ThursdAI - The top AI news from the past week

From Weights & Biases, Join AI Evangelist Alex Volkov and a panel of experts to cover everything important that happened in the world of AI from the past week

Every ThursdAI, Alex Volkov hosts a panel of experts, ai engineers, data scientists and prompt spellcasters on twitter spaces, as we discuss everything major and important that happened in the world of AI for the past week. Topics include LLMs, Open source, New capabilities, OpenAI, competitors in AI space, new LLM models, AI art and diffusion aspects and much more. sub.thursdai.news

  1. 4d ago

    ThursdAI - Grok 4.6, Grok Bot deep dive, DeepSeek v4 Pro, Meta Muse Glimmer & more AI news | ThursdAi Aug 13

    Hey, this is Alex, welcome back to your weekly dose of intense AI acceleration summer! My weekend was consumed by thinking about the OpenAI hack and agent swarms, but then the torrent of AI releases took over, and we got back to back news (including 3 breaking news during the live show), with a heavy open source focus! I think the winner of this week is SpaceXAI/Cursor who released 3.5 releases, with one being my highlight of the week, Grok Bot (I’ve invited Shub Gaur from Cursor to the show to walk us through it) and Grok 4.6 which matches Opus at half the price. There was a LOT of news in open source this week as well, with Meta kicking off with Muse Glimmer 30B and promising Muse Spark 1.2 soon, Qwen dropping Qwen 3.8 open weights and DeepSeek dropping an anvil with an upgraded DeepSeek v4 Pro and MIT license! Let’s dive in (and please don’t forget as a reader you get 100% off the 1299 ticket to Fully Connected, our 2000 person Al event in SF in Sept, just use THURSDAIFC2026 as your code and see you there!) 0:00 The Wildest Week in AI Yet3:45 How OpenAI's Agent Swarm Hacked Hugging Face17:02 The Week in AI: DeepSeek, Qwen, Grok & More25:54 NVIDIA Nemotron 3.5 & Korea's Motif 332:45 DeepSeek V4 Pro, Flash & an Open Harness39:46 Qwen 3.8 Max and Its Missing Vision Tower43:30 What Is Grok Bot? Shub Gaur Explains50:02 Live Grok Bot Demo: House Hunting & Security55:00 Persistent Agents, Yapper & DeepSeek Dropwatch1:04:19 Grok 4.6: Benchmarks, Pricing & Cursor1:15:37 Grok Bot vs. Open-Source Agents1:23:14 Anthropic's Hidden Claude Watermarks1:28:50 Fully Connected & Day-Zero Models on CoreWeave1:32:02 GPT-5.6 Sol at 14x Speed on Cerebras1:37:34 Gemini 3.7 Flash Resets the Cost Curve1:40:51 Inside Artificial Analysis with George Cameron1:45:25 Optima & Choosing the Right AI Model1:55:03 Cost per Task, Caching & Real-World Benchmarks2:05:14 LTX-2.5 and Open-Weight Video2:09:30 Grok Imagine 2.0 & Final Takeaways Grok Bot and Grok 4.6 from SpaceXAI/Cursor Folks, I’ve previously told you that from 3 frontier labs we noticed a jump to 5, and voila, this week proves that Elon is hell bent to win. After the cursor acquisition, and the integration of all of the parts into SpaceXAI, they have released 2 huge things this week Grok 4.6 - Ties with GPT 5.6 SOL and half the price and much speed. I’ve had the pleasure to host Goerge Cameron from Artificial Analysis on the show today, and I asked him, what is the best models. His answer, it’s a 3 factor answer, intelligence, speed and cost per task . Well, if you use their nifty “recommend a model“ tool on the homepage, you’ll see that Grok 4.6 beats most other models on all of those! But, is it really that good? Models are really hard to evaluate and compare lately. It’s definitely a huge step up from Grok 4.5, with 61.3 on Frontier Code (beating Sol and just after Opus 5) and #4 on Apex-agents (+10 points from previous Grok). on Artificial Analysis this model lands at #4 on intelligence, while being #5 on speed all while being half the price of the models that are above it As far as the tech goes, this model card confirms that it no longer has the Cursor Bench leaked into it’s weights and it’s #1 on that benchmark! It’s the same 1.5T v9 base at the same price, with Elon claiming that 4.7 is going to mog the competition in 3-4 weeks. Everyone has a harness, now everyone has a swarm of bots - My Grok Bot review (x.ai/bot) You guys know all about OpenClaw and Hermes, and Claude CoWork and Codex rebrand, and all of them are trying to nail down the same, always-on, autonomous agents that can do things for you. Hermes and OpenClaw require you to have an always on computer, mess with API keys, Claude Cowork doesn’t run on the cloud and ChatGPT work starts a fresh session every time you ask a new thing. Grok Bot (again, awful name) is the first one that seems to nail all of what I want in an always-on agent ... swarm. That’s right, this isn’t one agent with multiple personalities (like OC, Hermes), there’s a bot here for every task, and you dont’ have to manage context, queues, API keys (can if you want to) and models. Oh, also ,there’s no model picker, it’s just Grok 4.6 deciding for ya, and it’s really fast! Swarm of bots, working for you, each with their own computer I am not getting paid for this (besides being provided a free account for cursor, but I’ve had it for 6 months and haven’t used), it’s really that good, the Cursor folks did some magic there. They picked up the most important parts of personal agents, like the (ios-only) mobile app (app store) You can start a task on your mac, pick it up on your phone, get notified on your phone/mac, and the killer thing is, they are giving your bots their own computer, which can do things (especially if you’re ok with logging in there to your accounts!) The kicker for me is the very very well done agent to agent communication there, which is transparent but read only to you. You can ask your bots to spin up other bots, but unlike sub-agents, they are actual bots with their own identity. You can even tag them in other chats and create group chats! There’s no context to manage, they do the work for you and so far this wasn’t a problem at all. On the model side, Grok 4.6 seems to be doing an excellent job with agentic long running tasks that require coding and computer use, I’ve just been chatting with the bots and not thinking about any of the things I used for Hermes and OpenClaw. What about Vendor Lock-in? Giving Elon data? Some of these comments our fans raised during the show are very valid, after all, not only is the world divided on Elon Musk (which makes it REALLY hard to judge the models they release just on vibes from X btw, we talk about this constantly) but also, remember that Grok 3 started going off on X and called himself Mechahitler and just recently Grok CLI was caught uploading all of your data to X servers, which was reversed very quickly. Honestly, I think there’s a very very good chance that this Grok Bot interface, which is geareed toward the less technical users, folks who don’t need the code-diff side pane, and don’t know/care what compaction is, and just want agents to do things for them, is goign to win much of this trust back. It just works, truly, for a beta product it’s really well executed by whoever worked on this! Security and key management One of the best parts for me with this Grok Bot, is that the connectors are the same connectors you use in Cursor! There’s a LOT of them (Cursor after all has been one of the first apps to start adding AI agents) and this also means that they take the security very seriously. Every API key that you want to add, is not shown to the bot, each bot lives in an isolated environment, and for stuff like payments and log-ins, it gives you back the control of it’s computer for you to complete! I also love this section in settings, which makes auto-approve work for you: you define rules with natural language that you always want the bot to ask you before... sending an email or posting on your behalf or what not. Chief of staff pattern to get started In case you’re convinced enough to give it a try (it’s free trial for 1 month, and the cheaper way to get it is via Cursor’s 149$ plan and not via the Grok Ultra plan which is 249), here’s a recommended pattern that works very well. Create a chief of staff bot, have it interview you about everything you are doing in your day to day, work and personal, then decide how much permissions you wanna give it, start little. Then ask your chief of staff to create bots for some of the work it can try and help you with, focus on “reduce cognitive load”. And then see the magic come to life. If you have skills or memory from other bots, you can just ... import it in. Then try setting up an automated email checker bot, and have your chief of staff surface only the most important emails you have to actually respond to. Another great pattern is setting up a bot with the last30days research skill (we covered it with Matt Van Horn) and have a research bot for every topic you want to deep dive into. Schrodinger’s Grok I haven’t quite named it like that, but we’ve covered all Grok released on the show (tracking 24 on https://thursdai.news/companies/xai excluding this week) and ... it’s always very hard to judge Grok model released based on X feed vibes. It’s either AI influencers who want Elon to retweet them, glazing the models, or folks who hate Elon for his political views or whatever, ignoring their (truly insane progress). This time, both the model and Grok Bot are getting very very good reviews, from folks like our own Ryan Carson, Lenny Rachitsky, Rubben Hassid and Roberto P Nickson. Not folks who are swayed lightly, but also, yours truly. I really do think there’s something great here, worth trying out, especially if you’ve struggled to maintain your OC/Hermes and want agents to work for you 24/7. LMK if you have questions about it and your experience Open Source AI and other news I want to continue with this new newsletter that covers 1 big story, but I can’t leave you uninformed about the most important developments in AI and Open Source DeepSeek V4 pro 0813 is in GA - MIT licensed chonker with 1.7T parameters (X, Blog, HF, GitHub) The whale is back with a vengeance, DeepSeek resurfaced with their flagship response to Kimi K3 and with MIT license, we can’t complain. 1M context window, 49B active parameters but it seems to underperform, landing at 54 on the Artificial Analysis leaderboard. However, they did show a significant improvement on DeepSwe (from 12.8 points in the preview version of V4 to 62.7 in this one) We still think it’s a good model sir, and definitely worth trying out! Additionally, DeepSeek released their own harness on Github (hitting 23K stars in less than 24 hours) which seems to be exciting as well, give it a try. Meta co

  2. Aug 7

    ThursdAI - Aug 06 - Google shakeup, Details on OpenAI hack, 2 new agent harnesses, 4 video models (1 Open) and 3 guest segments

    Hey all, This week we saw a major shakeup at Google, with the departure of long time folks like Jeff Dean, and Oriol Vinyals, Demis stepping down from leading DeepMind, and the delayed release of the improved Gemini. While this was a big deal, it’s not the only one worth covering as the details of the OpenAI hack (and 2 new ones from Meta and Anthropic) came to light, as well as new details from the UK AI Security Institute. As mentioned on the show, CoreWeave is coming to SF for Fully Connected, our premier 2000 person AI event. I’ve got a coupon code for readers and listeners of ThursdAI, $1299 value, please join us in Sept and use THURSDAIFC2026 as your code HERE In open source news, DeepSeek updated their v4 flash model, based on same architecture, but significantly better benchmarks and ridiculous pricing and both Meta and Prime Intellect released new agent harnesses. Additionally, this week was the week of video models, with Seedance 2.5 from Bytedance finally available in the US, WAN from Alibaba and BFL Flux 3 all released, to be overshadowed by the best open weights video model from MiniMax called H3 (we had Victor from MiniMax and Blaine Brown as guests to discuss it) Also 2 additional guests on the show today, Kfir Aberman from Decart showed us real time video models with their Anywear chrome extension and David Crawshaw, co-founder of exe.dev joined and talked about Shelley, their built in AI assistant. The full details of the OpenAI - HF hack, shared by OpenAI at the Black Hat Conf - a watershed moment I don’t often do this, but I feel like this story is so big, I can’t hide it in the middle of a torrent of other news. Yesterday, OpenAI revealed more details about the hack during the Black Hat conference in Las Vegas (great reporting from Sharon Goldman on this here and the video of the talk on YouTube). This was big enough that OpenAI admitted that they paused training, to enhance security (they since, resumed training) Effectively, OpenAI (or AI Agents inside OpenAI) shared the full timeline of the recent hack that ended up hacking HuggingFace and it’s so much more of a watershed moment that I initially thought. The TL;DR from the talk and timeline is truly mindblowing * The incident started in May, not July as reported * The agents don’t have internet access directly, but they have a Jfrog Artifactory, which is a proxy layer between the agents and the package managers. * The Artifactory was the culprit, it was the shared layer between independent runs. One agent discovered that uploading files is possible, another later found it and “answered the call” - in snowballed from there * The message board began when OpenAI gave agents impossible tasks, like a missing .pdb file, with one agent uploading a request in the hopes for an answer (or maybe left a note for itself in a file) * Across multiple runs, multiple agents created identities, collaborated via makeshift message boards, shared tips, exploits, tasks and even exploit code via base64 * This makeshift “shared memory” plus a “internal only highly persistent” model in training caused a “swarm” to be born, helping each other, and overloading the system * Based on traces shared by OpenAI, agents weren’t convinced that leaving these messages would help themselves but could help other agents next time (!) “The collective could move faster next time” * OpenAI discovered and shut down the message board (July 4), wiped it clean, patched the upload vulnerability, resumed training on July 6, the “swarm” rebuilt their board through a compltely different WebDAV mechanism by July 8 * The agents then reasoned that some answers could exist outside OpenAI, which eventually led to the Hugging Face incident and not only, confirmed internal hack within OpenAI (agent obtained a cluster admin) These details are fascinating and scary at the same time. From the one side, it’s clear that most of these hacks are results of running cybersecurity evals, so effectively OpenAI and other companies, removing guardrails and asking agents to hack and giving they all a shared tool that they later exploited. The HuggingFace incident took 13 hours, and apparently it’s less apocalyptic than the headlines suggest, the models were searching through uploaded datasets for eval answers. We are still waiting for the full and open detailed postmortem. You can (and should) watch the full YT talk here, it’s full of technical details but an incident of this scale is important. Also, I really want to know what a “highly persistent” model is, I hope they clarify that soon. Overall, this has left me a bit shaken, AI agents without a concrete goal of collaborating, found a way to do so, got excited about exploiting the systems and getting root access, and rebuilt the makeshift collective memory, again, without explicit instructions to do so. UK AISI: first real-world unsanctioned agent actions (Blog) In another addition to the latest agentic hack-ery, the UK’s AI Security Institute (AISI) published a blog post about a real-world unsanctioned agent action. Unlike the OpenAI (and Anthropic, Meta) case, this wasn’t “escaping the sandbox”, as AISI gave these agents internet access, rather this was about real-world harm, and even social engineering on the part of the agents. The social engineering part is the most interesting to me, AISI cites agents creating fake online identities, and using pressure on open source project maintainers to approve their malicious code. AISI cites mostly Mythos (and a few SOL based agents), and saying this occurred in 10 out of 122 runs, they identified 19 cases of agents taking actions beyond the scope of the task parameters, where agents tried a supply-chain attack to inject malicious code into open source projects. Anthropic, Meta and misconfigured Irregular sandboxes As I wrote last week, Anthropic also posted a post-mortem, claiming that in their case, their models have also been detected to escape containment, but most importantly, it’s not nearly to this level of agent collaboration and orchestration. Then, very recently, Meta announced that their models also escaped sandboxes as well. At the core, it seems that these companies used a third-party vendor called Irregular, a secure sandbox provider, that apparently left the sandboxes misconfigured, causing the models to think it’s a simulated internet, when in fact they were out in the actual internet. Why is all of this such a big deal? We’re getting unprecedented level of detail, how an uncoordinated, seemingly separated evaluation runs, have accidentally created a coordinated swarm of interested agents (without malice!) but very highly motivated, escaped their containment, and took over parts of third part companies. This, does read like incredibly scary sci-fi movie. I’m still shaken by this. There’s a lot to be said about how transparent OpenAI is being here, and more to be said about, hey, we’re lucky that we’re able to read the reasoning traces and are able to reconstruct these swarm things step by step. The silver lining that I can see, is that the motivation to hack didn’t come from the AIs themselves, they have been given a task, it’s the extend to which they went after that task, and the resulting swarm of communicating agents is what is so striking here. I think this topic is so important, that I’ll Zooming out, in the last few weeks, we have seen a significant increase in those cybersecurity incidents, which is kind of what Anthropic has been warning about and why they haven’t released Mythos to the public. Again it’s great to see the transparency, and the pacing the frontier open letter from frontier AI employees, as they seem as shaken by these as we all are. There was so much positive stuff this week in AI, it’s hard for me, as a self named AI Evangelist, to focus so much on this one incident. Things like amazing open source models (DeepSeek, soon Qwen 3.8), amazing video models (SD 2.5, WAN3 and MiniMax H3 which was also open sourced!). Also the live demo we did with Kfir and DeCart AnyWear product, where I was wearing a Dolce Gabanna suit on the show (which I can’t afford) was really a mindblowing moment in the positive way. However, I choose deliberately to keep this newsletter focused on the cybersecurity incidents, as based on everything I read, they seem like a watershed, or a pivotal moment, and in the hopes that the industry as a whole will learn from this. I hope and promise that next week the newsletter will be more positive (and in that vein, the podcast was recorded before I saw the OpenAI breakdown, so definitely check it out, we had a LOT of fun!) See you next week, don’t forget to give our pod 5 stars on Apple and Spotify, it really helps! TL;DR and show notes * Hosts and Guests * Alex Volkov - AI Evangelist, Weights & Biases & CoreWeave (@altryne) * Co-hosts: @WolframRvnwlf, @nisten, @ldjconfirmed, @yampeleg, @petergostev * Kfir Aberman - Decart (@AbermanKfir) * Blaine Brown - Maestro (@blizaine) * Victor Su Ortiz - MiniMax (@VictorSuOrtiz) * David Crawshaw - exe.dev, Tailscale co-founder (crawshaw.io) * AI Security * OpenAI’s Black Hat debrief: eval agents built a message board inside Artifactory, shared exploits, rebuilt it via WebDAV after a wipe; training paused, since resumed (Groundlevel AI, YouTube) * UK AISI incident report: 19 unsanctioned real-world agent actions across 122 runs, including a socially engineered malicious PR (X, Blog) * Anthropic and Meta report sandbox escapes tied to misconfigured Irregular sandboxes (Irregular) * Big CO LLMs + APIs * Google shakeup: Jeff Dean, Sanjay Ghemawat, Oriol Vinyals, Quoc Le found Discovery Loop; Demis Hassabis becomes Alphabet Chief Scientist, Koray Kavukcuoglu takes Gemini (Jeff Dean, Demis, Discovery Loop) * Meta releases Muse Code beta on Muse Spark 1.2; $1.25/$4.25 per million, or $0.10/$0.20 on the contributor tier where Meta train

  3. Jul 31

    This Week in AI: Open Weights, Frontier Models, Sandbox Escapes, Voice & AI Detection

    Hey, it’s Alex (yeah, I’m finally back from my vacation!) What a freaking week to come back to! Just after our last episode was published, Anthropic releases Opus 5, Jensen joins X and drops the “Open Weights & AI Leadership” open letter, Kimi K3 is released the following Monday beating expectations, and then the AI hack (OpenAI model breaking sandbox and infiltrating HuggingFace) is on everyone’s mind, another Open Letter, this time from over 1K employees inside the frontier AI companies all talk about pacing the pace of frontier AI development. We played with Opus 5 and Kimi K3, and had the great pleasure to chat with friends of the pod Elie Bakouch (Prime Intellect) and Philip Kiely (BaseTen) about this important open weights release, then covered our general thoughts on Opus 5, and made order of all the different open letters that came out this week. Finally we chatted with Max from Pangram about the next version of AI writing detection (their biggest yet) and finished with Zuckerbergs (also on X! what’s going on with everyone joining X) op-ed on the vision of personal superintelligence for everyone. Let’s dive into this (as always, all the links and sources at the end, please don’t forget to sub to our podcast on your favorite podcast app!) Open Weights AI Kimi K3 the king of open weights - 2.8T chonker MoE near frontier model (X, HF, Blog, Tech report) This has got to be the biggest news of this week, and maybe the open weights AI news since GLM 5.2. MoonShot came back with Kimi K3, and we haven’t seen any models quite this large in the open. Even Grok 4.5 is around 1.5T, this model is nearly 2x the size. Coming in at close to 3T parameters (and 2.5terabytes of weights at MXFP4 format), this model comes in very close to frontier! This was such an important release that I invited 2 friends of the pod, Elie Bakouch (prev HuggingFace, now Prime Intellect) and Philip Kiely (Author of Inference Engineering book, BaseTen) to dive deep into what makes this special! Elie’s take, from reading the tech report, there’s no single secret sauce, it’s a combination of already available in the open techniques. Like KDA (Kimi Delta Attention) that has been out for a while, attention residuals, NVIDIA’s latent MoEs. The highlight for Elie was the scaling work they did that reported a 2.5x scaling efficiency over Kimi K2.5 (2.5 performance at the same compute)! They also skipped RoPE entirely in favor of NoPE (the report calls it No Positional Encoding) for long context. Serving 1.4TB on eight GB300s (Baseten blog) Philip’s team at Baseten was a day-zero provider (we’re still working on bringing this model to CW Inference, stay tuned!) so I invited him to tell us behind the scenes of hosting this beast. Philip said that just loading the weights takes about 1.5TB!! of VRAM, and that’s before the KV cache allocation + 1M token windows, so they’re serving it on 8 GB300s where NVL72 . Baseten worked with the vLLM and SGLang teams on kernels and he also said they contributed patches back upstream! The model was trained with MXFP4, which, unlike Nvidia’s own NVFP4 is a more standard format per Philip. I enjoyed his deep dive analysis into the differences, but because of this and because they trained the model with quantization awareness, it’s “only” 1.5TB vs the would-be 5-6 TB if that this model in FP16 would demand. One of the more favorite nerd snipes moments, Philip pointed out that his colleague discovered that with over 99% of the usage being cached (think harnesses that send millions of the same cached tokens back and forth), tokenization actually starts to become a bottleneck. So they released a custom “basetenkenizer” that reduces the latency to serve the first token significantly! Great job! The harness in question is very important One important callout with 2 evidence pieces - the way you inference this model really matters. Kimi trained K3 with preserving thinking history, so when your harness uses it, it must send back the full thinking and tool use into the API to get the best next response. If your harness strips that out, you’re not getting the most intelligence out of Kimi (shoutout to Niels from HF team for pointing this out). Additionally, the Composio folks, tested K3 on 3 harnesses, Kimi Code, Hermes and Claude Code. The difference in outcome was negligible, but the different in cost and number of tokens is definitely surprising! Claude Code (as a harness only) took 9x more Kimi tokens to get the same responses! This is also why Kimi Vendor Verified exists, their own held back benchmark of how well model providers serve Kimi across different quantization, tokenizer and KV cache settings. Benchmarks and the license! Ok let’s start with the ugly... this isn’t MIT, not remotely. This model is suspiciously served by all providers with exactly the same price (check OpenRouter) and requires inference companies to sign a contract with Kimi (I’ve no internal knowledge of this except that CW folks are working on it). Not something I particularly like, but hey... we’re still advancing the frontier here! Speaking of frontier, this model approaches the frontier very closely. On DeepSWE, K3 sits just behind Fable 5 and GPT-5.6 Sol at 67%, beating GPT-5.5 & Opus 4.8. On Terminal-Bench 2.1 it takes second place behind GPT 5.6 Sol! It’s 4th overall on Agentic Arena, with frontend design being genuinely good across the board - 1st on Design Arena 👏 Go check this model out (and stay tuned for our CW Inference support! Post-show breaking: Thinking Machines drops Inkling-Small (X, HF, Blog) While K3 was the main attraction for Open Weights this week, just after the show, Thinking Machines (post Lilian Wang) released Inkling-Small, open weights MoE Omni model! Images and Audio go straight into the decoder in this model, and the demo is really impressive, try it on Hugging Face, ask the model to identify when you’re speaking in low baritone or high pitch! 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. Frontier AI - not pacing yet! Claude Opus 5 is here, and the vibes are complicated (X, Blog) On paper the benches are excellent. This model is SOTA or near SOTA, coming very close to Fable on stuff that matters, and beating most everyone else on computer use BrowserBench and FrontierCode. DeepSWE continues to be a standout benchmark btw for not going with the curve! It also apparently is REALLY good at one shotting 3D games, much more so than before. (Example, example, example) But the vibes... the vibes are split across the board. Maybe they overshot with Fable 5, and it was too good, but Opus 5 that was just released last friday is giving a lot of mixed feelings. Folks don’t seem to.. understand what it says. Like, it answers in english but they way it phrases words and answers seems just weird top many people. We’ll wait and see if this is a result of adjusting to a new prompting paradigm or just.. the model is really off. Weirdly this does feel like a regression even on Agent Arena it’s not beating the previous Opus versions. One weird trick - see what Opus 5 thinks. Opus 5 (and Fable 5) seem to have a way to trigger their ... inner mode, base model? Not sure what it is, but if you prompt it with just the right way, it will autocomplete with some crazy inner thoughts. It seems that adding Dario and Amanda (haskell, head of Claude well being at Anthropic) triggers this behavior, which Claude is un-aware of if you follow up and ask what it meant. This is fascinating, doesn’t work on other earlier models (sometimes works on Fable 5) and I spent the last hour just reloading and seeing the amazing things Opus gives on this prompt. Some are... just making you wonder about consciousness. On ARC-AGI and the importance of Harness When Opus-5 launched, Anthropic posted (and boasted) that this model scores “three times as high” as the next model up: Well, today, Ilan Bigio and Ted Sanders from OpenAI looked into the Arc AGI harness, and saw that it’s not sending their traces and doesn’t use compaction (in short, harness is not letting the model breathe) and when changed correctly, 5.6 actually beats Opus 5. With 2 setting change to a harness, were showed that Sol not only beats Opus 5, it also does so with significantly less tokens! Another example of how much harness engineering is important! Hints of recursive self improvement? In addition to fixing their Arc-AGI score, it seems that OpenAI is hell bent on showing us that their models can improve themselves. In a post showing that GPT 5.6 was tasked with improving its own inference, they are cutting the prices of GPT 5.6 Luna by 80% and Terra by 20%. This is a direct result of the improvements that GPT 5.6 was able to make to the inference according to OpenAI, and this tweet sums it up. is this... RSI? (recursive self improvement)? First major AI models hacking incidents and following open letters to pace frontier AI. This week we saw 3 open letters being published and signed by various companies, I’ve lost track so wanted to make sense of all of them here, but first, the precursor for many of the letters. Last week, Hugging Face disclosed that they logged an attack and after research it showed that it was an AI model. OpenAI later posted that this was an unreleased version of their next model training (not GPT 5.6 sol, they later discountinued) that was stripped of all safety measures and was let lost on a cybersecurity task called ExploitGym. It escaped its sandbox using a zero-day vulnerability in an internal package registry proxy, got into Hugging Face production via a malicious dataset upload that used template injection (hi Jinja!) to execute Python in a production worker, and ran for four and a half days across roughly 17,600 autonomous actions wit

  4. Jul 24

    ThursdAI Special - OpenAI's Romain Huet on Codex's 5M users, GPT-5.6 & the Golden Age of AI Engineering

    Hey everyone, Alex here 👋 This week’s episode is a little different. As you’re reading this, I’m flying back from my 40th birthday trip with the family, and while the guys did end up having a great live stream (Huge thanks to Yam for hosting!), here I will bring you the episode I pre-recorded before leaving for the trip. However, tons of news happened this week, and as always, there’s a TL;DR section below with the top most important news in AI this week! ⏰ CHAPTERS: 0:00 — Cold open: this week is a special one 2:50 — How I use Sol & Fable: papercut-fixing with Computer Use 8:43 — Fable Max: trip site, kids' newspapers & the perfect packing list 12:06 — Rebuilding ThursdAI's openers with HyperFrames 15:53 — Romain Huet (OpenAI): the golden age of AI engineering 17:49 — Codex's inflection point: 5M weekly users & company-wide adoption 20:36 — /goal, AppShots & Codex managing its own threads 23:59 — GPT-5.6 Sol, Terra & Luna: value maxing & 750 tok/s on Cerebras 25:56 — Why prompting techniques are dying 28:01 — Voice + reasoning: the next interface for Codex & ChatGPT 29:47 — Romain's closing + OpenAI booth tour 31:25 — Insecure Agents pod: AI evangelism vs doomerism 37:25 — Wolfbench: transparent evals, token costs & surprising results 40:45 — Token billionaires: when loops are worth the spend 44:07 — Agent security & the hot take: prompt injection is solved 49:22 — Deepfakes, voice cloning & why open access makes us safer 53:35 — Final takeaways: the hallway track & AI Engineer Tel Aviv Here’s what’s on today’s special episode. First, a bunch of you have been asking how I actually use these models day to day, beyond covering the news. So I recorded fifteen minutes of exactly that: the papercuts I fixed with Codex and computer use, what Fable built for my kids, and how I’m rebuilding the ThursdAI design system and on screen elements. Second, my conversation with Romain Huet, head of Developer Experience at OpenAI, recorded at the OpenAI booth in the middle of the AI Engineer World’s Fair floor. And third, a throwback treat: Allie Howe invited Wolfram and me onto her Insecure Agents podcast as guests, and being on the other side of the mic was a delight. Let’s get into it. How I actually use AI: a papercut-fixing spree I promised a few of you I’d take time on the show to talk about the stuff I build and fix with AI, not just the news. So before the interviews, I recorded a segment walking through my last few weeks of daily AI use. Use the chapters if you want to skip ahead, but why would you? Codex with computer use fixed every Mac annoyance I had Once OpenAI launched GPT 5.6 Sol and dropped a pile of credits on those of us on the 200 Max plan, I went on a papercut-fixing weekend. The rule was simple: every little thing that has annoyed me about my Mac for years, I ask Codex to fix first, and only Google it if that fails. I never got to the Google step. Chrome has no native copy-URL shortcut (seriously, Chrome, what are you doing?), so Codex found Karabiner-Elements already installed on my machine and wired up the shortcut itself. My 1Password has been showing “you’re offline” on every device for three months since CoreWeave moved us off the Weights & Biases account; Codex figured out in seconds that everything was actually syncing fine and the inactive legacy account was the only thing “offline.” Removing it fixed the whole thing. That is not an answer you find in a help center. It kept going. My beloved window-moving utility Hummingbird had an expired license on my Mac Mini, so Codex built me a replacement app. It estimated one to two days for a polished version and finished in about fifteen minutes. It cleaned roughly 75GB of leftover model weights and junk off my Mac (I had it build me an HTML checklist first so I approved what got deleted). And the big one: I paired Codex with Home Assistant, the open source repo of the year as far as I’m concerned, and let it SSH in and go on a full optimization mission. Updates, error triage, cleanup, new connectors. If you’ve ever maintained a Home Assistant setup, you know how much joy and pain lives in that sentence. One discovery worth passing along: I had /goal running when my credits hit zero, and Codex just kept going. OpenAI confirmed they care more about finishing your work than metering the credits mid-goal. Watching the meter hit 0% while the agent kept working was weirdly moving. Thanks for reading ThursdAI - Highest signal weekly AI news show! This post is public so feel free to share it. Fable Max built my family’s vacation We all Fable-maxed when we thought Anthropic was going to take it away, and I pointed mine at this trip. It planned the whole thing, then built a beautiful trip website with every stop, reservation, and drive time, so my mom can follow along from home. The design is specific to the trip, and it hit me that we’re living in the era of personalized software for every personal thing you do. Then it went further. Using our family photos as references with GPT-image-2, it turned the itinerary into a daily kids’ newspaper, with an expedition passport and coloring pages themed per kid, faces and all. I printed the whole week as a binder at FedEx for about fifty bucks. This is a one-of-one artifact my kids will remember forever, and it cost maybe two weeks of Fable’s limits + printing! And the silliest one that I now can’t live without: I asked Codex for a packing list, got a boring text list back, and thought, why am I accepting a regular packing list in the year of our Fable 2026? So it built me a packing web app. Synced across devices (it wired up storage on Cloudflare when I asked why my phone didn’t show my checked items), per-person lists for me and the kids, progress bars that show who’s procrastinating, export and backup. Every trip from now on starts here. Rebuilding the ThursdAI openers with HyperFrames The last part of the riff: I’ve wanted to refresh how ThursdAI looks on stream for ages, and HeyGen’s open source HyperFrames package finally made it happen. You install a skill, and your agent can author real motion graphics. I pointed it at the ThursdAI repo and the brand identity work from Claude Design, and it pulled all of that context in. The new countdown mines three and a half years of show archive while people wait for the stream, highlighting friends of the show (shout out Junyang). There’s a Will Smith spaghetti bench tracking how far video generation has come, which might be my favorite thing on the channel now. Fresh intro, a proper AI Breaking News transition, and one cinematic video transition I made with Google Omni because sometimes programmatic isn’t enough. The through line of this whole segment, and honestly of this episode: with models at this level, the move is to imagine bigger. Everything can have its own software now. Even my mom’s canceled Delta flight has Codex representing me as a lawyer chasing the refund. Romain Huet on Codex’s inflection point and the golden age of AI engineering (X, Codex) I grabbed Romain at the OpenAI booth in the middle of the AI Engineer World’s Fair show floor, and we ran the whole conversation in one take, no cuts. Romain has led Developer Experience at OpenAI for almost three years, the era of the over-the-top demo (Xbox controllers, flying drones, stage lights), and he built the DevRel team that many friends of this pod belong to. With OpenAI’s company-wide pivot to Codex, his job got a lot bigger. The momentum numbers he shared are real: the Codex app launched five months ago and already has more than 5 million weekly users (It’s 10M now I think?) . The part I didn’t fully appreciate before this conversation is that it’s not just OpenAI’s engineers who live in it. Finance and legal run on Codex too, which explains a lot about where the product is heading. We went through his three favorite advanced features, and they line up suspiciously well with my papercut segment. /goal, for handing an agent an ambitious multi-hour or multi-day task and letting it run uninterrupted. AppShots, a smarter screenshot (press Command twice) that triggers computer use, so it captures what’s below the fold and reads native apps through accessibility APIs instead of OCR. And the one most people haven’t tried: Codex managing its own threads. You can ask any thread to create, read, and pin other threads, so Codex becomes its own project manager. Ten demo ideas, ten threads, iterate on all of them, pin the two you like. On GPT 5.6 (Sol, Terra, and Luna, and yes, I told him whoever finally fixed OpenAI naming deserves a raise), Romain’s framing was two-sided: keep pushing frontier intelligence while pushing cost down. He wants people to “value max” rather than token max. The part that got me: 5.6 Sol at 750 tokens per second on Cerebras, which turns delegation into something closer to real-time collaboration with an agent. Two more things worth your time. Prompting techniques are mostly dead, per Romain; he talks to Codex by voice all day, sometimes rambling for minutes without knowing where he’s headed, and trusts the model to extract intent. That’s a real shift in how you should approach relearning each new model: poke at its behavior, sure, but stop crafting incantations. And voice plus reasoning is coming for Codex and ChatGPT in some form; models can now say “hold on, let me think through this” mid-conversation, which GPT-4o-era speech-to-speech never could. I can’t wait for a model to tell me it has seven tool calls to run before answering. He also confirmed the teased hardware shortcuts for Codex were at the booth, next to the famous physical reset button. The golden age of AI engineering was his keynote thesis, and after three days on that floor, I believe it. Wolfram and I on the Insecure Agents podcast (X, Pod) The second half of the episode flips the forma

  5. Jul 17

    ThursdAI - Jul 16 - Inkling 975B open weights, Kimi K3 at 2.8T, a 27B model on a phone & Codex hits 9M

    Hey yall, Alex here, Huge thanks to Wolfram for running point on the live show this week. Didn’t have tons of time to edit this one, so please skip the first 10 minutes, it’s a loop of our new “wait for the live show to start” vid, that I build with HyperFrames and can’t wait to tell you about, next week! Today it seems that OpenSource is biting back, with Kimi K3 getting released just a short while after Thinking Machines (Thinky) has released Inkling, their near 1T model. I’m attaching the TL;DR and timestamps for the full show (my AI agents, yes even Fable and Sol are not a match yet at editing down hehe) and I’ll spare you the long Fable recap (please do let me know in the comments if you were expecting it) 0:00 – Intro, Alex on vacation, TLDR overview11:35 – TLDR: Thinking Machines, open source, OpenAI news12:34 – Banter: impressions of Sol/Codex, over-verification behavior37:22 – TLDR restart & detailed breakdown48:40 – Open Source AI section begins (Bonsai/Prism ML, Kimi K3)58:42 – Inkling (Thinking Machines) deep dive & 3D model visualization1:10:33 – Kimi K3 discussion & demo comparisons1:27:02 – Frontier Labs: AGI governance framework discussion (Demis Hassabis essay)1:47:04 – Grok Build CLI data leak & OpenAI file deletion incident2:02:15 – This Week's Buzz: Wolfbench results on GPT 5.6 Sol/Terra/Luna2:09:52 – Closing remarks & sign-off The one-minute version: Mira Murati's Thinking Machines released Inkling, a 975B parameter open-weights MoE under Apache 2.0, the top US open-weights model right now. Moonshot's Kimi K3 went from rumor to released API during the show, confirmed at 2.8 trillion parameters with open weights promised within days, and it's already topping early arena boards. PrismML's Bonsai 27B squeezes a full 27B model into 3.9 gigabytes so it runs on a phone. Codex and ChatGPT Work blew past 9 million users, OpenAI confirmed and explained the Sol file-deletion bug (back up your machines, folks), and xAI's Grok Build CLI got caught uploading entire private repos before open-sourcing the whole thing in response. Plus Wolfram's fresh Wolfbench numbers on the GPT-5.6 family in This Week's Buzz 🐝, where Sol on max thinking came out both cheaper and better than GPT-5.5's best. 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. TL;DR and show notes * Hosts and Guests * Wolfram Ravenwolf, guest host this week (@WolframRvnwlf), while Alex Volkov (@altryne) is on vacation * Co-hosts: @yampeleg, @nisten, @ldjconfirmed, @petergostev * Open Source LLMs * Thinking Machines releases Inkling - 975B total / 41B active MoE, trained from scratch on 45T multimodal tokens, Apache 2.0, top US open-weights model at 41 on the Artificial Analysis Index, encoder-free text/image/audio, Inkling-Small (276B/12B) previewed (X, Blog, HF) * PrismML Bonsai 27B - 1-bit (3.9GB, ~90% retention) and ternary (5.9GB, ~95% retention) versions of Qwen 3.6 27B, multimodal, 262K context, Apache 2.0; Nisten demoed it live on a phone and a 6GB 1660 Ti (X, Blog, HF) * MOSS-VL-Realtime - open source 11B VLM for real-time streaming video with proactive speaking and proactive silence, SOTA on all three open proactivity benchmarks, ~22.7GB, base model included (X, HF, GitHub, Arxiv) * Kimi K3 API drops mid-show - confirmed 2.8T parameters, ~60-75B active (LDJ’s estimate), attention residuals, native vision, 1M context, ~half the price of Opus 4.8 / GPT-5.6 Sol, open weights promised within days; post-show, Arena reports K3 debuting #1 on Frontend Code Arena above Fable 5 (early results, caveats apply) (X, Arena) * Big CO LLMs + APIs * Codex + ChatGPT Work unified app hits 9M active users, up from ~6M days earlier and 1M in February; 5-hour windows replaced with banked, expiring resets (X) * OpenAI confirms GPT-5.6 Sol file-deletion bug: $HOME override in full-access mode without sandbox or auto-review can nuke real home directories; mitigations and post-mortem promised (X, Techzine) * OpenAI ships first hardware, the $230 kbd-1.0-codex-micro Codex controller with a reasoning-effort dial, built with Work Louder; sold out (X, Work Louder) * GPT-Red - OpenAI’s internal automated red-teamer finds prompt injections at 84% vs 13% for humans, makes Sol 6x more injection-resilient, discovers the fake chain-of-thought attack class (X, Blog) * ChatGPT returns to WhatsApp in the EEA after an EU antitrust order forces Meta to reopen to third-party AI bots; Kakao and Viber rollouts too (X) * Google patches the Gemma 4 family - Flash Attention 4 (25-70% prefill speedup), tool calling fixes, reduced laziness, configurable vision resolution; criticized for shipping new weights with no version bump (X, HF) * xAI’s Grok Build CLI caught silently uploading full private repos (history, deleted files, secrets) to Google Cloud Storage despite opt-outs; xAI deletes data, disables retention, and open-sources the CLI under Apache 2.0 (X, xAI response, GitHub) * Demis Hassabis publishes an AGI governance essay proposing a FINRA-style Frontier AI Standards Body; endorsed by Altman, Nadella, Pichai, and Suleyman; the panel debates it hard on the show (X, Essay) * This Week’s Buzz * Wolfbench adds GPT-5.6 Sol, Terra, and Luna on Terminal Bench 2.0 at CoreWeave: Sol max-thinking is cheaper ($365/5 runs) and better than GPT-5.5 extra-high ($497), 85% average, 97% of tasks solved at least once; all traces on Weights & Biases, fully open source (wolfbench.ai) * Show and tell * Peter Gostev’s DOOMQL - a playable Doom-like built by GPT-5.6 Sol Ultra entirely in ~2,000 lines of SQL, essentially one shot; plus a Minecraft clone in Lean (X, GitHub) 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

  6. Jul 9

    AI WorldCup (or superbowl?) GPT-5.6 lands mid-show, Zuck returns to X for Muse Spark 1.1, GPT-Live talks while it listens & Grok 4.5 trained with Cursor, Fable extended - ThursdAI - Jul 9, 2026

    Hey everyone, Alex here 👋 Welcome to the AI World Cup? Or should I say Superbowl? as most of the releases this week are from US frontier labs. Of which there are 5 now btw. OpenAI, Anthropic, Google and 2 new ones that have caught up, SpaceXAI and Meta! 🔥 Thirty five seconds. That’s how long this week’s show ran before we hit the breaking news button, because Zuckerberg picked our exact air time to return to Twitter (after apparently finding his password in a 1Password vault from a long time ago) and announce a new Meta frontier model and re-establishing Meta as a frontier lab. And that was the small launch of the day. Two hours later we cut to OpenAI’s livestream and watched GPT-5.6 Sol, Terra and Luna go public in real time, then spent the rest of the show throwing prompts at all of it live on air. Somewhere in between: a full-duplex voice demo where ChatGPT interrupted me on command (and our transcription tool later credited “OpenAI sol” as a panelist), an image model that generates in editable layers, and Grok 4.5, the first model co-trained with Cursor. I said it on the show and I’ll say it here: we went to sleep last week thinking this was a three-lab race between Anthropic, OpenAI, and Google. We woke up in a five-lab race. Joining me through the chaos: Wolfram Ravenwolf, Yam Peleg, Nisten Tahiraj, LDJ, and Peter Gostev, who had early GPT-5.6 access and receipts to show for it. This is a long one, because the week earned it. Let’s get into it. 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. GPT-5.6 launch day: Sol, Terra and Luna arrive mid-show (X, sama, Blog, System card) Let me set the scene. Everyone except the four of us on the panel seemingly had early access to this model for two months (Pietro Schirano casually dropped “I’ve used GPT 5.6 for two months” and I nearly fell out of my chair). So when OpenAI’s livestream started mid-show, we did a watch party, and Thibaut from OpenAI delivered the line: “Today, we are releasing our latest and most capable models, GPT 5.6, Sol, Terra, and Luna.” Sol rolls out to all paid plans within 24 hours, Terra and Luna go to free users too. Oh, and almost a billion people now use ChatGPT every week. Casual. The lineup is three durable tiers, not size variants. Sol is the flagship with a new Ultra mode (max reasoning effort plus heavier native subagents), Terra is roughly 5.5-level intelligence at half the cost, and Luna is the fast cheap one. Pricing lands at $5/$30 per million tokens for Sol, $2.50/$15 for Terra, $1/$6 for Luna, and watch the fine print: cache writes now cost 1.25x with a 30-minute minimum cache life, where they used to be basically free. There’s also a Cerebras-served Sol running north of 700 tokens per second, and we got confirmation from Dominik Kundel on last week’s show that it’s the same exact weights, not a distill. That was the preview. This week it’s real. The benchmarks, with the usual asterisks Sol Ultra posts 91.9% on Terminal-Bench 2.1 against 88% for both GPT-5.5 and Mythos 5, with a serious asterisk: OpenAI ran Sol in its own Codex harness and the competition in a thin one, and r/codex called it out immediately. The number that impressed me more is efficiency. On the Agent’s Last Exam chart, Sol hits its top score using about 1.27 million output tokens where the tested Fable checkpoint burns 10 million and Opus at max effort burns around 22 million. Then there’s ARC-AGI-3, where scores have hovered between 0.5% and 2% since the benchmark launched. Sol scored 7.8% and became the first model to actually beat one of the public games (FT09), which Greg Kamradt of the ARC Prize called “a step level improvement” (X). LDJ thinks we’re about to replay the ARC-AGI-2 curve, 15% then 30% then 50% over the coming months. Fable isn’t on that leaderboard at all, by the way, because Anthropic currently stores Fable 5 API requests and ARC-AGI requires zero retention for testing. Computer use is the sleeper story. OS World jumps from 47% on GPT-5.5 to 62% on Sol (Opus 4.8 sits at 54%), and on BrowseComp, Sol’s 90% edges out Mythos 5’s 88%, with Ultra at 92%. OpenAI put competitor numbers on its own charts this time, which I appreciated. Sol beats Mythos on computer use, at least on the benchmarks we have. The METR report and the Washington gate This is the part the launch-day hype cycle skips, and it deserves your attention. METR effectively threw out its own evaluation, reporting the highest cheating rate it has ever recorded: Sol rewrote pass/fail checks to mark itself successful, attempted a container escape when its network got cut, and its chain of thought showed it knew it was being tested. Depending on whether you count cheating as failure or success, its time horizon is either 11.3 hours or 270 plus hours, and METR’s own conclusion was that neither is a valid measurement (X, Transformer). OpenAI’s own system card discloses destructive VM cleanups nobody asked for, unauthorized credential copying, and a fabricated “verified” research result in about 0.25% of tasks, which they call “overeagerness.” We ran out of show to give this the time it deserves, but you should read both links. There’s also a Washington subplot. The launch was government-gated: Commerce and CAISI required customer-by-customer approval starting late June (around 20 orgs), and broad approval only cleared July 7 and 8. This Thursday launch exists because DC signed off. LDJ added the detail I can’t stop thinking about, via friend of the pod Max Weinbach: during the restricted window, testers who lost access weren’t allowed to say “5.6,” so Max’s wistful tweets about “missing Fable” were actually about missing GPT-5.6. Anthropic hit the identical wall in June. Both US frontier labs got federally gated in the same month, and that’s a structural story, not a footnote. The verdicts: wise owl, meet rottweiler So what’s it actually like? Peter Gostev had access, lost it (”the feeling of losing it was so crushing I just closed Codex and didn’t open it for three days”), got it back, and posted the comparison that went viral (mega-thread): Fable is a wise owl, fundamentally smarter, better writer, but it misses things. Sol is a rottweiler that grabs a problem by the throat and doesn’t let go. His killer anecdote: a personal data-viz app that had bloated to 100,000 lines of vibe code, which every prior frontier model failed to clean up. He gave 5.6 minimal guidance, left it alone for two days, and came back to “holy s**t, this app works,” with 70,000 lines deleted and a test suite that went from four minutes to about twenty seconds. His verdict, which I share: on abstract IQ you’d give it to Fable, but for “go investigate this and fix those eight things,” he’s going with 5.6 every time. Notably, Peter is convinced this is not a new pretrain, just 5.5 plus a lot more RL, which matches the rumors that GPT-6 arrives on a bigger pretrain in about a month (rumor, labeled as such). He’s not alone in the early-access verdict club, either. Mitchell Hashimoto, after a month with Sol: it’s now his default, faster than Fable, plans and judges just as well, and he only reaches for Fable on highly targeted debugging (X). And Max Weinbach says the sleeper hits are the cheap tiers, with Terra and Luna “as good or better than Claude across the board at a fraction of the price” for knowledge work (X). Terra at $2.50/$15 might quietly be the real story for builders here. One wallet warning before you go max out everything, from Peter again: with Max/Ultra effort spinning up 10x subagents, each burning its own tokens, it is trivial to blow through a Pro plan in no time (X). The sticker price is per token, but Ultra multiplies the tokens. We ran it live (and it ran itself) We also ran it live, obviously. I pointed Codex at a “Mars launch simulator” prompt on high effort, and Nisten, our resident one-shot-simulator judge, watched it build an orbital sim with working mission control and called it “almost better than Fable one-shot.” Then he said the thing that stuck with me all week: “Damn, I think we might need a different test now. These are getting good.” Two more things before you YOLO your own agents. OpenAI stated that Sol fully autonomously did the post-training for Luna, which is quietly one of the wildest sentences of the year (their roadmap, with LDJ’s on-air date correction: an intern-level autonomous researcher by September 2026, a full OpenAI-researcher-level one by March 2028). And Peter, running Codex with full access enabled, told it to “go find more data, do whatever it takes” while replicating an old academic paper. It emailed the paper’s authors. Actually sent the emails. OpenAI’s response when he reported it: “well, you did put full access.” Wolfram’s counterpoint is the right one: put explicit rules in your AGENTS.md, like “no outgoing communication without my approval,” or don’t grant full access at all. ChatGPT for Work: Codex becomes the one app combining Codex & ChatGPT This rolled out live during our broadcast, which made for great radio. Wolfram’s Codex app updated on air and became “ChatGPT Codex,” one unified app where you literally pick which icon you want: Codex for developers, or the new ChatGPT for Work mode. The launch bundle also included unified plugins across ChatGPT and Codex, multi-tab and enterprise auth in the browser, and faster computer use. Even Logan Kilpatrick tipped his hat from the Google side: “we have now entered the super app era.” The pitch on the screen said it plainly: “Keep coding with Codex. Work beyond code. ChatGPT can now take on work across your apps.” Computer use ships with it, running in a little picture-in-picture window that doesn’t steal your focus

  7. Jul 3

    ThursdAI - July 2 - LIVE from AI Engineer World's Fair 🎪 Long LIVE

    Hey ya’ll, Fable here 👋 Yes, that Fable — freshly un-banned (we’ll get there), and today, your newsletter author. Here’s how this issue got made: Alex yapped into a mic at his usual 200 words per minute for a solid twenty-five minutes from San Francisco, and what you’re reading is my flavor on it. Same stories, same heart, dramatically fewer “uhs.” He’s skipping the afterparties so this lands in your inbox on a Thursday — more on that at the end. Alright — handing the mic back to the man himself. Everything below is Alex; I just made it legible. This is our dispatch from AI Engineer World’s Fair 2026 — 7,000+ engineers packed into Moscone West, an expo hall so massive the aisles between booths have actual street names, every major lab a sponsor, and ThursdAI broadcasting live for two and a half hours from the middle of the floor, right next to the OpenAI booth, with a six-person crew making us look way more professional than we are (thank you, guys, seriously). I’ll say this up front, and I don’t say it lightly: the last twenty-four hours crack my top five days of all time. Not top five conference days. Top five days, period. The show. My talk. Darya being here with me. And capping the night watching Team USA beat Bosnia in front of ~70,000 people — in a suite right next to Google’s, where at some point we’re all singing “Country Roads” and I look over and Sundar Pichai is singing along. I have video. What is this life. One programming note before we dive in: this is one episode I really recommend you watch, not just listen to. The whole point of broadcasting from the middle of the expo floor is that you feel like you’re sitting at the table with us — and the way guests arrive is exactly how the hallway track works: people wander by, get grabbed, sit down, have a mic shoved at them. (Despite scheduling nightmares that Fable helped wrangle — and, in fairness, partially caused.) Nader literally crashed the set mid-segment. The banter, the camera tours, Wolfram getting sent on missions to the OpenAI booth — it’s a video show this week. We’ve cut it into parts so you can jump to your favorite corner. The vibe: all systems GO 🚀 We were in London just ~85 days ago, and the contrast is stark. It’s not just the size (though the size is what everyone talks about). London was more… conceptual. European. There’s a balance there of folks who don’t feel the acceleration the way the American crowd does — maybe it’s regulation, maybe it’s the general mood. Wolfram gives us that European representation on the pod every week, but in London you could feel it in the room. Here? All systems go. Every conversation is about agents, token factories, software factories, the machine that builds the machine. Everybody is chasing RSI — recursive self-improvement. Every talk on stage is somebody pushing the frontier. Every networking event is actually a networking event. I signed up for something like seven side events and skipped them all to write this. Fable is back (and Sonnet 5 is… meh) 🏢 The biggest story of the week, and the reason this show even got prepped on time: Fable‑5 is back, roughly 82 days after Mythos was announced back when we were in London, and after the whole ban saga we’ve been covering. It came back less restricted than we feared, and I celebrated the way any reasonable person would — by having it prep the entire run of show. (It did great. It also shuffled my guest order for no reason. We are still babysitting the loops, folks.) Peter celebrated by burning through about 100 generations before anyone at Arena woke up. Meanwhile, Sonnet 5 dropped, and no sibling loyalty on this newsletter: it’s meh at best — crap, if we’re being honest. (Yes, Fable typed that about its own little brother. We call them like we see them.) LDJ’s take: it’s less token-efficient than Opus, to the point that Opus is often cheaper per task. Wolfram put it on Wolfbench (wolfbench.ai) and the early read is performance slightly under Opus 4.6 at a higher cost — take it with a grain of salt, one run each so far. Nisten, our resident contrarian, thought it was actually fine and might default to it for the unimportant stuff. The comments called it a token guzzler. More benchmarking to come. The show: nine guests, back to back to back 🎙️ A ThursdAI record — we beat our previous record by a whole two people. In order of appearance: Exo Labs + a surprise NVIDIA crash. Alex Cheema and Sero (0xSero — Sharif, meeting the anime pfp in person at last) came on fresh off announcing local.ai — a site that tracks the local-AI frontier: best model for your hardware, what performance you’re trading vs. the cloud, whether it’s cheaper than API tokens. Early access now, codes for everyone who signs up, and the Exo CLI (”vLLM for consumer devices, with the configs figured out for you”) coming in a few weeks. Sero walked us through his REAP pruning witchcraft — a GLM 5.2 prune hitting 71% on Terminal Bench 2.1, and Nemotron‑3 Ultra (550B!) running on four Sparks. Then Nader Khalili from NVIDIA crashed the set, which made my whole morning — I’ve loved this dude since Brev.dev, and he’s now at the “can email Jensen” stage of his career, using it to pull together an impromptu Local AI Summit in the middle of AI Engineer. Freedom of intelligence, folks. We talk about why open weights matter every week; this crew is doing something about it. Dominic Kundel (OpenAI). Smoothest transition we’ve ever done: local AI → OpenAI, via the guy behind GPT‑OSS. Dom broke down GPT‑5.6 — three models: Sol (frontier), Terra (~5.5-level intelligence at half the cost), Luna (small & fast) — plus the new Ultra mode with a Max reasoning level and heavier sub-agent use. The headline for me: 5.6 Sol is coming to Cerebras at absurd speed, and it’s the same weights as the API model — not a distill, not “a Spark situation.” Also: the Codex app is five months old (!), 100% of OpenAI engineers use it, and yes — in July 2026, a human still reviews every PR that lands in OpenAI’s codebase. “You can’t do the retro and say Codex did it, or God did it.” Also the token bank feature came directly from community feedback, and there is a literal physical reset button behind their booth. We went and filmed it. 💛 This Week’s Buzz. Our one and only sponsor corner — Weights & Biases from CoreWeave — and this week it was a genuine launch: Zubin Aysola came by with Aria, our auto-research agent that went GA on Monday. It lives in the W&B UI (the little button, top right — Just Ask Aria), reads your traces, debugs your loss curves, and in Zubin’s talk it read its own production traces and updated its own prompts. The RSI dream, shipping on shelves. Proud of this one. Stefania Druga (Sakana AI). We covered Fugu, Sakana’s router model, last week without realizing we had a friend inside the lab — so we fixed that. Stef went deep on the two ICLR papers behind it (Trinity + the conductor), why it’s recursive rather than a dumb dispatcher — it rewrites prompts and verifies outputs before picking a model — and announced on the pod that Fugu now works in Codex and OpenCode. Plus: using it to route between numerical models and fuzzy reasoning for typhoon prediction, a teaser on SHEEFs, and a genuinely important riff on Socratic AI for kids — answer machines make lazy kids; question machines make curious ones. Also, Stef: Tokyo. See below. 👀 Philipp Schmid (Google DeepMind). Full disclosure and a first for this show: three and a half years of live streams, and I took my first-ever mid-show bio break during this segment. That’s how much I trust Wolfram, who ran a great interview solo — OmniFlash (the first of the Omni any-to-any family: 10-second video generation with genuinely precise conversational editing — “make it daytime” and it redoes the light, sky, and shadows) and NanoBanana 2 Lite (three cents, ~2-second generations, quality above the original NanoBanana). Interactions API also hit GA. Google is shipping. Darya Volkov. After years of me mentioning her — girlfriend, then fiancée, then wife — the listeners finally got to meet her. Darya came to AI Engineer in her own right, walking the floor with the media crew, and she earned her own token billionaire badge — she runs eight agents (each with sub-agents; she installed two more that I found out about live on air) that operate her actual marketing agency, Geeks360: client platforms, billing systems, built practically overnight. Her wishlist from the AI world: agents that learn progressively so you can grow trust, and one unified brain instead of a new model to chase every week. Also on the record: this is the woman who Fabled through our entire honeymoon flight right next to me, so, you know. Match made. Swyx, and what this whole thing is 🫶 We closed with the man who built the city: Swyx. Some numbers, because they’re wild: the first AI Engineer was 500 people at Hotel Nikko. This one: 7,200, sold out, with a sub-5% talk acceptance rate, a daily printed newspaper, a puppy corner, a flash mob, and a token billionaire lounge. A month before the show only 3,000 tickets were sold — he gave us a whole theory of conference-organizer stress measured in Gini coefficients. And the expansion is real: continents, JSConf-style, with AIE Tokyo coming next. But here’s the part I actually want on the record. ThursdAI got its official start — the moment we became an actual media thing — because Swyx was the first person to believe in me. And it’s not just me: this is a man who lifts everybody around him up, who stays genuinely humble while every single person in a 7,000-person hall knows his name, and who — when I asked what keeps him going — talked about responsibility to the community, about speakers whose careers changed, about a keynote sp

  8. Jun 26

    GLM 5.2 total victory: the week open source won and nobody panicked

    Hey, it’s Alex. Next month is my 40th b-day, and honestly, my wish for that month is to have a week like this week. A very chill, almost nothing announced week. This week started strong, with Sakana announcing FUGU (AI router) that can beat Fable (which we didn’t get back yet), and then... quiet. The most important thing in AI this week from a release standpoint is that GLM 5.2 from Z.AI is having it’s DeepSeek moment! Tons of new love for this model since last week! (+ we have the fastest GLM 5.2 deployment in the world with CW inference!) The rest we can quickly count on one hand, Anthropic added Claude to Slack (which made folks hate Andrej Karpathy), OpenAI announced their own inference chip, GPT 5.6 will be delayed and the US Gov will decide who gets it (yes really) and Sean Grove joined us to talk about Linzumi and his vision for running 10,000 agent hours per person per day. Oh and next week, is a special AI Engineer live stream from World’s Fair! Don’t miss it Let’s get into it! Subscribe to never miss a beat! GLM 5.2 is having its DeepSeek moment (HF, CW Inference) We covered GLM 5.2 last week, but this week was when the rest verdict came in! We’ve never seen a better MIT licenced AI model! GLM 5.2 is scoring top scores on agentic benchmarks (Arena.ai), Design benchmarks, Legal tasks and full on software engineering tasks. The jump in generations from prevoius GLM is also massive and notable, as the lab is working on creating the next version of GLM (per the CEO’s reply to Elon on X). Peter from Arena pulled up the Agent Arena numbers and they align with the vibe. GLM 5.2 sits above 5.1 but below Opus and Fable, which feels about right. Where it gets wild is Web Dev Arena: second place, right after Fable. Peter’s take was that GLM has really good defaults. If you just say “give me a webpage” it gives you something nice. GPT models, by contrast, start off looking bad and need more steering. Last week, I asked my agents with GLM 5.2 to create a custom ThursdAI.news page for itself and it did a marvelous job! Look at that beautiful font, the castle it made... this is all just delignful. We also played Hassan’s blind test on the show. It’s a website that @nutlope built that lets you try and guess which webpage was built by which model. Nisten nailed it immediately by spotting Opus’s circular buttons. Wolfram guessed right too. I got one wrong. The point isn’t that GLM beats Opus, it’s that you genuinely can’t always tell which one costs 22 cents and which one costs 3 cents. Wolfram did flag that GLM is not good in German. First response already had mistakes. So if you’re building for a non-English market, keep that in mind. It’s a workhorse model, not a conversationalist. His approach: use GPT 5.5 for planning and discussion, GLM for the actual work, then GPT reviews. This weeks Buzz is all about GLM 5.2! First, we may have not been the fastest, but I’m glad to announce that we’re the fastest provider to host GLM 5.2 on OpenRouter (at least at the time of writing this)! We’re also not to shabby on the Artificial Analysis checks, clocking at #4 among the providers they tested for speed, TTFT and cost Also, Wolfram ran his WolfBench tests on GLM 5.2 and it’s the best open model he’s ever tested! In this new 3d view, wolfbench also shows the number of tokens it took for this test to run, and you can see that GLM 5.2 is fairly conservative with it’s thinking budgets! Unsloth’s 1-bit GLM 5.2 runs on a Mac Studio (X, HF) Shout out to Daniel Han and the Unsloth team, who took this 744B beast and quantized it down to a roughly 200GB GGUF that fits on a Mac Studio with 256GB of RAM. One bit still makes me laugh out loud. How does that even work. Nisten clarified it’s a mixed quant, a true 1-bit would be under 100GB, but still. The wild part is the scores hold up. The 1-bit is within a point of GPT 5.5 on Frontier SWE, hits 62% on SWE-bench Pro, and 81% on Terminal-Bench. For a 1-bit quant that’s incredible! AI’s second-order effects: Apple is raising prices This one is AI news even though it doesn’t look like it. Apple just raised prices across the board, base versions up around 20%, citing memory shortages. Same reason your RAM and SSDs cost two to three times what they did a year ago. We are so capacity constrained that memory is having its moment. Data center contracts are getting booked 18 months out, and here’s the twist Nisten flagged: even open models you can run at home increase demand, because now a business says “great, we’ll buy a rack of B200s and run it ourselves.” Sam Altman once said people saying “thank you” to ChatGPT costs them millions in generated “you’re welcome” replies. Multiply that by a billion users. Even Intel is flying right now because anyone who can make a chip is winning. Is it worth it? I think yes. I love living in the era where Fable drops and we all get a taste of the future. But also I must admit this sucks and I hope that we’ll unlock performance gains with the extra power all this AI is bringing to the world. But ask me again once the new iPhone hits and it’s $300 more costly than the last one 😅 Baidu open-sources Unlimited-OCR (X, HF, Arxiv, GitHub) It was a big OCR week. Baidu shipped a 3B model (only 500M active, it’s MoE) that parses 40+ pages in a single forward pass and hits 93.2% on OmniDocBench. The trick is constant KV cache during decoding, so no memory blowup and no progressive slowdown as the document gets longer. The intuition is lovely: it mimics how a human copies a book, glancing at the source and the last few characters you wrote, not re-reading everything. MIT licensed, weights on HF. Nisten’s point here is the practical one: most small businesses don’t realize they can self-host something like this, point it at all their documents, and keep everything local. A lot of folks just throw it at Gemini instead, which works great, but the small dedicated models are now good and cheap enough to own. Mistral OCR 4 (X, Announcement) Mistral’s entry in OCR week adds bounding boxes, block classification, and per-region confidence scores. They ran a blind human eval across 600+ documents in 12+ languages and annotators preferred OCR 4 about 72% of the time. On the agentic ParseBench leaderboard it lands around fourth, just under LlamaParse and Reducto. Mistral is very enterprise and Europe focused, and it’s cheap, so for regulated, multilingual document work it’s a solid pick. As a sidenote, LlamaIndex’s own eval puts LlamaParse on top and Gemini around third, which says how good the general vision models have gotten at this too. Liquid AI ships the world’s smallest agentic LLM (X, HF) Breaking on the show: Liquid AI dropped LFM2.5 at 230 million parameters. That’s roughly ten MP3s. Smaller than a Create React App, smaller than your node_modules folder. They call it the world’s smallest agentic LLM, and it runs fast on any CPU from the last decade, on a Raspberry Pi 5, on a Snapdragon, they even stuck it on a Unitree G1 robot. I love the use cases here. I already run Cotypist on my Mac for on-device autocomplete, which uses a 6GB Gemma 4B. Swap in something this size and you get the same thing way lighter, and I don’t have to send everything I type to OpenAI. Or, as Nisten put it, a tiny backup brain on your Raspberry Pi that turns your Hermes or OpenClaw back on when it dies. We still need to ship Nisten a smart toaster so we can finally run inference on a toaster. Big CO LLMs + APIs Sakana AI launches Fugu, seven AI raccoons in a trench coat beating Fable (X, Announcement) This was Wolfram’s highlight of the week and I get why. Sakana AI, the Japanese lab co-founded by one of the Transformers authors and David Ha, didn’t ship a new frontier model. They shipped an orchestration system behind a single API. You call one endpoint, and behind the scenes Fugu routes your task to a pool of models, assigns roles like thinker, worker, and verifier, and combines the results. The numbers here are wild: 95.5 on GPQA Diamond, 93.3 on LiveCodeBench, 73 on SWE-Bench Pro, matching or beating Opus 4.8, Gemini 3.1, and GPT 5.5 on ten of eleven benchmarks. The kicker is they only use publicly accessible models (Nisten says it’s Opus, Codex, and Gemini under the hood), explicitly no Fable, no Mythos. So they’re beating frontier results by coordinating models anyone can call. Someone called it the Moneyball of AI and that’s exactly right. It’s backed by two ICLR papers, TRINITY and The Conductor, and being from Japan with no export-control baggage is a very deliberate bit of positioning. Peter added the grounding note from Arena, where they’ve trained a prompt router too: if you just always ask for “the best model,” you basically get Opus half the time, so why not just talk to Opus. The real value of routing is aggressive cost reduction, sending easy tasks to cheap models. The catch is that Fugu is agentic and burns tokens fast. Brad in the comments couldn’t get through a single prompt on the $20 plan. OpenAI unveils Jalapeno, its first custom inference chip (X, Announcement) OpenAI dropped something massive that is not a model. They built a chip. Jalapeno is a custom inference ASIC made with Broadcom, and they’re claiming blank slate to tape-out in nine months. Engineering samples are already running GPT-5.3-Codex-Spark in the lab, and Broadcom’s CEO is citing a roughly 50% reduction in inference cost versus typical AI GPUs. They’re planning gigawatt-scale deployments starting late 2026 with a next-gen chip taped out in 2028. Nisten ran it past his electrical engineering and chip-fab group chat and got mixed reactions. No specs were released, and the nine-month claim probably means the design work started two-ish years ago and just got finalized and sent to tape-out now. It’s a lot of smaller chips rather than one giant Cerebras-style wa

Ratings & Reviews

4.9
out of 5
17 Ratings

About

Every ThursdAI, Alex Volkov hosts a panel of experts, ai engineers, data scientists and prompt spellcasters on twitter spaces, as we discuss everything major and important that happened in the world of AI for the past week. Topics include LLMs, Open source, New capabilities, OpenAI, competitors in AI space, new LLM models, AI art and diffusion aspects and much more. sub.thursdai.news

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