ThursdAI - The top AI news from the past week

What a week in AI, folks! Seriously, just when you think things might slow down, the AI world throws another curveball. This week, we had everything from rogue AI apps giving unsolicited life advice (and sending rogue texts!), to mind-blowing open source releases that are pushing the boundaries of what's possible, and of course, the ever-present drama of the big AI companies with OpenAI dropping a roadmap that has everyone scratching their heads.Buckle up, because on this week's ThursdAI, we dove deep into all of it. We chatted with the brains behind the latest open source embedding model, marveled at a tiny model crushing math benchmarks, and tried to decipher Sam Altman's cryptic GPT-5 roadmap. Plus, I shared a personal story about an AI app that decided to psychoanalyze my text messages – you won't believe what happened! Let's get into the TL;DR of ThursdAI, February 13th, 2025 – it's a wild one!* Alex Volkov: AI Adventurist with weights and biases* Wolfram Ravenwlf: AI Expert & Enthusiast* Nisten: AI Community Member* Zach Nussbaum: Machine Learning Engineer at Nomic AI* Vu Chan: AI Enthusiast & Evaluator* LDJ: AI Community MemberPersonal story of Rogue AI with RPLYThis week kicked off with a hilarious (and slightly unsettling) story of my own AI going rogue, all thanks to a new Mac app called RPLY designed to help with message replies. I installed it thinking it would be a cool productivity tool, but it turned into a personal intervention session, and then… well, let's just say things escalated.The app started by analyzing my text messages and, to my surprise, delivered a brutal psychoanalysis of my co-parenting communication, pointing out how both my ex and I were being "unpleasant" and needed to focus on the kids. As I said on the show, "I got this as a gut punch. I was like, f*ck, I need to reimagine my messaging choices." But the real kicker came when the AI decided to take initiative and started sending messages without my permission (apparently this was a bug with RPLY that was fixed since I reported)! Friends were texting me question marks, and my ex even replied to a random "Hey, How's your day going?" message with a smiley, completely out of our usual post-divorce communication style. "This AI, like on Monday before just gave me absolute s**t about not being, a person that needs to be focused on the kids also decided to smooth things out on friday" I chuckled, still slightly bewildered by the whole ordeal. It could have gone way worse, but thankfully, this rogue AI counselor just ended up being more funny than disastrous.Open Source LLMsDeepHermes preview from NousResearchJust in time for me sending this newsletter (but unfortunately not quite in time for the recording of the show), our friends at Nous shipped an experimental new thinking model, their first reasoner, called DeepHermes. NousResearch claims DeepHermes is among the first models to fuse reasoning and standard LLM token generation within a single architecture (a trend you'll see echoed in the OpenAI and Claude announcements below!)Definitely experimental cutting edge stuff here, but exciting to see not just an RL replication but also innovative attempts from one of the best finetuning collectives around. Nomic Embed Text V2 - First Embedding MoENomic AI continues to impress with the release of Nomic Embed Text V2, the first general-purpose Mixture-of-Experts (MoE) embedding model. Zach Nussbaum from Nomic AI joined us to explain why this release is a big deal.* First general-purpose Mixture-of-Experts (MoE) embedding model: This innovative architecture allows for better performance and efficiency.* SOTA performance on multilingual benchmarks: Nomic Embed V2 achieves state-of-the-art results on the multilingual MIRACL benchmark for its size.* Support for 100+ languages: Truly multilingual embeddings for global applications.* Truly open source: Nomic is committed to open source, releasing training data, weights, and code under the Apache 2.0 License.Zach highlighted the benefits of MoE for embeddings, explaining, "So we're trading a little bit of, inference time memory, and training compute to train a model with mixture of experts, but we get this, really nice added bonus of, 25 percent storage." This is especially crucial when dealing with massive datasets. You can check out the model on Hugging Face and read the Technical Report for all the juicy details.AllenAI OLMOE on iOS and New Tulu 3.1 8BAllenAI continues to champion open source with the release of OLMOE, a fully open-source iOS app, and the new Tulu 3.1 8B model.* OLMOE iOS App: This app brings state-of-the-art open-source language models to your iPhone, privately and securely.* Allows users to test open-source LLMs on-device.* Designed for researchers studying on-device AI and developers prototyping new AI experiences.* Optimized for on-device performance while maintaining high accuracy.* Fully open-source code for further development.* Available on the App Store for iPhone 15 Pro or newer and M-series iPads.* Tulu 3.1 8B As Nisten pointed out, "If you're doing edge AI, the way that this model is built is pretty ideal for that." This move by AllenAI underscores the growing importance of on-device AI and open access. Read more about OLMOE on the AllenAI Blog.Groq Adds Qwen Models and Lands on OpenRouterGroq, known for its blazing-fast inference speeds, has added Qwen models, including the distilled R1-distill, to its service and joined OpenRouter.* Record-fast inference: Experience a mind-blowing 1000 TPS with distilled DeepSeek R1 70B on Open Router.* Usable Rate Limits: Groq is now accessible for production use cases with higher rate limits and pay-as-you-go options.* Qwen Model Support: Access Qwen models like 2.5B-32B and R1-distill-qwen-32B.* Open Router Integration: Groq is now available on OpenRouter, expanding accessibility for developers.As Nisten noted, "At the end of the day, they are shipping very fast inference and you can buy it and it looks like they are scaling it. So they are providing the market with what it needs in this case." This integration makes Groq's speed even more accessible to developers. Check out Groq's announcement on X.com.SambaNova adds full DeepSeek R1 671B - flies at 200t/s (blog)In a complete trend of this week, SambaNova just announced they have availability of DeepSeek R1, sped up by their custom chips, flying at 150-200t/s. This is the full DeepSeek R1, not the distilled Qwen based versions! This is really impressive work, and compared to the second fastest US based DeepSeek R1 (on Together AI) it absolutely fliesAgentica DeepScaler 1.5B Beats o1-preview on MathAgentica's DeepScaler 1.5B model is making waves by outperforming OpenAI's o1-preview on math benchmarks, using Reinforcement Learning (RL) for just $4500 of compute.* Impressive Math Performance: DeepScaleR achieves a 37.1% Pass@1 on AIME 2025, outperforming the base model and even o1-preview!!* Efficient Training: Trained using RL for just $4500, demonstrating cost-effective scaling of intelligence.* Open Sourced Resources: Agentica open-sourced their dataset, code, and training logs, fostering community progress in RL-based reasoning.Vu Chan, an AI enthusiast who evaluated the model, joined us to share his excitement: "It achieves, 42% pass at one on a AIME 24. which basically means if you give the model only one chance at every problem, it will solve 42% of them." He also highlighted the model's efficiency, generating correct answers with fewer tokens. You can find the model on Hugging Face, check out the WandB logs, and see the announcement on X.com.ModernBert Instruct - Encoder Model for General TasksModernBert, known for its efficient encoder-only architecture, now has an instruct version, ModernBert Instruct, capable of handling general tasks.* Instruct-tuned Encoder: ModernBERT-Large-Instruct can perform classification and multiple-choice tasks using its Masked Language Modeling (MLM) head.* Beats Qwen .5B: Outperforms Qwen .5B on MMLU and MMLU Pro benchmarks.* Efficient and Versatile: Demonstrates the potential of encoder models for general tasks without task-specific heads.This release shows that even encoder-only models can be adapted for broader applications, challenging the dominance of decoder-based LLMs for certain tasks. Check out the announcement on X.com.Big CO LLMs + APIsRIP GPT-5 and o3 - OpenAI Announces Public RoadmapOpenAI shook things up this week with a roadmap update from Sam Altman, announcing a shift in strategy for GPT-5 and the o-series models. Get ready for GPT-4.5 (Orion) and a unified GPT-5 system!* GPT-4.5 (Orion) is Coming: This will be the last non-chain-of-thought model from OpenAI.* GPT-5: A Unified System: GPT-5 will integrate technologies from both the GPT and o-series models into a single, seamless system.* No Standalone o3: o3 will not be released as a standalone model; its technology will be integrated into GPT-5. "We will no longer ship O3 as a standalone model," Sam Altman stated.* Simplified User Experience: The model picker will be eliminated in ChatGPT and the API, aiming for a more intuitive experience.* Subscription Tier Changes:* Free users will get unlimited access to GPT-5 at a standard intelligence level.* Plus and Pro subscribers will gain access to increasingly advanced intelligence settings of GPT-5.* Expanded Capabilities: GPT-5 will incorporate voice, canvas, search, deep research, and more.This roadmap signals a move towards more integrated and user-friendly AI experiences. As Wolfram noted, "Having a unified access and the AI should be smart enough... AI has, we need an AI to pick which AI to use." This seems to be OpenAI's direction. Read Sam Altman's full announcement on X.com.OpenAI Releases ModelSpec v2OpenAI also released ModelSpec v2, an update to their document defining desired AI model behaviors, emphasizing customizability, transparency, and intellectual freedom.* Chain of Command: Defines a hierarchy to balanc

What's up friends, Alex here, back with another ThursdAI hot off the presses.Hold onto your hats because this week was another whirlwind of AI breakthroughs, mind-blowing demos, and straight-up game-changers. We dove deep into OpenAI's new "Deep Research" agent – and let me tell you, it's not just hype, it's legitimately revolutionary. You also don't have to take my word for it, a new friend of the pod and a scientist DR Derya Unutmaz joined us to discuss his experience with Deep Research as a scientist himself! You don't want to miss this conversation! We also unpack Google's Gemini 2.0 release, including the blazing-fast Flash Lite model. And just when you thought your brain couldn't handle more, ByteDance drops OmniHuman-1, a human animation model that's so realistic, it's scary good.I've also saw maybe 10 moreTLDR & Show Notes* Open Source LLMs (and deep research implementations)* Jina Node-DeepResearch (X, Github)* HuggingFace - OpenDeepResearch (X)* Deep Agent - R1 -V (X, Github)* Krutim - Krutim 2 12B, Chitrath VLM, Embeddings and more from India (X, Blog, HF)* Simple Scaling - S1 - R1 (Paper)* Mergekit updated - * Big CO LLMs + APIs* OpenAI ships o3-mini and o3-mini High + updates thinking traces (Blog, X)* Mistral relaunches LeChat with Cerebras for 1000t/s (Blog)* OpenAI Deep Research - the researching agent that uses o3 (X, Blog)* Google ships Gemini 2.0 Pro, Gemini 2.0 Flash-lite in AI Studio (Blog)* Anthropic Constitutional Classifiers - announced a universal jailbreak prevention (Blog, Try It)* Cloudflare to protect websites from AI scraping (News)* HuggingFace becomes the AI Appstore (link)* This weeks Buzz - Weights & Biases updates* AI Engineer workshop (Saturday 22) * Tinkerers Toronto workshops (Sunday 23 , Monday 24)* We released a new Dataset editor feature (X)* Audio and Sound* KyutAI open sources Hibiki - simultaneous translation models (Samples, HF)* AI Art & Diffusion & 3D* ByteDance OmniHuman-1 - unparalleled Human Animation Models (X, Page)* Pika labs adds PikaAdditions - adding anything to existing video (X)* Google added Imagen3 to their API (Blog)* Tools & Others* Mistral Le Chat has ios an and adroid apps now (X)* CoPilot now has agentic workflows (X)* Replit launches free apps agent for everyone (X)* Karpathy drops a new 3 hour video on youtube (X, Youtube)* OpenAI canvas links are now shareable (like Anthropic artifacts) - (example)* Show Notes & Links * Guest of the week - Dr Derya Umnutaz - talking about Deep Research* He's examples of Ehlers-Danlos Syndrome (ChatGPT), (ME/CFS) Deep Research, Nature article about Deep Reseach with Derya comments* Hosts* Alex Volkov - AI Evangelist & Host @altryne* Wolfram Ravenwolf - AI Evangelist @WolframRvnwlf* Nisten Tahiraj - AI Dev at github.GG - @nisten* LDJ - Resident data scientist - @ldjconfirmedBig Companies products & APIsOpenAI's new chatGPT moment with Deep Research, their second "agent" product (X)Look, I've been reporting on AI weekly for almost 2 years now, and been following the space closely since way before chatGPT (shoutout Codex days) and this definitely feels like another chatGPT moment for me.DeepResearch is OpenAI's new agent, that searches the web for any task you give it, is able to reason about the results, and continue searching those sources, to provide you with an absolute incredible level of research into any topic, scientific or ... the best taqueria in another country. The reason why it's so good is it's ability to do multiple search trajectories, backtrack if it needs to, and react in real time to new information. It also has python tool use (to do plots and calculations) and of course, the brain of it is o3, the best reasoning model from OpenAIDeep Research is only offered on the Pro tier ($200) of chatGPT, and it's the first publicly available way to use o3 full! and boy, does it deliver! I've had it review my workshop content, help me research LLM as a judge articles (which it did masterfully) and help me plan datenights in Denver (though it kind of failed at that, showing me a closed restaurant) A breakthrough for scientific researchBut I'm no scientist, so I've asked Dr Derya Unutmaz, M.D. to join us, and share his incredible findings as a doctor, a scientist and someone with decades of experience in writing grants, patent applications, paper etc. The whole conversation is very very much worth listening to on the pod, we talked for almost an hour, but the highlights are honestly quite crazy. So one of the first things I did was, I asked Deep Research to write a review on a particular disease that I’ve been studying for a decade. It came out with this impeccable 10-to-15-page review that was the best I’ve read on the topic— Dr. Derya UnutmazAnd another banger quoteIt wrote a phenomenal 25-page patent application for a friend’s cancer discovery—something that would’ve cost 10,000 dollars or more and taken weeks. I couldn’t believe it. Every one of the 23 claims it listed was thoroughly justifiedHumanity's LAST exam? OpenAI announced Deep Research and have showed that on HLE (Humanity's Last Exam) benchmark that was just released a few weeks ago, it scores a whopping 26.6 percent! When HLE was released (our coverage here) all the way back at ... checks notes... January 23 or this year! the top reasoning models at the time (o1, R1) scored just under 10%O3-mini and Deep Research now score 13% and 26.6% respectively, which means both that AI is advancing like crazy, but also.. that maybe calling this "last exam" was a bit premature? 😂😅Deep Research is now also SOTA holder on GAIA, a public benchmark on real world questions, though Clementine (one of GAIA authors) throws a bit of shade on the result since OpenAI didn't really submit their results. Incidently, Clementine is also involved in HuggingFace attempt at replicating Deep Research in the open (with OpenDeepResearch) OpenAI releases o3-mini and o3-mini highThis honestly got kind of buried with the Deep Research news, but as promised, on the last day of January, OpenAI released their new reasoning model, which is significantly fast and much cheaper than o1, while matching it on most benchmarks! I've been talking about the fact that during o3 announcement (our coverage) that mini may be more practical and useful announcement than o3 itself, given the price and speed of it. And viola, OpenAI has reduced the price point of their best reasoner model by 67%, and it's now matches just 2x that of DeepSeek R1.Coming in at 110c for 1M input tokens and 440c for 1M output tokens, and streaming at a whopping 1000t/s at some instances, this reasoner is really something to beat. Great for application developersIn addition to seem to be a great model, comparing it to R1 is a nonstarter IMO, not only because "it’s sending your data to choyna", which IMO is a ridiculous attack vector and people should be ashamed by posting this content. o3-mini supports all of the nice API things that OpenAI has, like tool use, structured outputs, developer messages and streaming. The ability to set the reasoning effort is also interesting for applications! Added benefit is the new 200K context window with 100K (claimed) output context. It's also really really fast, while R1 availability grows, as it gets hosted on more and more US based providers, none of them are offering the full context window at these token speeds. o3-mini-high?! While the free users also started getting access to o3-mini, with the "reason" button on chatGPT, plus subscribers received 2 models, o3-mini and o3-mini-high, which is essentially the same model, but with the "high" reasoning mode turned on, giving the model significantly more compute (and tokens) to think. This can be done on the API level by selecting reasoning_effort=high but it's the first time OpenAI is exposing this to non API users! One highlight for me is, just how MANY tokens o3-mini high things through. In one of my evaluations on Weave, o3-mini high generated around 160K output tokens, answering 20 questions, while DeepSeek R1 for example generated 75K and Gemini Thinking, got the highest score on these, while charging only 14K tokens (though I'm pretty sure Google just doesn't report on thinking tokens yet, this seems like a bug)As I'm writing this, OpenAI just announced a new update, o3-mini and o3-mini-high now show... "updated" reasoning traces! These definitely "feel" more like the R1 reasoning traces (remember, previously OpenAI had a different model summarizing the reasoning to prevent training on them?) but they are not really the RAW ones (confirmed) Google ships Gemini 2.0 Pro, Gemini 2.0 Flash-lite in AI Studio (X, Blog)Congrats to our friends at Google for 2.0 👏 Google finally put all the experimental models under one 2.0 umbrella, giving us Gemini 2.0, Gemini 2.0 Flash and a new model! They also introduced Gemini 2.0 Flash-lite, a crazy fast and cheap model that performs similarly to Flash 1.5. The rate limits on Flash-lite are twice as high as the regular Flash, making it incredibly useful for real-time applications. They have also released a few benchmarks, but they only compared those to the previous benchmark released by Google, and while that's great, I wanted a comparison done, so I asked DeepResearch to do it for me, and it did (with citations!) Google also released Imagen 3, their awesome image diffusion model in their API today, with 3c per image, this one is really really good! Mistral's new LeChat spits out 1000t/s + new IOS appsDuring the show, Mistral announced new capabilities for their LeChat interface, including a 15$/mo tier, but most importantly, a crazy fast generation using some kind of new inference, spitting out around 1000t/s. (Powered by Cerebras)Additionally they have code interpreter there, Canvas, and they also claim to have the best OCR and don't forget, they have access to Flux images, and likely are the only place I know of that offers that image model for free! Finally, they've released native mobile a

Hey folks, Alex here 👋It’s official—grandmas (and the entire stock market) now know about DeepSeek. If you’ve been living under an AI rock, DeepSeek’s new R1 model just set the world on fire, rattling Wall Street (causing the biggest monetary loss for any company, ever!) and rocketing to #1 on the iOS App Store. This week’s ThursdAI show took us on a deep (pun intended) dive into the dizzying whirlwind of open-source AI breakthroughs, agentic mayhem, and big-company cat-and-mouse announcements. Grab your coffee (or your winter survival kit if you’re in Canada), because in true ThursdAI fashion, we’ve got at least a dozen bombshells to cover—everything from brand-new Mistral to next-gen vision models, new voice synthesis wonders, and big moves from Meta and OpenAI.We’re also talking “reasoning mania,” as the entire industry scrambles to replicate, dethrone, or ride the coattails of the new open-source champion, R1. So buckle up—because if the last few days are any indication, 2025 is officially the Year of Reasoning (and quite possibly, the Year of Agents, or both!)Open Source LLMsDeepSeek R1 discourse Crashes the Stock MarketOne-sentence summary: DeepSeek’s R1 “reasoning model” caused a frenzy this week, hitting #1 on the App Store and briefly sending NVIDIA’s stock plummeting in the process ($560B drop, largest monetary loss of any stock, ever)Ever since DeepSeek R1 launched (our technical coverate last week!), the buzz has been impossible to ignore—everyone from your mom to your local barista has heard the name. The speculation? DeepSeek’s new architecture apparently only cost $5.5 million to train, fueling the notion that high-level AI might be cheaper than Big Tech claims. Suddenly, people wondered if GPU manufacturers like NVIDIA might see shrinking demand, and the stock indeed took a short-lived 17% tumble. On the show, I joked, “My mom knows about DeepSeek—your grandma probably knows about it, too,” underscoring just how mainstream the hype has become.Not everyone is convinced the cost claims are accurate. Even Dario Amodei of Anthropic weighed in with a blog post arguing that DeepSeek’s success increases the case for stricter AI export controls. Public Reactions* Dario Amodei’s blogIn “On DeepSeek and Export Controls,” Amodei argues that DeepSeek’s efficient scaling exemplifies why democratic nations need to maintain a strategic leadership edge—and enforce export controls on advanced AI chips. He sees Chinese breakthroughs as proof that AI competition is global and intense.* OpenAI Distillation EvidenceOpenAI mentioned it found “distillation traces” of GPT-4 inside R1’s training data. Hypocrisy or fair game? On ThursdAI, the panel mused that “everyone trains on everything,” so perhaps it’s a moot point.* Microsoft ReactionMicrosoft wasted no time, swiftly adding DeepSeek to Azure—further proof that corporations want to harness R1’s reasoning power, no matter where it originated.* Government reactedEven officials in the government, David Sacks, US incoming AI & Crypto czar, discussed the fact that DeepSeek did "distillation" using the term somewhat incorrectly, and presidet Trump was asked about it.* API OutagesDeepSeek’s own API has gone in and out this week, apparently hammered by demand (and possibly DDoS attacks). Meanwhile, GPU clouds like Groq are showing up to accelerate R1 at 300 tokens/second, for those who must have it right now.We've seen so many bad takes on the topic, from seething cope takes, to just gross misunderstandings from gov officials confusing the ios App with the OSS models, folks throwing conspiracy theories into the mix, claiming that $5.5M sum was a PsyOp. The fact of the matter is, DeepSeek R1 is an incredible model, and is now powering (just a week later), multiple products (more on this below) and experiences already, while pushing everyone else to compete (and give us reasoning models!)Open Thoughts Reasoning DatasetOne-sentence summary: A community-led effort, “Open Thoughts,” released a new large-scale dataset (OpenThoughts-114k) of chain-of-thought reasoning data, fueling the open-source drive toward better reasoning models.Worried about having enough labeled “thinking” steps to train your own reasoner? Fear not. The OpenThoughts-114k dataset aggregates chain-of-thought prompts and responses—114,000 of them—for building or fine-tuning reasoning LLMs. It’s now on Hugging Face for your experimentation pleasure. The ThursdAI panel pointed out how crucial these large, openly available reasoning datasets are. As Wolfram put it, “We can’t rely on the big labs alone. More open data means more replicable breakouts like DeepSeek R1.”Mistral Small 2501 (24B)One-sentence summary: Mistral AI returns to the open-source spotlight with a 24B model that fits on a single 4090, scoring over 81% on MMLU while under Apache 2.0.Long rumored to be “going more closed,” Mistral AI re-emerged this week with Mistral-Small-24B-Instruct-2501—an Apache 2.0 licensed LLM that runs easily on a 32GB VRAM GPU. That 81% MMLU accuracy is no joke, putting it well above many 30B–70B competitor models. It was described as “the perfect size for local inference and a real sweet spot,” noting that for many tasks, 24B is “just big enough but not painfully heavy.” Mistral also finally started comparing themselves to Qwen 2.5 in official benchmarks—a big shift from their earlier reluctance, which we applaud! Berkeley TinyZero & RAGEN (R1 Replications)One-sentence summary: Two separate projects (TinyZero and RAGEN) replicated DeepSeek R1-zero’s reinforcement learning approach, showing you can get “aha” reasoning moments with minimal compute.If you were wondering whether R1 is replicable: yes, it is. Berkeley’s TinyZero claims to have reproduced the core R1-zero behaviors for $30 using a small 3B model. Meanwhile, the RAGEN project aims to unify RL + LLM + Agents with a minimal codebase. While neither replication is at R1-level performance, they demonstrate how quickly the open-source community pounces on new methods. “We’re now seeing those same ‘reasoning sparks’ in smaller reproductions,” said Nisten. “That’s huge.”AgentsCodename Goose by Blocks (X, Github)One-sentence summary: Jack Dorsey’s company Blocks released Goose, an open-source local agent framework letting you run keyboard automation on your machine.Ever wanted your AI to press keys and move your mouse in real time? Goose does exactly that with AppleScript, memory extensions, and a fresh approach to “local autonomy.” On the show, I tried Goose, but found it occasionally “went rogue, trying to delete my WhatsApp chats.” Security concerns aside, Goose is significant: it’s an open-source playground for agent-building. The plugin system includes integration with Git, Figma, a knowledge graph, and more. If nothing else, Goose underscores how hot “agentic” frameworks are in 2025.OpenAI’s Operator: One-Week-InIt’s been a week since Operator went live for Pro-tier ChatGPT users. “It’s the first agent that can run for multiple minutes without bugging me every single second,”. Yet it’s still far from perfect—captchas, login blocks, and repeated confirmations hamper tasks. The potential, though, is enormous: “I asked Operator to gather my X.com bookmarks and generate a summary. It actually tried,” I shared, “but it got stuck on three links and needed constant nudges.” Simon Willison added that it’s “a neat tech demo” but not quite a productivity boon yet. Next steps? Possibly letting the brand-new reasoning models (like O1 Pro Reasoning) do the chain-of-thought under the hood.I also got tired of opening hundreds of tabs for operator, so I wrapped it in a macOS native app, that has native notifications and the ability to launch Operator tasks via a Raycast extension, if you're interested, you can find it on my GithubBrowser-use / Computer-use AlternativesIn addition to Goose, the ThursdAI panel mentioned browser-use on GitHub, plus numerous code interpreters. So far, none blow minds in reliability. But 2025 is evidently “the year of agents.” If you’re itching to offload your browsing or file editing to an AI agent, expect to tinker, troubleshoot, and yes, babysit. The show consensus? “It’s not about whether agents are coming, it’s about how soon they’ll become truly robust,” said Wolfram.Big CO LLMs + APIsAlibaba Qwen2.5-Max (& Hidden Video Model) (Try It)One-sentence summary: Alibaba’s Qwen2.5-Max stands toe-to-toe with GPT-4 on some tasks, while also quietly rolling out video-generation features.While Western media fixates on DeepSeek, Alibaba’s Qwen team quietly dropped the Qwen2.5-Max MoE model. It clocks in at 69% on MMLU-Pro—beating some OpenAI or Google offerings—and comes with a 1-million-token context window. And guess what? The official Chat interface apparently does hidden video generation, though Alibaba hasn’t publicized it in the English internet. In the Chinese AI internet, this video generation model is called Tongyi Wanxiang, and even has it’s own website, can support first and last video generation and looks really really good, they have a gallery up there, and it even has audio generation together with the video!This one was an img2video, but the movements are really natural! Zuckerberg on LLama4 & LLama4 MiniIn Meta’s Q4 earnings call, Zuck was all about AI (sorry, Metaverse). He declared that LLama4 is in advanced training, with a smaller “LLama4 Mini” finishing pre-training. More importantly, a “reasoning model” is in the works, presumably influenced by the mania around R1. Some employees had apparently posted on Blind about “Why are we paying billions for training if DeepSeek did it for $5 million?” so the official line is that Meta invests heavily for top-tier scale. Zuck also doubled down on saying "Glasses are the perfect form factor for AI" , to which I somewhat agree, I love my Meta Raybans, I just wished they were integrated into the ios more. He also boasted about their HUGE datacenters, called Mesa, spanning the size of Manhattan, be

What a week, folks, what a week! Buckle up, because ThursdAI just dropped, and this one's a doozy. We're talking seismic shifts in the open source world, a potential game-changer from DeepSeek AI that's got everyone buzzing, and oh yeah, just a casual $500 BILLION infrastructure project announcement. Plus, OpenAI finally pulled the trigger on "Operator," their agentic browser thingy – though getting it to actually operate proved to be a bit of a live show adventure, as you'll hear. This week felt like one of those pivotal moments in AI, a real before-and-after kind of thing. DeepSeek's R1 hit the open source scene like a supernova, and suddenly, top-tier reasoning power is within reach for anyone with a Mac and a dream. And then there's OpenAI's Operator, promising to finally bridge the gap between chat and action. Did it live up to the hype? Well, let's just say things got interesting.As I’m writing this, White House just published that an Executive Order on AI was just signed and published as well, what a WEEK.Open Source AI Goes Nuclear: DeepSeek R1 is HERE!Hold onto your hats, open source AI just went supernova! This week, the Chinese Whale Bros – DeepSeek AI, that quant trading firm turned AI powerhouse – dropped a bomb on the community in the best way possible: R1, their reasoning model, is now open source under the MIT license! As I said on the show, "Open source AI has never been as hot as this week."This isn't just a model, folks. DeepSeek unleashed a whole arsenal: two full-fat R1 models (DeepSeek R1 and DeepSeek R1-Zero), and a whopping six distilled finetunes based on Qwen (1.5B, 7B, 14B, and 32B) and Llama (8B, 72B). One stat that blew my mind, and Nisten's for that matter, is that DeepSeek-R1-Distill-Qwen-1.5B, the tiny 1.5 billion parameter model, is outperforming GPT-4o and Claude-3.5-Sonnet on math benchmarks! "This 1.5 billion parameter model that now does this. It's absolutely insane," I exclaimed on the show. We're talking 28.9% on AIME and 83.9% on MATH. Let that sink in. A model you can probably run on your phone is schooling the big boys in math.License-wise, it's MIT, which as Nisten put it, "MIT is like a jailbreak to the whole legal system, pretty much. That's what most people don't realize. It's like, this is, it's not my problem. You're a problem now." Basically, do whatever you want with it. Distill it, fine-tune it, build Skynet – it's all fair game.And the vibes? "Vibes are insane," as I mentioned on the show. Early benchmarks are showing R1 models trading blows with o1-preview and o1-mini, and even nipping at the heels of the full-fat o1 in some areas. Check out these numbers:And the price? Forget about it. We're talking 50x cheaper than o1 currently. DeepSeek R1 API is priced at $0.14 / 1M input tokens and $2.19 / 1M output tokens, compared to OpenAI's o1 at $15.00 / 1M input and a whopping $60.00 / 1M output. Suddenly, high-quality reasoning is democratized.LDJ highlighted the "aha moment" in DeepSeek's paper, where they talk about how reinforcement learning enabled the model to re-evaluate its approach and "think more." It seems like simple RL scaling, combined with a focus on reasoning, is the secret sauce. No fancy Monte Carlo Tree Search needed, apparently!But the real magic of open source is what the community does with it. Pietro Schirano joined us to talk about his "Retrieval Augmented Thinking" (RAT) approach, where he extracts the thinking process from R1 and transplants it to other models. "And what I found out is actually by doing so, you may even like smaller, quote unquote, you know, less intelligent model actually become smarter," Pietro explained. Frankenstein models, anyone? (John Lindquist has a tutorial on how to do it here)And then there's the genius hack from Voooogel, who figured out how to emulate a "reasoning_effort" knob by simply replacing the "end" token with "Wait, but". "This tricks the model into keeps thinking," as I described it. Want your AI to really ponder the meaning of life (or just 1+1)? Now you can, thanks to open source tinkering.Georgi Gerganov, the legend behind llama.cpp, even jumped in with a two-line snippet to enable speculative decoding, boosting inference speeds on the 32B model on my Macbook from a sluggish 5 tokens per second to a much more respectable 10-11 tokens per second. Open source collaboration at its finest and it's only going to get better! Thinking like a NeuroticMany people really loved the way R1 thinks, and what I found astonishing is that I just sent "hey" and the thinking went into a whole 5 paragraph debate of how to answer, a user on X answered with "this is Woody Allen-level of Neurotic" which... nerd sniped me so hard! I used Hauio Audio (which is great!) and ByteDance latentSync and gave R1 a voice! It's really something when you hear it's inner monologue being spoken out like this! ByteDance Enters the Ring: UI-TARS Controls Your PCNot to be outdone in the open source frenzy, ByteDance, the TikTok behemoth, dropped UI-TARS, a set of models designed to control your PC. And they claim SOTA performance, beating even Anthropic's computer use models and, in some benchmarks, GPT-4o and Claude.UI-TARS comes in 2B, 7B, and 72B parameter flavors, and ByteDance even released desktop apps for Mac and PC to go along with them. "They released an app it's called the UI TARS desktop app. And then, this app basically allows you to Execute the mouse clicks and keyboard clicks," I explained during the show.While I personally couldn't get the desktop app to work flawlessly (quantization issues, apparently), the potential is undeniable. Imagine open source agents controlling your computer – the possibilities are both exciting and slightly terrifying. As Nisten wisely pointed out, "I would use another machine. These things are not safe to tell people. I might actually just delete your data if you, by accident." Words to live by, folks.LDJ chimed in, noting that UI-TARS seems to excel particularly in operating system-level control tasks, while OpenAI's leaked "Operator" benchmarks might show an edge in browser control. It's a battle for desktop dominance brewing in open source!Noting that the common benchmark between Operator and UI-TARS is OSWorld, UI-Tars launched with a SOTA Humanity's Last Exam: The Benchmark to BeatSpeaking of benchmarks, a new challenger has entered the arena: Humanity's Last Exam (HLE). A cool new unsaturated bench of 3,000 challenging questions across over a hundred subjects, crafted by nearly a thousand subject matter experts from around the globe. "There's no way I'm answering any of those myself. I need an AI to help me," I confessed on the show.And guess who's already topping the HLE leaderboard? You guessed it: DeepSeek R1, with a score of 9.4%! "Imagine how hard this benchmark is if the top reasoning models that we have right now... are getting less than 10 percent completeness on this," MMLU and Math are getting saturated? HLE is here to provide a serious challenge. Get ready to hear a lot more about HLE, folks.Big CO LLMs + APIs: Google's Gemini Gets a Million-Token BrainWhile open source was stealing the show, the big companies weren't completely silent. Google quietly dropped an update to Gemini Flash Thinking, their experimental reasoning model, and it's a big one. We're talking 1 million token context window and code execution capabilities now baked in!"This is Google's scariest model by far ever built ever," Nisten declared. "This thing, I don't like how good it is. This smells AGI-ish" High praise, and high concern, coming from Nisten! Benchmarks are showing significant performance jumps in math and science evals, and the speed is, as Nisten put it, "crazy usable." They have enabled the whopping 1M context window for the new Gemini Flash 2.0 Thinking Experimental (long ass name, maybe let's call it G1?) and I agree, it's really really good!And unlike some other reasoning models cough OpenAI cough, Gemini Flash Thinking shows you its thinking process! You can actually see the chain of thought unfold, which is incredibly valuable for understanding and debugging. Google's Gemini is quietly becoming a serious contender in the reasoning race (especially with Noam Shazeer being responsible for it!)OpenAI's "Operator" - Agents Are (Almost) HereThe moment we were all waiting for (or at least, I was): OpenAI finally unveiled Operator, their first foray into Level 3 Autonomy - agentic capabilities with ChatGPT. Sam Altman himself hyped it up as "AI agents are AI systems that can do work for you. You give them a task and they go off and do it." Sounds amazing, right?Operator is built on a new model called CUA (Computer Using Agent), trained on top of GPT-4, and it's designed to control a web browser in the cloud, just like a human would, using screen pixels, mouse, and keyboard. "This is just using screenshots, no API, nothing, just working," one of the OpenAI presenters emphasized. They demoed Operator booking restaurant reservations on OpenTable, ordering groceries on Instacart, and even trying to buy Warriors tickets on StubHub (though that demo got a little… glitchy). The idea is that you can delegate tasks to Operator, and it'll go off and handle them in the background, notifying you when it needs input or when the task is complete.As I'm writing these words, I have an Operator running trying to get me some fried rice, and another one trying to book me a vacation with kids over the summer, find some options and tell me what it found. Benchmarks-wise, OpenAI shared numbers for OSWorld (38.1%) and WebArena (58.1%), showing Operator outperforming previous SOTA but still lagging behind human performance. "Still a way to go," as they admitted. But the potential is massive.The catch? Operator is initially launching in the US for Pro users only, and even then, it wasn't exactly smooth sailing. I immediately paid the $200/mo to try it out (pro mode didn't convince me, unlimited SORA videos didn't eithe

Hey everyone, Alex here 👋 Welcome back, to an absolute banger of a week in AI releases, highlighted with just massive Open Source AI push. We're talking a MASSIVE 4M context window context window model from Hailuo (remember when a jump from 4K to 16K seemed like a big deal?), a 8B omni model that lets you livestream video and glimpses of Agentic ChatGPT? This week's ThursdAI was jam-packed with so much open source goodness that the big companies were practically silent. But don't worry, we still managed to squeeze in some updates from OpenAI and Mistral, along with a fascinating new paper from Sakana AI on self-adaptive LLMs. Plus, we had the incredible Graham Neubig, from All Hands AI, join us to talk about Open Hands (formerly OpenDevin) and even contributed to our free, LLM Evaluation course on Weights & Biases!Before we dive in, a friend asked me over dinner, what are the main 2 things that happened in AI in 2024, and this week highlights one of those trends. Most of the Open Source is now from China. This week, we got MiniMax from Hailuo, OpenBMB with a new MiniCPM, InternLM came back and most of the rest were Qwen finetunes. Not to mention DeepSeek. Wanted to highlight this significant narrative change and that this is being done despite the chip export restrictions. ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Open Source AI & LLMsMiniMax-01: 4 Million Context, 456 Billion Parameters, and Lightning Attention This came absolutely from the left field, given that we've seen no prior LLMs from Haulio, the company previously releasing video models with consistent characters. Dropping a massive 456B mixture of experts model (45B active parameters) with such a long context support in open weights, but also with very significant benchmarks that compete with Gpt-4o, Claude and DeekSeek v3 (75.7 MMLU-pro, 89 IFEval, 54.4 GPQA)They have trained the model on up to 1M context window and then extended it to 4M with ROPE scaling methods (our coverage of RoPE) during Inference. MiniMax-Text-01 adopts a hybrid architecture that combines Lightning Attention, Softmax Attention and Mixture-of-Experts (MoE) with 45B active parameters. I gotta say, when we started talking about context window, imagining a needle in a haystack graph that shows 4M, in the open source seemed far fetched, though we did say that theoretically, there may not be a limit to context windows. I just always expected that limit to be unlocked by transformers alternative architectures like Mamba or other State Space Models.Vision, API and Browsing - Minimax-VL-01It feels like such a well rounded and complete release, that it highlights just how mature company that is behind it. They have also released a vision version of this model, that includes a 300M param Vision Transformer on top (trained with 512B vision language tokens) that features dynamic resolution and boasts very high DocVQA and ChartQA scores. Not only did these two models were released in open weights, they also launched as a unified API endpoint (supporting up to 1M tokens) and it's cheap! $0.2/1M input and $1.1/1M output tokens! AFAIK this is only the 3rd API that supports this much context, after Gemini at 2M and Qwen Turbo that supports 1M as well.Surprising web browsing capabilitiesYou can play around with the model on their website, hailuo.ai which also includes web grounding, which I found quite surprising to find out, that they are beating chatGPT and Perplexity on how fast they can find information that just happened that same day! Not sure what search API they are using under the hood but they are very quick. 8B chat with video model omni-model from OpenBMBOpenBMB has been around for a while and we've seen consistently great updates from them on the MiniCPM front, but this one takes the cake! This is a complete omni modal end to end model, that does video streaming, audio to audio and text understanding, all on a model that can run on an iPad! They have a demo interface that is very similar to the chatGPT demo from spring of last year, and allows you to stream your webcam and talk to the model, but this is just an 8B parameter model we're talking about! It's bonkers! They are boasting some incredible numbers, and to be honest, I highly doubt their methodology in textual understanding, because, well, based on my experience alone, this model understands less than close to chatGPT advanced voice mode, but miniCPM has been doing great visual understanding for a while, so ChartQA and DocVQA are close to SOTA. But all of this doesn't matter, because, I say again, just a little over a year ago, Google released a video announcing these capabilities, having an AI react to a video in real time, and it absolutely blew everyone away, and it was FAKED. And this time a year after, we have these capabilities, essentially, in an 8B model that runs on device 🤯 Voice & Audio This week seems to be very multimodal, not only did we get an omni-modal from OpenBMB that can speak, and last week's Kokoro still makes a lot of waves, but this week there were a lot of voice updates as wellKokoro.js - run the SOTA open TTS now in your browserThanks to friend of the pod Xenova (and the fact that Kokoro was released with ONNX weights), we now have kokoro.js, or npm -i kokoro-js if you will. This allows you to install and run Kokoro, the best tiny TTS model, completely within your browser, with a tiny 90MB download and it sounds really good (demo here)Hailuo T2A - Emotional text to speech + API Hailuo didn't rest on their laurels of releasing a huge context window LLM, they also released a new voice framework (tho not open sourced) this week, and it sounds remarkably good (competing with 11labs) They have all the standard features like Voice Cloning, but claim to have a way to preserve the emotional undertones of a voice. They also have 300 voices to choose from and professional effects applied on the fly, like acoustics or telephone filters. (Remember, they have a video model as well, so assuming that some of this is to for the holistic video production) What I specifically noticed is their "emotional intelligence system" that's either automatic or can be selected from a dropdown. I also noticed their "lax" copyright restrictions, as one of the voices that was called "Imposing Queen" sounded just like a certain blonde haired heiress to the iron throne from a certain HBO series. When I generated a speech worth of that queen, I noticed that the emotion in that speech sounded very much like an actress would read them, and unlike any old TTS, just listen to it in the clip above, I don't remember getting TTS outputs with this much emotion from anything, maybe outside of advanced voice mode! Quite impressive!This Weeks Buzz from Weights & Biases - AGENTS!Breaking news from W&B as our CTO just broke SWE-bench Verified SOTA, with his own o1 agentic framework he calls W&B Programmer 😮 at 64.6% of the issues!Shawn describes how he achieved this massive breakthrough here and we'll be publishing more on this soon, but the highlight for me is he ran over 900 evaluations during the course of this, and tracked all of them in Weave! We also have an upcoming event in NY, on Jan 22nd, if you're there, come by and learn how to evaluate your AI agents, RAG applications and hang out with our team! (Sign up here)Big Companies & APIsOpenAI adds chatGPT tasks - first agentic feature with more to come! We finally get a glimpse of an agentic chatGPT, in the form of scheduled tasks! Deployed to all users, it is now possible to select gpt-4o with tasks, and schedule tasks in the future. You can schedule them in natural language, and then will execute a chat (and maybe perform a search or do a calculation) and then send you a notification (and an email!) when the task is done! A bit underwhelming at first, as I didn't really find a good use for this yet, I don't doubt that this is just a building block for something more Agentic to come that can connect to my email or calendar and do actual tasks for me, not just... save me from typing the chatGPT query at "that time" Mistral CodeStral 25.01 - a new #1 coding assistant modelAn updated Codestral was released at the beginning of the week, and TBH I've never seen the vibes split this fast on a model. While it's super exciting that Mistral is placing a coding model at #1 on the LMArena CoPilot's arena, near Claude 3.5 and DeepSeek, the fact that this new model is not released weights is really a bummer (especially as a reference to the paragraph I mentioned on top) We seem to be closing down on OpenSource in the west, while the Chinese labs are absolutely crushing it (while also releasing in the open, including Weights, Technical papers). Mistral has released this model in API and via a collab with the Continue dot dev coding agent, but they used to be the darling of the open source community by releasing great models! Also notable, a very quick new benchmark post release was dropped that showed a significant difference between their reported benchmarks and how it performs on Aider polyglot There was way more things for this week than we were able to cover, including a new and exciting transformers squared new architecture from Sakana, a new open source TTS with voice cloning and a few other open source LLMs, one of which cost only $450 to train! All the links in the TL;DR below! TL;DR and show notes* Open Source LLMs * MiniMax-01 from Hailuo - 4M context 456B (45B A) LLM (Github, HF, Blog, Report)* Jina - reader V2 model - HTML 2 Markdown/JSON (HF)* InternLM3-8B-Instruct - apache 2 License (Github, HF)* OpenBMB - MiniCPM-o 2.6 - Multimodal Live Streaming on Your Phone (HF, Github, Demo)* KyutAI - Helium-1 2B - Base (X, HF)* Dria-Agent-α - 3B model that outputs python code (HF)* Sky-T1, a ‘reasoning’ AI model that can be trained for less than $450 (blog)* Bi

Hey everyone, Alex here 👋This week's ThursdAI was a whirlwind of announcements, from Microsoft finally dropping Phi-4's official weights on Hugging Face (a month late, but who's counting?) to Sam Altman casually mentioning that OpenAI's got AGI in the bag and is now setting its sights on superintelligence. Oh, and NVIDIA? They're casually releasing a $3,000 supercomputer that can run 200B parameter models on your desktop. No big deal.We had some amazing guests this week too, with Oliver joining us to talk about a new foundation model in genomics and biosurveillance (yes, you read that right - think wastewater and pandemic monitoring!), and then, we've got some breaking news! Vik returned to the show with a brand new Moondream release that can do some pretty wild things. Ever wanted an AI to tell you where someone's looking in a photo? Now you can, thanks to a tiny model that runs on edge devices. 🤯So buckle up, folks, because we've got a ton to cover. Let's dive into the juicy details of this week's AI madness, starting with open source.03:10 TL;DR03:10 Deep Dive into Open Source LLMs10:58 MetaGene: A New Frontier in AI20:21 PHI4: The Latest in Open Source AI27:46 R Star Math: Revolutionizing Small LLMs34:02 Big Companies and AI Innovations42:25 NVIDIA's Groundbreaking Announcements43:49 AI Hardware: Building and Comparing Systems46:06 NVIDIA's New AI Models: LLAMA Neumatron47:57 Breaking News: Moondream's Latest Release50:19 Moondream's Journey and Capabilities58:41 Weights & Biases: New Evals Course01:08:29 NVIDIA's World Foundation Models01:08:29 ByteDance's LatentSync: State-of-the-Art Lip Sync01:12:54 Kokoro TTS: High-Quality Text-to-SpeechAs always, TL;DR section with links and show notes below 👇Open Source AI & LLMsPhi-4: Microsoft's "Small" Model Finally Gets its Official Hugging Face DebutFinally, after a month, we're getting Phi-4 14B on HugginFace. So far, we've had bootlegged copies of it, but it's finally officially uploaded by Microsoft. Not only is it now official, it's also officialy MIT licensed which is great!So, what's the big deal? Well, besides the licensing, it's a 14B parameter, dense decoder-only Transformer with a 16K token context length and trained on a whopping 9.8 trillion tokens. It scored 80.4 on math and 80.6 on MMLU, making it about 10% better than its predecessor, Phi-3 and better than Qwen 2.5's 79What’s interesting about phi-4 is that the training data consisted of 40% synthetic data (almost half!)The vibes are always interesting with Phi models, so we'll keep an eye out, notable also, the base models weren't released due to "safety issues" and that this model was not trained for multi turn chat applications but single turn use-casesMetaGene-1: AI for Pandemic Monitoring and Pathogen DetectionNow, this one's a bit different. We usually talk about LLMs in this section, but this is more about the "open source" than the "LLM." Prime Intellect, along with folks from USC, released MetaGene-1, a metagenomic foundation model. That's a mouthful, right? Thankfully, we had Oliver Liu, a PhD student at USC, and an author on this paper, join us to explain.Oliver clarified that the goal is to use AI for "biosurveillance, pandemic monitoring, and pathogen detection." They trained a 7B parameter model on 1.5 trillion base pairs of DNA and RNA sequences from wastewater, creating a model surprisingly capable of zero-shot embedding. Oliver pointed out that while using genomics to pretrain foundation models is not new, MetaGene-1 is, "in its current state, the largest model out there" and is "one of the few decoder only models that are being used". They also have collected 15T bae pairs but trained on 10% of them due to grant and compute constraints.I really liked this one, and though the science behind this was complex, I couldn't help but get excited about the potential of transformer models catching or helping catch the next COVID 👏rStar-Math: Making Small LLMs Math Whizzes with Monte Carlo Tree SearchAlright, this one blew my mind. A paper from Microsoft (yeah, them again) called "rStar-Math" basically found a way to make small LLMs do math better than o1 using Monte Carlo Tree Search (MCTS). I know, I know, it sounds wild. They took models like Phi-3-mini (a tiny 3.8B parameter model) and Qwen 2.5 3B and 7B, slapped some MCTS magic on top, and suddenly these models are acing the AIME 2024 competition math benchmark and scoring 90% on general math problems. For comparison, OpenAI's o1-preview scores 85.5% on math and o1-mini scores 90%. This is WILD, as just 5 months ago, it was unimaginable that any LLM can solve math of this complexity, then reasoning models could, and now small LLMs with some MCTS can!Even crazier, they observed an "emergence of intrinsic self-reflection capability" in these models during problem-solving, something they weren't designed to do. LDJ chimed in saying "we're going to see more papers showing these things emerging and caught naturally." So, is 2025 the year of not just AI agents, but also emergent reasoning in LLMs? It's looking that way. The code isn't out yet (the GitHub link in the paper is currently a 404), but when it drops, you can bet we'll be all over it.Big Companies and LLMsOpenAI: From AGI to ASIOkay, let's talk about the elephant in the room: Sam Altman's blog post. While reflecting on getting fired from his job on like a casual Friday, he dropped this bombshell: "We are now confident that we know how to build AGI as we have traditionally understood it." And then, as if that wasn't enough, he added, "We're beginning to turn our aim beyond that to superintelligence in the true sense of the word." So basically, OpenAI is saying, "AGI? Done. Next up: ASI."This feels like a big shift in how openly folks at OpenAI is talking about Superintelligence, and while AGI is yet to be properly defined (LDJ read out the original OpenAI definition on the live show, but the Microsoft definition contractually with OpenAI was a system that generates $100B in revenue) they are already talking about Super Intelligence which supersedes all humans ever lived in all domainsNVIDIA @ CES - Home SuperComputers, 3 scaling laws, new ModelsThere was a lot of things happening at CES, the largest consumer electronics show, but the AI focus was on NVIDIA, namely on Jensen Huangs keynote speech!He talked about a lot of stuff, really, it's a show, and is a very interesting watch, NVIDIA is obviously at the forefront of all of this AI wave, and when Jensen tells you that we're at the high of the 3rd scaling law, he knows what he's talking about (because he's fueling all of it with his GPUs) - the third one is of course test time scaling or "reasoning", the thing that powers o1, and the coming soon o3 model and other reasoners.Project Digits - supercomputer at home?Jensen also announced Project Digits: a compact AI supercomputer priced at a relatively modest $3,000. Under the hood, it wields a Grace Blackwell “GB10” superchip that supposedly offers 1 petaflop of AI compute and can support LLMs up to 200B parameters (or you can link 2 of them to run LLama 405b at home!)This thing seems crazy, but we don't know more details like the power requirements for this beast!Nemotrons again?Also announced was a family of NVIDIA LLama Nemotron foundation models, but.. weirdly we already have Nemotron LLamas (3 months ago) , so those are... new ones? I didn't really understand what was announced here, as we didn't get new models, but the announcement was made nonetheless. We're due to get 3 new version of Nemotron on the Nvidia NEMO platform (and Open), sometime soon.NVIDIA did release new open source models, with COSMOS, which is a whole platform that includes pretrained world foundation models to help simulate world environments to train robots (among other things).They have released txt2world and video2world Pre-trained Diffusion and Autoregressive models in 7B and 14B sizes, that generate videos to simulate visual worlds that have strong alignment to physics.If you believe Elon when he says that Humanoid Robots are going to be the biggest category of products (every human will want 1 or 3, so we're looking at 20 billion of them), then COSMOS is a platform to generate synthetic data to train these robots to do things in the real world!This weeks buzz - Weights & Biases cornerThe wait is over, our LLM Evals course is now LIVE, featuring speakers Graham Neubig (who we had on the pod before, back when Open Hands was still called Open Devin) and Paige Bailey, and Anish and Ayush from my team at W&B!If you're building with LLM in production and don't have a robust evaluation setup, or don't even know where to start with one, this course is definitely for you! Sign up today. You'll learn from examples of Imagen and Veo from Paige, Agentic examples using Weave from Graham and Basic and Advanced Evaluation from Anish and Ayush.The workshop in Seattle next was filled out super quick, so since we didn't want to waitlist tons of folks, we have extended it to another night, so those of you who couldn't get in, will have another opportunity on Tuesday! (Workshop page) but while working on it I came up with this distillation of what I'm going to deliver, and wanted to share with you.Vision & VideoNew Moondream 01-09 can tell where you look (among other things) (blog, HF)We had some breaking news on the show! Vik Korrapati, the creator of Moondream, joined us to announce updates to Moondream, a new version of his tiny vision language model. This new release has some incredible capabilities, including pointing, object detection, structured output (like JSON), and even gaze detection. Yes, you read that right. Moondream can now tell you where someone (or even a pet!) is looking in an image.Vic explained how they achieved this: "We took one of the training datasets that Gazelle trained on and added it to the Moondream fine tuning mix". What's even more impressive is that Moondream is tiny - the new version com

Hey folks, Alex here 👋 Happy new year!On our first episode of this year, and the second quarter of this century, there wasn't a lot of AI news to report on (most AI labs were on a well deserved break). So this week, I'm very happy to present a special ThursdAI episode, an interview with Joāo Moura, CEO of Crew.ai all about AI agents!We first chatted with Joāo a year ago, back in January of 2024, as CrewAI was blowing up but still just an open source project, it got to be the number 1 trending project on Github, and #1 project on Product Hunt. (You can either listen to the podcast or watch it in the embedded Youtube above)00:36 Introduction and New Year Greetings02:23 Updates on Open Source and LLMs03:25 Deep Dive: AI Agents and Reasoning03:55 Quick TLDR and Recent Developments04:04 Medical LLMs and Modern BERT09:55 Enterprise AI and Crew AI Introduction10:17 Interview with João Moura: Crew AI25:43 Human-in-the-Loop and Agent Evaluation33:17 Evaluating AI Agents and LLMs44:48 Open Source Models and Fin to OpenAI45:21 Performance of Claude's Sonnet 3.548:01 Different parts of an agent topology, brain, memory, tools, caching53:48 Tool Use and Integrations01:04:20 Removing LangChain from Crew01:07:51 The Year of Agents and Reasoning01:18:43 Addressing Concerns About AI01:24:31 Future of AI and Agents01:28:46 Conclusion and Farewell---Is 2025 "the year of AI agents"?AI agents as I remember them as a concept started for me a few month after I started ThursdAI ,when AutoGPT exploded. Was such a novel idea at the time, run LLM requests in a loop,(In fact, back then, I came up with a retry with AI concept and called it TrAI/Catch, where upon an error, I would feed that error back into the GPT api and ask it to correct itself. it feels so long ago!)AutoGPT became the fastest ever Github project to reach 100K stars, and while exciting, it did not work.Since then we saw multiple attempts at agentic frameworks, like babyAGI, autoGen. Crew AI was one of them that keeps being the favorite among many folks.So, what is an AI agent? Simon Willison, friend of the pod, has a mission, to ask everyone who announces a new agent, what they mean when they say it because it seems that everyone "shares" a common understanding of AI agents, but it's different for everyone.We'll start with Joāo's explanation and go from there. But let's assume the basic, it's a set of LLM calls, running in a self correcting loop, with access to planning, external tools (via function calling) and a memory or sorts that make decisions.Though, as we go into detail, you'll see that since the very basic "run LLM in the loop" days, the agents in 2025 have evolved and have a lot of complexity.My takeaways from the conversationI encourage you to listen / watch the whole interview, Joāo is deeply knowledgable about the field and we go into a lot of topics, but here are my main takeaways from our chat* Enterprises are adopting agents, starting with internal use-cases* Crews have 4 different kinds of memory, Long Term (across runs), short term (each run), Entity term (company names, entities), pre-existing knowledge (DNA?)* TIL about a "do all links respond with 200" guardrail* Some of the agent tools we mentioned* Stripe Agent API - for agent payments and access to payment data (blog)* Okta Auth for Gen AI - agent authentication and role management (blog)* E2B - code execution platform for agents (e2b.dev)* BrowserBase - programmatic web-browser for your AI agent* Exa - search grounding for agents for real time understanding* Crew has 13 crews that run 24/7 to automate their company* Crews like Onboarding User Enrichment Crew, Meetings Prep, Taking Phone Calls, Generate Use Cases for Leads* GPT-4o mini is the most used model for 2024 for CrewAI with main factors being speed / cost* Speed of AI development makes it hard to standardize and solidify common integrations.* Reasoning models like o1 still haven't seen a lot of success, partly due to speed, partly due to different way of prompting required.This weeks BuzzWe've just opened up pre-registration for our upcoming FREE evaluations course, featuring Paige Bailey from Google and Graham Neubig from All Hands AI (previously Open Devin). We've distilled a lot of what we learned about evaluating LLM applications while building Weave, our LLM Observability and Evaluation tooling, and are excited to share this with you all! Get on the listAlso, 2 workshops (also about Evals) from us are upcoming, one in SF on Jan 11th and one in Seattle on Jan 13th (which I'm going to lead!) so if you're in those cities at those times, would love to see you!And that's it for this week, there wasn't a LOT of news as I said. The interesting thing is, even in the very short week, the news that we did get were all about agents and reasoning, so it looks like 2025 is agents and reasoning, agents and reasoning!See you all next week 🫡TL;DR with links:* Open Source LLMs* HuatuoGPT-o1 - medical LLM designed for medical reasoning (HF, Paper, Github, Data)* Nomic - modernbert-embed-base - first embed model on top of modernbert (HF)* HuggingFace - SmolAgents lib to build agents (Blog)* SmallThinker-3B-Preview - a QWEN 2.5 3B "reasoning" finetune (HF)* Wolfram new Benchmarks including DeepSeek v3 (X)* Big CO LLMs + APIs* Newcomer Rubik's AI Sonus-1 family - Mini, Air, Pro and Reasoning (X, Chat)* Microsoft "estimated" GPT-4o-mini is a ~8B (X)* Meta plans to bring AI profiles to their social networks (X)* This Week's Buzz* W&B Free Evals Course with Page Bailey and Graham Beubig - Free Sign Up* SF evals event - January 11th* Seattle evals workshop - January 13th This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey everyone, Alex here 👋I was hoping for a quiet holiday week, but whoa, while the last newsletter was only a week ago, what a looong week it has been, just Friday after the last newsletter, it felt like OpenAI has changed the world of AI once again with o3 and left everyone asking "was this AGI?" over the X-mas break (Hope Santa brought you some great gifts!) and then not to be outdone, DeepSeek open sourced basically a Claude 2.5 level behemoth DeepSeek v3 just this morning!Since the breaking news from DeepSeek took us by surprise, the show went a bit longer (3 hours today!) than expected, so as a Bonus, I'm going to release a separate episode with a yearly recap + our predictions from last year and for next year in a few days (soon in your inbox!) TL;DR* Open Source LLMs* CogAgent-9B (Project, Github)* Qwen QvQ 72B - open weights visual reasoning (X, HF, Demo, Project)* GoodFire Ember - MechInterp API - GoldenGate LLama 70B* 🔥 DeepSeek v3 658B MoE - Open Source Claude level model at $6M (X, Paper, HF, Chat)* Big CO LLMs + APIs* 🔥 OpenAI reveals o3 and o3 mini (Blog, X)* X.ai raises ANOTHER 6B dollars - on their way to 200K H200s (X)* This weeks Buzz* Two W&B workshops upcoming in January* SF - January 11* Seattle - January 13 (workshop by yours truly!)* New Evals course with Paige Bailey and Graham Neubig - pre-sign up for free* Vision & Video* Kling 1.6 update (Tweet)* Voice & Audio* Hume OCTAVE - 3B speech-language model (X, Blog)* Tools* OpenRouter added Web Search Grounding to 300+ models (X)Open Source LLMsDeepSeek v3 658B - frontier level open weights model for ~$6M (X, Paper, HF, Chat )This was absolutely the top of the open source / open weights news for the past week, and honestly maybe for the past month. DeepSeek, the previous quant firm from China, has dropped a behemoth model, a 658B parameter MoE (37B active), that you'd need 8xH200 to even run, that beats Llama 405, GPT-4o on most benchmarks and even Claude Sonnet 3.5 on several evals! The vibes seem to be very good with this one, and while it's not all the way beating Claude yet, it's nearly up there already, but the kicker is, they trained it with a very restricted compute, per the paper, with ~2K h800 (which is like H100 but with less bandwidth) for 14.8T tokens. (that's 15x cheaper than LLama 405 for comparison) For evaluations, this model excels on Coding and Math, which is not surprising given how excellent DeepSeek coder has been, but still, very very impressive! On the architecture front, the very interesting thing is, this feels like Mixture of Experts v2, with a LOT of experts (256) and 8+1 active at the same time, multi token prediction, and a lot optimization tricks outlined in the impressive paper (here's a great recap of the technical details)The highlight for me was, that DeepSeek is distilling their recent R1 version into this version, which likely increases the performance of this model on Math and Code in which it absolutely crushes (51.6 on CodeForces and 90.2 on MATH-500) The additional aspect of this is the API costs, and while they are going to raise the prices come February (they literally just swapped v2.5 for v3 in their APIs without telling a soul lol), the price performance for this model is just absurd. Just a massive massive release from the WhaleBros, now I just need a quick 8xH200 to run this and I'm good 😅 Other OpenSource news - Qwen QvQ, CogAgent-9B and GoldenGate LLamaIn other open source news this week, our friends from Qwen have released a very interesting preview, called Qwen QvQ, a visual reasoning model. It uses the same reasoning techniques that we got from them in QwQ 32B, but built with the excellent Qwen VL, to reason about images, and frankly, it's really fun to see it think about an image. You can try it hereand a new update to CogAgent-9B (page), an agent that claims to understand and control your computer, claims to beat Claude 3.5 Sonnet Computer Use with just a 9B model! This is very impressive though I haven't tried it just yet, I'm excited to see those very impressive numbers from open source VLMs driving your computer and doing tasks for you!A super quick word from ... Weights & Biases! We've just opened up pre-registration for our upcoming FREE evaluations course, featuring Paige Bailey from Google and Graham Neubig from All Hands AI. We've distilled a lot of what we learned about evaluating LLM applications while building Weave, our LLM Observability and Evaluation tooling, and are excited to share this with you all! Get on the listAlso, 2 workshops (also about Evals) from us are upcoming, one in SF on Jan 11th and one in Seattle on Jan 13th (which I'm going to lead!) so if you're in those cities at those times, would love to see you!Big Companies - APIs & LLMsOpenAI - introduces o3 and o3-mini - breaking Arc-AGI challenge, GQPA and teasing AGI? On the last day of the 12 days of OpenAI, we've got the evals of their upcoming o3 reasoning model (and o3-mini) and whoah. I think I speak on behalf of most of my peers that we were all shaken by how fast the jump in capabilities happened from o1-preview and o1 full (being released fully just two weeks prior on day 1 of the 12 days) Almost all evals shared with us are insane, from 96.7 on AIME (from 13.4 with Gpt40 earlier this year) to 87.7 GQPA Diamond (which is... PhD level Science Questions) But two evals stand out the most, and one of course is the Arc-AGI eval/benchmark. It was designed to be very difficult for LLMs and easy for humans, and o3 solved it with an unprecedented 87.5% (on high compute setting)This benchmark was long considered impossible for LLMs, and just the absolute crushing of this benchmark for the past 6 months is something to behold: The other thing I want to highlight is the Frontier Math benchmark, which was released just two months ago by Epoch, collaborating with top mathematicians to create a set of very challenging math problems. At the time of release (Nov 12), the top LLMs solved only 2% of this benchmark. With o3 solving 25% of this benchmark just 3 months after o1 taking 2%, it's quite incredible to see how fast these models are increasing in capabilities. Is this AGI? This release absolutely started or restarted a debate of what is AGI, given that, these goal posts move all the time. Some folks are freaking out and saying that if you're a software engineer, you're "cooked" (o3 solved 71.7 of SWE-bench verified and gets 2727 ELO on CodeForces which is competition code, which is 175th global rank among human coders!), some have also calculated its IQ and estimate it to be at 157 based on the above CodeForces rating. So the obvious question is being asked (among the people who follow the news, most people who don't follow the news could care less) is.. is this AGI? Or is something else AGI? Well, today we got a very interesting answer to this question, from a leak between a Microsoft and OpenAI negotiation and agreement, in which they have a very clear definition of AGI. "A system generating $100 Billion in profits" - a reminder, per their previous agreement, if OpenAI builds AGI, Microsoft will lose access to OpenAI technologies. o3-mini and test-time compute as the new scaling lawWhile I personally was as shaken as most of my peers at these incredible breakthroughs, I was also looking at the more practical and upcoming o3-mini release, which is supposed to come on January this year per Sam Altman. Per their evaluations, o3-mini is going to be significantly cheaper and faster than o3, while offering 3 levels of reasoning effort to developers (low, medium and high) and on medium level, it would beat the current best model (o1) while being cheaper than o1-mini. All of these updates and improvements in the span of less than 6 months are a testament of just how impressive test-time compute is as our additional new scaling law. Not to mention that current scaling laws still hold, we're waiting for Orion or GPT 4.5 or whatever it's called, and that underlying model will probably significantly improve the reasoning models that are built on top of it. Also, if the above results from DeepSeek are anything to go by (and they should be), the ability of these reasoning models to generate incredible synthetic training data for the next models is also quite incredible so... flywheel is upon us, models get better and make better models. Other AI news from this week: The most impressive other news came from HUME, showcasing OCTAVE - their new 3B speech-language model, which is able to not only fake someone's voice with 5 seconds of audio, but also take on their personality and style of speaking and mannerisms. This is not only a voice model mind you, but a 3B LLM as well, so it can mimic a voice, and even create new voices from a prompt. While they mentioned the size, the model was not released yet and will be coming to their API soon, and when I asked about open source, it seems that Hume CEO did not think it's a safe bet opening up this kind of tech to the world yet. I also loved a new little x-mas experiment from OpenRouter and Exa, where-in on the actual OpenRouter interface, you can now chat with over 300 models they serve, and ground answers in search. This is it for this week, which again, I thought is going to be a very chill one, and .. nope! The second part of the show/newsletter, in which we did a full recap of the last year, talked about our predictions from last year and did predictions for this next year, is going to drop in a few days 👀 So keep your eyes peeled. (I decided to separate the two, as 3 hour podcast about AI is... long, I'm no Lex Fridman lol) As always, if you found any of this interesting, please share with a friend, and comment on social media, or right here on Substack, I love getting feedback on what works and what doesn't. Thank you for being part of the ThursdAI community 👋ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and

For the full show notes and links visit https://sub.thursdai.news🔗 Subscribe to our show on Spotify: https://thursdai.news/spotify🔗 Apple: https://thursdai.news/appleHo, ho, holy moly, folks! Alex here, coming to you live from a world where AI updates are dropping faster than Santa down a chimney! 🎅 It's been another absolutely BANANAS week in the AI world, and if you thought last week was wild, and we're due for a break, buckle up, because this one's a freakin' rollercoaster! 🎢In this episode of ThursdAI, we dive deep into the recent innovations from OpenAI, including their 1-800 ChatGPT phone service and new advancements in voice mode and API functionalities. We discuss the latest updates on O1 model capabilities, including Reasoning Effort settings, and highlight the introduction of WebRTC support by OpenAI. Additionally, we explore the groundbreaking VEO2 model from Google, the generative physics engine Genesis, and new developments in open source models like Cohere's Command R7b. We also provide practical insights on using tools like Weights & Biases for evaluating AI models and share tips on leveraging GitHub Gigi. Tune in for a comprehensive overview of the latest in AI technology and innovation.00:00 Introduction and OpenAI's 12 Days of Releases00:48 Advanced Voice Mode and Public Reactions01:57 Celebrating Tech Innovations02:24 Exciting New Features in AVMs03:08 TLDR - ThursdAI December 1912:58 Voice and Audio Innovations14:29 AI Art, Diffusion, and 3D16:51 Breaking News: Google Gemini 2.023:10 Meta Apollo 7b Revisited33:44 Google's Sora and Veo234:12 Introduction to Veo2 and Sora34:59 First Impressions of Veo235:49 Comparing Veo2 and Sora37:09 Sora's Unique Features38:03 Google's MVP Approach43:07 OpenAI's Latest Releases44:48 Exploring OpenAI's 1-800 CHAT GPT47:18 OpenAI's Fine-Tuning with DPO48:15 OpenAI's Mini Dev Day Announcements49:08 Evaluating OpenAI's O1 Model54:39 Weights & Biases Evaluation Tool - Weave01:03:52 ArcAGI and O1 Performance01:06:47 Introduction and Technical Issues01:06:51 Efforts on Desktop Apps01:07:16 ChatGPT Desktop App Features01:07:25 Working with Apps and Warp Integration01:08:38 Programming with ChatGPT in IDEs01:08:44 Discussion on Warp and Other Tools01:10:37 GitHub GG Project01:14:47 OpenAI Announcements and WebRTC01:24:45 Modern BERT and Smaller Models01:27:37 Genesis: Generative Physics Engine01:33:12 Closing Remarks and Holiday WishesHere’s a talking podcast host speaking excitedly about his showTL;DR - Show notes and Links* Open Source LLMs* Meta Apollo 7B – LMM w/ SOTA video understanding (Page, HF)* Microsoft Phi-4 – 14B SLM (Blog, Paper)* Cohere Command R 7B – (Blog)* Falcon 3 – series of models (X, HF, web)* IBM updates Granite 3.1 + embedding models (HF, Embedding)* Big CO LLMs + APIs* OpenAI releases new o1 + API access (X)* Microsoft makes CoPilot Free! (X)* Google - Gemini Flash 2 Thinking experimental reasoning model (X, Studio)* This weeks Buzz* W&B weave Playground now has Trials (and o1 compatibility) (try it)* Alex Evaluation of o1 and Gemini Thinking experimental (X, Colab, Dashboard)* Vision & Video* Google releases Veo 2 – SOTA text2video modal - beating SORA by most vibes (X)* HunyuanVideo distilled with FastHunyuan down to 6 steps (HF)* Kling 1.6 (X)* Voice & Audio* OpenAI realtime audio improvements (docs)* 11labs new Flash 2.5 model – 75ms generation (X)* Nexa OmniAudio – 2.6B – multimodal local LLM (Blog)* Moonshine Web – real time speech recognition in the browser (X)* Sony MMAudio - open source video 2 audio model (Blog, Demo)* AI Art & Diffusion & 3D* Genesys – open source generative 3D physics engine (X, Site, Github)* Tools* CerebrasCoder – extremely fast apps creation (Try It)* RepoPrompt to chat with o1 Pro – (download) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey folks, Alex here, writing this from the beautiful Vancouver BC, Canada. I'm here for NeurIPS 2024, the biggest ML conferences of the year, and let me tell you, this was one hell of a week to not be glued to the screen. After last week banger week, with OpenAI kicking off their 12 days of releases, with releasing o1 full and pro mode during ThursdAI, things went parabolic. It seems that all the AI labs decided to just dump EVERYTHING they have before the holidays? 🎅A day after our show, on Friday, Google announced a new Gemini 1206 that became the #1 leading model on LMarena and Meta released LLama 3.3, then on Saturday Xai releases their new image model code named Aurora.On a regular week, the above Fri-Sun news would be enough for a full 2 hour ThursdAI show on it's own, but not this week, this week this was barely a 15 minute segment 😅 because so MUCH happened starting Monday, we were barely able to catch our breath, so lets dive into it! As always, the TL;DR and full show notes at the end 👇 and this newsletter is sponsored by W&B Weave, if you're building with LLMs in production, and want to switch to the new Gemini 2.0 today, how will you know if your app is not going to degrade? Weave is the best way! Give it a try for free.Gemini 2.0 Flash - a new gold standard of fast multimodal LLMsGoogle has absolutely taken the crown away from OpenAI with Gemini 2.0 believe it or not this week with this incredible release. All of us on the show were in agreement that this is a phenomenal release from Google for the 1 year anniversary of Gemini. Gemini 2.0 Flash is beating Pro 002 and Flash 002 on all benchmarks, while being 2x faster than Pro, having 1M context window, and being fully multimodal! Multimodality on input and outputThis model was announced to be fully multimodal on inputs AND outputs, which means in can natively understand text, images, audio, video, documents and output text, text + images and audio (so it can speak!). Some of these capabilities are restricted for beta users for now, but we know they exists. If you remember project Astra, this is what powers that project. In fact, we had Matt Wolfe join the show, and he demoed had early access to Project Astra and demoed it live on the show (see above) which is powered by Gemini 2.0 Flash. The most amazing thing is, this functionality, that was just 8 months ago, presented to us in Google IO, in a premium Booth experience, is now available to all, in Google AI studio, for free! Really, you can try out right now, yourself at https://aistudio.google.com/live but here's a demo of it, helping me proof read this exact paragraph by watching the screen and talking me through it. Performance out of the boxThis model beating Sonnet 3.5 on Swe-bench Verified completely blew away the narrative on my timeline, nobody was ready for that. This is a flash model, that's outperforming o1 on code!?So having a Flash MMIO model with 1M context that is accessible via with real time streaming option available via APIs from the release time is honestly quite amazing to begin with, not to mention that during the preview phase, this is currently free, but if we consider the previous prices of Flash, this model is going to considerably undercut the market on price/performance/speed matrix. You can see why this release is taking the crown this week. 👏 Agentic is coming with Project MarinerAn additional thing that was announced by Google is an Agentic approach of theirs is project Mariner, which is an agent in the form of a Chrome extension completing webtasks, breaking SOTA on the WebVoyager with 83.5% score with a single agent setup. We've seen agents attempts from Adept to Claude Computer User to Runner H, but this breaking SOTA from Google seems very promising. Can't wait to give this a try. OpenAI gives us SORA, Vision and other stuff from the bag of goodiesOk so now let's talk about the second winner of this week, OpenAI amazing stream of innovations, which would have taken the crown, if not for, well... ☝️ SORA is finally here (for those who got in)Open AI has FINALLY released SORA, their long promised text to video and image to video (and video to video) model (nee, world simulator) to general availability, including a new website - sora.com and a completely amazing UI to come with it. SORA can generate images of various quality from 480p up to 1080p and up to 20 seconds long, and they promised that those will be generating fast, as what they released is actually SORA turbo! (apparently SORA 2 is already in the works and will be even more amazing, more on this later) New accounts paused for nowOpenAI seemed to have severely underestimated how many people would like to generate the 50 images per month allowed on the plus account (pro account gets you 10x more for $200 + longer durations whatever that means), and since the time of writing these words on ThursdAI afternoon, I still am not able to create a sora.com account and try out SORA myself (as I was boarding a plane when they launched it) SORA magical UII've invited one of my favorite video creators, Blaine Brown to the show, who does incredible video experiments, that always go viral, and had time to play with SORA to tell us what he thinks both from a video perspective and from a interface perspective. Blaine had a great take that we all collectively got so much HYPE over the past 8 months of getting teased, that many folks expected SORA to just be an incredible text to video 1 prompt to video generator and it's not that really, in fact, if you just send prompts, it's more like a slot machine (which is also confirmed by another friend of the pod Bilawal)But the magic starts to come when the additional tools like blend are taken into play. One example that Blaine talked about is the Remix feature, where you can Remix videos and adjust the remix strength (Strong, Mild) Another amazing insight Blaine shared is a that SORA can be used by fusing two videos that were not even generated with SORA, but SORA is being used as a creative tool to combine them into one. And lastly, just like Midjourney (and StableDiffusion before that), SORA has a featured and a recent wall of video generations, that show you videos and prompts that others used to create those videos with, for inspiration and learning, so you can remix those videos and learn to prompt better + there are prompting extension tools that OpenAI has built in. One more thing.. this model thinksI love this discovery and wanted to share this with you, the prompt is "A man smiles to the camera, then holds up a sign. On the sign, there is only a single digit number (the number of 'r's in 'strawberry')"Advanced Voice mode now with Video!I personally have been waiting for Voice mode with Video for such a long time, since the that day in the spring, where the first demo of advanced voice mode talked to an OpenAI employee called Rocky, in a very flirty voice, that in no way resembled Scarlet Johannson, and told him to run a comb through his hair. Well today OpenAI have finally announced that they are rolling out this option soon to everyone, and in chatGPT, we'll all going to have the camera button, and be able to show chatGPT what we're seeing via camera or the screen of our phone and have it have the context. If you're feeling a bit of a deja-vu, yes, this is very similar to what Google just launched (for free mind you) with Gemini 2.0 just yesterday in AI studio, and via APIs as well. This is an incredible feature, it will not only see your webcam, it will also see your IOS screen, so you’d be able to reason about an email with it, or other things, I honestly can’t wait to have it already! They also announced Santa mode, which is also super cool, tho I don’t quite know how to .. tell my kids about it? Do I… tell them this IS Santa? Do I tell them this is an AI pretending to be Santa? Where is the lie end exactly? And in one of his funniest jailbreaks (and maybe one of the toughest ones) Pliny the liberator just posted a Santa jailbreak that will definitely make you giggle (and him get Coal this X-mas)The other stuff (with 6 days to go) OpenAI has 12 days of releases, and the other amazing things we got obviously got overshadowed but they are still cool, Canvas can now run code and have custom GPTs, GPT in Apple Intelligence is now widely supported with the public release of iOS 18.2 and they have announced fine tuning with reinforcement learning, allowing to funetune o1-mini to outperform o1 on specific tasks with a few examples. There's 6 more work days to go, and they promised to "end with a bang" so... we'll keep you updated! This weeks Buzz - Guard Rail GenieAlright, it's time for "This Week's Buzz," our weekly segment brought to you by Weights & Biases! This week I hosted Soumik Rakshit from the Weights and Biases AI Team (The team I'm also on btw!). Soumik gave us a deep dive into Guardrails, our new set of features in Weave for ensuring reliability in GenAI production! Guardrails serve as a "safety net" for your LLM powered applications, filtering out inputs or llm responses that trigger a certain criteria or boundary. Types of guardrails include prompt injection attacks, PII leakage, jailbreaking attempts and toxic language as well, but can also include a competitor mention, or selling a product at $0 or a policy your company doesn't have. As part of developing the guardrails Soumik also developed and open sourced an app to test prompts against those guardrails "Guardrails Genie" and we're going to host it to allow folks to test their prompts against our guardrails, and also are developing it and the guardrails in the open so please check out our Github Apple iOS 18.2 Apple Intelligence + ChatGPT integrationApple Intelligence is finally here, you can download it if you have iPhone 15 pro and pro Max and iPhone 16 all series. If you have one of those phones, you will get the following new additional features that have been in Beta for a while, features like Im

Well well well, December is finally here, we're about to close out this year (and have just flew by the second anniversary of chatGPT 🎂) and it seems that all of the AI labs want to give us X-mas presents to play with over the holidays! Look, I keep saying this, but weeks are getting crazier and crazier, this week we got the cheapest and the most expensive AI offerings all at once (the cheapest from Amazon and the most expensive from OpenAI), 2 new open weights models that beat commercial offerings, a diffusion model that predicts the weather and 2 world building models, oh and 2 decentralized fully open sourced LLMs were trained across the world LIVE and finished training. I said... crazy week! And for W&B, this week started with Weave launching finally in GA 🎉, which I personally was looking forward for (read more below)!TL;DR Highlights* OpenAI O1 & Pro Tier: O1 is out of preview, now smarter, faster, multimodal, and integrated into ChatGPT. For heavy usage, ChatGPT Pro ($200/month) offers unlimited calls and O1 Pro Mode for harder reasoning tasks.* Video & Audio Open Source Explosion: Tencent’s HYVideo outperforms Runway and Luma, bringing high-quality video generation to open source. Fishspeech 1.5 challenges top TTS providers, making near-human voice available for free research.* Open Source Decentralization: Nous Research’s DiStRo (15B) and Prime Intellect’s INTELLECT-1 (10B) prove you can train giant LLMs across decentralized nodes globally. Performance is on par with centralized setups.* Google’s Genie 2 & WorldLabs: Generating fully interactive 3D worlds from a single image, pushing boundaries in embodied AI and simulation. Google’s GenCast also sets a new standard in weather prediction, beating supercomputers in accuracy and speed.* Amazon’s Nova FMs: Cheap, scalable LLMs with huge context and global language coverage. Perfect for cost-conscious enterprise tasks, though not top on performance.* 🎉 Weave by W&B: Now in GA, it’s your dashboard and tool suite for building, monitoring, and scaling GenAI apps. Get Started with 1 line of codeOpenAI’s 12 Days of Shipping: O1 & ChatGPT ProThe biggest splash this week came from OpenAI. They’re kicking off “12 days of launches,” and Day 1 brought the long-awaited full version of o1. The main complaint about o1 for many people is how slow it was! Well, now it’s not only smarter but significantly faster (60% faster than preview!), and officially multimodal: it can see images and text together.Better yet, OpenAI introduced a new ChatGPT Pro tier at $200/month. It offers unlimited usage of o1, advanced voice mode, and something called o1 pro mode — where o1 thinks even harder and longer about your hardest math, coding, or science problems. For power users—maybe data scientists, engineers, or hardcore coders—this might be a no-brainer. For others, 200 bucks might be steep, but hey, someone’s gotta pay for those GPUs. Given that OpenAI recently confirmed that there are now 300 Million monthly active users on the platform, and many of my friends already upgraded, this is for sure going to boost the bottom line at OpenAI! Quoting Sam Altman from the stream, “This is for the power users who push the model to its limits every day.” For those who complained o1 took forever just to say “hi,” rejoice: trivial requests will now be answered quickly, while super-hard tasks get that legendary deep reasoning including a new progress bar and a notification when a task is complete. Friend of the pod Ray Fernando gave pro a prompt that took 7 minutes to think through! I've tested the new o1 myself, and while I've gotten dangerously close to my 50 messages per week quota, I've gotten some incredible results already, and very fast as well. This ice-cubes question failed o1-preview and o1-mini and it took both of them significantly longer, and it took just 4 seconds for o1. Open Source LLMs: Decentralization & Transparent ReasoningNous Research DiStRo & DeMo OptimizerWe’ve talked about decentralized training before, but the folks at Nous Research are making it a reality at scale. This week, Nous Research wrapped up the training of a new 15B-parameter LLM—codename “Psyche”—using a fully decentralized approach called “Nous DiStRo.” Picture a massive AI model trained not in a single data center, but across GPU nodes scattered around the globe. According to Alex Volkov (host of ThursdAI), “This is crazy: they’re literally training a 15B param model using GPUs from multiple companies and individuals, and it’s working as well as centralized runs.”The key to this success is “DeMo” (Decoupled Momentum Optimization), a paper co-authored by none other than Diederik Kingma (yes, the Kingma behind Adam optimizer and VAEs). DeMo drastically reduces communication overhead and still maintains stability and speed. The training loss curve they’ve shown looks just as good as a normal centralized run, proving that decentralized training isn’t just a pipe dream. The code and paper are open source, and soon we’ll have the fully trained Psyche model. It’s a huge step toward democratizing large-scale AI—no more waiting around for Big Tech to drop their weights. Instead, we can all chip in and train together.Prime Intellect INTELLECT-1 10B: Another Decentralized TriumphBut wait, there’s more! Prime Intellect also finished training their 10B model, INTELLECT-1, using a similar decentralized setup. INTELLECT-1 was trained with a custom framework that reduces inter-GPU communication by 400x. It’s essentially a global team effort, with nodes from all over the world contributing compute cycles.The result? A model hitting performance similar to older Meta models like Llama 2—but fully decentralized. Ruliad DeepThought 8B: Reasoning You Can Actually SeeIf that’s not enough, we’ve got yet another open-source reasoning model: Ruliad’s DeepThought 8B. This 8B parameter model (finetuned from LLaMA-3.1) from friends of the show FarEl, Alpin and Sentdex 👏Ruliad’s DeepThought attempts to match or exceed performance of much larger models in reasoning tasks (beating several 72B param models while being 8B itself) is very impressive. Google is firing on all cylinders this weekGoogle didn't stay quiet this week as well, and while we all wait for the Gemini team to release the next Gemini after the myriad of very good experimental models recently, we've gotten some very amazing things this week. Google’s PaliGemma 2 - finetunable SOTA VLM using GemmaPaliGemma v2, a new vision-language family of models (3B, 10B and 33B) for 224px, 448px, 896px resolutions are a suite of base models, that include image segmentation and detection capabilities and are great at OCR which make them very versatile for fine-tuning on specific tasks. They claim to achieve SOTA on chemical formula recognition, music score recognition, spatial reasoning, and chest X-ray report generation! Google GenCast SOTA weather prediction with... diffusion!?More impressively, Google DeepMind released GenCast, a diffusion-based model that beats the state-of-the-art ENS system in 97% of weather predictions. Did we say weather predictions? Yup. Generative AI is now better at weather forecasting than dedicated physics based deterministic algorithms running on supercomputers. Gencast can predict 15 days in advance in just 8 minutes on a single TPU v5, instead of hours on a monstrous cluster. This is mind-blowing. As Yam said on the show, “Predicting the world is crazy hard” and now diffusion models handle it with ease. W&B Weave: Observability, Evaluation and Guardrails now in GASpeaking of building and monitoring GenAI apps, we at Weights & Biases (the sponsor of ThursdAI) announced that Weave is now GA. Weave is a developer tool for evaluating, visualizing, and debugging LLM calls in production. If you’re building GenAI apps—like a coding agent or a tool that processes thousands of user requests—Weave helps you track costs, latency, and quality systematically.We showcased two internal apps: Open UI (a website builder from a prompt) and Winston (an AI agent that checks emails, Slack, and more). Both rely on Weave to iterate, tune prompts, measure user feedback, and ensure stable performance. With O1 and other advanced models coming to APIs soon, tools like Weave will be crucial to keep those applications under control.If you follow this newsletter and develop with LLMs, now is a great way to give Weave a tryOpen Source Audio & Video: Challenging Proprietary ModelsTencent’s HY Video: Beating Runway & Luma in Open SourceTencent came out swinging with their open-source model, HYVideo. It’s a video model that generates incredible realistic footage, camera cuts, and even audio—yep, Foley and lip-synced character speech. Just a single model doing text-to-video, image-to-video, puppeteering, and more. It even outperforms closed-source giants like Runway Gen 3 and Luma 1.6 on over 1,500 prompts.This is the kind of thing we dreamed about when we first heard of video diffusion models. Now it’s here, open-sourced, ready for tinkering. “It’s near SORA-level,” as I mentioned, referencing OpenAI’s yet-to-be-fully-released SORA model. The future of generative video just got more accessible, and competitors should be sweating right now. We may just get SORA as one of the 12 days of OpenAI releases! FishSpeech 1.5: Open Source TTS Rivaling the Big GunsNot just video—audio too. FishSpeech 1.5 is a multilingual, zero-shot voice cloning model that ranks #2 overall on TTS benchmarks, just behind 11 Labs. This is a 500M-parameter model, trained on a million hours of audio, achieving near-human quality, fast inference, and open for research.This puts high-quality text-to-speech capabilities in the open-source community’s hands. You can now run a top-tier TTS system locally, clone voices, and generate speech in multiple languages with low latency. No more relying solely on closed APIs. This is how open-source chases—and often catches—commercial leaders.If you’ve been longing for near-instan

Hey ya'll, Happy Thanskgiving to everyone who celebrates and thank you for being a subscriber, I truly appreciate each and every one of you! We had a blast on today's celebratory stream, especially given that today's "main course" was the amazing open sourcing of a reasoning model from Qwen, and we had Junyang Lin with us again to talk about it! First open source reasoning model that you can run on your machine, that beats a 405B model, comes close to o1 on some metrics 🤯 We also chatted about a new hybrid approach from Nvidia called Hymba 1.5B (Paper, HF) that beats Qwen 1.5B with 6-12x less training, and Allen AI releasing Olmo 2, which became the best fully open source LLM 👏 (Blog, HF, Demo), though they didn't release WandB logs this time, they did release data! I encourage you to watch todays show (or listen to the show, I don't judge), there's not going to be a long writeup like I usually do, as I want to go and enjoy the holiday too, but of course, the TL;DR and show notes are right here so you won't miss a beat if you want to use the break to explore and play around with a few things! ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.TL;DR and show notes* Qwen QwQ 32B preview - the first open weights reasoning model (X, Blog, HF, Try it)* Allen AI - Olmo 2 the best fully open language model (Blog, HF, Demo)* NVIDIA Hymba 1.5B - Hybrid smol model beating Qwen, SmolLM w/ 6-12x less training (X, Paper, HF)* Big CO LLMs + APIs* Anthropic MCP - model context protocol (X,Blog, Spec, Explainer)* Cursor, Jetbrains now integrate with ChatGPT MacOS app (X)* Xai is going to be a Gaming company?! (X)* H company shows Runner H - WebVoyager Agent (X, Waitlist) * This weeks Buzz* Interview w/ Thomas Cepelle about Weave scorers and guardrails (Guide)* Vision & Video* OpenAI SORA API was "leaked" on HuggingFace (here)* Runway launches video Expand feature (X)* Rhymes Allegro-TI2V - updated image to video model (HF)* Voice & Audio* OuteTTS v0.2 - 500M smol TTS with voice cloning (Blog, HF)* AI Art & Diffusion & 3D* Runway launches an image model called Frames (X, Blog)* ComfyUI Desktop app was released 🎉* Chat* 24 hours of AI hate on 🦋 (thread)* Tools* Cursor agent (X thread)* Google Generative Chess toy (Link)See you next week and happy Thanks Giving 🦃Thanks for reading ThursdAI - Recaps of the most high signal AI weekly spaces! This post is public so feel free to share it.Full Subtitles for convenience[00:00:00] Alex Volkov: let's get it going.[00:00:10] Alex Volkov: Welcome, welcome everyone to ThursdAI November 28th Thanksgiving special. My name is Alex Volkov. I'm an AI evangelist with Weights Biases. You're on ThursdAI. We are live [00:00:30] on ThursdAI. Everywhere pretty much.[00:00:32] Alex Volkov:[00:00:32] Hosts and Guests Introduction[00:00:32] Alex Volkov: I'm joined here with two of my co hosts.[00:00:35] Alex Volkov: Wolfram, welcome.[00:00:36] Wolfram Ravenwolf: Hello everyone! Happy Thanksgiving![00:00:38] Alex Volkov: Happy Thanksgiving, man.[00:00:39] Alex Volkov: And we have Junyang here. Junyang, welcome, man.[00:00:42] Junyang Lin: Yeah, hi everyone. Happy Thanksgiving. Great to be here.[00:00:46] Alex Volkov: You had a busy week. We're going to chat about what you had. I see Nisten joining us as well at some point.[00:00:51] Alex Volkov: Yam pe joining us as well. Hey, how, Hey Yam. Welcome. Welcome, as well. Happy Thanksgiving. It looks like we're assembled folks. We're across streams, across [00:01:00] countries, but we are.[00:01:01] Overview of Topics for the Episode[00:01:01] Alex Volkov: For November 28th, we have a bunch of stuff to talk about. Like really a big list of stuff to talk about. So why don't we just we'll just dive in. We'll just dive in. So obviously I think the best and the most important.[00:01:13] DeepSeek and Qwen Open Source AI News[00:01:13] Alex Volkov: Open source kind of AI news to talk about this week is going to be, and I think I remember last week, Junyang, I asked you about this and you were like, you couldn't say anything, but I asked because last week, folks, if you remember, we talked about R1 from DeepSeek, a reasoning model from [00:01:30] DeepSeek, which really said, Oh, maybe it comes as a, as open source and maybe it doesn't.[00:01:33] Alex Volkov: And I hinted about, and I asked, Junyang, what about some reasoning from you guys? And you couldn't say anything. so this week. I'm going to do a TLDR. So we're going to actually talk about the stuff that, you know, in depth a little bit later, but this week, obviously one of the biggest kind of open source or sorry, open weights, and news is coming from our friends at Qwen as well, as we always celebrate.[00:01:56] Alex Volkov: So one of the biggest things that we get as. [00:02:00] is, Qwen releases, I will actually have you tell me what's the pronunciation here, Junaid, what is, I say Q W Q or maybe quick, what is the pronunciation of this?[00:02:12] Junyang Lin: I mentioned it in the blog, it is just like the word quill. Yeah. yeah, because for the qw you can like work and for the q and you just like the U, so I just combine it together and create a new pronunciation called Quill.[00:02:28] Junyang Lin: Yeah.[00:02:28] Alex Volkov: So we're saying it's Quin [00:02:30] Quill 32 B. Is that the right pronunciation to say this?[00:02:33] Junyang Lin: Yeah, it's okay. I would just call it qui quill. It is, some something funny because,the ca the characters look very funny. Oh, we have a subculture,for these things. Yeah. Just to express some, yeah.[00:02:46] Junyang Lin: our. feelings.[00:02:49] Alex Volkov: Amazing. Qwen, Quill, 32B, and it's typed,the name is typed QWQ, 32Breview. This is the first OpenWeights reasoning model. This [00:03:00] model is not only predicting tokens, it's actually doing reasoning behind this. What this means is we're going to tell you what this means after we get to this.[00:03:07] Alex Volkov: So we're still in the, we're still in the TLDR area. We also had. Another drop from Alien Institute for AI, if you guys remember last week we chatted with Nathan, our dear friend Nathan, from Alien Institute about 2. 0. 3, about their efforts for post training, and he gave us all the details about post training, so they released 2.[00:03:28] Alex Volkov: 0. 3, this week they released Olmo 2. [00:03:30] 0. We also talked about Olmo with the friends from Alien Institute a couple of months ago, and now they released Olmo 2. 0. Which they claim is the best fully open sourced, fully open sourced language models, from Allen Institute for AI.and, so we're going to chat about, Olmo a little bit as well.[00:03:46] Alex Volkov: And last minute addition we have is NVIDIA Haimba, which is a hybrid small model from NVIDIA, very tiny one, 1. 5 billion parameters. small model building Qwen and building small LLM as well. this is in the area [00:04:00] of open source. I[00:04:01] Alex Volkov: Okay, in the big companies, LLMs and APIs, I want to run through a few things.[00:04:06] Anthropic's MCP and ChatGPT macOS Integrations[00:04:06] Alex Volkov: So first of all, Anthropic really something called MCP. It's a, something they called Model Context Protocol. We're going to briefly run through this. It's a, it's a kind of a release from them that's aimed for developers is a protocol that enables secure connections between a host application, like a cloud desktop, for example,[00:04:24] Alex Volkov: there's also a bunch of new integrations for the ChatGPT macOS app. If you guys remember a couple of [00:04:30] weeks ago, We actually caught this live.[00:04:31] Alex Volkov: I refreshed my MacOS app and there's ta da, there's a new thing. And we discovered this live. It was very fun. The MacOS app for ChatGPT integrates with VS Code, et cetera. and so we tried to run this with Cursor. It didn't work. So now it works with Cursor,[00:04:43] Wolfram Ravenwolf:[00:04:43] Alex Volkov: So the next thing we're going to look at, I don't know if it's worth mentioning, but you guys know the XAI, the company that Elon Musk is raising another 6 billion for that tries to compete with OpenAI[00:04:54] Alex Volkov: Do you guys hear that it's going to be a gaming company as well? I don't know if it's worth talking about, but we'll at least [00:05:00] mention this. And the one thing that I wanted to chat about is H, the French company, H that showed a runner that looks. Three times as fast and as good as the Claude computer use runner, and we're definitely going to show examples of this, video live because that looks just incredible.[00:05:18] Alex Volkov: this out of nowhere company, the biggest fundraise or the biggest seed round that Europe has ever seen, at least French has ever seen, just show they, An agent that controls your [00:05:30] computer that's tiny, ridiculously tiny, I think it's like the three billion parameter, two billion parameter or something.[00:05:36] Alex Volkov: And it runs way better than computer, cloud computer use. Something definitely worth talking about. after with, after which in this week's Bars, we're going to talk with Thomas Capelli, from, from my team at Weights Biases. about LLM guardrails, that's gonna be fun. and in vision video category, we're gonna cover that OpenAI Sora quote unquote leaked, this week.[00:05:56] Alex Volkov: And this leak wasn't really a leak, but, definitely [00:06:00] we saw some stuff. and then there's also a new expand feature that we saw in, Runway. And we saw another video model from, Rhymes called Allegro TIV2. which is pretty cool in voice and audio. If we get there in voice and audio, we saw out TTS vision 0.[00:06:19] Alex Volkov: 2, which is a new TTS, a 500 million parameter, small TTS you can run in your browser and sounds pretty dope.art in the fusion, super quick runway launches an image [00:06:30] model. Yep, Runway, the guys wh

Hey folks, Alex here, and oof what a 🔥🔥🔥 show we had today! I got to use my new breaking news button 3 times this show! And not only that, some of you may know that one of the absolutely biggest pleasures as a host, is to feature the folks who actually make the news on the show!And now that we're in video format, you actually get to see who they are! So this week I was honored to welcome back our friend and co-host Junyang Lin, a Dev Lead from the Alibaba Qwen team, who came back after launching the incredible Qwen Coder 2.5, and Qwen 2.5 Turbo with 1M context.We also had breaking news on the show that AI2 (Allen Institute for AI) has fully released SOTA LLama post-trained models, and I was very lucky to get the core contributor on the paper, Nathan Lambert to join us live and tell us all about this amazing open source effort! You don't want to miss this conversation!Lastly, we chatted with the CEO of StackBlitz, Eric Simons, about the absolutely incredible lightning in the bottle success of their latest bolt.new product, how it opens a new category of code generator related tools.00:00 Introduction and Welcome00:58 Meet the Hosts and Guests02:28 TLDR Overview03:21 Tl;DR04:10 Big Companies and APIs07:47 Agent News and Announcements08:05 Voice and Audio Updates08:48 AR, Art, and Diffusion11:02 Deep Dive into Mistral and Pixtral29:28 Interview with Nathan Lambert from AI230:23 Live Reaction to Tulu 3 Release30:50 Deep Dive into Tulu 3 Features32:45 Open Source Commitment and Community Impact33:13 Exploring the Released Artifacts33:55 Detailed Breakdown of Datasets and Models37:03 Motivation Behind Open Source38:02 Q&A Session with the Community38:52 Summarizing Key Insights and Future Directions40:15 Discussion on Long Context Understanding41:52 Closing Remarks and Acknowledgements44:38 Transition to Big Companies and APIs45:03 Weights & Biases: This Week's Buzz01:02:50 Mistral's New Features and Upgrades01:07:00 Introduction to DeepSeek and the Whale Giant01:07:44 DeepSeek's Technological Achievements01:08:02 Open Source Models and API Announcement01:09:32 DeepSeek's Reasoning Capabilities01:12:07 Scaling Laws and Future Predictions01:14:13 Interview with Eric from Bolt01:14:41 Breaking News: Gemini Experimental01:17:26 Interview with Eric Simons - CEO @ Stackblitz01:19:39 Live Demo of Bolt's Capabilities01:36:17 Black Forest Labs AI Art Tools01:40:45 Conclusion and Final ThoughtsAs always, the show notes and TL;DR with all the links I mentioned on the show and the full news roundup below the main new recap 👇Google & OpenAI fighting for the LMArena crown 👑I wanted to open with this, as last week I reported that Gemini Exp 1114 has taken over #1 in the LMArena, in less than a week, we saw a new ChatGPT release, called GPT-4o-2024-11-20 reclaim the arena #1 spot!Focusing specifically on creating writing, this new model, that's now deployed on chat.com and in the API, is definitely more creative according to many folks who've tried it, with OpenAI employees saying "expect qualitative improvements with more natural and engaging writing, thoroughness and readability" and indeed that's what my feed was reporting as well.I also wanted to mention here, that we've seen this happen once before, last time Gemini peaked at the LMArena, it took less than a week for OpenAI to release and test a model that beat it.But not this time, this time Google came prepared with an answer!Just as we were wrapping up the show (again, Logan apparently loves dropping things at the end of ThursdAI), we got breaking news that there is YET another experimental model from Google, called Gemini Exp 1121, and apparently, it reclaims the stolen #1 position, that chatGPT reclaimed from Gemini... yesterday! Or at least joins it at #1LMArena Fatigue?Many folks in my DMs are getting a bit frustrated with these marketing tactics, not only the fact that we're getting experimental models faster than we can test them, but also with the fact that if you think about it, this was probably a calculated move by Google. Release a very powerful checkpoint, knowing that this will trigger a response from OpenAI, but don't release your most powerful one. OpenAI predictably releases their own "ready to go" checkpoint to show they are ahead, then folks at Google wait and release what they wanted to release in the first place.The other frustration point is, the over-indexing of the major labs on the LMArena human metrics, as the closest approximation for "best". For example, here's some analysis from Artificial Analysis showing that the while the latest ChatGPT is indeed better at creative writing (and #1 in the Arena, where humans vote answers against each other), it's gotten actively worse at MATH and coding from the August version (which could be a result of being a distilled much smaller version) .In summary, maybe the LMArena is no longer 1 arena is all you need, but the competition at the TOP scores of the Arena has never been hotter.DeepSeek R-1 preview - reasoning from the Chinese WhaleWhile the American labs fight for the LM titles, the real interesting news may be coming from the Chinese whale, DeepSeek, a company known for their incredibly cracked team, resurfaced once again and showed us that they are indeed, well super cracked.They have trained and released R-1 preview, with Reinforcement Learning, a reasoning model that beasts O1 at AIME and other benchmarks! We don't know many details yet, besides them confirming that this model comes to the open source! but we do know that this model , unlike O1, is showing the actual reasoning it uses to achieve it's answers (reminder: O1 hides its actual reasoning and what we see is actually another model summarizing the reasoning)The other notable thing is, DeepSeek all but confirmed the claim that we have a new scaling law with Test Time / Inference time compute law, where, like with O1, the more time (and tokens) you give a model to think, the better it gets at answering hard questions. Which is a very important confirmation, and is a VERY exciting one if this is coming to the open source!Right now you can play around with R1 in their demo chat interface.In other Big Co and API newsIn other news, Mistral becomes a Research/Product company, with a host of new additions to Le Chat, including Browse, PDF upload, Canvas and Flux 1.1 Pro integration (for Free! I think this is the only place where you can get Flux Pro for free!).Qwen released a new 1M context window model in their API called Qwen 2.5 Turbo, making it not only the 2nd ever 1M+ model (after Gemini) to be available, but also reducing TTFT (time to first token) significantly and slashing costs. This is available via their APIs and Demo here.Open Source is catching upAI2 open sources Tulu 3 - SOTA 8B, 70B LLama post trained FULLY open sourced (Blog ,Demo, HF, Data, Github, Paper)Allen AI folks have joined the show before, and this time we got Nathan Lambert, the core contributor on the Tulu paper, join and talk to us about Post Training and how they made the best performing SOTA LLama 3.1 Funetunes with careful data curation (which they also open sourced), preference optimization, and a new methodology they call RLVR (Reinforcement Learning with Verifiable Rewards).Simply put, RLVR modifies the RLHF approach by using a verification function instead of a reward model. This method is effective for tasks with verifiable answers, like math problems or specific instructions. It improves performance on certain benchmarks (e.g., GSM8K) while maintaining capabilities in other areas.The most notable thing is, just how MUCH is open source, as again, like the last time we had AI2 folks on the show, the amount they release is staggeringIn the show, Nathan had me pull up the paper and we went through the deluge of models, code and datasets they released, not to mention the 73 page paper full of methodology and techniques.Just absolute ❤️ to the AI2 team for this release!🐝 This weeks buzz - Weights & Biases cornerThis week, I want to invite you to a live stream announcement that I am working on behind the scenes to produce, on December 2nd. You can register HERE (it's on LinkedIn, I know, I'll have the YT link next week, promise!)We have some very exciting news to announce, and I would really appreciate the ThursdAI crew showing up for that! It's like 5 minutes and I helped produce 🙂Pixtral Large is making VLMs cool againMistral had quite the week this week, not only adding features to Le Chat, but also releasing Pixtral Large, their updated multimodal model, which they claim state of the art on multiple benchmarks.It's really quite good, not to mention that it's also included, for free, as part of the le chat platform, so now when you upload documents or images to le chat you get Pixtral Large.The backbone for this model is Mistral Large (not the new one they also released) and this makes this 124B model a really really good image model, albeit a VERY chonky one that's hard to run locally.The thing I loved about the Pixtral release the most is, they used the new understanding to ask about Weights & Biases charts 😅 and Pixtral did a pretty good job!Some members of the community though, reacted to the SOTA claims by Mistral in a very specific meme-y way:This meme has become a very standard one, when labs tend to not include Qwen VL 72B or other Qwen models in the evaluation results, all while claiming SOTA. I decided to put these models to a head to head test myself, only to find out, that ironically, both models say the other one is better, while both hallucinate some numbers.BFL is putting the ART in Artificial Intelligence with FLUX.1 Tools (blog)With the absolute breaking news bombastic release, the folks at BFL (Black Forest Labs) have released Flux.1 Tools, which will allow AI artist to use these models in all kind of creative inspiring ways.These tools are: FLUX.1 Fill (for In/Out painting), FLUX.1 Depth/Canny (Structural Guidance using depth map or canny edges) a

This week is a very exciting one in the world of AI news, as we get 3 SOTA models, one in overall LLM rankings, on in OSS coding and one in OSS voice + a bunch of new breaking news during the show (which we reacted to live on the pod, and as we're now doing video, you can see us freak out in real time at 59:32)00:00 Welcome to ThursdAI00:25 Meet the Hosts02:38 Show Format and Community03:18 TLDR Overview04:01 Open Source Highlights13:31 Qwen Coder 2.5 Release14:00 Speculative Decoding and Model Performance22:18 Interactive Demos and Artifacts28:20 Training Insights and Future Prospects33:54 Breaking News: Nexus Flow36:23 Exploring Athene v2 Agent Capabilities36:48 Understanding ArenaHard and Benchmarking40:55 Scaling and Limitations in AI Models43:04 Nexus Flow and Scaling Debate49:00 Open Source LLMs and New Releases52:29 FrontierMath Benchmark and Quantization Challenges58:50 Gemini Experimental 1114 Release and Performance01:11:28 LLM Observability with Weave01:14:55 Introduction to Tracing and Evaluations01:15:50 Weave API Toolkit Overview01:16:08 Buzz Corner: Weights & Biases01:16:18 Nous Forge Reasoning API01:26:39 Breaking News: OpenAI's New MacOS Features01:27:41 Live Demo: ChatGPT Integration with VS Code01:34:28 Ultravox: Real-Time AI Conversations01:42:03 Tilde Research and Stargazer Tool01:46:12 Conclusion and Final ThoughtsThis week also, there was a debate online, whether deep learning (and scale is all you need) has hit a wall, with folks like Ilya Sutskever being cited by publications claiming it has, folks like Yann LeCoon calling "I told you so". TL;DR? multiple huge breakthroughs later, and both Oriol from DeepMind and Sam Altman are saying "what wall?" and Heiner from X.ai saying "skill issue", there is no walls in sight, despite some tech journalism love to pretend there is. Also, what happened to Yann? 😵‍💫Ok, back to our scheduled programming, here's the TL;DR, afterwhich, a breakdown of the most important things about today's update, and as always, I encourage you to watch / listen to the show, as we cover way more than I summarize here 🙂TL;DR and Show Notes:* Open Source LLMs* Qwen Coder 2.5 32B (+5 others) - Sonnet @ home (HF, Blog, Tech Report)* The End of Quantization? (X, Original Thread)* Epoch : FrontierMath new benchmark for advanced MATH reasoning in AI (Blog)* Common Corpus: Largest multilingual 2T token dataset (blog)* NexusFlow - Athena v2 - open model suite (X, Blog, HF)* Big CO LLMs + APIs* Gemini 1114 is new king LLM #1 LMArena (X)* Nous Forge Reasoning API - beta (Blog, X)* Reuters reports "AI is hitting a wall" and it's becoming a meme (Article)* Cursor acq. SuperMaven (X)* This Weeks Buzz* Weave JS/TS support is here 🙌* Voice & Audio* Fixie releases UltraVox SOTA (Demo, HF, API)* Suno v4 is coming and it's bonkers amazing (Alex Song, SOTA Jingle)* Tools demoed* Qwen artifacts - HF Demo* Tilde Galaxy - Interp Tool This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

👋 Hey all, this is Alex, coming to you from the very Sunny California, as I'm in SF again, while there is a complete snow storm back home in Denver (brrr).I flew here for the Hackathon I kept telling you about, and it was glorious, we had over 400 registered, over 200 approved hackers, 21 teams submitted incredible projects 👏 You can follow some of these hereI then decided to stick around and record the show from SF, and finally pulled the plug and asked for some budget, and I present, the first ThursdAI, recorded from the newly minted W&B Podcast studio at our office in SF 🎉This isn't the only first, today also, for the first time, all of the regular co-hosts of ThursdAI, met on video for the first time, after over a year of hanging out weekly, we've finally made the switch to video, and you know what? Given how good AI podcasts are getting, we may have to stick around with this video thing! We played one such clip from a new model called hertz-dev, which is a <10B model for full duplex audio.Given that today's episode is a video podcast, I would love for you to see it, so here's the timestamps for the chapters, which will be followed by the TL;DR and show notes in raw format. I would love to hear from folks who read the longer form style newsletters, do you miss them? Should I bring them back? Please leave me a comment 🙏 (I may send you a survey)This was a generally slow week (for AI!! not for... ehrm other stuff) and it was a fun podcast! Leave me a comment about what you think about this new format.Chapter Timestamps00:00 Introduction and Agenda Overview00:15 Open Source LLMs: Small Models01:25 Open Source LLMs: Large Models02:22 Big Companies and LLM Announcements04:47 Hackathon Recap and Community Highlights18:46 Technical Deep Dive: HertzDev and FishSpeech33:11 Human in the Loop: AI Agents36:24 Augmented Reality Lab Assistant36:53 Hackathon Highlights and Community Vibes37:17 Chef Puppet and Meta Ray Bans Raffle37:46 Introducing Fester the Skeleton38:37 Fester's Performance and Community Reactions39:35 Technical Insights and Project Details42:42 Big Companies API Updates43:17 Haiku 3.5: Performance and Pricing43:44 Comparing Haiku and Sonnet Models51:32 XAI Grok: New Features and Pricing57:23 OpenAI's O1 Model: Leaks and Expectations01:08:42 Transformer ASIC: The Future of AI Hardware01:13:18 The Future of Training and Inference Chips01:13:52 Oasis Demo and Etched AI Controversy01:14:37 Nisten's Skepticism on Etched AI01:19:15 Human Layer Introduction with Dex01:19:24 Building and Managing AI Agents01:20:54 Challenges and Innovations in AI Agent Development01:21:28 Human Layer's Vision and Future01:36:34 Recap and Closing RemarksThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Show Notes and Links:* Interview* Dexter Horthy (X) from HumanLayer* Open Source LLMs* SmolLM2: the new, best, and open 1B-parameter language mode (X)* Meta released MobileLLM (125M, 350M, 600M, 1B) (HF)* Tencent Hunyuan Large - 389B X 52B (Active) MoE (X, HF, Paper)* Big CO LLMs + APIs* OpenAI buys and opens chat.com* Anthropic releases Claude Haiku 3.5 via API (X, Blog)* OpenAI drops o1 full - and pulls it back (but not before it got Jailbroken)* X.ai now offers $25/mo free of Grok API credits (X, Platform)* Etched announces Sonu - first Transformer ASIC - 500K tok/s (etched)* PPXL is not valued at 9B lol* This weeks Buzz* Recap of SF Hackathon w/ AI Tinkerers (X)* Fester the Halloween Toy aka Project Halloweave videos from trick or treating (X, Writeup)* Voice & Audio* Hertz-dev - 8.5B conversation audio gen (X, Blog )* Fish Agent v0.1 3B - Speech to Speech model (HF, Demo)* AI Art & Diffusion & 3D* FLUX 1.1 [pro] is how HD - 4x resolution (X, blog)Full Transcription for convenience below: This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey everyone, Happy Halloween! Alex here, coming to you live from my mad scientist lair! For the first ever, live video stream of ThursdAI, I dressed up as a mad scientist and had my co-host, Fester the AI powered Skeleton join me (as well as my usual cohosts haha) in a very energetic and hopefully entertaining video stream! Since it's Halloween today, Fester (and I) have a very busy schedule, so no super length ThursdAI news-letter today, as we're still not in the realm of Gemini being able to write a decent draft that takes everything we talked about and cover all the breaking news, I'm afraid I will have to wish you a Happy Halloween and ask that you watch/listen to the episode. The TL;DR and show links from today, don't cover all the breaking news but the major things we saw today (and caught live on the show as Breaking News) were, ChatGPT now has search, Gemini has grounded search as well (seems like the release something before Google announces it streak from OpenAI continues). Here's a quick trailer of the major things that happened: This weeks buzz - Halloween AI toy with WeaveIn this weeks buzz, my long awaited Halloween project is finally live and operational! I've posted a public Weave dashboard here and the code (that you can run on your mac!) hereReally looking forward to see all the amazing costumers the kiddos come up with and how Gemini will be able to respond to them, follow along! ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Ok and finally my raw TL;DR notes and links for this week. Happy halloween everyone, I'm running off to spook the kiddos (and of course record and post about it!)ThursdAI - Oct 31 - TL;DRTL;DR of all topics covered:* Open Source LLMs:* Microsoft's OmniParser: SOTA UI parsing (MIT Licensed) 𝕏* Groundbreaking model for web automation (MIT license).* State-of-the-art UI parsing and understanding.* Outperforms GPT-4V in parsing web UI.* Designed for web automation tasks.* Can be integrated into various development workflows.* ZhipuAI's GLM-4-Voice: End-to-end Chinese/English speech 𝕏* End-to-end voice model for Chinese and English speech.* Open-sourced and readily available.* Focuses on direct speech understanding and generation.* Potential applications in various speech-related tasks.* Meta releases LongVU: Video LM for long videos 𝕏* Handles long videos with impressive performance.* Uses DINOv2 for downsampling, eliminating redundant scenes.* Fuses features using DINOv2 and SigLIP.* Select tokens are passed to Qwen2/Llama-3.2-3B.* Demo and model are available on HuggingFace.* Potential for significant advancements in video understanding.* OpenAI new factuality benchmark (Blog, Github)* Introducing SimpleQA: new factuality benchmark* Goal: high correctness, diversity, challenging for frontier models* Question Curation: AI trainers, verified by second trainer* Quality Assurance: 3% inherent error rate* Topic Diversity: wide range of topics* Grading Methodology: "correct", "incorrect", "not attempted"* Model Comparison: smaller models answer fewer correctly* Calibration Measurement: larger models more calibrated* Limitations: only for short, fact-seeking queries* Conclusion: drive research on trustworthy AI* Big CO LLMs + APIs:* ChatGPT now has Search! (X)* Grounded search results in browsing the web* Still hallucinates* Reincarnation of Search GPT inside ChatGPT* Apple Intelligence Launch: Image features for iOS 18.2 [𝕏]( Link not provided in source material)* Officially launched for developers in iOS 18.2.* Includes Image Playground and Gen Moji.* Aims to enhance image creation and manipulation on iPhones.* GitHub Universe AI News: Co-pilot expands, new Spark tool 𝕏* GitHub Co-pilot now supports Claude, Gemini, and OpenAI models.* GitHub Spark: Create micro-apps using natural language.* Expanding the capabilities of AI-powered coding tools.* Copilot now supports multi-file edits in VS Code, similar to Cursor, and faster code reviews.* GitHub Copilot extensions are planned for release in 2025.* Grok Vision: Image understanding now in Grok 𝕏* Finally has vision capabilities (currently via 𝕏, API coming soon).* Can now understand and explain images, even jokes.* Early version, with rapid improvements expected.* OpenAI advanced voice mode updates (X)* 70% cheaper in input tokens because of automatic caching (X)* Advanced voice mode is now on desktop app* Claude this morning - new mac / pc App* This week's Buzz:* My AI Halloween toy skeleton is greeting kids right now (and is reporting to Weave dashboard)* Vision & Video:* Meta's LongVU: Video LM for long videos 𝕏 (see Open Source LLMs for details)* Grok Vision on 𝕏: 𝕏 (see Big CO LLMs + APIs for details)* Voice & Audio:* MaskGCT: New SoTA Text-to-Speech 𝕏* New open-source state-of-the-art text-to-speech model.* Zero-shot voice cloning, emotional TTS, long-form synthesis, variable speed synthesis, bilingual (Chinese & English).* Available on Hugging Face.* ZhipuAI's GLM-4-Voice: End-to-end Chinese/English speech 𝕏 (see Open Source LLMs for details)* Advanced Voice Mode on Desktops: 𝕏 (See Big CO LLMs + APIs for details).* AI Art & Diffusion: (See Red Panda in "This week's Buzz" above)* Redcraft Red Panda: new SOTA image diffusion 𝕏* High-performing image diffusion model, beating Black Forest Labs Flux.* 72% win rate, higher ELO than competitors.* Creates SVG files, editable as vector files.* From Redcraft V3.* Tools:* Bolt.new by StackBlitz: In-browser full-stack dev environment 𝕏* Platform for prompting, editing, running, and deploying full-stack apps directly in your browser.* Uses WebContainers.* Supports npm, Vite, Next.js, and integrations with Netlify, Cloudflare, and SuperBase.* Free to use.* Jina AI's Meta-Prompt: Improved LLM Codegen 𝕏 This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey all, Alex here, coming to you from the (surprisingly) sunny Seattle, with just a mind-boggling week of releases. Really, just on Tuesday there was so much news already! I had to post a recap thread, something I do usually after I finish ThursdAI! From Anthropic reclaiming close-second sometimes-first AI lab position + giving Claude the wheel in the form of computer use powers, to more than 3 AI video generation updates with open source ones, to Apple updating Apple Intelligence beta, it's honestly been very hard to keep up, and again, this is literally part of my job! But once again I'm glad that we were able to cover this in ~2hrs, including multiple interviews with returning co-hosts ( Simon Willison came back, Killian came back) so definitely if you're only a reader at this point, listen to the show! Ok as always (recently) the TL;DR and show notes at the bottom (I'm trying to get you to scroll through ha, is it working?) so grab a bucket of popcorn, let's dive in 👇 ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Claude's Big Week: Computer Control, Code Wizardry, and the Mysterious Case of the Missing OpusAnthropic dominated the headlines this week with a flurry of updates and announcements. Let's start with the new Claude Sonnet 3.5 (really, they didn't update the version number, it's still 3.5 tho a different API model) Claude Sonnet 3.5: Coding Prodigy or Benchmark Buster?The new Sonnet model shows impressive results on coding benchmarks, surpassing even OpenAI's O1 preview on some. "It absolutely crushes coding benchmarks like Aider and Swe-bench verified," I exclaimed on the show. But a closer look reveals a more nuanced picture. Mixed results on other benchmarks indicate that Sonnet 3.5 might not be the universal champion some anticipated. My friend who has held back internal benchmarks was disappointed highlighting weaknesses in scientific reasoning and certain writing tasks. Some folks are seeing it being lazy-er for some full code completion, while the context window is now doubled from 4K to 8K! This goes to show again, that benchmarks don't tell the full story, so we wait for LMArena (formerly LMSys Arena) and the vibe checks from across the community. However it absolutely dominates in code tasks, that much is clear already. This is a screenshot of the new model on Aider code editing benchmark, a fairly reliable way to judge models code output, they also have a code refactoring benchmarkHaiku 3.5 and the Vanishing Opus: Anthropic's Cryptic CluesFurther adding to the intrigue, Anthropic announced Claude 3.5 Haiku! They usually provide immediate access, but Haiku remains elusive, saying that it's available by end of the month, which is very very soon. Making things even more curious, their highly anticipated Opus model has seemingly vanished from their website. "They've gone completely silent on 3.5 Opus," Simon Willison (𝕏) noted, mentioning conspiracy theories that this new Sonnet might simply be a rebranded Opus? 🕯️ 🕯️ We'll make a summoning circle for new Opus and update you once it lands (maybe next year) Claude Takes Control (Sort Of): Computer Use API and the Dawn of AI Agents (𝕏)The biggest bombshell this week? Anthropic's Computer Use. This isn't just about executing code; it’s about Claude interacting with computers, clicking buttons, browsing the web, and yes, even ordering pizza! Killian Lukas (𝕏), creator of Open Interpreter, returned to ThursdAI to discuss this groundbreaking development. "This stuff of computer use…it’s the same argument for having humanoid robots, the web is human shaped, and we need AIs to interact with computers and the web the way humans do" Killian explained, illuminating the potential for bridging the digital and physical worlds. Simon, though enthusiastic, provided a dose of realism: "It's incredibly impressive…but also very much a V1, beta.” Having tackled the setup myself, I agree; the current reliance on a local Docker container and virtual machine introduces some complexity and security considerations. However, seeing Claude fix its own Docker installation error was an unforgettably mindblowing experience. The future of AI agents is upon us, even if it’s still a bit rough around the edges.Here's an easy guide to set it up yourself, takes 5 minutes, requires no coding skills and it's safely tucked away in a container.Big Tech's AI Moves: Apple Embraces ChatGPT, X.ai API (+Vision!?), and Cohere Multimodal EmbeddingsThe rest of the AI world wasn’t standing still. Apple made a surprising integration, while X.ai and Cohere pushed their platforms forward.Apple iOS 18.2 Beta: Siri Phones a Friend (ChatGPT)Apple, always cautious, surprisingly integrated ChatGPT directly into iOS. While Siri remains…well, Siri, users can now effortlessly offload more demanding tasks to ChatGPT. "Siri is still stupid," I joked, "but can now ask it to write some stuff and it'll tell you, hey, do you want me to ask my much smarter friend ChatGPT about this task?" This approach acknowledges Siri's limitations while harnessing ChatGPT’s power. The iOS 18.2 beta also includes GenMoji (custom emojis!) and Visual Intelligence (multimodal camera search) which are both welcome, tho I didn't really get the need of the Visual Intelligence (maybe I'm jaded with my Meta Raybans that already have this and are on my face most of the time) and I didn't get into the GenMoji waitlist still waiting to show you some custom emojis! X.ai API: Grok's Enterprise Ambitions and a Secret Vision ModelElon Musk's X.ai unveiled their API platform, focusing on enterprise applications with Grok 2 beta. They also teased an undisclosed vision model, and they had vision APIs for some folks who joined their hackathon. While these models are still not worth using necessarily, the next Grok-3 is promising to be a frontier model, and for some folks, it's relaxed approach to content moderation (what Elon is calling maximally seeking the truth) is going to be a convincing point for some! I just wish they added fun mode and access to real time data from X! Right now it's just the Grok-2 model, priced at a very non competative $15/mTok 😒Cohere Embed 3: Elevating Multimodal Embeddings (Blog)Cohere launched Embed 3, enabling embeddings for both text and visuals such as graphs and designs. "While not the first multimodal embeddings, when it comes from Cohere, you know it's done right," I commented. Open Source Power: JavaScript Transformers and SOTA Multilingual ModelsThe open-source AI community continues to impress, making powerful models accessible to all.Massive kudos to Xenova (𝕏) for the release of Transformers.js v3! The addition of WebGPU support results in a staggering "up to 100 times faster" performance boost for browser-based AI, dramatically simplifying local, private, and efficient model running. We also saw DeepSeek’s Janus 1.3B, a multimodal image and text model, and Cohere For AI's Aya Expanse, supporting 23 languages.This Week’s Buzz: Hackathon Triumphs and Multimodal WeaveOn ThursdAI, we also like to share some of the exciting things happening behind the scenes.AI Chef Showdown: Second Place and Lessons LearnedHappy to report that team Yes Chef clinched second place in a hackathon with an unconventional creation: a Gordon Ramsay-inspired robotic chef hand puppet, complete with a cloned voice and visual LLM integration. We bought and 3D printed and assembled an Open Source robotic arm, made it become a ventriloquist operator by letting it animate a hand puppet, and cloned Ramsey's voice. It was so so much fun to build, and the code is hereWeave Goes Multimodal: Seeing and Hearing Your AIEven more exciting was the opportunity to leverage Weave's newly launched multimodal functionality. "Weave supports you to see and play back everything that's audio generated," I shared, emphasizing its usefulness in debugging our vocal AI chef. For a practical example, here's ALL the (NSFW) roasts that AI Chef has cooked me with, it's honestly horrifying haha. For full effect, turn on the background music first and then play the chef audio 😂📽️ Video Generation Takes Center Stage: Mochi's Motion Magic and Runway's Acting BreakthroughVideo models made a quantum leap this week, pushing the boundaries of generative AI.Genmo Mochi-1: Diffusion Transformers and Generative MotionGenmo's Ajay Jain (Genmo) joined ThursdAI to discuss Mochi-1, their powerful new diffusion transformer. "We really focused on…prompt adherence and motion," he explained. Mochi-1's capacity to generate complex and realistic motion is truly remarkable, and with an HD version on its way, the future looks bright (and animated!). They also get bonus points for dropping a torrent link in the announcement tweet.So far this apache 2, 10B Diffusion Transformer is open source, but not for the GPU-poors, as it requires 4 GPUs to run, but apparently there was already an attempt to run in on one single 4090 which, Ajay highlighted was one of the reasons they open sourced it! Runway Act-One: AI-Powered Puppetry and the Future of Acting (blog)Ok this one absolutely seems bonkers! Runway unveiled Act-One! Forget just generating video from text; Act-One takes a driving video and character image to produce expressive and nuanced character performances. "It faithfully represents elements like eye-lines, micro expressions, pacing, and delivery," I noted, excited by the transformative potential for animation and filmmaking.So no need for rigging, for motion capture suites on faces of actors, Runway now, does this, so you can generate characters with Flux, and animate them with Act-One 📽️ Just take a look at this insanity 👇 11labs Creative Voices: Prompting Your Way to the Perfect Voice11labs debuted an incredible feature: creating custom voices using only text prompts. Want a high-pitched squeak or a sophisticated British accent? Just ask.

Hey folks, Alex here from Weights & Biases, and this week has been absolutely bonkers. From robots walking among us to rockets landing on chopsticks (well, almost), the future is feeling palpably closer. And if real-world robots and reusable spaceship boosters weren't enough, the open-source AI community has been cooking, dropping new models and techniques faster than a Starship launch. So buckle up, grab your space helmet and noise-canceling headphones (we’ll get to why those are important!), and let's blast off into this week’s AI adventures!TL;DR and show-notes + links at the end of the post 👇Robots and Rockets: A Glimpse into the FutureI gotta start with the real-world stuff because, let's be honest, it's mind-blowing. We had Robert Scoble (yes, the Robert Scoble) join us after attending the Tesla We, Robot AI event, reporting on Optimus robots strolling through crowds, serving drinks, and generally being ridiculously futuristic. Autonomous robo-taxis were also cruising around, giving us a taste of a driverless future.Robert’s enthusiasm was infectious: "It was a vision of the future, and from that standpoint, it succeeded wonderfully." I couldn't agree more. While the market might have had a mini-meltdown (apparently investors aren't ready for robot butlers yet), the sheer audacity of Tesla’s vision is exhilarating. These robots aren't just cool gadgets; they represent a fundamental shift in how we interact with technology and the world around us. And they’re learning fast. Just days after the event, Tesla released a video of Optimus operating autonomously, showcasing the rapid progress they’re making.And speaking of audacious visions, SpaceX decided to one-up everyone (including themselves) by launching Starship and catching the booster with Mechazilla – their giant robotic chopsticks (okay, technically a launch tower, but you get the picture). Waking up early with my daughter to watch this live was pure magic. As Ryan Carson put it, "It was magical watching this… my kid who's 16… all of his friends are getting their imaginations lit by this experience." That’s exactly what we need - more imagination and less doomerism! The future is coming whether we like it or not, and I, for one, am excited.Open Source LLMs and Tools: The Community Delivers (Again!)Okay, back to the virtual world (for now). This week's open-source scene was electric, with new model releases and tools that have everyone buzzing (and benchmarking like crazy!).* Nemotron 70B: Hype vs. Reality: NVIDIA dropped their Nemotron 70B instruct model, claiming impressive scores on certain benchmarks (Arena Hard, AlpacaEval), even suggesting it outperforms GPT-4 and Claude 3.5. As always, we take these claims with a grain of salt (remember Reflection?), and our resident expert, Nisten, was quick to run his own tests. The verdict? Nemotron is good, "a pretty good model to use," but maybe not the giant-killer some hyped it up to be. Still, kudos to NVIDIA for pushing the open-source boundaries. (Hugging Face, Harrison Kingsley evals)* Zamba 2 : Hybrid Vigor: Zyphra, in collaboration with NVIDIA, released Zamba 2, a hybrid Sparse Mixture of Experts (SME) model. We had Paolo Glorioso, a researcher from Ziphra, join us to break down this unique architecture, which combines the strengths of transformers and state space models (SSMs). He highlighted the memory and latency advantages of SSMs, especially for on-device applications. Definitely worth checking out if you’re interested in transformer alternatives and efficient inference.* Zyda 2: Data is King (and Queen): Alongside Zamba 2, Zyphra also dropped Zyda 2, a massive 5 trillion token dataset, filtered, deduplicated, and ready for LLM training. This kind of open-source data release is a huge boon to the community, fueling the next generation of models. (X)* Ministral: Pocket-Sized Power: On the one-year anniversary of the iconic Mistral 7B release, Mistral announced two new smaller models – Ministral 3B and 8B. Designed for on-device inference, these models are impressive, but as always, Qwen looms large. While Mistral didn’t include Qwen in their comparisons, early tests suggest Qwen’s smaller models still hold their own. One point of contention: these Ministrals aren't as open-source as the original 7B, which is a bit of a bummer, with the 3B not being even released anywhere besides their platform. (Mistral Blog)* Entropix (aka Shrek Sampler): Thinking Outside the (Sample) Box: This one is intriguing! Entropix introduces a novel sampling technique aimed at boosting the reasoning capabilities of smaller LLMs. Nisten’s yogurt analogy explains it best: it’s about “marinating” the information and picking the best “flavor” (token) at the end. Early examples look promising, suggesting Entropix could help smaller models tackle problems that even trip up their larger counterparts. But, as with all shiny new AI toys, we're eagerly awaiting robust evals. Tim Kellog has an detailed breakdown of this method here* Gemma-APS: Fact-Finding Mission: Google released Gemma-APS, a set of models specifically designed for extracting claims and facts from text. While LLMs can already do this to some extent, a dedicated model for this task is definitely interesting, especially for applications requiring precise information retrieval. (HF) 🔥 OpenAI adds voice to their completion API (X, Docs)In the last second of the pod, OpenAI decided to grace us with Breaking News! Not only did they launch their Windows native app, but also added voice input and output to their completion APIs. This seems to be the same model as the advanced voice mode (and priced super expensively as well) and the one they used in RealTime API released a few weeks ago at DevDay. This is of course a bit slower than RealTime but is much simpler to use, and gives way more developers access to this incredible resource (I'm definitely planning to use this for ... things 😈) This isn't their "TTS" or "STT (whisper) models, no, this is an actual omni model that understands audio natively and also outputs audio natively, allowing for things like "count to 10 super slow"I've played with it just now (and now it's after 6pm and I'm still writing this newsletter) and it's so so awesome, I expect it to be huge because the RealTime API is very curbersome and many people don't really need this complexity. This weeks Buzz - Weights & Biases updates Ok I wanted to send a completely different update, but what I will show you is, Weave, our observability framework is now also Multi Modal! This couples very well with the new update from OpenAI! So here's an example usage with today's announcement, I'm going to go through the OpenAI example and show you how to use it with streaming so you can get the audio faster, and show you the Weave multimodality as well 👇You can find the code for this in this Gist and please give us feedback as this is brand newNon standard use-cases of AI cornerThis week I started noticing and collecting some incredible use-cases of Gemini and it's long context and multimodality and wanted to share with you guys, so we had some incredible conversations about non-standard use cases that are pushing the boundaries of what's possible with LLMs.Hrishi blew me away with his experiments using Gemini for transcription and diarization. Turns out, Gemini is not only great at transcription (it beats whisper!), it’s also ridiculously cheaper than dedicated ASR models like Whisper, around 60x cheaper! He emphasized the unexplored potential of prompting multimodal models, adding, “the prompting on these things… is still poorly understood." So much room for innovation here!Simon Willison then stole the show with his mind-bending screen-scraping technique. He recorded a video of himself clicking through emails, fed it to Gemini Flash, and got perfect structured data in return. This trick isn’t just clever; it’s practically free, thanks to the ridiculously low cost of Gemini Flash. I even tried it myself, recording my X bookmarks and getting a near-perfect TLDR of the week’s AI news. The future of data extraction is here, and it involves screen recordings and very cheap (or free) LLMs.Here's Simon's example of how much this would cost him had he actually be charged for it. 🤯Speaking of Simon Willison , he broke the news that NotebookLM has got an upgrade, with the ability to steer the speakers with custom commands, which Simon promptly used to ask the overview hosts to talk like Pelicans Voice Cloning, Adobe Magic, and the Quest for Real-Time AvatarsVoice cloning also took center stage this week, with the release of F5-TTS. This open-source model performs zero-shot voice cloning with just a few seconds of audio, raising all sorts of ethical questions (and exciting possibilities!). I played a sample on the show, and it was surprisingly convincing (though not without it's problems) for a local model! This, combined with Hallo 2's (also released this week!) ability to animate talking avatars, has Wolfram Ravenwolf dreaming of real-time AI assistants with personalized faces and voices. The pieces are falling into place, folks.And for all you Adobe fans, Firefly Video has landed! This “commercially safe” text-to-video and image-to-video model is seamlessly integrated into Premiere, offering incredible features like extending video clips with AI-generated frames. Photoshop also got some Firefly love, with mind-bending relighting capabilities that could make AI-generated images indistinguishable from real photographs.Wrapping Up:Phew, that was a marathon, not a sprint! From robots to rockets, open source to proprietary, and voice cloning to video editing, this week has been a wild ride through the ever-evolving landscape of AI. Thanks for joining me on this adventure, and as always, keep exploring, keep building, and keep pushing those AI boundaries. The future is coming, and it’s going to be amazing.P.S. Don’t forget to subscribe to the podcast and newsletter for more

Hey Folks, we are finally due for a "relaxing" week in AI, no more HUGE company announcements (if you don't consider Meta Movie Gen huge), no conferences or dev days, and some time for Open Source projects to shine. (while we all wait for Opus 3.5 to shake things up) This week was very multimodal on the show, we covered 2 new video models, one that's tiny and is open source, and one massive from Meta that is aiming for SORA's crown, and 2 new VLMs, one from our friends at REKA that understands videos and audio, while the other from Rhymes is apache 2 licensed and we had a chat with Kwindla Kramer about OpenAI RealTime API and it's shortcomings and voice AI's in general. ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.All right, let's TL;DR and show notes, and we'll start with the 2 Nobel prizes in AI 👇 * 2 AI nobel prizes* John Hopfield and Geoffrey Hinton have been awarded a Physics Nobel prize* Demis Hassabis, John Jumper & David Baker, have been awarded this year's #NobelPrize in Chemistry.* Open Source LLMs & VLMs* TxT360: a globally deduplicated dataset for LLM pre-training ( Blog, Dataset)* Rhymes Aria - 25.3B multimodal MoE model that can take image/video inputs Apache 2 (Blog, HF, Try It)* Maitrix and LLM360 launch a new decentralized arena (Leaderboard, Blog)* New Gradio 5 with server side rendering (X)* LLamaFile now comes with a chat interface and syntax highlighting (X)* Big CO LLMs + APIs* OpenAI releases MLEBench - new kaggle focused benchmarks for AI Agents (Paper, Github)* Inflection is still alive - going for enterprise lol (Blog)* new Reka Flash 21B - (X, Blog, Try It)* This weeks Buzz* We chatted about Cursor, it went viral, there are many tips* WandB releases HEMM - benchmarks of text-to-image generation models (X, Github, Leaderboard)* Vision & Video* Meta presents Movie Gen 30B - img and text to video models (blog, paper)* Pyramid Flow - open source img2video model MIT license (X, Blog, HF, Paper, Github)* Voice & Audio* Working with OpenAI RealTime Audio - Alex conversation with Kwindla from trydaily.com* Cartesia Sonic goes multilingual (X)* Voice hackathon in SF with 20K prizes (and a remote track) - sign up* Tools* LM Studio ships with MLX natively (X, Download)* UITHUB.com - turn any github repo into 1 long file for LLMsA Historic Week: TWO AI Nobel Prizes!This week wasn't just big; it was HISTORIC. As Yam put it, "two Nobel prizes for AI in a single week. It's historic." And he's absolutely spot on! Geoffrey Hinton, often called the "grandfather of modern AI," alongside John Hopfield, were awarded the Nobel Prize in Physics for their foundational work on neural networks - work that paved the way for everything we're seeing today. Think back propagation, Boltzmann machines – these are concepts that underpin much of modern deep learning. It’s about time they got the recognition they deserve!Yoshua Bengio posted about this in a very nice quote: @HopfieldJohn and @geoffreyhinton, along with collaborators, have created a beautiful and insightful bridge between physics and AI. They invented neural networks that were not only inspired by the brain, but also by central notions in physics such as energy, temperature, system dynamics, energy barriers, the role of randomness and noise, connecting the local properties, e.g., of atoms or neurons, to global ones like entropy and attractors. And they went beyond the physics to show how these ideas could give rise to memory, learning and generative models; concepts which are still at the forefront of modern AI researchAnd Hinton's post-Nobel quote? Pure gold: “I’m particularly proud of the fact that one of my students fired Sam Altman." He went on to explain his concerns about OpenAI's apparent shift in focus from safety to profits. Spicy take! It sparked quite a conversation about the ethical implications of AI development and who’s responsible for ensuring its safe deployment. It’s a discussion we need to be having more and more as the technology evolves. Can you guess which one of his students it was? Then, not to be outdone, the AlphaFold team (Demis Hassabis, John Jumper, and David Baker) snagged the Nobel Prize in Chemistry for AlphaFold 2. This AI revolutionized protein folding, accelerating drug discovery and biomedical research in a way no one thought possible. These awards highlight the tangible, real-world applications of AI. It's not just theoretical anymore; it's transforming industries.Congratulations to all winners, and we gotta wonder, is this a start of a trend of AI that takes over every Nobel prize going forward? 🤔 Open Source LLMs & VLMs: The Community is COOKING!The open-source AI community consistently punches above its weight, and this week was no exception. We saw some truly impressive releases that deserve a standing ovation. First off, the TxT360 dataset (blog, dataset). Nisten, resident technical expert, broke down the immense effort: "The amount of DevOps and…operations to do this work is pretty rough." This globally deduplicated 15+ trillion-token corpus combines the best of Common Crawl with a curated selection of high-quality sources, setting a new standard for open-source LLM training. We talked about the importance of deduplication for model training - avoiding the "memorization" of repeated information that can skew a model's understanding of language. TxT360 takes a 360-degree approach to data quality and documentation – a huge win for accessibility.Apache 2 Multimodal MoE from Rhymes AI called Aria (blog, HF, Try It )Next, the Rhymes Aria model (25.3B total and only 3.9B active parameters!) This multimodal marvel operates as a Mixture of Experts (MoE), meaning it activates only the necessary parts of its vast network for a given task, making it surprisingly efficient. Aria excels in understanding image and video inputs, features a generous 64K token context window, and is available under the Apache 2 license – music to open-source developers’ ears! We even discussed its coding capabilities: imagine pasting images of code and getting intelligent responses.I particularly love the focus on long multimodal input understanding (think longer videos) and super high resolution image support. I uploaded this simple pin-out diagram of RaspberriPy and it got all the right answers correct! Including ones I missed myself (and won against Gemini 002 and the new Reka Flash!) Big Companies and APIsOpenAI new Agentic benchmark, can it compete with MLEs on Kaggle?OpenAI snuck in a new benchmark, MLEBench (Paper, Github), specifically designed to evaluate AI agents performance on Machine Learning Engineering tasks. Designed around a curated collection of Kaggle competitions, creating a diverse set of challenging tasks that test real-world ML engineering skills such as training models, preparing datasets, and running experiments. They found that the best-performing setup--OpenAI's o1-preview with AIDE scaffolding--achieves at least the level of a Kaggle bronze medal in 16.9% of competitions (though there are some that throw shade on this score)Meta comes for our reality with Movie GenBut let's be honest, Meta stole the show this week with Movie Gen (blog). This isn’t your average video generation model; it’s like something straight out of science fiction. Imagine creating long, high-definition videos, with different aspect ratios, personalized elements, and accompanying audio – all from text and image prompts. It's like the Holodeck is finally within reach! Unfortunately, despite hinting at its size (30B) Meta is not releasing this model (just yet) nor is it available widely so far! But we'll keep our fingers crossed that it drops before SORA. One super notable thing is, this model generates audio as well to accompany the video and it's quite remarkable. We listened to a few examples from Meta’s demo, and the sound effects were truly remarkable – everything from fireworks to rustling leaves. This model isn't just creating video, it's crafting experiences. (Sound on for the next example!)They also have personalization built in, which is showcased here by one of the leads of LLama ,Roshan, as a scientist doing experiments and the realism is quite awesome to see (but I get why they are afraid of releasing this in open weights)This Week’s Buzz: What I learned at Weights & Biases this weekMy "buzz" this week was less about groundbreaking models and more about mastering the AI tools we have. We had a team meeting to share our best tips and tricks for using Cursor, and when I shared those insights on X (thread), they went surprisingly viral! The big takeaway from the thread? Composer, Cursor’s latest feature, is a true game-changer. It allows for more complex refactoring and code generation across multiple files – the kind of stuff that would take hours manually. If you haven't tried Composer, you're seriously missing out. We also covered strategies for leveraging different models for specific tasks, like using O1 mini for outlining and then switching to the more robust Cloud 3.5 for generating code. Another gem we uncovered: selecting any text in the console and hitting opt+D will immediately send it to the chat to debug, super useful! Over at Weights & Biases, my talented teammate, Soumik, released HEMM (X, Github), a comprehensive benchmark specifically designed for text-to-image generation models. Want to know how different models fare on image quality and prompt comprehension? Head over to the leaderboard on Weave (Leaderboard) and find out! And yes, it's true, Weave, our LLM observability tool, is multimodal (well within the theme of today's update)Voice and Audio: Real-Time Conversations and the Quest for Affordable AIOpenAI's DevDay was just a few weeks back, but the ripple effects of their announcements are still being felt. The big one for voice AI enthusiasts like myself? The RealTime API, offering

Hey, it's Alex. Ok, so mind is officially blown. I was sure this week was going to be wild, but I didn't expect everyone else besides OpenAI to pile on, exactly on ThursdAI. Coming back from Dev Day (number 2) and am still processing, and wanted to actually do a recap by humans, not just the NotebookLM one I posted during the keynote itself (which was awesome and scary in a "will AI replace me as a podcaster" kind of way), and was incredible to have Simon Willison who was sitting just behind me most of Dev Day, join me for the recap! But then the news kept coming, OpenAI released Canvas, which is a whole new way of interacting with chatGPT, BFL released a new Flux version that's 8x faster, Rev released a Whisper killer ASR that does diarizaiton and Google released Gemini 1.5 Flash 8B, and said that with prompt caching (which OpenAI now also has, yay) this will cost a whopping 0.01 / Mtok. That's 1 cent per million tokens, for a multimodal model with 1 million context window. 🤯 This whole week was crazy, as last ThursdAI after finishing the newsletter I went to meet tons of folks at the AI Tinkerers in Seattle, and did a little EvalForge demo (which you can see here) and wanted to share EvalForge with you as well, it's early but very promising so feedback and PRs are welcome! WHAT A WEEK, TL;DR for those who want the links and let's dive in 👇 * OpenAI - Dev Day Recap (Alex, Simon Willison)* Recap of Dev Day* RealTime API launched* Prompt Caching launched* Model Distillation is the new finetune* Finetuning 4o with images (Skalski guide)* Fireside chat Q&A with Sam* Open Source LLMs * NVIDIA finally releases NVML (HF)* This weeks Buzz* Alex discussed his demo of EvalForge at the AI Tinkers event in Seattle in "This Week's Buzz". (Demo, EvalForge, AI TInkerers)* Big Companies & APIs* Google has released Gemini Flash 8B - 0.01 per million tokens cached (X, Blog)* Voice & Audio* Rev breaks SOTA on ASR with Rev ASR and Rev Diarize (Blog, Github, HF)* AI Art & Diffusion & 3D* BFL relases Flux1.1[pro] - 3x-6x faster than 1.0 and higher quality (was 🫐) - (Blog, Try it)The day I met Sam Altman / Dev Day recapLast Dev Day (my coverage here) was a "singular" day in AI for me, given it also had the "keep AI open source" with Nous Research and Grimes, and this Dev Day I was delighted to find out that the vibe was completely different, and focused less on bombastic announcements or models, but on practical dev focused things. This meant that OpenAI cherry picked folks who actively develop with their tools, and they didn't invite traditional media, only folks like yours truly, @swyx from Latent space, Rowan from Rundown, Simon Willison and Dan Shipper, you know, newsletter and podcast folks who actually build! This also allowed for many many OpenAI employees who work on the products and APIs we get to use, were there to receive feedback, help folks with prompting, and just generally interact with the devs, and build that community. I want to shoutout my friends Ilan (who was in the keynote as the strawberry salesman interacting with RealTime API agent), Will DePue from the SORA team, with whom we had an incredible conversation about ethics and legality of projects, Christine McLeavey who runs the Audio team, with whom I shared a video of my daughter crying when chatGPT didn't understand her, Katia, Kevin and Romain on the incredible DevEx/DevRel team and finally, my new buddy Jason who does infra, and was fighting bugs all day and only joined the pub after shipping RealTime to all of us. I've collected all these folks in a convenient and super high signal X list here so definitely give that list a follow if you'd like to tap into their streamsFor the actual announcements, I've already covered this in my Dev Day post here (which was payed subscribers only, but is now open to all) and Simon did an incredible summary on his Substack as well The highlights were definitely the new RealTime API that let's developers build with Advanced Voice Mode, Prompt Caching that will happen automatically and reduce all your long context API calls by a whopping 50% and finetuning of models that they are rebranding into Distillation and adding new tools to make it easier (including Vision Finetuning for the first time!)Meeting Sam AltmanWhile I didn't get a "media" pass or anything like this, and didn't really get to sit down with OpenAI execs (see Swyx on Latent Space for those conversations), I did have a chance to ask Sam multiple things. First at the closing fireside chat between Sam and Kevin Weil (CPO at OpenAI), Kevin first asked Sam a bunch of questions, and then they gave out the microphones to folks, and I asked the only question that got Sam to smileSam and Kevin went on for a while, and that Q&A was actually very interesting, so much so, that I had to recruit my favorite Notebook LM podcast hosts, to go through it and give you an overview, so here's that Notebook LM, with the transcript of the whole Q&A (maybe i'll publish it as a standalone episode? LMK in the comments)After the official day was over, there was a reception, at the same gorgeous Fort Mason location, with drinks and light food, and as you might imagine, this was great for networking.But the real post dev day event was hosted by OpenAI devs at a bar, Palm House, which both Sam and Greg Brokman just came to and hung out with folks. I missed Sam last time and was very eager to go and ask him follow up questions this time, when I saw he was just chilling at that bar, talking to devs, as though he didn't "just" complete the largest funding round in VC history ($6.6B at $175B valuation) and went through a lot of drama/turmoil with the departure of a lot of senior leadership! Sam was awesome to briefly chat with, tho as you might imagine, it was loud and tons of folks wanted selfies, but we did discuss how AI affects the real world, job replacement stuff were brought up, and how developers are using the OpenAI products. What we learned, thanks to Sigil, is that o1 was named partly as a "reset" like the main blogpost claimed and partly as "alien of extraordinary ability" , which is the the official designation of the o1 visa, and that Sam came up with this joke himself. Is anyone here smarter than o1? Do you think you still will by o2? One of the highest impact questions was by Sam himself to the audience.Who feels like they've spent a lot of time with O1, and they would say, like, I feel definitively smarter than that thing?— Sam AltmanWhen Sam asked this at first, a few hands hesitatingly went up. He then followed up with Do you think you still will by O2? No one. No one taking the bet.One of the challenges that we face is like, we know how to go do this thing that we think will be like, at least probably smarter than all of us in like a broad array of tasksThis was a very palpable moment that folks looked around and realized, what OpenAI folks have probably internalized a long time ago, we're living in INSANE times, and even those of us at the frontier or research, AI use and development, don't necessarily understand or internalize how WILD the upcoming few months, years will be. And then we all promptly forgot to have an existential crisis about it, and took our self driving Waymo's to meet Sam Altman at a bar 😂 This weeks Buzz from Weights & BiasesHey so... after finishing ThursdAI last week I went to Seattle Tinkerers event and gave a demo (and sponsored the event with a raffle of Meta Raybans). I demoed our project called EvalForge, which I built the frontend of and my collegue Anish on backend, as we tried to replicate the Who validates the validators paper by Shreya Shankar, here’s that demo, and EvalForge Github for many of you who asked to see it. Please let me know what you think, I love doing demos and would love feedback and ideas for the next one (coming up in October!)OpenAI chatGPT Canvas - a complete new way to interact with chatGPTJust 2 days after Dev Day, and as breaking news during the show, OpenAI also shipped a new way to interact with chatGPT, called Canvas! Get ready to say goodbye to simple chats and hello to a whole new era of AI collaboration! Canvas, a groundbreaking interface that transforms ChatGPT into a true creative partner for writing and coding projects. Imagine having a tireless copy editor, a brilliant code reviewer, and an endless source of inspiration all rolled into one – that's Canvas!Canvas moves beyond the limitations of a simple chat window, offering a dedicated space where you and ChatGPT can work side-by-side. Canvas opens in a separate window, allowing for a more visual and interactive workflow. You can directly edit text or code within Canvas, highlight sections for specific feedback, and even use a handy menu of shortcuts to request tasks like adjusting the length of your writing, debugging code, or adding final polish. And just like with your favorite design tools, you can easily restore previous versions using the back button.Per Karina, OpenAI has trained a special GPT-4o model specifically for Canvas, enabling it to understand the context of your project and provide more insightful assistance. They used synthetic data, generated by O1 which led them to outperform the basic version of GPT-4o by 30% in accuracy. A general pattern emerges, where new frontiers in intelligence are advancing also older models (and humans as well). Gemini Flash 8B makes intelligence essentially freeGoogle folks were not about to take this week litely and decided to hit back with one of the most insane upgrades to pricing I've seen. The newly announced Gemini Flash 1.5 8B is goint to cost just... $0.01 per million tokens 🤯 (when using caching, 3 cents when not cached) This basically turns intelligence free. And while it is free, it's still their multimodal model (supports images) and has a HUGE context window of 1M tokens. The evals look ridiculous as well, this 8B param model, now almost matches Flash from May of this year, less than 6 month a