Höre ThursdAI - The top AI news from the past week

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 [https://twitter.com/kwindla/]), our resident voice expert, dropped in to talk about some mind-blowing TTS and her own open-source VAD release. Maziyar Panahi (@MaziyarPanahi [https://x.com/MaziyarPanahi]) gave us the inside scoop on OpenAI's recent meeting with the open source community. And Dex Horthy (@dexhorthy [https://x.com/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 Efficiency Even 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 Meeting Perhaps 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 [https://huggingface.co/spaces/nvidia/describe-anything-model-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 [https://x.com/reach_vb/status/1914962078571356656]) 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 [https://huggingface.co/nvidia/DAM-3B]), 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 [https://huggingface.co/datasets/nvidia/DescribeAnythingDataset]) 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 [https://www.reddit.com/media?url=https://i.redd.it/23ut7jd3klve1.jpeg]) 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 Conversations Even 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 [https://github.com/nari-labs/dia], HF [https://huggingface.co/nari-labs/Dia-1.6B]). What makes it special? The insane emotional range and natural interaction patterns. My Twitter post about it (X post [https://x.com/altryne/status/1914421814455099680]) 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 [https://narilabs.github.io/dia/] | Fal.ai Voice Clone Demo [https://fal.ai/models/fal-ai/dia-tts/voice-clone] Another 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 VAD Speaking 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 [https://github.com/pipecat-ai/smart-turn], HF Model [https://huggingface.co/pipecat-ai/smart-turn]) 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 [https://turn-training.pipecat.ai] site (Try It Demo [https://pcc-smart-turn.vercel.app/])! 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 [https://maven.com/pipecat/voice-ai-and-voice-agents-a-technical-deep-dive?utm_source=student&utm_campaign=welcome] 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 Hits A 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 [https://x.com/TencentHunyuan/status/1915026828013850791]. 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 Horthy With 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 [https://github.com/humanlayer/12-factor-agents/tree/main]), 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 Frameworks A 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 Context So, 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 [https://github.com/humanlayer/12-factor-agents/tree/main] and the webinar recording [https://lu.ma/12-factor-agent] for a deeper dive. Big Companies & APIs: GPT Image and Grok Get Developer Access While 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 [https://x.com/OpenAIDevs/status/1915097067023900883]) 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 [https://platform.openai.com/docs/guides/image-generation?image-generation-model=gpt-image-1]) . 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 [https://platform.openai.com/docs/api-reference/images]) 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 backgrounds I 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 [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 [https://docs.x.ai/docs/overview]). 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 [https://x.com/ebbyamir/status/1914820712092852430]) . 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 & More Just 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 [https://x.com/SandAI_HQ/status/1914303284954996749], GitHub [https://github.com/SandAI-org/Magi-1], PDF Report [https://static.magi.world/static/files/MAGI_1.pdf]). 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 VRAM Wolfram 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 [https://github.com/lllyasviel/FramePack]) Character AI AvatarFX Steps In Also in the visual space, Character AI announced AvatarFX in early access (Website [https://t.co/cdF6H58kBk]), 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 / Community Quick 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 [http://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 [fullyconnected.com]) Wrapping Up the "Chill" Week That Wasn't Quite Chill Phew! 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 [thursdai.news/spotify] 🔗 Apple: thursdai.news/apple [thursdai.news/apple] 🔗 Youtube: thursdai.news/yt [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!- Alex TL;DR and Show Notes (April 23rd, 2024) * Hosts and Guests * Alex Volkov - AI Evangelist & Weights & Biases @altryne [http://x.com/@altryne] * Co Hosts - Wolfram Ravenwlf @WolframRvnwlf [http://x.com/@WolframRvnwlf], Yam Peleg @yampeleg [x.com/@yampeleg], Nisten Tahiraj @nisten [http://x.com/@nisten], LDJ @ldjconfirmed [http://x.com/@ldjconfirmed] * Kwindla Kramer @kwindla [https://twitter.com/kwindla/] - Daily Co-Founder // Voice expert * Dexter Horthy @dexhorthy [https://x.com/dexhorthy] - HumanLayer // Agents expert * Maziyar Panahi @MaziyarPanahi [https://x.com/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 [https://x.com/reach_vb/status/1914962078571356656], HF model [https://huggingface.co/nvidia/DAM-3B], HF demo [https://huggingface.co/spaces/nvidia/describe-anything-model-demo]) * Google Gemma QAT: Quantization-Aware Training models (X [https://x.com/osanseviero/status/1913220285328748832], Blog [https://developers.googleblog.com/en/gemma-3-quantized-aware-trained-state-of-the-art-ai-to-consumer-gpus/]) * Big CO LLMs + APIs * OpenAI GPT Image 1 API: (X Post [https://x.com/OpenAIDevs/status/1915097067023900883], Docs [https://platform.openai.com/docs/guides/image-generation?image-generation-model=gpt-image-1], API Reference [https://platform.openai.com/docs/api-reference/images]) * Grok API & App Updates: Grok 3 and Grok 3 Mini available via API. (API Docs [https://docs.x.ai/docs/overview], App Update X Post [https://x.com/ebbyamir/status/1914820712092852430]) * This weeks Buzz - Weights & Biases * WeaveHacks SF: Hackathon May 17-18 at W&B HQ. lu.ma/weavehacks [http://lu.ma/weavehacks] * Fully Connected: W&B's 2-day conference, June 18-19 in SF fullyconnected.com [https://www.fullyconnected.com/] * Vision & Video * Send AI MAGI-1: 24B autoregressive diffusion model for long, streaming video (X Post [https://x.com/SandAI_HQ/status/1914303284954996749], GitHub [https://github.com/SandAI-org/Magi-1], PDF Report [https://static.magi.world/static/files/MAGI_1.pdf], HF Repo [https://huggingface.co/sand-ai/MAGI-1]) * Character AI AvatarFX: Early access for creating speaking/emoting avatars from images . (Website [https://t.co/cdF6H58kBk]) * Framepack: Mentioned for long video generation (120s) on low VRAM (6GB). (Project Page [https://framepack.github.io/]) * Voice & Audio * Nari Labs Dia: 1.6B param OSS TTS model (X Post Highlight [https://x.com/altryne/status/1914421814455099680], HF Model [https://huggingface.co/nari-labs/Dia-1.6B], Github [https://github.com/nari-labs/dia], Fal.ai Demo [https://fal.ai/models/fal-ai/dia-tts/voice-clone]) * PipeCat Smart-Turn VAD: Open source semantic VAD model (Github [https://github.com/pipecat-ai/smart-turn], HF Model [https://huggingface.co/pipecat-ai/smart-turn], Fal.ai Playground [https://fal.ai/models/fal-ai/smart-turn/playground], Try It Demo [https://pcc-smart-turn.vercel.app/]) * AI Art & Diffusion & 3D * Hunyuan 3D 2.5 (Tencent): 10B param update [0:09:06]. Higher res geometry, PBR textures, improved rigging. (X Post [https://x.com/TencentHunyuan/status/1915026828013850791]) * Agents , Tools & Links * 12 Factor Agents: Discussion with Dex Horthy on building robust agents (Github Repo [https://github.com/humanlayer/12-factor-agents/tree/main]) 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 [https://sub.thursdai.news/subscribe?utm_medium=podcast&utm_campaign=CTA_2]

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 + APIs OpenAI o3 & o4‑mini: SOTA Reasoning Meets Tool‑Use (Blog [https://openai.com/index/introducing-o3-and-o4-mini/], Watch Party [https://youtube.com/live/2G-VwWxKCkk?feature=share]) 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 images This 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 Performance Yam 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 [https://www.youtube.com/live/A5-Zxj816J0], Prompting guide [https://x.com/noahmacca/status/1911898549308280911]) 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 [https://wandb.ai/site/weave?utm_source=thursdai&utm_medium=referral&utm_campaign=apr17]. 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 News Just 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 [https://ai.google.dev/docs/gemini_api_overview] (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 2025 How 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 [https://ai.google.dev/docs] and Gemini Cookbook [https://ai.google.dev/examples]. Open Source: LLMs, Tools & more OpenAI open sources MRCR eval and Codex (Mrcr HF [https://huggingface.co/datasets/openai/mrcr], Codex Github [https://github.com/openai/codex]) 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 Updates While OpenAI's contributions were notable, it wasn't the only action this week: * Microsoft's BitNet v1.5 (HF [https://huggingface.co/collections/microsoft/BitNet]): 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 [https://www.primeintellect.ai/blog/intellect-2], X [https://x.com/primeintellect_ai]): 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 [Z.ai] (Formerly ChatGLM) & GLM-4 Family (X [https://x.com/Zai_org/status/1779846143024941199], HF [https://huggingface.co/collections/THUDM], GitHub [https://github.com/THUDM/GLM-4]): The team behind ChatGLM rebranded to Z.ai [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 A2A On 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 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Fweave_wb%2Fstatus%2F1912246450857341092]) 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 Segal This 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 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fgithub.com%2Fgoogle%2FA2A]), 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 Slicker The visual AI space keeps advancing rapidly. Veo-2 Video Generation Hits GA in Vertex AI & Gemini App (Blog [https://developers.googleblog.com/en/veo-2-video-generation-now-generally-available/], Try It [http://ai.dev]) 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 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Faltryne%2Fstatus%2F1912043121497850242], Blog [https://www.google.com/url?sa=E&q=https%3A%2F%2Fklingai.com]) 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 [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 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fblog.google%2Ftechnology%2Fai%2Fdolphingemma%2F]) 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 Champs Seedream 3.0: ByteDance's Bilingual Image Powerhouse (Tech post [https://www.google.com/url?sa=E&q=https%3A%2F%2Fteam.doubao.com%2Fen%2Ftech%2Fseedream3_0], arXiv [https://www.google.com/url?sa=E&q=https%3A%2F%2Farxiv.org%2Fabs%2F2504.11346], AIbase news [https://www.google.com/url?sa=E&q=https%3A%2F%2Fwww.aibase.com%2Fnews%2F17208]) 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 [http://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! Alex TL;DR and Show Notes Everything we covered today in bite-sized pieces with links! * Hosts and Guests * Alex Volkov - AI Evangelist & Weights & Biases (@altryne [https://www.google.com/url?sa=E&q=http%3A%2F%2Fx.com%2F%40altryne]) * Co Hosts - @WolframRvnwlf [https://www.google.com/url?sa=E&q=http%3A%2F%2Fx.com%2F%40WolframRvnwlf] @yampeleg [https://www.google.com/url?sa=E&q=x.com%2F%40yampeleg] @nisten [https://www.google.com/url?sa=E&q=http%3A%2F%2Fx.com%2F%40nisten] @ldjconfirmed [https://www.google.com/url?sa=E&q=http%3A%2F%2Fx.com%2F%40ldjconfirmed]) * Todd Segal - Principal Software Engineer @ Google - Working on A2A Protocol * Big CO LLMs + APIs * 👑 OpenAI launches o3 and o4-mini in chatGPT & API (Blog [https://www.google.com/url?sa=E&q=https%3A%2F%2Fopenai.com%2Findex%2Fintroducing-o3-and-o4-mini%2F], Our Coverage [https://www.google.com/url?sa=E&q=https%3A%2F%2Fyoutube.com%2Flive%2F2G-VwWxKCkk], o3 and o4-mini announcement [https://www.google.com/url?sa=E&q=https%3A%2F%2Fopenai.com%2Findex%2Fintroducing-o3-and-o4-mini%2F]) * OpenAI launches GPT 4.1, 4.1-mini and 4.1-nano in API (Our Coverage [https://www.google.com/url?sa=E&q=https%3A%2F%2Fwww.youtube.com%2Flive%2FA5-Zxj816J0], Prompting guide [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Fnoahmacca%2Fstatus%2F1911898549308280911]) * 🚨 Google launches Gemini 2.5 Flash with controllable thinking budgets (Blog Post - Placeholder Link [https://www.google.com/url?sa=E&q=https%3A%2F%2Fblog.google%2Ftechnology%2Fai%2Fgoogle-gemini-update-flash-extension%2F], API Docs [https://www.google.com/url?sa=E&q=https%3A%2F%2Fai.google.dev%2Fdocs]) * Mistral classifiers Factory * Claude does research + workspace integration (Blog [https://www.google.com/url?sa=E&q=https%3A%2F%2Fwww.anthropic.com%2Fnews%2Fresearch]) * Cohere Embed‑4 — Multimodal embeddings for enterprise search (Blog [https://www.google.com/url?sa=E&q=https%3A%2F%2Fcohere.com%2Fblog%2Fembed-4], Docs Changelog [https://www.google.com/url?sa=E&q=https%3A%2F%2Fdocs.cohere.com%2Fv2%2Fchangelog%2Fembed-multimodal-v4], X [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Fcohere%2Fstatus%2F1912128813104078999]) * Open Source LLMs * OpenAI open sources MRCR Long‑Context Benchmark (Hugging Face [https://www.google.com/url?sa=E&q=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Fopenai%2Fmrcr]) * Microsoft BitNet v1.5 (HF [https://www.google.com/url?sa=E&q=https%3A%2F%2Fhuggingface.co%2Fcollections%2Fmicrosoft%2FBitNet]) * INTELLECT‑2 — Prime Intellect’s 32B “globally‑distributed RL” experiment (Blog [https://www.google.com/url?sa=E&q=https%3A%2F%2Fwww.primeintellect.ai%2Fblog%2Fintellect-2], X [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Fprimeintellect_ai]) * Z.ai [Z.ai] (previously chatGLM) + GLM‑4‑0414 open‑source family (X [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2FZai_org%2Fstatus%2F1779846143024941199], HF Collection [https://www.google.com/url?sa=E&q=https%3A%2F%2Fhuggingface.co%2Fcollections%2FTHUDM], GitHub [https://www.google.com/url?sa=E&q=https%3A%2F%2Fgithub.com%2FTHUDM%2FGLM-4]) * This weeks Buzz + MCP/A2A * Weave playground support for GPT 4.1 and o3/o4-mini models (X [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Fweave_wb%2Fstatus%2F1912246450857341092]) * Chat with Todd Segal - A2A Protocol (GitHub Spec [https://www.google.com/url?sa=E&q=https%3A%2F%2Fgithub.com%2Fgoogle%2FA2A]) * Vision & Video * Veo‑2 Video Generation in GA, Gemini App (Dev Blog [https://www.google.com/url?sa=E&q=https%3A%2F%2Fdevelopers.googleblog.com%2Fen%2Fveo-2-video-generation-now-generally-available%2F]) * Kling 2.0 Creative Suite (X [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Faltryne%2Fstatus%2F1912043121497850242], Blog [https://www.google.com/url?sa=E&q=https%3A%2F%2Fklingai.com]) * ByteDance public Seaweed-7B, a video generation foundation model (seaweed.video [https://www.google.com/url?sa=E&q=https%3A%2F%2Fseaweed.video%2F]) * Voice & Audio * DolphinGemma — Google AI tackles dolphin communication (Blog [https://www.google.com/url?sa=E&q=https%3A%2F%2Fblog.google%2Ftechnology%2Fai%2Fdolphingemma%2F]) * AI Art & Diffusion & 3D * Seedream 3.0 bilingual image diffusion – ByteDance (Tech post [https://www.google.com/url?sa=E&q=https%3A%2F%2Fteam.doubao.com%2Fen%2Ftech%2Fseedream3_0], arXiv [https://www.google.com/url?sa=E&q=https%3A%2F%2Farxiv.org%2Fabs%2F2504.11346], AIbase news [https://www.google.com/url?sa=E&q=https%3A%2F%2Fwww.aibase.com%2Fnews%2F17208]) * Tools * OpenAI debuts Codex CLI, an open source coding tool for terminals (Github [https://www.google.com/url?sa=E&q=https%3A%2F%2Fgithub.com%2Fopenai%2Fcodex]) * 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 [https://sub.thursdai.news/subscribe?utm_medium=podcast&utm_campaign=CTA_2]

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 [https://sub.thursdai.news/p/thursdai-july-12-show-recap-notes] 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 [https://ai.meta.com/blog/llama-4-multimodal-intelligence/], HF [https://huggingface.co/meta-llama], Try It [https://meta.ai/]) 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 released These 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 Zuck Not 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 benchmarks Too 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 Take Despite 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 [https://x.com/togethercompute/status/1909697124805333208], Blog [https://www.together.ai/blog/deepcoder]) 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 [https://huggingface.co/agentica-org/DeepCoder-14B-Preview]), the dataset (HF [https://huggingface.co/datasets/agentica-org/DeepCoder-Preview-Dataset]), the Weights & Biases logs [https://wandb.ai/mluo/deepcoder] and even the eval logs [https://drive.google.com/file/d/1tr_xXvCJnjU0tLO7DNtFL85GIr3aGYln/view?usp=sharing]! 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 [https://huggingface.co/nvidia/Llama-3_1-Nemotron-Ultra-253B-v1]) 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, Kimi The 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 [https://jina.ai/news/jina-reranker-m0-multilingual-multimodal-document-reranker/], HF [https://huggingface.co/jinaai/jina-reranker-m0]) * 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 [https://www.deepcogito.com/research/cogito-v1-preview], HF [https://huggingface.co/deepcogito/cogito-v1-preview-llama-70B]) * 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 [https://huggingface.co/collections/moonshotai/kimi-vl-a3b-67f67b6ac91d3b03d382dd85]) 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 [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 [https://observable.tools]) – 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 [https://wandb.me/mcp-spec] 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 [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) [oneb.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 A2A As mentioned earlier, we're also excited to be a launch partner for Google's new Agent2Agent [https://wandb.ai/wandb_fc/product-announcements-fc/reports/Powering-Agent-Collaboration-Weights-Biases-Partners-with-Google-Cloud-on-Agent2Agent-Interoperability-Protocol---VmlldzoxMjE3NDg3OA] (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 Upgrade While 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 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fblog.google%2Fproducts%2Fgoogle-cloud%2Fnext-2025%2F]) 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 [https://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 [https://twitter.com/demishassabis/status/1910107859041271977])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 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fdevelopers.googleblog.com%2Fen%2Fa2a-a-new-era-of-agent-interoperability%2F] , Spec [https://github.com/google/A2A], W&B Blog [https://www.google.com/url?sa=E&q=https%3A%2F%2Fwandb.ai%2Fwandb_fc%2Fproduct-announcements-fc%2Freports%2FPowering-Agent-Collaboration-Weights-Biases-Partners-with-Google-Cloud-on-Agent2Agent-Interoperability-Protocol---VmlldzoxMjE3NDg3OA])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 [https://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 Memory Right 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 VLMs The most impressive long form AI video paper dropped, showing that it's possible to create 1 minute long video, with incredible character and scene consistency This paper [https://t.co/agJKUAExpz] 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 Crown HiDream-I1-Dev 17B MIT license new leading open weights image gen! (HF [https://www.google.com/url?sa=E&q=https%3A%2F%2Fhuggingface.co%2Fcollections%2FHiDream-ai%2Fhidream-i1-67f3e90dd509fed088a158b3]) 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 Could GitMCP - turn any github repo into an MCP server (website [https://gitmcp.io]) 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). [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. Alex TL;DR and Show Notes * Hosts and Guests * Alex Volkov - AI Evangelist & Weights & Biases (@altryne [http://x.com/@altryne]) * Co Hosts - @WolframRvnwlf [http://x.com/@WolframRvnwlf] @yampeleg [http://x.com/@yampeleg] @nisten [http://x.com/@nisten] @ldjconfirmed [http://x.com/@ldjconfirmed] * Michael Luo @michaelzluo [http://x.com/michaelzluo] - CS PhD @ UC Berkeley; AI & Systems * Liad Yosef (@liadyosef [https://x.com/liadyosef]), Ido Salomon (@idosal1 [https://x.com/idosal1]) - GitMCP creators * Open Source LLMs * Meta drops LLama 4 (Scout 109B/17BA & Maverick 400B/17BA) - (Blog [https://ai.meta.com/blog/llama-4-multimodal-intelligence/], HF [https://huggingface.co/meta-llama], Try It [https://meta.ai/]) * Together AI and Agentica (UC Berkley) announce DeepCoder-14B (X [https://x.com/togethercompute/status/1909697124805333208], Blog [https://www.together.ai/blog/deepcoder]) * NVIDIA Nemotron Ultra is here! 253B pruned LLama 3-405B (X [https://x.com/kuchaev/status/1909444566379573646], HF [https://huggingface.co/nvidia/Llama-3_1-Nemotron-Ultra-253B-v1]) * Jina Reranker M0 - SOTA multimodal reranker model (Blog [https://jina.ai/news/jina-reranker-m0-multilingual-multimodal-document-reranker/], HF [https://huggingface.co/jinaai/jina-reranker-m0]) * DeepCogito - SOTA models 3-70B - beating DeepSeek 70B - (Blog [https://www.deepcogito.com/research/cogito-v1-preview], HF [https://huggingface.co/deepcogito/cogito-v1-preview-llama-70B]) * ByteDance new release - Seed-Thinking-v1.5 [https://github.com/ByteDance-Seed/Seed-Thinking-v1.5] * Big CO LLMs + APIs * Google announces TONS of new things 🙌 (Blog [https://blog.google/products/google-cloud/next-2025/]) * Google launches Firebase Studio (website [https://firebase.studio/]) * Google is announcing official support for MCP (X [https://x.com/demishassabis/status/1910107859041271977]) * Google announces A2A protocol - agent 2 agent communication (Blog [https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/], Spec [https://github.com/google/A2A], W&B Blog [https://wandb.ai/wandb_fc/product-announcements-fc/reports/Powering-Agent-Collaboration-Weights-Biases-Partners-with-Google-Cloud-on-Agent2Agent-Interoperability-Protocol---VmlldzoxMjE3NDg3OA]) * Cloudflare - new Agents SDK (Website [https://agents.cloudflare.com/]) * Anthropic MAX - $200/mo with more quota * Grok 3 finally launches API tier (API [https://docs.x.ai/docs/models#models-and-pricing]) * OPenAI adds enhanced memory to ChatGPT - can remember all your chats (X [https://x.com/OpenAI/status/1910378768172212636]) * This weeks Buzz - MCP and A2A * W&B launches the observable.tools [http://observable.tools] initiative & invite people to comment on the MCP RFC [http://wandb.me/mcp-spec] * W&B is the launch partner for Google's A2A (Blog [https://wandb.ai/wandb_fc/product-announcements-fc/reports/Powering-Agent-Collaboration-Weights-Biases-Partners-with-Google-Cloud-on-Agent2Agent-Interoperability-Protocol---VmlldzoxMjE3NDg3OA]) * Vision & Video * Kimi-VL and Kimi-VL-Thinking - A3B vision models (X, HF [https://t.co/cgCMsiHN8p]) * One-Minute Video Generation with Test-Time Training (Blog [https://t.co/BSHsucizoG], Paper [https://t.co/agJKUAExpz]) * Voice & Audio * Amazon - Nova Sonic - speech2speech foundational model (Blog [https://www.aboutamazon.com/news/innovation-at-amazon/nova-sonic-voice-speech-foundation-model]) * AI Art & Diffusion & 3D * HiDream-I1-Dev 17B MIT license new leading open weights image gen 0 passes Flux1.1[pro] ! (HF [https://huggingface.co/collections/HiDream-ai/hidream-i1-67f3e90dd509fed088a158b3]) * Tools * GitMCP - turn any github repo into an MCP server (try it [https://gitmcp.io/]) 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 [https://sub.thursdai.news/subscribe?utm_medium=podcast&utm_campaign=CTA_2]

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 Raised It 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 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Fkevinweil%2Fstatus%2F1906797119848988822] that they're working on a "highly capable open language model" and are actively seeking developer feedback through dedicated sessions (sign up here [https://www.google.com/url?sa=E&q=https%3A%2F%2Fopenai.com%2Fform%2Fopen-model-feedback] 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 <700M user license limit. Seeing OpenAI potentially re-embrace its "Open" roots with a potentially SOTA model is huge. We'll be watching like hawks! Second, they dropped PaperBench [https://www.google.com/url?sa=E&q=https%3A%2F%2Fopenai.com%2Findex%2Fpaperbench%2F], 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 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fgithub.com%2Fopenai%2Fpreparedness]). 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 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fgithub.com%2Fopenai%2Fpreparedness%2Ftree%2Fmain%2Fproject%2Fpaperbench] and read the full paper here [https://www.google.com/url?sa=E&q=https%3A%2F%2Fcdn.openai.com%2Fpapers%2F22265bac-3191-44e5-b057-7aaacd8e90cd%2Fpaperbench.pdf]. Third, the casual 40 Billion Dollars [https://openai.com/index/investing−in−our−mission/] 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 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2FOpenAI%2Fstatus%2F1907124258867982338] in advanced voice mode. It's surprisingly entertaining. Open Source Powerhouses: Nomic & OpenHands Deliver SOTA Beyond 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 Docs Our friends at Nomic AI are back with a killer release! We had Zach Nussbaum on the show discussing Nomic Embed Multimodal [https://www.google.com/url?sa=E&q=https%3A%2F%2Fwww.nomic.ai%2Fblog%2Fposts%2Fnomic-embed-multimodal]. These are new 3B & 7B parameter embedding models (available on Hugging Face [https://www.google.com/url?sa=E&q=https%3A%2F%2Fhuggingface.co%2Fcollections%2Fnomic-ai%2Fnomic-embed-multimodal-67e5ddc1a890a19ff0d58073]) 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 Coding Remember OpenDevin? It evolved into OpenHands, and the team just dropped their own OpenHands LM 32B [https://www.google.com/url?sa=E&q=https%3A%2F%2Fwww.all-hands.dev%2Fblog%2Fintroducing-openhands-lm-32b----a-strong-open-coding-agent-model]! We chatted with co-founder Xingyao "Elle" Wang about this impressive Qwen 2.5 finetune (MIT licensed, on Hugging Face [https://www.google.com/url?sa=E&q=https%3A%2F%2Fhuggingface.co%2Fall-hands%2Fopenhands-lm-32b-v0.1]). 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 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fapp.all-hands.dev%2F] ($50 free credit). Amazing progress from this team! Frontiers: Diffusion LMs & Superhuman Math Two other developments pushed the boundaries this week: Dream 7B: A Diffusion Language Model Challenger? This one's fascinating conceptually. Researchers unveiled Dream 7B [https://www.google.com/url?sa=E&q=https%3A%2F%2Fhkunlp.github.io%2Fblog%2F2025%2Fdream%2F], a language model based on diffusion, not auto-regression. The benchmarks they shared [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2FJiachengYe15%2Fstatus%2F1907430553369883017] show it competing strongly with top 7-8B models, and absolutely crushing tasks like Sudoku (81% vs <50% for others), potentially due to its parallel processing nature being better for global constraints. It's an exciting hint at alternative architectures, but the 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 Observable Amazon's Nova Act Agent & The Need for Access Amazon entered the agent chat with Nova Act [https://www.google.com/url?sa=E&q=https%3A%2F%2Flabs.amazon.science%2Fblog%2Fnova-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 Speeds Hardware keeps accelerating. CoreWeave announced hitting 800 Tokens/sec on Llama 3.1 405B [https://www.google.com/url?sa=E&q=https%3A%2F%2Fwww.prnewswire.com%2Fnews-releases%2Fcoreweave-achieves-new-record-breaking-ai-inferencing-benchmark-with-nvidia-gb200-grace-blackwell-superchips-302418682.html%3Fhss_channel%3Dtw-979803443681349632] 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 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fobservable.tools%2F] initiative. The goal: integrate observability into the MCP standard itself. Right now, that link redirects to a GitHub discussion [https://www.google.com/url?sa=E&q=https%3A%2F%2Fgithub.com%2Fmodel-context-protocol%2Fspecification%2Fdiscussions%2F18] 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 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Faltryne%2Fstatus%2F1906180131540066796] showed there's huge demand for usable MCP clients too – more on that soon!). Vision & Video: Entering the Uncanny Valley This space is moving at lightning speed. Runway Gen-4 [https://www.google.com/url?sa=E&q=https%3A%2F%2Frunwayml.com%2Fresearch%2Fintroducing-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 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fdreamina.capcut.com%2Fai-tool%2Fvideo%2Flip-sync%2Fgenerate] (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 [https://www.hedra.com/] feels actually better (and much much faster) Meta's MoCHA [https://www.google.com/url?sa=E&q=https%3A%2F%2Fcongwei1230.github.io%2FMoCha%2F] 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-02 While Gladia launched their Solaria STT [https://www.google.com/url?sa=E&q=https%3A%2F%2Fwww.gladia.io%2Fsolaria], the standout for me was Hailuo's Speech-02 TTS API [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2FHailuo_AI%2Fstatus%2F1906723587379101923]. 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 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fblog.google%2Ftechnology%2Fgoogle-labs%2Fnotebooklm-discover-sources%2F] 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 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fthursdai.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 Notes Host, Guests, and Co-hosts * Host: Alex Volkov - AI Evangelist & Weights & Biases (@altryne [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Faltryne]) * Co-Hosts: * LDJ (@ldjconfirmed [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Fldjconfirmed]) * Yam Peleg (@yampeleg [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Fyampeleg]) * Guests: * Zach Nussbaum (@zach_nussbaum [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Fzach_nussbaum]) - Nomic AI * Xingyao Wang (@xingyaow_ [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Fxingyaow_]) - All Hands AI / OpenHands * Cong Wei (@CongWei1230 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2FCongWei1230]) - Meta AI / MoCha Key Topics & Links * OpenAI's Big Week: * Teasing highly capable Open Source Reasoner Model [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Fkevinweil%2Fstatus%2F1906797119848988822] (seeking feedback [https://www.google.com/url?sa=E&q=https%3A%2F%2Fopenai.com%2Fform%2Fopen-model-feedback]). * Released PaperBench [https://www.google.com/url?sa=E&q=https%3A%2F%2Fopenai.com%2Findex%2Fpaperbench%2F] eval (code [https://www.google.com/url?sa=E&q=https%3A%2F%2Fgithub.com%2Fopenai%2Fpreparedness%2Ftree%2Fmain%2Fproject%2Fpaperbench], paper [https://www.google.com/url?sa=E&q=https%3A%2F%2Fcdn.openai.com%2Fpapers%2F22265bac-3191-44e5-b057-7aaacd8e90cd%2Fpaperbench.pdf]) & Nano-Eval framework [https://www.google.com/url?sa=E&q=https%3A%2F%2Fgithub.com%2Fopenai%2Fpreparedness]. * Raised $40B at $300B valuation [https://www.google.com/url?sa=E&q=https%3A%2F%2Fopenai.com%2Findex%2Finvesting-in-our-mission%2F]. * New EMO "Monday" voice [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2FOpenAI%2Fstatus%2F1907124258867982338] in ChatGPT. * Open Source Powerhouses: * Nomic Embed Multimodal [https://www.google.com/url?sa=E&q=https%3A%2F%2Fwww.nomic.ai%2Fblog%2Fposts%2Fnomic-embed-multimodal]: SOTA visual doc embeddings (3B & 7B, Apache 2.0 for 7B [https://www.google.com/url?sa=E&q=https%3A%2F%2Fhuggingface.co%2Fcollections%2Fnomic-ai%2Fnomic-embed-multimodal-67e5ddc1a890a19ff0d58073]). * OpenHands LM 32B [https://www.google.com/url?sa=E&q=https%3A%2F%2Fwww.all-hands.dev%2Fblog%2Fintroducing-openhands-lm-32b----a-strong-open-coding-agent-model]: SOTA-level coding agent model (Qwen finetune, MIT License [https://www.google.com/url?sa=E&q=https%3A%2F%2Fhuggingface.co%2Fall-hands%2Fopenhands-lm-32b-v0.1], 37.2% SWE-Bench, #2 Live SWE-Bench). Cloud version available [https://www.google.com/url?sa=E&q=https%3A%2F%2Fapp.all-hands.dev%2F]. * Frontier Models & Capabilities: * Dream 7B [https://www.google.com/url?sa=E&q=https%3A%2F%2Fhkunlp.github.io%2Fblog%2F2025%2Fdream%2F]: Promising diffusion LM shows strong benchmark results (esp. Sudoku [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2FJiachengYe15%2Fstatus%2F1907430553369883017]), but weights not yet released. * Gemini 2.5: Crushes hard USAMO math eval (24.4% vs <5% for others), showcasing superior reasoning. * Agents & Compute: * Amazon's Nova Act [https://www.google.com/url?sa=E&q=https%3A%2F%2Flabs.amazon.science%2Fblog%2Fnova-act] agent announced, claims SOTA but lacks public access (request form [https://www.google.com/url?sa=E&q=https%3A%2F%2Fnova.amazon.com]). * CoreWeave/NVIDIA [https://www.google.com/url?sa=E&q=https%3A%2F%2Fwww.prnewswire.com%2Fnews-releases%2Fcoreweave-achieves-new-record-breaking-ai-inferencing-benchmark-with-nvidia-gb200-grace-blackwell-superchips-302418682.html%3Fhss_channel%3Dtw-979803443681349632]: Massive inference speedups (800T/s on Llama 405B with GB200). * This Week's Buzz - MCP: * Observable Tools [https://www.google.com/url?sa=E&q=https%3A%2F%2Fobservable.tools%2F] initiative launched to add observability to MCP. * Proposal using OpenTelemetry posted for community feedback on GitHub [https://www.google.com/url?sa=E&q=https%3A%2F%2Fgithub.com%2Fmodel-context-protocol%2Fspecification%2Fdiscussions%2F18] - please support! * Huge demand shown for usable MCP clients (viral tweet [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Faltryne%2Fstatus%2F1906180131540066796]). * Vision & Video Highlights: * Runway Gen-4 [https://www.google.com/url?sa=E&q=https%3A%2F%2Frunwayml.com%2Fresearch%2Fintroducing-runway-gen-4] focuses on video consistency. * ByteDance OmniHuman [https://www.google.com/url?sa=E&q=https%3A%2F%2Fdreamina.capcut.com%2Fai-tool%2Fvideo%2Flip-sync%2Fgenerate] (image-to-avatar) now publicly available via Dreamina (example thread [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Faltryne%2Fstatus%2F1907173680456794187]). * Meta's MoCHA [https://www.google.com/url?sa=E&q=https%3A%2F%2Fcongwei1230.github.io%2FMoCha%2F]: Generates stunningly realistic, movie-grade talking characters from speech+text. * Voice Highlight: * Hailuo Speech-02 [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2FHailuo_AI%2Fstatus%2F1906723587379101923]: Impressive TTS API with excellent emotional control and voice cloning. * Tool Updates: * Windsurf adds deployments [https://www.google.com/url?sa=E&q=https%3A%2F%2Fx.com%2Fwindsurf_ai%2Fstatus%2F1907497638267924566] to Netlify. * Google NotebookLM adds source discovery [https://www.google.com/url?sa=E&q=https%3A%2F%2Fblog.google%2Ftechnology%2Fgoogle-labs%2Fnotebooklm-discover-sources%2F]. * 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 [https://sub.thursdai.news/subscribe?utm_medium=podcast&utm_campaign=CTA_2]

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 [https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025/], and seriously, go try it yourself at ai.dev [http://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 [https://x.com/arcprize/status/1905274808935608528], Interactive Blog [https://x.com/arcprize/status/1905274808935608528]) 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 4.5 How 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 LLMs The 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 [https://x.com/deepseek_ai/status/1904526863604883661], HF [https://huggingface.co/deepseek-ai/DeepSeek-V3-0324]), 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 [https://huggingface.co/Qwen/Qwen2.5-Omni-7B]). 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-regression This 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 [https://x.com/GrantSlatton/status/1904631016356274286]). 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 [https://x.com/mrgreen/status/1904886576951300495]) and even recreating entire movie trailers, like this unbelievable Lord of the Rings one (link [https://x.com/PJaccetturo/status/1905151190872309907]), 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 [https://reservoirsamples.substack.com/p/thoughts-on-setting-policy-for-new]). 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 [https://x.com/Taesung/status/1904220824435032528], Blog/News [https://decrypt.co/311375/new-reve-image-generator-beats-ai-art-heavyweights-midjourney-and-flux-at-a-penny-per-image], Try it [https://preview.reve.art/api/misc/sso_callback?code=4%2F0AQSTgQGhy3PinnDf0OVcAVGo3OwJMOe7uK2IRiA1b6mo6eWsKxOWCKibKLwCuhRP0O6KtQ&state=eyJob3N0IjoicHJldmlldy5yZXZlLmFydCIsImZsYXZvciI6InNpZ251cCIsInJrZXkiOiJzc28tYUNEd3RtZGxGRldQMm9kY2Y3dkQiLCJzaWciOiJFRmlhLzRtYUN1U0N4REd6VHF5R1BvRXVlVUNjZ0gxOUdVOG5JTDVOblFnPSJ9&error=&error_description=&id_token=]) 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 [https://about.ideogram.ai/3.0], Try it [https://about.ideogram.ai/3.0]), 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 [https://x.com/morgymcg/status/1904997037688385607], Github [https://github.com/wandb/MCP-server]!) 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 [https://wandb.ai/wandb-applied-ai-team/mcp-tests/reports/Model-Evaluation-Analysis--VmlldzoxMjAxMDQ1NA?accessToken=o11lv1bo38pz2xay3x0dwwlb04lovkvcd4f9getbfboe2i7yl00htggxzaqapvcd] 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 & MCP And 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 [https://openai.github.io/openai-agents-python/mcp/]). 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 & Audio Just one more quick update on the audio front: OpenAI Updates Advanced Voice Mode with Semantic VAD Alongside the image generation, OpenAI also quietly updated the advanced voice mode in ChatGPT (YT announcement [https://www.youtube.com/watch?v=mm4djPNO8os]). 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-Audio And speaking (heh) of audio and speech, we had the awesome Prince Canuma [https://x.com/Prince_Canuma/status/1903221389504430273] 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 [http://x.com/altryne])Co Hosts - Wolfram Ravenwlf (@WolframRvnwlf [https://twitter.com/WolframRvnwlf]), Nisten Tahiraj (@nisten [https://x.com/nisten/]), Yam Peleg (@yampeleg [http://x.com/@yampeleg]) * Tulsee Doshi - Head of Product, Gemini Models at Google DeepMind (@tulseedoshi [https://x.com/tulseedoshi]) * Morgan McQuire - Head of AI Applied Team at Weights & Biases (@morgymcg [https://x.com/morgymcg]) * Prince Canuma - ML Research Engineer, Creator of MLX Libraries (@PrinceCanuma [https://x.com/PrinceCanuma]) * Big CO LLMs + APIs * 🔥 Google reclaims #1 position with Gemini 2.5 Pro (thinking) - (X [https://x.com/JeffDean/status/1904580112248693039], Blog [https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025/], Try it [http://ai.dev]) * ARC-AGI 2 benchmark revealed - Base LLMs score 0%, thinking models 4%. * Open Source LLMs * Deepseek updates DeepSeek-V3-0324 685B params (X [https://x.com/deepseek_ai/status/1904526863604883661], HF [https://huggingface.co/deepseek-ai/DeepSeek-V3-0324]) - MIT License! * Qwen launches an Omni 7B model - perceives text, image, audio, video & generates text and speech (HF [https://huggingface.co/Qwen/Qwen2.5-Omni-7B]) * AI Art & Diffusion & Auto-regression * OpenAI launches native image support in GPT-4o (Model Card, X thread [https://x.com/GrantSlatton/status/1904631016356274286], Ad threads [https://x.com/mrgreen/status/1904886576951300495], Full Lord of the Rings trailer [https://x.com/PJaccetturo/status/1905151190872309907], Model Card [https://cdn.openai.com/11998be9-5319-4302-bfbf-1167e093f1fb/Native_Image_Generation_System_Card.pdf]) * Reve - new SOTA diffusion image gen claims (X [https://x.com/Taesung/status/1904220824435032528], Blog/News [https://decrypt.co/311375/new-reve-image-generator-beats-ai-art-heavyweights-midjourney-and-flux-at-a-penny-per-image], Try [https://preview.reve.art/api/misc/sso_callback?code=4%2F0AQSTgQGhy3PinnDf0OVcAVGo3OwJMOe7uK2IRiA1b6mo6eWsKxOWCKibKLwCuhRP0O6KtQ&state=eyJob3N0IjoicHJldmlldy5yZXZlLmFydCIsImZsYXZvciI6InNpZ251cCIsInJrZXkiOiJzc28tYUNEd3RtZGxGRldQMm9kY2Y3dkQiLCJzaWciOiJFRmlhLzRtYUN1U0N4REd6VHF5R1BvRXVlVUNjZ0gxOUdVOG5JTDVOblFnPSJ9&error=&error_description=&id_token=]) * Ideogram 3 launched - another SOTA claim, strong on text/logos, realism, style refs (Blog [https://about.ideogram.ai/3.0], Try it [https://about.ideogram.ai/3.0]) * This weeks Buzz + MCP * Weights & Biases Weave official MCP server tool - talk to your evals! (X [https://x.com/morgymcg/status/1904997037688385607], Github [https://github.com/wandb/MCP-server]) * Agents , Tools & MCP * OpenAI has added support for MCP - MCP WON! (Docs [https://openai.github.io/openai-agents-python/mcp/]) * Voice & Audio * OpenAI updates advanced voice mode with semantic VAD for more natural conversations (YT announcement [https://www.youtube.com/watch?v=mm4djPNO8os]). * MLX-Audio v0.0.3 released by Prince Canuma (Github [https://github.com/Blaizzy/mlx-audio]) * Show Notes and other Links * Catch the show live & subscribe to the newsletter/YouTube: thursdai.news/yt [thursdai.news/yt] * Try Gemini 2.5 Pro: AI.dev [http://ai.dev] * Learn more about MCP from our previous episode (March 6th [https://thursdai.news/mar-6]). 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 [https://sub.thursdai.news/subscribe?utm_medium=podcast&utm_campaign=CTA_2]