia e lea agaga o le tuu i lalo o le Upu a le Atua i lou loto, ma faatupuina lagona e leai sa mana i le Upu a le Atua. E faatupuina foi ou manatu e lē moni le Upu a le Atua, e faatumuina lou loto i le lē mautonu. Afai o sauaina pea oe e le agaga o le masalosalo ma le lē faatuatua, ou te tatalo ia tuliesea ma oe i le taimi nei, i le suafa o Iesu. Na faatūina e Iesu le afafine o Iairo i le Mareko 5:35-43 i lana Upu. Ina ua ia taunuu atu i le fale o Iairo na ia faatonuina i latou o fetagisi i le maliu e ō i fafo ona faatonuina lea o le teine oti e tū i luga. Na faia foi e Peteru le faiga e tasi ina ua faaaogaina e le Atua o ia e faatuina Toreka mai le oti i le Galuega 9:36-42, na ia faatonuina i latou o fetagisi i le maliu e ō i fafo ona ia faatonuina lea o le tinā maliu e tū i luga. Le au pele e, e leai se tapulaa o le tulaga e mafai ona e ausia pe a e tautala i le Upu a le Atua i le faatuatua. E pei ona fetalai Iesu i le faitauga mai le Tusi Paia o le asō, e leai se mea e lē mafaia ia te oe, pe a e tautala i le upu a le Atua i le faatuatua e aunoa ma le masalosalo poo le lē faatuatua. E finagalo le Atua ia e faatuatua i lana Upu ma savali ai. E finagalo e te alu i tulaga ua leai se faamoemoe ina ia mafai ona ia faaali atu i le lalolagi o loo iai pea le faamoemoe e tauala atu ia te oe. E finagalo e na te faaaogaina oe ma le matautia ma susulu atu ai lona malamalama ia te oe ina ia viia ai pea o ia (Mataio 5:16). Peitai e tatau ona aoaoina oe e faatuatua i lana Upu ma le atoatoa, ia folafola lana Upu ma le mana ma le toa i soo se tulaga o feagai ma oe. Tau ina ia avea oe ma auala e faaali atu ai le mana matautia o le Atua i soo se mea e te alu iai, i le suafa o Iesu, Amene. 

En este episodio de Hemispherics hablamos sobre el daño axonal difuso tras un traumatismo craneoencefálico, una de las formas de lesión cerebral más frecuentes y, al mismo tiempo, más difíciles de comprender desde la clínica y la neuroimagen convencional. A lo largo del episodio revisamos cómo las fuerzas de aceleración y rotación pueden producir una lesión de desconexión en las redes cerebrales, profundizando en conceptos como la axotomía secundaria, la neuroinflamación, la vía del SARM1 o la lesión axonal traumática. También abordamos qué sabemos actualmente sobre resonancia magnética, tensor de difusión y biomarcadores como GFAP, UCH-L1 o neurofilamento ligero. Más allá de la biología, el episodio intenta trasladar todo esto a la realidad clínica y terapéutica. Hablamos de las expresiones cognitivas, conductuales y motoras que pueden aparecer en estos pacientes, de las limitaciones actuales del pronóstico y de cómo entender el daño axonal difuso no como una única lesión focal, sino como una alteración dinámica de redes cerebrales. Referencias del episodio: 1. Adams, J. H., Doyle, D., Ford, I., Gennarelli, T. A., Graham, D. I., & McLellan, D. R. (1989). Diffuse axonal injury in head injury: definition, diagnosis and grading. Histopathology, 15(1), 49–59. https://doi.org/10.1111/j.1365-2559.1989.tb03040.x (https://pubmed.ncbi.nlm.nih.gov/2767623/). 2. Bayley, M. T., Janzen, S., Harnett, A., Teasell, R., Patsakos, E., Marshall, S., Bragge, P., Velikonja, D., Kua, A., Douglas, J., Togher, L., Ponsford, J., & McIntyre, A. (2023). INCOG 2.0 Guidelines for Cognitive Rehabilitation Following Traumatic Brain Injury: Methods, Overview, and Principles. The Journal of head trauma rehabilitation, 38(1), 7–23. https://doi.org/10.1097/HTR.0000000000000838 (https://pubmed.ncbi.nlm.nih.gov/36594856/). 3. Castaño-Leon, A. M., Sánchez Carabias, C., Hilario, A., Ramos, A., Navarro-Main, B., Paredes, I., Munarriz, P. M., Panero, I., Eiriz Fernández, C., García-Pérez, D., Moreno-Gomez, L. M., Esteban-Sinovas, O., Garcia Posadas, G., Gomez, P. A., & Lagares, A. (2022). Serum assessment of traumatic axonal injury: the correlation of GFAP, t-Tau, UCH-L1, and NfL levels with diffusion tensor imaging metrics and its prognosis utility. Journal of neurosurgery, 138(2), 454–464. https://doi.org/10.3171/2022.5.JNS22638 (https://pubmed.ncbi.nlm.nih.gov/35901687/). 4. Frati, A., Cerretani, D., Fiaschi, A. I., Frati, P., Gatto, V., La Russa, R., Pesce, A., Pinchi, E., Santurro, A., Fraschetti, F., & Fineschi, V. (2017). Diffuse Axonal Injury and Oxidative Stress: A Comprehensive Review. International journal of molecular sciences, 18(12), 2600. https://doi.org/10.3390/ijms18122600 (https://pubmed.ncbi.nlm.nih.gov/29207487/). 5. Geiger, P., Gmeiner, R., Schön, V., Petr, O., Thomé, C., & Pinggera, D. (2025). Timing of Magnetic Resonance Imaging (MRI) in Moderate and Severe TBI: A Systematic Review. Journal of clinical medicine, 14(12), 4078. https://doi.org/10.3390/jcm14124078 (https://pubmed.ncbi.nlm.nih.gov/40565823/). 6. Henninger, N., Bouley, J., Sikoglu, E. M., An, J., Moore, C. M., King, J. A., Bowser, R., Freeman, M. R., & Brown, R. H., Jr (2016). Attenuated traumatic axonal injury and improved functional outcome after traumatic brain injury in mice lacking Sarm1. Brain : a journal of neurology, 139(Pt 4), 1094–1105. https://doi.org/10.1093/brain/aww001 (https://pubmed.ncbi.nlm.nih.gov/26912636/). 7. Johnson, V. E., Stewart, W., & Smith, D. H. (2013). Axonal pathology in traumatic brain injury. Experimental neurology, 246, 35–43. https://doi.org/10.1016/j.expneurol.2012.01.013 (https://pubmed.ncbi.nlm.nih.gov/22285252/). 8. Lagares, A., de la Cruz, J., Terrisse, H., Mejan, O., Pavlov, V., Vermorel, C., Payen, J. F., & of the BRAINI participants and investigators (2024). An automated blood test for glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) to predict the absence of intracranial lesions on head CT in adult patients with mild traumatic brain injury: BRAINI, a multicentre observational study in Europe. EBioMedicine, 110, 105477. https://doi.org/10.1016/j.ebiom.2024.105477 (https://pmc.ncbi.nlm.nih.gov/articles/PMC11647500/). 9. Mac Donald, C. L., Dikranian, K., Song, S. K., Bayly, P. V., Holtzman, D. M., & Brody, D. L. (2007). Detection of traumatic axonal injury with diffusion tensor imaging in a mouse model of traumatic brain injury. Experimental neurology, 205(1), 116–131. https://doi.org/10.1016/j.expneurol.2007.01.035 (https://pubmed.ncbi.nlm.nih.gov/17368446/). 10. Mac Donald, C. L., Yuh, E. L., Vande Vyvere, T., Edlow, B. L., Li, L. M., Mayer, A. R., Mukherjee, P., Newcombe, V. F. J., Wilde, E. A., Koerte, I. K., Yurgelun-Todd, D., Wu, Y. C., Duhaime, A. C., Awwad, H. O., Dams-O'Connor, K., Doperalski, A., Maas, A. I. R., McCrea, M. A., Umoh, N., & Manley, G. T. (2025). Neuroimaging Characterization of Acute Traumatic Brain Injury with Focus on Frontline Clinicians: Recommendations from the 2024 National Institute of Neurological Disorders and Stroke Traumatic Brain Injury Classification and Nomenclature Initiative Imaging Working Group. Journal of neurotrauma, 42(13-14), 1056–1064. https://doi.org/10.1089/neu.2025.0079 (https://pubmed.ncbi.nlm.nih.gov/40393517/). 11. Muehlschlegel, S., Rajajee, V., Wartenberg, K. E., Alexander, S. A., Busl, K. M., Creutzfeldt, C. J., Fontaine, G. V., Hocker, S. E., Hwang, D. Y., Kim, K. S., Madzar, D., Mahanes, D., Mainali, S., Meixensberger, J., Sakowitz, O. W., Varelas, P. N., Weimar, C., & Westermaier, T. (2024). Guidelines for Neuroprognostication in Critically Ill Adults with Moderate-Severe Traumatic Brain Injury. Neurocritical care, 40(2), 448–476. https://doi.org/10.1007/s12028-023-01902-2 (https://pubmed.ncbi.nlm.nih.gov/38366277/). 12. Ponsford, J. L., Downing, M. G., Olver, J., Ponsford, M., Acher, R., Carty, M., & Spitz, G. (2014). Longitudinal follow-up of patients with traumatic brain injury: outcome at two, five, and ten years post-injury. Journal of neurotrauma, 31(1), 64–77. https://doi.org/10.1089/neu.2013.2997 (https://pubmed.ncbi.nlm.nih.gov/23889321/). 13. Sassani, M., Ghafari, T., Arachchige, P. R. W., Idrees, I., Gao, Y., Waitt, A., Weaver, S. R. C., Mazaheri, A., Lyons, H. S., Grech, O., Thaller, M., Witton, C., Bagshaw, A. P., Wilson, M., Park, H., Brookes, M., Novak, J., Mollan, S. P., Hill, L. J., Lucas, S. J. E., … Fernández-Espejo, D. (2025). Current and prospective roles of magnetic resonance imaging in mild traumatic brain injury. Brain communications, 7(2), fcaf120. https://doi.org/10.1093/braincomms/fcaf120 (https://pubmed.ncbi.nlm.nih.gov/40241788/). 14. Siedler, D. G., Chuah, M. I., Kirkcaldie, M. T., Vickers, J. C., & King, A. E. (2014). Diffuse axonal injury in brain trauma: insights from alterations in neurofilaments. Frontiers in cellular neuroscience, 8, 429. https://doi.org/10.3389/fncel.2014.00429 (https://pubmed.ncbi.nlm.nih.gov/25565963/). 15. Smith, D. H., Hicks, R., & Povlishock, J. T. (2013). Therapy development for diffuse axonal injury. Journal of neurotrauma, 30(5), 307–323. https://doi.org/10.1089/neu.2012.2825 (https://pubmed.ncbi.nlm.nih.gov/23252624/). 16. Wofford, K. L., Loane, D. J., & Cullen, D. K. (2019). Acute drivers of neuroinflammation in traumatic brain injury. Neural regeneration research, 14(9), 1481–1489. https://doi.org/10.4103/1673-5374.255958 (https://pmc.ncbi.nlm.nih.gov/articles/PMC6557091/).

On today's episode of the Squad Games Podcast, we are joined by Shane from Command Point for another Warhammer 40k Kill Team Hot Takes call-in show. We talk through the newest balance dataslate, whether Hierotek Circle is back, if climbing is actually fixed, and how the latest Tac Op changes affect the meta. The community calls in with takes on GW map design, Octarius vs. Volkus, Tau teams not being able to shoot twice, Exaction Squad being underrated, day-one patches, rosters, heavy weapons, and more. We also get into a bigger conversation about the state of the Kill Team community, whether the game is in a better place than people give it credit for, and why negativity around competitive play can sometimes drown out the fun. Join the Squad Games Discord to submit your own hot takes and be part of a future live call-in episode. Scorpion 3+save Shirts! Baby Shower Registry Try Out Squad Ops! Pick Up These Brushes from Highlander Hobbies Join the Squad-Games Discord Watch the Latest Kill Team Battle Report Squad-Games Stuff: Join our Patreon and help us bring you guys more episodes! @squad_games_entertainment Other Socials and Stuff https://www.squad-games.net/ @Squad_Games_Entertainment @Wargaming_Studios @TableTopMayhem

Die minister van Handel, Nywerheid en Mededinging, Parks Tau, sê swart ekonomiese bemagtiging sal steeds die hefboom wees wat die regering gebruik om Suid-Afrikaners te bevry. Dit volg nadat Business Leadership South Africa gewaarsku het die departement se voorgestelde wysigings aan die B-BBEE-regulasies kan jare se nywerheidsontwikkeling ondermyn. Tau sê B-BBEE moet versterk en hersien word, maar sal nie laat vaar word nie. Hy sê ekonomiese bevryding is die maatreël van die land se vryheid:

OPEN HEAVENSMATALA LE LAGI MO LE ASO TOFI 7 ME 2026(tusia e Pastor EA Adeboye) Manatu Autu: Mealilo o le soifua umi 4 (Secrets to longevity 4)Tauloto Tusi Paia: Luka 6:21 “Amu‘ia ‘outou e fia ‘a‘ai nei; auā e mā‘o‘ona ‘outou. ‘Amu‘ia ‘outou e tagi nei; auā e featani ‘outou.”Faitauga - Tusi Paia: Salamo 107:8-9 A'o faasolo atu pea le aoaoga i mealilo o le soifua umi, ou te fia talanoa atu i le taua o le toe faatumu pea ao savali i le ala ua faasino e le Atua i ou luma. O le fiafia ma manuia i le soifua umi aemaise i le malo o le Atua, e tatau ona toe faatumu pea i le tele o taimi. O le mafuaaga lea na ia faia ai e mulimuli mai le po i le ao ina ia mafai ona galue malosi i le ao, ona e moe lea i le po ma toe faafoisia ai lou malosi. E faapena foi pe a uma le ti o le taeao, o se tulaga masani le toe fiaai pe a maea ni nai itula, pe afai o loo faaagaoina pea lou malosi. Na faia oe e le Atua e ai faaitula ina ia toe faafoisia lou malosi. O le mafai ona faaaogaina o se fana, e tatau ona toe utu pulu pe a maea ona fana. E faapena foi le kerisiano faatuatua e tatau lava ona faaolaola pea ona ma'a faaleagaga. Mo se faataitaiga, e le mafai e se kerisiano ona ola i le upu a le Atua na faalogo ma aoao ai i le tausaga talu ai, poo le anapogi ma tatalo na faia i le tolu tausaga talu ai. O lea kerisiano e matua vaivai ma e faigofie ona maileiina e le tiapolo. O lona uiga, e tatau ona faifai pea le fafagaina ina ia malosi lelei i le agaga. Fai mai le Mataio 5:6 “Amu‘ia ē fia ‘a‘ai ma fia inu i le amiotonu; auā e mā‘o‘ona i latou.”Na faia e le Atua le fiaai e faamanatu mai ai ua maualalo le malosi, ma ua tatau ona ai e toe faafoisia le malosi. E pei a ona ia faia le fiaai i le tino o se faailoga e tatau ai ona ‘ai ia maua le malosi, na ia faia foi le fiaai i le agaga o se faailoga e tatau ona faafoisia ai le malosi i le agaga. A e fiaai mo le Upu a le Atua, e te fiafia pe a oo i le taimi o aoga Tusi Paia, fiafia pe a oo i taimi o fono tatalo, o loo e malosi faaleagaga. Peitai, afai e te le faia se mea i le fiaai, e te ono le toe faalogoina pe a maea sina taimi. Afai e le toe fiaai se kerisiano faatuatua i mea faaleagaga, o lona uiga ua matutū o ia ma ua lata ina oti i le agaga. Le au pele e, e leai seisi e umi i le malo o le Atua e aunoa ma le fiaai i taimi uma, ina ia toe faatumu faaleagaga. Afai e te oo i le tulaga ua e lē toe fiaai i le Upu a le Atua, tagi atu i le Atua e toe faafou lou fiaai faaleagaga. Tau ia e fiaai ma fiainu pea i le amiotonu ina ia faamalosia oe e faataunuuina le finagalo ma le fuafuaga a le Atua mo lou olaga, i le suafa o Iesu. Tatalo, Tamā, faamolemole, faafou lo'u fiaai faaleagaga ma fesoasoani mai ia te a'u e saili ia te oe i taimi uma ma lo'u loto atoa, i le suafa o Iesu, Amene. 

Tau muaj ib tsab ntawv cej luam tshiab ntawm lub chaw teeb txheeb John Curtin Research Centre nqua hu kom tsoom fwv teb chaws Australia tsim cov national strategy los tswj cov kev siv cov AI (Artificial Intelligence) technology ntawm tej chaw ua hauj lwm. Vim rau qhov lawv cav tias yog tsis ua tib zoo tswj ces tej technology no kuj yuav muaj peev xwm muaj tau teeb meem loj sib npaug zos tib yam tej txiaj ntsim uas yuav tau thiab.

Alytaus kultūrinėje bendruomenėje vis garsiau keliami klausimai dėl mažėjančio finansavimo ir sprendimų skaidrumo. Vienas ryškiausių pavyzdžių – šių metų projektinė iniciatyva „Tau, Alytau“, kurios finansavimas sumažintas beveik perpus jau po ekspertų komisijos rekomendacijų.Nors miesto biudžetas auga, investicijos į turinio įvairovę ir nevyriausybinį sektorių drastiškai traukiasi, o Alytus pagal kultūros indeksą užima vieną žemiausių vietų Lietuvoje. Pokalbio tikslas yra paskatinti bendruomenę garsiai kelti klausimus dėl sisteminių spragų ir priešintis požiūriui, jog kultūra yra tik paviršutiniški masiniai renginiai, o ne esminis miesto gyvybingumo pamatas. Apie situaciją FM99 studijoje kalbėta su kultūrinio lauko atstove Karole Jakūnaite.

What if you could know 20 YEARS before symptoms appear whether you're heading for Alzheimer's? And what if there was a proven way to reverse early cognitive decline? In this episode, I sit down with my dear friend Dr. Dave Jenkins — the leading Dr. Dale Bredesen Protocol practitioner in the Southern Hemisphere — to unpack the p-Tau 217 blood test revolution and the stunning results from Bredesen's latest randomised controlled trial. Dr. Dave breaks down how a simple finger-prick blood test can now detect the Alzheimer's process with 95% certainty up to TWO DECADES before memory symptoms begin. This isn't diagnosing Alzheimer's — it's diagnosing the process, which means you have 20 years to intervene. We dive into Bredesen's 2024 multi-site RCT showing the ReCODE precision medicine protocol is 6–7x MORE POWERFUL than the best Alzheimer's drug currently available in America (lecanemab) — a drug with devastating side effects including brain bleeds and even death. Dr. Dave shares real clinical insights from his Bali longevity practice including the 30–60 "holes in the roof" driving cognitive decline, cutting-edge peptides (Semax, Selank, Cerebrolysin, Dihexa), bioregulators, and his personal experience with Klotho gene therapy that took his memory scores from the 70th to the 97th percentile in just 6 weeks. This is essential listening for anyone with a family history of Alzheimer's, anyone watching a loved one decline, and anyone who wants to take brain health seriously BEFORE it becomes a crisis. ⏰ CHAPTERS: [to be generated after edit]

Alzheimer's disease unfolds over many years through a complex interplay of amyloid, tau, genetics, lipid biology, and the brain's immune response. John Hardy, Ph.D., explains how rare inherited forms of Alzheimer's disease helped shape current thinking about how the disease begins, then connects those discoveries to broader questions about late onset disease and why it develops differently across people. Hardy shows that amyloid and tau are linked but not identical, and argues that problems with protein buildup and clearance both matter in understanding the disease. He also emphasizes that Alzheimer's is not a single event but a long process, which makes prediction, diagnosis, and treatment especially difficult. While current amyloid-targeting therapies can help and show measurable benefit, Hardy says they do not stop the disease, underscoring the need for earlier diagnosis, better treatments, and wider access to care Series: "Shiley Endowed Lecture" [Health and Medicine] [Show ID: 41250]

Alzheimer's disease unfolds over many years through a complex interplay of amyloid, tau, genetics, lipid biology, and the brain's immune response. John Hardy, Ph.D., explains how rare inherited forms of Alzheimer's disease helped shape current thinking about how the disease begins, then connects those discoveries to broader questions about late onset disease and why it develops differently across people. Hardy shows that amyloid and tau are linked but not identical, and argues that problems with protein buildup and clearance both matter in understanding the disease. He also emphasizes that Alzheimer's is not a single event but a long process, which makes prediction, diagnosis, and treatment especially difficult. While current amyloid-targeting therapies can help and show measurable benefit, Hardy says they do not stop the disease, underscoring the need for earlier diagnosis, better treatments, and wider access to care Series: "Shiley Endowed Lecture" [Health and Medicine] [Show ID: 41250]

Alzheimer's disease unfolds over many years through a complex interplay of amyloid, tau, genetics, lipid biology, and the brain's immune response. John Hardy, Ph.D., explains how rare inherited forms of Alzheimer's disease helped shape current thinking about how the disease begins, then connects those discoveries to broader questions about late onset disease and why it develops differently across people. Hardy shows that amyloid and tau are linked but not identical, and argues that problems with protein buildup and clearance both matter in understanding the disease. He also emphasizes that Alzheimer's is not a single event but a long process, which makes prediction, diagnosis, and treatment especially difficult. While current amyloid-targeting therapies can help and show measurable benefit, Hardy says they do not stop the disease, underscoring the need for earlier diagnosis, better treatments, and wider access to care Series: "Shiley Endowed Lecture" [Health and Medicine] [Show ID: 41250]

Alzheimer's disease unfolds over many years through a complex interplay of amyloid, tau, genetics, lipid biology, and the brain's immune response. John Hardy, Ph.D., explains how rare inherited forms of Alzheimer's disease helped shape current thinking about how the disease begins, then connects those discoveries to broader questions about late onset disease and why it develops differently across people. Hardy shows that amyloid and tau are linked but not identical, and argues that problems with protein buildup and clearance both matter in understanding the disease. He also emphasizes that Alzheimer's is not a single event but a long process, which makes prediction, diagnosis, and treatment especially difficult. While current amyloid-targeting therapies can help and show measurable benefit, Hardy says they do not stop the disease, underscoring the need for earlier diagnosis, better treatments, and wider access to care Series: "Shiley Endowed Lecture" [Health and Medicine] [Show ID: 41250]

Alzheimer's disease unfolds over many years through a complex interplay of amyloid, tau, genetics, lipid biology, and the brain's immune response. John Hardy, Ph.D., explains how rare inherited forms of Alzheimer's disease helped shape current thinking about how the disease begins, then connects those discoveries to broader questions about late onset disease and why it develops differently across people. Hardy shows that amyloid and tau are linked but not identical, and argues that problems with protein buildup and clearance both matter in understanding the disease. He also emphasizes that Alzheimer's is not a single event but a long process, which makes prediction, diagnosis, and treatment especially difficult. While current amyloid-targeting therapies can help and show measurable benefit, Hardy says they do not stop the disease, underscoring the need for earlier diagnosis, better treatments, and wider access to care Series: "Shiley Endowed Lecture" [Health and Medicine] [Show ID: 41250]

Nyumba Radio is a monthly Afro House and Afro Tech radio show built around deep listening, long-form journeys, and intentional curation. Guided by a monthly theme, each episode opens with a Nyumba-curated hour before transitioning into an uninterrupted 60-minute guest mix. A home for sound with meaning. ⚡️Like the Show? Click the [Repost] ↻ button so more people can hear it!

Varėnos rajone, greta Lavyso kaimo užfiksuotas incidentas, kai nuaidi sprogimas, horizonte nušvinta ugnies liepsnos ir pasipila degančios objekto nuolaužos. Kol kas neaišku, koks objektas galėjo nukristi Varėnos rajone.Baltarusija paskelbė, kad keturis mėnesius šalyje įstrigę vilkikai su lietuviškais numeriais gali išvykti. Tiesa, tik tuomet, kai susimokės už stovėjimo aikšteles. Šių įkainiai taip pat sumažinti, tačiau Lietuvos verslininkai abejoja, ar įstengs susimokėti ir tokius, o gal dalis vilkikų taip ir liks Baltarusijoje.Slovėnijoje vyko parlamento, Prancūzijoje ir Vokietijoje - savivaldos rinkimai. Čekijoje tūkstančiai žmonių protestavo prieš Andrejaus Babišo vyriausybę. Rinkimai artėja ir Vengrijoje.Seimo nariai aiškinasi, kodėl Valstybinę miškų tarnyba palieka ilgametę patirtį turintys specialistai. Parlamentarai taip pat kelia klausimą, kiek teisėtos su verslininku Gediminu Žiemeliu siejamos statybos Vilniaus rajone, miške šalia Taučiliškių ežero.Ved. L. Želnienė

Why do so many talented professionals feel stuck even when they have the skills and ambition to succeed? In this episode, Dr. Len Tau sits down with executive coach Thomas Passalacqua to explore how mindset, self-awareness, and leadership clarity can help dental professionals move past fear, doubt, and imposter syndrome. In this episode of the Raving Patients Podcast, Dr. Len Tau welcomes Thomas Passalacqua, certified executive coach and founder of Ascend Professional Pathways. Tom specializes in helping leaders, dentists, and ambitious professionals overcome internal barriers that prevent them from reaching their full potential. Drawing on his experience in education, dental sales, and business development, Tom shares how leadership success often comes down to mastering the human side of decision making. Dr. Tau and Tom discuss why professionals often feel stuck in their careers, how imposter syndrome holds leaders back, and why most decisions should be made quickly rather than overanalyzed. Tom also explains his Ascend Clarity Process, a framework designed to help professionals gain perspective, identify obstacles, and take meaningful action toward their goals. Whether you are a practice owner, associate dentist, or team leader, this conversation offers practical strategies for developing confidence, making better decisions, and building momentum in your career. What You'll Learn Why many successful professionals still feel stuck or uncertain How leadership challenges often come from internal mindset barriers The difference between coaching, consulting, and advising The impact of imposter syndrome in dentistry and leadership How the 98% Decision Rule can reduce overthinking Strategies for gaining clarity and building forward momentum How executive coaching helps professionals unlock their potential Why focusing on what you can control is key to growth Key Takeaways 00:48 Introduction and sponsor acknowledgments 03:00 Meet Thomas Passalacqua and his coaching background 04:40 Leadership coaching and the human side of business 07:00 Why professionals often feel stuck 09:20 Building momentum and shifting perspective 11:34 Coaching vs consulting: What makes executive coaching different 13:30 Who Tom works with and common leadership challenges 14:47 Understanding imposter syndrome in leadership 17:10 Case study: Helping an associate dentist regain confidence 19:25 Coaching business owners through growth and team leadership 22:05 The Ascend Clarity Process explained 24:50 Maintaining momentum after breakthrough progress 27:22 Following your internal compass and career transitions 28:55 Common internal struggles professionals face 29:59 Lightning round Q&A with Thomas   — Connect with Thomas Website: https://ascendpropathways.com Email: thomas@ascendpropathways.com Special Offer: Mention the code RAVING when reaching out to receive 10% off executive coaching and leadership development programs.

Die minister van Handel, Nywerheid en Mededinging, Parks Tau, waarsku 'n drie-maande oorlog in die Midde-Ooste kan veroorsaak dat Suid-Afrika ongeveer 13,8-miljard Suid-Afrikaanse rand in uitvoere verloor. Verlede jaar is daar ongeveer 258-miljard-rand in handel met die Midde-Oosterse streek aangeteken. Die ontwrigting in die wêreldwye olievoorraad, veral deur die strategiese Seestraat van Hormuz, het veroorsaak dat oliepryse die hoogte inskiet. Tau het aan parlementslede gesê 'n gesamentlike poging word nou benodig om deur hierdie storm te kom:

Fr Peter George Flynn talks about the Tau is a letter from the Greek (and also Hebrew) alphabet shaped like a T, resembling a cross. St. Francis used it as his personal signature and spiritual emblem. Its biblical roots come from the Book of Ezekiel, where those marked with a Tau are spared and protected. […] L'articolo The Franciscan Hour – The Tau emblem of the Franciscan Order and a personal “signature” of Saint Francis of Assisi.– Fr Peter George Flynn proviene da Radio Maria.

Loch an Loch und hält doch: Frost und Tau haben den Leipziger Straßen ordentlich zugesetzt – überall Schlaglöcher und Risse im Asphalt. Wie das Tiefbauamt die Schäden jetzt angehen will und warum das Ganze wohl etwas dauert, erfahrt Ihr in der neuen Folge von HELDENSTADT, dem Leipziger Wohnzimmerpodcast der LVZ. Wir, Eure Hosts Daniel Heinze und Guido Corleone, quatschen wieder über alles, was Leipzig gerade bewegt: In Lausen-Grünau hat unsachgemäß entsorgter Hygiene-Kram einen echten Abwasserschaden from hell verursacht (und niemand hat's für Instagram gefilmt!). Außerdem wächst der Widerstand gegen die Reform der Buslinie 89 – und wir erklären, warum auch wir weiter gerne gemütlich im Elektrobus durch die Fußgängerzone der Innenstadt zuckeln würden. Aufnahmestopp im Mieterverein: Weil dort die Arbeit überquillt, können aktuell keine neuen Mitglieder aufgenommen werden. Eigenbedarfskündigungen, steigende Nebenkosten und Mieterhöhungen haben in den letzten Monaten für einen regelrechten Run auf die Angebote des Vereins gesorgt. Be-Beat-and-Rhythm: In den Veranstaltungstipps raten wir Euch zu den Konzerten von Marc Broussard im Werk 2, Mel D in Naumanns Tanzlokal und Mallorca in Ilses Erika - und feiern die frisch sanierte und wiedereröffnete naTo am Südplatz. Tja, und dann haben wir noch das Gipfeltreffen der Heldenstadt-Podcast-Rubriken für Euch: Reddit meets Waschbär! Ihr hört, wie ein Waschbär auf einem Leipziger Dachgeschoss-Balkon zum heißen Thema auf Reddit wurde. Jede Menge Facts und Fun aus Eurer Lieblingsstadt zum Hören - viel Spaß mit dem Frühlingsgefühl unter den Leipzig-Podcasts: „HELDENSTADT. Der LVZ-Podcast aus Leipzig mit Daniel Heinze und Guido Corleone“, Folge vom 9. März 2026! [FOLGT DEM PODCAST, damit neue Episoden automatisch angezeigt werden! Und los, jeder erzählt einer anderen Person von HELDENSTADT!]

In this episode, Professor Louise Serpell is joined by 2026 Rainwater Prize winners Professor Dennis Dickson, Professor Melissa Murray and Dr Marc Busche. They talk about their work and the science that led to them earning this much deserved award, reflecting on decades of research into tau and its role in neurodegenerative disease. The conversation explores how tau functions in the healthy brain, how it becomes harmful in conditions such as Alzheimer's disease and progressive supranuclear palsy, and why certain brain regions are especially vulnerable. The discussion covers different forms of tau, including soluble species that may disrupt how neurons fire before visible tangles appear. Brain banking, imaging and fluid biomarkers are highlighted as key tools for understanding disease differences and improving diagnosis. The importance of rare MAPT mutations and what they can teach us about future treatments is also explored. Alongside the science, there are thoughtful reflections on mentorship, risk taking and the value of asking ambitious questions in dementia research. 10 Key Takeaways

MERCH: https://orchideight.com/collections/poorhammer TWITCH: https://www.twitch.tv/poorhammer PATREON: https://www.patreon.com/SolelySingleton   On this week's episode, Brad and Eric take a look at the Combat Patrols of 40K 10th Edition and try to make them a better product in hopes that someone at Game's Workshop watches this and presents the new improved combo boxes as their idea to their boss. It's Ok. We don't need any credits. Just give us these combat patrols!   SHOW LINKS: Brad's Bsky: https://bsky.app/profile/drruler.bsky.social Eric's Bsky: https://bsky.app/profile/onekuosora.bsky.social   OTHER EPISODES OF THIS SERIES: Tyranids, Orks and Guard: https://www.youtube.com/watch?v=r5AQ1mjhy6E Deathwatch, Tau, Dark Angels, Nurgle, Aeldari, Harlequins: https://www.youtube.com/watch?v=q0wc28ka2aE EC, Necrons, Salamanders, Ad Mech: https://www.youtube.com/watch?v=uuhbo-RzVAE Tsons, Death Guard, Votann, Slaanesh, Agents: https://www.youtube.com/watch?v=96mjO4WSFjY Building Better Battleforce Boxes: https://www.youtube.com/watch?v=oiTLhDJvfFI Building Bolder Battleforce Boxes: https://www.youtube.com/watch?v=EXV-_Rpvim0 Building WORSE Combat Patrols: https://www.youtube.com/watch?v=3ThBplwgIZM   TIMESTAMPS: 00:00 Hello and Welcome 00:33 Further Ado 02:52 The Anatomy of a Combat Patrol 05:15 Adepta Sororitas 08:54 Grey Knights 14:27 Chaos Daemons - Tzeentch 20:18 World Eaters 23:22 We can't make an episode without committing a crime 28:03 I need a good adjective for the title of the next episode on this series 29:50 Alright Audio Audience Hows It Going   Contact Information: You can interact with Solely Singleton by joining the hosts on discord and Twitter to give input to improve the show. Feel free to email more detailed questions and suggestions to the show's email address.   Your Hosts: Brad (DrRuler) & Eric (OnekuoSora) Brad's Bsky: https://bsky.app/profile/drruler.bsky.social Eric's Bsky: https://bsky.app/profile/onekuosora.bsky.social Show Email: thepoorhammerpodcast@gmail.com Merch Website: http://www.poorhammer.com/ Edited by: Menino Berilio Show Mailing Address: PO Box 70893 Rochester Hills, MI 48307   Licensed Music Used By This Program: "Night Out" by LiQWYD CC BY "Thursday & Snow (Reprise)" by Blank & Kytt CC BY "First Class" by Peyruis CC BY "Funky Souls" by Amaria CC BY  

MERCH: https://orchideight.com/collections/poorhammer TWITCH: https://www.twitch.tv/poorhammer PATREON: https://www.patreon.com/SolelySingleton   On this week's episode, Brad and Eric take a look at the Combat Patrols of 40K 10th Edition and try to make them a better product in hopes that someone at Game's Workshop watches this and presents the new improved combo boxes as their idea to their boss. It's Ok. We don't need any credits. Just give us these combat patrols!   SHOW LINKS: Brad's Bsky: https://bsky.app/profile/drruler.bsky.social Eric's Bsky: https://bsky.app/profile/onekuosora.bsky.social   OTHER EPISODES OF THIS SERIES: Tyranids, Orks and Guard: https://www.youtube.com/watch?v=r5AQ1mjhy6E Deathwatch, Tau, Dark Angels, Nurgle, Aeldari, Harlequins: https://www.youtube.com/watch?v=q0wc28ka2aE EC, Necrons, Salamanders, Ad Mech: https://www.youtube.com/watch?v=uuhbo-RzVAE Tsons, Death Guard, Votann, Slaanesh, Agents: https://www.youtube.com/watch?v=96mjO4WSFjY Building Better Battleforce Boxes: https://www.youtube.com/watch?v=oiTLhDJvfFI Building Bolder Battleforce Boxes: https://www.youtube.com/watch?v=EXV-_Rpvim0 Building WORSE Combat Patrols: https://www.youtube.com/watch?v=3ThBplwgIZM   TIMESTAMPS: 00:00 Hello and Welcome 00:33 Further Ado 02:52 The Anatomy of a Combat Patrol 05:15 Adepta Sororitas 08:54 Grey Knights 14:27 Chaos Daemons - Tzeentch 20:18 World Eaters 23:22 We can't make an episode without committing a crime 28:03 I need a good adjective for the title of the next episode on this series 29:50 Alright Audio Audience Hows It Going   Contact Information: You can interact with Solely Singleton by joining the hosts on discord and Twitter to give input to improve the show. Feel free to email more detailed questions and suggestions to the show's email address.   Your Hosts: Brad (DrRuler) & Eric (OnekuoSora) Brad's Bsky: https://bsky.app/profile/drruler.bsky.social Eric's Bsky: https://bsky.app/profile/onekuosora.bsky.social Show Email: thepoorhammerpodcast@gmail.com Merch Website: http://www.poorhammer.com/ Edited by: Menino Berilio Show Mailing Address: PO Box 70893 Rochester Hills, MI 48307   Licensed Music Used By This Program: "Night Out" by LiQWYD CC BY "Thursday & Snow (Reprise)" by Blank & Kytt CC BY "First Class" by Peyruis CC BY "Funky Souls" by Amaria CC BY

Basti war in Mailand beim olympischen Eishockey und berichtet über dubiose Häuser und Schiedsrichter aus Garmisch. Außerdem tauschen wir uns über industriell verarbeitetes Essen, Haustiere und Ärzte aus, und versuchen am Schaubild nachzuvollziehen, ob Winfried in Anke verliebt ist. Anschließend zieht sich „Der Fußballtrainer“ im Allgemeinen und Besonderen wie ein roter Faden durch die Sendung. Wir sprechen über Kwasniok, und geraten in eine wilde Diskussion, was dessen Einstellung für den Effzeh bedeutet, bewerten Rieras Riposte auf Penis-Fragen und decken die Urs-Fisch-ZDF-Verschwörung auf. Basti und David gründen eine eigene Liga, bei der ganz normale Sachen passieren und alle Spiele in Bonn stattfinden. Zum Schluss klären wir euch noch auf, warum wir möglicherweise in den USA in einer Zelle neben Kim Dotcom sitzen und machen weiter mit unserer Bewertung der WM-Spielort-Plakate. Dabei ist ein rätselhafter Regenwurm eventuell ein Tau, Enzo vergibt Punkte für blaues Wasser, und das Boston-Plakat führt zu weit divergent auseinanderdriftenden Meinungsbildern. Viel Spaß!

with Pastor Micheal Oxentenko

In this episode we examine the Kill Team narrative potential of the Tau empire. It's for the greater good.Once Upon a Kill Team - https://www.instagram.com/onceuponakillteam/Jason - https://www.instagram.com/citizendisco/ Seán - https://www.instagram.com/uberstrata_makes/ Josh - https://www.instagram.com/dogtowndistillers/If you want to support this podcast - https://ko-fi.com/onceuponakillteamIf you want to join our discord - https://discord.gg/2bmRUFPXHj

Adam, Andy and Dawfydd sit around the Fluffenfire to discuss their memories of the Tau

Hello you soulful delights, this week we dive into the world of the tuna-beef and have 3 amazing guests on to discuss the journey of Tau through this 10th edition. With Durante Bozzini, Will B and Kyle Grundy giving us their thoughts about the codex and all things greater goodness.Hope you enjoy!

Podcast available on Spotify "Keep It a Buck Daily"LIKE - COMMENT - PLEASE SUBSCRIBETIMESTAMPS:(0:01) - Intro / Thoughts on Paramount + Production for UFC 324(15:07) - UFC Bonuses / Betting Scandals / Fights dropped from UFC 324 Card(30:00) - UFC 324 Recap / Discussion (30:30) - Paddy vs Gaethje (44:00) - O'Malley vs Song (49:45) - Cortes-Acosta vs Lewis(55:35) - Silva vs Namajunas (56:06) - Silva vs Allen (1:01:40) - UFC 324 Prelims (Skim Through)(1:10:20) - Batbayar vs Tau (1:12:46) - Lui vs Sulangrangbo (1:14:00) - Szalay vs Nakamura (1:15:50) - Mar Fan vs Kim (1:17:07) - Ofli vs Yizha (1:20:30) - Micallef vs Elliott (1:24:10) - Malkoun vs Finney (1:28:10) - Rowston vs Brundage (1:30:45) - Tafa vs Elekana UFC 325 MAIN CARD(1:33:05) Salkilld vs Mullarkey (1:34:40) Tuivasa vs Teixeira(1:39:50) Fiziev vs Ruffy (1:46:42) CO MAIN: Hooker vs BSD(1:53:20) MAIN: Volkanovski vs Lopes I post all my final picks on my social media accounts down below. FOLLOW AND SUB THE Social Media accountsTWITTER / X Account: @KIABmediaInstagram: @keepitabuck_mediaTik Tok: @ kiabmedia_

MMA Lock of the Night is back to give you breakdowns and predictions for UFC 325: Volkanovski vs Lopes 2. Also on the card, Hooker vs Saint Denis, Fiziev vs Ruffy, Tuivasa vs Teixeira, and Salkilld vs Mullarkey.

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Tau muaj ib co kev teeb txheeb tshiab qhia tias tej neeg zejzog coj ntseeg ntau yam kab lis kev cai yog cov raug tub ceev xwm cia li tshawb lawv ntau txog li 3.5 npaug piv rau tej zejzog neeg dawb. Thiab pom tias tub ceev xwm Victoria tej fwj chim tshawg neeg no tau ua rau tej neeg coj ntseeg ntau yam kab lis kev cai raug tshawb yam tsis ncaj ncees thiab cuam tshuam rau lawv tej privacy thiab lawv lub neej.

Tau win some of the biggest events of the weekend, we see the first results of 3W Victrix Guard and C'tan on the metagame, and your usual dose of Warhammer Adjacent chatter.   ➡ Support the work we do:   / statcheck   ➡ Check out the Meta Data Dashboard: https://www.stat-check.com/the-meta ➡ Stat Check coaching: https://www.stat-check.com/coaching ➡ Stat Check Merch: https://bit.ly/statcheckmerch ➡ Check out our sponsor the Red Dragon (Stat Check Patrons get 15% off the entire store) at https://red-dragon.ca/ ➡ Check out our sponsor Saltire Games: https://www.saltiregames.co.uk/ ➡ Shop amazing WTC terrain at Weyland-Yutani and save 5% with the code "STATCHECK5": https://www.weyland-yutani-inc.com/ ➡ Looking for GW-style US Open terrain? Check out J15 Games (10% off with code STATCHECK) at https://www.etsy.com/shop/j15games   #warhammer40k #warhammer #wh40k #competitivewarhammer #statcheck

Alzheimer's Disease is a neurodegenerative disease characterized by the buildup of Amyloid Beta plaques and Tau proteins. The initial symptoms often manifest as a loss of cognitive function, especially with learning and memory. Currently, there are numerous pharmaceutical ways to treat the symptoms of Alzheimer's disease, including drugs to manage the severity of symptoms and clearing plaque. However, a recent paper from the Proceedings of the National Academy of Sciences of the United States of America (PNAS) shows that a new non-pharmaceutical treatment may be a valuable prospect in future Alzheimer's research.This paper details the use of an auditory stimulation of 40Hz on Rhesus macaques possibly clearing Amyloid Beta plaques from the brains of elderly macaques with Alzheimer's pathology. Today, Dr. Jonathan Karp and student producer Kaya Basatemur discuss this paper and what it could mean for future Alzheimer's research and theoretical treatments.

In this powerful episode of Daily Influence, Gregg-Brooke Koleno sits down with Dr. Zuri Tau—Founder and CEO of Social Insights, a pioneering organization helping foundations, nonprofits, and government agencies turn data into deeper understanding and measurable, community-centered change. With over two decades at the intersection of research, justice, and healing, Dr. Tau invites us to rethink how we define “success,” why metrics alone are never enough, and how true accountability begins with empathy. She shares her journey from early roots in service and community organizing to reshaping how organizations listen, learn, and respond to the people they aim to serve. Together, Gregg and Zuri explore: ✨ How awareness drives responsible influence inside organizations ✨ Why stories, lived experience, and somatic wisdom matter as much as data ✨ The challenge of aligning good intentions with real-world outcomes ✨ How to stay grounded in leadership while facing resistance to change ✨ What it means to “come close” when work—and the world—feels overwhelming ✨ The importance of trying without perfection, taking risks, and stepping toward what brings meaning Dr. Tau's perspective is a timely reminder that influence isn't just about sharing ideas—it's about shaping outcomes that honor people, community, and collective possibility. Her insights call us to reflect not just on what we're doing, but why and how we're doing it. If you're ready to lead with more clarity, courage, and compassion, this is an episode you won't want to miss. Learn More At: https://www.socinsights.com/ https://www.instagram.com/p/CVQrD1VLZk2/ https://

don't miss George's AIE talk: https://www.youtube.com/watch?v=sRpqPgKeXNk —- From launching a side project in a Sydney basement to becoming the independent gold standard for AI benchmarking—trusted by developers, enterprises, and every major lab to navigate the exploding landscape of models, providers, and capabilities—George Cameron and Micah Hill-Smith have spent two years building Artificial Analysis into the platform that answers the questions no one else will: Which model is actually best for your use case? What are the real speed-cost trade-offs? And how open is "open" really? We discuss: The origin story: built as a side project in 2023 while Micah was building a legal AI assistant, launched publicly in January 2024, and went viral after Swyx's retweet Why they run evals themselves: labs prompt models differently, cherry-pick chain-of-thought examples (Google Gemini 1.0 Ultra used 32-shot prompts to beat GPT-4 on MMLU), and self-report inflated numbers The mystery shopper policy: they register accounts not on their own domain and run intelligence + performance benchmarks incognito to prevent labs from serving different models on private endpoints How they make money: enterprise benchmarking insights subscription (standardized reports on model deployment, serverless vs. managed vs. leasing chips) and private custom benchmarking for AI companies (no one pays to be on the public leaderboard) The Intelligence Index (V3): synthesizes 10 eval datasets (MMLU, GPQA, agentic benchmarks, long-context reasoning) into a single score, with 95% confidence intervals via repeated runs Omissions Index (hallucination rate): scores models from -100 to +100 (penalizing incorrect answers, rewarding "I don't know"), and Claude models lead with the lowest hallucination rates despite not always being the smartest GDP Val AA: their version of OpenAI's GDP-bench (44 white-collar tasks with spreadsheets, PDFs, PowerPoints), run through their Stirrup agent harness (up to 100 turns, code execution, web search, file system), graded by Gemini 3 Pro as an LLM judge (tested extensively, no self-preference bias) The Openness Index: scores models 0-18 on transparency of pre-training data, post-training data, methodology, training code, and licensing (AI2 OLMo 2 leads, followed by Nous Hermes and NVIDIA Nemotron) The smiling curve of AI costs: GPT-4-level intelligence is 100-1000x cheaper than at launch (thanks to smaller models like Amazon Nova), but frontier reasoning models in agentic workflows cost more than ever (sparsity, long context, multi-turn agents) Why sparsity might go way lower than 5%: GPT-4.5 is ~5% active, Gemini models might be ~3%, and Omissions Index accuracy correlates with total parameters (not active), suggesting massive sparse models are the future Token efficiency vs. turn efficiency: GPT-5 costs more per token but solves Tau-bench in fewer turns (cheaper overall), and models are getting better at using more tokens only when needed (5.1 Codex has tighter token distributions) V4 of the Intelligence Index coming soon: adding GDP Val AA, Critical Point, hallucination rate, and dropping some saturated benchmarks (human-eval-style coding is now trivial for small models) — Artificial Analysis Website: https://artificialanalysis.ai (https://artificialanalysis.ai ("https://artificialanalysis.ai")) George Cameron on X: https://x.com/grmcameron (https://x.com/grmcameron ("https://x.com/grmcameron")) Micah Hill-Smith on X: https://x.com/_micah_h (https://x.com/_micah_h ("https://x.com/_micah_h")) Chapters 00:00:00 Introduction: Full Circle Moment and Artificial Analysis Origins 00:01:08 Business Model: Independence and Revenue Streams 00:04:00 The Origin Story: From Legal AI to Benchmarking 00:07:00 Early Challenges: Cost, Methodology, and Independence 00:16:13 AI Grant and Moving to San Francisco 00:18:58 Evolution of the Intelligence Index: V1 to V3 00:27:55 New Benchmarks: Hallucination Rate and Omissions Index 00:33:19 Critical Point and Frontier Physics Problems 00:35:56 GDPVAL AA: Agentic Evaluation and Stirrup Harness 00:51:47 The Openness Index: Measuring Model Transparency 00:57:57 The Smiling Curve: Cost of Intelligence Paradox 01:04:00 Hardware Efficiency and Sparsity Trends 01:07:43 Reasoning vs Non-Reasoning: Token Efficiency Matters 01:10:47 Multimodal Benchmarking and Community Requests 01:14:50 Looking Ahead: V4 Intelligence Index and Beyond

Happy New Year! You may have noticed that in 2025 we had moved toward YouTube as our primary podcasting platform. As we'll explain in the next State of Latent Space post, we'll be doubling down on Substack again and improving the experience for the over 100,000 of you who look out for our emails and website updates!We first mentioned Artificial Analysis in 2024, when it was still a side project in a Sydney basement. They then were one of the few Nat Friedman and Daniel Gross' AIGrant companies to raise a full seed round from them and have now become the independent gold standard for AI benchmarking—trusted by developers, enterprises, and every major lab to navigate the exploding landscape of models, providers, and capabilities.We have chatted with both Clementine Fourrier of HuggingFace's OpenLLM Leaderboard and (the freshly valued at $1.7B) Anastasios Angelopoulos of LMArena on their approaches to LLM evals and trendspotting, but Artificial Analysis have staked out an enduring and important place in the toolkit of the modern AI Engineer by doing the best job of independently running the most comprehensive set of evals across the widest range of open and closed models, and charting their progress for broad industry analyst use.George Cameron and Micah-Hill Smith have spent two years building Artificial Analysis into the platform that answers the questions no one else will: Which model is actually best for your use case? What are the real speed-cost trade-offs? And how open is “open” really?We discuss:* The origin story: built as a side project in 2023 while Micah was building a legal AI assistant, launched publicly in January 2024, and went viral after Swyx's retweet* Why they run evals themselves: labs prompt models differently, cherry-pick chain-of-thought examples (Google Gemini 1.0 Ultra used 32-shot prompts to beat GPT-4 on MMLU), and self-report inflated numbers* The mystery shopper policy: they register accounts not on their own domain and run intelligence + performance benchmarks incognito to prevent labs from serving different models on private endpoints* How they make money: enterprise benchmarking insights subscription (standardized reports on model deployment, serverless vs. managed vs. leasing chips) and private custom benchmarking for AI companies (no one pays to be on the public leaderboard)* The Intelligence Index (V3): synthesizes 10 eval datasets (MMLU, GPQA, agentic benchmarks, long-context reasoning) into a single score, with 95% confidence intervals via repeated runs* Omissions Index (hallucination rate): scores models from -100 to +100 (penalizing incorrect answers, rewarding ”I don't know”), and Claude models lead with the lowest hallucination rates despite not always being the smartest* GDP Val AA: their version of OpenAI's GDP-bench (44 white-collar tasks with spreadsheets, PDFs, PowerPoints), run through their Stirrup agent harness (up to 100 turns, code execution, web search, file system), graded by Gemini 3 Pro as an LLM judge (tested extensively, no self-preference bias)* The Openness Index: scores models 0-18 on transparency of pre-training data, post-training data, methodology, training code, and licensing (AI2 OLMo 2 leads, followed by Nous Hermes and NVIDIA Nemotron)* The smiling curve of AI costs: GPT-4-level intelligence is 100-1000x cheaper than at launch (thanks to smaller models like Amazon Nova), but frontier reasoning models in agentic workflows cost more than ever (sparsity, long context, multi-turn agents)* Why sparsity might go way lower than 5%: GPT-4.5 is ~5% active, Gemini models might be ~3%, and Omissions Index accuracy correlates with total parameters (not active), suggesting massive sparse models are the future* Token efficiency vs. turn efficiency: GPT-5 costs more per token but solves Tau-bench in fewer turns (cheaper overall), and models are getting better at using more tokens only when needed (5.1 Codex has tighter token distributions)* V4 of the Intelligence Index coming soon: adding GDP Val AA, Critical Point, hallucination rate, and dropping some saturated benchmarks (human-eval-style coding is now trivial for small models)Links to Artificial Analysis* Website: https://artificialanalysis.ai* George Cameron on X: https://x.com/georgecameron* Micah-Hill Smith on X: https://x.com/micahhsmithFull Episode on YouTubeTimestamps* 00:00 Introduction: Full Circle Moment and Artificial Analysis Origins* 01:19 Business Model: Independence and Revenue Streams* 04:33 Origin Story: From Legal AI to Benchmarking Need* 16:22 AI Grant and Moving to San Francisco* 19:21 Intelligence Index Evolution: From V1 to V3* 11:47 Benchmarking Challenges: Variance, Contamination, and Methodology* 13:52 Mystery Shopper Policy and Maintaining Independence* 28:01 New Benchmarks: Omissions Index for Hallucination Detection* 33:36 Critical Point: Hard Physics Problems and Research-Level Reasoning* 23:01 GDP Val AA: Agentic Benchmark for Real Work Tasks* 50:19 Stirrup Agent Harness: Open Source Agentic Framework* 52:43 Openness Index: Measuring Model Transparency Beyond Licenses* 58:25 The Smiling Curve: Cost Falling While Spend Rising* 1:02:32 Hardware Efficiency: Blackwell Gains and Sparsity Limits* 1:06:23 Reasoning Models and Token Efficiency: The Spectrum Emerges* 1:11:00 Multimodal Benchmarking: Image, Video, and Speech Arenas* 1:15:05 Looking Ahead: Intelligence Index V4 and Future Directions* 1:16:50 Closing: The Insatiable Demand for IntelligenceTranscriptMicah [00:00:06]: This is kind of a full circle moment for us in a way, because the first time artificial analysis got mentioned on a podcast was you and Alessio on Latent Space. Amazing.swyx [00:00:17]: Which was January 2024. I don't even remember doing that, but yeah, it was very influential to me. Yeah, I'm looking at AI News for Jan 17, or Jan 16, 2024. I said, this gem of a models and host comparison site was just launched. And then I put in a few screenshots, and I said, it's an independent third party. It clearly outlines the quality versus throughput trade-off, and it breaks out by model and hosting provider. I did give you s**t for missing fireworks, and how do you have a model benchmarking thing without fireworks? But you had together, you had perplexity, and I think we just started chatting there. Welcome, George and Micah, to Latent Space. I've been following your progress. Congrats on... It's been an amazing year. You guys have really come together to be the presumptive new gardener of AI, right? Which is something that...George [00:01:09]: Yeah, but you can't pay us for better results.swyx [00:01:12]: Yes, exactly.George [00:01:13]: Very important.Micah [00:01:14]: Start off with a spicy take.swyx [00:01:18]: Okay, how do I pay you?Micah [00:01:20]: Let's get right into that.swyx [00:01:21]: How do you make money?Micah [00:01:24]: Well, very happy to talk about that. So it's been a big journey the last couple of years. Artificial analysis is going to be two years old in January 2026. Which is pretty soon now. We first run the website for free, obviously, and give away a ton of data to help developers and companies navigate AI and make decisions about models, providers, technologies across the AI stack for building stuff. We're very committed to doing that and tend to keep doing that. We have, along the way, built a business that is working out pretty sustainably. We've got just over 20 people now and two main customer groups. So we want to be... We want to be who enterprise look to for data and insights on AI, so we want to help them with their decisions about models and technologies for building stuff. And then on the other side, we do private benchmarking for companies throughout the AI stack who build AI stuff. So no one pays to be on the website. We've been very clear about that from the very start because there's no use doing what we do unless it's independent AI benchmarking. Yeah. But turns out a bunch of our stuff can be pretty useful to companies building AI stuff.swyx [00:02:38]: And is it like, I am a Fortune 500, I need advisors on objective analysis, and I call you guys and you pull up a custom report for me, you come into my office and give me a workshop? What kind of engagement is that?George [00:02:53]: So we have a benchmarking and insight subscription, which looks like standardized reports that cover key topics or key challenges enterprises face when looking to understand AI and choose between all the technologies. And so, for instance, one of the report is a model deployment report, how to think about choosing between serverless inference, managed deployment solutions, or leasing chips. And running inference yourself is an example kind of decision that big enterprises face, and it's hard to reason through, like this AI stuff is really new to everybody. And so we try and help with our reports and insight subscription. Companies navigate that. We also do custom private benchmarking. And so that's very different from the public benchmarking that we publicize, and there's no commercial model around that. For private benchmarking, we'll at times create benchmarks, run benchmarks to specs that enterprises want. And we'll also do that sometimes for AI companies who have built things, and we help them understand what they've built with private benchmarking. Yeah. So that's a piece mainly that we've developed through trying to support everybody publicly with our public benchmarks. Yeah.swyx [00:04:09]: Let's talk about TechStack behind that. But okay, I'm going to rewind all the way to when you guys started this project. You were all the way in Sydney? Yeah. Well, Sydney, Australia for me.Micah [00:04:19]: George was an SF, but he's Australian, but he moved here already. Yeah.swyx [00:04:22]: And I remember I had the Zoom call with you. What was the impetus for starting artificial analysis in the first place? You know, you started with public benchmarks. And so let's start there. We'll go to the private benchmark. Yeah.George [00:04:33]: Why don't we even go back a little bit to like why we, you know, thought that it was needed? Yeah.Micah [00:04:40]: The story kind of begins like in 2022, 2023, like both George and I have been into AI stuff for quite a while. In 2023 specifically, I was trying to build a legal AI research assistant. So it actually worked pretty well for its era, I would say. Yeah. Yeah. So I was finding that the more you go into building something using LLMs, the more each bit of what you're doing ends up being a benchmarking problem. So had like this multistage algorithm thing, trying to figure out what the minimum viable model for each bit was, trying to optimize every bit of it as you build that out, right? Like you're trying to think about accuracy, a bunch of other metrics and performance and cost. And mostly just no one was doing anything to independently evaluate all the models. And certainly not to look at the trade-offs for speed and cost. So we basically set out just to build a thing that developers could look at to see the trade-offs between all of those things measured independently across all the models and providers. Honestly, it was probably meant to be a side project when we first started doing it.swyx [00:05:49]: Like we didn't like get together and say like, Hey, like we're going to stop working on all this stuff. I'm like, this is going to be our main thing. When I first called you, I think you hadn't decided on starting a company yet.Micah [00:05:58]: That's actually true. I don't even think we'd pause like, like George had an acquittance job. I didn't quit working on my legal AI thing. Like it was genuinely a side project.George [00:06:05]: We built it because we needed it as people building in the space and thought, Oh, other people might find it useful too. So we'll buy domain and link it to the Vercel deployment that we had and tweet about it. And, but very quickly it started getting attention. Thank you, Swyx for, I think doing an initial retweet and spotlighting it there. This project that we released. And then very quickly though, it was useful to others, but very quickly it became more useful as the number of models released accelerated. We had Mixtrel 8x7B and it was a key. That's a fun one. Yeah. Like a open source model that really changed the landscape and opened up people's eyes to other serverless inference providers and thinking about speed, thinking about cost. And so that was a key. And so it became more useful quite quickly. Yeah.swyx [00:07:02]: What I love talking to people like you who sit across the ecosystem is, well, I have theories about what people want, but you have data and that's obviously more relevant. But I want to stay on the origin story a little bit more. When you started out, I would say, I think the status quo at the time was every paper would come out and they would report their numbers versus competitor numbers. And that's basically it. And I remember I did the legwork. I think everyone has some knowledge. I think there's some version of Excel sheet or a Google sheet where you just like copy and paste the numbers from every paper and just post it up there. And then sometimes they don't line up because they're independently run. And so your numbers are going to look better than... Your reproductions of other people's numbers are going to look worse because you don't hold their models correctly or whatever the excuse is. I think then Stanford Helm, Percy Liang's project would also have some of these numbers. And I don't know if there's any other source that you can cite. The way that if I were to start artificial analysis at the same time you guys started, I would have used the Luther AI's eval framework harness. Yup.Micah [00:08:06]: Yup. That was some cool stuff. At the end of the day, running these evals, it's like if it's a simple Q&A eval, all you're doing is asking a list of questions and checking if the answers are right, which shouldn't be that crazy. But it turns out there are an enormous number of things that you've got control for. And I mean, back when we started the website. Yeah. Yeah. Like one of the reasons why we realized that we had to run the evals ourselves and couldn't just take rules from the labs was just that they would all prompt the models differently. And when you're competing over a few points, then you can pretty easily get- You can put the answer into the model. Yeah. That in the extreme. And like you get crazy cases like back when I'm Googled a Gemini 1.0 Ultra and needed a number that would say it was better than GPT-4 and like constructed, I think never published like chain of thought examples. 32 of them in every topic in MLU to run it, to get the score, like there are so many things that you- They never shipped Ultra, right? That's the one that never made it up. Not widely. Yeah. Yeah. Yeah. I mean, I'm sure it existed, but yeah. So we were pretty sure that we needed to run them ourselves and just run them in the same way across all the models. Yeah. And we were, we also did certain from the start that you couldn't look at those in isolation. You needed to look at them alongside the cost and performance stuff. Yeah.swyx [00:09:24]: Okay. A couple of technical questions. I mean, so obviously I also thought about this and I didn't do it because of cost. Yep. Did you not worry about costs? Were you funded already? Clearly not, but you know. No. Well, we definitely weren't at the start.Micah [00:09:36]: So like, I mean, we're paying for it personally at the start. There's a lot of money. Well, the numbers weren't nearly as bad a couple of years ago. So we certainly incurred some costs, but we were probably in the order of like hundreds of dollars of spend across all the benchmarking that we were doing. Yeah. So nothing. Yeah. It was like kind of fine. Yeah. Yeah. These days that's gone up an enormous amount for a bunch of reasons that we can talk about. But yeah, it wasn't that bad because you can also remember that like the number of models we were dealing with was hardly any and the complexity of the stuff that we wanted to do to evaluate them was a lot less. Like we were just asking some Q&A type questions and then one specific thing was for a lot of evals initially, we were just like sampling an answer. You know, like, what's the answer for this? Like, we didn't want to go into the answer directly without letting the models think. We weren't even doing chain of thought stuff initially. And that was the most useful way to get some results initially. Yeah.swyx [00:10:33]: And so for people who haven't done this work, literally parsing the responses is a whole thing, right? Like because sometimes the models, the models can answer any way they feel fit and sometimes they actually do have the right answer, but they just returned the wrong format and they will get a zero for that unless you work it into your parser. And that involves more work. And so, I mean, but there's an open question whether you should give it points for not following your instructions on the format.Micah [00:11:00]: It depends what you're looking at, right? Because you can, if you're trying to see whether or not it can solve a particular type of reasoning problem, and you don't want to test it on its ability to do answer formatting at the same time, then you might want to use an LLM as answer extractor approach to make sure that you get the answer out no matter how unanswered. But these days, it's mostly less of a problem. Like, if you instruct a model and give it examples of what the answers should look like, it can get the answers in your format, and then you can do, like, a simple regex.swyx [00:11:28]: Yeah, yeah. And then there's other questions around, I guess, sometimes if you have a multiple choice question, sometimes there's a bias towards the first answer, so you have to randomize the responses. All these nuances, like, once you dig into benchmarks, you're like, I don't know how anyone believes the numbers on all these things. It's so dark magic.Micah [00:11:47]: You've also got, like… You've got, like, the different degrees of variance in different benchmarks, right? Yeah. So, if you run four-question multi-choice on a modern reasoning model at the temperatures suggested by the labs for their own models, the variance that you can see on a four-question multi-choice eval is pretty enormous if you only do a single run of it and it has a small number of questions, especially. So, like, one of the things that we do is run an enormous number of all of our evals when we're developing new ones and doing upgrades to our intelligence index to bring in new things. Yeah. So, that we can dial in the right number of repeats so that we can get to the 95% confidence intervals that we're comfortable with so that when we pull that together, we can be confident in intelligence index to at least as tight as, like, a plus or minus one at a 95% confidence. Yeah.swyx [00:12:32]: And, again, that just adds a straight multiple to the cost. Oh, yeah. Yeah, yeah.George [00:12:37]: So, that's one of many reasons that cost has gone up a lot more than linearly over the last couple of years. We report a cost to run the artificial analysis. We report a cost to run the artificial analysis intelligence index on our website, and currently that's assuming one repeat in terms of how we report it because we want to reflect a bit about the weighting of the index. But our cost is actually a lot higher than what we report there because of the repeats.swyx [00:13:03]: Yeah, yeah, yeah. And probably this is true, but just checking, you don't have any special deals with the labs. They don't discount it. You just pay out of pocket or out of your sort of customer funds. Oh, there is a mix. So, the issue is that sometimes they may give you a special end point, which is… Ah, 100%.Micah [00:13:21]: Yeah, yeah, yeah. Exactly. So, we laser focus, like, on everything we do on having the best independent metrics and making sure that no one can manipulate them in any way. There are quite a lot of processes we've developed over the last couple of years to make that true for, like, the one you bring up, like, right here of the fact that if we're working with a lab, if they're giving us a private endpoint to evaluate a model, that it is totally possible. That what's sitting behind that black box is not the same as they serve on a public endpoint. We're very aware of that. We have what we call a mystery shopper policy. And so, and we're totally transparent with all the labs we work with about this, that we will register accounts not on our own domain and run both intelligence evals and performance benchmarks… Yeah, that's the job. …without them being able to identify it. And no one's ever had a problem with that. Because, like, a thing that turns out to actually be quite a good… …good factor in the industry is that they all want to believe that none of their competitors could manipulate what we're doing either.swyx [00:14:23]: That's true. I never thought about that. I've been in the database data industry prior, and there's a lot of shenanigans around benchmarking, right? So I'm just kind of going through the mental laundry list. Did I miss anything else in this category of shenanigans? Oh, potential shenanigans.Micah [00:14:36]: I mean, okay, the biggest one, like, that I'll bring up, like, is more of a conceptual one, actually, than, like, direct shenanigans. It's that the things that get measured become things that get targeted by labs that they're trying to build, right? Exactly. So that doesn't mean anything that we should really call shenanigans. Like, I'm not talking about training on test set. But if you know that you're going to be great at another particular thing, if you're a researcher, there are a whole bunch of things that you can do to try to get better at that thing that preferably are going to be helpful for a wide range of how actual users want to use the thing that you're building. But will not necessarily work. Will not necessarily do that. So, for instance, the models are exceptional now at answering competition maths problems. There is some relevance of that type of reasoning, that type of work, to, like, how we might use modern coding agents and stuff. But it's clearly not one for one. So the thing that we have to be aware of is that once an eval becomes the thing that everyone's looking at, scores can get better on it without there being a reflection of overall generalized intelligence of these models. Getting better. That has been true for the last couple of years. It'll be true for the next couple of years. There's no silver bullet to defeat that other than building new stuff to stay relevant and measure the capabilities that matter most to real users. Yeah.swyx [00:15:58]: And we'll cover some of the new stuff that you guys are building as well, which is cool. Like, you used to just run other people's evals, but now you're coming up with your own. And I think, obviously, that is a necessary path once you're at the frontier. You've exhausted all the existing evals. I think the next point in history that I have for you is AI Grant that you guys decided to join and move here. What was it like? I think you were in, like, batch two? Batch four. Batch four. Okay.Micah [00:16:26]: I mean, it was great. Nat and Daniel are obviously great. And it's a really cool group of companies that we were in AI Grant alongside. It was really great to get Nat and Daniel on board. Obviously, they've done a whole lot of great work in the space with a lot of leading companies and were extremely aligned. With the mission of what we were trying to do. Like, we're not quite typical of, like, a lot of the other AI startups that they've invested in.swyx [00:16:53]: And they were very much here for the mission of what we want to do. Did they say any advice that really affected you in some way or, like, were one of the events very impactful? That's an interesting question.Micah [00:17:03]: I mean, I remember fondly a bunch of the speakers who came and did fireside chats at AI Grant.swyx [00:17:09]: Which is also, like, a crazy list. Yeah.George [00:17:11]: Oh, totally. Yeah, yeah, yeah. There was something about, you know, speaking to Nat and Daniel about the challenges of working through a startup and just working through the questions that don't have, like, clear answers and how to work through those kind of methodically and just, like, work through the hard decisions. And they've been great mentors to us as we've built artificial analysis. Another benefit for us was that other companies in the batch and other companies in AI Grant are pushing the capabilities. Yeah. And I think that's a big part of what AI can do at this time. And so being in contact with them, making sure that artificial analysis is useful to them has been fantastic for supporting us in working out how should we build out artificial analysis to continue to being useful to those, like, you know, building on AI.swyx [00:17:59]: I think to some extent, I'm mixed opinion on that one because to some extent, your target audience is not people in AI Grants who are obviously at the frontier. Yeah. Do you disagree?Micah [00:18:09]: To some extent. To some extent. But then, so a lot of what the AI Grant companies are doing is taking capabilities coming out of the labs and trying to push the limits of what they can do across the entire stack for building great applications, which actually makes some of them pretty archetypical power users of artificial analysis. Some of the people with the strongest opinions about what we're doing well and what we're not doing well and what they want to see next from us. Yeah. Yeah. Because when you're building any kind of AI application now, chances are you're using a whole bunch of different models. You're maybe switching reasonably frequently for different models and different parts of your application to optimize what you're able to do with them at an accuracy level and to get better speed and cost characteristics. So for many of them, no, they're like not commercial customers of ours, like we don't charge for all our data on the website. Yeah. They are absolutely some of our power users.swyx [00:19:07]: So let's talk about just the evals as well. So you start out from the general like MMU and GPQA stuff. What's next? How do you sort of build up to the overall index? What was in V1 and how did you evolve it? Okay.Micah [00:19:22]: So first, just like background, like we're talking about the artificial analysis intelligence index, which is our synthesis metric that we pulled together currently from 10 different eval data sets to give what? We're pretty much the same as that. Pretty confident is the best single number to look at for how smart the models are. Obviously, it doesn't tell the whole story. That's why we published the whole website of all the charts to dive into every part of it and look at the trade-offs. But best single number. So right now, it's got a bunch of Q&A type data sets that have been very important to the industry, like a couple that you just mentioned. It's also got a couple of agentic data sets. It's got our own long context reasoning data set and some other use case focused stuff. As time goes on. The things that we're most interested in that are going to be important to the capabilities that are becoming more important for AI, what developers are caring about, are going to be first around agentic capabilities. So surprise, surprise. We're all loving our coding agents and how the model is going to perform like that and then do similar things for different types of work are really important to us. The linking to use cases to economically valuable use cases are extremely important to us. And then we've got some of the. Yeah. These things that the models still struggle with, like working really well over long contexts that are not going to go away as specific capabilities and use cases that we need to keep evaluating.swyx [00:20:46]: But I guess one thing I was driving was like the V1 versus the V2 and how bad it was over time.Micah [00:20:53]: Like how we've changed the index to where we are.swyx [00:20:55]: And I think that reflects on the change in the industry. Right. So that's a nice way to tell that story.Micah [00:21:00]: Well, V1 would be completely saturated right now. Almost every model coming out because doing things like writing the Python functions and human evil is now pretty trivial. It's easy to forget, actually, I think how much progress has been made in the last two years. Like we obviously play the game constantly of like the today's version versus last week's version and the week before and all of the small changes in the horse race between the current frontier and who has the best like smaller than 10B model like right now this week. Right. And that's very important to a lot of developers and people and especially in this particular city of San Francisco. But when you zoom out a couple of years ago, literally most of what we were doing to evaluate the models then would all be 100% solved by even pretty small models today. And that's been one of the key things, by the way, that's driven down the cost of intelligence at every tier of intelligence. We can talk about more in a bit. So V1, V2, V3, we made things harder. We covered a wider range of use cases. And we tried to get closer to things developers care about as opposed to like just the Q&A type stuff that MMLU and GPQA represented. Yeah.swyx [00:22:12]: I don't know if you have anything to add there. Or we could just go right into showing people the benchmark and like looking around and asking questions about it. Yeah.Micah [00:22:21]: Let's do it. Okay. This would be a pretty good way to chat about a few of the new things we've launched recently. Yeah.George [00:22:26]: And I think a little bit about the direction that we want to take it. And we want to push benchmarks. Currently, the intelligence index and evals focus a lot on kind of raw intelligence. But we kind of want to diversify how we think about intelligence. And we can talk about it. But kind of new evals that we've kind of built and partnered on focus on topics like hallucination. And we've got a lot of topics that I think are not covered by the current eval set that should be. And so we want to bring that forth. But before we get into that.swyx [00:23:01]: And so for listeners, just as a timestamp, right now, number one is Gemini 3 Pro High. Then followed by Cloud Opus at 70. Just 5.1 high. You don't have 5.2 yet. And Kimi K2 Thinking. Wow. Still hanging in there. So those are the top four. That will date this podcast quickly. Yeah. Yeah. I mean, I love it. I love it. No, no. 100%. Look back this time next year and go, how cute. Yep.George [00:23:25]: Totally. A quick view of that is, okay, there's a lot. I love it. I love this chart. Yeah.Micah [00:23:30]: This is such a favorite, right? Yeah. And almost every talk that George or I give at conferences and stuff, we always put this one up first to just talk about situating where we are in this moment in history. This, I think, is the visual version of what I was saying before about the zooming out and remembering how much progress there's been. If we go back to just over a year ago, before 01, before Cloud Sonnet 3.5, we didn't have reasoning models or coding agents as a thing. And the game was very, very different. If we go back even a little bit before then, we're in the era where, when you look at this chart, open AI was untouchable for well over a year. And, I mean, you would remember that time period well of there being very open questions about whether or not AI was going to be competitive, like full stop, whether or not open AI would just run away with it, whether we would have a few frontier labs and no one else would really be able to do anything other than consume their APIs. I am quite happy overall that the world that we have ended up in is one where... Multi-model. Absolutely. And strictly more competitive every quarter over the last few years. Yeah. This year has been insane. Yeah.George [00:24:42]: You can see it. This chart with everything added is hard to read currently. There's so many dots on it, but I think it reflects a little bit what we felt, like how crazy it's been.swyx [00:24:54]: Why 14 as the default? Is that a manual choice? Because you've got service now in there that are less traditional names. Yeah.George [00:25:01]: It's models that we're kind of highlighting by default in our charts, in our intelligence index. Okay.swyx [00:25:07]: You just have a manually curated list of stuff.George [00:25:10]: Yeah, that's right. But something that I actually don't think every artificial analysis user knows is that you can customize our charts and choose what models are highlighted. Yeah. And so if we take off a few names, it gets a little easier to read.swyx [00:25:25]: Yeah, yeah. A little easier to read. Totally. Yeah. But I love that you can see the all one jump. Look at that. September 2024. And the DeepSeek jump. Yeah.George [00:25:34]: Which got close to OpenAI's leadership. They were so close. I think, yeah, we remember that moment. Around this time last year, actually.Micah [00:25:44]: Yeah, yeah, yeah. I agree. Yeah, well, a couple of weeks. It was Boxing Day in New Zealand when DeepSeek v3 came out. And we'd been tracking DeepSeek and a bunch of the other global players that were less known over the second half of 2024 and had run evals on the earlier ones and stuff. I very distinctly remember Boxing Day in New Zealand, because I was with family for Christmas and stuff, running the evals and getting back result by result on DeepSeek v3. So this was the first of their v3 architecture, the 671b MOE.Micah [00:26:19]: And we were very, very impressed. That was the moment where we were sure that DeepSeek was no longer just one of many players, but had jumped up to be a thing. The world really noticed when they followed that up with the RL working on top of v3 and R1 succeeding a few weeks later. But the groundwork for that absolutely was laid with just extremely strong base model, completely open weights that we had as the best open weights model. So, yeah, that's the thing that you really see in the game. But I think that we got a lot of good feedback on Boxing Day. us on Boxing Day last year.George [00:26:48]: Boxing Day is the day after Christmas for those not familiar.George [00:26:54]: I'm from Singapore.swyx [00:26:55]: A lot of us remember Boxing Day for a different reason, for the tsunami that happened. Oh, of course. Yeah, but that was a long time ago. So yeah. So this is the rough pitch of AAQI. Is it A-A-Q-I or A-A-I-I? I-I. Okay. Good memory, though.Micah [00:27:11]: I don't know. I'm not used to it. Once upon a time, we did call it Quality Index, and we would talk about quality, performance, and price, but we changed it to intelligence.George [00:27:20]: There's been a few naming changes. We added hardware benchmarking to the site, and so benchmarks at a kind of system level. And so then we changed our throughput metric to, we now call it output speed, and thenswyx [00:27:32]: throughput makes sense at a system level, so we took that name. Take me through more charts. What should people know? Obviously, the way you look at the site is probably different than how a beginner might look at it.Micah [00:27:42]: Yeah, that's fair. There's a lot of fun stuff to dive into. Maybe so we can hit past all the, like, we have lots and lots of emails and stuff. The interesting ones to talk about today that would be great to bring up are a few of our recent things, I think, that probably not many people will be familiar with yet. So first one of those is our omniscience index. So this one is a little bit different to most of the intelligence evils that we've run. We built it specifically to look at the embedded knowledge in the models and to test hallucination by looking at when the model doesn't know the answer, so not able to get it correct, what's its probability of saying, I don't know, or giving an incorrect answer. So the metric that we use for omniscience goes from negative 100 to positive 100. Because we're simply taking off a point if you give an incorrect answer to the question. We're pretty convinced that this is an example of where it makes most sense to do that, because it's strictly more helpful to say, I don't know, instead of giving a wrong answer to factual knowledge question. And one of our goals is to shift the incentive that evils create for models and the labs creating them to get higher scores. And almost every evil across all of AI up until this point, it's been graded by simple percentage correct as the main metric, the main thing that gets hyped. And so you should take a shot at everything. There's no incentive to say, I don't know. So we did that for this one here.swyx [00:29:22]: I think there's a general field of calibration as well, like the confidence in your answer versus the rightness of the answer. Yeah, we completely agree. Yeah. Yeah.George [00:29:31]: On that. And one reason that we didn't do that is because. Or put that into this index is that we think that the, the way to do that is not to ask the models how confident they are.swyx [00:29:43]: I don't know. Maybe it might be though. You put it like a JSON field, say, say confidence and maybe it spits out something. Yeah. You know, we have done a few evils podcasts over the, over the years. And when we did one with Clementine of hugging face, who maintains the open source leaderboard, and this was one of her top requests, which is some kind of hallucination slash lack of confidence calibration thing. And so, Hey, this is one of them.Micah [00:30:05]: And I mean, like anything that we do, it's not a perfect metric or the whole story of everything that you think about as hallucination. But yeah, it's pretty useful and has some interesting results. Like one of the things that we saw in the hallucination rate is that anthropics Claude models at the, the, the very left-hand side here with the lowest hallucination rates out of the models that we've evaluated amnesty is on. That is an interesting fact. I think it probably correlates with a lot of the previously, not really measured vibes stuff that people like about some of the Claude models. Is the dataset public or what's is it, is there a held out set? There's a hell of a set for this one. So we, we have published a public test set, but we we've only published 10% of it. The reason is that for this one here specifically, it would be very, very easy to like have data contamination because it is just factual knowledge questions. We would. We'll update it at a time to also prevent that, but with yeah, kept most of it held out so that we can keep it reliable for a long time. It leads us to a bunch of really cool things, including breakdown quite granularly by topic. And so we've got some of that disclosed on the website publicly right now, and there's lots more coming in terms of our ability to break out very specific topics. Yeah.swyx [00:31:23]: I would be interested. Let's, let's dwell a little bit on this hallucination one. I noticed that Haiku hallucinates less than Sonnet hallucinates less than Opus. And yeah. Would that be the other way around in a normal capability environments? I don't know. What's, what do you make of that?George [00:31:37]: One interesting aspect is that we've found that there's not really a, not a strong correlation between intelligence and hallucination, right? That's to say that the smarter the models are in a general sense, isn't correlated with their ability to, when they don't know something, say that they don't know. It's interesting that Gemini three pro preview was a big leap over here. Gemini 2.5. Flash and, and, and 2.5 pro, but, and if I add pro quickly here.swyx [00:32:07]: I bet pro's really good. Uh, actually no, I meant, I meant, uh, the GPT pros.George [00:32:12]: Oh yeah.swyx [00:32:13]: Cause GPT pros are rumored. We don't know for a fact that it's like eight runs and then with the LM judge on top. Yeah.George [00:32:20]: So we saw a big jump in, this is accuracy. So this is just percent that they get, uh, correct and Gemini three pro knew a lot more than the other models. And so big jump in accuracy. But relatively no change between the Google Gemini models, between releases. And the hallucination rate. Exactly. And so it's likely due to just kind of different post-training recipe, between the, the Claude models. Yeah.Micah [00:32:45]: Um, there's, there's driven this. Yeah. You can, uh, you can partially blame us and how we define intelligence having until now not defined hallucination as a negative in the way that we think about intelligence.swyx [00:32:56]: And so that's what we're changing. Uh, I know many smart people who are confidently incorrect.George [00:33:02]: Uh, look, look at that. That, that, that is very humans. Very true. And there's times and a place for that. I think our view is that hallucination rate makes sense in this context where it's around knowledge, but in many cases, people want the models to hallucinate, to have a go. Often that's the case in coding or when you're trying to generate newer ideas. One eval that we added to artificial analysis is, is, is critical point and it's really hard, uh, physics problems. Okay.swyx [00:33:32]: And is it sort of like a human eval type or something different or like a frontier math type?George [00:33:37]: It's not dissimilar to frontier frontier math. So these are kind of research questions that kind of academics in the physics physics world would be able to answer, but models really struggled to answer. So the top score here is not 9%.swyx [00:33:51]: And when the people that, that created this like Minway and, and, and actually off via who was kind of behind sweep and what organization is this? Oh, is this, it's Princeton.George [00:34:01]: Kind of range of academics from, from, uh, different academic institutions, really smart people. They talked about how they turn the models up in terms of the temperature as high temperature as they can, where they're trying to explore kind of new ideas in physics as a, as a thought partner, just because they, they want the models to hallucinate. Um, yeah, sometimes it's something new. Yeah, exactly.swyx [00:34:21]: Um, so not right in every situation, but, um, I think it makes sense, you know, to test hallucination in scenarios where it makes sense. Also, the obvious question is, uh, this is one of. Many that there is there, every lab has a system card that shows some kind of hallucination number, and you've chosen to not, uh, endorse that and you've made your own. And I think that's a, that's a choice. Um, totally in some sense, the rest of artificial analysis is public benchmarks that other people can independently rerun. You provide it as a service here. You have to fight the, well, who are we to, to like do this? And your, your answer is that we have a lot of customers and, you know, but like, I guess, how do you converge the individual?Micah [00:35:08]: I mean, I think, I think for hallucinations specifically, there are a bunch of different things that you might care about reasonably, and that you'd measure quite differently, like we've called this a amnesty and solutionation rate, not trying to declare the, like, it's humanity's last hallucination. You could, uh, you could have some interesting naming conventions and all this stuff. Um, the biggest picture answer to that. It's something that I actually wanted to mention. Just as George was explaining, critical point as well is, so as we go forward, we are building evals internally. We're partnering with academia and partnering with AI companies to build great evals. We have pretty strong views on, in various ways for different parts of the AI stack, where there are things that are not being measured well, or things that developers care about that should be measured more and better. And we intend to be doing that. We're not obsessed necessarily with that. Everything we do, we have to do entirely within our own team. Critical point. As a cool example of where we were a launch partner for it, working with academia, we've got some partnerships coming up with a couple of leading companies. Those ones, obviously we have to be careful with on some of the independent stuff, but with the right disclosure, like we're completely comfortable with that. A lot of the labs have released great data sets in the past that we've used to great success independently. And so it's between all of those techniques, we're going to be releasing more stuff in the future. Cool.swyx [00:36:26]: Let's cover the last couple. And then we'll, I want to talk about your trends analysis stuff, you know? Totally.Micah [00:36:31]: So that actually, I have one like little factoid on omniscience. If you go back up to accuracy on omniscience, an interesting thing about this accuracy metric is that it tracks more closely than anything else that we measure. The total parameter count of models makes a lot of sense intuitively, right? Because this is a knowledge eval. This is the pure knowledge metric. We're not looking at the index and the hallucination rate stuff that we think is much more about how the models are trained. This is just what facts did they recall? And yeah, it tracks parameter count extremely closely. Okay.swyx [00:37:05]: What's the rumored size of GPT-3 Pro? And to be clear, not confirmed for any official source, just rumors. But rumors do fly around. Rumors. I get, I hear all sorts of numbers. I don't know what to trust.Micah [00:37:17]: So if you, if you draw the line on omniscience accuracy versus total parameters, we've got all the open ways models, you can squint and see that likely the leading frontier models right now are quite a lot bigger than the ones that we're seeing right now. And the one trillion parameters that the open weights models cap out at, and the ones that we're looking at here, there's an interesting extra data point that Elon Musk revealed recently about XAI that for three trillion parameters for GROK 3 and 4, 6 trillion for GROK 5, but that's not out yet. Take those together, have a look. You might reasonably form a view that there's a pretty good chance that Gemini 3 Pro is bigger than that, that it could be in the 5 to 10 trillion parameters. To be clear, I have absolutely no idea, but just based on this chart, like that's where you would, you would land if you have a look at it. Yeah.swyx [00:38:07]: And to some extent, I actually kind of discourage people from guessing too much because what does it really matter? Like as long as they can serve it as a sustainable cost, that's about it. Like, yeah, totally.George [00:38:17]: They've also got different incentives in play compared to like open weights models who are thinking to supporting others in self-deployment for the labs who are doing inference at scale. It's I think less about total parameters in many cases. When thinking about inference costs and more around number of active parameters. And so there's a bit of an incentive towards larger sparser models. Agreed.Micah [00:38:38]: Understood. Yeah. Great. I mean, obviously if you're a developer or company using these things, not exactly as you say, it doesn't matter. You should be looking at all the different ways that we measure intelligence. You should be looking at cost to run index number and the different ways of thinking about token efficiency and cost efficiency based on the list prices, because that's all it matters.swyx [00:38:56]: It's not as good for the content creator rumor mill where I can say. Oh, GPT-4 is this small circle. Look at GPT-5 is this big circle. And then there used to be a thing for a while. Yeah.Micah [00:39:07]: But that is like on its own, actually a very interesting one, right? That is it just purely that chances are the last couple of years haven't seen a dramatic scaling up in the total size of these models. And so there's a lot of room to go up properly in total size of the models, especially with the upcoming hardware generations. Yes.swyx [00:39:29]: So, you know. Taking off my shitposting face for a minute. Yes. Yes. At the same time, I do feel like, you know, especially coming back from Europe, people do feel like Ilya is probably right that the paradigm is doesn't have many more orders of magnitude to scale out more. And therefore we need to start exploring at least a different path. GDPVal, I think it's like only like a month or so old. I was also very positive when it first came out. I actually talked to Tejo, who was the lead researcher on that. Oh, cool. And you have your own version.George [00:39:59]: It's a fantastic. It's a fantastic data set. Yeah.swyx [00:40:01]: And maybe it will recap for people who are still out of it. It's like 44 tasks based on some kind of GDP cutoff that's like meant to represent broad white collar work that is not just coding. Yeah.Micah [00:40:12]: Each of the tasks have a whole bunch of detailed instructions, some input files for a lot of them. It's within the 44 is divided into like two hundred and twenty two to five, maybe subtasks that are the level of that we run through the agenda. And yeah, they're really interesting. I will say that it doesn't. It doesn't necessarily capture like all the stuff that people do at work. No avail is perfect is always going to be more things to look at, largely because in order to make the tasks well enough to find that you can run them, they need to only have a handful of input files and very specific instructions for that task. And so I think the easiest way to think about them are that they're like quite hard take home exam tasks that you might do in an interview process.swyx [00:40:56]: Yeah, for listeners, it is not no longer like a long prompt. It is like, well, here's a zip file with like a spreadsheet or a PowerPoint deck or a PDF and go nuts and answer this question.George [00:41:06]: OpenAI released a great data set and they released a good paper which looks at performance across the different web chat bots on the data set. It's a great paper, encourage people to read it. What we've done is taken that data set and turned it into an eval that can be run on any model. So we created a reference agentic harness that can run. Run the models on the data set, and then we developed evaluator approach to compare outputs. That's kind of AI enabled, so it uses Gemini 3 Pro Preview to compare results, which we tested pretty comprehensively to ensure that it's aligned to human preferences. One data point there is that even as an evaluator, Gemini 3 Pro, interestingly, doesn't do actually that well. So that's kind of a good example of what we've done in GDPVal AA.swyx [00:42:01]: Yeah, the thing that you have to watch out for with LLM judge is self-preference that models usually prefer their own output, and in this case, it was not. Totally.Micah [00:42:08]: I think the way that we're thinking about the places where it makes sense to use an LLM as judge approach now, like quite different to some of the early LLM as judge stuff a couple of years ago, because some of that and MTV was a great project that was a good example of some of this a while ago was about judging conversations and like a lot of style type stuff. Here, we've got the task that the grader and grading model is doing is quite different to the task of taking the test. When you're taking the test, you've got all of the agentic tools you're working with, the code interpreter and web search, the file system to go through many, many turns to try to create the documents. Then on the other side, when we're grading it, we're running it through a pipeline to extract visual and text versions of the files and be able to provide that to Gemini, and we're providing the criteria for the task and getting it to pick which one more effectively meets the criteria of the task. Yeah. So we've got the task out of two potential outcomes. It turns out that we proved that it's just very, very good at getting that right, matched with human preference a lot of the time, because I think it's got the raw intelligence, but it's combined with the correct representation of the outputs, the fact that the outputs were created with an agentic task that is quite different to the way the grading model works, and we're comparing it against criteria, not just kind of zero shot trying to ask the model to pick which one is better.swyx [00:43:26]: Got it. Why is this an ELO? And not a percentage, like GDP-VAL?George [00:43:31]: So the outputs look like documents, and there's video outputs or audio outputs from some of the tasks. It has to make a video? Yeah, for some of the tasks. Some of the tasks.swyx [00:43:43]: What task is that?George [00:43:45]: I mean, it's in the data set. Like be a YouTuber? It's a marketing video.Micah [00:43:49]: Oh, wow. What? Like model has to go find clips on the internet and try to put it together. The models are not that good at doing that one, for now, to be clear. It's pretty hard to do that with a code editor. I mean, the computer stuff doesn't work quite well enough and so on and so on, but yeah.George [00:44:02]: And so there's no kind of ground truth, necessarily, to compare against, to work out percentage correct. It's hard to come up with correct or incorrect there. And so it's on a relative basis. And so we use an ELO approach to compare outputs from each of the models between the task.swyx [00:44:23]: You know what you should do? You should pay a contractor, a human, to do the same task. And then give it an ELO and then so you have, you have human there. It's just, I think what's helpful about GDPVal, the OpenAI one, is that 50% is meant to be normal human and maybe Domain Expert is higher than that, but 50% was the bar for like, well, if you've crossed 50, you are superhuman. Yeah.Micah [00:44:47]: So we like, haven't grounded this score in that exactly. I agree that it can be helpful, but we wanted to generalize this to a very large number. It's one of the reasons that presenting it as ELO is quite helpful and allows us to add models and it'll stay relevant for quite a long time. I also think it, it can be tricky looking at these exact tasks compared to the human performance, because the way that you would go about it as a human is quite different to how the models would go about it. Yeah.swyx [00:45:15]: I also liked that you included Lama 4 Maverick in there. Is that like just one last, like...Micah [00:45:20]: Well, no, no, no, no, no, no, it is the, it is the best model released by Meta. And... So it makes it into the homepage default set, still for now.George [00:45:31]: Other inclusion that's quite interesting is we also ran it across the latest versions of the web chatbots. And so we have...swyx [00:45:39]: Oh, that's right.George [00:45:40]: Oh, sorry.swyx [00:45:41]: I, yeah, I completely missed that. Okay.George [00:45:43]: No, not at all. So that, which has a checkered pattern. So that is their harness, not yours, is what you're saying. Exactly. And what's really interesting is that if you compare, for instance, Claude 4.5 Opus using the Claude web chatbot, it performs worse than the model in our agentic harness. And so in every case, the model performs better in our agentic harness than its web chatbot counterpart, the harness that they created.swyx [00:46:13]: Oh, my backwards explanation for that would be that, well, it's meant for consumer use cases and here you're pushing it for something.Micah [00:46:19]: The constraints are different and the amount of freedom that you can give the model is different. Also, you like have a cost goal. We let the models work as long as they want, basically. Yeah. Do you copy paste manually into the chatbot? Yeah. Yeah. That's, that was how we got the chatbot reference. We're not going to be keeping those updated at like quite the same scale as hundreds of models.swyx [00:46:38]: Well, so I don't know, talk to a browser base. They'll, they'll automate it for you. You know, like I have thought about like, well, we should turn these chatbot versions into an API because they are legitimately different agents in themselves. Yes. Right. Yeah.Micah [00:46:53]: And that's grown a huge amount of the last year, right? Like the tools. The tools that are available have actually diverged in my opinion, a fair bit across the major chatbot apps and the amount of data sources that you can connect them to have gone up a lot, meaning that your experience and the way you're using the model is more different than ever.swyx [00:47:10]: What tools and what data connections come to mind when you say what's interesting, what's notable work that people have done?Micah [00:47:15]: Oh, okay. So my favorite example on this is that until very recently, I would argue that it was basically impossible to get an LLM to draft an email for me in any useful way. Because most times that you're sending an email, you're not just writing something for the sake of writing it. Chances are context required is a whole bunch of historical emails. Maybe it's notes that you've made, maybe it's meeting notes, maybe it's, um, pulling something from your, um, any of like wherever you at work store stuff. So for me, like Google drive, one drive, um, in our super base databases, if we need to do some analysis or some data or something, preferably model can be plugged into all of those things and can go do some useful work based on it. The things that like I find most impressive currently that I am somewhat surprised work really well in late 2025, uh, that I can have models use super base MCP to query read only, of course, run a whole bunch of SQL queries to do pretty significant data analysis. And. And make charts and stuff and can read my Gmail and my notion. And okay. You actually use that. That's good. That's, that's, that's good. Is that a cloud thing? To various degrees of order, but chat GPD and Claude right now, I would say that this stuff like barely works in fairness right now. Like.George [00:48:33]: Because people are actually going to try this after they hear it. If you get an email from Micah, odds are it wasn't written by a chatbot.Micah [00:48:38]: So, yeah, I think it is true that I have never actually sent anyone an email drafted by a chatbot. Yet.swyx [00:48:46]: Um, and so you can, you can feel it right. And yeah, this time, this time next year, we'll come back and see where it's going. Totally. Um, super base shout out another famous Kiwi. Uh, I don't know if you've, you've any conversations with him about anything in particular on AI building and AI infra.George [00:49:03]: We have had, uh, Twitter DMS, um, with, with him because we're quite big, uh, super base users and power users. And we probably do some things more manually than we should in. In, in super base support line because you're, you're a little bit being super friendly. One extra, um, point regarding, um, GDP Val AA is that on the basis of the overperformance of the models compared to the chatbots turns out, we realized that, oh, like our reference harness that we built actually white works quite well on like gen generalist agentic tasks. This proves it in a sense. And so the agent harness is very. Minimalist. I think it follows some of the ideas that are in Claude code and we, all that we give it is context management capabilities, a web search, web browsing, uh, tool, uh, code execution, uh, environment. Anything else?Micah [00:50:02]: I mean, we can equip it with more tools, but like by default, yeah, that's it. We, we, we give it for GDP, a tool to, uh, view an image specifically, um, because the models, you know, can just use a terminal to pull stuff in text form into context. But to pull visual stuff into context, we had to give them a custom tool, but yeah, exactly. Um, you, you can explain an expert. No.George [00:50:21]: So it's, it, we turned out that we created a good generalist agentic harness. And so we, um, released that on, on GitHub yesterday. It's called stirrup. So if people want to check it out and, and it's a great, um, you know, base for, you know, generalist, uh, building a generalist agent for more specific tasks.Micah [00:50:39]: I'd say the best way to use it is get clone and then have your favorite coding. Agent make changes to it, to do whatever you want, because it's not that many lines of code and the coding agents can work with it. Super well.swyx [00:50:51]: Well, that's nice for the community to explore and share and hack on it. I think maybe in, in, in other similar environments, the terminal bench guys have done, uh, sort of the Harbor. Uh, and so it's, it's a, it's a bundle of, well, we need our minimal harness, which for them is terminus and we also need the RL environments or Docker deployment thing to, to run independently. So I don't know if you've looked at it. I don't know if you've looked at the harbor at all, is that, is that like a, a standard that people want to adopt?George [00:51:19]: Yeah, we've looked at it from a evals perspective and we love terminal bench and, and host benchmarks of, of, of terminal mention on artificial analysis. Um, we've looked at it from a, from a coding agent perspective, but could see it being a great, um, basis for any kind of agents. I think where we're getting to is that these models have gotten smart enough. They've gotten better, better tools that they can perform better when just given a minimalist. Set of tools and, and let them run, let the model control the, the agentic workflow rather than using another framework that's a bit more built out that tries to dictate the, dictate the flow. Awesome.swyx [00:51:56]: Let's cover the openness index and then let's go into the report stuff. Uh, so that's the, that's the last of the proprietary art numbers, I guess. I don't know how you sort of classify all these. Yeah.Micah [00:52:07]: Or call it, call it, let's call it the last of like the, the three new things that we're talking about from like the last few weeks. Um, cause I mean, there's a, we do a mix of stuff that. Where we're using open source, where we open source and what we do and, um, proprietary stuff that we don't always open source, like long context reasoning data set last year, we did open source. Um, and then all of the work on performance benchmarks across the site, some of them, we looking to open source, but some of them, like we're constantly iterating on and so on and so on and so on. So there's a huge mix, I would say, just of like stuff that is open source and not across the side. So that's a LCR for people. Yeah, yeah, yeah, yeah.swyx [00:52:41]: Uh, but let's, let's, let's talk about open.Micah [00:52:42]: Let's talk about openness index. This. Here is call it like a new way to think about how open models are. We, for a long time, have tracked where the models are open weights and what the licenses on them are. And that's like pretty useful. That tells you what you're allowed to do with the weights of a model, but there is this whole other dimension to how open models are. That is pretty important that we haven't tracked until now. And that's how much is disclosed about how it was made. So transparency about data, pre-training data and post-training data. And whether you're allowed to use that data and transparency about methodology and training code. So basically, those are the components. We bring them together to score an openness index for models so that you can in one place get this full picture of how open models are.swyx [00:53:32]: I feel like I've seen a couple other people try to do this, but they're not maintained. I do think this does matter. I don't know what the numbers mean apart from is there a max number? Is this out of 20?George [00:53:44]: It's out of 18 currently, and so we've got an openness index page, but essentially these are points, you get points for being more open across these different categories and the maximum you can achieve is 18. So AI2 with their extremely open OMO3 32B think model is the leader in a sense.swyx [00:54:04]: It's hooking face.George [00:54:05]: Oh, with their smaller model. It's coming soon. I think we need to run, we need to get the intelligence benchmarks right to get it on the site.swyx [00:54:12]: You can't have it open in the next. We can not include hooking face. We love hooking face. We'll have that, we'll have that up very soon. I mean, you know, the refined web and all that stuff. It's, it's amazing. Or is it called fine web? Fine web. Fine web.Micah [00:54:23]: Yeah, yeah, no, totally. Yep. One of the reasons this is cool, right, is that if you're trying to understand the holistic picture of the models and what you can do with all the stuff the company's contributing, this gives you that picture. And so we are going to keep it up to date alongside all the models that we do intelligence index on, on the site. And it's just an extra view to understand.swyx [00:54:43]: Can you scroll down to this? The, the, the, the trade-offs chart. Yeah, yeah. That one. Yeah. This, this really matters, right? Obviously, because you can b

From creating SWE-bench in a Princeton basement to shipping CodeClash, SWE-bench Multimodal, and SWE-bench Multilingual, John Yang has spent the last year and a half watching his benchmark become the de facto standard for evaluating AI coding agents—trusted by Cognition (Devin), OpenAI, Anthropic, and every major lab racing to solve software engineering at scale. We caught up with John live at NeurIPS 2025 to dig into the state of code evals heading into 2026: why SWE-bench went from ignored (October 2023) to the industry standard after Devin's launch (and how Walden emailed him two weeks before the big reveal), how the benchmark evolved from Django-heavy to nine languages across 40 repos (JavaScript, Rust, Java, C, Ruby), why unit tests as verification are limiting and long-running agent tournaments might be the future (CodeClash: agents maintain codebases, compete in arenas, and iterate over multiple rounds), the proliferation of SWE-bench variants (SWE-bench Pro, SWE-bench Live, SWE-Efficiency, AlgoTune, SciCode) and how benchmark authors are now justifying their splits with curation techniques instead of just “more repos,” why Tau-bench's “impossible tasks” controversy is actually a feature not a bug (intentionally including impossible tasks flags cheating), the tension between long autonomy (5-hour runs) vs. interactivity (Cognition's emphasis on fast back-and-forth), how Terminal-bench unlocked creativity by letting PhD students and non-coders design environments beyond GitHub issues and PRs, the academic data problem (companies like Cognition and Cursor have rich user interaction data, academics need user simulators or compelling products like LMArena to get similar signal), and his vision for CodeClash as a testbed for human-AI collaboration—freeze model capability, vary the collaboration setup (solo agent, multi-agent, human+agent), and measure how interaction patterns change as models climb the ladder from code completion to full codebase reasoning.We discuss:* John's path: Princeton → SWE-bench (October 2023) → Stanford PhD with Diyi Yang and the Iris Group, focusing on code evals, human-AI collaboration, and long-running agent benchmarks* The SWE-bench origin story: released October 2023, mostly ignored until Cognition's Devin launch kicked off the arms race (Walden emailed John two weeks before: “we have a good number”)* SWE-bench Verified: the curated, high-quality split that became the standard for serious evals* SWE-bench Multimodal and Multilingual: nine languages (JavaScript, Rust, Java, C, Ruby) across 40 repos, moving beyond the Django-heavy original distribution* The SWE-bench Pro controversy: independent authors used the “SWE-bench” name without John's blessing, but he's okay with it (”congrats to them, it's a great benchmark”)* CodeClash: John's new benchmark for long-horizon development—agents maintain their own codebases, edit and improve them each round, then compete in arenas (programming games like Halite, economic tasks like GDP optimization)* SWE-Efficiency (Jeffrey Maugh, John's high school classmate): optimize code for speed without changing behavior (parallelization, SIMD operations)* AlgoTune, SciCode, Terminal-bench, Tau-bench, SecBench, SRE-bench: the Cambrian explosion of code evals, each diving into different domains (security, SRE, science, user simulation)* The Tau-bench “impossible tasks” debate: some tasks are underspecified or impossible, but John thinks that's actually a feature (flags cheating if you score above 75%)* Cognition's research focus: codebase understanding (retrieval++), helping humans understand their own codebases, and automatic context engineering for LLMs (research sub-agents)* The vision: CodeClash as a testbed for human-AI collaboration—vary the setup (solo agent, multi-agent, human+agent), freeze model capability, and measure how interaction changes as models improve—John Yang* SWE-bench: https://www.swebench.com* X: https://x.com/jyangballinFull Video EpisodeTimestamps00:00:00 Introduction: John Yang on SWE-bench and Code Evaluations00:00:31 SWE-bench Origins and Devon's Impact on the Coding Agent Arms Race00:01:09 SWE-bench Ecosystem: Verified, Pro, Multimodal, and Multilingual Variants00:02:17 Moving Beyond Django: Diversifying Code Evaluation Repositories00:03:08 Code Clash: Long-Horizon Development Through Programming Tournaments00:04:41 From Halite to Economic Value: Designing Competitive Coding Arenas00:06:04 Ofir's Lab: SWE-ficiency, AlgoTune, and SciCode for Scientific Computing00:07:52 The Benchmark Landscape: TAU-bench, Terminal-bench, and User Simulation00:09:20 The Impossible Task Debate: Refusals, Ambiguity, and Benchmark Integrity00:12:32 The Future of Code Evals: Long Autonomy vs Human-AI Collaboration00:14:37 Call to Action: User Interaction Data and Codebase Understanding Research Get full access to Latent.Space at www.latent.space/subscribe

From creating SWE-bench in a Princeton basement to shipping CodeClash, SWE-bench Multimodal, and SWE-bench Multilingual, John Yang has spent the last year and a half watching his benchmark become the de facto standard for evaluating AI coding agents—trusted by Cognition (Devin), OpenAI, Anthropic, and every major lab racing to solve software engineering at scale. We caught up with John live at NeurIPS 2025 to dig into the state of code evals heading into 2026: why SWE-bench went from ignored (October 2023) to the industry standard after Devin's launch (and how Walden emailed him two weeks before the big reveal), how the benchmark evolved from Django-heavy to nine languages across 40 repos (JavaScript, Rust, Java, C, Ruby), why unit tests as verification are limiting and long-running agent tournaments might be the future (CodeClash: agents maintain codebases, compete in arenas, and iterate over multiple rounds), the proliferation of SWE-bench variants (SWE-bench Pro, SWE-bench Live, SWE-Efficiency, AlgoTune, SciCode) and how benchmark authors are now justifying their splits with curation techniques instead of just "more repos," why Tau-bench's "impossible tasks" controversy is actually a feature not a bug (intentionally including impossible tasks flags cheating), the tension between long autonomy (5-hour runs) vs. interactivity (Cognition's emphasis on fast back-and-forth), how Terminal-bench unlocked creativity by letting PhD students and non-coders design environments beyond GitHub issues and PRs, the academic data problem (companies like Cognition and Cursor have rich user interaction data, academics need user simulators or compelling products like LMArena to get similar signal), and his vision for CodeClash as a testbed for human-AI collaboration—freeze model capability, vary the collaboration setup (solo agent, multi-agent, human+agent), and measure how interaction patterns change as models climb the ladder from code completion to full codebase reasoning. We discuss: John's path: Princeton → SWE-bench (October 2023) → Stanford PhD with Diyi Yang and the Iris Group, focusing on code evals, human-AI collaboration, and long-running agent benchmarks The SWE-bench origin story: released October 2023, mostly ignored until Cognition's Devin launch kicked off the arms race (Walden emailed John two weeks before: "we have a good number") SWE-bench Verified: the curated, high-quality split that became the standard for serious evals SWE-bench Multimodal and Multilingual: nine languages (JavaScript, Rust, Java, C, Ruby) across 40 repos, moving beyond the Django-heavy original distribution The SWE-bench Pro controversy: independent authors used the "SWE-bench" name without John's blessing, but he's okay with it ("congrats to them, it's a great benchmark") CodeClash: John's new benchmark for long-horizon development—agents maintain their own codebases, edit and improve them each round, then compete in arenas (programming games like Halite, economic tasks like GDP optimization) SWE-Efficiency (Jeffrey Maugh, John's high school classmate): optimize code for speed without changing behavior (parallelization, SIMD operations) AlgoTune, SciCode, Terminal-bench, Tau-bench, SecBench, SRE-bench: the Cambrian explosion of code evals, each diving into different domains (security, SRE, science, user simulation) The Tau-bench "impossible tasks" debate: some tasks are underspecified or impossible, but John thinks that's actually a feature (flags cheating if you score above 75%) Cognition's research focus: codebase understanding (retrieval++), helping humans understand their own codebases, and automatic context engineering for LLMs (research sub-agents) The vision: CodeClash as a testbed for human-AI collaboration—vary the setup (solo agent, multi-agent, human+agent), freeze model capability, and measure how interaction changes as models improve — John Yang SWE-bench: https://www.swebench.com X: https://x.com/jyangballin Chapters 00:00:00 Introduction: John Yang on SWE-bench and Code Evaluations 00:00:31 SWE-bench Origins and Devon's Impact on the Coding Agent Arms Race 00:01:09 SWE-bench Ecosystem: Verified, Pro, Multimodal, and Multilingual Variants 00:02:17 Moving Beyond Django: Diversifying Code Evaluation Repositories 00:03:08 Code Clash: Long-Horizon Development Through Programming Tournaments 00:04:41 From Halite to Economic Value: Designing Competitive Coding Arenas 00:06:04 Ofir's Lab: SWE-ficiency, AlgoTune, and SciCode for Scientific Computing 00:07:52 The Benchmark Landscape: TAU-bench, Terminal-bench, and User Simulation 00:09:20 The Impossible Task Debate: Refusals, Ambiguity, and Benchmark Integrity 00:12:32 The Future of Code Evals: Long Autonomy vs Human-AI Collaboration 00:14:37 Call to Action: User Interaction Data and Codebase Understanding Research

As little as 3000 steps per day can slow progression to Alzheimer's Disease; Self-reports of memory impairment soaring among young people; New study vindicates unprocessed red meat—and even often-vilified processed red meat—for cancer and overall health. Prostate artery embolization (PAE) offers new non-invasive option for men's age-related urinary problems; Targeting the mitochondria and the microbiome for Parkinson's Disease; Popular prostate and hair loss prevention drugs linked to depression and suicide—while Cialis for urinary symptoms may stave off cardiovascular disease; Discovery that a safe, cheap medication may increase survival after breast cancer surgery. 

Episode 267 of The Independent Characters arrives right on schedule, and it's a big one, both in content and in subject matter. This time, we're striding into the battlefield with not one, but two colossal armies in our latest Show of Force. Join us as we explore both the Imperial Knights and their dark reflections, the Chaos Knights. These towering engines of war have a presence on the tabletop and in the lore that is impossible to ignore, and we're diving deep into every aspect of them. Edit: So as we started recording – we realize that this would turn into a 5-6 hour episode. Carl was, once again, too optimistic regarding how much we could cram into an episode. So we made the decision to divide this topic into two parts. Episode 268 will cover the Chaos Knights as Part 2 of Oaths and Damnation. Over the course of the episode, we cover their origins and history, the unique culture of the Knight Houses, and how those sworn oaths of loyalty can either lead to noble service to the Imperium or a fall into the corrupting grip of the Dark Gods. From lore to codex, units to battlefield roles, we've got it all packed in. As our Show of Force Episodes go – we'll also take a look back at their evolution over the years – how these armies have grown in design, rules, and miniature range to become some of the most striking models in the Warhammer 40,000 universe. Carl and Adan – as well as a special guest, will unpack the details of these armies over the course of several hours. Fire up your void-shields because Episode 267 releases on October 18th, 2025. Time Stamps: 0:00:00 – Show Intro, Elite Choice, Hobby Progress 1:00:40 – Oaths & Damnation – Part 1 (First Half) 1:54:10 – Oaths & Damnation – Part 1 (Second Half) 3:23:10 – War on The Shore Narrative Event! 3:46:30 – Final Thoughts and show closing Relevant Links: The Independent Characters Patreon Tablewar! – SPONSOR Herrick Games & Hobbies – SPONSOR Imperium Maledictum by Cubicle7 Goonhammer War on The Shore 2026 Adepticon Games Workshop The Black Library