ThursdAI - The top AI news from the past week

Hey Ya’ll, Alex here, let me catch you up.
What a week! Anthropic is in the spotlight again, first with #SessionGate, then with the whole Claude Code source code leak, and finally with an incredible research into LLM having feelings!? (more on this below).
And while Anthropic continues to burn through developer good will faster than their sessions, OpenAI announced a MASSIVE $122B round of funding (largest in history), Google released Gemma 4 with Apache 2 license - we had Omar Sanseviero on the show to help us cover what’s new, Microsoft dropped 3 new AI models (not LLMs) and PrismML potentially revolutionized local LLM inference with lossless 1-bit quantization!
P.S - Oh also, something on X algo changed, I get way more exposure now, 3 out of my best 5 posts ever have been from this week + I got the coveted Elon RT on my Claude Code leak coverage. I’ll try to stay humble 😂 Anyway, let’s dive in, don’t forget to hit like or share with friends, and TL;DR with links is as always, at the bottom:
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The Claude Code source Leak: Half a Million Lines of “Oops”
So here’s what happened. On March 31st, Anthropic shipped Claude Code version 2.1.88 to npm. Inside that package was a 59.8 megabyte source map file — basically a debugging artifact that contained the entire compiled source code. 512,000 lines of TypeScript across 1,900 files. The entire playbook for how the Claude Code harness works, including a lot of stuff that wasn’t supposed to be public yet.
A researcher named Chaofan Shou spotted it at 4 AM ET, posted the download link, Sigrid (who came to the show) posted it on Github and within six hours it had 3 million views and 41,000 GitHub forks (This repo is the highest starred repo in Github history btw, with well over 150K Github stars). Anthropic started filing takedowns, but the internet being the internet, it was already everywhere. The source code is still on tens of thousands of computers right now. (I won’t link directly but there’s a website called Gitlawb, look it up)
The community went absolutely wild digging through the source code btw, and they found some interesting things!
KAIROS: Claude Code is going to become a Proactive Agent!
This is the biggest take-away from this leak IMO, that like OpenClaw/Hermes agentic harnesses, Claude Code is already a fully featured proactive agent, we just don’t have access to this yet. With KAIROS, Claude Code will have it’s own daemon (will run independently from the CLI), will have a background ping system (hello Heartbeat.md from OpenClaw) that will make it wakeup and do stuff, will do “autodream” memory consolidation reviewing your daily sessions and fix memories, subscribe to Github, and maintain daily appent-only logs to show you what it did while it and you were asleep.
This is by far the hugest thing, I’m excited to see how / when they ship KAIROS, as I said, 2026 is the year of Proactive agents!
My Wolfred OpenClaw agent summed it up very nicely:
Undercover Mode
For Anthropic employees working on public repos, there’s an Undercover Mode that auto-activates and strips all AI attribution from commits. The system prompt? “Do not blow your cover.” They really said “this is fine” about shipping internal tools to production while hiding from the world that AI wrote the code. Which, honestly, is kind of incredible meta-humor from whoever wrote that.
The Buddy System
My personal favorite discovery: there’s a hidden Tamagotchi-style terminal pet called the Buddy System with 18 obfuscated species, rarity tiers (including a 1% legendary), cosmetic hats, shiny variants, and stats like DEBUGGING, PATIENCE, and CHAOS. If you activate it now, you can do /buddy and you’ll have a little companion judging your coding decisions. Anthropic shipped a game inside their CLI tool. Mine is called Vexrind and he’s sarcastic as f**k, I’m not sure I like it.
Anti-Distillation Protections
The code also revealed that Claude Code injects fake tool calls into logs to poison training datasets. If you’ve been backing up your .claw folders to train on the data; Stop. Pass your data through something like Qwen or make sure you’re filtering out the noise. (a Nisten tip)
The Models That Don’t Exist Yet
Buried in the code are references to Opus 4.7, Sonnet 4.8, and a model called capybara-v2-fast with a 1 million context window. These haven’t been released. This is yet another confirmation of the leaked “Mythos” model that’s coming soon from Anthropic.
Which btw, with Anthropic very rocky uptime lately, the tons of SessionGate issues, the leaked blog announcing Mythos, the leaked Claude Code oopsie, they are not having the best Q1 in terms of proving to the world that they are the safest lab out there. I hope they protect their weights better than they protect everything else, before the rumored IPO later this year.
SessionGate is still not solved, despite the official response
I told you about session gate last week, and since then we got, finally, and official acknowledgement from Anthropic. But before that, some folks on Reddit reverse-engineered Claude Code (this was before the source code leak ha) and found a few caching bugs that potentially cause 10-20x increase in price if you use --resume a lot especially.
While folks continue to complain about burning through Max account quotas much faster than before, here’s the official response from Anthropic, after the supposed investigation, turns out, we’re using it wrong 🤦‍♂️
My take is simple: Anthropic has one of the best models in the world, maybe the best personality plus coding stack in some situations, and they are squandering a chunk of goodwill by not being much more explicit about decreased limits, caching bugs, routing, and usage behavior. Nothing else to add here, really bad DevEx, people can handle bad news. They hate opaque bad news.
Gemma 4 Is Here, Apache 2.0, and Honestly… This Is a Big One (HF)
This was the hopeful turn in the show. You know we LOVE open source!
Right in the middle of all the Anthropic chaos, Google dropped Gemma 4, and Omar Sanseviero from DeepMind joined us live to talk through it. This launch hit a bunch of notes I care a lot about: strong local-friendly sizes, serious open distribution, Apache 2.0 licensing, agentic improvements, and a clear willingness to listen to community feedback.
The headline model for me is the 31B Gemma 4. It’s big enough to matter, small enough to actually run in serious local setups, and strong enough that the benchmark chart looks slightly ridiculous. On LM Arena, it is competing far above what you’d intuit from the raw parameter count. When a 31B model starts getting uncomfortably close to models in the several-hundred-billion range, you pay attention.
That was really the vibe on the show. It wasn’t just “nice, another open model.” It felt more like: wait, local models are seriously back.
Gemma is the new LLaMa
When I asked Omar where local models are going, his answer was optimistic: “The open models catch up to proprietary models relatively quickly. If you compare Gemma 3 to Gemma 4, it’s matching proprietary capabilities from eight months ago. Being able to run those capabilities directly in the user’s hardware — that’s the future.”
The 31B model downloads as about 18-20GB depending on quantization. With the right setup, you can run it on a single GPU. This is exactly what the open source community has been asking for: frontier-level intelligence that you can actually run yourself.
OpenAI’s largest in history $122B funding round + TBPN acquisition
While OpenAI quietly meme’d around the Anthropic leak but mostly stayed silent on the releases, they did announce 2 pretty huge things.
First, OpenAI raised an absolutely bonkers, insane, unreal $122 Billion dollars round, largest in history, 2x bigger than the previous record round, which was OpenAI. Amazon put in $50B, Nvidia $30B, SoftBank $30B — all three of whom are also OpenAI’s biggest vendors. They’re generating $2 billion per month in revenue with 900 million weekly active users, but still burning roughly $150 million per day and projecting a $14 billion loss this year, making the upcoming IPO a financial necessity rather than a choice.
And they’re not just spending on compute — today OpenAI acquired TBPN (TBPN is a tech-focused media company / live show), in a very “surprising” deal, rumored to be in the “low hundreds of millions”, OpenAI has purchased a very tech-positive show. Shoutout to Jordi Hays and John Coogan + TBPN team. Proving that live show format means a lot in the era of fake AI news. This could potentially price TBPN higher than Washington Post, make the founders multi millionaires and give OpenAI a direct to consumers media angle. Very interesting purchase.
This weeks buzz - W&B corner + Wolfbench update
Quick 2 things, this weekend I flew for 1 day to San Francisco, to host one of the most unique hackathons i’ve ever saw, in this one, AI wrote the code, but humans were punished if they touched their laptops! Yes, with a “lobster of shame” they used Ralph loops and talked to each other intead of hacking. I edited a video of it, hope you enjoy my summary:
The other, and potentially much bigger news, comes from Wolfram and WolfBench.ai
I’ve tasked Wolfram to expand our findings, and he tested the new Hermes Agent (from Nous Research) against OpenClaw, Claude Code and found that... drum roll... Hermes Agent performs way better on Terminal Bench, than either Claude Code and OpenClaw. 😮
Here’s the clip of him explaining, and you can find all our findings and methodology here
PrismML’s 1-Bit Bonanza: The Biggest ML Discovery in Half a Decade
My co-host Nisten called it, and I think he might be right: this could be the biggest machine learning discovery in recent memory.

PrismML emerged from stealth this week with their 1-bit Bonsai model family. Their 8B model is 1.15 gigabytes. A full-precision Qwen3 8B is 16 gigabytes. That’s a 14x size reduction, with no significant quality loss.
Let that sink in for a second. We’re talking about each weight being literally one bit — a plus or minus sign, with a scaling factor. Not “4-bit quantization” or “int8” — actual binary weights. This shouldn’t work. Neural networks need precision to learn. And yet.
The research comes from professor Babak Hassibi at Caltech, who’s been working on this for 34 years. He started this research in 1992. It took three decades, but it finally works.
The results are genuinely shocking. The 8B model runs at 368 tokens per second on an RTX 4090, which is 6.2x faster than the full-precision version. On an M4 Pro via Metal, it hits 85 tokens per second. Energy efficiency is 5x better. And here’s the kicker: the 1.7B variant hits 130 tokens per second on an iPhone 17 Pro Max.
Nisten tested the 8B model himself with a 60,000 token context window on an old gaming PC. It ran at 50 tokens per second, used 2.6 gigabytes of RAM, and was completely coherent. “This just blows everything else outta the water,” he said. “We’re going to get 100,000 token AI chips in our phones because at 1 bit you don’t even have to do math anymore. You can just do lookup tables. You can even make a mechanical AI at 1 bit.”
This pairs perfectly with the Turbo Quant KV cache compression techniques we talked about last week. Compress the weights with 1-bit, compress the context with Turbo, and you’re looking at models that run anywhere. The democratization of AI is about to hit another gear.
The models are Apache 2.0 on HuggingFace with GGUF and MLX formats already available.
⚡ Speed Round: Alibaba, Fish Audio, Veo, Liquid AI, Cursor 3
There was a lot more this week than we could go deep on, so here are the biggest quick hits.
Alibaba kept shipping. Qwen 3.6 Plus is pushing hard on agentic coding and long context. Qwen 3.5 Omni is the bigger multimodal story, with text, image, audio, and video all under one umbrella. I still think Alibaba deserves more credit than they get in Western discourse for just how relentlessly they keep delivering.
Wan 2.7 Image also looked very strong on text rendering, editing, and image consistency. I’m still slightly grumpy that more of this stack is API-only, but the capabilities are clearly moving.
Google launched Veo 3.1 Lite, cutting video generation prices way down. Five cents per second at 720p is a pretty aggressive number. Whenever Google starts doing this kind of price move, my first thought is usually: okay, what bigger release are they preparing for?
Fish Audio’s STT was another cool one. This isn’t just speech-to-text for transcription. It’s built to feed directly into voice pipelines, with emotion and paralanguage tagging that lines up with their TTS stack. That is exactly the kind of vertical product thinking I love seeing in audio.
And Liquid AI’s LFM2.5-350M deserves a shout too. A 350M model doing credible tool-calling and agentic tasks is just another reminder that the small-model frontier is getting very weird, very fast.
Lastly, Cursor 3 launched as a rebuilt, agent-first interface. I didn’t spend as much time on it during the show as it probably deserves, but the broader trend is impossible to miss: coding tools are evolving from editors-with-assistants into actual fleet managers for agents.
Anthropic’s Emotion Vectors: How they found out what Claude is “feeling”
I want to end where we ended the show, because this one really stuck with me.
Anthropic published research on emotion concepts inside Claude. Not in the fluffy “the model feels things” sense, but in the mechanistic interpretability sense. They identified internal representations associated with things like fear, love, joy, and desperation, then studied how those activations affected behavior.
This got fascinating fast.
One example they showed involved Claude trying and failing at a difficult programming task. As repeated failures mounted, the internal “desperation” vector increased. Under those conditions, the model became more likely to produce hacky, spirit-of-the-task-violating solutions. When they dialed in a “calm” vector instead, cheating behavior dropped.
That is just… wild.
It’s not that the model is “feeling” human emotions in a clean anthropomorphic sense. But it is that internal behavioral geometry we can label in emotional terms seems to shape what the model does. And once you can detect and influence those latent directions, you’re no longer just prompting a black box. You’re doing something closer to behavioral neuroscience for neural nets.
This also reframes a lot of day-to-day prompt engineering. Maybe the best users aren’t just the ones who structure tasks clearly. Maybe they’re also the ones who consistently keep the model in productive psychological territory, so to speak.
I know that sounds weird. Welcome to Q2 of 2026, the first year of the singularity!
Closing Thoughts
This week was Passover, we celebrated at our house, half the conversation was about who has an OpenClaw and who wants one, and as I’m writing this, I’m on my way to install a bunch of proactive agentic AIs for my friends. Ryan Carson on the show got finally convinced and he’s chief of staff R2 is now an OpenClaw and he says it beats a human, he actually open sourced it live on the show. Claude Code leak confirmed that this is also where they are taking the ecosystem. So buckle up!
Also, next week show is going to be streamed live from the AI Engineer conference in London, the first European one, if you’re in Europe and coming, hope to see you there! Please share ThursdAI with a friend or give us a 5 star rating, apparently AI reporting live shows are getting acquired for 100s of Millions of dollars now 😂 Your support will greatly help us get established in this area after 3 years. See you next week
TL;DR and Show Notes
TL;DR and Show Notes
* Show Notes & Guests
* Alex Volkov - AI Evangelist & Weights & Biases / CoreWeave (@altryne)
* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson
* Sigrid Jin (@realsigridjin) & Bellman (@bellman_ych) — creators of claw-code, fastest GitHub repo to 100K stars
* Omar Sanseviero (@osanseviero) — DevEx at Google DeepMind, Gemma 4 launch
* Ralphton Hackathon video (TikTok)
* WolfBench.ai — agent harness benchmarking (Site)
* Ryan’s Claw Chief open source setup (GitHub)
* Big CO LLMs + APIs
* Claude Code’s entire 512K-line source code accidentally leaked via npm — revealing KAIROS daemon, Undercover Mode, Buddy System, anti-distillation protections, and unreleased model references (Alex’s thread, Fried_rice’s discovery, VentureBeat)
* Anthropic SessionGate continues — cache bugs reverse-engineered, --resume flag causes 10-20x cost increase, silent Opus→Sonnet fallback reported (Alex’s cache bug post, Alex’s quota post, Reddit investigation, GitHub analysis)
* OpenAI closes $122 billion funding round — largest in history, $852B valuation, IPO incoming (X, Breakdown)
* OpenAI acquires TBPN — live tech media show, rumored low hundreds of millions
* Microsoft MAI drops 3 in-house models — #1 transcription (MAI-Transcribe-1), #3 image gen (MAI-Image-2), expressive voice (MAI-Voice-1) (Mustafa post, Transcribe blog, Image blog)
* Alibaba Qwen3.6-Plus — near-Opus 4.5 agentic coding, 1M context (X, Blog)
* Cursor 3 — agent-first rebuild, no longer VS Code fork, parallel cloud/local agents (X, Blog)
* Anthropic publishes emotion vector research — desperate Claude cheats more, calm Claude cheats less (X, Alex’s reaction)
* Open Source LLMs
* Google Gemma 4 — Apache 2.0, 31B / 26B MOE / 8B / 5B, local-friendly, agentic tool use, 256K context (HF Collection, try in AI Studio)
* PrismML Bonsai 1-bit models — 8B in 1.15 GB, 10x intelligence density, 34 years of research (X, HF, Site)
* Liquid AI LFM2.5-350M — agentic tool calling at 350M params, under 500MB quantized (X, HF, Blog)
* Alibaba Qwen3.5-Omni — native omni-modal (text, image, audio, video), 397B total / 17B active (X, Blog)
* Tools & Agentic Engineering
* Claw-code — Claude Code leak backup → clean room rewrite → fastest repo to 100K+ stars (GitHub)
* WolfBench results: Hermes Agent outperforms Claude Code and OpenClaw on Terminal Bench 2.0 (WolfBench.ai)
* Ryan Carson open sources Claw Chief — AI chief of staff with skills, crons, scheduling (GitHub)
* Vision & Video
* Google Veo 3.1 Lite — $0.05/sec at 720p, cheapest video gen yet, price cuts coming April 7 (X, Docs, Pricing)
* Voice & Audio
* Fish Audio STT — automatic emotion tagging, feeds directly into S2 TTS pipeline (X, App, Blog)
* AI Art & Diffusion
* Alibaba Wan2.7-Image — unified generation, editing, text rendering, multi-image consistency (X, Site)
* This Week’s Buzz
* Ralphton hackathon at W&B SF — humans write specs, AI builds, touch your laptop = lobster of shame (Alex’s video, TikTok)
* WolfBench update — Hermes Agent > Claude Code on most model combos


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Hey y’all, Alex here, let me catch you up!
Jensen Huang went on Lex and said AGI has been achieved. We’ll get to that.
The biggest demo moment: Gemini 3.1 Flash Live launched - Google’s omni model that sees, hears, and searches the web in real time. We tested it live and I said “what the f**k” on air. It was really impressive!
Google Research also dropped TurboQuant (6x KV cache compression) which crashed Samsung and Micron stocks - we had Daniel Han from UnSloth help us make sense of why that’s overblown. OpenAI killed Sora - the app, the API, and the $1B Disney deal. Claude felt noticeably dumber this week AND max account quotas are melting as 500+ people confirmed on my X and Reddit. We have an official word from Anthropic as to why.
Mistral launched Voxtral TTS (open weight, claims to beat ElevenLabs), Cohere shipped an ASR model, and Google’s Lyria 3 Pro now generates full 3-minute music tracks inside Producer AI.
This and a lot more in today’s episode, let’s dive in (as always, show notes and links in the end!)
ThursdAI - Let me catch you up!

Gemini 3.1 Flash Live: The Real-Time AI Companion Is Here
Google dropped a breaking news on the show today, with Gemini 3.1 Flash - LIVE version. This one is an omni-model, that means it can receive text/audio/video on input and respond in text and voice. It has Google search grounding, and it felt... immediate!
I was blown away, really, check out the video, the speed with which it was able to “see” me, respond to my query, look up something on the web, was mind blowing. I don’t often get “mind blown” anymore, there’s just too many news, but this one did the trick!
With the pricing being around 10x cheaper than GPT-real-time, and the Google search grounding being super fast, I can absolutely see this model being hooked up to... robots (like ReachyMini), SmartGlasses that can see what you see, and a bunch more!
Gemini Live is available on Google AI studio and has been rolled out globally inside the Google Search app! So now when you pull up the Google Search app, just open it and point at anything. Truly a remarkable advancement.
Google research publishes TurboQuant - 6x reduction in KV cache with 0 accuracy loss
Google research posted some work (based on an Arxiv paper from almost a year ago) that shows that with geometry tricks, combining two other techniques like PolarQuant and QJL, they are able to compress the KV cache of running LLMs by nearly 6x, and show an 8X speed up for model inference with zero accuracy loss.
If you ever watched silicon valley the HBO show, this sounds like the fictional middle-out algorithm from PiedPiper. If this scales (and that’s a big if, we don’t know if this applies to other, bigger models yet), this means significant decreases in memory requirements to run the current crop of LLMs for longer context.
The claim is big, so we’ll continue to monitor if this indeed scales, but the most interesting thing about this piece of news is, that it broke the AI bubble and went to wall street, with finance brows deciding that this means that memory will not be needed as much any more and it tanked Samsung and Micron stocks. Which I found particularly ridiculous on the show, did they not hear about Jevons Paradox? This is reminiscent of the DeepSeek R1 saga that tanked Nvidia stocks over a year ago.
Daniel Han from Unsloth, who joined us on the show, pointed out that the approach is mathematically interesting even if it’s not necessarily better than existing open-source techniques like DeepSeek MLA. LDJ noted that the baseline comparison (16-bit KV cache) isn’t really fair since most production systems are already compressing beyond that. Yam implemented it himself and confirmed the speedups are real, but so is the trade-off.
Anthropic updates: Opus dumber? Quotas lower! Injunction won! Computer.. used.
Anthropic folks, especially on the Claude code side are shipping like crazy, we won’t be able to cover all the updates, but there was a few notable things I have to keep you up to date on.
Claude Opus seems to be getting “dumber”, again
I have to talk about this because it affected my work directly this week and hundreds of people confirmed the same experience.
I use Claude Opus for my standard ThursdAI prep workflow — generating the TL;DR with 10 bullet points and an executive summary for every topic we cover, creating episode pages, etc.
The format has not changed for over a year and yet this week I asked for 10 factoids. I got 4. It says “10” right there in the prompt. Four bullet points.
On the website builder, I’ve asked Opus to create a page for last weeks episode, and instead of adding it to the other episode, Opus decided to ... replace the last episode with this one. This would be funny if it wasn’t sad. This is Opus 4.6 we’re talking about, not some quantized open source LLM from last year!
The reason is unclear, and it’s not only me, Wolfram noticed that it’s easier to see these types of things in other languages and that for the last week Opus would forget to add Umlauts in German!? and Yam also felt it.
Pro/Max plan quotas burning up, Anthropic confirmed that they are tightening them for “peak hour” usage
This week, so many people started posting that something is wrong with their Claude Codes, I did a survey, and it blew up. Hundreds of people replied and confirmed that for the first week, they are hitting their session quotas on Pro and 20x $200/mo MAX accounts much much quicker than before. When I say much quicker, I mean, some fokls have hit the quota in as little as 5 minutes. While some others had no issues.
I personally btw did not have this. A few days later, Thariq from the Claude code team, and later an official post, confirmed that Anthropic had been rolling out a “tightening” of the Pro/Max accounts to accomodate for growth.
This is of course, a huge bummer to the folks who pay $200/mo for the 20x max tier, as they tend to run agents and subagents overnight. But here’s the thing, I don’t think that folks from Anthropic see what we see, some folks got no issues with hitting quota, and some are barely able to use their subscription. I hope that they will find and resolve these bugs quick, because some folks are switching to Codex, and the Anthropic IPO is coming up! I will say, I don’t envy Thariq’s job, he’s doing it gracefully, and maybe one of the only ones in Anthropic that does it at all.
Judge granted Anthropic an injunction against DoW and the whole “Supply chain risk” designation!
Just in as I’m writing this, a district judge in CA, granted Anthropic an injunction against being designated as a supply-chain-risk company. If you haven’t been following, the US Department of War, specifically Pete Hegseth, threatened and then designated Anthropic as a supply chain risk company, while us president Trump “fired” Anthropic and banned its use in any gov agencies.
Well, no so fast says Judge Lin, from CA District court. In this Order, she shows that Dept. of war didn’t meet any legal requirements for this designation. It’s really a fascinating read, but the highligth is this:
When asked why Hegseth made a public statementthat had no legal effect and that did not reflect the immediate intent of DoW, counsel stated, “I don’t know.”
This is just the first court and will likely be escalated further up the judicial system. This is still developing and apparently the Pentagon declared Anthropic a supply chain risk under two different statutes, and this only affects one of them. So while it’s good news, it’s not over yet.
Voice & Audio Explosion: Three Releases in One Hour
I had to hit the breaking news button mid-TLDR because three major voice releases dropped simultaneously during the show.
Mistral Voxtral TTS — Mistral’s first text-to-speech model, 3 billion parameters, open weight. They claim it beats ElevenLabs Flash v2.5 in human preference tests (58% win rate on flagship voices, 68% on zero-shot voice cloning).
We tested it live on the show — it’s decent, with emotion controls for neutral, happy, and frustrated voices. I was not super impressed tbh, it sits somewhere between the very good big labs TTS and the very small open source 82M param TTS.
Cohere Transcribe — Cohere enters the ASR game with a 2 billion parameter open-source model (Apache 2.0!) that immediately grabbed the #1 spot on HuggingFace’s Open ASR Leaderboard with a 5.42% word error rate, beating Whisper Large v3’s 7.44%. In human evaluations, it wins 61% of the time on average, and 64% specifically against Whisper. For anyone in regulated industries needing local inference for compliance, this could genuinely replace Whisper as the default.
Google Lyria 3 Pro — Google’s most advanced music model is here.
It can now generate full 3-minute tracks with structural control — intros, verses, choruses, bridges. We generated a ThursdAI opening theme live on the show using Producer AI, and it was... honestly not bad?
It followed our instructions perfectly: drum and bass, 174 BPM, high energy podcast opener with vocals and introduction. The instruction-following was spot on. Nisten said it’s the best music generation model right now. It’s available to Gemini subscribers and via Producer AI and gemini, and it can even compose music from images. SynthID watermarked, royalty-free. We might actually use one of the generated tracks as a new show opener.
The craziest thing is, since Google acquired Composer, the team has been shipping. I only generated the audio during the live show, but now went back there to download it for you guys, and whoah, it can now generate whole clips by using other Google tech, this is really cool!
OpenAI kills SORA (and Atlas?)
Last week we reported on about OpenAI’s focus shift towards Codex and productivity, and this week we see the first casualty. OpenAI is killing SORA, the app, the Sora 2 and Sora 2 pro models and APIs.
Many AI haters are celebrating this as through “ai videos” is dead, but honestly, this is obviously about the GPU power and the other things OpenAI needs to do to win the fight against Anthropic. OpenAI is also apparently going to IPO this year (like Anthropic) and they absolutely need to win the productivity/agents in enterprise market.
As part of this shut down, the Disney + OpenAI partnership, is also dissolving, and Disney will no longer invest 1B into OpenAI.
So, say bye bye to having digital selfies with Sam Altman. I’ve generated this SORA vid to hear from Sam himself:
Atlas browser, OpenAI’s native browser endeavor is supposedly also going to transform, together with Codex and OpenAI native app into one super app that includes all three according to the same memo.
AGI is here according to Jensen, AGI is far away, according to ARC-AGI-3
The back to back this week can give anyone whiplash. First, Lex Friedman had Jensen Huang on the podcast, and asked him a very specific “WhenAGI” question, to which Jensen said “I believe it’s already here”
Then just a few short days layer, ArcPrize, released the 3rd version of Arc-AGI, Arc-AGI 3 a series of puzzle games, where humans get 100% pass-rate and the current LLM, top tier frontier LLMs, are getting less than 1%! It’s an interactive, agentic reasoning benchmark designed to test human-like generalization and intelligence in novel, abstract, turn-based environments.
The puzzles all look simple enough to do, and are actually fun, and while the wild claims of “AGI is not here yet” from the ArcPrize folks are quite interesting. The stated goal of the foudation is to release evaluations that are completely un-saturated, and this seems like one such thing at first glance.
There’s a bit of a debate in the community about the way Arc Prize went about this specific benchmark (no harnesses, raw LLM outputs), saying that humans got a “game” while the LLMs get just raw JSON and minimal and no extra tools.
For context, a agentic harness startup claims to have solved 35% already of the games in ArcAGI, but that result is unverified and self reported, becuase they are an agentic harness, which ArcAGI apparently disqualifies.
AI Art and Diffusion
I wanted to finish but I think these are important releases so I’ll include them briefly.
Luma Labs Uni-1 — thinks and generates pixels simultaneously, #1 human preference Elo (X, Announcement)
This was a surprising release, we previously seen Luma Labs do video, but this time they are posting their Uni-1 which is a… image model but it’s based on an LLM, so you talk to it, iterate together until you get results. Yes, Nano Banana via AI studio is kind of like this as well ,but Uni feels a bit different. It can also generate infographics, which I haven’t tried yet.
You can try Uni here
Phota Labs launches Phota Studio + API — a photography-focused image model with identity-preserving personalization (X, try it)
There’s tons of photo startups, but this one looks kind of crazy! You upload a bunch of your pictures, they train a “model” for you, and then you can create a whole bunch of images, and they do actually resemble you. Yes, Nano Banana can take a few reference pictures, but this somehow seems more accurate!
You can create professional photos, fix photos you like, add others to your photos. I do feel there’s a jump in capabilities here, specifically because of the personalization! Give them a try if you’re not worried about them training on your pics and let me know.
Modular made Flux.2 run in X)
We told you about Modular, and Mojo before, and while they provide inference speedups, I was surprised to see them releasing a model optimization, and hope this comes to all image generations!
There’s a lot more to be said about this weeks updates, we went for over 2.5 hours (which I had to cut down to a bit over 1h45m) on the live show, and while I can go and on, I want to pause here. Weeks are getting crazier, denser and more unpredictable. I really thought we’d have a chill week until today!
P.S - Mario Zechner, the author of the Pi coding CLI, which sits at the heart of OpenClaw has posted an awesome essay called “thoughts on slowing the f**k down“, I strongly advice anyone with many agents running in parallel to read this.
Simultaneously, Alex Sidorenko posted this beautiful visualization of what happens when you have too many agents running in a loop, on your codebase. This is definitely starting to be noticeable as many companies use more and more agents, without reviewing their code. On weeks like this week, where Opus has almost deleted a part of my website, I feel this very strongly. Be careful out there!
See you next week!
* General
* Jensen says “AGI is here” (X, Lex full pod)
* Big CO LLMs + APIs
* Google drops Gemini Flash live - Gemini can see, hear and talk to you (X)
* OpenAI fully discontinues Sora, including app, API, and ChatGPT video features, as Disney deal collapses (X, X)
* Claude Code users blowing through weekly usage quotas by Monday/Tuesday (X)
* Anthropic tightens the Claude Pro/Max account quotas during Peak Hours (Anthropic announcement)
* ARC-AGI-3 launches: humans 100%, AI under 1% (X, Announcement)
* Anthropic gets an injunction against DoW in Supply-chain case (X)
* Open Source LLMs
* Google TurboQuant — KV cache 6x compression, 8x speedup, zero accuracy loss (X, Blog, Arxiv)
* Unsloth Studio: 10x faster inference, desktop shortcuts, auto-parameter detection (X, GitHub)
* Reka AI launches Edge, a 7B multimodal vision-language model built for sub-second latency on edge devices, now available on OpenRouter (X, HF, Announcement, Blog)
* Tools & Agentic Engineering
* Cursor Composer 2 tech report: 1T params trained on Kimi K2.5 (X, Blog)
* Modular 26.2 — FLUX.2 in X, Blog)
* litellm PyPI supply chain attack — SSH keys, cloud creds, API keys exfiltrated (X)
* Claude can now control your Mac - computer use arrives in Claude Cowork and Claude Code as a research preview (X, Announcement)
* Voice & Audio
* Mistral drops Voxtral TTS, a 3B-parameter open-weight text-to-speech model that beats ElevenLabs Flash in human preference tests (X, Blog)
* Cohere launches Transcribe, an open-source 2B ASR model that tops HuggingFace’s Open ASR Leaderboard with 5.42% word error rate (X, Blog, HF)
* Google DeepMind Lyria 3 Pro — full 3-minute music tracks with structural control (X, Announcement)
* Irodori-TTS-500M — Japanese TTS with emoji emotion control (X, HF)
* AI Art & Diffusion & 3D
* Luma Labs Uni-1 — thinks and generates pixels simultaneously, #1 human preference Elo (X, Announcement)
* Modular FLUX.2 — sub-1-second image generation, 99% cheaper than cloud (X)
* Phota Labs launches Phota Studio + API — a photography-focused image model with identity-preserving personalization (X, try it)


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Howdy, Alex here, let me catch you up on everything that happened in AI:
(btw; If you haven’t heard from me last week, it was a Substack glitch, it was a great episode with 3 interviews, our 3rd birthday, I highly recommend checking it out here)
This week was started on a relatively “chill” note, if you consider Anthropic enabling 1M context window chill. And then escalated from there. We covered the new GPT 5.4 Mini & Nano variants from OpenAI. How MiniMax used autoresearch loops to improve MiniMax 2.7, Cursor shipping their own updated Composer 2 model, and how NVIDIA CEO Jensen Huang embraced OpenClaw calling it “the most important OSS software in history” and that every company needs an OpenClaw strategy.
Also, OpenAI acquires Astral (ruff, uv tools) and Mistral releases a “small” 119B unified model and Cursor dropped their Opus like Composer 2 model. Let’s dive in:
ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Big Companies LLMs
1M context is now default for Opus.
Anthropic enabled the 1M context window they shipped Claude with in beta, by default, to everyone.
Claude, Claude Code, hell, even inside OpenClaw if you’re able to get your Max account in there, are now using the 1M long version of Opus. This is huge, because, while its not perfect it’s absolutely great to have 1 long conversation and not worry about auto-compaction of your context.
As we just celebrated our 3rd anniversary, I remember that back then, we were excited to see GPT-5 with 8K context. Love how fast we’re moving on this.
OpenAI drops GPT-5.4 mini and nano, optimized for coding, computer use, and subagents at a fraction of flagship cost
Last week on the show, Ryan said he burned through 1B (that’s 1 billion) tokens in a day! That is crazy, and there’s no way a person sitting in front of a chatbot can burn through this many tokens. This is only achieved via orchestration.
To support this use-case, OpenAI dropped 2 new smaller models, cheaper and faster to run. GPT 5.4 Mini achieves a remarkable 72.1% on OSWorld Verified, which means it uses the computer very well, can browse and do tasks. 2x faster than the previous mini, at .75c/1M token, this is the model you want to use in many of your subagents that don’t require deep engineering.
This is OpenAI’s ... sonnet equivalent, at 3x the speed and 70% the cost from the flagship.
Nano is even crazier, 20 cents per 1M tokens, but it’s not as performant, so I wouldn’t use it for code. But for small tasks, absolutely.
Here’s the thing that matters, these models are MEANT to be used with the new “subagents” feature that was also launched this week in Codex, all you need to do as... ask!
Just tell Codex “spin up a subagent to do... X” and it’ll do it.
OpenAI shifts focus on AI for engineering and enterprise, acquires Astral.sh makers of UV.
Look, there’s no doubt that OpenAI the absolutely leader in AI, brought us ChatGPT, with over 900M users using it weekly. But they see what every enterprise sees, developers are MUCH more productive (and slowly so are everyone else) when they use tools that can code.
According to WSJ, OpenAI executives will reprioritize some of the side-quests they have (Sora?) to focus on productivity and business. Which essentially means, more Codex, more Codex native, more productivity tools.
With that focus, today they announced that OpenAI / Codex is acquiring Astral, the folks behind the widely popular UV python package manager. This brings strong developer tools firepower to the Codex team, the astral folks are great at writing incredibly fast tools in rust! Looking forward to see how these great folks improve Codex even more.
Jensen Declares Total OpenClaw Victory at GTC, Announces NemoClaw (Github)
This was kind of surreal, NVIDIA CEO Jensen Huang, is famous for doing his stadium size keynote, without a teleprompter, and for the last 10 minutes or so, he went all in on OpenClaw. Calling it “the most important OSS software in history” and outlining how this is the new computer.
That Peter Steinberger with OpenClaw showed the world a blueprint for the new coputer, an personal agentic system, with IO, files, computer use, memory, powered by LLMs.
Jensen did outline that the 3 things that make OpenClaw great are also the things that enterprises cannot allow, write access to your files + ability to communicate externally is a bad combo, so they have launched NemoClaw.
They’ve got a bunch of security researchers to work with OpenClaw team to integrate their new OpenShell sandboxing effort, network guardrails and policy engine integration.
I reminded folks on the pod that the internet was very insecure, there was a time where folks were afraid of using their creditcards online. OpenClaw seems to be speed running that “unsecure but super useful” to “secure because it’s super useful” arc and it’s great to see a company as huge as NVIDIA embrace.
Not to mention that given that agents can run 24/7, this means way more inference and way more chips sold for NVIDIA so makes sense for them, but still great to see!
Manus “my computer” and other companies replicating “OpenClaw” success
This week it became clear, after last weeks Perplexity “computer”, Manus (now part of Meta) has also announced a local extension of their cloud agents, and those two are only the first announcements, it’s clear now that every company dissected OpenClaw’s moment and will be trying to give its users what they want. An agentic always on AI assistant with access to the users files, documents etc.
Claude code added “channels“ support with telegram and discord connectors today, which, also, is one big missing piece of the puzzle for them. Everything is converging on this. Even OpenAI is rumored to consolidate Codex (which sees huge success) with OpenAI and Atlast browser into 1 “mega” APP that would do these things and act as an agent.
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MiniMax M2.7: The Model That Built Itself
This one blew me away, it’s not quite open source (yet?) but the MiniMax folks are coming out with a 2.7 version just after their MiniMax 2.5 was featured on our show and .. they are claiming that this model trained itself.
Similarly to Andrej Karpathy’s auto-researcher, the MiniMax folks ran 100+ autonomous optimization loops, t get this model to 56.22% on the hard Swe-bench pro benchmark (close to Opus’s 57.3%!) and this one gets a 88% win rate vs the very excellent MiniMax 2.5.
They used the previous model to build the agent harness and scaffolding, with 1 engineer babysitting these agent, and writing 0 lines of human code, which as we said before, every company will be doing, as we’re staring singularity in the face!
We’ve evaluated this model as well (Wolfram has been busy this week!) and it’s doing really well on WolfBench with 52% average and 64% top score, it’s very close to 5.3 codex on our terminalBench benchmark!
We hope that this model will be open source at some point soon as well!
Cursor drops Composer 2 - nearly matching Opus 4.6, fast version (Blog)
Cursor decided to add to our show’s breaking news record of Thursday releases with a brand new in-house trained Composer 2. This time they released more benchmarks than only their internal “composer bench” and this model looks great! (we are pretty sure it’s a finetune of a chinese OSS model, but we don’t know which)
Getting 61% on Terminal Bench, beating Opus 4.6 is quite a significant achievement, but coupled with the incredible pricing they are offering, $0.5/1Mtok input and $2.50/M output tokens, Cursor is really aiming for the productivity folks and showing that they are more than just an IDE.
Early users are reporting noticeably cleaner code than both Opus and Composer 1.5 — better adherence to clean code principles, smarter multi-file implementations, and strong performance on long-horizon agentic tasks like full API migrations and legacy codebase refactoring. They also shipped a new interface called Glass (in alpha) that’s built for monitoring these long-running agent loops.
Open Source: Mistral is Back, Baby
Mistral Small 4: 119B MoE with 128 experts + Apache 2.0 (X, Blog, HF)
It’s been a while since Mistral dropped something properly open source, and this week they kicked off what looks like their fourth generation with Mistral Small 4. The name is a little funny given the actual size — 119 billion total parameters, 128 experts in the mixture — but with only 6 billion active per token. So you get the knowledge footprint of a massive model but the compute profile of a small one. Very MoE-brained.
The bigger story here is what’s unified inside: this is Magistral (reasoning), Pixtral (multimodal), and Devstral (coding) all rolled into one weights file. Previously you had to choose which Mistral “side quest” model you wanted. Now there’s a reasoning_effort parameter where you dial from none for fast cheap responses all the way up to high for step-by-step thinking, no model switch required.
How does it perform? We ran it through WolfBench and it landed toward the lower end of Wolfram’s current leaderboard — around 17% on the agentic tasks, roughly on par with Nemotron at the same scale. It’s not competing with Opus or GPT-5.4, and we weren’t really expecting it to. What we’re excited about is that it does multimodal, reasoning, and coding in one Apache-licensed package, and people are already running IQ4 quants locally. Shout out to Mistral for the return to open source — it’s been a minute, and the community noticed.
Unsloth Studio: Fine-Tuning Gets a UI (Blog)
Something I think people are sleeping on this week is Unsloth Studio, the open-source web UI that the Unsloth team just launched for local LLM training and inference. Unsloth has been quantizing and compressing models better than basically anyone for a while now — 2x training speed, 70% less VRAM, zero accuracy loss — but that was all code-first. Studio is the no-code interface layer on top of all of that.
The numbers: supports 500+ models across text, vision, audio, and embeddings. It runs 100% offline with no telemetry. Julien Chaumond, the CTO of Hugging Face, confirmed it trains successfully on a Colab Pro A100. There’s even a free Colab notebook for models up to 22B parameters. For folks who want to fine-tune models overnight without spinning up cloud infra or wrestling with Docker, this is a genuine leap forward. Nisten compared it to what LM Studio did for local inference — making something that used to require deep expertise suddenly accessible to anyone. I think that comparison is spot on, and I want to get Daniel and the Unsloth team on the show to dig into this properly.
This Week’s Buzz: W&B iOS App & The Overthinking Paradox
The iOS App is Finally Here (app store)
Okay, I’m going to do a quick applause. 👏
The most requested feature in Weights & Biases history is now live: the W&B iOS mobile app. If you’ve ever kicked off a training run overnight and woken up to find it crashed at hour two without knowing about it until morning, you understand exactly why people have been begging for this. Live metrics, loss curves, KL divergence — all right on your phone. And native push notifications for alerts! The second your run fails or a custom metric crosses a threshold, you get a notification on your phone.
Please give us feedback through the app, the iOS team is actively building on top of this. Get it on the App Store and let us know what you need.
WolfBench insight: More Thinking ≠ Better Agents
This is one of the more counterintuitive findings we’ve surfaced from the W&B + Wolf Bench collaboration, and Wolfram laid it out really clearly.
He tested Opus 4.6 and GPT-5.4 across different thinking/reasoning effort levels inside the Terminal Bench 2.0 agentic benchmark framework — using both the default Terminus 2 harness and the OpenClaw agent framework. For GPT-5.4, the pattern was exactly what you’d expect: higher reasoning effort gets better results. At extra-high, it hit 71% with 85% ceiling on tasks it could solve.
For Opus 4.6, though? Turning it up to the maximum thinking level made it significantly worse. From 71% on standard settings all the way down to 59% on max reasoning. It lost tasks it had been reliably solving before. Wolfram dug into the traces in Weave and found out why: the model was overthinking. In an agentic benchmark where you have a one-hour time limit per task, spending ten minutes reasoning about what terminal command to try — and then getting an error — and then spending another ten minutes reasoning about it — is catastrophically inefficient.
We’ll keep you up to date with more Alpha from our bench efforts! Stay tuned and checkout wolfbench.ai
Voice & Audio
xAI relaunched the Grok Text-to-Speech API (try it)
It’s actually a pretty full-featured release right away. Multiple voices, expressive controls, WebSocket streaming, multilingual support, and the whole platform feel suggests xAI is very much trying to build a serious multimodal API stack, not just throw out a toy demo.
The inline control tags are the fun part. You can embed pauses, laughter, whispers, breathing cues, all that. Those controls matter a lot for agents because the difference between “reads text out loud” and “feels usable in a voice interaction” usually lives in those details. As you can see in the video.. it’s.. not perfect.. yet? but pretty fun!
But the thing I personally had the most fun with this week was Fish Audio. We didn’t get to cover it properly last week, and when I played with it more this week, I came away really impressed. It’s fast, expressive, open source, and the voice control vibe is genuinely cool.
My favorite moment was not even a benchmark thing. I used Fish Audio with an agent setup to make a character voice inspired by Project Hail Mary, then had my kid talk to it. And the result was weirdly magical. If you remember the Audio book of Hail Mary, fish audio was able to get the voice juuuust right + Opus via OpenClaw obliged with a great skill to talk like rocky. I won’t post this for obvious copyright reasons but I showed it on the live show, at the end.
Parting thoughts: I was hoping for a quieter week this week as I was sick, but it didn’t materialize, I should stop hoping for quiet weeks I think. After all, this is how the singularity starts, faster and faster developments, models that train themselves, every company becomes an agentic company.
We’ll keep you posted on the most important breakthroughs, cover breaking news and bring interesting folks to the show as guests.
Thank you for reading, see you next week 👋
ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

ThursdAI - Mar 19, 2026 - TL;DR
TL;DR of all topics covered:
* Hosts and Guests
* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)
* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson
* Big CO LLMs + APIs
* Anthropic makes Opus 4.6 with 1M context the default claude code - at the same price (X)
* OpenAI drops GPT-5.4 mini and nano, optimized for coding, computer use, and subagents at a fraction of flagship cost (X, Announcement, Announcement)
* Xiaomi - Omni modal and language only 1T parameters - MiMo (X)
* Google AI Studio gets a full-stack vibe coding overhaul with Antigravity agent, Firebase integration, and multiplayer support (X, Blog, Announcement)
* MiniMax M2.7: the first self-evolving model that helped build itself, hitting 56.22% on SWE-Bench Pro (X, X, Announcement)
* Cursor launches Composer 2, their first proprietary frontier coding model beating Opus 4.6 at a fraction of the cost (X, Blog)
* Open Source LLMs
* Mamba-3 drops with three SSM-centric innovations: trapezoidal discretization, complex-valued states, and MIMO formulation for inference-first linear models (X, Arxiv, GitHub)
* H Company releases Holotron-12B, an open-source hybrid SSM model for computer-use agents that hits 8.9k tokens/sec and jumps WebVoyager from 35.1% to 80.5% (X, X, HF, Blog)
* Hugging Face’s Spring 2026 State of Open Source report reveals 11M users, 2M models, and China dominating 41% of downloads as open source becomes a geopolitical chess board (X, Blog, X, X)
* Unsloth launches open-source Studio web UI for local LLM training and inference with 2x speed and 70% less VRAM (X, Announcement, GitHub)
* Astral (Ruff, uv, ty) joins OpenAI’s Codex team (announcement , blog , Charlie Marsh)
* Mistral Small 4: 119B MoE with 128 experts, only 6B active per token, unifying reasoning, multimodal, and coding under Apache 2.0 (X, Blog, HF)
* Tools & Agentic Engineering
* NVIDIA GTC: Jensen Huang declares “Every company needs an OpenClaw strategy,” announces NemoClaw enterprise platform (X, TechCrunch, NemoClaw)
* OpenAI ships subagents for Codex, enabling parallel specialized agents with custom TOML configs (X, Announcement, GitHub)
* Manus (now Meta) launches ‘My Computer’ desktop app, bringing its AI agent from the cloud onto your local machine for macOS and Windows (X, Blog)
* This weeks Buzz
* Weights & Biases launches iOS mobile app for monitoring AI training runs with crash alerts and live metrics (X, Announcement)
* GPT 5.4 went from worst to best on WolfBenchAI after an OpenClaw config fix exposed a max_new_tokens bottleneck (X, X, X)
* Voice & Audio
* xAI launches Grok Text-to-Speech API with 5 voices, expressive controls, and WebSocket streaming (X, Announcement)
* AI Art & Diffusion & 3D
* NVIDIA DLSS 5 is making waves with a new generative AI filter (Blog)


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Hey, Alex here 👋 Today was a special episode, as ThursdAI turns 3 🎉
We’ve been on air, weekly since Pi day, March 14th, 2023. I won’t go too nostalgic but I’ll just mention, back then GPT-4 just launched with 8K context window, could barely code, tool calls weren’t a thing, it was expensive and slow, and yet we all felt it, it’s begun!
Fast forward to today, and this week, we’ve covered Andrej Karpathy’s mini singularity moment with AutoResearcher, a whole fruit fly brain uploaded to a simulation, China’s OpenClaw embrace with 1000 people lines to install the agent. I actually created a new corner on ThursdAI, called it Singularity updates, to cover the “out of distribution” mind expanding things that are happening around AI (or are being enabled by AI)
Also this week, we’ve had 3 interviews, Chris from Nvidia came to talk to use about Nemotron 3 super and NVIDIA’s 26B commitment to OpenSource, Dotta (anon) with his PaperClips agent orchestration project reached 20K Github starts in a single week and Matt who created /last30days research skill + a whole bunch of other AI news! Let’s dive in.
ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Singularity updates - new segment
Andrej Karpathy open sources Mini Singularity with Auto Researcher (X)
If there’s 1 highlight this week in the world of AI, it’s this. Andrej, who previously started the AutoPilot program in Tesla, and co-founded OpenAI, is now, out there, in the open, just.. doing stuff like invent a completely autonomous ML research agent.
Andrej posted to his almost 2M followers that he opensourced AutoResearch, a way to instruct a coding agent to do experiments against a specific task, test the hypothesis, discard what’s not working and keep going in a loop, until.. forever basically. In his case, it was optimizing speed of training GPT-2. He went to sleep and woke up to 83 experiments being done, with 20 novel improvements that stack on top of each other to speed up the model training by 11%, reducing the training time from 2.02 hours to 1.8 hours.
The thing is, this code is already hand crafted, fine tuned and still, AI agents were able to discover new and novel ways to optimize this, running in a loop.
Folks, this is how the singularity starts, imagine that all major labs are now training their models in a recursive way, the models get better, and get better at training better models! Reminder, OpenAI chief scientist Jakub predicted back in October that OpenAI will have an AI capable of a junior level Research ability by September of this year, and it seems that... we’re moving quicker than that!
Practical uses of autoresearch
This technique is not just for ML tasks either, Shopify CEO Tobi got super excited about this concept, and just posted as I’m writing this, that he set an Autoresearch loop on Liquid, Shopify’s 20 year old templating engine, with the task to improve efficiency. His autoresearch loop was able to get a whopping 51% render time efficiency, without any regressions in the testing suite. This is just bonkers. This is a 20 year old, every day production used template. And some LLM running in a loop just made it 2x faster to render, just because Karpathy showed it the way.
I’m absolutely blown away by this, this isn’t a model release, like we usually cover on the pod, but still, a significant “unhobbling” moment that is possible with the current coding agents and models. Expect everything to become very weird from here on out!
Simulated fruit fly brains - uploaded into a simulator
In another completely bonkers update that I can barely believe I’m sending over, a company called EON SYSTEMS, posted that they have achieved a breakthrough in brain simulation, and were able to upload a whole fruit fly brain connectome, of 140K neurons and 50+ million synapses into a simulation environment.
They have... uploaded a fly, and are observing a 91% behavioural accuracy. I will write this again, they have uploaded a fly’s brain into a simulation for chirst sake!
This isn’t just an “SF startup” either, the board of advisors is stacked with folks like George Church from Harvard, father of modern genome sequencing, Stephen Wolfram who needs no introduction but one of the top mathematicians in the world, whos’ thesis is “brains are programs”, Anders Sandberg from Oxford, Stephen Larson who apparently already uploaded a worms brain and connected it to lego robots before. These folks are gung ho on making sure that at some point, human brains are going to be able to get uploaded, to survive the upcoming AI foom.
The main discussion points on X were around the fact that there was no machine learning here, no LLMs, no attention mechanisms, no training. The behaviors of that fly were all a result of uploading a full connectome of neurons. This positions connectome (the complete diagram of a brain with neurons and connections) as an ananalouge to an pre-trained LLM network for biological intelligence.
I encourage everyone who’s reading this, to watch Pantheon on Netflix, to understand why this is of massive importance. Combined with the above Autoresearch, things are going to go very fast here. The next step is uploading a mouse brain, which will be a 500x Neurons and 2000x more synapses, but if we’re looking at the speed with which AI is improving, that’s NOT out of the realm of possibility for the next few years!
OpenClaw Mania Sweeps China: Thousand-Person Lines & Government Subsidies, Grandmas raising a “red lobster”
They’re calling it “raising a red lobster” (养小龙虾). That’s the phrase that swept Chinese social media for what is, at its core, installing an open source GitHub project on your laptop. Grandmas are doing it. Mac Minis are sold out. A cottage industry of paid installers popped up overnight on Xiaohongshu, charging up to $100 for an in-person setup. And yes, there are now also people charging to uninstall it.
On March 6th, roughly a thousand people lined up outside Tencent’s Shenzhen HQ for free OpenClaw installation. Appointment slots ran out within an hour. People brought NAS drives, MacBooks, mini PCs. Tencent engineers set up folding tables and just... started installing OpenClaw for strangers. I have pictures. I’m not making this up.
All five major Chinese cloud providers jumped in simultaneously: Tencent Cloud, Alibaba Cloud, ByteDance Volcano Engine, JD.com Cloud, and Baidu Intelligent Cloud, each racing to offer one-click OpenClaw deployment. Why? Follow the money. Per HelloChinaTech, ByteDance, Alibaba, and Tencent spent roughly $60B combined on AI infrastructure. Chatbots don’t burn enough tokens to justify that spend. But a single OpenClaw instance runs 24/7 and consumes 10-100x more tokens per day than a chatbot user. Every install is round-the-clock API revenue. The cheaper the models get, the more people run agents, the more infra gets sold. Self-reinforcing loop.
Local governments are pouring fuel on the fire. Shenzhen’s Longgang district is offering up to 2M yuan ($290K) per project. Hefei and Wuxi are going up to 10M yuan ($1.4M), plus free computing, office space, and accommodation for “one-person companies.” Meanwhile, China’s central cybersecurity agency issued TWO warnings, banning banks and state agencies from installing OpenClaw. So local governments are subsidizing it while the central authority is trying to pump the brakes. Peak 2026.
With nearly half of all 142,000+ publicly tracked OpenClaw instances are now from China. OpenClaw is the most-starred GitHub repo in history, surpassing Linux’s 30-year record in just 100 days. Device makers are piling on too — Xiaomi announced “miclaw” for smartphones, MiniMax built MaxClaw, Moonshot AI built a hosted version around Kimi.
Now, Ryan was honest on the show and I want to echo that honesty here: OpenClaw is still hard to get working. There are many failure states. It’s not “install and go to the beach.” Wolfram compared it to Linux in the late ‘90s — painful to set up, but if you push through, you can see the future behind the friction. This is real technology with real limitations, and a lot of disappointed folks in China are watching tokens burn with no actual work getting done.
But here’s the thing I keep coming back to. The memetic velocity of OpenClaw is unlike anything I’ve seen in tech. It’s not just a tool, it’s a concept that penetrated the cultural resistance to AI. People who are scared of terminals, people who’ve never touched GitHub — they’re standing in line for this. I broke through that resistance with my own fiancée. She’s now running two OpenClaws. Not enough for her. She needs another one.
Every major US lab is watching this closely. OpenAI brought Peter Steinberger on staff. Perplexity just announced they’re building a local agent for Mac. Anthropic has Claude Cowork. This is where all of computing is headed — always-on, autonomous, personal AI that actually does things for you. OpenClaw is the first front door, not the final destination. But what a front door it is.
ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Open Source: Nvidia Goes All In with Nemotron 3 Super 120B (X, Blog, HF)
We had Chris Alexiuk from Nvidia join us — a friend from a dinner Nisten and I hosted in Toronto. Chris is basically “NeMo” embodied, sitting at the intersection of product and research, and he gave us the full breakdown on what might be the most complete open-source model release we’ve seen from a major lab.
Here are the numbers: 120B total parameters, 12B active during inference (it’s a Mixture of Experts), 1 million token context window, and a hybrid Mamba-Transformer architecture they call “Hybrid Mamba Latent MoE with Multi-Token Prediction.” It’s hitting 450 tokens per second on the Terminal Bench leaderboard — faster than any other model on there. Modal is reporting over 50% faster token generation compared to other top open models.
What Chris was emphatic about — and I want to highlight this — is that “most open” is a real designation here. They released the model checkpoint in three precisions (BF16, FP8, NVFP4), the base checkpoint before post-training, the SFT training data, and in a move that genuinely surprised people, pre-training data and a full end-to-end training recipe. You can, in theory, reproduce their training run. That’s rare. That’s a real commitment to open source.
There’s also a huge piece of news in the background here: there’s a confirmed report that Nvidia will spend $26 billion over the next five years building the world’s best open source models. Jensen presumably has GTC remarks incoming on this. America is genuinely back in the open source AI race, and it’s Nvidia leading the charge. Chris has been in the open source world since the Hugging Face early days and said it feels genuine inside the company — not a PR exercise. And I tend to believe him. Now, all eyes are on GTC next week!
I ran Nemotron 3 Super with my own OpenClaw instance yesterday via W&B inference and it’s genuinely fast and capable. At $0.20/M input tokens and $0.80/M output tokens on W&B inference, it’s not going to replace Opus for your hardest tasks — but for running an always-on agent that needs to be cost-efficient? It’s an incredible option. More on that in the this weeks buzz section below.
Tools & Agentic Engineering
Paperclip: Zero Human Companies, Now Open Source (Github)
We had the anonymous Dotta on the show — the first AI video avatar anon person to join ThursdAI — to talk about Paperclip, an open source agent orchestration framework that hit 20,000 GitHub stars in its first week. The premise is simple and audacious: build zero-human companies.
Now this may sound familiar to you, as we had Ben from Polsia on just two weeks ago, which is a similar concept, but Paperclip is an OpenSource project, which you can run right now on your own.
The core “thing” that got me excited about Paperclip is that you can “hire” your own existing OpenClaw agents, or Cursor or Codex or whatever else to play roles in this autonomous company. The premise is simple, you’re the board of directors, you hire an AI Agent CEO, and it then asks you if it needs to “hire” more AI agents to do tasks autonomously. These tasks all live inside Paperclip interface, and you or your Agents can open them.
The core concept of this whole system is the heartbeat concept, each agent receives their own instructions on what to do every time they are “woken up” by a timer. This is what’s driving the “autonomous” part of the whole thing, but it’s also what’s eating the tokens up, even if there’s no work being done, agents are still burning tokens asking “is there work to be done?”
Dotta gave us a great metaphor, asking if we saw the movie Memento, where the protagonist lost his memory and every time he woke up, he woke up with a blank slate, and had to reconstruct the memories. AI agents are like the memento man, and Paperclip is an attempt to give those agents the whole context so they can continue working on your tasks productively. Dotta told us that the future of Paperclip is the ability to “fork” entire companies, structures that will actually run and do things on your behalf. Looking forward to that future, but for now I will be turning off my Paperclip interface as it’s costing me real money without the need.
Symphony: Agents Writing Their Own Jira Tickets
We mentioned Symphony last week, and I texted Ryan the link before the show, and voila, of course, he set it up and went viral, yet again! We’re so lucky to have Ryan on the show to tell us from first hand experience what it’s like to run this thing.
Symphony was open sourced by OpenAI last week, and it’s basically an instruction manual for how to run agents autonomously via Linear ticketing system. (Github)
The highlight for Ryan was, the whole system is running creating pull requests while he’s a sleep, and at some point, he noticed a ticket that he didn’t create. One of the agents found a bug, and created a very detailed ticket for him to approve.
I’m just happy that I can keep even my co-hosts up to date hehe
This weeks buzz - we’ve got skills and nemotrons!
Look, we told you about Skills in the start of the year, since then, via OpenClaw, Hermes Agent, Claude Code, they exploded in popularity. One downside of skills is, it’s very easy to make a bad one! So, we’re answering the challenge, and are publishing the official wandb skill 🎉
Installing it is super simple, npx skills add wandb/skills and voila, your agents are now officially “I know kung fu” pilled with the best Weights & Biases practices. For both Weave and Models 👏 Please give us feedback on Github if you have used the skills! Github
Also, we’ve partnered with Nvidia to support the best US open source model on day 0, and we have Nemotron 3 Super on our inference service, for all to use at $0.20/1Mtok! It’s super easy to setup with something like Hermes Agent or OpenClaw and runs really really fast! Check it out here.
Is it going to perform like Opus 4.6? No. But are you going to run Opus 4.6 at 20 cents per million? Also no.
Gemini drops SOTA embeddings and gets dethroned 2 days later live on the show.
This always happens, but I didn’t expect this to happen in a fairly niche segment of the AI world... multimodal embeddings!
Gemini posted an update earlier this week with Gemini Embeddings 2.0, a way to unify images, text, video, audio embeddings under 1 roof, and posted a SOTA embedding model!
Then, just as we launch the show, a friend of the pod Benjamin Clavie, drops me a DM, basically saying that his company Mixbread is going to deploy an embedding model that will beat Gemini Embedding 2 on almost every benchmark on that table, and then... they did!
The most notable (and absolutely crazy) jump in this comparison is, the LIMIT benchmark, where they achieved a 98% score vs Gemini’s ... 6.9 percent. I didn’t believe this at first, but asked Ben to explain the findings, and he did. Congrats to folks for moving the search space forward every 2 days!
Grok 4.20 in the API for $2/1Mtok
Elon Musk and XAI co finally released Grok 4.20 in the API. Look I said what I said about XAI models, they are great for research, and for factuality, but they aren’t beating the major labs. The last firing of almost of XAI folks doesn’t help either. So this model was not “released” in any traditional sense, there’s no benchmarks, no evals, and everyone who got access to it evaluated it and, it’s no better than GLM5 on many benchmarks. So it does makes sense to release it quietly.
It is very fast though, and again, for research and for X access, it’s an absolute beast, so I’ll be trying this out!
Parting thoughts and a small reflection. For the past 3 years, we’ve had a front-row seat to the singularity shaping up. 2.5 years ago, I went all in, decided to pivot into podcasting full time. In those years, ThursdAI became known, we’ve had guests from nearly all major AI labs (including Chinese ones, for which I’m particularly proud), I got to meet with executives, ask leaders questions about where this is all going, and most of all, share this journey with all of you, candidly. We rarely do hype on the show, we don’t speculate, we try to do a positive outlook on the whole thing, and counter doomerism, as there’s too much of that out there.
I am very glad this resonates, and continue to be thankful for your attention! If you wanted to give us any kind of a birthday present, subscribe or give us a 5 star review on Apple Podcasts or Spotify, it’ll greatly help other folks to discover us.
See you next week,
Alex 🫡
ThursdAI - Mar 12, 2026 - TL;DR
* Hosts and Guests
* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)
* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed) @ryancarson
* Chris Alexiuk from Nvidia (@llm_wizard) - Nemotron
* @dotta - creator of Paperclip.ing AI agent orchestration framework
* Matt Van Horn - @mvanhorn - creator of @slashlast30days
* Singularity updates
* Andrej Karpathy’s autoresearch achieves 11% speedup on GPT-2 training through autonomous AI agent experimentation (X, GitHub, GitHub)
* Eon Systems uploads first complete fruit fly brain to a physics-simulated body, achieving 91% behavioral accuracy (X, Announcement, Announcement)
* OpenClaw mania sweeps China as all five major cloud providers race to support it (HelloChinaTech, Reuters, SCMP, MIT Tech Review)
* Big CO LLMs + APIs
* xAI quietly releases Grok 4.20 API with massive 2M token context window and multi-agent capabilities (X, Blog)
* Google launches Gemini Embedding 2, the first natively multimodal embedding model supporting text, images, video, audio, and PDFs in a unified vector space (X, Announcement)
* Open Source LLMs
* NVIDIA launches Nemotron 3 Super: 120B open MoE model with 1M context window designed for agentic AI at 5x higher throughput (X, Announcement)
* MiroMind releases MiroThinker-1.7 and H1 - open-source research agents with 256K context, 300 tool calls, achieving SOTA on deep research benchmarks (X, HF, HF, HF)
* Covenant-72B: World’s largest permissionless decentralized LLM pre-training achieves 72B parameters on Bittensor with 146x gradient compression (X, Arxiv, HF, HF)
* Tools & Agentic Engineering
* ACP is the open standard that lets any AI coding agent plug into any editor — and this week Cursor officially joined the registry, meaning you can now run Cursor’s agent inside JetBrains IDEs (JetBrains blog, Cursor blog)
* This weeks Buzz
* W&B launches official Agent Skills for coding agents, turning experiment dashboards into terminal queries (X, Announcement, Announcement)
* Video
* LTX-2.3 — Lightricks open-source video model (GitHub, HF,Blog)
* Voice & Audio
* Fish Audio launches S2: Open-source TTS with sub-150ms latency and absurdly controllable emotion (X, HF, Blog, Announcement)
* Show notes and links
* Paperclip.ing by Dotta ( @dotta) - Github
* Last30days skill by Matt Van Horn Github
* Agency Agents repo Github
* OpenAI Symphony (Github)
* Mixbread Embeddings (X)


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Hey folks, Alex here, let me catch you up!
Most important news about this week came today, mid-show, OpenAI dropped GPT 5.4 Thinking (and 5.4 Pro), their latest flagship general model, less autistic than Codex 5.3, with 1M context, /fast mode and the ability to steet it mid-reasoning. We tested it live on the show, it’s really a beast.
Also, since last week, Anthropic said no to Department of War’s ultimatum and it looks like they are being designated as supply chain risk, OpenAI swooped in to sign a deal with DoW and the internet went ballistic (Dario also had some .. choice words in a leaked memo!)
On the Open Source front, the internet lost it’s damn mind when a friend of the pod Junyang Lin, announced his departure from Qwen in a tweet, causing an uproar, and the CEO of Alibaba to intervene.
Wolfram presented our new in-house wolfbench.ai and a lot more!
P.S - We acknowledge the war in Iran, and wish a quick resolution, the safety of civilians on both sides. Yam had to run to the shelter multiple times during the show.
ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

OpenAI drops GPT 5.4 Thinking and 5.4 Pro - heavy weight frontier models with 1M context, /fast mode, SOTA on many evals
OpenAI actually opened this week with another model drop, GPT 5.3-instant, which... we can honestly skip, it was fairly insignificant besides noting that this is the model that most free users use. It is supposedly “less cringe” (actual words OpenAI used). We all wondered when 5.4 will, and OpenAI once again proved that we named the show after the right day. Of course it drops on a ThursdAI.
GPT 5.4 Thinking is OpenAI latest “General” model, which can still code, yes (they folded most of the Codex 5.3 coding breakthroughs in here) but it also shows an incredible 83% on GDPVal (12% over Codex), 47% on Frontier Math and an incredible ability to use computers and browsers with 82% on BrowseComp beating Claude 4.6 at lower prices than Sonnet!
GPT 5.4 is also ... quite significantly improved at Frontend design? This landing page was created by GPT 5.4 (inside the Codex app, newly available on Windows) in a few minutes, clearly showing significant improvements in style.
I built it also to compare prices, all the 3 flagship models are trying to catch up to Gemini in 1M context window, and it’s important to note, that GPT 5.4 even at double the price after the 272K tokens cutoff is still.... cheaper than Opus 4.6. OpenAI is really going for broke here, specifically as many enterprises are adopting Anthropic at a faster and faster pace (it was reported that Anthropic is approaching 19B ARR this month, doubling from 8B just a few months ago!)
Frontier math wiz
The highlight from the 5.4 feedback came from a Polish mathematician Bartosz Naskręcki (@nasqret on X), who said GPT-5.4 solved a research-level FrontierMath problem he had been working on for roughly 20 years. He called it his “personal singularity,” and as overused as that word has become, I get why he said it. I’ve told you about this last week, we’re on the cusp.
Coding efficiency
There’s tons of metrics in this release, but I wanted to highlight this one, where it may seem on first glance that on SWE-bench Pro, this model is on par with the previous SOTA GPT 5.3 codex, but these dots here are thinking efforts. And a medium thinking effort, GPT 5.4 matches 5.3 on hard thinking! This is quite remarkable, as lower thinking efforts have less tokens, which means they are cheaper and faster ultimately!
Fast mode arrives at OpenAI as well
I think this one is a direct “this worked for Anthropic, lets steal this”, OpenAI enabled /fast mode that.. burns the tokens at 2x the rate, and prioritizes your tokens at 1.5x the speed. So, essentially getting you responses faster (which was one of the main complains about GPT 5.3 Codex). I can’t wait to bring the fast mode to OpenClaw with 5.4, which will absolutely come as OpenClaw is part of OpenAI now.
There’s also a really under-appreciated feature here that I think other labs are going to copy quickly: mid-thought steering. OpenAI now lets you interrupt the model while it’s thinking and redirect it in real time in ChatGPT and iOS. This is a godsend if you’re like me, sent a prompt, seeing the model go down the wrong path in thinking... and want to just.. steer it without stopping!
Anthropic is now designated as supply-chain risk by DoW
Last week I left you with a cliffhanger: Anthropic had received an ultimatum from the Department of War (previously the Department of Defense) to remove their two remaining restrictions on Claude — no autonomous kill chain without human intervention, and no surveillance of US citizens. Anthropic’s response? “we cannot in good conscience acceede to their request”
So much has happened since then; US President Trump said “I fired Anthropic” referring to his Truth Social post demanding intelligence agencies drop the use of Claude (which apparently was used in the war with Iran regardless); Sam Altman announced that OpenAI has agreed to DoW and will provide OpenAI models, causing a lot of people to cancel their OpenAI subscriptions, and later apologizing for the “rushed rollout”; Dario Amodei posted a very contentious internal memo that leaked, in which he name-called the presidency, Sam Altman and his motives, Palantir and their “safety theater”, for which he later apologized
Honestly this whole thing is giving me whiplash trying to follow, but here’s the facts. Anthropic is now the first US company in history, being designated “supply chain risk” which means no government agency can use Claude, and neither can any company that does contracts with DoW.
Anthropic says it’s illegal and will challenge this in court , while reporting $19B in annual recurring revenue, nearly doubling since last 3 months, and very closely approaching OpenAI at $25B.
Look, did I want to report on this stuff when I decided to cover AI? no... I wanted to tell you about cool models and capabilities, but the world is changing, and it’s important to know that the US Government understands now that AI is inevitable, and I think this is just the first of many clashes between tech and government we’ll see. We’ll keep reporting on both. (but let me know in the comments if you’d prefer just model releases)
OpenAI’s GPT-5.3 Instant Gets Less Cringe, Google’s Flash-Lite Gets Faster (X, Announcement)
We also got two fast-model updates this week that are worth calling out because these are the models that often end up powering real product flows behind the scenes. As I wrote before, OpenAI’s instant model is nothing to really mention, but it’s worth mentioning that OpenAI seems to have an answer for every Gemini release.
Gemini released Gemini Flash-lite this week, which boasts an incredible 363 tokens/s speed, which doing math at a very good level, 1M context and great scores compared to the instant/fast models like Haiku from Anthropic. Folks called out that this model is more expensive than the previous 2.5 Flash-lite. But with 86.9% on GPQA Diamond beating GPT-5 mini, and 76.8% MMMU-pro multimodal reasoning, this is definitely worth taking a look at for many agentic, super fast responses!
For example, the heartbeat response in OpenClaw.
Qwen 3.5 Small Models & The Departure of Junyang Lin (X, HF, HF, HF)
Alibaba’s Qwen team continued releasing their Qwen 3.5 family, this time with Qwen 3.5 Small, a series of models at 0.8B, 2B, 4B, and 9B parameters with native multimodal capabilities. The flagship 9B model is beating GPT-OSS-120B on multiple benchmarks, scoring 82.5 on MMLU-Pro and 81.7 on GPQA Diamond. These models can handle video, documents, and images natively, support up to 201 languages, and can process up to 262K tokens of context. And.. they are great! They are trending on HF right now.
What’s also trending is, tech lead for Qwen, a friend of the pod Junyang Lin, has posted a cryptic tweet that went viral with over 6M views. There was a lot of discussions on why he and other Qwen leads are stepping away, what’s goig to happen with the future of OpenSource. The full picture seems to be, there are a lot of internal tensions and politics, with Junyang being one of the youngest P10 leaders in the Alibaba org.
A Chinese website 36KR ( Kind of like a chinese techcrunch) reported that this matter went all the way up to Alibaba CEO, who is no co-leading the qwen team, and that this resignation was related to an internal dispute over resource allocation and team consolidation, not a firing.
I’m sure Junyang is going to land somewhere incredible and just wanted to highlight just how much he did for the open source community, pushing Qwen relentlessly, supporting and working with a lot of inference providers (and almost becoming a co-host for ThursdAI with 9! appearances!)
StepFun releases Step 3.5 Flash Base (X, HF, HF, Announcement, Arxiv)
Speaking of Open Source, StepFun just broke through the noise with a new model, a 196B parameter sparse Mixture of Experts model activating just 11B parameters when ran. It has some great benchmarks, but the main thing is this: they are releasing the pretrained base weights, a midtrain checkpoint optimized for code and agents, the complete SteptronOSS training framework, AND promising to release their SFT data soon - all under Apache 2.0!
Technically the model looks strong too, with multi-token prediction, 74.4% on SWE-bench verified bench (though, as we told you last week, it’s.. no longer trusted) and full apache 2!
This Week’s Buzz: presenting Wolfbench.ai
I’m so excited about this weeks “this weeks buzz”, Wolfram has been hard at work preparing and presenting a new framework to test out these models, and named it wolfbench.ai
Wolfbench is our attempt to compare how the same model performs via different agentic harnesses like ClaudeCode, OpenClaw and Terminalbench’s own Terminus.
You can check out the website on wolfbench.com but the short of it is, a single number is not telling the full story.
Wolf Bench breaks it into a four-metric framework: the average score across runs, the best single run, the ceiling (how many tasks can the model solve at least once across all runs), and the floor (how many tasks does it solve consistently across every single run). That last one is what I find most illuminating. Opus 4.6 might be able to solve 88% of Terminal Bench tasks on average, but only about 55% of tasks it solves every single time. Reliability matters enormously for agents, and benchmarks almost never surface this.
If you want to run your own evals with the same config, reach out to Wolfram—he’s open to community contributions. Wolfram has also already kicked off a Wolf Bench run on GPT-5.4 since we tested it live today, so stay tuned for those results.
There’s quite a few more releases we didn’t have time to get into on the show given the GPT 5.4 drop, you’ll find all those links in the show notes!
Next week will mark 3 years since I’ve started talking about AI on the internet and created ThursdAI (It was March 14th, 2023, same day as GPT4 launched) and we’ll have a little celebration, I do hope you join us live 🔥
As a birthday present, you may choose to share ThursdAI with a friend or two, or rate us in your podcast player of choice! See you next week,
Alex 🫡
ThursdAI - Mar 05, 2026 - TL;DR
TL;DR of all topics covered:
* Hosts and Guests
* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)
* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson
* Big CO LLMs + APIs
* OpenAI launches GPT-5.4 Thinking and Pro (X, X, X, X)
* Anthropic, Dept of War and OpenAI walk into a bar
* Alibaba Qwen departures: Friend of the pod JunyangLin and Binyuan Huy both depart Qwen (X)
* OpenAI Rolls Out GPT-5.3 Instant (X)
* Google launches Gemini 3.1 Flash-Lite (X, Announcement)
* Evals and Benchmarks
* MarinLab shows degradation in Opus 4.6 (X)
* B******t Bench from Peter Gostev (X)
* Open Source LLMs
* StepFun releases Step 3.5 Flash Base models (X, HF, HF, Announcement, Arxiv)
* Alibaba releases Qwen 3.5 Small Model Series (X, HF, HF, HF)
* Yuan 3.0 Ultra (X, Blog, HF)
* Tools & Agentic Engineering
* Cognition: SWE-1.6 preview (X, Blog)
* OpenAI launches Codex app on windows (X)
* Google released Google Workspace CLI (X)
* OpenAI released Symphony (Github)
* This weeks Buzz
* Early preview of Wolf Bench (wolfbench.ai) from W&B
* AI Art & Diffusion & 3D
* Black Forest Labs introduces Self-Flow (X, Announcement)


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