Podcasts about Tejo

“Veio-me à cabeça a imagem do general Garibaldi quando, ao partirem de Roma, disse aos soldados que lhes oferecia sede e calor durante o dia, fome durante a noite e perigo a toda a hora…” Isto serve como impulso se nos viramos para aquele outro lado do qual só recebemos notícias quando algo da ordem da catástrofe nos faz sentir como a realidade é hoje outro nome para o esquecimento. Esses lugares por onde anda a monte, sem prece que o alcance, e a coçar-se contra tudo o deus dos secretos, senhor de vidas inesperadas, que não quadram, não encontram rima neste mundo, mas são contíguos a desertos, serras floridas, e mato agreste, afiados instintos de tanta dar caça a bichos difíceis de explicar, enumerar, armar ciladas aos pássaros, naças aos peixes no mais fundo rio, pescarias ali onde o rio faz d'água uma mansa colheita, e às vezes distraído num gesto mais largo, molhava n'água amara, e compelia/ a recolher a roxa tarde e breve”, depois servia-se da capela abandonada como despensa, usava os santos quebrados para esfacelar a carne. Chamam casa a estes lugares que começam onde se chama campo ao que mais ninguém quis. Tudo saqueado, vendido, traído, tudo roído por uma angústia esfomeada. “Vês o tempo como foge/ que parece que não toca?” Como querem então fazer deste tempo qualquer coisa que se sinta, que de si possa fazer exemplo, deixar algo em conserva, penetrar com um perfume apenas seu esses esforços de memória? “Correm os nossos tempos de maneira,/ Antes no mal parece que estão quedos,/ por mais que mude o sol sua carreira,/ Tantos os males são, tantos os medos,/ Que não há vale cá, não há ribeira,/ Por onde soem já cantares ledos;/ Dos tristes ouvi esses, entretanto/ Dará o céu matéria a melhor canto.”… Há tanto tempo já que não cantamos, e parece até que grão mal adivinhamos. Parece que erram buscando saber o que vão por aí inda dizendo os poetas, mas estes, pior que as silvas, têm só esta estratégia de viverem virados para si mesmos, fazendo o seu, como quem oferece caução, sem levantar ondas, e depois esperar que se insista nesse triste enredo que foi o da eternidade, como se eles disso tivessem notícias mais do que as enfermidades de retardo que nos servem de quotidiano. “O vosso Tejo vai de sangue tinto./ Tal vai o nosso Douro, tal o Lima,/ E vão ainda pior do que te pinto./ Aquele que mais pode não estima/ Entrar por onde quer, saqueia tudo,/ O fogo traz na mão, a maça e a lima./ O dono do curral há-de ser mudo,/ Se não quer, em soltando uma só fala,/ Provar com dano seu, seu aço agudo.” Só vagos ventos sem origem nem nenhuma espécie de sentido andam pelos fundos da língua, a fazer que vivam antigas imagens, muito repetidas, muito usadas para ajudar a despertar fantasmas um pouco mais doces, como o dessa Leanor descalça, que vai pela verdura até à fonte e… “A talha leva pedrada,/ pucarinho de feição,/ sai de cor de limão,/ beatilha soqueixada; cantando de madrugada,/ pisa as flores na verdura:/ Vai fermosa e não segura.” O campo hoje é mais um enredo que o ouvido capta escutando os ecos na sua intimidade ajeitados a modos bravios fazendo por se reproduzirem. “O maravilhoso move-se tão próximo/ das casas sujas e decrépitas…” E o que temos nós ainda de ligação com isto, ainda somos capazes com o nosso peso de assentá-lo em qualquer pegada que faça florescer a verdura? Somos vistos lá onde o tempo se faz outro de tão longe, e temos alguma semelhança muito lavada com esses de olhos castanhos, a tez soleada, a fala cantada de só saber das coisas o recorte emprestado pelo ar. Outros ouvirão falar de um país esquecido, entregue à sua bárbara implosão, num mundo entregue ao desaire de envelhecer, enrijar, ossificar-se sem mais distracção que a própria destruição… Essa é a sua musa, e desperta nele uma intenção terrível, a de um mundo que deita um olhar envilecido a tudo o que de fora só vem para roubar-lhe a paz, incomodá-lo. Eram mentira os idílios, e mesmo desses lendas cheias do unto verboso foi tendo outra impressão… “Um dia vi o amor – era medonho:/ tinha olhos convulsos de anjo bêbado/ e a máscara do ódio.” Os que eram daqui, de tanto se desfazerem contra os trabalhos ordinários que aos demais serviam de ilustração, impulsos para que a lira se entregasse às suas perras entoações, tão fartos de terra, de séculos sentindo os ossos lentamente esmagados contra ela, com vergões e cicatrizes herdadas na pele, e nenhum entusiasmo por esses nomes que a nós nos sabem a mel e cheiram a madressilva. Mal se puderam ver livres de tudo isto, deram cabo dela e de tudo o que lhes lembrasse, nesse crime passional de que fala o Rui Lage. Preferem-lhe tudo o que sirva para enforcar a vista, essas grandes casas, edifícios que fecham a vista, escondem o horizonte, empurram o olhar para longe de todo o céu, fartos-fartos da terra, das infinitas extensões que lhes causavam vertigem pois só viam o imenso trabalho que tudo isso lhes dava. Se nós vamos ao campo em passeio, gozar do prazer de ver a terra presa aos astros, alguns vêm a ígnea tela bárbara de espanto, conhecem os infinitos cansaços de “um povo que vivia a suicidar-se, arando a terra, abrindo a derradeira cama”. Esse povo que hoje nos custa reconhecer como a nossa mais funda tradição, povo para quem o trigo é pão em flor, povo para quem a verdadeira flor era o pão. E é deles sobre nós que sentimos assentarem como uma esparsa maldição esses olhos rasos de um espanto podre, vozes misturadas ao silêncio, um engolir a seco nas serras onde irá a enterrar por estes dias o último pastor, lugares à morte entregues todo-ouvidos. Esta a corografia que se apropriará dos nossos restos, o país das “cabras e carrascos”… “É no teu chão dorido/ Que gasto, em paz, os cascos/ Deu fauno envelhecido…” Escreva-se o requiem, então, sendo certo que de nós nada irá notar-se que não comece ali, que se esboce entre aquela névoa: “A morte/ em flor/ dos camponeses/ tão chegados à terra/ que são folhas/ e ervas de nada/ passa no vento/ e eu julgo ouvir/ ao longe/ nos recessos da névoa/ os animais feridos/ do Início.” Tão poucas páginas daquilo que se resolve antologias fazem ferida como esta. Um pó que soa, um brilho que nos chama para a infinidade dessas noites em que não havia mais que acumular o resíduo de estrelas, vê-lo pairar, como uma essência estranha àquela terra que se fazia sentir com a sua imensidão nos corpos, o peso deles também a decompor-se, sem dar notícia, nesse pouco som enfrentando os currais sem gado que ruíram de pobreza. O sofrimento é a única história, mas desta talvez só o musgo dê, “em seu discurso esquivo de água e indiferença alguma ideia disto”. E, por isso, neste tempo que é sempre depois, só nos resta passar por lá em prosa, para não nos entregarmos a essa inane torpeza de quem canta seja o que for, e se põe a soprar aos pés de um enforcado a ver se o faz balouçar… O enforcado de quem ainda alguns têm muita vergonha… “No gesto suspensivo de um sobreiro,/ o enforcado.// Badalo que ninguém ouve,/ espantalho que ninguém vê,/ suas botas recusam o chão que o rejeitou.// Dele sobra o cajado.” É uma forma de dizer mal disto tudo, outra é lançar um fósforo e rir-se ao vê-los naquela dança dos noticiários, estes que só sabem soletrar o desastre quando o campo, a paisagem deles, surge carbonizado. Quando já não é possível trocar coisa nenhuma por nada que valha. Um fim muito claro, muito fácil de entender, traduzindo em cinzas aquilo que de outro modo não era senão “um pó que nem se palpa/ na peneira do mundo”. E de toda aquela história resta o quê? Além da dúvida de um tempo incerto, sem ciclos, sem estações sequer, os campos tão sós… “Tão longe/ dos homens, as largas plantações, ermos/ sem lar, sem fumos, sequer sem espectros/ dos antigos habitantes vivos.” Aos poucos o bucolismo já não aguenta canto seja de que espécie for, morrem as espécies e só se gera já “crias das bestas e dos homens”, um hálito desolador e “oposto ao antigo sopro do Génesis; que gera/ criaturas como se meramente simulasse/ a vida. E a paisagem torna-se aparência,/ semente simulacro e armadilha”. Teremos, então, de nos contar não tanto com os resíduos de estrelas, que já quase não se vêem, mas com os resíduos do campo: “É o oco interior de alguns/ quintais. O bailado surdo/ e brusco das asas/ da galinha./ A caleira podre aonde/ chora um pingo/ – o derradeiro.// É o mundo minúsculo/ dos canteiros; a vida/ nos degraus da planta; a sesta/ de uma gata que por acaso/ insiste em ser novelo.// É este chão cinzento./ A carne entumescida das paredes./ As espinhas reunidas/ do que foi um peixe.// E as armas toscas de matar/ o tempo: colheres, comida, insectos que tentam/ (ao menos) um mundo/ irrequieto./ É a noite que tem as mãos/ suspensas sobre um alguidar/aonde bóia o dia/ pequeno/ de todas as crianças.// Em certas casas constroem-se/ filhos: a música suave/ que se ouve nas camas./ Resíduos da canção/ a única/ que este povo/ ainda sabe/ e canta.” E com este balanço todo que levamos, colhido na mais recente antologia da poesia portuguesa que nos ofereceu Rui Lage, aquela que reza sobre os campos afinal tão infelizes que foram mantidos até meados do século passado num epílogo do Neolítico, parece que deste lado já estamos safos. Mas, entretanto, se a natureza só é vista em trânsito, cada vez mais embaraçada, a vida cedeu toda ela a um comércio passageiro, e se antes Deus se pagava com o seu próprio dinheiro (lombarda, vinho, feijão-verde e batata nova entre outras espécies), agora parece que a própria vida lírica está inteiramente nos velhos, os que tendo memória de outro mundo, estão invadidos de um infinita suspeita, e tossem, conspiram contra este com uma militância certamente desencantada, mas talvez já só haja algum encanto em ser contra. “Sempre se busca alguma espécie de/ mortal eternidade e a escolha/ da terra é a melhor// forma de amar um tempo destinado/ a mostrar que a linguagem por mais/ ninguém usada// como poesia/ o mortal corpo de quem/ a usou há-de por fim dilacerar”. De resto, que resta? Talvez já só esse resíduo de alvoroço, andar para trás e recompor com toda a dificuldade uma pequena porção de toda aquela dor, emocionar-se diante de algo como um arado, que hoje adquire as feições de um passado remoto, mais parecendo o seu esqueleto. Contra a tecnologia toda que se alimenta de nós, espantar-se diante desse ser já sem mundo… “A mecânica do arado é rudimentar,/ clarividente e sóbria. Nada tem/ em demasia: o que a função requer/ e nada mais.// No perfil eficiente do arado/ há qualquer coisa de navalha, qualquer coisa/ de falo em riste, em transe de fecundar.// de facto, noutros tempos,/ era o arado que rasgava a terra,/ fazia dela um ventre aconchegado –/ cenário certo para o deflagrar da vida/ que vai dentro das sementes.// isto foi no tempo em que havia agricultura/ nos gestos quotidianos dos homens/ e das mulheres.” Agora, o campo na linguagem parece também ele algo que se trafica na sua versão transgénica. Vemos aquele talento para combinar os termos e favorecer um apelo rústico, na poesia como na gastronomia ou nos empreendimentos de turismo-rural… Os poemas dos nossos neo-bucólicos, estão cheios de tojo, restolho e urze, giestas, estalidos, de folhas secas, água a correr, das vozes distantes que chamam dos quintais, e das “casinhas/ com papás, vovós e manos, talvez/ com uma sentida perda/ de um talher à mesa e uma/ horta, couves, alfaces, a doméstica/ economia dos quintalórios/ com um cão cativo a ladrar/ à sina e à honestidade das batatas/ que as mães ou avós ainda esmagam/ na sopa com uns pingos de azeite e/ enfado. Pequeno país do/ gasóleo e futebol, memórias/ de mercados e feiras buliçosas,/ de escolinhas rústicas, agora desertas,/ com a cruz e os presidentes na parede,/ pequeno país de bravia/ palavra, sofrida crueza/ de mato ardido e estrumes, sucatas,/ detritos, o hábito endurecido dos/ pequenos holocaustos/ diários.” E para que mais queremos o espaço, a terra, o país propriamente, esse que serve de luxo de passagem, com todas essas aldeias com abismos e alguma ribeira ao fundo. Carbonizadas aldeias que parece que se deitaram para sempre, e estão por aí como ruínas de embalar, “como se nenhum de nós conseguisse entrar nesse obscuro mundo de leis e direcções invisíveis”. E olhamos para tudo isso e aqueles que lhe escaparam de algum modo talvez se sintam como se reconfortados, como se não pudesse mesmo haver volta, e não quisessem daquele mundo outra coisa além desse “mecanismo triste/ movendo a boca breve”. E o fogo talvez seja a última honestidade de que somos capazes. Talvez, de algum modo, nós sejamos toda a destruição que sonharam e convocaram essas tantas gerações que ali no campo “nasciam, penavam e pereciam no anonimato e no isolamento mais cru”, como nos diz Rui Lage. “A luta pelo pão de cada dia exauria a força vital, conduzida para o braço que fazia descer a enxada e o mangual, que pilotava o arado, que cegava as espigas no braseiro do estio e tocava o gado pelos montes. Do berço à cova, a existência do camponês compunha-se de agruras e privações inumeráveis. Ninguém disse tal condição em verso tão cortante quanto Gil Vicente, pela voz do lavrador da Barca do Purgatório (1518): ‘Sempre é morto quem do arado/ há-de viver'. Afinal, nesse auto medial da trilogia das barcas, o Lavrador anuncia-se ao Anjo não como debutante da morte, mas como seu veterano: ‘Da morte venho eu cansado'. Séculos a fio, o adeus aos campos infelizes foi um gesto vedado. A aldeia fazia as vezes de um cativeiro sem grades.”

Um pedido de isenção temporária de portagens na A13 e na A10 , com carácter de urgência. As subidas das águas dos rios Almansor, Sorraia e Tejo,  levaram ao corte de várias vias rodoviárias. Edição de Cláudia Costa.

Apesar da descida dos caudais do Tejo, João Teixeira Leite não acredita que, na globalidade, o pior já tenha passado. Mostra-se especialmente preocupado com as encostas no concelho de Santarém.See omnystudio.com/listener for privacy information.

Na Golegã, a última semana deixou marcas visíveis e outras, menos óbvias, mas igualmente pesadas: estradas cortadas, serviços condicionados, equipamentos municipais danificados e o dia-a-dia mudou. Ao mesmo tempo, o concelho viu adiada a votação da segunda volta das eleições presidenciais, para dia 15 de Fevereiro. Rui Xavier, membro da Assembleia Municipal da Golegã, vive na região há cerca de um ano e meio. Não é natural do concelho, mas fala já com a atenção de quem aprendeu a ler o terreno, os ritmos do rio e os sinais que circulam entre vizinhos. “Aqui é um sítio que tradicionalmente teve e tem muitas cheias”, começa por explicar. E, no entanto, sublinha que desta vez houve um elemento novo: “Em relação à tempestade de vento, chuva, as pessoas mais velhas dizem que não têm memória de uma tempestade deste género.” Apesar do impacto, Rui Xavier faz questão de relativizar a gravidade local face ao resto do país. “Aqui, embora tenha sido razoável em algumas empresas e com a destruição de parte de árvores, ainda assim não tem comparação com o que aconteceu, por exemplo, no epicentro, ali em Leiria e noutras zonas do país.” O prejuízo mais evidente, diz, está na sede do concelho. E aponta casos concretos: “As piscinas municipais têm uma área muito grande envidraçada. Uma parede toda em vidro foi completamente destruída.” O problema, explica, vai além do custo. “Vamos ver o tempo que agora vai demorar a recuperar-se aquele equipamento que é um dos centros da comunidade.” E enumera, com precisão, o que está em causa: “É um sítio onde têm aulas os miúdos das escolas. Há, para além da unidade escolar para pessoas idosas, natação livre.” Ele próprio usa o espaço: “Eu faço muito regularmente lá.” Para Rui Xavier, a dimensão do dano não é apenas material: é logística, social e comunitária. “A recuperação não é só uma questão de dinheiro, é mesmo uma questão agora logística.” Luz intermitente e água a subir Nas freguesias do Pombalinho e da Azinhaga, onde vive, o impacto foi mais contido. “Houve falta de luz durante as primeiras 48 horas na sede do concelho, portanto na vila da Golegã.” Já ali, diz, o cenário foi diferente: “Aqui onde nós estamos, que é o Pombalinho e a Azinhaga, ela foi sendo intermitente. Mas nunca houve um período, creio que mais do que algumas 6 horas, em que tivéssemos estado sem energia.” O concelho vive encostado à água e isso molda tudo. “Nós estamos muito próximos do rio Tejo e aqui, no caso de Pombalinho e Azinhaga, do rio Almonda.” Quando chove a sério, o que acontece é quase previsível: “Sempre que há chuvas mais intensas, o caudal do rio aumenta.” A estrada cortada que muda a vida: 8kms passam a 30kms A consequência mais pesada, sublinha, não foi a destruição de casas, foi a interrupção do movimento. “A estrada que liga a Golegã, a Azinhaga e vice-versa (…) é muito comum ficar cortada.” Mas desta vez, insiste, a duração surpreendeu: “Desta vez ficou cortada e ainda está cortada durante muito mais dias.” E é aqui que o dia-a-dia se encarece. “O caminho entre a Azinhaga e a Golegã são à volta de 8 km.” Agora, diz, a realidade é outra: “Tenho vizinhos, amigos, que estão a fazer 20, 25, 30kms.” Rui Xavier chama-lhe pelo nome certo: impacto económico. “O impacto económico na vida das pessoas é muito grande.” Mesmo sem “um grande impacto no edificado”, a factura chega de outra forma: “A possibilidade de deslocação ou haver uma deslocação que de repente passa a ser três vezes maior.” E remata: “Os valores que as pessoas dispendem nessas deslocações têm um impacto muito grande nas contas do fim do mês.” A forma como estas comunidades vivem a cheia é, para quem chega de fora, quase desconcertante. Rui Xavier reconhece-o: “Eu estou cá há pouco tempo e vou aprendendo.” Mudou-se de Lisboa com a mulher, por gosto e por escolha. Mas, diz, uma preocupação esteve sempre presente: “Sabendo que há cheias regulares nesta zona, estávamos num sítio em que a água facilmente cá chegasse.” A surpresa veio depois: “Percebemos a forma como as pessoas lidam com o caudal a aumentar e a transbordar.” Porque aqui, ao contrário do que se vê na televisão, a água não é apenas medo: é também fertilidade e continuidade. “Tudo aqui à volta, a grande fertilidade dos solos depende em muito de ciclicamente serem alagados.” E aponta a paisagem por trás da sua casa como exemplo. “A água, como nós vemos aqui na parte de trás da minha casa, desde que mantenha estes níveis, é quase uma coisa óptima e uma bênção.” Cita, sem romantizar, o que ouve dos mais velhos: “As pessoas mais velhas dizem mesmo isto: ‘Assim tá óptimo.'” E a condição é clara: “Desde que não tenha impacto na casa das pessoas e que não suba muito mais.” A memória agrícola é antiga. “Uma das pessoas mais velhas disse-me (…) que isto era fantástico, porque aqui há umas décadas (…) se o ano fosse mais ou menos seco (…) alagavam os campos através de valas.” Um saber acumulado, transmitido e adaptado: “Todo esse conhecimento acumulado mantém-se.” Para Rui Xavier, a palavra-chave é relação: “Há uma relação muito mais simbiótica com a natureza e até com a proximidade da água.” “A lei da gravidade cumpre-se" Hoje existem réguas hidrométricas, alertas, Protecção Civil e medições em tempo real. Rui Xavier reconhece: “A informação flui de uma maneira que não tem comparação com há décadas atrás.” E elogia o papel local: “As juntas de freguesia tiveram um trabalho muito importante em manter a população informada.” Mas há outra camada, mais antiga, mais humana, mais exacta do que parece: a leitura do território. “As pessoas aqui têm um conhecimento empírico disso, de observação, muitas vezes baseadas em marcos de construção.” Conta um episódio que vale por um tratado de geografia local. A estrada que liga o Pombalinho a Mate Miranda foi cortada por precaução. Rui Xavier falava com o vizinho Manuel, 90 anos, que viveu todas as grandes cheias do século passado e deste século, em 2013. A resposta do homem foi imediata: “Eles cortaram a estrada por precaução, mas ainda se passa lá.” E como é que se sabe? Rui Xavier explica o critério: “Para as pessoas da idade dele, é ter água acima do joelho ou na cintura.” A razão é simples: “Porque já não dá para passar de bicicleta, porque é assim que as pessoas se deslocavam aqui durante décadas.” E continua, “O Manel ainda hoje, com 90 anos, todos os dias anda de bicicleta.” O momento culmina numa frase que Rui Xavier repete com admiração: “Para não passar na estrada de Mate Miranda, a água tem que chegar aqui a este poste.” E depois a conclusão perfeita: “A lei da gravidade cumpre-se. E a água é autonivelante.” O conhecimento do terreno, diz, é tão profundo que dispensa deslocações. “Sabem que quando isto acontece aqui tem implicações ali e não precisam de ir lá sequer ver. Têm a certeza.” Voto adiado para domingo, 15 de Fevereiro No meio deste cenário, o adiamento da votação na segunda volta das presidenciais deixou frustração e um debate inevitável. Rui Xavier não esconde a sua posição: “Eu preferia ter podido votar este domingo” E acrescenta: “As condições climatéricas estão razoáveis e acho que seria possível votarmos.” Ainda assim, não aponta o dedo. “Compreendo que as autoridades tenham avaliado com antecedência e tenham avaliado o risco.” E lembra que a sucessão de tempestades foi imprevisível: “Estavam anunciadas estas outras, embora não me agrade não poder votar, eu compreendo essa precaução.” A frase que usa é rara na política portuguesa, como ele próprio nota: “Parece uma coisa nada portuguesa, mas vale prevenir.” A garantia que o tranquiliza é simples: “Eu sei que vou votar no próximo domingo (…) e que o meu voto também conta.” E deixa um apelo directo à participação: “Acho que é uma obrigação a nossa voz também ser ouvida.” Rui Xavier admite o incómodo, mas recusa dramatismos: “Não é o ideal, mas é o possível.” E insiste na ideia central: segurança primeiro. “Pôs-se em prioridade a possibilidade das pessoas poderem votar em segurança e o processo ser mais razoável.” O concelho, lembra, está em situação de calamidade. E faz um exercício concreto: “Imaginemos que estas últimas 24 horas tinham sido realmente muito fustigadoras.” Estradas cortadas, comboios interrompidos, pessoas a deslocarem-se de fora para votar: “Isso não era muito razoável.” No fim, regressa à mesma lógica que viu nos cortes de estrada e nos avisos de cheia: precaução. “Compreendo que uma estrada seja cortada quando há pouca água a passar por cima, mas que ainda assim é um risco para a população.” A Golegã, como tantas vezes, volta a ser um território entre dois movimentos: o da água que sobe e o do país que tenta avançar. Aqui, as cheias são antigas — e a democracia, por uma semana, ficou à espera.

Imagens da Força Aérea mostram cheias no Mondego, Tejo e Sado9fa6f132-9d02-f111-8330-6045

Presidentes de Câmara de Vila Franca de Xira e Abrantes preocupado com subida do caudal do Tejo. Pedem maior intervenção na zona ribeirinha e deixam apelo de cuidado à população. See omnystudio.com/listener for privacy information.

Com o Mondego a galgar margens, Alcácer debaixo de água e as aldeias ribeirinhas do Tejo a fazerem figas, parece que nos lembrámos da utilidade das barragens. Pena que lhes tenhamos complicado a vida.See omnystudio.com/listener for privacy information.

Jorge Miguel Miranda, ex-presidente do IPMA, alerta para chuva persistente e possíveis cheias no Tejo, Sado e Mondego. Pede prudência à população e apoio às zonas afetadas.See omnystudio.com/listener for privacy information.

In der ersten Folge des neuen Jahres reisen Jenny und Malte nach Lissabon – ins Verride Palácio de Santa Catarina. Ein ehemaliger Stadtpalast hoch über dem Tejo, der Geschichte, Zurückhaltung und luxuriöse Gelassenheit auf ganz besondere Weise vereint.

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

Xavier Barreto identifica falta de meios na Margem Sul do Tejo para responder à procura atual e propõe solução no imediato: "Libertar internamentos sociais" caso contrário, novas mortes são hipótese.See omnystudio.com/listener for privacy information.

Andrej Hauptman je eno izmed najbolj prepoznavnih imen slovenskega športa, ko govorimo o vrhunskem kolesarstvu, fokusu in zmagovalnem mindsetu. Nekdanji vrhunski kolesar in dolgoletni trener danes stoji ob strani najboljšim kolesarjem sveta, a njegovo delo presega šport.Poslišaj kakšne Fuck-Upe je ušpičil Andrej Andrej Hauptman je bil večkratni državni pravk, leta 2001 je postal prvi slovenski kolesar, ki je osvojil bronasto medaljo na svetovnem prvenstvu v cestnem kolesarstvu, ki je potekalo v Lizboni na Portugalskem. S tem je postavil slovensko kolesarstvo na svetovni zemljevid.Po koncu tekmovalne kariere je postal kolesarski trener in pomagal trenirati in kovati mlade talente. Bil je osebni trener slovenskega kolesarja Tadej Pogačar ter tudi glavni trener in vodja selektorjev slovenske kolesarske reprezentance. V preteklosti je vodil tudi ekipo Pogi Team Gusto Ljubljana, kjer je med drugim vodil Pogačarja skozi njegovo kategorijo do 23 let. Andrej je treniral tudi Primoža Rogliča, ko je ta prešel iz smučarskih skokov v kolesarstvo in vozil za razvojno ekipo moštva. Maja 2019 se je Andrej pridružil moštvu UAE Team Emirates kot športni direktor (directeur sportif), potem ko se je Pogačar pridružil tej ekipi. Od takrat ekipa kroji sam vrh in vsako leto znova preseže svoje rezultate, ki so izjemni. V letu 2025 so ponovno podirali rekorde in osvojili kar 95 zmag. Od leta 2019, ko se je pridružil Andrej, so osvojili tudi 4 naslove Tour de France, kjer je blestel Tadej Pogačar, ki že velja za enega najboljših kolesarjev vseh časov in izjemen talent. Andrej ima tudi dva otroka in izjemno ženo Tejo ter tudi psa in mačka, kar ga še dodatno izpopolnjuje. 

Uma viagem à Antártica, a sustentabilidade na aviação e o romance sobre a mediática queda de um hidroavião no Tejo (que até deu um filme em Hollywood) marcam o regresso de José Correia GuedesSee omnystudio.com/listener for privacy information.

No último episódio da terceira temporada do podcast NARRATIVA, atravessamos o Tejo até à Ponta dos Corvos com Nuno Andrade, fotógrafo e arquitecto, para quem a margem é lugar de encontro, liberdade e pertença. Em PicNic, o autor revela gestos simples de convivência e celebração, transformando um território muitas vezes visto como periférico num espaço vivo de comunidade.Com exposições realizadas em Portugal, França, Finlândia e Índia, e depois de ter sido distinguido com o Prémio de Fotografia HSBC em 2019 — ano em que publica o seu primeiro livro — Nuno constrói um percurso consistente, marcado por um olhar persistente sobre a geografia humana que o rodeia. PicNic surge agora como um marco decisivo: o projecto vale-lhe o Prémio NARRATIVA – FUJIFILM, tornando-o o primeiro vencedor desta distinção dedicada à fotografia contemporânea em Portugal.Neste episódio, conversamos com o autor sobre fotografia como ferramenta de desconstrução de estigmas, sobre o trabalho no terreno e sobre o futuro deste projecto, que abre a programação anual da NARRATIVA com uma exposição individual com inauguração a 10 de Janeiro de 2026 — encerrando a temporada com um convite a repensar a ideia de margem e comunidade.Guião e moderação de Bárbara MonteiroEdição de som de Bárbara MonteiroJingle de António QuintinoDesign de Alex Paganelli

Andrej Hauptman je eno izmed najbolj prepoznavnih imen slovenskega športa, ko govorimo o vrhunskem kolesarstvu, fokusu in zmagovalnem mindsetu. Nekdanji vrhunski kolesar in dolgoletni trener danes stoji ob strani najboljšim kolesarjem sveta, a njegovo delo presega šport. Andrej Hauptman je bil večkratni državni pravk, leta 2001 je postal prvi slovenski kolesar, ki je osvojil bronasto medaljo na svetovnem prvenstvu v cestnem kolesarstvu, ki je potekalo v Lizboni na Portugalskem. S tem je postavil slovensko kolesarstvo na svetovni zemljevid.Po koncu tekmovalne kariere je postal kolesarski trener in pomagal trenirati in kovati mlade talente. Bil je osebni trener slovenskega kolesarja Tadej Pogačar ter tudi glavni trener in vodja selektorjev slovenske kolesarske reprezentance. V preteklosti je vodil tudi ekipo Pogi Team Gusto Ljubljana, kjer je med drugim vodil Pogačarja skozi njegovo kategorijo do 23 let. Andrej je treniral tudi Primoža Rogliča, ko je ta prešel iz smučarskih skokov v kolesarstvo in vozil za razvojno ekipo moštva. Maja 2019 se je Andrej pridružil moštvu UAE Team Emirates kot športni direktor (directeur sportif), potem ko se je Pogačar pridružil tej ekipi. Od takrat ekipa kroji sam vrh in vsako leto znova preseže svoje rezultate, ki so izjemni. V letu 2025 so ponovno podirali rekorde in osvojili kar 95 zmag. Od leta 2019, ko se je pridružil Andrej, so osvojili tudi 4 naslove Tour de France, kjer je blestel Tadej Pogačar, ki že velja za enega najboljših kolesarjev vseh časov in izjemen talent. Andrej ima tudi dva otroka in izjemno ženo Tejo ter tudi psa in mačka, kar ga še dodatno izpopolnjuje.  Najljubša serija: Kriminalne serijeHobiji: KolesarjenjeNajljubša hrana: Italijanska hranaNajljubši podjetnik: veliko Slovenskih podjetnikov, ki krojijo sam vrh svetaNajljubša aplikacija: WhatsAppZaključni nauk:Sledite svojim sanjam in če nekaj verjamete s srcem, to storite.

Javier Figueredo, alcalde de El Espinal, nos habla sobre la situación de la presa de El Tejo

En la 1439-a E_elsendo el la 25.11.2025 ĉe www.pola-retradio.org: • Nian hodiaŭan felietonon ni dediĉas al la varsovia verdaĵplena kvartalo Marysin Wawerski, en kies tombejo okazis ĉi matene la lasta adiaŭo de la konata pola esperantisto, Roman Dobrzyński. Nian programinformon akompanas foto pri „Sobieski-arbaro”, la dua plej granda komplekso de arbaraj parkoj en Varsovio, troviĝanta i.a. najbare de Marysin Wawerski; • En la kulturtema kroniko – post la kalendarfoliaj informoj, ligitaj kun historiaj eventoj de  25.11 – ni informas pri ekspozicio de skulptaĵoj de Szymon Ołtarzewski en Romo; pri la premio Živa, plia nunjara distingo por la Muzeo de Siberiekzilitoj en Bjalistoko; pri la varsovia ekspozicio de Adam Kossowski, kies 120-a naskiĝdatreveno pasos la 5-an de decembro; • En la E-komunuma segmento ni informas pri Erasmus+ por studi Esperanton kiel interfakan kampon por transnacia novigado; pri TEJO en la estraro de CoNGO; pri kelkaj E-eldonaĵoj kun hispanlingva tradukparto prezentitaj al la ekstera publiko de Madrido; • Muzike akompanas nin fragmente la kanto de TEAM „Rakonta silentado”'; • En unuopaj rubrikoj de nia paĝo eblas konsulti la paralele legeblajn kaj aŭdeblajn tekstojn el niaj elsendoj, kio estas tradicio de nia redakcio ekde 2003. La elsendo estas aŭdebla en Jutubo ĉe la adreso: https://www.youtube.com/results?q=pola+retradio&sp=CAI%253D Ineralie pere de Jutubo, konforme al individua bezono, eblas rapidigi aŭ malrapidigi la parolritmon de la sondokumentoj; eblas transsalti al ajna serĉata fragmento de la elsendo.

Correr é feito de contrastes: os dias em que tudo flui e aqueles em que nada parece correr bem. Neste episódio exploramos este yin e yang da corrida, partilhando experiências de recordes pessoais, lesões, pausas forçadas e o regresso ao prazer de treinar.Partilhamos ainda experiências recentes em provas como a Corrida do Tejo, debatemos a importância de relativizar bons e maus momentos, e exploramos como o treino, a recuperação e até o reforço muscular fora da corrida moldam não só o desempenho, mas também a forma como vivemos este estilo de vida.Anunciamos também o início da parceria do podcast com a Snupe Nutrition, marca nacional de nutrição desportiva que se junta ao Pace Setters e que já foi convidada do nosso podcast anteriormente. Para mais informação, falem connosco!RecomendaçõesSneaker Wars: Adidas v Puma - Docuseries (Disney+) - https://www.imdb.com/title/tt11382826Once a Runner - Livro (John L. Parker) - https://www.goodreads.com/book/show/98250.Once_a_RunnerObtém 7% de desconto com o cupão: PACESETTERSAproveita já em https://anadias.run!Ajudem-nos a crescer pelo preço de um ☕️ - https://www.buymeacoffee.com/pacesettersSegue-nos nas redes sociais e Youtube!Vítor Oliveira - Aquele Que Gosta de Correr- IG: https://www.instagram.com/aquelequegostadecorrer/- Facebook: https://www.facebook.com/aquelequegostadecorrer- YouTube: @AqueleQueGostaDeCorrer- Blog: https://www.aquelequegostadecorrer.com/Luís Machado - Quero, Posso e Corro- IG: https://www.instagram.com/queropossoecorro/- Blog: https://queropossoecorro.com/

Neste episódio falamos de um membro esquecido da Dinastia de Avis: o príncipe D. Afonso, filho e herdeiro de D. João II. Abordamos o seu nascimento e criação, a estadia como refém durante as Terçarias de Moura, o seu casamento com uma princesa castelhana e as faustosas celebrações do matrimónio em Évora, até concluirmos com a sua trágica morte junto ao Tejo em 1491.Sugestões de leitura1. Paulo Drumond Braga – O Príncipe D. Afonso, filho de D. João II: uma vida entre a guerra e a paz. Edições Colibri, 20082. Luís Adão da Fonseca – D. João II. Temas e Debates, 2007.-----Obrigado aos patronos do podcast:André Silva, Bruno Ricardo Neves Figueira, Cláudio Batista, Gustavo Fonseca, Isabel Yglesias de Oliveira, Joana Figueira, NBisme, Oliver Doerfler;Alessandro Averchi, Alexandre Carvalho, Andre Oliveira, Carla Pinelas, Carlos Castro, Cláudia Conceição, Daniel Murta, David Fernandes, Domingos Ferreira, É Manel, Francisco, Hugo Picciochi, João Cancela, João Carreiro, João Pedro Tuna Moura Guedes, Jorge Filipe, Luís André Agostinho, Manuel Prates, Miguel Vidal, Patrícia Gomes, Pedro Almada, Pedro Alves, Pedro Ferreira, Rui Roque, Tiago Pereira, Vera Costa;Adriana Vazão, Ana Gonçalves, Ana Sofia Agostinho, André Abrantes, Andre de Oliveira, André Silva, António Farelo, António Silva , Bruno Luis, Carlos Afonso, Carlos Ribeiro, Carlos Ribeiro, Catarina Ferreira, Civiforum, Lda., Diogo Camoes, Diogo Freitas, Eugenia Capela, Fábio Videira Santos, Francisco Fernandes, Gn, Gonçalo Pedro, Hugo Palma, Hugo Vieira, Igor Silva, João Barbosa, João Canto, João Carlos Braga Simões, João Diamantino, João Félix, João Ferreira, Joao Godinho, João Pedro Mourão, Joel José Ginga, Johnniedee, José Beleza, José Santos, Luis Colaço, Miguel Brito, Miguel Gama, Miguel Gonçalves Tomé, Miguel Oliveira, Miguel Salgado, Nuno Carvalho, Nuno Esteves, Nuno Silva, Parte Cóccix, Paulo Silva, Pedro, Pedro Cardoso, Pedro Oliveira, Pedro Simões, Ricardo Pinho, Ricardo Santos, Rui Curado Silva, Rui Rodrigues, Simão, Simão Ribeiro, Sofia Silva, Thomas Ferreira, Tiago Matias, Tiago Sequeira, Tomás Matos Pires, Vitor Couto.-----Ouve e gosta do podcast?Se quiser apoiar o Falando de História, contribuindo para a sua manutenção, pode fazê-lo via Patreon: https://patreon.com/falandodehistoria-----Música: “Five Armies” e “Magic Escape Room” de Kevin MacLeod (incompetech.com); Licensed under Creative Commons: By Attribution 4.0 License, ⁠http://creativecommons.org/licenses/by/4.0⁠Edição de Marco António.

Nuno Duarte passou de desconhecido do público leitor a vencedor do Prémio LeYa. Em 2024, foi o escolhido pelo júri com o seu primeiro livro, “Pés de Barro”, em que ficciona a construção da Ponte sobre o Tejo - hoje Ponte 25 de Abril -, a partir de um pátio em Alcântara, onde vive Victor, que vem trabalhar na ponte, e Dália, a muda que cheira a chocolate.A que chegou a ser Ponte Salazar era, para o escritor, o “símbolo máximo do Estado Novo”. E, nesta entrevista a Magda Cruz, deixa um ponto assente: não podia escrever um livro passado durante o Estado Novo que não batesse no regime. Nuno Duarte nasceu anos antes da Revolução dos Cravos, detesta a ditadura e sublinha que é um tempo a que não quer voltar, apesar de sentir algum saudosismo, nos dias de hoje, vindo de algumas pessoas.Neste episódio do “Ponto Final, Parágrafo”, Nuno Duarte reflete sobre a importância do Prémio LeYa, sobre se tornar escritor e sobre como não sente pressão do mercado editorial para escrever um novo romance. Aliás, já escrevia o segundo livro quando nem sabia da atribuição do prémio, e ideias para três ou quatro livros não lhe faltam, garante.Considera apoiar o podcast no Patreon: patreon.com/pontofinalparagrafoContacto do podcast: ⁠⁠pontofinalparagrafo.fm@gmail.com⁠⁠Segue o Ponto Final, Parágrafo nas redes sociais: Instagram, Twitter e FacebookProdução, apresentação e edição: Magda CruzGenérico: Nuno ViegasLogótipo: Gonçalo Pinto com fotografia de João Pedro Morais

Jony Ive ha vuelto. En forma de chapas.El tema de la semana obviamente es nuestro amigo y fiel oyente Juanito el Tejo; que se alía con OpenAI para intentar llegar donde el iPhone no llegó o no llegará. Eso dicen. Para muchos tertulianos es una clara señal negativa para el futuro de Apple, pero nosotros no lo tenemos claro.Un segundo tema clave es el análisis crítico de la estrategia de Apple en IA hasta ahora liderada por John Giannandrea, y contrastada con empresas como Meta o Google. Debatimos si la cautela de Apple —priorizando privacidad y hardware local— es una ventaja o un obstáculo frente a competidores que entrenan modelos con grandes datos.Coincidimos en que Apple debe equilibrar innovación con pragmatismo, evitando productos redundantes (como el Humane AI Pin o Rabbit R1) y enfocándose en integraciones útiles en sus ecosistemas. A ver si en el WWDC de este año vemos algo.Nos vamos como es habitual hablando de Apple TV+, on el estreno de Matabot, la gran primera temporada de The Studio y la inminente llegada de Fundación. La Conferencia Mundial de Desarrolladores de Apple comienza el 9 de junio - Apple (CO) Apple trabaja en LLM Siri, una versión mejorada de su asistente que competirá con ChatGPT WIRED OpenAI to Buy Apple Veteran Jony Ive's AI Device Startup in $6.5 Billion Deal - Bloomberg Sam and Jony introduce io OpenAI Jony Ive's AI gadget rumored to be ‘slightly larger' than Humane's AI pin The Verge Murderbot Rotten Tomatoes Sistemas críticos: Los diarios de Matabot (Alethé) : Wells, Martha, BATALLER ESTRUCH, CARLA: Amazon.es: Libros Martha Wells (Author of All Systems Red) The Studio | Rotten Tomatoes Michael J. Fox joins ‘Shrinking' cast as guest star for Season 3 - 9to5Mac

Escuche esta y más noticias de LA PATRIA Radio de lunes a viernes por los 1540 AM de Radio Cóndor en Manizales y en www.lapatria.com, encuentre videos de las transmisiones en nuestro Facebook Live: www.facebook.com/lapatria.manizales/videos

It's been a while. But come with us as we shit talk our way through the Premier season, discuss the end of VCT split 1, and oh, were there some Tejo changes? Discord: https://discord.gg/n5eP3XxzuGSubreddit: www.reddit.com/r/drunkvalorantpodcast

Prelego: pri Tejo kaj la Internacia junalara Kongreso de Hoan Tran kaj Arya Bhaskara Ferduzi. Kanto: el la kompaktdisko Marta kaj JoMo kantas Mayoma “mi revenas Nakozonga”.  Legado: Franciska el la libro Volontuloj kun okulvitroj de Jef Last kaj Nordhal Grieg ‘  Unua majtago je la fronto”. Kanto : el la kompaktdisko Dezertoj de Armel […]

Gastas ĉe nia mikrofono Tyron SURMON, prezidinto de TEJO por 2023-24.Kun li ni multe diskutas kio funkcias bone kaj malbone en TEJO kaj en la movado ĝenerale, kia estas la sperto servi en estraro aŭ eĉ prezidi organizon, kaj kion ni prognozas por la estonteco de la movado.Kiel juna esperantisto trovas sian vojon al TEJO? Kial TEJO-anoj apud la aĝlimo malpli ofte trovas sian vojon aktiviĝi en UEA? Ĝis kiu grado respondecas Hanso por la plej enliberafoliigindaj aferoj?Registrita la 3an de majo, 2025LigilojSubstako de Usone PersoneSubstako de BrandonoNASKDulanda Kongreso 2025KER-ekzamenojLibroj:La BiblioBridge of WordsThe Secret HistoryLa Kaŝita Vivo de ZamenhofLa Sindikato de Jidaj PolicistojBaza Literatura KrestomatioParnasa GvidlibroMi Stelojn Jungis al RevadoDankesprimoj:Ni volas elkore danki niajn subtenantojn Matt Brooks kaj Phillip David Morgan. Dankon al ili ambaŭ! Get full access to Usone Persone at usonepersone.substack.com/subscribe

In the first episode of this series, Eoin takes you to Colombia, where our first destination is the city of Medellin. Here, there are football ultras trying to make the world a better place, and there is a football club trying to get away from the historic association with Pablo Escobar.There is the national sport of Tejo, where drinking beer is encouraged and making things explode is the aim of the game.In Bogota, we hear about a national obsession with cycling, and how it's intertwined - and not for the better - with the current politics of Colombia. There is better news on the women's football front, however, as the national team gains positive headlines.There is also alcohol tasting, a bus that gets stuck in the highland mud and a dog-friendly MMA gym.Before all that, there's part one of the Patagonian hitchhiking journey, as Eoin tries to get a lift out of the town of Bariloche.Follow Eoin…https://www.instagram.com/eoinsheahan/ https://x.com/EoinSheahan https://www.tiktok.com/@eoinsheahan

Coming to your OTB Daily feed this Tuesday (March 25th) at 10am - the first episode of Eoin Sheahan's Diverted. Before the first episode went live, Eoin chatted to OTB's Mick McCarthy about the 10-part series, which you can find each Tuesday in the OTB Daily feed, and in its own podcast feed. You can access that here to subscribe. Follow Eoin on this not-so-smooth journey from the Deep South of the USA to the southern tip of Argentina, as he tries to recover from missing out on the Biggest Party In The History of The Universe (™).Each week, this podcast will travel to a new country, and we will bring you to a ritualistic fighting festival in rural Bolivia, to the basketball-crazy highlands of Guatemala and to the equine sport heartlands of Argentina's gaucho country. We will become experts in Jai-Alai and Tejo, we will speak extensively with a survivor of the 1972 Andes plane crash, we will become fans of FC Palestino and hang out with lucha libre fighters. There are carnivals and psychedelics and broken bones, as we run from Andean karma but towards any presence of Maradona and Messi.

Por la kvina epizodo de nia tria sezono, venis al nia tablo nia kara Stela Besenyei-Merger, kiu lastatempe estis dufoje premiita, kiel la Esperantisto de la Jaro por 2024, kaj kiel la unua ricevanto de la Premio Maertens. Ni invitis ŝin por paroli pri ŝia sperto, kion signifis por ŝi tiuj premioj, kaj kial gravas rekoni la laboron kaj kontribuojn de tiuj, kiuj dediĉas sin al la afero.Ni longe diskutis pri kial gravas lerni de veteranaj movadanoj, kaj agnoski ties kontribuojn. Nur kelkajn minutojn post la registrado, ni lernis pri la forpaso de Renato CORSETTI (1941-2025). Renato kontribuis dum jardekoj al nia movado. Inter alie li prezidis por TEJO (1971-1973) kaj UEA (2001-2007), kaj estis delonge tre aktiva membro de la komitato de UEA. Krom tio, li estis la edzo de Anna Löwenstein, nia lastatempa gasto, kaj ni sendas niajn kondolencojn al ŝi kaj al la infanoj de Anna kaj Renato. Ni bedaŭras, ke ni neniam havis okazon gastigi lin, kaj dediĉas la epizodon al li. Registrita la 1-an de februaro, 2025.Foto de Renato Corsetti: Aleks Aɴᴅʀᴇ - Propra verko, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=48489480LigilojSubstako de Usone PersoneNovaĵo pri forpaso de Renato CORSETTIEsperantisto de la JaroPremio Grégoire MaertensIntervjuo de Mark Fettes al StelaLibrojY2KMonstersPoemo de UtnoaPromeso en obskuroOur EveningsLingua, Politica, Cultura. Serta Gratulatoria in Honorem Renato Corsetti: Festlibro en Esperanto, la itala kaj la angla (ne menciita dum la epizodo, sed bona komencpunkto por orientiĝi pri la vivo kaj verki de Renato Corsetti) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit usonepersone.substack.com

Earlier on the show we found ourselves talking about ways to track down a wasps nest. One suggestion was to lightly freeze the wasp, tie a string to its leg and watch where it goes.  Listener, Tejo, chats with Jesse Mulligan about how they caught a wasp with a new type of 'wasp catching mist' from the Australian Entomological Society, and gave it ago.

Head to https://factormeals.com/VALORANT50OFF for 50% off your first order + free shipping!

With Chase carousing down south, SwaNiac joins us as in welcoming the new Columbian initiator as he makes an explosive entrance. Has Riot finally returned to releasing agents that aren't a huge disappointment at launch? And are they trying to Flex on the playerbase by releasing something no one wants and using that to increase the price of weapon skins (no, that's fucking ridiculous)? We're so (2/3) back baby!Discord: https://discord.gg/n5eP3XxzuGSubreddit: www.reddit.com/r/drunkvalorantpodcast

No quarto show da turnê do Boia por terras lusitanas, tivemos como convidados especialíssimos MP, vulgo Miguel Pedreira, o paralelo português de Bruno Bocayuva no quesito nomes, números e datas do surfe, e Tito Rosemberg, em áudio no Pra Lá de Marrakesh e ao vivo, comentando suas próprias palavras. O impacto econômico da WSL em Portugal, o Festival de Curtas da Lourinhã, a entrega de prêmios das ondas grandes na Nazaré, a nova edição da ressuscitada SURFER, gaita de foles, os segredos da Margem Sul do Tejo e muito mais em maís um episódio antológico do podcast mais delirante dos sete mares. O episódio começa com o velho canalha Serge Gainsbourg cantando Vielle Canalle, segue com Jamie Hinckson e uma versão jazz de Waiting In Vain e a novidade portuguesa A Garota Não, com Dilúvio e encerra com o encontro luso-tupiniquim de Sérgio Godinho e Caetano Veloso com uma versão de Lisboa Que Amanhece de fazer inveja a Burt Bacharach.

A violência que irrompeu na grande Lisboa, a norte e sul do Tejo, nas últimas noites, depois da morte de Odair Moniz, vítima de disparos da polícia, na madrugada da última segunda-feira, no bairro da Cova da Moura é o tema central deste Eixo do Mal em podcast, com Daniel Oliveira, Luís Pedro Nunes, Pedro Marques Lopes e Clara Ferreira Alves. O inquérito está em andamento, tudo ainda bastante incerto, apesar das certezas de muitos ao longo dos dias. Ouça aqui a emissão de 24 de outubro na SIC Notícias.See omnystudio.com/listener for privacy information.

Former door guy and all around great guy, Jesus Trejo sits down with Eleanor for a fun conversation about The Store, standup, family and landscaping (...?). Jesus gives us a cover reveal for his second illustrated children's book Mama's Magnificent Dancing Plantitas! Go buy Papá's Magical Water-Jug Clock - https://bit.ly/PapásMagicalWaterJugClock Follow https://www.instagram.com/jesustrejo1 Watch Roots of Comedy - https://www.pbs.org/show/roots-of-comedy-with-jesus-trejo/ Visit his website for all things Jesus Trejo - https://jesustrejo.komi.io/ Watch Eleanor's special No Country for Old Women OUT NOW https://www.youtube.com/watch?v=HZE8P7d-jyA Follow us on IG Rick Ingraham - https://www.instagram.com/rickingraham/ Eleanor Kerrigan - https://www.instagram.com/ejkerrigan The Comedy Store - https://www.instagram.com/thecomedystore Comedy Store Studios - https://www.instagram.com/comedystorestudios Wanna buy something with our logo on it? https://shop.comedystore.com/ and https://comedystorerecords.myshopify.com Learn more about your ad choices. Visit megaphone.fm/adchoices