ThursdAI - Apr 3rd - OpenAI Goes Open?! Gemini Crushes Math, AI Actors Go Hollywood & MCP, Now with Observability?

Hey everyone, Alex here 👋Welcome back to ThursdAI! After what felt like ages of non-stop, massive model drops (looking at you, O3 and GPT-4!), we finally got that "chill week" we've been dreaming of since maybe... forever? It seems the big labs are taking a breather, probably gearing up for even bigger things next week (maybe some open source 👀).But "chill" doesn't mean empty! This week was packed with fascinating developments, especially in the open source world and with long-awaited API releases. We actually had time to dive deeper into things, which was a refreshing change. We had a fantastic lineup of guests joining us too: Kwindla Kramer (@kwindla), our resident voice expert, dropped in to talk about some mind-blowing TTS and her own open-source VAD release. Maziyar Panahi (@MaziyarPanahi) gave us the inside scoop on OpenAI's recent meeting with the open source community. And Dex Horthy (@dexhorthy) from HumanLayer shared some invaluable insights on building robust AI agents that actually work in the real world. It was great having them alongside the usual ThursdAI crew: LDJ, Yam, Wolfram, and Nisten!So, instead of rushing through a million headlines, we took a more relaxed pace. We explored NVIDIA's cool new Describe Anything model, dug into Google's Quantization Aware Training for Gemma, celebrated the much-anticipated API release for OpenAI's GPT Image generation (finally!), checked out the new Grok API, got absolutely blown away by a tiny, open-source TTS model from Korea called Dia, and debated the principles of building better AI agents. Plus, a surprise drop from Send AI with a powerful video model!Let's dive in!Open Source AI Highlights: Community, Vision, and EfficiencyEven with the big players quieter on the model release front, the open source scene was buzzing. It feels like this "chill" period gave everyone a chance to focus on refining tools, releasing datasets, and engaging with the community.OpenAI Inches Closer to Open Source? Insights from the Community MeetingPerhaps the biggest non-release news of the week was OpenAI actively engaging with the open source community. Friend of the show Maziyar Panahi was actually in the room (well, the Zoom room) and joined us to share what went down It sounds like OpenAI came prepared, with Sam Altman himself spending significant time answering questions . Maziyar gave us the inside scoop, mentioning that OpenAI's looking to offload some GPU pressure by embracing open source – a win-win where they help the community, and the community helps lighten their load. He painted a picture of a company genuinely trying to listen and figure out how to best contribute. It felt less like a checkbox exercise and more like genuine engagement, which is awesome to see.What did the community ask for? Based on Maziyar's recap, there was a strong consensus on several key points:* Model Size: The sweet spot seemed to be not tiny, but not astronomically huge either. Something in the 70B-200B parameter range that could run reasonably on, say, 4 GPUs, leaving room for other models. People want power they can actually use without needing a supercomputer.* Capabilities: A strong desire for reliable structured output. Surprisingly, there was less emphasis on complex, built-in reasoning, or at least the ability to toggle reasoning off. This likely stems from practical concerns about cost and latency in production environments. The community seems to value control and efficiency for specific tasks.* Multilingual: Good support for European languages (at least 20) was a major request, reflecting the global nature of the open source community. Needs to be as good as English support.* Base Models: A huge ask was for OpenAI to release base models. The reasoning? Empower the community to handle fine-tuning for specific tasks like coding, roleplay, or supporting underrepresented languages . Let the experts in those niches build on a solid foundation.* Focus: Usefulness over chasing leaderboard glory. The community urged OpenAI to provide a solid, practical model rather than aiming for a temporary #1 spot that gets outdated in days or weeks . Stability, reliability, and long-term utility were prized over fleeting benchmark wins.* Safety: A preference for separate guardrail models (similar to LlamaGuard or GemmaGuard) rather than overly aligning the main model, which often hurts performance and flexibility . Give users the tools to implement safety layers as needed, rather than baking in limitations that might stifle creativity or utility.Perhaps most excitingly, Maziyar mentioned OpenAI seemed committed to regular open model releases, not just a one-off thin=! This, combined with recent moves like approving a community Pull Request to make their open-source Codex agent work with non-OpenAI models (as Yam Peleg excitedly pointed out!), suggests a potentially significant shift. Remember, it's been a long time since GPT-2 and Whisper were OpenAI's main open contributions! We're definitely watching this space closely. Huge shout out to OpenAI for listening and engaging with the builders.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.NVIDIA's DAM: Describe Anything Model (and Dataset!)NVIDIA dropped something really cool this week: the Describe Anything Model (DAM), specifically DAM-3B, a 3 billion parameter multimodal model for region-based image and video captioning. Think Meta's Segment Anything (SAM), but instead of just segmenting, it also tells you what you've segmented, in detail.We played around with the image demo on the show (HF demo) . You hover over an image, things get segmented on the fly (you can use points, boxes, scribbles, or masks), you click, and boom – a detailed description pops up for that specific region: "A brown bear with a thick, dense coat of fur..." . It's pretty slick and responsive!While the demo didn't showcase video, the project page (X post) shows it working on videos too (DAM-3B-Video), tracking and describing objects like fish even as they move. This capability really impressed Yam, who rightly pointed out that tracking objects consistently over video is hard, so having a base model that understands this level and embeds it in language is seriously impressive. The model uses a "focal prompt" and gated cross-attention to fuse the full scene context with the selected region.Nisten reminded us that our friend Piotr Skalski from Roboflow basically built a pipeline for this a while back by combining SAM with description models like Microsoft Florence . But DAM integrates it all into one efficient 3B parameter model (HF model), setting a new state-of-the-art on their introduced DLC-Bench (Detailed Localized Captioning).Crucially, NVIDIA didn't just drop the model; they also released the Describe Anything Dataset (HF dataset) used to train it (built on subsets like COCO, Paco, SAM) and the code under a research-only license. This is fantastic for researchers and builders. Imagine using this for precise masking before sending an image to the new GPT Image API for editing – super useful! Big props to NVIDIA and their collaborators at UC Berkeley and UCSF for this contribution.Gemma Gets Quantization Aware Training (QAT): Smaller Footprint, Sassy Attitude?Google also pushed the open source envelope by releasing Gemma models trained with Quantization Aware Training (QAT). This isn't your standard post-training quantization; QAT involves incorporating the impact of quantization during the training process itself. As LDJ explained, this allows the model to adapt, potentially resulting in a quantized state with much higher quality and less performance degradation compared to just quantizing a fully trained model afterwards.The results? Significant reductions in VRAM requirements across the board. The 27B parameter Gemma 3, for example, drops from needing a hefty 54GB to just 14.1GB ! Even the 1B model goes from 2GB to just half a gig. This makes running these powerful models much more accessible on consumer hardware. Folks are already running them in MLX, llama.cpp, LM Studio, etc. (Reddit thread)Wolfram already took the 4B QAT model for a spin using LM Studio . The good news: it ran easily, needing only 5-6GB of RAM. The quirky news: it seemed to struggle a bit with prompt adherence in his tests, even giving Wolfram a sassy, winking-emoji response about ignoring the "fine print" in his complex system prompt when called out on a language switching error: "Who reads a fine print? 😉" ! He did note Gemma 3 now supports system prompts (unlike Gemma 2), which is a definite improvement .(While NVIDIA also released OpenMath Nemotron, we didn't dive deep in the show, but worth noting its AIMO win and accompanying open dataset release!)Voice and Audio Innovations: Emotional TTS and Smarter ConversationsEven in a "chill" week, the audio space delivered some serious excitement. Kwindla Kramer joined us to break down two major developments.Dia TTS: Unhinged Emotion from a Small Open Model 🤯This one absolutely blew up Twitter, and for good reason. Dia, from Nari Labs (essentially a student and a half in Korea!), is a 1.6 billion parameter open-weights (MIT licensed) text-to-dialogue model (Github, HF). What makes it special? The insane emotional range and natural interaction patterns. My Twitter post about it (X post) went viral, getting half a million views !We played some examples, and they are just wild. You have to hear this to believe it:* Check the Demos: Dia Demo Page | Fal.ai Voice Clone DemoAnother crazy thing is how it handles non-verbal cues like laughs or coughs specified in the text (e.g., (laughs)) . Instead of just tacking on a generic sound, it inflects the preceding words leading into the laugh, making it sound incredibly natural. It even handles interruptions seamlessly, cutting off one speaker realistically when another starts .Kwin, our voice expert, offered some valuable perspective . While Dia is undeniably awesome and shows what's possible, it's very much a research model – likely unpredictable ("unhinged" was his word!) and probably required cherry-picking the best demos. Production models like 11Labs need predictability. Kwin also noted the dataset is probably scraped from YouTube (a common practice, explaining the lack of open audio data) and that the non-speech sounds are a key takeaway – the bar for TTS is rising beyond just clear speech .PipeCat SmartTurn: Fixing Awkward AI Silences with Open Source VADSpeaking of open audio, Kwin and the team at Daily/Pipecat had their own breaking news: they released an open-source checkpoint for their SmartTurn model – a semantic Voice Activity Detection (VAD) system (Github, HF Model) What's the problem SmartTurn solves? That annoying thing where voice assistants interrupt you mid-thought just because you paused for a second. I've seen this happen with my kids all the time, making interaction frustrating! Semantic VAD, or "Smart Turn," is much smarter. It considers not just silence but also the context – audio patterns (like intonation suggesting you're not finished) and linguistic cues (like ending on "and..." or "so...") to make a much better guess about whether you're truly done talking. This is crucial for natural-feeling voice interactions, especially for kids or multilingual speakers (like me!) who might pause more often to find the right word.And the data part is key here. They're building an open dataset for this, hosted on Hugging Face. You can even contribute your own voice data by playing simple games on their turn-training.pipecat.ai site (Try It Demo)! The cool incentive? The more diverse voice data they get (especially for different languages!), the better these systems will work for everyone. If your voice is in the dataset, future AI agents might just understand you a little better!Kwin also mentioned their upcoming Voice AI Course co-created with friend-of-the-pod Swyx, hosted on Maven . It aims to be a comprehensive guide with code samples, community interaction, and insights from experts (including folks from Weights & Biases!). Check it out if you want to dive deep into building voice AI. AI Art & Diffusion & 3D: Quick HitsA slightly quieter week for major art model releases, but still some significant movement:* OpenAI's GPT Image 1 API: We'll cover this in detail in the Big Companies section below, but obviously relevant here too as a major new tool for developers creating AI art and image editing applications .* Hunyuan 3D 2.5 (Tencent): Tencent released an update to their 3D generation model, now boasting 10 billion parameters (up from 1B!) . They're highlighting massive leaps in precision (1024-resolution geometry), high-quality textures with PBR support, and improved skeletal rigging for animation X Post. Definitely worth keeping an eye on as 3D generation matures and becomes more accessible (they doubled the free quota and launched an API).Agent Development Insights: Building Robust Agents with Dex HorthyWith things slightly calmer, it was the perfect time to talk about AI agents – a space buzzing with activity, frameworks, and maybe even a little bit of drama. We brought in Dex Horthy, founder of HumanLayer and author of the insightful "12 Factor Agent" essay (Github Repo), to share his perspective on what actually works when building agents for production.Dex builds SDKs to help create agents that feel more like digital humans, aiming to deploy them where users already are (Slack, email, etc.), moving beyond simple chat interfaces. His experience led him to identify common patterns and pitfalls when trying to build reliable agents.The Problem with Current Agent FrameworksA key takeaway Dex shared? Many teams building serious, production-ready agents end up writing large parts from scratch. Why? Because existing frameworks often fall short in providing the necessary control and reliability for complex tasks. The common "prompt + bag of tools + figure it out" approach, while great for demos, struggles with reliability over longer, multi-step workflows . Think about it: even if each step is 92% reliable, after 10 steps, your overall success rate plummets due to compounding errors. That's just not good enough for customer-facing applications.Key Principles: Small Agents, Owning ContextSo, what does work today according to Dex's 12 factors?* Small, Focused Agents: Instead of one giant, monolithic agent trying to do everything, the more reliable approach is to build smaller "micro-agents" that handle specific, well-defined parts of a workflow ]. As models get smarter, these micro-agents might grow in capability, but the principle of breaking down complexity holds. Find something at the edge of the model's capability and nail it consistently .* Own Your Prompts & Context: Don't let frameworks abstract away control over the exact tokens going into the LLM or how the context window is managed. This is crucial for performance tuning. Even with massive context windows (like Gemini's 2M tokens), smaller, carefully curated context often yields better results and lower costs . Maximum performance requires owning every single token.Dex's insights provide a crucial dose of pragmatism for anyone building or thinking about building AI agents in this rapidly evolving space. Check out his full 12 Factor Agent essay and the webinar recording for a deeper dive.Big Companies & APIs: GPT Image and Grok Get Developer AccessWhile new foundation models were scarce from the giants this week, they did deliver on the API front, opening up powerful capabilities to developers.OpenAI Finally Releases GPT Image 1 API! (X Post)This was a big one many developers were waiting for. OpenAI's powerful image generation capabilities, previously locked inside ChatGPT, are now available via API under the official name gpt-image-1 (Docs) . No more awkward phrasing like "the new image generation capabilities within chat gpt"!Getting access requires organizational verification, which involved a slightly intense biometric scan process for me – feels like they're taking precautions given the model's realism and potential for misuse . Understandable, but something developers need to be aware of .The API (API Reference) offers several capabilities:* Generations: Creating images from scratch based on text prompts.* Edits: Modifying existing images using a new prompt, crucially supporting masking for partial edits. This is huge for targeted changes and perfect for combining with segmentation models like NVIDIA's DAM!There's a nice playground interface in the console, and you have interesting controls over the output:* Quality: Instead of distinct models, you select a quality level (standard/HD) which impacts the internal "thinking time" and cost . It seems to be a reasoning model under the hood, so quality relates to compute/latency.* Number: Generate up to 10 images at once.* Transparency: Supports generating images with transparent backgroundsI played around with it, generating ads and even trying to get it to make a ThursdAI thumbnail with my face. The text generation is excellent – it nailed "ThursdAI" perfectly on an unhinged speaker ad Nisten prompted! It follows complex style prompts well.However, generating realistic faces, especially matching a specific person like me, seems... really hard right now . Even after many attempts providing a source image and asking it to replace a face, the results were generic or only vaguely resembled me. It feels almost intentionally nerfed, maybe as a safety measure to prevent deepfakes? I still used it for the thumbnail, but yeah, it could be better on faces.OpenAI launched with several integration partners like Adobe, Figma, Wix, HeyGen, and Fal.ai already onboard. Expect to see these powerful image generation capabilities popping up everywhere!Grok 3 Mini & Grok 3 Now Available via API (+ App Updates)Elon's xAI also opened the gates this week, making Grok 3 Mini and Grok 3 available via API (Docs).The pricing structure is fascinating and quite different from others. Grok 3 Mini is incredibly cheap for input ($0.30 / 1M tokens) with only a modest bump for output ($0.50 / 1M). The "Fast" versions, however, cost significantly more, especially for output tokens (Grok 3 Fast is $5 input / $25 output per million!) . It seems like a deliberate play on the "fast, cheap, smart" triangle, giving developers explicit levers to pull based on their needs.Benchmarks provided by xAI position Grok 3 Mini competitively against other small models like Gemini 2.5 Flash and O4 Mini, scoring well on AIME (93%) and coding benchmarks. Speaking of the app, the iOS version got a significant update adding a live video view (let Grok see what you see through your camera) and multilingual audio support (X Post) . Prepare for some potentially unhinged, real-time video roasting if you use the fun mode with the camera on ! Multilingual audio and search are also rolling out to SuperGrok users on Android.(Side note: We briefly touched on O3's recent wonkiness in following instructions for tone, despite its amazing GeoGuessr abilities! Something feels off there lately.)Vision and Video: Send AI's Surprise Release & MoreJust when we thought the week was winding down on model releases...Send AI Drops MAGI-1: 24B Video Model with Open Weights! 🔥Out of seemingly nowhere, a company called Send AI released details (and then the weights!) for MAGI-1, a 24 billion parameter autoregressive diffusion model for video generation (X Post, GitHub, PDF Report).The demos looked stunning, showcasing impressive long-form video generation with remarkable character consistency – often the Achilles' heel of AI video . Nisten speculated this could be a major step towards usable AI-generated movies, solving the critical face/character consistency problem . They achieve this by predicting video in 24-frame chunks with causal attention between them, allowing for real-time streaming generation where compute doesn't scale with length. They also highlighted an "infinite extension" capability, allowing users to build out longer scenes by injecting new prompts or continuing footage.Their technical report dives into the architecture, mentioning novel techniques like a custom "MagiAttention" kernel that scales to massive contexts and helps achieve the temporal consistency. It also sets SOTA on VBench-I2V and Physics-IQ benchmarks.And the biggest surprise? They released the model weights under an Apache 2.0 license on Hugging Face ! This is huge! Just as we sometimes lament the lack of open source momentum from certain players, Send AI drops this 24B parameter beast with open weights. Amazing! Go download it!Framepack: Long Videos on Low VRAMWolfram also flagged Framepack, another interesting video development from the research world from the creator of ControlNet. FramePack is a next-frame (next-frame-section) prediction neural network structure that generates videos progressively. (Github)Character AI AvatarFX Steps InAlso in the visual space, Character AI announced AvatarFX in early access (Website), stepping into the realm of animated, speaking visual avatars derived from images. It seems like everyone wants to bring characters to life visually now.This Week's Buzz from W&B / CommunityQuick hits on upcoming events and community stuff:* WeaveHacks Coming to SF! Mark your calendars! We're hosting a hackathon focused on building with W&B Weave at the Weights & Biases office in San Francisco on May 17th-18th [0:06:15]. If you're around, especially if you're coming into town for Google I/O the week after, come hang out, build cool stuff, and say hi! We're planning to go all out with sponsors and prizes (announcements coming soon). lu.ma/weavehacks * Fully Connected Conference Reminder: Our flagship W&B conference, Fully Connected, is happening in San Francisco on June 18th [0:06:30]. It's where our customers, partners, and the community come together for two days of talks, workshops, and networking focused on production AI. It's always an incredible event. (fullyconnected.com)Wrapping Up the "Chill" Week That Wasn't Quite ChillPhew! See? Even a "chill" week in AI is overflowing with news when you actually have time to stop and breathe for a second. From OpenAI's fascinating open source tango and the practical (and long-awaited!) API releases of GPT Image and Grok, to the sheer creative potential shown by indie projects like Dia and Send AI's Maggie, and the grounding principles for building agents that actually work from Dex – there was a ton to absorb and discuss. It felt good to have the space to go a little deeper.It was fantastic having Kwin, Maziar, and Dex join the regulars (LDJ, Yam, Wolfram, Nisten) to share their expertise and firsthand insights. A huge thank you to them and to everyone tuning in live across X, YouTube, LinkedIn, and participating in the chat! Your questions and comments make the show what it is.Don't forget, if you missed anything, the full show is available as a podcast (search "ThursdAI" wherever you get your podcasts)🔗 Subscribe to our show on Spotify: thursdai.news/spotify🔗 Apple: thursdai.news/apple🔗 Youtube: thursdai.news/yt Next week? The rumors suggest the big labs might be back with major releases . The brief calm might be over! Buckle up! We'll be here to break it all down.See you next ThursdAI!- AlexTL;DR and Show Notes (April 23rd, 2024)* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases @altryne* Co Hosts - Wolfram Ravenwlf @WolframRvnwlf, Yam Peleg @yampeleg, Nisten Tahiraj @nisten, LDJ @ldjconfirmed* Kwindla Kramer @kwindla - Daily Co-Founder // Voice expert* Dexter Horthy @dexhorthy - HumanLayer // Agents expert* Maziyar Panahi @MaziyarPanahi - OSS maintainer* Open Source AI - LLMs, Vision, Voice & more* OpenAI OSS Meeting: Insights from Maziar [0:16:37]. * NVIDIA Describe Anything (DAM-3B): 3B param multimodal LLM for region-based image/video captioning. (X Post, HF model, HF demo)* Google Gemma QAT: Quantization-Aware Training models (X, Blog) * Big CO LLMs + APIs* OpenAI GPT Image 1 API: (X Post, Docs, API Reference)* Grok API & App Updates: Grok 3 and Grok 3 Mini available via API. (API Docs, App Update X Post)* This weeks Buzz - Weights & Biases* WeaveHacks SF: Hackathon May 17-18 at W&B HQ. lu.ma/weavehacks * Fully Connected: W&B's 2-day conference, June 18-19 in SF fullyconnected.com* Vision & Video* Send AI MAGI-1: 24B autoregressive diffusion model for long, streaming video (X Post, GitHub, PDF Report, HF Repo)* Character AI AvatarFX: Early access for creating speaking/emoting avatars from images . (Website)* Framepack: Mentioned for long video generation (120s) on low VRAM (6GB). (Project Page)* Voice & Audio* Nari Labs Dia: 1.6B param OSS TTS model (X Post Highlight, HF Model, Github, Fal.ai Demo)* PipeCat Smart-Turn VAD: Open source semantic VAD model (Github, HF Model, Fal.ai Playground, Try It Demo)* AI Art & Diffusion & 3D* Hunyuan 3D 2.5 (Tencent): 10B param update [0:09:06]. Higher res geometry, PBR textures, improved rigging. (X Post)* Agents , Tools & Links* 12 Factor Agents: Discussion with Dex Horthy on building robust agents (Github Repo) 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 👋Wow. Just… wow. What a week, folks. Seriously, this has been one for the books. This week was dominated by OpenAI's double whammy: first the GPT-4.1 family dropped with a mind-boggling 1 million token context window, followed swiftly by the new flagship reasoning models, o3 and o4-mini, which are already blowing minds with their agentic capabilities. We also saw significant moves from Google with VEO-2 going GA, the fascinating A2A protocol launch (we had an amazing interview with Google's Todd Segal about it!), and even an attempt to talk to dolphins with DolphinGemma. Kling stepped up its video game, Cohere dropped SOTA multimodal embeddings, and ByteDance made waves in image generation. Plus, the open-source scene had some interesting developments, though perhaps overshadowed by the closed-source giants this time.o3 has absolutely taken the crown as the conversation piece, so lets start with it (as always, TL;DR and shownotes at the end, and here's the embedding of our live video show) Big Company LLMs + APIsOpenAI o3 & o4‑mini: SOTA Reasoning Meets Tool‑Use (Blog, Watch Party)The long awaited o3 models (promised to us in the last days of x-mas) is finally here, and it did NOT disappoint and well.. even surprised! o3 is not only SOTA on nearly all possible logic, math and code benchmarks, which is to be expected from the top reasoning model, it also, and I think for the first time, is able to use tools during its reasoning process. Tools like searching the web, python coding, image gen (which it... can zoom and rotate and crop images, it's nuts) to get to incredible responses faster. Tool using reasoner are... almost AGI? This is the headline feature for me. For the first time, these o-series models have full, autonomous access to all built-in tools (web search, Python code execution, file search, image generation with Sora-Image/DALL-E, etc.). They don't just use tools when told; they decide when and how to chain multiple tool calls together to solve a problem. We saw logs with 600+ consecutive tool calls! This is agent-level reasoning baked right in.Anecdote: We tested this live with a complex prompt: "generate an image of a cowboy that on his head is the five last digits of the hexadecimal code of the MMMU score of the latest Gemini model." o3 navigated this multi-step task flawlessly: figuring out the latest model was Gemini 2.5, searching for its MMMU score, using the Python tool to convert it to hex and extract the digits, and then using the image generation tool. It involved multiple searches and reasoning steps. Absolutely mind-blowing 🤯.Thinking visually with imagesThis one also blew my mind, this model is SOTA on multimodality tasks, and a reason for this, is these models can manipulate and think about the images they received. Think... cropping, zooming, rotating. The models can now perform all these tasks to multimodal requests from users. Sci-fi stuff! Benchmark Dominance: As expected, these models crush existing benchmarks.o3 sets new State-of-the-Art (SOTA) records on Codeforces (coding competitions), SWE-bench (software engineering), MMMU (multimodal understanding), and more. It scored a staggering $65k on the Freelancer eval (simulating earning money on Upwork) compared to o1's $28k!o4-mini is no slouch either. It hits 99.5% on AIME (math problems) when allowed to use its Python interpreter and beats the older o3-mini on general tasks. It’s a reasoning powerhouse at a fraction of the cost.Incredible Long Context PerformanceYam highlighted this – on the Fiction Life benchmark testing deep comprehension over long contexts, o3 maintained nearly 100% accuracy up to 120,000 tokens, absolutely destroying previous models including Gemini 2.5 Pro and even the new GPT-4.1 family on this specific eval. While its context window is currently 200k (unlike 4.1's 1M), its performance within that window is unparalleled.Cost-Effective Reasoning: They're not just better, they're cheaper for the performance you get.* o3: $10 input / $2.50 cached / $40 output per million tokens.* o4-mini: $1.10 input / $0.275 cached / $4.40 output per million tokens. (Cheaper than GPT-4.0!)Compute Scaling Validated: OpenAI confirmed these models used >10x the compute of o1 and leverage test-time compute scaling (spending longer on harder problems), further proving their scaling law research.Memory Integration: Both models integrate with ChatGPT's recently upgraded memory feature which has access to all your previous conversations (which we didn't talk about but is absolutely amazing, try asking o3 stuff it knows about you and have ti draw conclusions!)Panel Takes & Caveats:While the excitement was palpable, Yam noted some community observations about potential "rush" – occasional weird hallucinations or questionable answers compared to predecessors, possibly a side effect of cramming so much training data. Nisten, while impressed, still found the style of GPT-4.1 preferable for specific tasks like generating structured medical notes in his tests. It highlights that benchmarks aren't everything, and specific use cases require evaluation (shameless plug: use tools like W&B Weave for this!).I'll add my own, I use all the models every week to help me draft posts, and o3 was absolute crap about matching my tone. % of what's written above it was able to mimic. Gemini remains undefeated for me and this task.Though, Overall, o3 and o4-mini feel like a paradigm shift towards more autonomous, capable AI assistants. The agentic future feels a whole lot closer.OpenAI Launches GPT-4.1 Family: 1 Million Tokens & Killing 4.5! (Our Coverage, Prompting guide)Before the o3 shockwave, Monday brought its own major AI update: the GPT-4.1 family. This was the API-focused release, delivering massive upgrades for developers.The Headline: One Million Token Context Window! 🤯 Yes, you read that right. All three new models – GPT-4.1 (flagship), GPT-4.1 mini (cheaper/faster), and GPT-4.1 nano (ultra-cheap/fast) – can handle up to 1 million tokens. This is a monumental leap, enabling use cases that were previously impossible or required complex chunking strategies.Key Details:Goodbye GPT-4.5! In a surprising twist, OpenAI announced they are deprecating the recently introduced (and massive) GPT-4.5 model within 90 days in the API. Why? Because GPT-4.1 actually outperforms it on key benchmarks like SW-Bench, Aider Polyglot, and the new long-context MRCR eval, while being far cheaper to run. It addresses the confusion many had: why was 4.5 seemingly worse than 4.1? It seems 4.5 was a scaling experiment, but 4.1 represents a more optimized, better-trained checkpoint on superior data. RIP 4.5, we hardly knew ye (in the API).The Prompt Sandwich Surprise! 🥪: This was wild. Following OpenAI's new prompting guide, I tested the "sandwich" technique (instructions -> context -> instructions again) on my hard reasoning eval using W&B Weave.For GPT-4.1, it made no difference (still got 48%). But for GPT-4.1 mini, the score jumped from 31% to 49% – essentially matching the full 4.1 model just by repeating the prompt! That's a crazy performance boost for a simple trick. Even nano saw a slight bump. Lesson: Evaluate prompt techniques! Don't assume they won't work.Million-Token Recall Confirmed: Using Needle-in-Haystack and their newly open-sourced MRCR benchmark (Multi-round Co-reference Resolution – more in Open Source), OpenAI showed near-perfect recall across the entire 1 million token window for all three models, even nano! This isn't just a theoretical limit; the recall seems robust.Multimodal Gains: Impressively, 4.1 mini hit 72% on Video-MME, pushing SOTA for long-video Q&A in a mid-tier model by analyzing frame sequences. 4.1 mini seems to be the absolute powerhouse of this release cycle, it nearly matches the intelligence of the previous 4o, while being significantly cheaper and much much faster with 1M context window! Windsurf (and Cursor) immediately made the 4.1 family available, offering a free week for users to test them out (likely to gather feedback and maybe influenced by certain acquisition rumors 😉). Devs reported them feeling snappier and less verbose than previous models.Who Should Use Which OpenAI API?My initial take:* For complex reasoning, agentic tasks, or just general chat: Use o3 (if you need the best) or o4-mini (for amazing value/speed).* For API development, especially coding or long-context tasks: Evaluate the GPT-4.1 family. Start with 4.1 mini – it's likely the sweet spot for performance/cost, especially with smart prompting. Use 4.1 if mini isn't quite cutting it. Use nano for simple, high-volume tasks like translation or basic classification.The naming is still confusing (thanks Nisten for highlighting the UI nightmare!), but the capability boost across the board is undeniable.Hold the Phone! 🚨 Google Fires Back with Gemini 2.5 Flash in Breaking NewsJust when we thought the week couldn't get crazier, Google, likely reacting to OpenAI's rapid-fire launches, just dropped Gemini 2.5 Flash into preview via the Gemini API (in AI Studio and Vertex AI). This feels like Google's direct answer, aiming to blend reasoning capabilities with speed and cost-effectiveness.The Big Twist: Controllable Thinking Budgets!Instead of separate models like OpenAI, Gemini 2.5 Flash tries to do both reasoning and speed/cost efficiency in one model. The killer feature? Developers can set a "thinking budget" (0 to 24,576 tokens) per API call to control the trade-off:* Low/Zero Budget: Prioritizes speed and low cost (very cheap: $0.15 input / $0.60 output per 1M tokens), great for simpler tasks.* Higher Budget: Allows the model multi-step reasoning "thinking" for better accuracy on complex tasks, at a higher cost ($3.50 output per 1M tokens, including reasoning tokens).This gives granular control over the cost/quality balance within the same model.Performance & Specs:Google claims strong performance, ranking just behind Gemini 2.5 Pro on Hard Prompts in ChatBot Arena and showing competitiveness against o4-mini and Sonnet 3.7 in their benchmarks, especially given the flexible pricing.Key specs are right up there with the competition:* Multimodal Input: Text, Images, Video, Audio* Context Window: 1 million tokens (matching GPT-4.1!)* Knowledge Cutoff: January 2025How to Control Thinking:Simply set the thinking_budget parameter in your API call (Python/JS examples available in their docs). If unspecified, the model decides automatically.My Take: This is a smart play by Google. The controllable thinking budget is a unique and potentially powerful feature for optimizing across different use cases without juggling multiple models. With 1M context and competitive pricing, Gemini 2.5 Flash is immediately a major contender in the ongoing AI arms race. Definitely one to evaluate! Find more in the developer docs and Gemini Cookbook.Open Source: LLMs, Tools & moreOpenAI open sources MRCR eval and Codex (Mrcr HF, Codex Github)Let's face it, this isn't the open source OpenAI coverage I was hoping for, Sam promised us an open source model, and they are about to drop this, I'd assume close to Google IO (May 20th) to steal thunder. But OpenAI did make OpenSource waves this week in addition to the above huge stories. MRCR is a way to evaluate long context complex tasks, and they have taken this Gemini research and open sourced a dataset for this eval. 👏 But also, they have dropped the Codex CLI tool, which is a coding partner using o4-mini and o3 and made that tool open source as well (Unlike anthropic with Claude Code), which in turn saw 86+ Pull Requests approved within the first 24 hours! The best part about this CLI, is that it's hardened security, using Apple Seatbelt which limits it execution to the current directory + temp files (on a mac at least) Other Open Source UpdatesWhile OpenAI's contributions were notable, it wasn't the only action this week:* Microsoft's BitNet v1.5 (HF): Microsoft quietly dropped updates to BitNet, continuing their exploration into ultra-low-bit (ternary) models for efficiency. As Nisten pointed out on the show though, keep in mind these still use some higher-precision layers, so they aren't purely 1.5-bit in practice just yet. Important research nonetheless!* INTELLECT-2 Distributed RL (Blog, X): Prime Intellect did something wild – training INTELLECT-2, a 32B model, using globally distributed, permissionless reinforcement learning. Basically, anyone with a GPU could potentially contribute. Fascinating glimpse into decentralized training!* Z.ai (Formerly ChatGLM) & GLM-4 Family (X, HF, GitHub): The team behind ChatGLM rebranded to Z.ai and released their GLM-4 family (up to 32B parameters) under the very permissive MIT license. They're claiming performance competitive with much larger models like Qwen 72B, which is fantastic news for commercially usable open source!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.This Week's Buzz: Playground Updates & A Deep Dive into A2AOn the Weights & Biases front, it's all about enabling developers to navigate this new model landscape.Weave Playground Supports GPT-4.1 and o3/o4-mini (X)With all these new models dropping, how do you actually choose which one is best for your application? You need to evaluate! Our W&B Weave Playground now has full support for the new GPT-4.1 family and the o3/o4-mini models.If you're using Weave to monitor your LLM apps in production, you can easily grab a trace of a real user interaction, open it in the Playground, and instantly retry that exact same call (with all its context and history) using any of the new models side-by-side. It’s the fastest way to see how o3 compares to 4.1-mini or how Claude 3.7 stacks up against o4-mini on your specific data. Essential for making informed decisions in this rapidly changing environment.Deep Dive: Understanding Google's A2A Protocol with Todd SegalThis was a highlight of the show for me. We were joined by Todd Segal, a Principal Software Engineer at Google working directly on the new Agent-to-Agent (A2A) protocol. There was some confusion initially about how A2A relates to the increasingly popular Model Context Protocol (MCP), so getting Todd's perspective was invaluable. W&B is a proud launch partner for the A2A protocol!Key Takeaways from our Chat:* A2A vs. MCP: Complementary, Not Competitive: Todd was clear: Google sees these as solving different problems. MCP is for Agents talking to Tools (structured, deterministic capabilities). A2A is for Agents talking to other Agents (unstructured, stateful, unpredictable, evolving interactions). Think of MCP like calling an API, and A2A like delegating a complex task to another expert service.* The Need for A2A: It emerged from the need for specialized, domain-expert agents (built internally or by partners like Salesforce) to collaborate on complex, long-running tasks (e.g., booking a multi-vendor trip, coordinating an enterprise workflow) where simple tool calls aren't enough. Google's Agent Space product heavily utilizes A2A internally.* Capability Discovery & Registries: A core concept is agents advertising their capabilities via an "agent card" (like a business card or resume). Todd envisions a future with multiple registries (public, private, enterprise-specific) where agents can discover other agents best suited for a task. This registry system is on the roadmap.* Async & Long-Running Tasks: A2A is designed for tasks that might take minutes, hours, or even days. It uses a central "Task" abstraction which is stateful. Agents communicate updates (status changes, generated artifacts, requests for more info) related to that task.* Push Notifications: For very long tasks, A2A supports a push notification mechanism. The client agent provides a secure callback URL, and the server agent can push updates (state changes, new artifacts) even if the primary connection is down. This avoids maintaining costly long-lived connections.* Multimodal Communication: The protocol supports negotiation of modalities beyond text, including rendering content within iframes (for branded experiences) or exchanging video/audio streams. Essential for future rich interactions.* Security & Auth: A2A deliberately doesn't reinvent the wheel. It relies on standard HTTP headers to carry authentication (OAuth tokens, internal enterprise credentials). Identity/auth handshakes happen "out of band" using existing protocols (OAuth, OIDC, etc.), and the resulting credentials are passed with A2A requests. Your user identity flows through standard mechanisms.* Observability: Todd confirmed OpenTelemetry (OTel) support is planned for the SDKs. Treating agents like standard microservices means leveraging existing observability tools (like W&B Weave!) is crucial for tracing and debugging multi-agent workflows.* Open Governance: While currently in a Google repo, the plan is to move A2A to a neutral foundation (like Linux Foundation) with a fully open governance model. They want this to be a true industry standard.* Getting Started: Check out the GitHub repo (github.com/google/A2A), participate in discussions, file issues, and send PRs!My take: A2A feels like a necessary piece of infrastructure for the next phase of AI agents, enabling complex, coordinated actions across different systems and vendors. While MCP handles the "how" of using tools, A2A handles the "who" and "what" of inter-agent delegation. Exciting times ahead! Big thanks to Todd for shedding light on this.Vision & Video: Veo-2 Arrives, Kling Gets SlickerThe visual AI space keeps advancing rapidly.Veo-2 Video Generation Hits GA in Vertex AI & Gemini App (Blog, Try It)Google's answer to Sora and Kling, Veo-2, is now Generally Available (GA) for all Google Cloud customers via Vertex AI. You can also access it in the Gemini app.Veo-2 produces stunningly realistic and coherent video, making it a top contender alongside OpenAI's Sora and Kling. Having it easily accessible in Vertex AI is a big plus for developers on Google Cloud.I've tried and keep tyring all of them, VEO2 is an absolute beast in realism. Kling 2.0 Creative Suite: A One-Stop Shop for Video AI? (X, Blog)Kuaishou's Kling model also got a major upgrade, evolving into a full Kling 2.0 Creative Suite.Anecdote: I actually stayed up quite late one night trying to piece together info from a Chinese live stream about this release! The dedication is real, folks. 😂What's New:* Kling 2.0 Master: The core video model, promising better motion, physics, and facial consistency (still 5s clips for now, but 30s/4K planned).* Kolors 2.0: An integrated image generation and restyling model (think Midjourney-style filters).* MVL (Multimodal Visual Language) Prompting: This is killer! You can now inline images directly within your text prompt for precise control (e.g., "Swap the hoodie in @video1 with the style of @image2"). This offers granular control artists have been craving.* Multi-Elements Editor: A timeline-based editor to stitch clips, add lip-sync, sound effects (including generated ones like "car horn"), and music.* Global Access: No more Chinese phone number requirement! Available worldwide at klingai.com.* Official API via FAL: Developers can now integrate Kling 2.0 via our friends at ⚡ FAL Generative Media Cloud.Kling is clearly aiming to be a holistic creative platform, reducing the need to jump between 17 different AI tools for image gen, video gen, editing, and sound. The MVL prompting is particularly innovative. Very impressive package.Voice & Audio: Talking to Dolphins? 🐬DolphinGemma: Google AI Listens to Flipper (Blog)In perhaps the most delightful news of the week, Google, in collaboration with Georgia Tech and the Wild Dolphin Project, announced DolphinGemma. It's a ~400M parameter audio model based on the Gemma architecture (using SoundStream for audio tokenization) trained specifically on decades of recorded dolphin clicks, whistles, and pulses.The goal? To decipher the potential syntax and structure within dolphin communication and eventually enable rudimentary two-way interaction using underwater communication devices. It runs on a Pixel phone for field deployment.This is just awesome. Using AI not just for human tasks but to potentially bridge the communication gap with other intelligent species is genuinely inspiring. We joked on the show about doing a segment of just dolphin noises – maybe next time if DolphinGemma gets an API! 🤣AI Art & Diffusion & 3D: Seedream Challenges the ChampsSeedream 3.0: ByteDance's Bilingual Image Powerhouse (Tech post, arXiv, AIbase news)ByteDance wasn't just busy with video; their Seed team announced Seedream 3.0, a powerful bilingual text-to-image model.Highlights:* Generates native 2048x2048 images.* Fast inference (~3 seconds for 1Kx1K on an A100).* Excellent bilingual (Chinese/English) text rendering, even small fonts.* Uses Scaled-ROPE-v2 for better high-resolution generation without artifacts.* Claims to outperform SDXL-Turbo and Qwen-Image on fidelity and prompt adherence benchmarks.* Available via Python SDK and REST API within their Doubao Studio and coming soon to dreamina.com Phew! We made it. What an absolute avalanche of news. OpenAI truly dominated with the back-to-back launches of the hyper-capable o3/o4-mini and the massively scaled GPT-4.1 family. Google countered strongly with the versatile Gemini 2.5 Flash, key GA releases like Veo-2, and the strategically important A2A protocol. The agent ecosystem took huge leaps forward with both A2A and broader MCP adoption. And we saw continued innovation in multimodal embeddings, video generation, and even niche areas like bioacoustics and low-bit models.If you feel like you missed anything (entirely possible this week!), the TL;DR and links below should help. Please subscribe if you haven't already, and share this with a friend if you found it useful – it's the best way to support the show!I have a feeling next week won't be any slower. Follow us on X/Twitter for breaking news between shows!Thanks for tuning in, keep building, keep learning, and I'll see you next Thursday!AlexTL;DR and Show NotesEverything we covered today in bite-sized pieces with links!* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed)* Todd Segal - Principal Software Engineer @ Google - Working on A2A Protocol* Big CO LLMs + APIs* 👑 OpenAI launches o3 and o4-mini in chatGPT & API (Blog, Our Coverage, o3 and o4-mini announcement)* OpenAI launches GPT 4.1, 4.1-mini and 4.1-nano in API (Our Coverage, Prompting guide)* 🚨 Google launches Gemini 2.5 Flash with controllable thinking budgets (Blog Post - Placeholder Link, API Docs)* Mistral classifiers Factory* Claude does research + workspace integration (Blog)* Cohere Embed‑4 — Multimodal embeddings for enterprise search (Blog, Docs Changelog, X)* Open Source LLMs* OpenAI open sources MRCR Long‑Context Benchmark (Hugging Face)* Microsoft BitNet v1.5 (HF)* INTELLECT‑2 — Prime Intellect’s 32B “globally‑distributed RL” experiment (Blog, X)* Z.ai (previously chatGLM) + GLM‑4‑0414 open‑source family (X, HF Collection, GitHub)* This weeks Buzz + MCP/A2A* Weave playground support for GPT 4.1 and o3/o4-mini models (X)* Chat with Todd Segal - A2A Protocol (GitHub Spec)* Vision & Video* Veo‑2 Video Generation in GA, Gemini App (Dev Blog)* Kling 2.0 Creative Suite (X, Blog)* ByteDance public Seaweed-7B, a video generation foundation model (seaweed.video)* Voice & Audio* DolphinGemma — Google AI tackles dolphin communication (Blog)* AI Art & Diffusion & 3D* Seedream 3.0 bilingual image diffusion – ByteDance (Tech post, arXiv, AIbase news)* Tools* OpenAI debuts Codex CLI, an open source coding tool for terminals (Github)* Use o3 with Windsurf (which OpenAI is rumored to buy at $3B) via the mac app integration + write back + multiple files 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, celebrating an absolutely crazy (to me) milestone, of #100 episodes of ThursdAI 👏 100 episodes in a year and a half (as I started publishing much later than I started going live, and the first episode was embarrassing), 100 episodes that documented INCREDIBLE AI progress, we mention on the show today, we used to be excited by context windows jumping from 4K to 16K! I want to extend a huge thank you to every one of you, who subscribes, listens to the show on podcasts, joins the live recording (we regularly get over 1K live viewers across platforms), shares with friends and highest thank you for the paid supporters! 🫶 Sharing the AI news progress with you, energizes me to keep going, despite the absolute avalanche of news every week.And what a perfect way to celebrate the 100th episode, on a week that Meta dropped Llama 4, sending the open-source world into a frenzy (and a bit of chaos). Google unleashed a firehose of announcements at Google Next. The agent ecosystem got a massive boost with MCP and A2A developments. And we had fantastic guests join us – Michael Lou diving deep into the impressive DeepCoder-14B, and Liad Yosef & Ido Salomon sharing their wild ride creating the viral GitMCP tool.I really loved today's show, and I encourage those of you who only read, to give this a watch/listen, and those of you who only listen, enjoy the recorded version (though longer and less edited!) Now let's dive in, there's a LOT to talk about (TL;DR and show notes as always, at the end of the newsletter) Open Source AI & LLMs: Llama 4 Takes Center Stage (Amidst Some Drama)Meta drops Llama 4 - Scout 109B/17BA & Maverick 400B/17BA (Blog, HF, Try It)This was by far the biggest news of this last week, and it dropped... on a Saturday? (I was on the mountain ⛷️! What are you doing Zuck) Meta dropped the long awaited LLama-4 models, huge ones this time* Llama 4 Scout: 17B active parameters out of ~109B total (16 experts).* Llama 4 Maverick: 17B active parameters out of a whopping ~400B total (128 experts).* Unreleased: Behemoth - 288B active with 2 Trillion total parameters chonker!* Both base and instruct finetuned models were releasedThese new models are all Multimodal, Multilingual MoE (mixture of experts) architecture, and were trained with FP8, for significantly more tokens (around 30 Trillion Tokens!) with interleaved attention (iRoPE), and a refined SFT > RL > DPO post-training pipeline.The biggest highlight is the stated context windows, 10M for Scout and 1M for Maverick, which is insane (and honestly, I haven't yet seen a provider that is even remotely able to support anything of this length, nor do I have the tokens to verify it) The messy release - Big Oof from Big ZuckNot only did Meta release on a Saturday, messing up people's weekends, Meta apparently announced a high LM arena score, but the model they provided to LMArena was... not the model they released!?It caused LMArena to release the 2000 chats dataset, and truly, some examples are quite damning and show just how unreliable LMArena can be as vibe eval. Additionally, during the next days, folks noticed discrepancies between the stated eval scores Meta released, and the ability to evaluate them independently, including our own Wolfram, who noticed that a quantized version of Scout, performed better on his laptop while HIGHLY quantized (read: reduced precision) than it was performing on the Together API inference endpoint!? We've chatted on the show that this may be due to some VLLM issues, and speculated about other potential reasons for this. Worth noting the official response from Ahmad Al-Dahle, head of LLama at Meta, who mentioned stability issues between providers and absolutely denied any training on any benchmarksToo big for its own good (and us?)One of the main criticism the OSS community had about these releases, is that for many of us, the reason for celebrating Open Source AI, is the ability to run models without network, privately on our own devices. Llama 3 was released in 8-70B distilled versions and that was incredible for us local AI enthusiasts! These models, despite being "only" 17B active params, are huge and way to big to run on most local hardware, and so the question then is, if we're getting a model that HAS to run on a service, why not use Gemini 2.5 that's MUCH better and faster and cheaper than LLama? Why didn't Meta release those sizes? Was it due to an inability to beat Qwen/DeepSeek enough? 🤔 My TakeDespite the absolutely chaotic rollout, this is still a monumental effort from Meta. They spent millions on compute and salaries to give this to the community. Yes, no papers yet, the LM Arena thing was weird, and the inference wasn't ready. But Meta is standing up for Western open-source in a big way. We have to celebrate the core contribution while demanding better rollout practices next time. As Wolfram rightly said, the real test will be the fine-tunes and distillations the community builds on these base models. Releasing the base weights is crucial for that. Let's see if the community can tame this beast once the inference dust settles. Shout out to Ahmed Al-Dahle and the whole Llama team at Meta – incredible work, messy launch, but thank you for pushing open source forward. 🎉Together AI & Agentica (Berkley) finetuned DeepCoder-14B with reasoning (X, Blog)Amidst the Llama noise, we got another stellar open-source release! We were thrilled to have Michael Lou from Agentica/UC Berkeley join us to talk about DeepCoder-14B-Preview which beats DeepSeek R1 and even o3-mini on several coding benchmarks. Using distributed Reinforcement Learning (RL), it achieves 60.6% Pass@1 accuracy on LiveCodeBench, matching the performance of models like o3-mini-2025-01-31 (Low) despite its smaller size.The stated purpose of the project is to democratize RL and they have open sourced the model (HF), the dataset (HF), the Weights & Biases logs and even the eval logs! Shout out to Michael, Sijun and Alpay and the rest of the team who worked on this awesome model! NVIDIA Nemotron ULTRA is finally here, 253B pruned Llama 3-405B (HF)While Llama 4 was wrapped in mystery, NVIDIA dropped their pruned and distilled finetune of the previous Llama chonker 405B model, turning at just about half the parameters. And they were able to include the LLama-4 benchmarks in their release, showing that the older Llama, finetuned can absolutely beat the new ones at AIME, GPQA and more. As a reminder, we covered the previous 2 NEMOTRONS and they are a combined reasoning and non reasoning models, so the jump is not that surprising, and it does seem like a bit of eval cherry picking happened here. Nemotron Ultra supports 128K context and fits on a single 8xH100 node for inference. Built on open Llama models and trained on vetted + synthetic data, it's commercially viable. Shout out to NVIDIA for releasing this, and especially for pushing open reasoning datasets which Nisten rightly praised as having long-term value beyond the models themselves.More Open Source Goodness: Jina, DeepCogito, KimiThe open-source train didn't stop there:* Jina Reranker M0: Our friends at Jina released a state-of-the-art multimodal reranker model. If you're doing RAG with images and text, this looks super useful for improving retrieval quality across languages and modalities (Blog, HF)* DeepCogito: A new company emerged releasing a suite of Llama fine-tunes (3B up to 70B planned, with larger ones coming) trained using a technique they call Iterated Distillation and Amplification (IDA). They claim their 70B model beats DeepSeek V2 70B on some benchmarks . Definitely one to watch. (Blog, HF)* Kimi-VL & Kimi-VL-Thinking: MoonShot, who sometimes get lost in the noise, released incredibly impressive Kimi Vision Language Models (VLMs). These are MoE models with only ~3 Billion active parameters, yet they're showing results on par with or even beating models 10x larger (like Gemma 2 27B) on benchmarks like MathVision and ScreenSpot. They handle high-res images, support 128k context, and crucially, include a reasoning VLM variant. Plus, they're MIT licensed! Nisten's been following Kimi and thinks they're legit, just waiting for easier ways to run them locally. Definitely keep an eye on Kimi. (HF)This Week's Buzz from Weights & Biases - Observable Tools & A2A!This week was personally very exciting on the W&B front, as I spearheaded and launched initiatives directly related to the MCP and A2A news!W&B launches the observable.tools initiative!As MCP takes off, one challenge becomes clear: observability. When your agent calls an external MCP tool, that part of the execution chain becomes a black box. You lose the end-to-end visibility crucial for debugging and evaluation.That's why I'm thrilled that we launched Observable Tools (Website) – an initiative championing full-stack agent observability, specifically within the MCP ecosystem. Our vision is to enable developers using tools like W&B Weave to see inside those MCP tool calls, getting a complete trace of every step.The core of this is Proposal RFC 269 on the official MCP GitHub spec, which I authored! (My first RFC, quite the learning experience!). It details how to integrate OpenTelemetry tracing directly into the MCP protocol, allowing tools to securely report detailed execution spans back to the calling client (agent). We went deep on the spec, outlining transmission mechanisms, schemas, and rationale.My ask to you, the ThursdAI community: Please check out observable.tools, read the manifesto, watch the fun video we made, and most importantly, go to the RFC 269 proposal (shortcut: wandb.me/mcp-spec). Read it, comment, give feedback, and upvote if you agree! We need community support to make this impossible for the MCP maintainers to ignore. Let's make observability a first-class citizen in the MCP world! We also invite our friends from across the LLM observability landscape (LangSmith, Braintrust, Arize, Galileo, etc.) to join the discussion and collaborate.W&B is a Launch Partner for Google's A2AAs mentioned earlier, we're also excited to be a launch partner for Google's new Agent2Agent (A2A) protocol. We believe standardized communication between agents is the next critical step, and we'll be supporting A2A alongside MCP in our tools. Exciting times for agent infrastructure! I've invited Google folks to next week to discuss the protocol in depth! 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.Big Company LLMs + APIs: Google's Onslaught & OpenAI's Memory UpgradeWhile open source had a wild week, the big players weren't sleeping. Google especially came out swinging at Google Next.Google announces TONS of new things at Next 🙌 (Blog)Google I/O felt like a preview, Google Next felt like the delivery truck backing up and dumping everything. Here's the whirlwind tour:* Gemini 2.5 Flash API: The faster, cheaper Gemini 2.5 model is coming soon to Vertex AI. (Still waiting on that general API access!).* Veo 2 Editing: Their top-tier video model (competing with Sora, Kling) gets editing capabilities. Very cool.* Imagen 3 Updates: Their image model gets improvements, including inpainting.* Lyria: Text-to-music model moves into preview.* TPU v7 (Ironwood): New TPU generation coming soon. As Nisten noted, Google's infrastructure uptime is consistently amazing, which could be a winning factor regardless of model SOTA status.* Chirp 3 HD Voices + Voice Cloning: This one raised eyebrows. The notes mentioned HD voices and voice cloning. Cloning is a touchy subject the big players usually avoid publicly (copyright, deepfakes). Still digging for confirmation/details on this – if Google is really offering public voice cloning, that's huge. Let me know if you find a link!* Deep Research gets Gemini 2.5 Pro: The experimental deep research feature in Gemini (their answer to OpenAI's research agent) now uses the powerful 2.5 Pro model. Google released comparison stats showing users strongly prefer it (70%) over OpenAI's offering, citing better instruction following and comprehensiveness. I haven't fully tested the 2.5 version yet, but the free tier access is amazing. and just look at those differences in preference compared to OAI Deep Research! Firebase Studio (firebase.studio): Remember Project IDX? It's been rebranded and launched as Firebase Studio. This is Google's answer to the wave of "vibe coding" web builders like Lovable, Bolt and a few more. It's a full-stack, cloud-based GenAI environment for building, testing, and deploying apps, integrated with Firebase and likely Gemini. Looks promising!Google Embraces MCP & Launches A2A Protocol!Two massive protocol announcements from Google that signal the maturation of the AI agent ecosystem:* Official MCP Support! (X)This is huge. Following Microsoft and AWS, Google (via both Sundar Pichai and Demis Hassabis) announced official support for Anthropic's Model Context Protocol (MCP) in Gemini models and SDKs. MCP is rapidly becoming the standard for how agents discover and use tools securely and efficiently. With Google onboard, there's basically universal major vendor support. MCP is here to stay.* Agent2Agent (A2A) Protocol (Blog , Spec, W&B Blog)Google also launched a new open standard, A2A, designed for interoperability between different AI agents. Think of agents built by different vendors (Salesforce, ServiceNow, etc.) needing to talk to each other securely to coordinate complex workflows across enterprise systems. Built on web standards (HTTP, SSE, JSON-RPC), it handles discovery, task management (long-running!), and modality negotiation. Importantly, Google positions A2A as complementary to MCP, not competitive. MCP is how an agent uses a tool, A2A is how an agent talks to another agent. Weights & Biases is proud to be one of the 50+ launch partners working with Google on this! We'll do a deeper dive soon, but this + MCP feels like the foundation for a truly interconnected agent future.Cloudflare - new Agents SDK (agents.cloudflare.com)Speaking of agents, Cloudflare launched their new Agents SDK (npm i agents). Built on their serverless infrastructure (Workers, Durable Objects), it offers a platform for building stateful, autonomous AI agents with a compelling pricing model (pay for CPU time, not wall time). This ties into the GitMCP story later – Cloudflare is betting big on the edge agent ecosystem.Other Big Co News:* Anthropic MAX: A new $200/month tier for Claude, offering higher usage quotas but no new models. Meh.* Grok 3 API: Elon's xAI finally launched the API tier for Grok 3 (plus Fast and Mini variants). Now you can test its capabilities yourself. We're still waiting for the promised Open Source Grok-2🚨 BREAKING NEWS 🚨 OpenAI Upgrades MemoryRight on cue during the show, OpenAI dropped a feature update! Sam Altman hyped something coming, and while it wasn't the o3/o4-mini models (those are coming next), it's a significant enhancement to ChatGPT Memory.Previously, Memory tried to selectively save key facts. Now, when enabled, it can reference ALL of your past chats to personalize responses. Preferences, interests, past projects – it can potentially draw on everything. OpenAI states there's no storage limit for what it can reference.How? Likely some sophisticated RAG/vector search under the hood, not stuffing everything into context. LDJ mentioned he might have had this rolling out silently for weeks, and while the immediate difference wasn't huge, the potential is massive as models get better at utilizing this vast personal context.The immediate reaction? Excitement mixed with a bit of caution. As Wolfram pointed out, do I really want it remembering every single chat? Configurable memory (flagging chats for inclusion/exclusion) seems like a necessary follow-up. Thanks for the feature request, Wolfram! (And yes, Europe might not get this right away anyway...). This could finally stop ChatGPT from asking me basic questions it should know from our history!Prompt suggestion: Ask the new chatGPT with memory, a think that you asked it that you likely forgot.Just don't asked it what was the most boring thing you asked it, I got cooked I'm still feeling raw 😂 Vision & Video: Kimi Drops Tiny But Mighty VLMsThe most impressive long form AI video paper dropped, showing that it's possible to create 1 minute long video, with incredible character and scene consistencyThis paper introduces TTT layers (Test Time Training) to a pre-trained transformer, allowing it to one shot generate these incredibly consistent long scenes. Can't wait to see how the future of AI video evolves with this progress! AI Art & Diffusion & 3D: HiDream Takes the Open CrownHiDream-I1-Dev 17B MIT license new leading open weights image gen! (HF)Just when we thought the image gen space was settling, HiDream, a Chinese company, open-sourced their HiDream-I1 family under MIT license! This 17B parameter model comes in Dev, Full, and Fast variants.The exciting part? Based on early benchmarks (like Artificial Analysis Image Arena), HiDream-I1-Dev surpasses Flux 1.1 [Pro], Recraft V3, Reve and Imagen 3 while being open source! It boasts outstanding prompt following and text rendering capabilities.HiDream's API is coming soon too and I really hope it's finetunable! Tools: GitMCP - The Little Repo Tool That CouldGitMCP - turn any github repo into an MCP server (website)We wrapped up the show with a fantastic story from the community. We had Liad Yosef (Shopify) and Ido Salomon (Palo Alto Networks) join us to talk about GitMCP.It started with a simple problem: a 3MB LLM.txt file (a format proposed by Jeremy Howard for repo documentation) too large for context windows. Liad and Ido, working nights and weekends, built an MCP server that could ingest any GitHub repo (prioritizing LLM.txt if present, falling back to Readmes/code comments) and expose its documentation via MCP tools (semantic search, fetching).This means any MCP-compatible client (like Cursor, potentially future ChatGPT/Gemini) can instantly query the documentation of any public GitHub repo just by pointing to the GitMCP URL for that repo (e.g., https://gitmcp.io/user/repo). As Yam Peleg pointed out during the show, the genius here is dynamically generating a customized tool specifically for that repo, making it incredibly easy for the LLM to use.Then, the story got crazy. They launched, went viral, almost melted their initial Vercel serverless setup due to traffic and SSE connection costs (100$+ per hour!). DMs flew back and forth with Vercel's CEO, then Cloudflare's CTO swooped in offering to sponsor hosting on Cloudflare's unreleased Agents platform if they migrated immediately. A frantic weekend of coding ensued, culminating in a nail-biting domain switch and a temporary outage before getting everything stable on Cloudflare.The project has received massive praise (including from Jeremy Howard himself) and is solving a real pain point for developers wanting to easily ground LLMs in project documentation. Huge congrats to Liad and Ido for the amazing work and the wild ride! Check out gitmcp.io!Wrapping Up Episode 100!Whew! What a show. From the Llama 4 rollercoaster to Google's AI barrage, the rise of agent standards like MCP and A2A, groundbreaking open source models, and incredible community stories like GitMCP – this episode truly showed an exemplary week in AI and underlined the reason I do this every week. It's really hard to keep up, and so if I commit to you guys, I stay up to date myself! Hitting 100 episodes feels surreal. It's been an absolute privilege sharing this journey with Wolfram, LDJ, Nisten, Yam, all our guests, and all of you. Seeing the community grow, hitting milestones like 1000 YouTube subscribers today, fuels us to keep going 🎉 The pace isn't slowing down. If anything, it's accelerating. But we'll be right here, every Thursday, trying to make sense of it all, together.If you missed anything, don't worry! Subscribe to the ThursdAI News Substack for the full TL;DR and links below.Thanks again for making 100 episodes possible. Here's to the next 100! 🥂Keep tinkering, keep learning, and I'll see you next week.AlexTL;DR and Show Notes* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed* Michael Luo @michaelzluo - CS PhD @ UC Berkeley; AI & Systems* Liad Yosef (@liadyosef), Ido Salomon (@idosal1) - GitMCP creators* Open Source LLMs * Meta drops LLama 4 (Scout 109B/17BA & Maverick 400B/17BA) - (Blog, HF, Try It)* Together AI and Agentica (UC Berkley) announce DeepCoder-14B (X, Blog)* NVIDIA Nemotron Ultra is here! 253B pruned LLama 3-405B (X, HF)* Jina Reranker M0 - SOTA multimodal reranker model (Blog, HF)* DeepCogito - SOTA models 3-70B - beating DeepSeek 70B - (Blog, HF)* ByteDance new release - Seed-Thinking-v1.5* Big CO LLMs + APIs* Google announces TONS of new things 🙌 (Blog)* Google launches Firebase Studio (website)* Google is announcing official support for MCP (X)* Google announces A2A protocol - agent 2 agent communication (Blog, Spec, W&B Blog)* Cloudflare - new Agents SDK (Website)* Anthropic MAX - $200/mo with more quota* Grok 3 finally launches API tier (API)* OPenAI adds enhanced memory to ChatGPT - can remember all your chats (X)* This weeks Buzz - MCP and A2A* W&B launches the observable.tools initiative & invite people to comment on the MCP RFC* W&B is the launch partner for Google's A2A (Blog)* Vision & Video* Kimi-VL and Kimi-VL-Thinking - A3B vision models (X, HF)* One-Minute Video Generation with Test-Time Training (Blog, Paper)* Voice & Audio* Amazon - Nova Sonic - speech2speech foundational model (Blog)* AI Art & Diffusion & 3D* HiDream-I1-Dev 17B MIT license new leading open weights image gen 0 passes Flux1.1[pro] ! (HF)* Tools* GitMCP - turn any github repo into an MCP server (try it)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. 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

Woo! Welcome back to ThursdAI, show number 99! Can you believe it? We are one show away from hitting the big 100, which is just wild to me. And speaking of milestones, we just crossed 100,000 downloads on Substack alone! [Insert celebratory sound effect here 🎉]. Honestly, knowing so many of you tune in every week genuinely fills me with joy, but also a real commitment to keep bringing you the the high-signal, zero-fluff AI news you count on. Thank you for being part of this amazing community! 🙏And what a week it's been! I started out busy at work, playing with the native image generation in ChatGPT like everyone else (all 130 million of us!), and then I looked at my notes for today… an absolute mountain of updates. Seriously, one of those weeks where open source just exploded, big companies dropped major news, and the vision/video space is producing stuff that's crossing the uncanny valley.We’ve got OpenAI teasing a big open source release (yes, OpenAI might actually be open again!), Gemini 2.5 showing superhuman math skills, Amazon stepping into the agent ring, truly mind-blowing AI character generation from Meta, and a personal update on making the Model Context Protocol (MCP) observable. Plus, we had some fantastic guests join us live!So buckle up, grab your coffee (or whatever gets you through the AI whirlwind), because we have a lot to cover. Let's dive in! (as always, show notes and links in the end)OpenAI Makes Waves: Open Source Tease, Tough Evals & Billions RaisedIt feels like OpenAI was determined to dominate the headlines this week, hitting us from multiple angles.First, the potentially massive news: OpenAI is planning to release a new open source model in the "coming months"! Kevin Weil tweeted that they're working on a "highly capable open language model" and are actively seeking developer feedback through dedicated sessions (sign up here if interested) to "get this right." Word on the street is that this could be a powerful reasoning model. Sam Altman also cheekily added they won't slap on a Llama-style Second, they dropped PaperBench, a brutal new benchmark evaluating an AI's ability to replicate ICML 2024 research papers from scratch (read paper, write code, run experiments, match results - no peeking at original code!). It's incredibly detailed (>8,300 tasks) and even includes meta-evaluation for the LLM judge they built (Nano-Eval framework also open sourced). The kicker? Claude 3.5 Sonnet (New) came out on top with just 21.0% replication score (human PhDs got 41.4%). Props to OpenAI for releasing an eval where they don’t even win. That’s what real benchmarking integrity looks like. You can find the code on GitHub and read the full paper here.Third, the casual 40 Billion Dollars funding round led by SoftBank. Valuing the company at 300 Billion. Yes, Billion with a B. More than Coke, more than Disney. The blog post was hilariously short for such a massive number. They also mentioned500 million weekly ChatGPT usersand the insane onboarding rate (1M users/hr!) thanks to native image generation, especially seeing huge growth in India. The scale is just mind-boggling.Oh, and for fun, try the new grumpy, EMO "Monday" voice in advanced voice mode. It's surprisingly entertaining.Open Source Powerhouses: Nomic & OpenHands Deliver SOTABeyond the OpenAI buzz, the open source community delivered some absolute gems, and we had guests from two key projects join us!Nomic Embed Multimodal: SOTA Embeddings for Visual DocsOur friends at Nomic AI are back with a killer release! We had Zach Nussbaum on the show discussing Nomic Embed Multimodal. These are new 3B & 7B parameter embedding models (available on Hugging Face) built on Alibaba's excellent Qwen2.5-VL. They achieved SOTA on visual document retrieval by cleverly embedding interleaved text-image sequences – perfect for PDFs and complex webpages.Zach highlighted that they chose the Qwen base because high-performing open VLMs under 3B params are still scarce, making it a solid foundation. Importantly, the 7B model comes with an Apache 2.0 license, and they've open sourced weights, code, and data. They offer both a powerful multi-vector version (ColNomic) and a faster single-vector one. Huge congrats to Nomic!OpenHands LM 32B & Agent: Accessible SOTA CodingRemember OpenDevin? It evolved into OpenHands, and the team just dropped their own OpenHands LM 32B! We chatted with co-founder Xingyao "Elle" Wang about this impressive Qwen 2.5 finetune (MIT licensed, on Hugging Face).It hits a remarkable 37.2% on SWE-Bench Verified (a coding benchmark measuring real-world repo tasks), competing with much larger models. Elle stressed they didn't just chase code completion scores; they focused on tuning for agentic capabilities – tool use, planning, self-correction – using trajectories from their contamination-free Switch Execution dataset. This focus seems to be paying off, as the OpenHands agent also snagged the #2 spot on the brand new Live SWE-Bench leaderboard! Plus, the 32B model runs locally on a single 3090, making this power accessible. You can also try their managed OpenHands Cloud ($50 free credit). Amazing progress from this team!Frontiers: Diffusion LMs & Superhuman MathTwo other developments pushed the boundaries this week:Dream 7B: A Diffusion Language Model Challenger?This one's fascinating conceptually. Researchers unveiled Dream 7B, a language model based on diffusion, not auto-regression. The benchmarks they shared show it competing strongly with top 7-8B models, and absolutely crushing tasks like Sudoku (81% vs model weights aren't out yet, so we can't verify or play with it. Still, one to watch!Gemini 2.5 Obliterates Olympiad Math (24.4% on USAMO!)We already knew Gemini 2.5 was good, but wow. New results dropped showing its performance on the USA Math Olympiad (USAMO) – problems so hard most top models score under 5%. Gemini 2.5 Pro scored an incredible 24.4%!The gap between it and everything else is massive, highlighting the power of its reasoning and thinking capabilities (which you can inspect via its traces!). Having used it for complex tasks myself (like wrestling with tax forms!), I can attest to its depth. It's free in the Gemini app – go try it!Agents, Compute & Making MCP ObservableAmazon's Nova Act Agent & The Need for AccessAmazon entered the agent chat with Nova Act, designed for web browser actions. They claim it beats Claude 3.5 and OpenAI's QA model on some benchmarks, possibly leveraging acquired Adept talent. But... it's only available via an SDK with a request form. As Yam rightly pointed out on the show, these agent claims mean little until we can actually use them in the real world!CoreWeave + NVIDIA = Insane SpeedsHardware keeps accelerating. CoreWeave announced hitting 800 Tokens/sec on Llama 3.1 405B using NVIDIA's new GB200 Blackwell chips, and 33,000 T/s on Llama 2 70B with H200s. Inference is getting fast.This Week's Buzz: Let's Make MCP Observable!Okay, my personal mission this week builds on the growing Model Context Protocol (MCP) momentum. MCP is potentially the "HTTP for agents," enabling tool interoperability. But as tool use moves external, we lose visibility, making debugging and security harder.That's why I'm launching the Observable Tools initiative. The goal: integrate observability into the MCP standard itself. Right now, that link redirects to a GitHub discussion where I've proposed using the OpenTelemetry (OTel) standard to add tracing to MCP interactions. This would give developers clear visibility into tool usage, regardless of their observability platform.I need your help! Please check out the proposal, join the discussion, and show your support with a 👍 or 🚀 on GitHub. We need the community voice to make this happen! (And yes, my viral tweet showed there's huge demand for usable MCP clients too – more on that soon!).Vision & Video: Entering the Uncanny ValleyThis space is moving at lightning speed.Runway Gen-4 was announced, pushing for better consistency in AI video. Here's a few example videos showing incredible character and world consistency:ByteDance's impressive OmniHuman (single image to talking avatar) is now publicly usable via Dreamina website. For people it's really good, but for animated style images, Hedra Labs feels actually better (and much much faster)Meta's MoCHA is mind-blowing. We had researcher Cong Wei explain how it generates movie-grade, full-body, expressive talking characters directly from speech and text (no reference image needed!). Using Diffusion Transformers and clever attention mechanisms, the realism is startling, handling lip-sync, gestures, emotions, and even multi-character dialogue. Check the project page videos – some are truly uncanny. Just look at this one!Voice Highlight: Hailuo Speech-02While Gladia launched their Solaria STT, the standout for me was Hailuo's Speech-02 TTS API. The emotional control and voice cloning quality are, in my opinion, potentially SOTA right now, offering incredibly nuanced and realistic synthetic voices.Tool Update & Breaking News!* Google's NotebookLM now discovers related sources automatically.* BREAKING NEWS (Caught end of show): Devin 2.0 is out! Cognition Labs launched their AI software engineer V2 with a new IDE experience and, crucially, a $20/month starting price. Much more accessible to try!Phew! What a week. From OpenAI's big moves to Gemini's math prowess, stunning AI actors from Meta, and the push for an observable agent ecosystem – the field is accelerating like crazy.Alright folks, that’s a wrap for show #99! Thank you again for tuning in, for being part of the community, and for keeping us on our toes with your insights and feedback. Special thanks to our guests Zach Nussbaum (Nomic), Xingyao Wang (All Hands AI), and Cong Wei (Meta/MoCHA) for joining us!If you missed any part of the show, or want to grab any of the links, head over to ThursdAI.news. The full recording (video on YouTube, audio on Spotify, Apple Podcasts, etc.) and this blog post with all the notes will be up shortly.The best way to support the show? Share it with a friend or colleague who needs to stay up-to-date on AI, and drop us a 5-star review on your podcast platform! Financial support via Substack is also appreciated but never required.Get ready for Episode 100 next week! Until then, happy tinkering, stay curious, and I'll see you next ThursdAI!Bye bye everyone!TL;DR and Show NotesHost, Guests, and Co-hosts* Host: Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co-Hosts:* LDJ (@ldjconfirmed)* Yam Peleg (@yampeleg)* Guests:* Zach Nussbaum (@zach_nussbaum) - Nomic AI* Xingyao Wang (@xingyaow_) - All Hands AI / OpenHands* Cong Wei (@CongWei1230) - Meta AI / MoChaKey Topics & Links* OpenAI's Big Week:* Teasing highly capable Open Source Reasoner Model (seeking feedback).* Released PaperBench eval (code, paper) & Nano-Eval framework.* Raised $40B at $300B valuation.* New EMO "Monday" voice in ChatGPT.* Open Source Powerhouses:* Nomic Embed Multimodal: SOTA visual doc embeddings (3B & 7B, Apache 2.0 for 7B).* OpenHands LM 32B: SOTA-level coding agent model (Qwen finetune, MIT License, 37.2% SWE-Bench, #2 Live SWE-Bench). Cloud version available.* Frontier Models & Capabilities:* Dream 7B: Promising diffusion LM shows strong benchmark results (esp. Sudoku), but weights not yet released.* Gemini 2.5: Crushes hard USAMO math eval (24.4% vs * Agents & Compute:* Amazon's Nova Act agent announced, claims SOTA but lacks public access (request form).* CoreWeave/NVIDIA: Massive inference speedups (800T/s on Llama 405B with GB200).* This Week's Buzz - MCP:* Observable Tools initiative launched to add observability to MCP.* Proposal using OpenTelemetry posted for community feedback on GitHub - please support!* Huge demand shown for usable MCP clients (viral tweet).* Vision & Video Highlights:* Runway Gen-4 focuses on video consistency.* ByteDance OmniHuman (image-to-avatar) now publicly available via Dreamina (example thread).* Meta's MoCHA: Generates stunningly realistic, movie-grade talking characters from speech+text.* Voice Highlight:* Hailuo Speech-02: Impressive TTS API with excellent emotional control and voice cloning.* Tool Updates:* Windsurf adds deployments to Netlify.* Google NotebookLM adds source discovery.* Breaking News:* Devin 2.0 AI Software Engineer announced, starts at $20/month. 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 👋 Welcome back to ThursdAI! And folks, what an absolutely insane week it's been in the world of AI. Seriously, as I mentioned on the show, we don't often get weeks this packed with game-changing releases.We saw Google emphatically reclaim the #1 LLM spot with Gemini 2.5 Pro (and OpenAI try really hard to hit back with a new ChatGPT), DeepSeek dropped a monster 685B parameter open-source model, Qwen launched a tiny but mighty 7B Omni model that handles voice and video like a champ, and OpenAI finally gave us native image generation in GPT-4o, immediately unleashing a tidal wave of Ghibli-fication across the internet. It was intense, with big players seemingly trying to one-up each other constantly – remember when Sam Altman dropped Advanced Voice Mode right when Google was about to show Astra? This weeks was this, on steroids. We had a fantastic show trying to unpack it all, joined by the brilliant Tulsee Doshi from the Google Gemini team, my Weights & Biases colleague Morgan McQuire talking MCP tools, and the MLX King himself, Prince Canuma. Plus, my awesome co-hosts Wolfram, Nisten, and Yam were there to add their insights. (watch the LIVE recap or keep reading and listen to the audio pod) So, grab your beverage of choice, buckle up, and let's try to make sense of this AI whirlwind! (TL'DR and show notes at the bottom 👇)Big CO LLMs + APIs🔥 Google Reclaims #1 with Gemini 2.5 Pro (Thinking!)Okay, let's start with the big news. Google came out swinging this week, dropping Gemini 2.5 Pro and, based on the benchmarks and our initial impressions, taking back the crown for the best all-around LLM currently available. (Check out the X announcement, the official blog post, and seriously, go try it yourself at ai.dev).We were super lucky to have Tulsee Doshi, who leads the product team for Gemini modeling efforts at Google, join us on the show to give us the inside scoop. Gemini 2.5 Pro Experimental isn't just an incremental update; it's topping benchmarks in complex reasoning, science, math, and coding. As Tulsee explained, this isn't just about tweaking one thing – it's a combination of a significantly enhanced base model and improved post-training techniques, including integrating those "thinking" capabilities (like chain-of-thought) right into the core models.That's why they dropped "thinking" from the official name – it's not a separate mode anymore, it's becoming fundamental to how Gemini operates. Tulsee mentioned their goal is for the main line models to be thinking models, leveraging inference time when needed to get the best answer. This is a huge step towards more capable and reliable AI.The performance gains are staggering across the board. We saw massive jumps on benchmarks like AIME (up nearly 20 points!) and GPQA. But it's not just about the numbers. As Tulsee highlighted, Gemini 2.5 is proving to be incredibly well-rounded, excelling not only on academic benchmarks but also on human preference evaluations like LM Arena (where style control is key). The "vibes" are great, as Wolfram put it. My own testing on reasoning tasks confirms this – the latency is surprisingly low for such a powerful model (around 13 seconds on my hard reasoning questions compared to 45+ for others), and the accuracy is the highest I've seen yet at 66% on that specific challenging set.It also inherits the strengths of previous Gemini models – native multimodality and that massive long context window (up to 1M tokens!). Tulsee emphasized how crucial long context is, allowing the model to reason over entire code repos, large sets of financial documents, or research papers. The performance on long context tasks, like the needle-in-a-haystack test shown on Live Bench, is truly impressive, maintaining high accuracy even at 120k+ tokens where other models often falter significantly.Nisten mentioned on the show that while it's better than GPT-4o, it might not completely replace Sonnet 3.5 for him yet, especially for certain coding or medical tasks under 128k context. Still, the consensus is clear: Gemini 2.5 Pro is the absolute best model right now across categories. Go play with it!ARC-AGI 2 Benchmark Revealed (X, Interactive Blog)Also on the benchmark front, the challenging ARC-AGI 2 benchmark was revealed. This is designed to test tasks that are easy for humans but hard for LLMs. The initial results are sobering: base LLMs score 0% accuracy, and even current "thinking" models only reach about 4%. It highlights just how far we still have to go in developing truly robust AI reasoning, giving us another hill to climb.GPT-4o got another update (as I'm writing these words!) tied for #1 on LMArena, beating 4.5How much does Sam want to win over Google? So much he's letting it ALL out. Just now, we saw an update from LMArena and Sam, about a NEW GPT-4o (2025-03-26) which jumps OVER GPT 4.5 (like.. what?) and lands at number 2 on the LM Arena, jumping over 3o points.Tied #1 in Coding, Hard Prompts. Top-2 across ALL categories. Besides getting very close to Gemini but not quite beating it, I gotta ask, what's the point of 4.5 then? Open Source LLMsThe open-source community wasn't sleeping this week either, with some major drops!DeepSeek V3 Update - 685B Parameter Beast!The Whale Bros at DeepSeek silently dropped an update to their V3 model (X, HF), and it's a monster. We're talking 685 Billion parameters in a Mixture-of-Experts (MoE) architecture. This isn't R1 (their reasoning model), but the powerful base model that R1 was built upon (and supposedly R2 when it'll come out)The benchmark jumps from the previous version are huge, especially in reasoning:* MMLU-Pro: 75.9 → 81.2 (+5.3)* GPQA: 59.1 → 68.4 (+9.3)* AIME: 39.6 → 59.4 (+19.8) (Almost 20 points on competitive math!)* LiveCodeBench: 39.2 → 49.2 (+10.0)They're highlighting major boosts in reasoning, stronger front-end dev skills, and smarter tool use. Nisten noted it even gets some hard reasoning questions right that challenge other models. The "vibes" are reportedly great. Wolfram tried to run it locally but found even the 1-bit quantized version too large for his system (though it should theoretically fit in combined RAM/VRAM), but he's using it via API. It’s likely the best non-reasoning open model right now, potentially the best overall if you can run it.And huge news for the community – they've released these weights under the MIT License, just like R1! Massive respect to DeepSeek for continuing to push powerful models into the open.They also highlight being significantly better at Front End development and websites aesthetics. Qwen Launches Omni 7B Model - Voice & Video Chat!Our friends at Qwen (Alibaba) also came through with something super cool: Qwen2.5-Omni-7B (HF). This is an end-to-end multimodal model that can perceive text, images, audio, AND video, while generating both text and natural-sounding speech, potentially in real-time.They're using a "Thinker-Talker" architecture. What blew my mind is the size – it's listed as 7B parameters, though I saw a meme suggesting it might be closer to 11B internally (ah, the joys of open source!). Still, even at 11B, having this level of multimodal understanding and generation in a relatively small open model is fantastic. It understands voice and video natively and outputs text and voice. Now, when I hear "Omni," I start expecting image generation too (thanks, Google!), so maybe that's next for Qwen? 😉AI Art & Diffusion & Auto-regressionThis was arguably where the biggest "mainstream" buzz happened this week, thanks mainly to OpenAI.OpenAI Launches Native Image Support in GPT-4o - Ghibli Everywhere!This felt like a direct response to Gemini 2.5's launch, almost like OpenAI saying, "Oh yeah? Watch this!" They finally enabled the native image generation capabilities within GPT-4o (Blog, Examples). Remember that image Greg Brockman tweeted a year ago of someone writing on a blackboard with an old OpenAI logo, hinting at this? Well, it's here.And the results? Honestly, they're stunning. The prompt adherence is incredible. It actually listens to what you ask for in detail, including text generation within images, which diffusion models notoriously struggle with. The realism can be jaw-dropping, but it can also generate various styles.Speaking of styles... the internet immediately lost its collective mind and turned everything into the style of Studio Ghibli (great X thread here). My entire feed became Ghibli-fied. It's a testament to how accessible and fun this feature is. Wolfram even suggested we should have Ghibli avatars for the show!Interestingly, this image generation is apparently auto-regressive, not based on diffusion models like Midjourney or Stable Diffusion. This is more similar to how models like Grok's Aurora work, generating the image sequentially (top-to-bottom, kinda like how old GIFs used to load, as Yam pointed out we confirmed). This likely contributes to the amazing prompt adherence, especially with text.The creative potential is huge – people are generating incredible ad concepts (like this thread) and even recreating entire movie trailers, like this unbelievable Lord of the Rings one (link), purely through prompts in GPT-4o. It's wild.Now, this launch wasn't just about cool features; it also marked a significant shift in OpenAI's policy around image generation, aiming for what CEO Sam Altman called "a new high-water mark for us in allowing creative freedom." Joanne Jang, who leads model behavior at OpenAI, shared some fascinating insights into their thinking (Reservoir Samples post). She explained they're moving away from broad, blanket refusals (which often felt safest but limited creativity) towards a more precise approach focused on preventing real-world harm. This means trusting user creativity more, not letting hypothetical worst-case scenarios overshadow everyday usefulness (like generating memes!), and valuing the "unknown, unimaginable possibilities" that overly strict rules might stifle. It's a nuanced approach acknowledging that, as Joanne quoted, "Ships are safest in the harbor... But that’s not what ships or models are for." A philosophy change I definitely appreciate.Reve - New SOTA Diffusion Contender?While OpenAI grabbed headlines, another player emerged claiming State-of-the-Art results, this time in the diffusion space. Reve Image 1.0 (X, Blog/News, Try it) apparently beats Midjourney and Flux in benchmarks, particularly in prompt adherence, realism, and even text generation (though likely not as consistently as GPT-4o's native approach).It works on a credit system ($5 for 500 generations, ~1 cent per image) which is quite affordable. The editing seems a bit different, relying on chatting with the model rather than complex tools. It was kind of hidden/anonymous before, but now they've revealed themselves. Honestly, this would probably be huge news if OpenAI hadn't dropped their image bomb the same week.Ideogram 3 Also Launched - Another SOTA Claim!And just to make the AI art space even more crowded this week, Ideogram also launched version 3.0 (Blog, Try it), also claiming state-of-the-art performance!Ideogram has always been strong with text rendering and logos. Version 3.0 boasts stunning realism, creative design capabilities, and a new "Style References" feature where you can upload images to guide the aesthetic. They claim it consistently outperforms others in human evaluations. It's wild – we had at least three major image generation models/updates drop this week, all claiming top performance, and none of them seemed to benchmark directly against each other in their launch materials! It’s hard to keep track!This Week's Buzz + MCP (X, Github!)Bringing it back to Weights & Biases for a moment. We had Morgan McQuire on the show, who heads up our AI Applied team, to talk about something we're really excited about internally – integrating MCP with Weave, our LLM observability and evaluation tool. Morgan showed a demo and have shipped the MCP server, which you can try right now!Coming soon is the integration with wandb models, which will allows ML folks around the world to build agents that monitor loss curves for them! Weights & Biases Weave Official MCP Server Tool - Talk to Your Evals!We've launched an official MCP server tool for Weave! What does this mean? If you're using Weave to track your experiments, evaluations, prompts, etc. (and you should be!), you can now literally chat with that data. As Morgan demonstrated, you can ask questions like "Tell me about my last three evaluations," and the MCP tool, connected to your Weave data, will not only fetch and summarize that information for you directly in the chat interface (like Claude code or others that support MCP) but will generate a report and add visualizations! This is just the beginning of how we see MCP enhancing observability and interaction with ML workflows. Being able to query and analyze your runs and evaluations using natural language is incredibly powerful.Agents, Tools & MCPAnd speaking of MCP...OpenAI Adds Support for MCP - MCP WON!This was HUGE news, maybe slightly overshadowed by the image generation, but potentially far more impactful long-term, as Wolfram pointed out right at the start of the show. OpenAI officially announced support for the Model Context Protocol (MCP) (docs here).Why is this massive? Because Anthropic initiated MCP, and there was a real fear that OpenAI, being OpenAI, might just create its own competing standard for agent/tool communication, leading to fragmentation (think VHS vs. Betamax, or Blu-ray vs. HD DVD – standards wars suck!). Instead, OpenAI embraced the existing standard. As I said on the show, MCP WON!This is crucial for the ecosystem. It means developers can build tools and agents using the MCP standard, and they should (hopefully) work seamlessly across different models like Claude and GPT. OpenAI not only added support but released it in their Agents SDK and explicitly stated support is "coming soon" for the ChatGPT desktop app and response APIs. Yam expertly clarified the distinction: tools are often single API calls, while MCPs are servers that can maintain state, allowing for more complex, guided interactions. Qwen also adding MCP support to their UI just reinforces this – the standard is gaining traction fast. This standardization is absolutely essential for building a robust agentic future.Voice & AudioJust one more quick update on the audio front:OpenAI Updates Advanced Voice Mode with Semantic VADAlongside the image generation, OpenAI also quietly updated the advanced voice mode in ChatGPT (YT announcement). The key improvement is "Semantic VAD" (Voice Activity Detection). Instead of just cutting off when you pause (leading to annoying interruptions, especially for kids or people who think while speaking), it now tries to understand the meaning and tone to determine if you're actually finished talking.This should lead to a much more natural conversation flow. They also claim better personality, a more engaging natural tone (direct and concise), and less need for you to fill silence with "umms" just to keep the mic open. It might feel a tad slower because it waits a bit longer, but the improvement in interaction quality should be significant.MLX-AudioAnd speaking (heh) of audio and speech, we had the awesome Prince Canuma on the show! If you're into running models locally on Apple Silicon (Macs!), you probably know Prince. He's the MLX King, the creator and maintainer of essential libraries like MLX-VLM (for vision models), FastMLX, MLX Embeddings, and now, MLX-Audio. Seriously, huge props to Prince and the folks in the MLX community for making these powerful open-source models accessible on Mac hardware. It's an incredible contribution.This week, Prince released MLX-Audio v0.0.3. This isn't just text-to-speech (TTS); it aims to be a comprehensive audio package for MLX. Right now, it supports some of the best open TTS models out there:* Kokoro: The tiny, high-quality TTS model we've covered before.* Sesame 1B: Another strong contender.* Orpheus: From Canopy Labs (as Prince confirmed).* Suno Bark: Known for generating music and sound effects too.MLX-Audio makes running state-of-the-art speech synthesis locally on your Mac incredibly easy, basically offering a Hugging Face transformers pipeline equivalent but optimized for Apple Silicon. If you have a Mac, pip install mlx-audio and give it a spin! Prince also took a feature request on the show to allow text file input directly, so look out for that!Phew! See what I mean? An absolutely jam-packed week. Huge thanks again to Tulsee, Morgan, and Prince for joining us and sharing their insights, and to Wolfram, Nisten, and Yam for co-piloting through the news storm. And thank YOU all for tuning in! We'll be back next week, undoubtedly trying to catch our breath and make sense of whatever AI marvels (or madness) gets unleashed next. P.S - if the ghiblification trend didn’t get to your families as well, the alpha right now is… showing your kids how to be a magician and turn them into Ghibli characters, here are me and my kiddos (who asked to be pirates and princesses) TL;DR and Show Notes:* Guests and Cohosts* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)Co Hosts - Wolfram Ravenwlf (@WolframRvnwlf), Nisten Tahiraj (@nisten), Yam Peleg (@yampeleg)* Tulsee Doshi - Head of Product, Gemini Models at Google DeepMind (@tulseedoshi)* Morgan McQuire - Head of AI Applied Team at Weights & Biases (@morgymcg)* Prince Canuma - ML Research Engineer, Creator of MLX Libraries (@PrinceCanuma)* Big CO LLMs + APIs* 🔥 Google reclaims #1 position with Gemini 2.5 Pro (thinking) - (X, Blog, Try it)* ARC-AGI 2 benchmark revealed - Base LLMs score 0%, thinking models 4%.* Open Source LLMs* Deepseek updates DeepSeek-V3-0324 685B params (X, HF) - MIT License!* Qwen launches an Omni 7B model - perceives text, image, audio, video & generates text and speech (HF)* AI Art & Diffusion & Auto-regression* OpenAI launches native image support in GPT-4o (Model Card, X thread, Ad threads, Full Lord of the Rings trailer, Model Card)* Reve - new SOTA diffusion image gen claims (X, Blog/News, Try)* Ideogram 3 launched - another SOTA claim, strong on text/logos, realism, style refs (Blog, Try it)* This weeks Buzz + MCP* Weights & Biases Weave official MCP server tool - talk to your evals! (X, Github)* Agents , Tools & MCP* OpenAI has added support for MCP - MCP WON! (Docs)* Voice & Audio* OpenAI updates advanced voice mode with semantic VAD for more natural conversations (YT announcement).* MLX-Audio v0.0.3 released by Prince Canuma (Github)* Show Notes and other Links* Catch the show live & subscribe to the newsletter/YouTube: thursdai.news/yt* Try Gemini 2.5 Pro: AI.dev* Learn more about MCP from our previous episode (March 6th). 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, it's Alex, coming to you fresh off another live recording of ThursdAI, and what an incredible one it's been! I was hoping that this week will be chill with the releases, because of NVIDIA's GTC conference, but no, the AI world doesn't stop, and if you blinked this week, you may have missed 2 or 10 major things that happened. From Mistral coming back to OSS with the amazing Mistral Small 3.1 (beating Gemma from last week!) to OpenAI dropping a new voice generation model, and 2! new whisper killer ASR models with a Breaking News during our live show (there's a reason we're called ThursdAI) which we watched together and then dissected with Kwindla, our amazing AI VOICE and real time expert. Not to mention that we also had dedicated breaking news from friend of the pod Joseph Nelson, that came on the show to announce a SOTA vision model from Roboflow + a new benchmark on which even the top VL models get around 6%! There's also a bunch of other OSS, a SOTA 3d model from Tencent and more! And last but not least, Yam is back 🎉 So... buckle up and let's dive in. As always, TL;DR and show notes at the end, and here's the YT live version. (While you're there, please hit subscribe and help me hit that 1K subs on YT 🙏 )Voice & Audio: OpenAI's Voice Revolution and the Open Source EchoHold the phone, everyone, because this week belonged to Voice & Audio! Seriously, if you weren't paying attention to the voice space, you missed a seismic shift, courtesy of OpenAI and some serious open-source contenders.OpenAI's New Voice Models - Whisper Gets an Upgrade, TTS Gets Emotional!OpenAI dropped a suite of next-gen audio models: gpt-4o-mini-tts-latest (text-to-speech) and GPT 4.0 Transcribe and GPT 4.0 Mini Transcribe (speech-to-text), all built upon their powerful transformer architecture.To unpack this voice revolution, we welcomed back Kwindla Cramer from Daily, the voice AI whisperer himself. The headline news? The new speech-to-text models are not just incremental improvements; they’re a whole new ballgame. As OpenAI’s Shenyi explained, "Our new generation model is based on our large speech model. This means this new model has been trained on trillions of audio tokens." They're faster, cheaper (Mini Transcribe is half price of Whisper!), and boast state-of-the-art accuracy across multiple languages. But the real kicker? They're promptable!"This basically opens up a whole field of prompt engineering for these models, which is crazy," I exclaimed, my mind officially blown. Imagine prompting your transcription model with context – telling it you're discussing dog breeds, and suddenly, its accuracy for breed names skyrockets. That's the power of promptable ASR! I recorded a live reaction aftder dropping of stream, and I was really impressed with how I can get the models to pronounce ThursdAI by just... asking! But the voice magic doesn't stop there. GPT 4.0 Mini TTS, the new text-to-speech model, can now be prompted for… emotions! "You can prompt to be emotional. You can ask it to do some stuff. You can prompt the character a voice," OpenAI even demoed a "Mad Scientist" voice! Captain Ryland voice, anyone? This is a huge leap forward in TTS, making AI voices sound… well, more human.But wait, there’s more! Semantic VAD! Semantic Voice Activity Detection, as OpenAI explained, "chunks the audio up based on when the model thinks The user's actually finished speaking." It’s about understanding the meaning of speech, not just detecting silence. Kwindla hailed it as "a big step forward," finally addressing the age-old problem of AI agents interrupting you mid-thought. No more robotic impatience!OpenAI also threw in noise reduction and conversation item retrieval, making these new voice models production-ready powerhouses. This isn't just an update; it's a voice AI revolution, folks.They also built a super nice website to test out the new models with openai.fm ! Canopy Labs' Orpheus 3B - Open Source Voice Steps UpBut hold on, the open-source voice community isn't about to be outshone! Canopy Labs dropped Orpheus 3B, a "natural sounding speech language model" with open-source spirit. Orpheus, available in multiple sizes (3B, 1B, 500M, 150M), boasts zero-shot voice cloning and a glorious Apache 2 license. Wolfram noted its current lack of multilingual support, but remained enthusiastic, I played with them a bit and they do sound quite awesome, but I wasn't able to finetune them on my own voice due to "CUDA OUT OF MEMORY" alasI did a live reaction recording for this model on XNVIDIA Canary - Open Source Speech Recognition Enters the RaceSpeaking of open source, NVIDIA surprised us with Canary, a speech recognition and translation model. "NVIDIA open sourced Canary, which is a 1 billion parameter and 180 million parameter speech recognition and translation, so basically like whisper competitor," I summarized. Canary is tiny, fast, and CC-BY licensed, allowing commercial use. It even snagged second place on the Hugging Face speech recognition leaderboard! Open source ASR just got a whole lot more interesting.Of course, this won't get to the level of the new SOTA ASR OpenAI just dropped, but this can run locally and allows commercial use on edge devices! 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.Vision & Video: Roboflow's Visionary Model and Video Generation Gets MovingAfter the voice-apalooza, let's switch gears to the visual world, where Vision & Video delivered some knockout blows, spearheaded by Roboflow and StepFun.Roboflow's RF-DETR and RF100-VL - A New Vision SOTA EmergesRoboflow stole the vision spotlight this week with their RF-DETR model and the groundbreaking RF100-VL benchmark. We were lucky enough to have Joseph Nelson, Roboflow CEO, join the show again and give us the breaking news (they published the Github 11 minutes before he came on!) RF-DETR is Roboflow's first in-house model, a real-time object detection transformer that's rewriting the rulebook. "We've actually never released a model that we've developed. And so this is the first time where we've taken a lot of those learnings and put that into a model," Joseph revealed.And what a model it is! RF-DETR is not just fast; it's SOTA on real-world datasets and surpasses the 60 mAP barrier on COCO. But Joseph dropped a truth bomb: COCO is outdated. "The benchmark that everyone uses is, the COCO benchmark… hasn't been updated since 2017, but models have continued to get really, really, really good. And so they're saturated the COCO benchmark," he explained.Enter RF100-VL, Roboflow's revolutionary new benchmark, designed to evaluate vision-language models on real-world data. "We, introduced a benchmark that we call RF 100 vision language," Joseph announced. The results? Shockingly low zero-shot performance on real-world vision tasks, highlighting a major gap in current models. Joseph's quiz question about QwenVL 2.5's zero-shot performance on RF100-VL revealed a dismal 5.8% accuracy. "So we as a field have a long, long way to go before we have zero shot performance on real world context," Joseph concluded. RF100-VL is the new frontier for vision, and RF-DETR is leading the charge! Plus, it runs on edge devices and is Apache 2 licensed! Roboflow, you legends! Check out the RF-DETR Blog Post, the RF-DETR Github, and the RF100-VL Benchmark for more details!StepFun's Image-to-Video TI2V - Animating Images with StyleStepping into the video arena, StepFun released their image2video model, TI2V. TI2V boasts impressive motion controls and generates high-quality videos from images and text prompts, especially excelling in anime-style video generation. Dive into the TI2V HuggingFace Space and TI2V Github to explore further.Open Source LLMs: Mistral's Triumphant Return, LG's Fridge LLM, NVIDIA's Nemotron, and ByteDance's RL BoostLet's circle back to our beloved Open Source LLMs, where this week was nothing short of a gold rush!Mistral is BACK, Baby! - Mistral Small 3.1 24B (Again!)Seriously, Mistral AI's return to open source with Mistral Small 3.1 deserves another shoutout! "Mistral is back with open source. Let's go!" I cheered, and I meant it. This multimodal, Apache 2 licensed model is a powerhouse, outperforming Gemma 3 and ready for action on a single GPU. Wolfram, ever the pragmatist, noted, "We are in right now, where a week later, you already have some new toys to play with." referring to Gemma 3 that we covered just last week! Not only did we get a great new update from Mistral, they also cited our friends at Nous research and their Deep Hermes (released just last week!) for the reason to release the base models alongside finetuned models! Mistral Small 3.1 is not just a model; it's a statement: open source is thriving, and Mistral is leading the charge! Check out their Blog Post, the HuggingFace page, and the Base Model on HF. NVIDIA Nemotron - Distilling, Pruning, Making Llama's BetterNVIDIA finally dropped Llama Nemotron, and it was worth the wait! Nemotron Nano (8B) and Super (49B) are here, with Ultra (253B) on the horizon. These models are distilled, pruned, and, crucially, designed for reasoning with a hybrid architecture allowing you to enable and disable reasoning via a simple on/off switch in the system prompt! Beating other reasoners like QwQ on GPQA tasks, this distillined and pruned LLama based reasoner seems very powerful! Congrats to NVIDIAChris Alexius (a friend of the pod) who co-authored the announcement, told me that FP8 is expected and when that drops, this model will also fit on a single H100 GPU, making it really great for enterprises who host on their own hardware. And yes, it’s ready for commercial use. NVIDIA, welcome to the open-source LLM party! Explore the Llama-Nemotron HuggingFace Collection and the Dataset.LG Enters the LLM Fray with EXAONE Deep 32B - Fridge AI is Officially a ThingLG, yes, that LG, surprised everyone by open-sourcing EXAONE Deep 32B, a "thinking model" from the fridge and TV giant. "LG open sources EXAONE and EXAONE Deep 32B thinking model," I announced, still slightly amused by the fridge-LLM concept. This 32B parameter model claims "superior capabilities" in reasoning, and while my live test in LM Studio went a bit haywire, quantization could be the culprit. It's non-commercial, but hey, fridge-powered AI is now officially a thing. Who saw that coming? Check out my Reaction Video, the LG Blog, and the HuggingFace page for more info.ByteDance's DAPO - Reinforcement Learning Gets EfficientFrom the creators of TikTok, ByteDance, comes DAPO, a new reinforcement learning method that's outperforming GRPO. DAPO promises 50% accuracy on AIME 2024 with 50% less training steps. Nisten, our RL expert, explained it's a refined GRPO, pushing the boundaries of RL efficiency. Open source RL is getting faster and better, thanks to ByteDance! Dive into the X thread, Github, and Paper for the technical details.Big CO LLMs + APIs: Google's Generosity, OpenAI's Oligarch Pricing, and GTC ManiaSwitching gears to the Big CO LLM arena, we saw Google making moves for the masses, OpenAI catering to the elite, and NVIDIA… well, being NVIDIA.Google Makes DeepResearch Free and Adds CanvasGoogle is opening up DeepResearch to everyone for FREE! DeepResearch, Gemini's advanced search mode, is now accessible without a Pro subscription. I really like it's revamped UI where you can see the thinking and the sources! I used it live on the show to find out what we talked about in the latest episode of ThursdAI, and it did a pretty good job! Plus, Google unveiled Canvas, letting you "build apps within Gemini and actually see them." Google is making Gemini more accessible and more powerful, a win for everyone. Here's a Tetris game it built for me and here's a markdown enabled word counter I rebuild every week before I send ThursdAI (making sure I don't send you 10K words every week 😅)OpenAI's O1 Pro API - Pricey Power for the FewOpenAI, in contrast, released O1 Pro API, but with a price tag that's… astronomical. "OpenAI makes O1-pro API available to oligarchs ($600/1mtok output!)," I quipped, highlighting the exclusivity. $600 per million output tokens? "If you code with this, if you vibe code with this, you better already have VCs backing your startup," I warned. O1 Pro might be top-tier performance, but it's priced for the 0.1%.NVIDIA GTC Recap - Jensen's Hardware ExtravaganzaNVIDIA GTC was, as always, a hardware spectacle. New GPUs (Blackwell Ultra, Vera Rubin, Feynman!), the tiny DGX Spark supercomputer, the GR00T robot foundation model, and the Blue robot – NVIDIA is building the AI future, brick by silicon brick. Jensen is the AI world's rockstar, and GTC is his sold-out stadium show. Check out Rowan Cheung's GTC Recap on X for a quick overview.Shoutout to our team at GTC and this amazingly timed logo shot I took from the live stream! Antropic adds Web SearchWe had a surprise at the end of the show, with Antropic releasing web search. It's a small thing, but for folks who use Cloud AI, it's very important.You can now turn on web search directly on Claude which makes it... the last frontier lab to enable this feature 😂 Congrats! AI Art & Diffusion & 3D: Tencent's 3D RevolutionTencent Hunyuan 3D 2.0 MV and Turbo - 3D Generation Gets Real-TimeTencent updated Hunyuan 3D to 2.0 MV (MultiView) and Turbo, pushing the boundaries of 3D generation. Hunyuan 3D 2.0 surpasses SOTA in geometry, texture, and alignment, and the Turbo version achieves near real-time 3D generation – under one second on an H100! Try out the Hunyuan3D-2mv HF Space to generate your own 3D masterpieces! MultiView (MV) is another game-changer, allowing you to input 1-4 views for more accurate 3D models. "MV allows to generate 3d shapes from 1-4 views making the 3D shapes much higher quality" I explained. The demo of generating a 3D mouse from Gemini-generated images showcased the seamless pipeline from thought to 3D object. I literally just asked Gemini with native image generation to generate a character and then Holodecks are getting closer, folks!Closing Remarks and Thank YouAnd that's all she wrote, folks! Another week, another AI explosion. From voice to vision, open source to Big CO, this week was a whirlwind of innovation. Huge thanks again to our incredible guests, Joseph Nelson from Roboflow, Kwindla Cramer from Daily, and Lucas Atkins from ARCEE! And of course, massive shoutout to my co-hosts, Wolfram, Yam, and Nisten – you guys are the best!And YOU, the ThursdAI community, are the reason we do this. Thank you for tuning in, for your support, and for being as hyped about AI as we are. Remember, ThursdAI is a labor of love, fueled by Weights & Biases and a whole lot of passion.Missed anything? thursdai.news is your one-stop shop for the podcast, newsletter, and video replay. And seriously, subscribe to our YouTube channel! Let's get to 1000 subs!Helpful? We’d love to see you here again! TL;DR and Show Notes:* Guests and Cohosts* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)Co Hosts - @WolframRvnwlf @yampeleg @nisten* Sponsor - Weights & Biases Weave (@weave_wb)* Joseph Nelson - CEO Roboflow (@josephofiowa)* Kindwla Kramer - CEO Daily (@kwindla)* Lucas Atkins - Labs team at Arcee lead (@LukasAtkins7)* Open Source LLMs * Mistral Small 3.1 24B - Multimodal (Blog, HF, HF base)* LG open sources EXAONE and EXAONE Deep 32B thinking model (Alex Reaction Video, LG BLOG, HF)* ByteDance releases DAPO - better than GRPO RL Method (X, Github, Paper)* NVIDIA drops LLama-Nemotron (Super 49B, Nano 8B) with reasoning and data (X, HF, Dataset)* Big CO LLMs + APIs* Google makes DeepResearch free, Canvas added, Live Previews (X)* OpenAI makes O1-pro API available to oligarchs ($600/1mtok output!)* NVIDIA GTC recap - (X)* This weeks Buzz* Come visit the Weights & Biases team at GTC today! * Vision & Video* Roboflow drops RF-DETR a SOTA vision model + new eval RF100-VL for VLMs (Blog, Github, Benchmark)* StepFun dropped their image2video model TI2V (HF, Github)* Voice & Audio* OpenAI launches a new voice model and 2 new transcription models (Blog, Youtube)* Canopy Labs drops Orpheus 3B (1B, 500B, 150M versions) - natural sounding speech language model (Blog, HF, Colab)* NVIDIA Canary 1B/180M Flash - apache 2 speech recognition and translation LLama finetune (HF)* AI Art & Diffusion & 3D* Tencent updates Hunyuan 3D 2.0 MV (MultiView) and Turbo (HF)* Tools* ARCEE Conductor - model router (X)* Cursor ships Claude 3.7 MAX (X)* Notebook LM teases MindMaps (X)* Gemini Co-Drawing - using Gemini native image output for helping drawing (HF) 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

LET'S GO! Happy second birthday to ThursdAI, your favorite weekly AI news show! Can you believe it's been two whole years since we jumped into that random Twitter Space to rant about GPT-4? From humble beginnings as a late-night Twitter chat to a full-blown podcast, Newsletter and YouTube show with hundreds of thousands of downloads, it's been an absolutely wild ride! That's right, two whole years of me, Alex Volkov, your friendly AI Evangelist, along with my amazing co-hosts, trying to keep you up-to-date on the breakneck speed of the AI worldAnd what better way to celebrate than with a week PACKED with insane AI news? Buckle up, folks, because this week Google went OPEN SOURCE crazy, Gemini got even cooler, OpenAI created a whole new Agents SDK and the open-source community continues to blow our minds. We’ve got it all - from game-changing model releases to mind-bending demos.This week I'm also on the Weights & Biases company retreat, so TL;DR first and then the newsletter, but honestly, I'll start embedding the live show here in the substack from now on, because we're getting so good at it, I barely have to edit lately and there's a LOT to show you guys! TL;DR and Show Notes & Links* Hosts & Guests* Alex Volkov - AI Eveangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @ldjconfirmed @nisten * Sandra Kublik - DevRel at Cohere (@itsSandraKublik)* Open Source LLMs * Google open sources Gemma 3 - 1B - 27B - 128K context (Blog, AI Studio, HF)* EuroBERT - multilingual encoder models (210M to 2.1B params)* Reka Flash 3 (reasoning) 21B parameters is open sourced (Blog, HF)* Cohere Command A 111B model - 256K context (Blog)* Nous Research Deep Hermes 24B / 3B Hybrid Reasoners (X, HF)* AllenAI OLMo 2 32B - fully open source GPT4 level model (X, Blog, Try It)* Big CO LLMs + APIs* Gemini Flash generates images natively (X, AI Studio)* Google deep research is now free in Gemini app and powered by Gemini Thinking (Try It no cost)* OpenAI released new responses API, Web Search, File search and Computer USE tools (X, Blog)* This weeks Buzz * The whole company is at an offsite at oceanside, CA* W&B internal MCP hackathon and had cool projects - launching an MCP server soon!* Vision & Video* Remade AI - 8 LORA video effects for WANX (HF)* AI Art & Diffusion & 3D* ByteDance Seedream 2.0 - A Native Chinese-English Bilingual Image Generation Foundation Model by ByteDance (Blog, Paper)* Tools* Everyone's talking about Manus - (manus.im)* Google AI studio now supports youtube understanding via link droppingOpen Source LLMs: Gemma 3, EuroBERT, Reka Flash 3, and Cohere Command-A Unleashed!This week was absolutely HUGE for open source, folks. Google dropped a BOMBSHELL with Gemma 3! As Wolfram pointed out, this is a "very technical achievement," and it's not just one model, but a whole family ranging from 1 billion to 27 billion parameters. And get this – the 27B model can run on a SINGLE GPU! Sundar Pichai himself claimed you’d need "at least 10X compute to get similar performance from other models." Insane!Gemma 3 isn't just about size; it's packed with features. We're talking multimodal capabilities (text, images, and video!), support for over 140 languages, and a massive 128k context window. As Nisten pointed out, "it might actually end up being the best at multimodal in that regard" for local models. Plus, it's fine-tuned for safety and comes with ShieldGemma 2 for content moderation. You can grab Gemma 3 on Google AI Studio, Hugging Face, Ollama, Kaggle – everywhere! Huge shoutout to Omar Sanseviero and the Google team for this incredible release and for supporting the open-source community from day one! Colin aka Bartowski, was right, "The best thing about Gemma is the fact that Google specifically helped the open source communities to get day one support." This is how you do open source right!Next up, we have EuroBERT, a new family of multilingual encoder models. Wolfram, our European representative, was particularly excited about this one: "In European languages, you have different characters than in other languages. And, um, yeah, encoding everything properly is, uh, difficult." Ranging from 210 million to 2.1 billion parameters, EuroBERT is designed to push the boundaries of NLP in European and global languages. With training on a massive 5 trillion-token dataset across 15 languages and support for 8K context tokens, EuroBERT is a workhorse for RAG and other NLP tasks. Plus, how cool is their mascot?Reka Flash 3 - a 21B reasoner with apache 2 trained with RLOOAnd the open source train keeps rolling! Reka AI dropped Reka Flash 3, a 21 billion parameter reasoning model with an Apache 2.0 license! Nisten was blown away by the benchmarks: "This might be one of the best like 20B size models that there is right now. And it's Apache 2.0. Uh, I, I think this is a much bigger deal than most people realize." Reka Flash 3 is compact, efficient, and excels at chat, coding, instruction following, and function calling. They even used a new reinforcement learning technique called REINFORCE Leave One-Out (RLOO). Go give it a whirl on Hugging Face or their chat interface – chat.reka.ai!Last but definitely not least in the open-source realm, we had a special guest, Sandra (@itsSandraKublik) from Cohere, join us to announce Command-A! This beast of a model clocks in at 111 BILLION parameters with a massive 256K context window. Sandra emphasized its efficiency, "It requires only two GPUs. Typically the models of this size require 32 GPUs. So it's a huge, huge difference." Command-A is designed for enterprises, focusing on agentic tasks, tool use, and multilingual performance. It's optimized for private deployments and boasts enterprise-grade security. Congrats to Sandra and the Cohere team on this massive release!Big CO LLMs + APIs: Gemini Flash Gets Visual, Deep Research Goes Free, and OpenAI Builds for AgentsThe big companies weren't sleeping either! Google continued their awesome week by unleashing native image generation in Gemini Flash Experimental! This is seriously f*****g cool, folks! Sorry for my French, but it’s true. You can now directly interact with images, tell Gemini what to do, and it just does it. We even showed it live on the stream, turning ourselves into cat-confetti-birthday-hat-wearing masterpieces! Wolfram was right, "It's also a sign what we will see in, like, Photoshop, for example. Where you, you expect to just talk to it and have it do everything that a graphic designer would be doing." The future of creative tools is HERE.And guess what else Google did? They made Deep Research FREE in the Gemini app and powered by Gemini Thinking! Nisten jumped in to test it live, and we were all impressed. "This is the nicest interface so far that I've seen," he said. Deep Research now digs through HUNDREDS of websites (Nisten’s test hit 156!) to give you comprehensive answers, and the interface is slick and user-friendly. Plus, you can export to Google Docs! Intelligence too cheap to meter? Google is definitely pushing that boundary.Last second additions - Allen Institute for AI released OLMo 2 32B - their biggest open model yetJust as I'm writing this, friend of the pod, Nathan from Allen Institute for AI announced the release of a FULLY OPEN OLMo 2, which includes weights, code, dataset, everything and apparently it beats the latest GPT 3.5, GPT 4o mini, and leading open weight models like Qwen and Mistral. Evals look legit, but nore than that, this is an Apache 2 model with everything in place to advance open AI and open science! Check out Nathans tweet for more info, and congrats to Allen team for this awesome release! OpenAI new responses API and Agent ASK with Web, File and CUA toolsOf course, OpenAI wasn't going to let Google have all the fun. They dropped a new SDK for agents called the Responses API. This is a whole new way to build with OpenAI, designed specifically for the agentic era we're entering. They also released three new tools: Web Search, Computer Use Tool, and File Search Tool. The Web Search tool is self-explanatory – finally, built-in web search from OpenAI!The Computer Use Tool, while currently limited in availability, opens up exciting possibilities for agent automation, letting agents interact with computer interfaces. And the File Search Tool gives you a built-in RAG system, simplifying knowledge retrieval from your own files. As always, OpenAI is adapting to the agentic world and giving developers more power.Finally in the big company space, Nous Research released PORTAL, their new Inference API service. Now you can access their awesome models, like Hermes 3 Llama 70B and DeepHermes 3 8B, directly via API. It's great to see more open-source labs offering API access, making these powerful models even more accessible.This Week's Buzz at Weights & Biases: Offsite Hackathon and MCP Mania!This week's "This Week's Buzz" segment comes to you live from Oceanside, California! The whole Weights & Biases team is here for our company offsite. Despite the not-so-sunny California weather (thanks, storm!), it's been an incredible week of meeting colleagues, strategizing, and HACKING!And speaking of hacking, we had an MCP hackathon! After last week’s MCP-pilling episode, we were all hyped about Model Context Protocol, and the team didn't disappoint. In just three hours, the innovation was flowing! We saw agents built for WordPress, MCP support integrated into Weave playground, and even MCP servers for Weights & Biases itself! Get ready, folks, because an MCP server for Weights & Biases is COMING SOON! You'll be able to talk to your W&B data like never before. Huge shoutout to the W&B team for their incredible talent and for embracing the agentic future! And in case you missed it, Weights & Biases is now part of the CoreWeave family! Exciting times ahead!Vision & Video: LoRA Video Effects and OpenSora 2.0Moving into vision and video, Remade AI released 8 LoRA video effects for 1X! Remember 1X from Alibaba? Now you can add crazy effects like "squish," "inflate," "deflate," and even "cakeify" to your videos using LoRAs. It's open source and super cool to see video effects becoming trainable and customizable.And in the realm of open-source video generation, OpenSora 2.0 dropped! This 11 billion parameter model claims state-of-the-art video generation trained for just $200,000! They’re even claiming performance close to Sora itself on some benchmarks. Nisten checked out the demos, and while we're all a bit jaded now with the rapid pace of video AI, it's still mind-blowing how far we've come. Open source video is getting seriously impressive, seriously fast.AI Art & Diffusion & 3D: ByteDance's Bilingual Seedream 2.0ByteDance, the folks behind TikTok, released Seedream 2.0, a native Chinese-English bilingual image generation foundation model. This model, from ByteDream, excels at text rendering, cultural nuance, and human preference alignment. Seedream 2.0 boasts "powerful general capability," "native bilingual comprehension ability," and "excellent text rendering." It's designed to understand both Chinese and English prompts natively, generating high-quality, culturally relevant images. The examples look stunning, especially its ability to render Chinese text beautifully.Tools: Manus AI Agent, Google AI Studio YouTube Links, and Cursor EmbeddingsFinally, in the tools section, everyone's buzzing about Manus, a new AI research agent. We gave it a try live on the show, asking it to do some research. The UI is slick, and it seems to be using Claude 3.7 behind the scenes. Manus creates a to-do list, browses the web in a real Chrome browser, and even generates files. It's like Operator on steroids. We'll be keeping an eye on Manus and will report back on its performance in future episodes.And Google AI Studio keeps getting better! Now you can drop YouTube links into Google AI Studio, and it will natively understand the video! This is HUGE for video analysis and content understanding. Imagine using this for support, content summarization, and so much more.PHEW! What a week to celebrate two years of ThursdAI! From open source explosions to Gemini's visual prowess and OpenAI's agentic advancements, the AI world is moving faster than ever. As Wolfram aptly put it, "The acceleration, you can feel it." And Nisten reminded us of the incredible journey, "I remember I had early access to GPT-4 32K, and, uh, then... the person for the contract that had given me access, they cut it off because on the one weekend, I didn't realize how expensive it was. So I had to use $180 worth of tokens just trying it out." Now, we have models that are more powerful and more accessible than ever before. Thank you to Wolfram, Nisten, and LDJ for co-hosting and bringing their insights every week. And most importantly, THANK YOU to our amazing community for tuning in, listening, and supporting ThursdAI for two incredible years! We couldn't do it without you. Here's to another year of staying up-to-date so YOU don't have to! Don't forget to subscribe to the podcast, YouTube channel, and newsletter to stay in the loop. And share ThursdAI with a friend – it's the best birthday gift you can give us! Until next week, keep building and keep exploring the amazing world of AI! LET'S GO! 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

What is UP folks! Alex here from Weights & Biases (yeah, still, but check this weeks buzz section below for some news!) I really really enjoyed today's episode, I feel like I can post it unedited it was so so good. We started the show with our good friend Junyang Lin from Alibaba Qwen, where he told us about their new 32B reasoner QwQ. Then we interviewed Google's VP of the search product, Robby Stein, who came and told us about their upcoming AI mode in Google! I got access and played with it, and it made me switch back from PPXL as my main. And lastly, I recently became fully MCP-pilled, since we covered it when it came out over thanksgiving, I saw this acronym everywhere on my timeline but only recently "got it" and so I wanted to have an MCP deep dive, and boy... did I get what I wished for! You absolutely should tune in to the show as there's no way for me to cover everything we covered about MCP with Dina and Jason! ok without, further adieu.. let's dive in (and the TL;DR, links and show notes in the end as always!) 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.🤯 Alibaba's QwQ-32B: Small But Shocking Everyone!The open-source LLM segment started strong, chatting with friend of the show Junyang Justin Lin from Alibaba’s esteemed Qwen team. They've cooked up something quite special: QwQ-32B, a reasoning-focused, reinforcement-learning-boosted beast that punches remarkably above its weight. We're talking about a mere 32B parameters model holding its ground on tough evaluations against DeepSeek R1, a 671B behemoth!Here’s how wild this is: You can literally run QwQ on your Mac! Junyang shared that they applied two solid rounds of RL to amp its reasoning, coding, and math capabilities, integrating agents into the model to fully unlock its abilities. When I called out how insane it was that we’ve gone from "LLMs can't do math" to basically acing competitive math benchmarks like AIME24, Junyang calmly hinted that they're already aiming for unified thinking/non-thinking models. Sounds wild, doesn’t it?Check out the full QwQ release here, or dive into their blog post.🚀 Google Launches AI Mode: Search Goes Next-Level (X, Blog, My Live Reaction).For the past two years, on this very show, we've been asking, "Where's Google?" in the Gen AI race. Well, folks, they're back. And they're back in a big way.Next, we were thrilled to have Google’s own Robby Stein, VP of Product for Google Search, drop by ThursdAI after their massive launch of AI Mode and expanded AI Overviews leveraging Gemini 2.0. Robby walked us through this massive shift, which essentially brings advanced conversational AI capabilities straight into Google. Seriously — Gemini 2.0 is now out here doing complex reasoning while performing fan-out queries behind the scenes in Google's infrastructure.Google search is literally Googling itself. No joke. "We actually have the model generating fan-out queries — Google searches within searches — to collect accurate, fresh, and verified data," explained Robby during our chat. And I gotta admit, after playing with AI Mode, Google is definitely back in the game—real-time restaurant closures, stock analyses, product comparisons, and it’s conversational to boot. You can check my blind reaction first impression video here. (also, while you're there, why not subscribe to my YT?)Google has some huge plans, but right now AI Mode is rolling out slowly via Google Labs for Google One AI Premium subscribers first. More soon though!🐝 This Week's Buzz: Weights & Biases Joins CoreWeave Family!Huge buzz (in every sense of the word) from Weights & Biases, who made waves with their announcement this week: We've joined forces with CoreWeave! Yeah, that's big news as CoreWeave, the AI hyperscaler known for delivering critical AI infrastructure, has now acquired Weights & Biases to build the ultimate end-to-end AI platform. It's early days of this exciting journey, and more details are emerging, but safe to say: the future of Weights & Biases just got even more exciting. Congrats to the whole team at Weights & Biases and our new colleagues at CoreWeave!We're committed to all users of WandB so you will be able to keep using Weights & Biases, and we'll continuously improve our offerings going forward! Personally, also nothing changes for ThursdAI! 🎉MCP Takes Over: Giving AI agents super powers via standardized protocol Then things got insanely exciting. Why? Because MCP is blowing up and I had to find out why everyone's timeline (mine included) just got invaded.Welcoming Cloudflare’s amazing product manager Dina Kozlov and Jason Kneen—MCP master and creator—things quickly got mind-blowing. MCP servers, Jason explained, are essentially tool wrappers that effortlessly empower agents with capabilities like API access and even calling other LLMs—completely seamlessly and securely. According to Jason, "we haven't even touched the surface yet of what MCP can do—these things are Lego bricks ready to form swarms and even self-evolve."Dina broke down just how easy it is to launch MCP servers on Cloudflare Workers while teasing exciting upcoming enhancements. Both Dina and Jason shared jaw-dropping examples, including composing complex workflows connecting Git, Jira, Gmail, and even smart home controls—practically instantaneously! Seriously, my mind is still spinning.The MCP train is picking up steam, and something tells me we'll be talking about this revolutionary agent technology a lot more soon. Check out two great MCP directories that popped up this recently: Smithery, Cursor Directory and Composio.This show was one of the best ones we recorded, honestly, I barely need to edit it. It was also a really really fun livestream, so if you prefer seeing to listening, here's the lightly edited live streamThank you for being a ThursdAI subscriber, as always here's the TL:DR and shownotes for everything that happened in AI this week and the things we mentioned (and hosts we had)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 * Show Notes & Guests* Alex Volkov - AI Eveangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @ldjconfirmed @nisten* Junyang Justin Lin - Head of Qwen Team, Alibaba - @JustinLin610* Robby Stein - VP of Product, Google Search - @rmstein* Dina Kozlov - Product Manager, Cloudflare - @dinasaur_404* Jason Kneen - MCP Wiz - @jasonkneen* My Google AI Mode Blind Reaction Video (Youtube)* Sesame Maya Conversation Demo - (Youtube)* Cloudflare MCP docs (Blog)* Weights & Biases Agents Course Pre-signup - https://wandb.me/agents* Open Source LLMs* Qwen's latest reasoning model QwQ-32B - matches R1 on some evals (X, Blog, HF, Chat)* Cohere4ai - Aya Vision - 8B & 32B (X, HF)* AI21 - Jamba 1.6 Large & Jamba 1.6 Mini (X, HF)* Big CO LLMs + APIs* Google announces AI Mode & AI Overviews Gemini 2.0 (X, Blog, My Live Reaction)* OpenAI rolls out GPT 4.5 to plus users - #1 on LM Arena 🔥 (X)* Grok Voice is available for free users as well (X)* Elysian Labs launches Auren ios app (X, App Store)* Mistral announces SOTA OCR (Blog)* This weeks Buzz* Weights & Biases is acquired by CoreWeave 🎉 (Blog)* Vision & Video* Tencent HYVideo img2vid is finally here (X, HF, Try It)* Voice & Audio* NotaGen - symbolic music generation model high-quality classical sheet music Github, Demo, HF* Sesame takes the world by storm with their amazing voice model (My Reaction)* AI Art & Diffusion & 3D* MiniMax__AI - Image-01: A Versatile Text-to-Image Model at 1/10 the Cost (X, Try it)* Zhipu AI - CogView 4 6B - (X, Github)* Tools* Google - DataScience agent in GoogleColab Blog* Baidu Miaoda - nocode AI build 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, Alex here 👋What can I say, the weeks are getting busier , and this is one of those "crazy full" weeks in AI. As we were about to start recording, OpenAI teased GPT 4.5 live stream, and we already had a very busy show lined up (Claude 3.7 vibes are immaculate, Grok got an unhinged voice mode) and I had an interview with Kevin Hou from Windsurf scheduled! Let's dive in! 🔥 GPT 4.5 (ORION) is here - worlds largest LLM (10x GPT4o) OpenAI has finally shipped their next .5 model, which is 10x scale from the previous model. We didn't cover this on the podcast but did watch the OpenAI live stream together after the podcast concluded. A very interesting .5 release from OpenAI, where even Sam Altman says "this model won't crush on benchmarks" and is not the most frontier model, but is OpenAI's LARGEST model by far (folks are speculating 10+ Trillions of parameters) After 2 years of smaller models and distillations, we finally got a new BIG model, that shows scaling laws proper, and while on some benchmarks it won't compete against reasoning models, this model will absolutely fuel a huge increase in capabilities even for reasoners, once o-series models will be trained on top of this. Here's a summary of the announcement and quick vibes recap (from folks who had access to it before) * OpenAI's largest, most knowledgeable model.* Increased world knowledge: 62.5% on SimpleQA, 71.4% GPQA* Better in creative writing, programming, problem-solving (no native step-by-step reasoning).* Text and image input and text output* Available in ChatGPT Pro and API access (API supports Function Calling, Structured Output)* Knowledge Cutoff is October 2023.* Context Window is 128,000 tokens.* Max Output is 16,384 tokens.* Pricing (per 1M tokens): Input: $75, Output: $150, Cached Input: $37.50.* Foundation for future reasoning models4.5 Vibes RecapTons of folks who had access are pointing to the same thing, while this model is not beating others on evals, it's much better at multiple other things, namely creative writing, recommending songs, improved vision capability and improved medical diagnosis. Karpathy said "Everything is a little bit better and it's awesome, but also not exactly in ways that are trivial to point to" and posted a thread of pairwise comparisons of tone on his X threadThough the reaction is bifurcated as many are upset with the high price of this model (10x more costly on outputs) and the fact that it's just marginally better at coding tasks. Compared to the newerSonnet (Sonnet 3.7) and DeepSeek, folks are looking at OpenAI and asking, why isn't this way better? Anthropic's Claude 3.7 Sonnet: A Coding PowerhouseAnthropic released Claude 3.7 Sonnet, and the immediate reaction from the community was overwhelmingly positive. With 8x more output capability (64K) and reasoning built in, this model is an absolute coding powerhouse. Claude 3.7 Sonnet is the new king of coding models, achieving a remarkable 70% on the challenging SWE-Bench benchmark, and the initial user feedback is stellar, though vibes started to shift a bit towards Thursday.Ranking #1 on WebDev arena, and seemingly trained on UX and websites, Claude Sonnet 3.7 (AKA NewerSonner) has been blowing our collective minds since it was released on Monday, especially due to introducing Thinking and reasoning in a combined model. Now, since the start of the week, the community actually had time to play with it, and some of them return to sonnet 3.5 and saying that while the model is generally much more capable, it tends to generate tons of things that are unnecessary. I wonder if the shift is due to Cursor/Windsurf specific prompts, or the model's larger output context, and we'll keep you updated on if the vibes shift again. Open Source LLMsThis week was HUGE for open source, folks. We saw releases pushing the boundaries of speed, multimodality, and even the very way LLMs generate text!DeepSeek's Open Source SpreeDeepSeek went on an absolute tear, open-sourcing a treasure trove of advanced tools and techniques:This isn't your average open-source dump, folks. We're talking FlashMLA (efficient decoding on Hopper GPUs), DeepEP (an optimized communication library for MoE models), DeepGEMM (an FP8 GEMM library that's apparently ridiculously fast), and even parallelism strategies like DualPipe and EPLB.They are releasing some advanced stuff for training and optimization of LLMs, you can follow all their releases on their X accountDual Pipe seems to be the one that got most attention from the community, which is an incredible feat in pipe parallelism, that even got the cofounder of HuggingFace super excitedMicrosoft's Phi-4: Multimodal and Mini (Blog, HuggingFace)Microsoft joined the party with Phi-4-multimodal (5.6B parameters) and Phi-4-mini (3.8B parameters), showing that small models can pack a serious punch.These models are a big deal. Phi-4-multimodal can process text, images, and audio, and it actually beats WhisperV3 on transcription! As Nisten said, "This is a new model and, I'm still reserving judgment until, until I tried it, but it looks ideal for, for a portable size that you can run on the phone and it's multimodal." It even supports a wide range of languages. Phi-4-mini, on the other hand, is all about speed and efficiency, perfect for finetuning.Diffusion LLMs: Mercury Coder and LLaDA (X , Try it)This is where things get really interesting. We saw not one, but two diffusion-based LLMs this week: Mercury Coder from Inception Labs and LLaDA 8B. (Although, ok, to be fair, LLaDa released 2 weeks ago I was just busy)For those who don't know, diffusion is usually used for creating things like images. The idea of using it to generate text is like saying, "Okay, there's a revolutionary tool for painting; I'll write the code using it." Inception Labs' Mercury Coder is claiming over 1000 tokens per second on NVIDIA H100s – that's insane speed, usually only seen with specialized chips! Nisten spent hours digging into these, noting, "This is a complete breakthrough and, it just hasn't quite hit yet that this just happened because people thought for a while it should be possible because then you can do, you can do multiple token prediction at once". He explained that these models combine a regular LLM with a diffusion component, allowing them to generate multiple tokens simultaneously and excel at tasks like "fill in the middle" coding.LLaDA 8B, on the other hand, is an open-source attempt, and while it needs more training, it shows the potential of this approach. LDJ pointed out that LLaDA is "trained on like around five times or seven times less data while already like competing with LLAMA3 AP with same parameter count".Are diffusion LLMs the future? It's too early to say, but the speed gains are very intriguing.Magma 8B: Robotics LLM from MicrosoftMicrosoft dropped Magma 8B, a Microsoft Research project, an open-source model that combines vision and language understanding with the ability to control robotic actions.Nisten was particularly hyped about this one, calling it "the robotics. LLM." He sees it as a potential game-changer for robotics companies, allowing them to build robots that can understand visual input, respond to language commands, and act in the real world.OpenAI's Deep Research for Everyone (Well, Plus Subscribers)OpenAI finally brought Deep Research, its incredible web-browsing and research tool, to Plus subscribers.I've been saying this for a while: Deep Research is another ChatGPT moment. It's that good. It goes out, visits websites, understands your query in context, and synthesizes information like nothing else. As Nisten put it, "Nothing comes close to OpenAI's Deep Research...People like pull actual economics data, pull actual stuff." If you haven't tried it, you absolutely should.Our full coverage of Deep Research is here if you haven't yet listened, it's incredible. Alexa Gets an AI Brain Upgrade with Alexa+Amazon finally announced Alexa+, the long-awaited LLM-powered upgrade to its ubiquitous voice assistant.Alexa+ will be powered by Claude (and sometimes Nova), offering a much more conversational and intelligent experience, with integrations across Amazon services.This is a huge deal. For years, Alexa has felt… well, dumb, compared to the advancements in LLMs. Now, it's getting a serious intelligence boost, thanks to Anthropic's Claude. It'll be able to handle complex conversations, control smart home devices, and even perform tasks across various Amazon services. Imagine asking Alexa, "Did I let the dog out today?" and it actually checking your Ring camera footage to give you an answer! (Although, as I joked, let's hope it doesn't start setting houses on fire.)Also very intriguing is the new SDKs they are releasing to connect Alexa+ to all kinds of experience, I think this is huge and will absolutely create a new industry of applications built for voice Alexa. Alexa Web Actions for example will allow Alexa to navigate to a website and complete actions (think order Uber Eats) The price? 20$/mo but free if you're a Amazon Prime subscriber, which is most of the US households at this point. They are focusing on personalization and memory, though still unclear how that's going to be handled, and the ability to share documents like schedules I'm very much looking forward to smart Alexa, and to be able to say "Alexa, set a timer for the amount of time it takes to hard boil an egg, and flash my house lights when the timer is done" Grok Gets a Voice... and It's UNHINGEDGrok, Elon Musk's AI, finally got a voice mode, and… well, it's something else.One-sentence summary: Grok's new voice mode includes an "unhinged" 18+ option that curses like a sailor, along with other personality settings.Yes, you read that right. There's literally an "unhinged" setting in the UI. We played it live on the show, and... well, let's just say it's not for the faint of heart (or for kids). Here's a taste:Alex: "Hey there."Grok: "Yo, Alex. What's good, you horny b*****d? How's your day been so far? Fucked up or just mildly shitty?"Beyond the shock value, the voice mode is actually quite impressive in its expressiveness and ability to understand interruptions. It has several personalities, from a helpful "Grok Doc" to an "argumentative" mode that will disagree with everything you say. It's... unique.This Week's Buzz (WandB-Related News)Agents Course is Coming!We announced our upcoming agents course! You can pre-sign up HERE . This is going to be a deep dive into building and deploying AI agents, so don't miss it!AI Engineer Summit RecapWe briefly touched on the AI Engineer Summit in New York, where we met with Kevin Hou and many other brilliant minds in the AI space. The theme was "Agents at Work," and it was a fantastic opportunity to see the latest developments in agent technology. I gave a talk about reasoning agents and had a workshop about evaluations on Saturday, and saw many listeners of ThursdAI 👏 ✋ Interview with Kevin Hou from WindsurfThis week we had the pleasure of chatting with Kevin Hou from Windsurf about their revolutionary AI editor. Windsurf isn't just another IDE, it's an agentic IDE. As Kevin explained, "we made the pretty bold decision of saying, all right, we're not going to do chat... we are just going to [do] agent." They've built Windsurf from the ground up with an agent-first approach, and it’s making waves.Kevin walked us through the evolution of AI coding tools, from autocomplete to chat, and now to agents. He highlighted the "magical experiences" users are having, like debugging complex code with AI assistance that actually understands the context. We also delved into the challenges – memory, checkpointing, and cost.We also talked about the burning question: vibe coding. Is coding as we know it dead? Kevin’s take was nuanced: "there's an in between state that I really vibe or like gel with, which is,the scaffolding of what you want… Let's use, let's like vibe code and purely use the agent to accomplish this sort of commit." He sees AI agents raising the bar for software quality, demanding better UX, testing, and overall polish.And of course, we had to ask about the elephant in the room – why are so many people switching from Cursor to Windsurf? Kevin's answer was humble, pointing to user experience, the agent-first workflow, and the team’s dedication to building the best product. Check out our full conversation on the pod and download Windsurf for yourself: windsurf.aiVideo Models & Voice model updates There is so much happening in LLM world, that folks may skip over the other stuff, but there's so much happening in these world's as well this week! Here's a brief recap! * Alibaba's WanX: Open-sourced, cutting-edge video generation models making waves with over 250,000 downloads already. They claim to take SOTA on open source video generation evals and of course img2video of this high quality model will lead to ... folks using it for all kinds of things. * HUMEs Octave: A groundbreaking LLM model that genuinely understands context and emotion and does TTS. Blog Hume has been doing emotional TTS but with this TTS focused LLM we are now able to create voices with a prompt, and receive emotional responses that are inferred from the text. Think shyness, sarcasm, anger etc* 11labs’ Scribe: Beating Whisper 3 with impressive accuracy and diarization features, Scribe is raising the bar in speech-to-text quality. 11labs releasing their own ASR (automatic speech recognition) was not in my cards, and boy did they deliver. Beating whisper, with speaker separation (diarization), world level timestamps and much lower WER than other models, this is a very interesting entry to this space. However, free for now on their website, it's significantly slower than Gemini 2.0 and Whisper for me at least. * Sesame releases their conversational speech model (and promising to open source this) and it's honestly the best / least uncanny conversations I had with an AI. Check out my conversation with it * Lastly, VEO 2, the best video model around according to some, is finally available via API (though txt2video only) and it's fairly expensive, but gives some amazing results. You can try it out on FALPhew, it looks like we've made it! Huge huge week in AI, big 2 new models, tons of incredible updates on multimodality and voice as well 🔥If you enjoyed this summary, the best way to support us is to share with a friend (or 3) and give us a 5 start reviews on wherever you get your podcasts, it really does help! 👏 See you next week, Alex 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

Holy moly, AI enthusiasts! Alex Volkov here, reporting live from the AI Engineer Summit in the heart of (touristy) Times Square, New York! This week has been an absolute whirlwind of announcements, from XAI's Grok 3 dropping like a bomb, to Figure robots learning to hand each other things, and even a little eval smack-talk between OpenAI and XAI. It’s enough to make your head spin – but that's what ThursdAI is here for. We sift through the chaos and bring you the need-to-know, so you can stay on the cutting edge without having to, well, spend your entire life glued to X and Reddit.This week we had a very special live show with the Haize Labs folks, the ones I previously interviewed about their bijection attacks, discussing their open source judge evaluation library called Verdict. So grab your favorite caffeinated beverage, maybe do some stretches because your mind will be blown, and let's dive into the TL;DR of ThursdAI, February 20th, 2025!Participants* Alex Volkov: AI Evangelist with Weights and Biases* Nisten: AI Engineer and cohost* Akshay: AI Community Member* Nuo: Dev Advocate at 01AI* Nimit: Member of Technical Staff at Haize Labs* Leonard: Co-founder at Haize LabsOpen Source LLMsPerplexity's R1 7076: Censorship-Free DeepSeekPerplexity made a bold move this week, releasing R1 7076, a fine-tuned version of DeepSeek R1 specifically designed to remove what they (and many others) perceive as Chinese government censorship. The name itself, 1776, is a nod to American independence – a pretty clear statement! The core idea? Give users access to information on topics the CCP typically restricts, like Tiananmen Square and Taiwanese independence.Perplexity used human experts to identify around 300 sensitive topics and built a "censorship classifier" to train the bias out of the model. The impressive part? They claim to have done this without significantly impacting the model's performance on standard evals. As Nuo from 01AI pointed out on the show, though, he'd "actually prefer that they can actually disclose more of their details in terms of post training... Running the R1 model by itself, it's already very difficult and very expensive." He raises a good point – more transparency is always welcome! Still, it's a fascinating attempt to tackle a tricky problem, the problem which I always say we simply cannot avoid. You can check it out yourself on Hugging Face and read their blog post.Arc Institute & NVIDIA Unveil Evo 2: Genomics PowerhouseGet ready for some serious science, folks! Arc Institute and NVIDIA dropped Evo 2, a massive genomics model (40 billion parameters!) trained on a mind-boggling 9.3 trillion nucleotides. And it’s fully open – two papers, weights, data, training, and inference codebases. We love to see it!Evo 2 uses the StripedHyena architecture to process huge genetic sequences (up to 1 million nucleotides!), allowing for analysis of complex genomic patterns. The practical applications? Predicting the effects of genetic mutations (super important for healthcare) and even designing entire genomes. I’ve been super excited about genomics models, and seeing these alternative architectures like StripedHyena getting used here is just icing on the cake. Check it out on X.ZeroBench: The "Impossible" Benchmark for VLLMsNeed more benchmarks? Always! A new benchmark called ZeroBench arrived, claiming to be the "impossible benchmark" for Vision Language Models (VLLMs). And guess what? All current top-of-the-line VLLMs get a big fat zero on it.One example they gave was a bunch of scattered letters, asking the model to "answer the question that is written in the shape of the star among the mess of letters." Honestly, even I struggled to see the star they were talking about. It highlights just how much further VLLMs need to go in terms of true visual understanding. (X, Page, Paper, HF)Hugging Face's Ultra Scale Playbook: Scaling UpFor those of you building massive models, Hugging Face released the Ultra Scale Playbook, a guide to building and scaling AI models on huge GPU clusters.They ran 4,000 scaling experiments on up to 512 GPUs (nothing close to Grok's 100,000, but still impressive!). If you're working in a lab and dreaming big, this is definitely a resource to check out. (HF).Big CO LLMs + APIsGrok 3: XAI's Big Swing new SOTA LLM! (and Maybe a Bug?)Monday evening, BOOM! While some of us were enjoying President's Day, the XAI team dropped Grok 3. They announced it with a setting very similar to OpenAI announcements. They're claiming state-of-the-art performance on some benchmarks (more on that drama later!), and a whopping 1 million token context window, finally confirmed after some initial confusion. They talked a lot about agents and a future of reasoners as well.The launch was a bit… messy. First, there was a bug where some users were getting Grok 2 even when the dropdown said Grok 3. That led to a lot of mixed reviews. Even when I finally thought I was using Grok 3, it still flubbed my go-to logic test, the "Beth's Ice Cubes" question. (The answer is zero, folks – ice cubes melt!). But Akshay, who joined us on the show, chimed in with some love: "...with just the base model of Grok 3, it's, in my opinion, it's the best coding model out there." So, mixed vibes, to say the least! It's also FREE for now, "until their GPUs melt," according to XAI, which is great.UPDATE: The vibes are shifting, more and more of my colleagues and mutuals are LOVING grok3 for one shot coding, for talking to it. I’m getting convinced as well, though I did use and will continue to use Grok for real time data and access to X. DeepSearchIn an attempt to show off some Agentic features, XAI also launched a deep search (not research like OpenAI but effectively the same) Now, XAI of course has access to X, which makes their deep search have a leg up, specifically for real time information! I found out it can even “use” the X search! OpenAI's Open Source TeaseIn what felt like a very conveniently timed move, Sam Altman dropped a poll on X the same day as the Grok announcement: if OpenAI were to open-source something, should it be a small, mobile-optimized model, or a model on par with o3-mini? Most of us chose o3 mini, just to have access to that model and play with it. No indication of when this might happen, but it’s a clear signal that OpenAI is feeling the pressure from the open-source community.The Eval Wars: OpenAI vs. XAIThings got spicy! There was a whole debate about the eval numbers XAI posted, specifically the "best of N" scores (like best of 64 runs). Boris from OpenAI, and Aiden mcLau called out some of the graphs. Folks on X were quick to point out that OpenAI also used "best of N" in the past, and the discussion devolved from there.XAI is claiming SOTA. OpenAI (or some folks from within OpenAI) aren't so sure. The core issue? We can't independently verify Grok's performance because there's no API yet! As I said, "…we're not actually able to use this model to independently evaluate this model and to tell you guys whether or not they actually told us the truth." Transparency matters, folks!DeepSearch - How Deep?Grok also touted a new "Deep Search" feature, kind of like Perplexity or OpenAI's "Deep Research" in their more expensive plan. My initial tests were… underwhelming. I nicknamed it "Shallow Search" because it spent all of 34 seconds on a complex query where OpenAI's Deep Research took 11 minutes and cited 17 sources. We're going to need to do some more digging (pun intended) on this one.This Week's BuzzWe’re leaning hard into agents at Weights & Biases! We just released an agents whitepaper (check it out on our socials!), and we're launching an agents course in collaboration with OpenAI's Ilan Biggio. Sign up at wandb.me/agents! We're hearing so much about agent evaluation and observability, and we're working hard to provide the tools the community needs.Also, sadly, our Toronto workshops are completely sold out. But if you're at AI Engineer in New York, come say hi to our booth! And catch my talk on LLM Reasoner Judges tomorrow (Friday) at 11 am EST – it’ll be live on the AI Engineer YouTube channel (HERE)!Vision & VideoMicrosoft MUSE: Playable Worlds from a Single ImageThis one is wild. Microsoft's MUSE can generate minutes of playable gameplay from just a single second of video frames and controller actions.It's based on the World and Human Action Model (WHAM) architecture, trained on a billion gameplay images from Xbox. So if you’ve been playing Xbox lately, you might be in the model! I found it particularly cool: "…you give it like a single second of a gameplay of any type of game with all the screen elements, with percentages, with health bars, with all of these things and their model generates a game that you can control." (X, HF, Blog).StepFun's Step-Video-T2V: State-of-the-Art (and Open Source!)We got two awesome open-source video breakthroughs this week. First, StepFun's Step-Video-T2V (and T2V Turbo), a 30 billion parameter text-to-video model. The results look really good, especially the text integration. Imagine a Chinese girl opening a scroll, and the words "We will open source" appearing as she unfurls it. That’s the kind of detail we're talking about.And it’s MIT licensed! As Nisten noted "This is pretty cool. It came out. Right before Sora came out, people would have lost their minds." (X, Paper, HF, Try It).HAO AI's FastVideo: Speeding Up HY-VideoThe second video highlight: HAO AI released FastVideo, a way to make HY-Video (already a strong open-source contender) three times faster with no additional training! They call the trick "Sliding Tile Attention" apparently that alone provides enormous boost compared to even flash attention.This is huge because faster inference means these models become more practical for real-world use. And, bonus: it supports HY-Video's Loras, meaning you can fine-tune it for, ahem, all kinds of creative applications. I will not go as far as to mention civit ai. (Github)Figure's Helix: Robot Collaboration!Breaking news from the AI Engineer conference floor: Figure, the humanoid robot company, announced Helix, a Vision-Language-Action (VLA) model built into their robots!It has full upper body control!What blew my mind: they showed two robots working together, handing objects to each other, based on natural language commands! As I watched, I exclaimed, "I haven't seen a humanoid robot, hand off stuff to the other one... I found it like super futuristically cool." The model runs on the robot, using a 7 billion parameter VLM for understanding and an 80 million parameter transformer for control. This is the future, folks!Tools & OthersMicrosoft's New Quantum Chip (and State of Matter!)Microsoft announced a new quantum chip and a new state of matter (called "topological superconductivity"). "I found it like absolutely mind blowing that they announced something like this," I gushed on the show. While I'm no quantum physicist, this sounds like a big deal for the future of computing.Verdict: Hayes Labs' Framework for LLM JudgesAnd of course, the highlight of our show: Verdict, a new open-source framework from Hayes Labs (the folks behind those "bijection" jailbreaks!) for composing LLM judges. This is a huge deal for anyone working on evaluation. Leonard and Nimit from Hayes Labs joined us to explain how Verdict addresses some of the core problems with LLM-as-a-judge: biases (like preferring their own responses!), sensitivity to prompts, and the challenge of "meta-evaluation" (how do you know your judge is actually good?).Verdict lets you combine different judging techniques ("primitives") to create more robust and efficient evaluators. Think of it as "judge-time compute scaling," as Leonard called it. They're achieving near state-of-the-art results on benchmarks like ExpertQA, and it's designed to be fast enough to use as a guardrail in real-time applications!One key insight: you don't always need a full-blown reasoning model for judging. As Nimit explained, Verdict can combine simpler LLM calls to achieve similar results at a fraction of the cost. And, it's open source! (Paper, Github,X).ConclusionAnother week, another explosion of AI breakthroughs! Here are my key takeaways:* Open Source is THRIVING: From censorship-free LLMs to cutting-edge video models, the open-source community is delivering incredible innovation.* The Need for Speed (and Efficiency): Whether it's faster video generation or more efficient LLM judging, performance is key.* Robots are Getting Smarter (and More Collaborative): Figure's Helix is a glimpse into a future where robots work together.* Evaluation is (Finally) Getting Attention: Tools like Verdict are essential for building reliable and trustworthy AI systems.* The Big Players are Feeling the Heat: OpenAI's open-source tease and XAI's rapid progress show that the competition is fierce.I'll be back in my usual setup next week, ready to break down all the latest AI news. Stay tuned to ThursdAI – and don't forget to give the pod five stars and subscribe to the newsletter for all the links and deeper dives. There’s potentially an Anthropic announcement coming, so we’ll see you all next week.TLDR* Open Source LLMs* Perplexity R1 1776 - finetune of china-less R1 (Blog, Model)* Arc institute + Nvidia - introduce EVO 2 - genomics model (X)* ZeroBench - impossible benchmark for VLMs (X, Page, Paper, HF)* HuggingFace ultra scale playbook (HF)* Big CO LLMs + APIs* Grok 3 SOTA LLM + reasoning and Deep Search (blog, try it)* OpenAI is about to open source something? Sam posts a polls* This weeks Buzz* We are about to launch an agents course! Pre-sign up wandb.me/agents* Workshops are SOLD OUT* Watch my talk LIVE from AI Engineer - 11am EST Friday (HERE)* Keep watching AI Eng conference after the show on AIE YT* )* Vision & Video* Microsoft MUSE - playable worlds from one image (X, HF, Blog)* Microsoft OmniParser - Better, faster screen parsing for GUI agents with OmniParser v2 (Gradio Demo)* HAO AI - fastVIDEO - making HY-Video 3x as fast (Github)* StepFun - Step-Video-T2V (+Turbo), a SotA 30B text-to-video model (Paper, Github, HF, Try It)* Figure announces HELIX - vision action model built into FIGURE Robot (Paper)* Tools & Others* Microsoft announces a new quantum chip and a new state of matter (Blog, X)* Verdict - Framework to compose SOTA LLM judges with JudgeTime Scaling (Paper, Github,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