Podcasts about V4

Alex Marquez e Marco Bezzecchi, Ducati e Aprilia davanti a tutti dopo gli ultimi time attack e le varie simulazioni. Gli equilibri non sembrano tanto diversi dall'anno scorso, ma le novità viste sulle MotoGP 2026 sono tante e importanti. Le vedremo insieme. Quattro piloti italiani nei primi sei. Tra i debuttanti, Toprak diciottesimo davanti a Moreira. Sembra di cogliere una generale soddisfazione di piloti e tecnici. Analizzeremo casa per casa, con un occhio particolare a quella che sembra più in difficoltà: Yamaha. Già senza Quartararo fin dal primo giorno per una brutta caduta, al giorno 2 le nuove V4 sono state fermate per un problema tecnico da verificare, poi risolto.Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/dopogp-motogp-moto-it--4070022/support.

Désormais nonuple champion du Monde, Marc Márquez est sans aucun doute le favori pour un 10e titre en Grand Prix. Ses principaux rivaux : Marco Bezzecchi, Alex Marquez et Francesco Bagnaia seront-ils en mesure de le battre ? À la régulière ? Ponctuellement ? Ou le numéro 93 est-il indétrônable pour l'heure ?Le marché des transferts bat déjà son plein : Quartararo irait chez Honda, Bagnaia chez Aprilia, Acosta chez Ducati, Alex Marquez chez KTM. Mais alors que 2027 est synonyme de révolution technique, comment prendre sa décision ? Quel pari sera le plus risqué ? Malgré les grosses difficultés avec l'instauration du V4 pour Yamaha, la marque aux 3 diapasons pourra-t-elle tirer son épingle du jeu cette année ?Ce podcast est hébergé par Podcastics, la plateforme pour créer et diffuser votre podcast facilement.

1. V turnaji V4 sa Slovensko ocitlo na dne tabuľky 2. Ako je na tom Slovensko s infláciou 3. Umelá inteligencia pomáha novinárom nájsť trendy, ktoré by inak spozorovali ťažko 4. Krátko o aukcii dlhopisov, daňovej prognóze a úvahách o eure vo Švédsku

Prichádza nová éra česko-slovenských vzťahov. Takto premiér Robert Fico vítal českého podpredsedu vlády Karla Havlíčka v Bratislave. Ako bude vyzerať spolupráca medzi vládou Roberta Fica a Andreja Babiša? Podarí sa im zatočiť s cenami energií či nehnuteľností? A budeme ťahať za jeden koniec v rámci V4 či Európskej únie? Spýtali sme sa exkluzívne českého vicepremiéra a ministra priemyslu a obchodu Karla Havlíčka.

Björn Goop ger sin syn på söndagens V4 från Vincennes med Prix de France.

ClevelandMoto Podcast 536 Show Notes8 cylinders, 8 speed DCT, 1000 lbs. and sure, it's coming to America....yeah, right. Call me when it's sitting at a dealership. They tried this crap at AIM expo 2 years ago, this year they trotted it out at CES. https://www.advrider.com/the-gold-wing-dwarfing-gwm-souo-s2000-is-coming-to-america-allegedly/Oooh, let's all make fun of the silly Chinese company and their silly motorcycle! Right? Be careful tho' they sold 1.2 Million cars and trucks last year. That's just behind HONDA. https://www.gwm-global.com/news/3403831.html$60,000 is a lot of dosh. https://www.ft.com/content/d65acba7-33ca-4e43-8581-d71061543dd0?shareType=nongiftRemember this guy, we loved his 250cc 76 HP v-twin 2-strokes at $40,000 do we feel the same about his 185 HP Buell-Powered cafe racer? This is rough, probably rougher than you want, but it's a real 400cc V4 and I think it's gonna go cheap. https://iconicmotorbikeauctions.com/auction/1989-honda-vfr400-nc30-6/Triumph announces a "Limited Edition Cafe Racer 1200" Kind of their regular 1200 Bonneville with a $19,000 price tag. $5000 and 25 more HP than the standard model. $4400 more and the same HP as the Speed Twin 1200. https://www.triumphmotorcycles.com/motorcycles/classic/speed/speed-twin-cafe-racer-editionSince we're talking about high-dollar Retro bikes: coming in at a hair under $20,000 (if you want it in Black) is the newest Retro from Indian. 1890cc and about 120 torques. I actually love the look of this machine, but of course I do, it's a copy of a Kawasaki Drifter.  https://www.indianmotorcycle.com/en-us/chief-motorcycles/chief-vintage/Or Save $16,000 and have the same experience. https://atvhunt.com/l/10728353/2003-Kawasaki-Vulcan-800-Drifteror save $15,000 and get the 1500cc model: https://motohunt.com/l/4190659/1999-Kawasaki-Drifter-VN1500Support the showRemember folks...Ride Fast and Take Chances! check out our Youtube channel at https://www.youtube.com/c/ClevelandMoto

Retrouvez Eric Célis, Michel Turco et notre invité surprise : Johann Zarco pour #TalkbackGP !Votre rendez-vous #motogp

Thursday, February 5, 2026 - Week 6 Happy #RareDisease & #BlackHistory Month!   #NaturalHistory means how this disease progresses.  Reminder: We have only been at this for 17 years, first patients were identified via Hamdan, 2009. https://pubmed.ncbi.nlm.nih.gov/19196676/   Retrospective Digital NHS: cureSYNGAP1.org/Citizen (Growing list of tools available to families, for free)   Prospective Multi-disciplinary Multi-site NHS: ProMMiS cureSYNGAP1.org/ProMMiS   Reminder, only possible by CS1 support for non-CHOP sites and travel plus huge gift to Penn. https://www.chop.edu/news/25-million-gift-penn-medicine-and-children-s-hospital-philadelphia-establishes-center-epilepsy   Potential for being a control arm in the future.   Protocol: https://www.linkedin.com/posts/curesyngap1_syngap1-stxbp1-dee-activity-7425223573134327808-SVEQ & early data: https://pubmed.ncbi.nlm.nih.gov/40119723/   Join the ~160 families who have enjoyed excellent clinical care and contributed tot he future of SYNGAP1.  Today, a 4 month old is going! CHOP: 119 new, V2- 67, V3- 32, V4- 10, V5- 4 CHCO: 37 new, V2- 7 Stanford: 8 new, V2- 2 Total: 164 (double counting one family who goes to multiple sites)   Survey English: https://curesyngap1.org/SurveyProMMiS Spanish: https://curesyngap1.org/encuestaProMMiS   94 Responses to survey, so far: Why not? Did not receive an invitation, Too far to travel, Too expensive Barriers: Logistics, Cost, Time off, Behaviors, Insurance   ETC. Pubmed 2026 is at 6!  But will soon be 7 with the McKee paper! https://pubmed.ncbi.nlm.nih.gov/?term=syngap1&filter=years.2026-2026&sort=date   Biorepository needs more samples.  Check out the list and map here https://docs.google.com/presentation/d/1IjaHILXj7AlBDlbTJgvYrkBS_0bnI8VCnTIiPXJ7JGM/edit?usp=sharing and contribute blood.  The data and research we do with these samples is invaluable.   May 28, San Francisco, CA: cureSYNGAP1.org/SF26   SOCIAL MATTERS 4,668 LinkedIn.  https://www.linkedin.com/company/curesyngap1/ 1,520 YouTube.  https://www.youtube.com/@CureSYNGAP1 11.2k Twitter https://twitter.com/cureSYNGAP1 45k Insta https://www.instagram.com/curesyngap1/   $CAMP stock is at $3.59 on 5 Feb. ‘26 https://www.google.com/finance/beta/quote/CAMP:NASDAQ   Like and subscribe to this podcast wherever you listen.  https://curesyngap1.org/podcasts/syngap10/ Episode 198 of #Syngap10 #CureSYNGAP1 #Podcast

Imagine this: YouTube has paid over $100 billion to creators in the last four years. That's an enormous sum — far more than most TV networks spend on content today. In this episode, we take a closer look at what that means for creators and the platform as a whole. Further in this episode, we talk about plenty of other highlights, including two brand-new Hohem gimbals (which Nino reviewed), a range of innovative new lights from several manufacturers, and exciting new lenses — all covered in this episode of Focus Check. So, as always, hit that play button and enjoy. Chapters and Articles in This Episode   (00:00) – Intro (07:44) – Hohem iSteady MT3 & MT3 Pro Gimbal Review – Built-In AI Tracking and Pro Performance Without the Pro Price https://www.cined.com/hohem-isteady-mt3-mt3-pro-review-built-in-ai-tracking-and-pro-performance-without-the-pro-price/ (15:15) – DJI RS 5 Announced – Enhanced Intelligent Tracking Module, 5th-Gen Stabilization, and One-Hour Fast Charging https://www.cined.com/dji-rs-5-announced-enhanced-intelligent-tracking-module-5th-gen-stabilization-and-one-hour-fast-charging/ (25:31) – YouTube Paid Creators $100 Billion in Four Years – Here Are Their Priorities for 2026 https://www.cined.com/youtube-paid-creators-100-billion-in-four-years-here-are-their-priorities-for-2026/ (37:25) – Sony a7S III Firmware Update Version 5.00 Released with Expanded Autofocus Customization https://www.cined.com/sony-a7s-iii-firmware-update-version-5-00-released-with-expanded-autofocus-customization/ (40:50) – Sony Firmware Updates for VENICE 2 V4.1, BURANO V3.0, FX6 V6.0, and FR7 V4.0 Firmware at BSC Expo 2026 – New Anamorphic Modes, BIG6, Blackmagic RAW https://www.cined.com/sony-firmware-updates-for-venice-2-v4-1-burano-v3-0-fx6-v6-0-and-fr7-v4-0-firmware-at-bsc-expo-2026-new-anamorphic-modes-big6-blackmagic-raw/ (48:20) – Sony VENICE 2 Extension System Mini – Now Shipping https://www.cined.com/sony-venice-2-extension-system-mini-now-shipping/ (54:43) – Astera QuikBeam Announced – Ultra-Compact Fresnel with Multiple Powering Options https://www.cined.com/astera-quikbeam-announced-ultra-compact-fresnel-with-multiple-powering-options/ (58:17) – NANLITE FC-720B and FC-720C Announced – 750W Additions to the FC Series https://www.cined.com/nanlite-fc-720b-and-fc-720c-announced-750w-additions-to-the-fc-series/ (01:00:49) – Aputure NOVA 9° 2×1 and NOVA II 1×1 Announced – A 9° Long-Throw Panel and a High-Output 1×1 https://www.cined.com/aputure-nova-9-2x1-and-nova-ii-1x1-announced-a-9-long-throw-panel-and-a-high-output-1x1/ (01:05:51) – Leica Noctilux-M 35mm f/1.2 ASPH – Innovative and Exorbitantly Priced https://www.cined.com/leica-noctilux-m-35mm-f-1-2-asph-innovative-and-exorbitantly-priced/ (01:10:13) – Pixboom Spark First Production Unit Rolls Off Assembly Line – Mid-March Shipping Confirmed https://www.cined.com/pixboom-spark-first-production-unit-rolls-off-assembly-line-mid-march-shipping-confirmed/ (01:16:49) – Blazar Talon 1.5X AF Anamorphic Lenses Announced – World's First 1.5x Squeeze Autofocus System https://www.cined.com/blazar-talon-1-5x-af-anamorphic-lenses-announced-worlds-first-1-5x-squeeze-autofocus-system/ (01:23:00) – Eddie AI Adds Multi-Track Audio Support for Professional Editing Workflows https://www.cined.com/eddie-ai-adds-multi-track-audio-support-for-professional-editing-workflows/ (01:25:13) – FUJIFILM GFX ETERNA 55 Firmware Version 1.04 Released – Display Delay of External Monitor Improved https://www.cined.com/fujifilm-gfx-eterna-55-firmware-version-1-04-released-display-delay-of-external-monitor-improved/ (01:27:04) – Brightin Star 60mm f/2.8 II Macro Lens Announced https://www.cined.com/brightin-star-60mm-f-2-8-ii-macro-lens-announced/ (01:29:08) – Hollyland Solidcom M1 Pro Released – A Scalable 1.9 GHz Intercom for Medium-Scale Productions https://www.cined.com/hollyland-solidcom-m1-pro-released-a-scalable-1-9-ghz-intercom-for-medium-scale-productions/ (01:32:20) – Freeze, Move, Immerse – Bullet Time and Volumetric Capture in Action https://www.cined.com/freeze-move-immerse-bullet-time-and-volumetric-capture-in-action/ We hope you enjoyed this episode! You have feedback, comments, or suggestions? Write us at podcast@cined.com

Predictions: Chad: Increase in V4 engines Honda announces a new VFR Increase in Chinese motorcycles to the US More manufacturers enter three-wheel market Every major manufacturer will have double stack 1911 by end of the year (except Glock) More DA/SA handguns in the market Rich: Yamaha announces a street bike with V4 insprired by change in MotoGP More lighter ADV or dual sport motorcycles over (like IBEX 450, KLX300, etc) More double stack 1911 style handguns on the way More non-striker fires carry options

Retrouvez Eric Célis & Michel Turco pour ce nouvel épisode de #TalkbackGP !Votre rendez-vous #motogp

SHOW NOTES 535MotoMorini Vettore 450 will be in attendanceTriumph Triple Updates: The Trident 660 and Tiger Sport 660 received significant updates for 2026, featuring "punchier" engine layouts and revised chassis. They are expected in dealerships by March 2026.Honda Entry-Level Changes: The 2026 Rebel 300 has been updated with Honda's E-Clutch technology Yamaha's New Era: Yamaha has officially debuted its first full-factory V4-powered M1 for the 2026 MotoGP season, signaling a major technical shift from its traditional inline-four engine.Dakar Victory: Luciano Benavides and Red Bull KTM have beeKTM/Bajaj Restructuring: Following a major post-insolvency reset, Bajaj Auto has officially taken controlling interest of KTM AG. The company has announced plans to cut approximately 500 jobs as part of a global cost-reduction strategy.Market Trends: Reports indicate a challenging start to 2026, with nearly 120 U.S. dealerships closing due to sagging sales and rising costs. Conversely, BMW Motorrad reported record success, clearing 200,000 worldwide sales for the fourth consecutive year.New Brake Systems: WP Suspension (owned by KTM/Bajaj) has launched a new performance braking division, moving toward more in-house component integration for future models like the 390 DukeSupport the showRemember folks...Ride Fast and Take Chances! check out our Youtube channel at https://www.youtube.com/c/ClevelandMoto

Ministros de Agricultura de Eslovaquia y Chequia confían en una cooperación más estrecha dentro del V4, Resultados de encuesta Deportista del aňo 2025

Björn Goop ger sin syn på söndagens V4 på Vincennes med Prix d'Amérique.

Retrouvez Eric Célis & Michel Turco pour le lancement de la saison 2026 de #TalkbackGP !Votre rendez-vous #motogp

Czech–Hungarian alignment on migration, war and V4, Ice Age horse engraving found in Moravian Karst cave, history of license plates in Czech lands, Lesser Town 

Czech–Hungarian alignment on migration, war and V4, Ice Age horse engraving found in Moravian Karst cave, history of license plates in Czech lands, Lesser Town 

„The Czech Republic must follow good examples such as the USA, Hungary or Poland” – Andrej Babis Prime Minister said on MAXIMA. Babis, who stands on the top of of the new, barely one-month old Czech government, recalled his previous governmental term, when the Czechs were perhaps the best performing country of the V4, but nowadays they're nowhere to found, meanwhile Hungary is doing really well. The Prime Minister stated: from the Czech position it is „not possible” to grant further loans to Ukraine. He also stated that Ukraine is not ready for EU-membership. Andrej Babis, answering the questions of Zoltán Szalai, editor-in-chief and publishing director of Mandiner, spoke about why he considers Viktor Orbán „world-leading” politician, whether peace is possible without Donald Trump, and what kind of future awaits for Viktor Orbán's future project, the Patriots for Europe fraction, which he is also a member of.#mandiner #polgárikarakter #közélet #politika #magyarország #magyar #polgári #Budapest #európa #európai #eu #európaiunió #brüsszel #világ #nemzetközi #nemzetközipolitika #geopolitika #biztonságpolitika #külpolitika #hírek #elemzés #magyarázatMandiner – A polgári karakter. ► Mandiner: https://www.mandiner.hu/► Facebook: https://www.facebook.com/mandiner.hu/► Instagram: https://www.instagram.com/mandiner.hu/► TikTok: https://www.tiktok.com/@mandiner_official► X (Twitter): https://twitter.com/mandiner► LinkedIn: https://hu.linkedin.com/company/mandiner► Spotify: https://open.spotify.com/show/733tJfOPJ6yjcCnm2dAnEt► Előfizetés: https://mandiner.hu/elofizetes

#njpw #wwe #bulletclub

Björn Goop ger sin syn på söndagens V4 på Vincennes med Prix de Belgique där de sista kvalplatserna till Prix d'Amérique fördelas.

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

Asia Bureau Chief Jing Yang joins TITV Host Akash Pasricha to discuss exclusive reporting on DeepSeek's new V4 flagship model and its superior coding capabilities. We also talk with Cory Weinberg about Strava's confidential IPO and Lambda's $350M raise, and Every CEO Dan Shipper about how AI is blurring the lines between media and software. Then, Ro CEO Zach Reitano breaks down the launch of a new GLP-1 pill with Novo Nordisk and AI's role in modern healthcare. Lastly, we dive into the Silicon Valley obsession with biological age with Jemima McEvoy and the AI shopping race with Editors Martin Peers and Meredith Mazzilli.Articles discussed on this episode: https://www.theinformation.com/articles/deepseek-release-next-flagship-ai-model-strong-coding-abilityhttps://www.theinformation.com/articles/silicon-valley-obsessed-biological-age-tests-despite-dubious-sciencehttps://www.theinformation.com/articles/nvidia-backed-cloud-provider-lambda-talks-raise-350-million-ahead-ipohttps://www.theinformation.com/articles/strava-filed-confidentially-ipo-hired-goldman-sachsTITV airs on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Subscribe to: - The Information on YouTube: https://www.youtube.com/@theinformation- The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agenda

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

En esta tercera entrega dedicada a EICMA 2025, en Dame Rueda repasamos algunas de las novedades más potentes, extrañas y estratégicas del salón italiano. Desde prototipos V4 chinos que apuntan directamente a Ducati o BMW, hasta cruiser de más de 180 CV, eléctricas que por fin parecen motos “de verdad” y scooters que suben un escalón en prestaciones. Contenido del episodio: Superdeportivas CFMOTO V4 SR-RR (prototipo) La primera superbike V4 china: sin cifras oficiales, pero con clara inspiración MotoGP y una declaración de intenciones muy seria. Ducati Hypermotard V2 2026 Rediseño profundo, menos peso, más carácter y versiones Standard y SP con diferencias claras. Honda CBR500R 2026 La deportiva A2 que por fin recibe una parte ciclo a la altura de su nombre. Cruiser / Custom / Muscle-Bike Ducati Diavel V4 RS Una cruiser con corazón de MotoGP: 182 CV, electrónica de superbike y precio de infarto. Benda Dark Flag 950 La primera cruiser china con motor V4: cifras sorprendentes, estética agresiva y muchas incógnitas. ⚡ Eléctricas Balistron Sport TS16.12 Prototipo español eléctrico con planteamiento sport-touring real. Balistron Cruiser TCR15.12 Cruiser eléctrica que huye del concepto scooter. Honda WN7 eléctrica Por fin Honda entra en serio en la moto eléctrica “grande”, con cifras ya bastante claras. Scooter Aprilia SR GT 400 El scooter ADV urbano que sube de cilindrada y se coloca como referencia del segmento medio. ️ Participan en el episodio: Jose, Ramón, Javi, Clara y Jorge. Un programa cargado de datos técnicos, análisis y opiniones claras sobre hacia dónde va el mercado: China, electrificación, precios disparados y segmentos que empiezan a mezclarse peligrosamente. Si te interesa saber qué es humo, qué va en serio y qué puede cambiar el panorama en 2026, este episodio es para ti.

Proč Grolich nevyrazil s Kubou na lov odpadlíků; Ostřihomský summit V4 ukázal, že Visegrád žije. Na kapačkách; Stihneme to? Stihneme Putina zastavit? Europarlament přijal první program pro evropský obranný průmysl; Theodor Pištěk, nezapomenutelný solitér

Udělal někdo chybu v případě podmíněného propuštění vězně z Mírova, který po třech týdnech na svobodě znovu vraždil? Jak vidí další spolupráci zemí V4 prezidenti těchto středoevropských států? Je stavební trh v Česku přehřátý a pomůže mu pozastavení některých projektů?

Udělal někdo chybu v případě podmíněného propuštění vězně z Mírova, který po třech týdnech na svobodě znovu vraždil? Jak vidí další spolupráci zemí V4 prezidenti těchto středoevropských států? Je stavební trh v Česku přehřátý a pomůže mu pozastavení některých projektů?Všechny díly podcastu Hlavní zprávy - rozhovory a komentáře můžete pohodlně poslouchat v mobilní aplikaci mujRozhlas pro Android a iOS nebo na webu mujRozhlas.cz.

Zatímco ukrajinská delegace o víkendu zamířila na Floridu, kde jednala s Američany o podmínkách ukončení války, kterou před téměř čtyřmi lety rozpoutalo Rusko, v Moskvě probíhalo jiné setkání, a to mezi prezidentem Vladimirem Putinem a maďarským premiérem Viktorem Orbánem. Oficiálně spolu měli jednat o ruských dodávkách energií. Jak ale ve Výtahu Respektu říká Tomáš Brolík, byla to i součást Orbánovy kampaně před dubnovými volbami v Maďarsku. Může mu nadbíhání Rusku zafungovat? Mohlo by to ohrozit vztahy v rámci V4? A jak probíhají další jednání týkající se ukončení války na Ukrajině?

Na Slovensku sa zhruba za mesiac stali tri vlakové nešťastia s desiatkami zranených a jednou obeťou. Podľa dopravného analytika Iva Drahotského z Dopravnej fakulty Univerzity Pardubice nie je takáto frekvencia obvyklá. Pred niekoľkými rokmi zaznamenali aj v Česku sériu vlakových nešťastí, ktorá však odštartovala rýchlejšiu inštaláciu bezpečnostného systému ETCS do koľajníc i vlakov, ktorá pomáha podobným nehodám predchádzať. Na Slovensku tento proces mešká a v kombinácii s vysokým investičným dlhom výrazne znižuje bezpečnosť železničnej dopravy. Ivo Drahotský potvrdzuje, že českí dopravcovia sa sťažujú na stav slovenských koľajníc a musia tomu prispôsobovať svoju jazdu. „Slovenské železnice nespĺňajú ich technické a bezpečnostné parametre a české vlaky tak musia jazdiť pomalšie ako je obvyklé.“ Minister dopravy Jozef Ráž teraz sľubuje rýchle opatrenia na zvýšenie bezpečnosti, napríklad v podobe kamier inštalovaných v kabínach rušňovodičov. Podľa Drahotského to môže pomôcť eliminovať výskyt ľudských zlyhaní, ale Slovensko musí nevyhnutne pridať v investíciách do železničnej infraštruktúry. „Rýchle riešenia na zlepšenie bezpečnosti na železnici neexistujú,“ upozorňuje analytik. Okolité krajiny V4 už dlhšie investujú do železničnej infraštruktúry. Napríklad v Česku je aj vďaka dotáciám zavedený systém ETCS na dĺžke 1200 km tratí (12,5% siete), zatiaľ čo na Slovensku je to len na 204 kilometroch (7% siete). Rovnakým štandardom je vybavených aj 1803 vlakov Českých dráh, čo je realita len pre 89 rušňov slovenského dopravcu ZSSK. Napriek podfinancovaniu slovenských železníc štát uvoľnil dotáciu 5,2 milióna eur na leteckú linku Bratislava - Košice a naďalej platí zhruba 50 miliónov eur ročne dotáciu na vlaky zadarmo pre študentov a dôchodcov. Vlaky zadarmo zvažovali v minulosti aj v Česku, ale túto myšlienku zavrhli. „Ak je niečo zadarmo, ľudia si to nevážia a ak je to pre všetkých zadarmo, tak riskujete, že sa ľudia budú prevážať aj zbytočne,“ hovorí Ivo Drahotský. V rozhovore približuje aj nové trendy vo vlakovej doprave, napríklad v podobe autonómnych vlakov, ktoré už v Česku jazdia, alebo vysokorýchlostnej železnice, o ktorej česká vláda uvažuje. Moderuje Eva Mihočková. V článku sa dozviete: Aký zlý je technický stav slovenských železníc Ako funguje systém ETCS v Česku a na Slovensku Aký zmysel má dotovaná letecká linka Bratislava - Košice Prečo vlaky zadarmo nie sú dobrý nápad Kam smeruje budúcnosť železničnej dopravy a kde je Slovensko See omnystudio.com/listener for privacy information.

Vznikající česká vláda by ráda resuscitovala Visegrádskou skupinu, tedy spojenectví Česka, Slovenska, Maďarska a Polska. Proč je ale vlastně V4 ve stavu klinické smrti? A jak se vyvinuly vztahy zemí střední Evropy, které se po pádu železné opony snažily stavět na hodnotách demokratické evropské integrace? Hostem nové epizody Bruselských chlebíčků je novinář ze serveru Seznam Zprávy Filip Harzer.

Nové vedení TOP 09 a případný návrat ke kořenům; V4 už nemá společné zájmy, mrtvý projekt nelze oživit; Zapomněli jsme postavit protipožární zeď; EK chválí čtyři kandidátské země, sama však váhá s vlastními reformami; Zemědělství může zablokovat vstup Ukrajiny do EU. Unie se musí reformovat; Věra Křesadlová, symbol převratné doby

Predicazione espositiva del Pastore Daniel Ransom di Efesini capitolo 4 versetti da 4 a 6. Registrata presso il Centro Evangelico Battista di Perugia il 2 Novembre 2025.Titolo del messaggio: "3 motivi per cui l'unità dello Spirito deve essere la priorità del corpo"EFESINI 4 V4-64 Vi è un corpo solo e un solo Spirito, come pure siete stati chiamati a una sola speranza, quella della vostra vocazione. 5 V'è un solo Signore, una sola fede, un solo battesimo, 6 un solo Dio e Padre di tutti, che è al di sopra di tutti, fra tutti e in tutti.

Ako sme neoslavovali 107.výročie vzniku Československa. Pellegrini blúznil o obnovení V4 a Fico obhajoval svoje cesty za Putinom. Slovensko sa prepadá na dno v hodnotení právneho štátu.

Sometimes even Gundam Crossbone has good ideas. And they may not be good ideas leading up to them, or even following them. But for one shining moment, you get a cartridge-powered pile bunker leg driving a massive beam saber down, and everything is ok. ...Ok, that's unfair to the F89, that thing's fine. But boy. Anchor V2, V3, V4... it definitely goes downhill. Lighting can strike twice. But it usually doesn't. dont fact check dont fact check dont fact check You can find a video version of this podcast for free on Scanline Media's Patreon! If you want to find us on Bluesky, Dylan is lowpolyrobot.bsky.social and Six is six.scanlinemedia.com. Our opening theme is the Hangar Theme from Gundam Breaker 3, and our ending theme for this episode is Resumption from Gundam Breaker 4. Our podcast art is a fantastic piece of work from Twitter artist @fenfelt. Want to see a list of every unit we've covered from every episode, including variants and tangents? It's right here. Units discussed: F89 Gundam F89 Cristonian Ferley Danganloid MegaZord Gamma Kajimerian Ben G Doggie Kruger (Dekaranger) Doggie Kruger (SPD) F89 Gundam F89 (Final Equipment Type) F89 Gundam F89 (Chaser Equipment Type) F89 Gundam F89 (Commander Specifications) Anchor Anchor V2 Anchor V3 Anchor V4

Do platenej nenávistnej reklamy voči novinárom a mimovládkam najviac investuje vládna strana Smer. Spoločnosť Meta vďaka tomu počas uplynulého roka zarobila desaťtisíce eur.Analýza medzinárodného projektu „Platená nenávisť“ ukázala, že za posledný rok sa na sociálnych sieťach na Slovensku objavilo viac ako 500 platených politických útokov na novinárov a mimovládne organizácie. Slovensko sa tak v tomto nelichotivom rebríčku umiestnilo na prvom mieste spomedzi krajín V4, keďže takmer 70 percent všetkých identifikovaných nenávistných reklám pochádzalo práve od našich politikov. Viac o tejto téme nájdete tu.Ataky, ktorých cieľom je šíriť nenávistné a dezinformačné naratívy, pritom financovala najmä vládna strana Smer. Tá na svojom facebookovom profile za posledný rok zverejnila najmenej 117 útočných príspevkov namierených proti novinárom a mimovládkam, čo prestavuje až jednu pätinu z celkového počtu.O tom, prečo sa tak deje a aké dôsledky môžu mať takéto prejavy, sa budeme rozprávať s novinárkou Investigatívneho centra Jána Kuciaka a spoluautorkou spomínanej analýzy, Karin Kőváry Sólymos.

Štvrtá vláda Roberta Fica otočila zahraničnopolitický kurz Slovenska. Premiér hovorí o suverénnej zahraničnej politike orientovanej na všetky štyri svetové strany. Čo Slovensku premiérova nová zahraničná politika priniesla? Prečo naši exministri zahraničných vecí a diplomati hovoria, že nás to izoluje a ničí vzťahy s našimi spojencami?Minister zahraničných vecí protestuje proti účasti českého vicepremiéra na opozičnom proteste a Európski socialisti sa vraj definitívne chystajú vylúčiť Smer zo svojich radov. Rusi medzitým stále zabíjajú na Ukrajine civilistov, vrátane detí, desaťtisíce ich uniesli do Ruska a najnovšie sa ruské drony už objavili aj nad Poľskom či Rumunskom.Prečo si teda náš premiér stále podáva ruku s ruským prezidentom a Slovensko sa odmieta zbaviť ruského plynu a ropy aj keď nás už na to vyzýva aj americký prezident Trump? A ako dopadli rokovania premiéra s Ukrajinským prezidentom? V ako stave je spolupráca vo V4?Braňo Závodský sa rozprával s ministrom zahraničných vecí a európskych záležitostí a podpredsedom strany Smer – SD Jurajom Blanárom.

Adam, David and Neil are joined by friend and (incredibly well informed) journalist Thomas Baujard to talk about the themes emerging from the first day of the Circuit de Barcelona-Catalunya. Is Marc Marquez vulnerable? What were riders saying about their new contracts? Yamaha's V4 chatter, Barcelona bitumen and more predictions.

Com a V4, você estrutura um ano inteiro de marketing e comercial em 4 semanas

Join us as we take a deep dive into the more than 100 year old history of Lancia. From the first unibody construction to the first V4 engine to their championship-winning rally tech, Lancia has been on the forefront of automotive innovation since their inception. So how did they go from making some of the most beautiful, awe-inspiring machines to offering only one blobby uninspired hatchback? What the hell happened? This episode is brought to you by Underdog Fantasy! Sign up and deposit for Underdog HERE (www.underdogfantasy.com) with promo code GAS to get up to $1,000 in bonus cash and a free pick or download the app! This episode is sponsored by BetterHelp. Give online therapy a try at www.betterhelp.com/PASTGAS and get on your way to being your best self. Learn more about your ad choices. Visit podcastchoices.com/adchoices