ThursdAI - The top AI news from the past week

Hey guys, Alex here 👋 This week was so dense, that even my personal AI assistant Wolfred was struggling to help me keep up! Not to mention that we finally got to try one incredible piece of AI tech I’ve been waiting to get to try for a while!
Clawdbot we told you about last week exploded in popularity and had to rebrand to Molt...bot OpenClaw after Anthropic threatened the creators, Google is shipping like crazy, first adding Agentic features into Chrome (used by nearly 4B people daily!) then shipping a glimpse of a future where everything we see will be generated with Genie 3, a first real time, consistent world model you can walk around in!
Meanwhile in Open Source, Moonshot followed up with a .5 update to their excellent Kimi, our friends at Arcee launched Trinity Large (400B) and AI artists got the full Z-image. oh and Grok Imagine (their video model) now has an API, audio support and supposedly match Veo and Sora on quality while beating them on speed/price.
Tons to cover, let’s dive in, and of course, all the links and show notes are at the end of the newsletter.
Hey, if you’re in SF this weekend (Jan 31-Feb1), I’m hosting a self improving agents hackathon at W&B office, limited seats are left, Cursor is the surprise sponsor with $50/hacker credits + over $15K in cash prizes. lu.ma/weavehacks3 - Join us.
Play any reality - Google Genie3 launches to Ultra Subscribers
We got our collective minds blown by the videos of Genie-3 back in August (our initial coverage) and now, Genie is available to the public (Those who can pay for the Ultra tier, more on this later, I have 3 codes to give out!). You can jump and generate any world and any character you can imagine here!
We generated a blue hacker lobster draped in a yellow bomber jacket swimming with mermaids and honestly all of us were kind of shocked at how well this worked. The shadows on the rocks, the swimming mechanics, and poof, it was all over in 60 seconds, and we needed to create another world.
Thanks to the DeepMind team, I had a bit of an early access to this tech and had a chance to interview folks behind the model (look out for that episode soon) and the use-cases for this span from entertaining your kids all the way to “this may be the path to AGI, generating full simulated worlds to agents for them to learn”.
The visual fidelity, reaction speed and general feel of this far outruns the previous world models we showed you (WorldLabs, Mirage) as this model seems to have memory of every previous action (eg. if your character makes a trail, you turn around and the trail is still there!). Is it worth the upgrade to Ultra Gemini Plan? Probably not, it’s an incredible demo, but the 1 minute length is very short, and the novelty wears off fairly quick.
If you’d like to try, folks at Deepmind gave us 3 Ultra subscriptions to give out! Just tweet out the link to this episode and add #GenieThursdai and tag @altryne and I’ll raffle the ultra subscriptions between those who do
Chrome steps into Agentic Browsing with Auto Browse
This wasn’t the only mind blowing release from Gemini this week, the Chrome team upgraded the Gemini inside chrome to be actual helpful and agentic. And yes, we’ve seen this before, with Atlas from OpenAI, Comet from perplexity, but Google’s Chrome has a 70% hold on the browser market, and giving everyone with a Pro/Ultra subscription to “Auto Browse” is a huge huge deal.
We’ve tested the Auto Browse feature live on the show, and Chrome completed 77 steps! I asked it to open up each of my bookmarks in a separate folder and summarize all of them, and it did a great job!
Honestly, the biggest deal about this is not the capability itself, it’s the nearly 4B people this is now very close to, and the economic impact of this ability. IMO this may be the more impactful news out of Google this week!
Other news in big labs:
* Anthropic launches in chat applications based on the MCP Apps protocol. We interviewed the two folks behind this protocol back in November if you’d like to hear more about it. With connectors like Figma, Slack, Asana that can now show rich experiences
* Anthropic’s CEO Dario Amodei also published an essay called ‘The Adolescence of Technology” - warning of AI risks to national security
* Anthropic forced the creator of the popular open source AI Assistant Clawdbot to rename, they chose Moltbot as the name (apparently because crypto scammers stole a better name) EDIT: just after publishing this newsletter, the name was changed to OpenClaw, which we all agree is way way better.
Open Source AI
Kimi K2.5: Moonshot AI’s 1 Trillion Parameter Agentic Monster
Wolfram’s favorite release of the week, and for good reason. Moonshot AI just dropped Kimi K2.5, and this thing is an absolute beast for open source. We’re talking about a 1 trillion parameter Mixture-of-Experts model with 32B active parameters, 384 experts (8 selected per token), and 256K context length.
But here’s what makes this special — it’s now multimodal. The previous Kimi was already known for great writing vibes and creative capabilities, but this one can see. It can process videos. People are sending it full videos and getting incredible results.
The benchmarks are insane: 50.2% on HLE full set with tools, 74.9% on BrowseComp, and open-source SOTA on vision and coding with 78.5% MMMU Pro and 76.8% SWE-bench Verified. These numbers put it competitive with Claude 4.5 Opus and GPT 5.2 on many tasks. Which, for an open model is crazy.
And then there’s Agent Swarm — their groundbreaking feature that spawns up to 100 parallel sub-agents for complex tasks, achieving 4.5x speedups. The ex-Moonshot RL lead called this a “zero-to-one breakthrough” with self-directed parallel execution.
Now let’s talk about what matters for folks running agents and burning through tokens: pricing. Kimi K2.5 is $0.60 per million input tokens and $3 per million output. Compare that to Opus 4.5 at $4.50 input and $25 output per million. About a 10x price reduction. If you’re running OpenClas and watching your API bills climb with sub-agents, this is a game-changer. (tho I haven’t tested this myself)
Is it the same level of intelligence as whatever magic Anthropic cooks up with Opus? Honestly, I don’t know — there’s something about the Claude models that’s hard to quantify. But for most coding tasks on a budget, you can absolutely switch to Kimi and still get great results.
🦞 Clawdbot is no more, Moltbot is dead, Long Live OpenClaw
After we covered the incredible open source project last week, Clawdbot exploded in popularity, driven by Claude Max subscription, and a crazy viral loop where folks who try it, can’t wait to talk about it, it was everywhere! Apparently it was also on Anthropics’ lawyers minds, when they sent Peter Steinberger a friendly worded letter to rebrand and gave him like 12 hours.
Apparently, when pronounced, Claude and Clawd sound the same, and they are worried about copyright infringement (which makes sense, most of the early success of Clawd was due to Opus being amazing). The main issue is, due to the popularity of the project, crypto a******s sniped moltybot nickname on X so we got left with Moltbot, which is thematically appropriate, but oh so hard to remember and pronounce!
EDIT: OpenClaw was just announced as the new name, apparently I wasn’t the only one who absolutely hated the name Molt!
Meanwhile, rebrand or not, my own instance of OpenClaw created an X account, helped me prepare for ThursdAI (including generating a thumbnail), created a video for us today on the fly, and keeps me up to date on emails and unanswered messages via a daily brief. It really has showed me a glimpse of how a truly personal AI assistant can be helpful in a fast changing world!
I’ve shared a lot of tips and tricks, about memory, about threads and much more, as we all learn to handle this new ... AI agent framework! But I definitely feel that this is a new unlock in capability, for me and for many others. If you haven’t installed OpenClaw, lmk in the comments why not.
Arcee AI Trinity Large: The Western Open Source Giant
Remember when we had Lucas Atkins, Arcee’s CTO, on the show just as they were firing up their 2,000 NVIDIA B300 GPUs?
Well, the run is complete, and the results are massive. Arcee AI just dropped Trinity Large, a 400B parameter sparse MoE model (with a super efficient 13B active params via 4-of-256 routing) trained on a staggering 17 trillion tokens in just 33 days.
This represents the largest publicly announced pretraining run on B300 infrastructure, costing about $20M (and tracked with WandB of course!) and proves that Western labs can still compete at the frontier of open source. Best part? It supports 512K context and is free on OpenRouter until February 2026. Go try it now!
Quick open source hits: Trinity Large, Jan v3, DeepSeek OCR updated
* Jan AI released Jan v3, a 4B parameter model optimized for local inference. 132 tokens/sec on Apple Silicon, 262K context, 40% improvement on Aider benchmarks. This is the kind of small-but-mighty model you actually can run on your laptop for coding tasks.
* Nvidia released PersonaPlex-7B - full duplex voice AI that listens and speaks simultaneously with persona contol
* Moonshot AI also releases Kimi Code: Open-source Python-based coding agent with Apache 2.0 license
Vision, Video and AI art
xAI Grok Imagine API: #1 in Video Generation
xAI officially launched the Grok Imagine API with an updated model, and it’s now ranked #1 in both text-to-video and image-to-video on the Artificial Analysis leaderboards. It beats Runway Gen-4.5, Kling 2.5 Turbo, and Google Veo 3.1.
And of course, the pricing is $4.20 per minute. Of course it is. That’s cheaper than Veo 3.1 at $12/min and Sora 2 Pro at $30/min by 3-7x, with 45-second latency versus 68+ seconds for the competition.
During the show, I demoed this live with my AI assistant Wolfred. I literally sent him a message saying “learn this new API based on this URL, take this image of us in the studio, and create a video where different animals land on each of our screens.” He learned the API, generated the video (it showed wolves, owls, cats, and lions appearing on our screens with generated voice), and then when Nisten asked to post it to Twitter, Wolfred scheduled it on X and tagged everyone — all without me doing anything except asking.
Look, it’s not VEO but the price and the speed are crazy, XAI cooked with this model and you can try it on FAL and directly on XAI.
Decart - Lucy 2 - Real-time 1080p video transformation at 30 FPS with near-zero latency for $3/hour
This one also caught me by surprise, I read about it and said “oh this is cool, I’ll mention this on the show” and then we tried it in real time, and I approved my webcam, and I got transformed into Albert Einstein, and I could raise my hands and their model would in real time, raise Alberts hands!
The speed and fidelity of this model is something else, and yeah, after watching the Genie 3 world model, it’s hard to be impressed, but I was very impressed by this, as previous stuff from Decart was “only showing the future” and this one is a real time, 1080p quality web cam transformation!
You can try this yourself here: lucy.decart.ai, they let you create any kind of prompt!
AI Art Quick Hits:
* Tencent launches HunyuanImage 3.0-Instruct: 80B MoE model for precise image editing with chain-of-thought reasoning. It’s a VERY big model for AI Art standards but it’s becuase it has an LLM core and this make it much better for precise image editing.
* Tongyi Lab releases Z-Image, a full-capacity undistilled foundation model for image generation with superior diversity. We told you about the turbo version before, this one is its older brother and much higher quality!
The other highlight this week is that I got to record a show with Wolfram in person for the first time, as he’s now also an AI Evangelist with W&B and he’s here in SF for our hackathon (remember? you can still register lu.ma/weavehacks3 )
Huge shoutout to Chroma folks for hosting us at their amazing podcast studio (TJ, Jeff and other folks), if you need a memory for your AI assistant, check out chroma.db 🎉
Signing off as we have a hackathon to plan, see you guys next week (or this weekend!) 🫡
ThursdAI Jan 29 , TL;DR and show notes
* Hosts and Guests
* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)
* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson
* Open Source LLMs
* Moonshot AI releases Kimi K2.5 (X, HF)
* Arcee AI releases Trinity Large (X, Blog, HF, HF, HF)
* Jan AI releases Jan v3 (X, HF, HF, Blog)
* Big CO LLMs + APIs
* Google launches agentic Auto-Browse in Chrome with Gemini 3 (X, Blog)
* Anthropic launches MCP Apps (X)
* Google launches Agentic Vision in Gemini 3 Flash (X, Announcement)
* Anthropic CEO Dario Amodei publishes major essay ‘The Adolescence of Technology’ (X, Blog, Blog)
* This weeks Buzz
* WandB hackathon Weavehacks 3 - Jan 31-Feb1 in SF - limited seats available lu.ma/weavehacks3
* Vision & Video
* Google DeepMind launches Project Genie (X, Announcement)
* Voice & Audio
* NVIDIA releases PersonaPlex-7B (X, HF, Announcement)
* AI Art & Diffusion & 3D
* xAI launches Grok Imagine API (X, Announcement)
* Tencent launches HunyuanImage 3.0-Instruct (X, X)
* Tongyi Lab releases Z-Image (X, GitHub)
* Tools
* Moonshot AI releases Kimi Code (X, Announcement, GitHub)
* Andrej Karpathy shares his shift to 80% agent-driven coding with Claude (X)
* Clawdbot is forced to rename to Moltbot (Molty) becuase of Anthropic lawyers, then renames to OpenClaw


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Hey! Alex here, with another weekly AI update!
It seems like ThursdAI is taking a new direction, as this is our 3rd show this year, and a 3rd deep dive into topics (previously Ralph, Agent Skills), please let me know if the comments if you like this format.
This week’s deep dive is into Clawdbot, a personal AI assistant you install on your computer, but can control through your phone, has access to your files, is able to write code, help organize your life, but most importantly, it can self improve. Seeing Wolfred (my Clawdbot) learn to transcribe incoming voice messages blew my mind, and I wanted to share this one with you at length! We had Dan Peguine on the show for the deep dive + both Wolfram and Yam are avid users! This one is not to be missed. If ThursdAI is usually too technical for you, use Claude, and install Clawdbot after you read/listen to the deep dive!
Also this week, we read Claude’s Constitution that Anthropic released, heard a bunch of new TTS models (some are open source and very impressive) and talked about the new lightspeed coding model GLM 4.7 Flash. First the news, then deep dive, lets go 👇
ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Open Source AI
Z.ai’s GLM‑4.7‑Flash is the Local Agent Sweet Spot (X, HF)
This was the open‑source release that mattered this week. Z.ai (formerly Zhipu) shipped GLM‑4.7‑Flash, a 30B MoE model with only 3B active parameters per token, which makes it much more efficient for local agent work. We’re talking a model you can run on consumer hardware that still hits 59% on SWE‑bench Verified, which is uncomfortably close to frontier coding performance. In real terms, it starts to feel like “Sonnet‑level agentic ability, but local.” I know I know, we keep saying “sonnet at home” at different open source models, but this one slaps!
Nisten was getting around 120 tokens/sec on an M3 Ultra Mac Studio using MLX, and that’s kind of the headline. The model is fast and capable enough that local agent loops like RALPH suddenly feel practical. It also performs well on browser‑style agent tasks, which is exactly what you want for local automation without sending all your data to a cloud provider.
Liquid AI’s LFM2.5‑1.2B Thinking is the “Tiny but Capable” Class (X, HF)
Liquid AI released a 1.2B reasoning model that runs under 900MB of memory while still manages to be useful. This thing is built for edge devices and old phones, and the speed numbers are backing it up. We’re talking 239 tok/s decode on AMD CPU, 82 tok/s on mobile NPU, and prefill speeds that make long prompts actually usable. Nisten made a great point: on iOS, there’s a per‑process memory limit around 3.8GB, so a 1.2B model lets you spend your budget on context instead of weights.
This is the third class of models we’re now living with: not Claude‑scale, not “local workstation,” but “tiny agent in your pocket.” It’s not going to win big benchmarks, but it’s perfect for on‑device workflows, lightweight assistants, and local RAG.
Voice & Audio: Text To Speech is hot this week with 3 releases!
We tested three major voice releases this week, and I’m not exaggerating when I say the latency wars are now fully on.
Qwen3‑TTS: Open Source, 97ms Latency, Voice Cloning (X, HF)
Just 30 minutes before the show, Qwen released their first model of the year, Qwen3 TTS, with two models (0.6B and 1.7B). With support for Voice Cloning based on just 3 seconds of voice, and claims of 97MS latency, this apache 2.0 release looked very good on the surface!
The demos we did on stage though... were lackluster. TTS models like Kokoro previously impressed us with super tiny sizes and decent voice, while Qwen3 didn’t really perform on the cloning aspect. For some reason (I tested in Russian which they claim to support) the cloned voice kept repeating the provided sample voice instead of just generating the text I gave it. This confused me, and I’m hoping this is just a demo issue, not a problem with the model. They also support voice design where you just type in the type of voice you want, which to be fair, worked fairly well in our tests!
With Apache 2.0 and a full finetuning capability, this is a great release for sure, kudos to the Qwen team! Looking forward to see what folks do with this properly.
FlashLabs Chroma 1.0: Real-Time Speech-to-Speech, Open Source (X, HF)
Another big open source release in the audio category this week was Chroma 1.0 from FlashLabs, which claim to be the first speech2speech model (not a model that has the traditional ASR>LLM>TTS pipeline) and the claim 150ms end to end latency!
The issue with this one is, the company released an open source 4B model, and claimed that this model powers their chat interface demo on the web, but in the release notes they claim the model is english speaking only, while on the website it sounds incredible and I spoke to it in other languages 🤔 I think the mode that we’ve tested is not the open source one. I could’t confirm this at the time of writing, will follow on X with the team and let you guys know.
Inworld AI launches TTS-1.5: #1 ranked text-to-speech with sub-250ms latency at half a cent per minute (X, Announcement)
Ok this one is definitely in the realm of “voice realistic enough you won’t be able to tell” as this is not an open source model, it’s a new competitor to 11labs and MiniMax - the two leading TTS providers out there.
Inworld claims to achieve better results on the TTS Arena, while being significantly cheaper and faster (up to 25x less than leading providers like 11labs)
We tested out their voices and they sounded incredible, replied fast and generally was a very good experience. With 130ms response time for their mini version, this is a very decent new entry into the world of TTS providers.
Big Companies: Ads in ChatGPT + Claude Constitution
OpenAI is testing ads in ChatGPT’s free and Go tiers. Ads appear as labeled “Sponsored” content below responses, and OpenAI claim they won’t affect outputs. It’s still a major shift in the product’s business model, and it’s going to shape how people perceive trust in these systems. I don’t love ads, but I understand the economics, they have to make money somehow, with 900M weekly active users, many of them on the free tier, they are bound to make some money with this move. I just hope they won’t turn into a greedy ad optimizing AI machine.
Meanwhile, Anthropic released an 80‑page “New Constitution for Claude” that they use during training. This isn’t a prompt, it’s a full set of values baked into the model’s behavior. There’s a fascinating section where they explicitly talk about Claude’s potential wellbeing and how they want to support it. It’s both thoughtful and a little existential. I recommend reading it, especially if you care about alignment and agent design.
I applaud Anthropic for releasing this with Creative Commons license for public scrutiny and adoption 👏
This weeks buzz - come join the hackathon I’m hosting Jan 31 in SF
Quick plug, we have limited seats left open for the hackathon I’m hosting for Weights & Biases at the SF office, and if you’re reading this, and want to join, I’ll approve you if you mention ThursdAI in the application!
With sponsors like Redis, Vercel, BrowserBase, Daily, Google Cloud, we are going to give out a LOT of cash as prizes!
I’ve also invited a bunch of my friends from the top agentic AI places to be judges, it’s going to be awesome, come
Deep dive into Clawdbot: Local-First, Self-Improving, and Way Too Capable agent
Clawdbot (C‑L‑A‑W‑D) is that rare project where the hype is justified. It’s an open-source personal agent that runs locally on your Mac, but can talk to you through WhatsApp, Telegram, iMessage, Discord, Slack — basically wherever you already talk. What makes it different is not just the integrations; it’s the self‑improvement loop. You can literally tell it “go build a new skill,” and it will… build the skill, install it, then adopt it and start using it. It’s kind of wild to see it working for the first time. Now... it’s definitely not perfect, far far away from the polish of ChatGPT / Claude, but when it works, damn, it really is mindblowing.
That part actually happened live in the episode. Dan Peguine 🐧 showed how he had it create a skill to anonymize his own data so he could demo it on stream without leaking his personal life. Another example: I told my Clawdbot to handle voice notes in Telegram. It didn’t know how, so it went and found a transcription method, wrote itself a skill, saved it, and from that point on just… did the thing. That was the moment it clicked for me. (just before posting this, it forgot how to do it, I think I screwed something up)
Dan’s daily brief setup was wild too. It pulls from Apple Health, local calendars, weather, and his own projects, then produces a clean, human daily brief. It also lets him set reminders through WhatsApp and even makes its own decisions about how much to bother him based on context. He shared a moment where it literally told him, “I won’t bug you today because it’s your wife’s birthday.” That isn’t a hardcoded workflow — it’s reasoning layered on top of persistent memory.
And that persistent memory is a big deal. It’s stored locally as Markdown files and folders, Obsidian‑style, so you don’t lose your life every time you switch models. You can route the brain to Claude Opus 4.5 today and a local model tomorrow, and the memory stays with you. That is a huge step up from “ChatGPT remembers you unless you unsubscribe.”
There’s also a strong community forming around shared skills via ClawdHub. People are building everything from GA4 analytics skills to app testing automations to Tesla battery status checkers. The core pattern is simple but powerful: talk to it, ask it to build a skill, then it can run that skill forever.
I definitely have some issues with the security aspect, you are essentially giving full access to an LLM to your machine, so many folks are buying a specific home for their ClawdBot (Mac Mini seems to be the best option for many of them) and are giving it secure access to passwords via a dedicated 1Password vault. I’ll keep you up to date about my endeavors with Clawd but definitely do give it a try!
Installing
Installing Clawd on your machine is simple, go to clawd.bot and follow instructions. Then find the most convenient way for you to talk to it (for me it was telegram, creating a telegram token takes 20 seconds) and then, you can take it from there with Clawdbot itself! Ask it for something to do, like clear your inbox, or set a reminder, or.. a million other things that you need for your personal life, and enjoy the discovery of what a potential ever present always on AI can do!
Other news that we didn’t have time to cover at length but you should still now about:
* Overworld released an OpenSource realtime AI World model (X)
* Runway finally opened up their 4.5 video model, and it has Image2video capabilities, including multiple shots image to video (X)
* Vercel launches skills.sh, an “npm for AI agents skills”
* Anthropic’s Claude Code VS Code Extension Hits General Availability (X)
Ok, this is it for this week folks! I’m going to play with (and try to fix.. ) my clawdbot, and suggest you give it a try. Do let me know if the deepdives are a good format!
Show notes and links:
ThursdAI - Jan 22, 2026 - TL;DR and show notes
* Hosts and Guests
* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)
* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed
* Guest Dan Peguine ( @danpeguine )
* DeepDive - Clawdbot with Dan & Wolfram
* Clawdbot: Open-Source AI Agent Running Locally on macOS Transforms Personal Computing with Self-Improving Capabilities (X, Blog)
* Open Source LLMs
* Z.ai releases GLM-4.7-Flash, a 30B parameter MoE model that sets a new standard for lightweight local AI assistants (X, Technical Blog, HuggingFace)
* Liquid AI releases LFM2.5-1.2B-Thinking, a 1.2B parameter reasoning model that runs entirely on-device with under 900MB memory (X, HF, Announcement)
* Sakana AI introduces RePo, a new way for language models to dynamically reorganize their context for better attention (X, Paper, Website)
* Big CO LLMs + APIs
* OpenAI announces testing ads in ChatGPT free and Go tiers, prioritizing user trust and transparency (X)
* Anthropic publishes new 80-page constitution for Claude, shifting from rigid rules to explanatory principles that teach AI ‘why’ rather than ‘what’ to do (X, Blog, Announcement)
* This weeks Buzz
* WandB hackathon Weavehacks 3 - Jan 31-Feb1 in SF - limited seats available lu.ma/weavehacks3
* Vision & Video
* Overworld Releases Waypoint-1: Real-Time AI World Model Running at 60fps on Consumer GPUs (X, Announcement)
* Voice & Audio
* Alibaba Qwen Releases Qwen3-TTS: Full Open-Source TTS Family with 97ms Latency, Voice Cloning, and 10-Language Support (X, H, F, G, i, t, H, u, b)
* FlashLabs Releases Chroma 1.0: World’s First Open-Source Real-Time Speech-to-Speech Model with Voice Cloning Under 150ms Latency (X, HF, Arxiv)
* Inworld AI launches TTS-1.5: #1 ranked text-to-speech with sub-250ms latency at half a cent per minute (X, Announcement)
* Tools
* Vercel launches skills.sh, an “npm for AI agents” that hit 20K installs within hours (X, Vercel Changelog, GitHub)
* Anthropic’s Claude Code VS Code Extension Hits General Availability, Bringing Full Agentic Coding to the IDE (X, VS Code Marketplace, Docs)


This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey ya’ll, Alex here, and this week I was especially giddy to record the show! Mostly because when a thing clicks for me that hasn’t clicked before, I can’t wait to tell you all about it!
This week, that thing is Agent Skills! The currently best way to customize your AI agents with domain expertise, in a simple, repeatable way that doesn’t blow up the context window! We mentioned skills when Anthropic first released them (Oct 16) and when they became an open standard but it didn’t really click until last week! So more on that below.
Also this week, Anthropic released a research preview of Claude Cowork, an agentic tool for non coders, OpenAI finally let loos GPT 5.2 Codex (in the API, it was previously available only via Codex), Apple announced a deal with Gemini to power Siri, OpenAI and Anthropic both doubled down on healthcare and much more! We had an incredible show, with an expert in Agent Skills, Eleanor Berger and the usual gang on co-hosts, strongly recommend watching the show in addition to the newsletter!
Also, I vibe coded skills support for all LLMs to Chorus, and promised folks a link to download it, so look for that in the footer, let’s dive in!
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Big Company LLMs + APIs: Cowork, Codex, and a Browser in a Week
Anthropic launches Claude Cowork: Agentic AI for Non‑Coders (research preview)
Anthropic announced Claude Cowork, which is basically Claude Code wrapped in a friendly UI for people who don’t want to touch a terminal. It’s a research preview available on the Max tier, and it gives Claude read/write access to a folder on your Mac so it can do real work without you caring about diffs, git, or command line.
The wild bit is that Cowork was built in a week and a half, and according to the Anthropic team it was 100% written using Claude Code. This feels like a “we’ve crossed a threshold” moment. If you’re wondering why this matters, it’s because coding agents are general agents. If a model can write code to do tasks, it can do taxes, clean your desktop, or orchestrate workflows, and that means non‑developers can now access the same leverage developers have been enjoying for a year.
It also isn’t just for files—it comes with a Chrome connector, meaning it can navigate the web to gather info, download receipts, or do research and it uses skills (more on those later)
Earlier this week I recorded this first reactions video about Cowork and I’ve been testing it ever since, it’s a very interesting approach of coding agents that “hide the coding” to just... do things. Will this become as big as Claude Code for anthropic (which is reportedly a 1B business for them)? Let’s see!
There are real security concerns here, especially if you’re not in the habit of backing up or using git. Cowork sandboxes a folder, but it can still delete things in that folder, so don’t let it loose on your whole drive unless you like chaos.
GPT‑5.2 Codex: Long‑Running Agents Are Here
OpenAI shipped GPT‑5.2 Codex into the API finally! After being announced as the answer for Opus 4.5 and only being available in Codex. The big headline is SOTA on SWE-Bench and long‑running agentic capability. People describe it as methodical. It takes longer, but it’s reliable on extended tasks, especially when you let it run without micromanaging.
This model is now integrated into Cursor, GitHub Copilot, VS Code, Factory, and Vercel AI Gateway within hours of launch. It’s also state‑of‑the‑art on SWE‑Bench Pro and Terminal‑Bench 2.0, and it has native context compaction. That last part matters because if you’ve ever run an agent for long sessions, the context gets bloated and the model gets dumber. Compaction is an attempt to keep it coherent by summarizing old context into fresh threads, and we debated whether it really works. I think it helps, but I also agree that the best strategy is still to run smaller, atomic tasks with clean context.
Cursor vibe-coded browser with GPT-5.2 and 3M lines of code
The most mind‑blowing thing we discussed is Cursor letting GPT‑5.2 Codex run for a full week to build a browser called FastRenderer. This is not Chromium‑based. It’s a custom HTML parser, CSS cascade, layout engine, text shaping, paint pipeline, and even a JavaScript VM, written in Rust, from scratch. The codebase is open source on GitHub, and the full story is on Cursor’s blog
It took nearly 30,000 commits and millions of lines of code. The system ran hundreds of concurrent agents with a planner‑worker architecture, and GPT‑5.2 was the best model for staying on task in that long‑running regime. That’s the real story, not just “lol a model wrote a browser.” This is a stress test for long‑horizon agentic software development, and it’s a preview of how teams will ship in 2026.
I said on the show, browsers are REALLY hard, it took two decades for the industry to settle and be able to render websites normally, and there’s a reason everyone’s using Chromium. This is VERY impressive 👏
Now as for me, I began using Codex again, but I still find Opus better? Not sure if this is just me expecting something that’s not there? I’ll keep you posted
Gemini Personal Intelligence: The Data Moat king is back!
What kind of car do you drive? Does ChatGPT know that? welp, it turns our Google does (based on your emails, Google photos) and now Gemini can tap into this personal info (if you allow it, they are stressing privacy), and give you much more personalized answers!
Flipping this Beta feature on, lets Gemini reason across Gmail, YouTube, Photos, and Search with explicit opt‑in permissions, and it’s rolling out to Pro and Ultra users in the US first.
I got to try it early, and it’s uncanny. I asked Gemini what car I drive, and it told me I likely drive a Model Y, but it noticed I recently searched for a Honda Odyssey and asked if I was thinking about switching. It was kinda... freaky because I forgot I had early access and this was turned on 😂
Pro Tip: if you’re brave enough to turn this on, ask for a complete profile on you 🙂
Now the last piece is for Gemini to become proactive, suggesting things for me based on my needs!
Apple & Google: The Partnership (and Drama Corner)
We touched on this in the intro, but it’s official: Apple Intelligence will be powered by Google Gemini for “world knowledge” tasks. Apple stated that after “careful evaluation,” Google provided the most capable foundation model for their.. apple foundation models. It’s confusing, I agree.
Honestly? I got excited about Apple Intelligence, but Siri is still... Siri. It’s 2026 and we are still struggling with basic intents. Hopefully, plugging Gemini into the backend changes that?
In other drama: The silicon valley carousel continues. 3 Co-founders (Barret Zoph, Sam Schoenholz and Luke Metz) from Thinking Machines (and former OpenAI folks) have returned to the mothership (OpenAI), amid some vague tweets about “unethical conduct.” It’s never a dull week on the timeline.
This Week’s Buzz: WeaveHacks 3 in SF
I’ve got one thing in the Buzz corner this week, and it’s a big one. WeaveHacks 3 is back in San Francisco, January 31st - February 1st. The theme is self‑improving agents, and if you’ve been itching to build in person, this is it. We’ve got an amazing judge lineup, incredible sponsors, and a ridiculous amount of agent tooling to play with.
You can sign up here: https://luma.com/weavehacks3
If you’re coming, add to the form you heard it on ThursdAI and we’ll make sure you get in!
Deep Dive: Agent Skills With Eleanor Berger
This was the core of the episode, and I’m still buzzing about it. We brought on Eleanor Berger, who has basically become the skill evangelist for the entire community, and she walked us through why skills are the missing layer in agentic AI.
Skills are simple markdown files with a tiny bit of metadata in a directory together optional scripts, references, and assets. The key idea is progressive disclosure. Instead of stuffing your entire knowledge base into the context, the model only sees a small list of skills and let it load only what it needs. That means you can have hundreds of skills without blowing your context window (and making the model dumber and slower in result)
The technical structure is dead simple, but the implications are huge. Skills create a portable, reusable, composable way to give agents domain expertise, and they now work across most major harnesses. That means you can build a skill once and use it in Claude, Cursor, AMP, or any other agent tool that supports the standard.
Eleanor made the point that skills are an admission that we now have general‑purpose agents. The model can do the work, but it doesn’t know your preferences, your domain, your workflows. Skills are how you teach it those things. We also talked about how scripts inside skills reduce variance because you’re not asking the model to invent code every time; you’re just invoking trusted tools.
What really clicked for me this week is how easy it is to create skills using an agent. You don’t need to hand‑craft directories. You can describe your workflow, or even just do the task once in chat, and then ask the agent to turn it into a skill. It really is very very simple! And that’s likely the reason everyone is adopting this simple formart for extension their agents knowledge.
Get started with skills
If you use Claude Chat, the simplest way to get started is ask Claude to review your previous conversations and suggest a skill for you. Or, at the end of a long chat where you went back and forth with Claude on a task, ask it to distill the important parts into a skill. If you want to use other people’s skills, and you are using Claude Code, or any of the supported IDE/Agents, here’s where to download the folders and install them:
If you aren’t a developer and don’t subscribe to Claude, well, I got good news for you! I vibecoded skill support for every LLM 👇
The Skills Demo That Changed My Mind
I was resistant to skills at first, mostly because I wanted them inside my chat interface and not just in CLI tools. And I wasn’t subscribed to Claude for a while. Then I realized I could add skill support directly to Chorus, the open‑source multi‑model chat app, and I used Claude Code plus Ralph loops to vibe code it in a few hours. Now I can run skills with GPT‑5.2 Codex, Claude Opus, and Gemini from the same chat interface. That was my “I know kung fu” moment.
If you want to try Chorus with skills enabled, you can download my release here! Only for mac, and they are unsigned, mac will not like it, but you can run them anyway.
And if you want to explore more awesome skills, check out Vercel’s React Best Practices skills and UI Skills. It’s the beginning of a new kind of distribution: knowledge packaged as skills, shared like open source libraries (or paid for!) and
Open Source AI
Baichuan-M3 is a 235B medical LLM fine-tuned from Qwen3, released under Apache 2.0. The interesting claim here is that it beats GPT-5.2 on OpenAI’s HealthBench, including a remarkably low 3.5% hallucination rate.
What makes it different from typical medical models is that it’s trained to run actual clinical consultations asking follow-up questions and reasoning through differential diagnoses rather than just spitting out answers. Nisten pointed out that if you’re going to fine-tune something for healthcare, Qwen3 MoE is an excellent base because of its multilingual capabilities, which matters a lot in clinical settings. You can run it with vLLM or SGLang if you’ve got the hardware. (HF)
LongCat-Flash-Thinking-2601 from Meituan is a 560B MoE (27B active) released fully MIT-licensed. It’s specifically built for agentic tasks, scoring well on tool-use benchmarks like τ²-Bench and BrowseComp.
There’s a “Heavy Thinking” mode that pushes AIME-25 to 100%. What I like about this one is the training philosophy, they inject noise and broken tools during RL to simulate messy real-world conditions, which is exactly what production agents deal with. You can try it at longcat.chat and Github
We also saw Google release MedGemma this week (blog) a 4B model optimized for medical imaging like X-rays and CT scans and TranslateGemma (X) a family of on device translations (4B, 12B and 27B) which seem kind of cool! Didn’t have tons of time to dive into them unfortunately.
Vision, Voice & Art (Rapid Fire)
* Veo 3.1 adds native vertical video, 4K output, and better consistency in the Gemini API. Huge for creators (blog)
* Viral Kling motion‑transfer vids are breaking people’s brains about what AI video pipelines will look like.
* Pocket TTS from Kyutai Labs: a 100M‑parameter open‑source TTS model that runs on CPU and clones voices from seconds of audio (X)
* GLM‑Image drops as an open‑source hybrid AR + diffusion image model with genuinely excellent text rendering but pretty bad for everything else
* Black Forest Labs drops open source Flux.2 [Klein] 4B and 9B small models that create images super fast! (X, Fal, HF)
Phew, ok. I was super excited about this one and I’m really really happy with the result. I was joking on the pod that to prepare for this podcast, I not only had to collect all the news, I also had to ramp up on Agent Skills, and I wish we had an ability to upload information like the Matrix, but alas we didn’t. I also really enjoyed vibecoding a whole feature into Chorus just to explore skills fully, mind was absolutely blown when it worked after 3 hours of Ralphing!
See you next week, I think I have one more super exciting thing to play with this week before I talk about it!
TL;DR and Show Notes
* Hosts & Guests
* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)
* Co-Hosts: Wolfram Ravenwolf (@WolframRvnwlf), Yam Peleg (@yampeleg), Nisten Tahiraj (@nisten), LDJ (@ldjconfirmed)
* Guest: Eleanor Berger (@intellectronica)
* Open Source LLMs
* Baichuan-M3 - A 235B open-source medical LLM that beats GPT-5.2 on HealthBench with a 3.5% hallucination rate, featuring full clinical consultation capabilities. (HF, Blog, X Announcement)
* LongCat-Flash-Thinking-2601 - Meituan’s 560B MoE (27B active) agentic reasoning model, fully MIT licensed. Features “Heavy Thinking” mode scoring 100% on AIME-25. (GitHub, Demo, X Announcement)
* TranslateGemma - Google’s open translation family (4B, 12B, 27B) supporting 55 languages. The 4B model runs entirely on-device. (Arxiv, Kaggle, X Announcement)
* MedGemma 1.5 & MedASR - Native 3D imaging support (CT/MRI) and a speech model that beats Whisper v3 by 82% on clinical dictation error rates. (MedGemma HF, MedASR HF, Arxiv)
* Big CO LLMs + APIs
* Claude Cowork - Anthropic’s new desktop agent allows non-coders to give Claude file system and browser access to perform complex tasks. (TechCrunch, X Coverage)
* GPT-5.2 Codex - Now in the API ($1.75/1M input). Features native context compaction and state-of-the-art performance for long-running agentic loops. (Blog, Pricing)
* Cursor & FastRenderer - Cursor used GPT-5.2 Codex to build a 3M+ line Rust browser from scratch in one week of autonomous coding. (Blog, GitHub, X Thread)
* Gemini Personal Intelligence - Google leverages its data moat, letting Gemini reason across Gmail, Photos, and Search for hyper-personalized proactive help. (Blog, X Announcement)
* Partnerships & Drama
* Apple + Gemini - Apple officially selects Gemini to power Siri backend capabilities.
* OpenAI + Cerebras - A $10B deal for 750MW of high-speed compute through 2028. (Announcement)
* Thinking Machines - Co-founders and CTO return to OpenAI amidst drama; Soumith Chintala named new CTO.
* This Week’s Buzz
* WeaveHacks 3 - Self-Improving Agents Hackathon in SF (Jan 31-Feb 1). (Sign Up Here)
* Vision, Voice & Audio
* Veo 3.1 - Native 9:16 vertical video, 4K resolution, and reference image support in Gemini API. (Docs)
* Pocket TTS - A 100M parameter CPU-only model from Kyutai Labs that clones voices from 5s of audio. (GitHub, HF)
* GLM-Image - Hybrid AR + Diffusion model with SOTA text rendering. (HF, GitHub)
* FLUX.2 [klein] - Black Forest Labs releases fast 4B (Apache 2.0) and 9B models for sub-second image gen. (HF Collection, X Announcement)
* Kling Motion Transfer - Viral example of AI video pipelines changing Hollywood workflows. (X Thread)
* Deep Dive: Agent Skills
* Vercel React Best Practices - Pre-packaged skills for agents. (Blog)
* UI Skills - Documentation and skill standards. (Docs)
* Chorus with Skills - My fork of Chorus enabling skills for all LLMs. (Release)


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Hey folks, Alex here from Weights & Biases, with your weekly AI update (and a first live show of this year!)
For the first time, we had a co-host of the show also be a guest on the show, Ryan Carson (from Amp) went supernova viral this week with an X article (1.5M views) about Ralph Wiggum (yeah, from Simpsons) and he broke down that agentic coding technique at the end of the show.
LDJ and Nisten helped cover NVIDIA’s incredible announcements during CES with their Vera Rubin upcoming platform (4-5X improvements) and we all got excited about AI medicine with ChatGPT going into Health officially!
Plus, a bunch of Open Source news, let’s get into this:
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Open Source: The “Small” Models Are Winning
We often talk about the massive frontier models, but this week, Open Source came largely from unexpected places and focused on efficiency, agents, and specific domains.
Solar Open 100B: A Data Masterclass
Upstage released Solar Open 100B, and it’s a beast. It’s a 102B parameter Mixture-of-Experts (MoE) model, but thanks to MoE magic, it only uses about 12B active parameters during inference. This means it punches incredibly high but runs fast.
What I really appreciated here wasn’t just the weights, but the transparency. They released a technical report detailing their “Data Factory” approach. They trained on nearly 20 trillion tokens, with a huge chunk being synthetic. They also used a dynamic curriculum that adjusted the difficulty and the ratio of synthetic data as training progressed. This transparency is what pushes the whole open source community forward.
Technically, it hits 88.2 on MMLU and competes with top-tier models, especially in Korean language tasks. You can grab it on Hugging Face.
MiroThinker 1.5: The DeepSeek Moment for Agents?
We also saw MiroThinker 1.5, a 30B parameter model that is challenging the notion that you need massive scale to be smart. It uses something they call “Interactive Scaling.”
Wolfram broke this down for us: this agent forms hypotheses, searches for evidence, and then iteratively revises its answers in a time-sensitive sandbox. It effectively “thinks” before answering. The result? It beats trillion-parameter models on search benchmarks like BrowseComp. It’s significantly cheaper to run, too. This feels like the year where smaller models + clever harnesses (harnesses are the software wrapping the model) will outperform raw scale.
Liquid AI LFM 2.5: Running on Toasters (Almost)
We love Liquid AI and they are great friends of the show. They announced LFM 2.5 at CES with AMD, and these are tiny ~1B parameter models designed to run on-device. We’re talking about running capable AI on your laptop, your phone, or edge devices (or the Reachy Mini bot that I showed off during the show! I gotta try and run LFM on him!)
Probably the coolest part is the audio model. Usually, talking to an AI involves a pipeline: Speech-to-Text (ASR) -> LLM -> Text-to-Speech (TTS). Liquid’s model is end-to-end. It hears audio and speaks audio directly. We watched a demo from Maxime Labonne where the model was doing real-time interaction, interleaving text and audio. It’s incredibly fast and efficient. While it might not write a symphony for you, for on-device tasks like summarization or quick interactions, this is the future.
NousCoder-14B and Zhipu AI IPO
A quick shoutout to our friends at Nous Research who released NousCoder-14B, an open-source competitive programming model that achieved a 7% jump on LiveCodeBench accuracy in just four days of RL training on 48 NVIDIA B200 GPUs. The model was trained on 24,000 verifiable problems, and the lead researcher Joe Li noted it achieved in 4 days what took him 2 years as a teenager competing in programming contests. The full RL stack is open-sourced on GitHub and Nous published a great WandB results page as well!
And in historic news, Zhipu AI (Z.ai)—the folks behind the GLM series—became the world’s first major LLM company to IPO, raising $558 million on the Hong Kong Stock Exchange. Their GLM-4.7 currently ranks #1 among open-source and domestic models on both Artificial Analysis and LM Arena. Congrats to them!
Big Companies & APIs
NVIDIA CES: Vera Rubin Changes Everything
LDJ brought the heat on this one covering Jensen’s CES keynote that unveiled the Vera Rubin platform, and the numbers are almost hard to believe. We’re talking about a complete redesign of six chips: the Rubin GPU delivering 50 petaFLOPS of AI inference (5x Blackwell), the Vera CPU with 88 custom Olympus ARM cores, NVLink 6, ConnectX-9 SuperNIC, BlueField-4 DPU, and Spectrum-6 Ethernet.
Let me put this in perspective using LDJ’s breakdown: if you look at FP8 performance, the jump from Hopper to Blackwell was about 5x. The jump from Blackwell to Vera Rubin is over 3x again—but here’s the kicker—while only adding about 200 watts of power draw. That’s insane efficiency improvement.
The real-world implications Jensen shared: training a 10 trillion parameter mixture-of-experts model now requires 75% fewer GPUs compared to Blackwell. Inference token costs drop roughly 10x—a 1MW cluster goes from 1 million to 10 million tokens per second at the same power. HBM4 memory delivers 22 TB/s bandwidth with 288GB capacity, exceeding NVIDIA’s own 2024 projections by nearly 70%.
As Ryan noted, when people say there’s an AI bubble, this is why it’s hilarious. Jensen keeps saying the need for inference is unbelievable and only going up exponentially. We all see this. I can’t get enough inference—I want to spin up 10 Ralphs running concurrently! The NVL72 rack-scale system achieves 3.6 exaFLOPS inference with 20.7TB total HBM, and it’s already shipping. Runway 4.5 is already running on the new platform, having ported their model from Hopper to Vera Rubin NVL72 in a single day.
NVIDIA also recently acqui-hidred Groq (with a Q) in a ~$20 billion deal, bringing the inference chip expertise from the guy who created Google’s TPUs in-house.
Nemotron Speech ASR & The Speed of Voice (X, HF, Blog)
NVIDIA also dropped Nemotron Speech ASR. This is a 600M parameter model that offers streaming transcription with 24ms latency.
We showed a demo from our friend Kwindla Kramer at Daily. He was talking to an AI, and the response was virtually instant. The pipeline is: Nemotron (hearing) -> Llama/Nemotron Nano (thinking) -> Magpie TTS (speaking). The total latency is under 500ms. It feels like magic. Instant voice agents are going to be everywhere this year.
XAI Raises $20B While Grok Causes Problems (Again)
So here’s the thing about covering anything Elon-related: it’s impossible to separate signal from noise because there’s an army of fans who hype everything and an army of critics who hate everything. But let me try to be objective here.
XAI raised another massive Round E of $20 billion! at a $230 billion valuation, with NVIDIA and Cisco as strategic investors. The speed of their infrastructure buildout is genuinely incredible. Grok’s voice mode is impressive. I use Grok for research and it’s really good, notable for it’s unprecedented access to X !
But. This raise happened in the middle of a controversy where Grok’s image model was being used to “put bikinis” on anyone in reply threads, including—and this is where I draw a hard line—minors. As Nisten pointed out on the show, it’s not even hard to implement guardrails. You just put a 2B VL model in front and ask “is there a minor in this picture?” But people tested it, asked Grok not to use the feature, and it did it anyway. And yeah, putting Bikini on Claude is funny, but basic moderation is lacking!
The response of “we’ll prosecute illegal users” is stupid when there’s no moderation built into the product. There’s an enormous difference between Photoshop technically being able to do something after hours of work, and a feature that generates edited images in one second as the first comment to a celebrity, then gets amplified by the platform’s algorithm to millions of people. One is a tool. The other is a product with amplification mechanics. Products need guardrails. I don’t often link to CNN (in fact this is the first time) but they have a great writeup about the whole incident here which apparently includes the quitting of a few trust and safety folks and Elon’s pushback on guardrails. Crazy
That said, Grok 5 is in training and XAI continues to ship impressive technology. I just wish they’d put the same engineering effort into safety as they do into capabilities!
OpenAI Launches GPT Health
This one’s exciting. OpenAI CEO Fidji Simo announced ChatGPT Health, a privacy-first space for personalized health conversations that can connect to electronic health records, Apple Health, Function Health, Peloton, and MyFitnessPal.
Here’s why this matters: health already represents about 5% of all ChatGPT messages globally and touches 25% of weekly active users—often outside clinic hours or in underserved areas. People are already using these models for health advice constantly.
Nisten, who has worked on AI doctors since the GPT-3 days and even published papers on on-device medical AI, gave us some perspective: the models have been fantastic for health stuff for two years now. The key insight is that medical data seems like a lot, but there are really only about 2,000 prescription drugs and 2,000 diseases (10,000 if you count rare ones). That’s nothing for an LLM. The models excel at pattern recognition across this relatively contained dataset.
The integration with Function Health is particularly interesting to me. Function does 160+ lab tests, but many doctors won’t interpret them because they didn’t order them. ChatGPT could help bridge that gap, telling you “hey, this biomarker looks off, you should discuss this with your doctor.” The bad news is, this is just a waitlist and you can add yourself to the waitlist here, we’ll keep monitoring the situation and let you know when it opens up
Doctronic: AI Prescribing Without Physician Oversight
Speaking of healthcare, Doctronic launched a pilot in Utah where AI can autonomously renew prescriptions for chronic conditions without any physician in the loop. The system covers about 190 routine medications (excluding controlled substances) at just $4 per renewal. Trial data showed 99.2% concordance with physician treatment plans, and they’ve secured pioneering malpractice insurance that treats the AI like a clinician.
Nisten made the case that it’s ethically wrong to delay this kind of automation when ER wait times keep increasing and doctors are overworked. The open source models are already excellent at medical tasks. Governments should be buying GPUs rather than creating administrative roadblocks. Strong strong agree here!
Google Brings Gmail into the Gemini Era (X)
Breaking news from the day of our show: Google announced Gmail’s biggest AI transformation since its 2004 launch, powered by Gemini 3. This brings AI Overviews that summarize email threads, natural language queries (”Who gave me a plumber quote last year?”), Help Me Write, contextual Suggested Replies matching your writing style, and the upcoming AI Inbox that filters noise to surface VIPs and urgent items.
For 3 billion Gmail users, this is huge. I’m very excited to test it—though not live on the show because I don’t want you reading my emails.
This weeks buzz - covering Weights & Biases updates
Not covered on the show, but a great update on stuff from WandB, Chris Van Pelt (@vanpelt), one of the 3 co-founders released a great project I wanted to tell you about! For coders, this is an app that allows you to run multiple Claude Codes on free Github sandboxes, so you can code (or Ralph) and control everything away from home!
GitHub gives personal users 120 free Codespaces hours/month, and Catnip automatically shuts down inactive instances so you can code for quite a while with Catnip!
It’s fully open source on Github and you can download the app here
Interview: Ryan Carson - What the hell is Ralph Wiggum?
Okay, let’s talk about the character everyone is seeing on their timeline: Ralph Wiggum. My co-host Ryan Carson went viral this week with an article about this technique, and I had to have him break it down.
Ralph isn’t a new model; it’s a technique for running agents in a loop to perform autonomous coding. The core idea is deceptively simple: Ralph is a bash script that loops an AI coding agent. In a loop, until it a certain condition is met. But why is it blowing up?
Normally when you use a coding agent like Cursor, Claude Code, or AMP, you need to be in the loop. You approve changes, look at code, fix things when the agent hits walls or runs out of context. Ralph solves this by letting the agent run autonomously while you sleep.
Here’s how it works: First, you write a Product Requirements Doc (PRD) by talking to your agent for a few minutes about what you want to build. Then you convert that PRD into a JSON file containing atomic user stories with clear acceptance criteria. Each user story is small enough for the agent to complete in one focused thread.
The Ralph script then loops: it picks the first incomplete user story, the agent writes code to implement it, tests against the acceptance criteria, commits the changes, marks the story as complete, writes what it learned to a shared “agents.md” file, and loops to the next story. That compound learning step is crucial—without it, the agent would keep making the same mistakes.
What makes this work is the pre-work. As Ryan put it, “no real work is done one-shot.” This is how software engineering has always worked—you break big problems into smaller problems into user stories and solve them incrementally. The innovation is letting AI agents work through that queue autonomously while you sleep! Ryan’s excellent (and viral) X article is here!
Vision & Video
LTX-2 Goes Fully Open Source (HF, Paper)
Lightricks finally open-sourced LTX-2, marking a major milestone as the first fully open audio-video generation model. This isn’t just “we released the weights” open—it’s complete model weights (13B and 2B variants), distilled versions, controllable LoRAs, a full multimodal trainer, benchmarks, and evaluation scripts. For a video model that is aiming to be the open source SORA, supports audio and lipsync
The model generates synchronized audio and video in a single DiT-based architecture—motion, dialogue, ambience, and music flow simultaneously. Native 4K at up to 50 FPS with audio up to 10 seconds. And there’s also a distilled version (Thanks Pruna AI!) hosted on Replicate
ComfyUI provided day-0 native support, and community testing shows an A6000 generating 1280x720 at 120 frames in 50 seconds. This is near Sora-level quality that you can fine-tune on your own data for custom styles and voices in about an hour.
What a way to start 2026. From chips that are 5x faster to AI doctors prescribing meds in Utah, the pace is only accelerating. If anyone tells you we’re in an AI bubble, just show them what we covered today. Even if the models stopped improving tomorrow, the techniques like “Ralph” prove we have years of work ahead of us just figuring out how to use the intelligence we already have.
Thank you for being a ThursdAI subscriber. See you next week!
As always, here’s the show notes and TL;DR links:
* Hosts & Guests
* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)
* Co-Hosts - @WolframRvnwlf, @nisten, @ldjconfirmed
* Special Guest - Ryan Carson (@ryancarson) breaking down the Ralph Wiggum technique.
* Open Source LLMs
* Solar Open 100B - Upstage’s 102B MoE model. Trained on 19.7T tokens with a heavy focus on “data factory” synthetic data and high-performance Korean reasoning (X, HF, Tech Report).
* MiroThinker 1.5 - A 30B parameter search agent that uses “Interactive Scaling” to beat trillion-parameter models on search benchmarks like BrowseComp (X, HF, GitHub).
* Liquid AI LFM 2.5 - A family of 1B models designed for edge devices. Features a revolutionary end-to-end audio model that skips the ASR-LLM-TTS pipeline (X, HF).
* NousCoder-14B - competitive coding model from Nous Research that saw a 7% LiveCodeBench accuracy jump in just 4 days of RL (X, WandB Dashboard).
* Zhipu AI IPO - The makers of GLM became the first major LLM firm to go public on the HKEX, raising $558M (Announcement).
* Big Co LLMs & APIs
* NVIDIA Vera Rubin - Jensen Huang’s CES reveal of the next-gen platform. Delivers 5x Blackwell inference performance and 75% fewer GPUs needed for MoE training (Blog).
* OpenAI ChatGPT Health - A privacy-first vertical for EHR and fitness data integration (Waitlist).
* Google Gmail Era - Gemini 3 integration into Gmail for 3 billion users, featuring AI Overviews and natural language inbox search (Blog).
* XAI $20B Raise - Elon’s XAI raises Series E at a $230B valuation, even as Grok faces heat over bikini-gate and safety guardrails (CNN Report).
* Doctronic - The first US pilot in Utah for autonomous AI prescription renewals without a physician in the loop (Web).
* Alexa+ Web - Amazon brings the “Smart Alexa” experience to browser-based chat (Announcement).
* Autonomous Coding & Tools
* Ralph Wiggum - The agentic loop technique for autonomous coding using small, atomic user stories. Ryan Carson’s breakdown of why this is the death of “vibe coding” (Viral X Article).
* Catnip by W&B - Chris Van Pelt’s open-source iOS app to run Claude Code anywhere via GitHub Codespaces (App Store, GitHub).
* Vision & Video
* LTX-2 - Lightricks open-sources the first truly open audio-video generation model with synchronized output and full training code (GitHub, Replicate Demo).
* Avatar Forcing - KAIST’s framework for real-time interactive talking heads with ~500ms latency (Arxiv).
* Qwen Edit 2512 - Optimized by PrunaAI to generate high-res realistic images in under 7 seconds (Replicate).
* Voice & Audio
* Nemotron Speech ASR - NVIDIA’s 600M parameter streaming model with sub-100ms stable latency for massive-scale voice agents (HF).


This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey all,
Happy new year! This is Alex, writing to you for the very fresh start of this year, it’s 2026 already, can you believe it?
There was no live stream today, I figured the cohosts deserve a break and honestly it was a very slow week. Even the chinese labs who don’t really celebrate X-mas and new years didn’t come out with a banger AFAIK.
ThursdAI - AI moves fast, we’re here to make sure you never miss a thing! Subscribe :)

Tho I thought it was an incredible opportunity to finally post the Will Brow interview I recorded in November during the AI Engineer conference.
Will is a researcher at Prime Intellect (big fans on WandB btw!) and is very known on X as a hot takes ML person, often going viral for tons of memes!
Will is the creator and maintainer of the Verifiers library (Github) and his talk at AI Engineer was all about RL Environments (what they are, you can hear in the interview, I asked him!)
TL;DR last week of 2025 in AI
Besides this, my job here is to keep you up to date, and honestly this was very easy this week, as… almost nothing has happened, but here we go:
Meta buys Manus
The year ended with 2 huge acquisitions / aquihires. First we got the news from Alex Wang that Meta has bought Manus.ai which is an agentic AI startup we covered back in March for an undisclosed amount (folks claim $2-3B)
The most interesting thing here is that Manus is a Chinese company, and this deal requires very specific severance from Chinese operations.
Jensen goes on a new years spending spree, Nvidia buys Groq (not GROK) for $20B
Groq which we covered often here, and are great friends, is going to NVIDIA, in a… very interesting acqui-hire, which is a “non binding license” + most of Groq top employees apparently are going to NVIDIA. Jonathan Ross the CEO of Groq, was the co-creator of the TPU chips at Google before founding Groq, so this seems like a very strategic aquihire for NVIDIA! Congrats to our friends from Groq on this amazing news for the new year!
Tencent open-sources HY-MT1.5 translation models with 1.8B edge-deployable and 7B cloud variants supporting 33 languages (X, HF, HF, GitHub)
It seems that everyone’s is trying to de-throne whisper and this latest attempt from Tencent is a interesting one. a 1.8B and 7B translation models with very interesting stats.
Alibaba’s Qwen-Image-2512 drops on New Year’s Eve as strongest open-source text-to-image model, topping AI Arena with photorealistic humans and sharper textures (X, HF, Arxiv)
Our friends in Tongyi decided to give is a new years present in the form of an updated Qwen-image, with much improved realism
That’s it folks, this was a quick one, hopefully you all had an amazing new year celebration, and are gearing up to an eventful and crazy 2026.
I wish you all happiness, excitement and energy to keep up with everything in the new year, and will make sure that we’re here to keep you up to date as always!

P.S - I got a little news of my own this yesterday, not related to AI. She said yes 🎉



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