📅 ThursdAI - Apr 30 - DeepSeek V4 (1.6T MoE), Cursor SDK Wins WolfBench, Mayo's REDMOD Saves Lives, Stripe Gives Agents a Wallet & more

Good morning.

Good morning everyone. This is Alex ov. Welcome to ThursdAI, April 30th. tomorrow is already may. Can you believe it? Welcome to ThursdAI, your weekly live AI news show. To cover everything. with me, I have Yam Peleg. We're gonna have more folks here. Welcome Yam. How are you doing, sir? I'm gonna

Tired.

Tired? I'm tired boss.

Tired boss.

This has been a long week, but this is why we're here. As a reminder, this is why we're here. yam. You remember when we used to cover everything that happens in the ai, including some time for papers? That was a long time ago.

Yeah.

It's impossible. This,

no, it's impossible.

So basically

for a long time, yeah.

Basically the show now is, about preparedness and curation.

This is what ThursdAI is right now. And so we're very happy that you guys are choosing us for the whatever time you dedicate with us on a Thursday. Let me add Nisten. 'cause Nisten is very important for you to participate in this specific news item that I'm gonna about to bring. folks, this is what we're here for. This is what we're here for. There's announcement from Mayo Clinic that AI with Mayo Clinic. this is published in an actual medical journal. Mayo Clinic AI detects pancreatic cancer up to three years before diagnosis. Red mod achieves 73% sensitivity versus 39 for radiologists on the same scans, 73 versus 39 for human radiologist. This is crazy. And specifically this is, as far as I'm concerned, this is the one use of AI that we need to accelerate even more. There's this meme about, we need GPOs to solve cancer, right? And this is like looking at the solve cancer, right? So this is literally solving cancer. This is this gives people more like time to live and I'm absolutely for it. lemme see if I can zoom in for you guys a little bit more. Redmont analyzes hundreds of radio features, tissue texture and structure pattern invisible to the human eye on routine CT scans. originally read as normal. Dr. Ajit Wanka from Mayo Clinic says The greatest barrier to saving lives from pancreatic cancer has been our inability to see the disease when it's still curable. They say, I can now identify the signature of cancer from a normal appearing camp pancreas. I know we usually talk about models, about open source, about releases, about big companies, et cetera, but this is absolutely something that I wanted to bring here to the show to say, Hey, this is what we're all waiting for. We're all are waiting for AI to accelerate our health. For AI to do customized medicine based on DNA. All of this is possible. We know it's possible. It's just extremely stupidly expensive. And for AI to finally solve cancer, deaf dog with a comment of pancreatic cancer is what got Steve Jobs. Folks, imagine that. Like Steve Jobs had an AI three years before he went to a diagnosis, to tell them like, Hey, you should go handle this. This is an absolutely incredible big deal. And I just wanted to bring this to the show to talk to folks, because I think it's very important. Three years early folks, what do they think? I think this is incredible news. I fucking love this. I think it's incredible news. I want more. This is not some, some open source person making claims. This is Mayo Clinic. Most of AI LLMs right now have been trained on Mayo Clinic and their purpose of data in the world. Mr. You talk to doctors all the time. What's your take on this, brother?

yeah.

Seriously, I'm very annoyed by how much, more can be done in this field. Yes. And, as someone that is working, like on a full digital twin product to bring together all the, all the data, genomics data, drug data about any person and even vis visualize stuff and what's going on in reactions, the data is all there. and the LLMs and Theis are yeah. the AI solutions, whether it's like labeling stuff to, they're very good at pattern matching, and it's not that hard to do all of this. There's just like a lot of hurdles that are non-technical in the way. But, yeah, I think we're about to see a lot more, it looks like in this case they grabbed, CT scans and now that, I'm just guessing here. So I don't know what they actually did. I just scanned.

Yep.

it's a CT scan and I, my notes are like excel at this point. I might not say. it's designed to run on CT scans. People are already getting for other reasons. No extra screening is needed for this. And the specifically, the gap gets even bigger the earlier you go at more than two years before diagnosis, the AI catches nearly three x as many cases, especially to radiologists. So there's two things that are happening here. One of them is, this unlocks regular CT scans. I don't know how cheap this is. We don't know the details about models. This is published in an actual magazine. one of them is, regular CT scans will be now scanned for pancreatic cancer, for example, which has, what, 15% survival rate. 85% of the patients get nagged after it's already spread. This is like the hugest deal with this cancer specifically. So I think it's, it's absolutely like a breakthrough. Not only this specific breakthrough, it just shows that the world that we're all advocating for the past three and a half years, we're sitting here and telling you, Hey, a doctor, AI can help you. get better and that future needs to come soon, and that future is coming and I'm very excited.

and I'll quickly say stuff that comes from other doctors that

I hear that 90%, especially the ones that are in policy positions, 90% of the care happens outside a hospital. So if you can improve that and you can, you can make such a big difference for people to not end up in a hospital in the first place. Yeah. So every, yeah, every little thing like this, helps a lot. I'm guessing here for CT scans, since it's like, it's a lot of multiple, cross-section images that are put together. So you do have pretty high resolution, with that you can, and it looks like a radiologist did detect this too. They just did not have a very, a very high rate. So this is one thing where just applying the power of AI where, I don't know, different machines take different amounts of images that they stitch together in CP scans, so they can take a couple hundred or maybe a couple thousand, I may be wrong on that. and then they put 'em together into a 3D image. But now that you have very long context LMS and stuff, you can fit hundreds of images into the context, or you can fit 30, 40 and then you. yeah. and that's how you can improve the accuracy with better data. Again, I am mostly guessing this. I don't know exactly what they did, but yeah, just based on this limited information, this is pretty cool. We need to see way more of it though. We,

I'm ready, dude.

I'm, I think like a subsection or a Friday AI for just how, how AI solves longevity and sickness. I'm down to do a whole nother show just for, how AI is gonna improve our lives, like in a material way, not just in a digital world. It looks like folks in the comments are having, a lot of engagement about this. Folks leave us a comment if you're impacted by this, really like very strongly fuck cancer. Yeah. Alright folks, I think it's time to continue moving on. It's just if every week we get to open the show with positive news like this, I'm down. I'm absolutely down Fuck cancer and it's time to, to solve this, once and for all. folks, let's move on. very quick round of, what's the top news in AI from, since last we chatted. And then we're gonna dive into the TLDR. There's a lot to talk about. We also have a guest on the show today. If you ever looked at a piece of text like wondered, I wonder if this is AI written or not. sometimes you can know if people use like the lower models. Sometimes you can know, if it's using the very famous, it's not x it's y statement. Or if you see a lot of m dashes that people don't usually do, you can tell. But, what happens if you can't tell? So there is this whole thing about AI detection, AI writing detection, and Pangram is one of the companies that, leads the pack looks like. And so we're gonna have max Firo from Pangram come to the show and talk to us about the new Chrome extension, the text, whether or not everything you read on X and LinkedIn is AI generated. You will be shocked how much it is. and so I'm excited to have Max on, very soon. Meanwhile, would love to go around the table here to talk about what stands out in this week. You guys feel free, welcome to refer to notes or just from the top of your head. I will also invite our audience to also say. Folks I see a bunch of folks in the comments as well already on YouTube, NX and everywhere where we're streaming right now. Join us and tell us what is the one thing that we absolutely must cover on the show, because again, it's all about curation right now. It's less about we're covering everything. It's impossible. it's all about curation. So we are very interested about, the co-host here and also, the audience. What is the one most important thing to cover this week? we'll start with, LDJ. LDJ. Welcome.

Yeah, when it comes to, I guess it was kinda at the very end or a little

after our show, but GPT 5.5 last week and now that we've actually had some time to get to know it and get to use it, it's yeah, just so much faster. It's feels

the best.

It's

the best.

Yeah.

It's,

it's just the best.

Yeah.

And I guess be, besides that, 'cause that's not, we covered that a little bit, last Thursday, but deep seek vision paper. I guess that's something interesting. They basically show a technique for having models do reasoning, basically using visual tokens and then visual information and kind of piecing those together. So I, that was pretty interesting.

That's great.

let's go to Yam. 'cause Yam was already wearing glasses, but you cannot say GPT. Five point. No, you can, it's fine.

Okay.

So G PT 5.5 Cyber. Oh,

what

you gonna do about this?

Okay.

tell me

five, five.

Tell me.

I only have this in my notes, to be honest. It's just like a recent release, I think.

Okay.

Mytho, which is still a mytho. we haven't got to play with it. So he's still a mytho, like

Yeah.

Kind of a myth.

Yeah. Yeah. there is a, there had been, a Finetune of GPT 5.5 allegedly. not allegedly actually. Actually For real.

Yeah, for

real.

Now there is, yeah. That is, pretty good at cyber as well. mytho allegedly is too dangerous to release because, it can break the law. not just saying that every other model is very much capable of breaking the law already, but, Anyway, a GPT 5.5 is no less powerful on cyber. than mythos. And, and we are talking about detecting unknown and not known yet. security vulnerabilities in computers, in software, in apps that we simply don't know about, which can be actually dangerous. GPT 5.5 is very good at that G pt, 5.5 cyber. So I'm just saying you can't avoid it. Pretty much. It's the best at the moment.

Yeah, it is the best.

My one thing comes from yesterday, Stripe, had Stripe sessions yesterday. They announced a bunch of things. It's a sign of things to come when most of Sessions is talking about how the world is moving to agentic and how the economy is reshaping around ai. And specifically the one thing that I would love to tell you about later down the show is that Stripe has this concept called Link where you can store your credit cards and just one click payment across the internet. link now has CLI for agents, and so you can get your clankers to use your credit cards and get a notification approval on your phone and say, yeah, I'm okay with this. They can give it a daily budget, but also you can like just approve every transaction. And I did that yesterday and, a little bit of a sneak big preview about the next week. I'm going to get married next week, so I'm not gonna be part of the show. but I asked, thank you. I asked my clancher to go buy us a gift. Using the Sprite link and send notification. And I asked my open cloud to go buy me a gift. And, I'll tell you the funny story about this down the show, but it's really something to be able to securely give your clinkers a credit card and to be able to approve every transaction at transaction layer. Stripe announced a bunch of other things, but I think the fact that I gave my planker a credit card yesterday without being afraid that this is gonna go crazy. I think that's the top for me, like we're going into agentic, commerce, and I think it's very important for us to cover. I talks, I think it's time for TLDR.

All righty. This is the TLDR of Thursday Eye for, April 30th. This is everything that we've gathered from this week's news. there's a lot, so I cannot claim anymore that we're covering everything, but this is the top news that we're going to cover. As we already mentioned, Mayo Clinic Redmont. This is an AI that detects pancreatic cancer on routine CT scans up to three years before radiologist. and the clinical diagnostic, I think it's incredibly cool. in open source we have the return of the whale Dipe V four preview has dropped open source 1.6 trillion, mixture of experts with 50 49 billion per active parameters. 1 million context, claim to arrival opus and GBD 5.4 at Fran fraction of the cost. it did not break the internet like Dip R one did, a year and something ago. so we're definitely gonna mention this. since time since Nova UU won, it's Apache two unified, multimodal, no visual encoder Nova e 8 billion parameter plus 3 billion active, mixture of experts. also some model that we talk need to talk about. IBM came out with IBM Granite 4.1, IBM, the a hundred. Plus something year old company in computing. I don't know how old IBM is at this point, came out with a family of three B, eight B, and 30 B dense non-thinking models, and they have vision in there and speech, IBM Guardian is part of this, BFCL, which is Berkeley function. Calling leaderboard, I think is, 73. so they beat Gemma, 31 B on this and very talking efficient, definitely very interesting news in open source. I wish. Sure, there's more. this is the top ones that I have. folks please send me more open source news, so we'll cover them as well. open source ish. Misra Medium came out, 3.5. It's 128 B dense model. It's not an MOE. This is like one of the biggest dense models that we've seen in the past months. It's very expensive. Mr also came out with Vibe. Myra Vibe is their coding thing, with remote parallel agents and session teleport. so very interesting stuff from straw. And Baidu released Ernie 5.1 preview. It's, it's also a big model from number one, from a Chinese lab, above deep sick and Qwen. And some folks are speculating. It's a hundred billion parameter, 36 billion active, MOE. we're gonna chat about Baidu, Ernie as well. We mentioned Ernie before. This is Ernie 5.1. there's a bunch of other news in, in, in big companies in the API in big companies. Let me pull them up. so obviously it's big news in companies, but we're still in the TLDR, the Elon Musk versus Open The eye trials happening right now. Elon Musk is testifying for the third day in a row. We, we don't want cover everything because it's really hard to catch everything that's going on there. We'll just tell you that this is like something that's happening. and there is coverage right now. also in Big Labs. Let me go find, there's, the release about Open the Eyes is goblin mode from, GPT 5.5. we should at least mention that this is something that happens. Somebody found that, codex has instructions to never, ever talk about goblin strolls, et cetera. And, there's a whole discussion about why that is in g PT 5.5. So we'll definitely chat about this. And also in big companies, I think it goes into big companies. Stripe sessions happened and Stripe released a bunch of stuff. Specifically Stripe released, link Wallet for agents at Sessions given AI agents their own payment credentials with human approval at every purchase. And I think there's a huge news that I would love to tell you all about my experience about this and how my clinker got a credit card from me and went shopping. there's a new update to Gemini as well in big companies. So Gemini can generate an export docs, sheets, slides, and PDFs. let's see, what else is interesting? I think this is huge. OpenAI ends Microsoft Azure exclusivity and goes multi-cloud with AWS and other clouds. So for the longest time since Microsoft invested the first $10 billion into OpenAI and Microsoft had an exclusivity clause, no longer. Now, if you're running on Bedrock, you can use OpenAI models and other models. This is huge for many startups that haven't been able to use OpenAI just because they're locked into AWS for example. And, I think that's pretty much it on the big companies and APIs. Anything else from big companies that I missed? Folks that there is must be talked about. Alright, LDJ, go ahead.

Yeah, I think what's interesting with the exclusivity between Microsoft

and OpenAI, there's also this portion, if you guys remember that they mentioned, basically it's up to 2032 or until OpenAI reaches a GI and a GI would be determined by an independent panel and everything. And they actually dropped the a GI part. so they dropped that part of the clause. And then with this new contract that OpenAI has with AWS, apparently that contract now has this a GI clause. So it's interesting there.

All righty.

welcoming Wolfram back and then, welcome to the show. Okay. So this week's buzz the corner where we talk to you about everything that happens in the world of Weights, &, Biases, core, we've, et cetera. we actually have, IBM granite is live on, on Weights, & Biases, inference at 5 cents per million tokens and 10 cents per output token. That's very cheap. that's cheaper than most our models. So go test them out. 128 contacts Apache to license, open air and barrel endpoint. And also in news, Wolfram is gonna talk about this, but Wolfram is just blowing. Wolf bench is blowing up. I have a tweet about Wolf Bench, where we tested Cursor. And Cursor is now the top model harness, sorry, GPT 5.5 with the Cursor harness is the top one, is the top combo that we've tested on Wolf Bench ai. And, that tweet is going kind of viral. The folks from course are obviously loving this 'cause they have a great, harness. so we'll chat about this wolf and I would love, to talk about Wolf Bench and those results. it's really cool. Cool. Always easy.

When always need to share when you are the number one.

Oh, a hundred percent.

when you get number one. we'll tell you about Wolf Bench, as well in that section, we have a very big corner that we usually lately cover in AI coding and the genetic engineering, cognition. The company behind Devon announced Devon for Terminal. It's a local CLI agent, which we haven't tested in Wolf Bench yet, but we'll definitely get there because Devon is incredible as well. I'm really excited to see if Cursor beats Devon or if Devon Beats cursor, you can hand off to Cloud Devon also and then close your laptop and it just keeps going. cursor that we've tested is just their CLI, but Cursor now has an SDKI think it's a huge news folks. Basically Cursor now has, a way for you to put cursor in your apps and cursor. SDK is blowing up and people are building incredible things with it. OpenAI also has some like this by the way. There's the Codex Apps server, and that's a very similar thing. You can just build things with with Codex app server, but cursor release there is decay and it's only in TypeScript, but then Notion and, rippling and other folks are just like building Inside Cursor. so they're building cursor inside their, their products. I think it's very cool. This one also from Stripe, but, this is, this is, this has been out there for a while. Stripe projects. Do you guys know about Stripe projects? So we're moving toward the Agentic web. The agent interface across everything. Stripe Projects is this way for your agents to basically provision itself with every tool that you need, every API talking that you need by on its own. So like 32 companies now, providers are provision via Stripe projects, including paying for them. for example, your engine, your clinker can now go and provision CloudFlare and then browser base and they tone and netlify and auth zero and Algolia all these things. and create an API key for itself and pay for itself. Like all of this is now automatable, so you don't have to sit there like me and Wolfram did beginning of open claw excitement and said, okay, you need this API key copy paste, create this in this like in this, save it in one password, give it to your open claw. So Stripe project is like very cool. I wanted to talk about this for the longest time. we're Stripe heavy show this week. but definitely very interesting for Gentech engineering. this one I mentioned. This is a very cool thing as well. Your agents can now use your credit cards in a secure way with a human gate in the beginning. Stripe Link is now has a CLI and agent gets its own link payment credentials, and the, your real card is never exposed. They're giving it like a proxy card. I don't know how, and human approves for every purchase. This is sick. I, yesterday did this with my clinker and we can do something I think on the show and you guys won't even see my credit card 'cause it's not exposed. So like we can actually go and ask our clinker to, buy something a subscription to ThursdAI, for example. Ooh, that'd be cool. That'd be cool. we'll definitely do that later in the show. okay. g BT 5.5 Codex System. Prompt leak. We mentioned this already in the TLDR, but somebody found that found Codex is open source. Nobody found this. Went to the GitHub Ripon and read this, system prompt that, the anti creature instructions like never talk about goblins, gremlins, raccoons, trolls, Nisten raccoons Junior 5.5 cannot talk about raccoons. T Charles orders, pigeons, it appears twice. the viral X hit 5.5, 3 million views and people from open the eyes started commenting with goblins, everywhere. So goblin mode, somebody already built goblin mode, so it unlocks it back. I think, what else do we have in voice and audio? We have 11 Labs launches 11 music, which is their Suno competitor is a full music platform, where you discover and remix and create music. It's, they say it's Spotify meets Suno, but with remix at its core. So you listen to tunes and you re remix out, and they have a royalty model that they pay out tokens, for, they, they pay out actual royalties for folks. So maybe we can listen to that. I think that'd be cool. last week I told you about hyper frames from Hagen, which is a way to create a video. And I actually told you that the 10 minute segment that starts the show was created with Claude and hyper Frames. now it's live inside Claude Design. So two weeks ago, or a week ago, I think it was a week ago, two weeks ago it released, but a week ago we also told you about cloud design. Last week show was crazy. I don't know if you guys caught up or not. cloud design is this new UI from cloud, which is incredible, and the rate limits are ridiculous, so nobody can actually use this. but now you can create viral videos within cloud design with hyper frames. I think it's very cool. you can do motion graphics based on your, company's brand identity and guidelines. Okay. Last but not least, our guest for today is gonna be Max, from Pangram, which released the Chrome extension that detects real-time ai content flagging when X, LinkedIn, Reddit, substack, and Medium. and they claim 99.98% accuracy. And we will definitely talk with Max about that because the whole area of AI writing detection has been murky. It wasn't clear if it's actually like a thing if people, if people do, believe in AI detection or if it's false. I think that this is most of the stuff that we want to talk about. Oh, deep seek paper. We must absolutely cover the dips seek paper. So in addition to our model, there's a dips seek paper with, insane attention innovation, 1% KV cache and 1 million context at five gigabytes, possibly First Frontier model trained across multiple data centers. we must talk about the deep seek center. I think that's pretty much it, folks. this is the show for today. I think. Let's start with open source as we, we must cover at least the deep seek news. Yeah, let's do open source. I'm still waiting for folks from the comments to definitely send us, definitely send us comments as well. Nisten, you're sending us Send Nova demo. Okay, we'll look at this. All right, let's start with open source.

Open source ai. Let's get it started. All right, let's talk about open source. Listen, I think you started with Sense Nova. This one is open source as well, right? Why? Why don't we start there?

Yeah, we can.

So I was mis scratching up. I just found out about this. this is a very strange model. So they had a paper at ICLR last year, but, they didn't show a whole lot at, at that paper. And, now they released a model and I just tried it out on their website. So what they have done here is they have built a true multimodal, it's also an image generator. Yeah, I think you have to click on the Google. I saw a Google in there. yeah. If you click the main button and there's a Google button. Yeah. That's how I was able to log in.

Okay.

they have built a image generator, like a true multimodal

without a variable, auto encoder. So I don't know how they do it on a, like a per pixel basis. It's supposed to save the amount of tokens you need when you input an image. what I did here, I just typed in, Hey, draw me a city on Mars with the exact statistics of Mount Olympus. you can just type in by the way.

Oh, here?

yeah.

just directly. You can just say, draw me a city on Mars with the statistics of Mountain Olympus.

Okay.

so yeah, they, this, it is a completely new architecture and,

yes, it's an MOE and stuff too. But it looks like the mixture of experts, the experts, they route to the image generator part, and then they can combine both the text and the images very accurately. You have to refresh the page after a bit. it will take a bit for it to generate it.

I'll try again.

Yeah.

Or, or, I can show it to you because Yeah. The page went blank for me and then Yeah,

it went blank

and then I just, I just hit refresh on it, with that link

because it created a session link. And then, it was able to do, but yeah, so when it's generating an image, it routes to both the text generator and the image generator part, and then it puts the image together. I need more time to actually look. How, how this works. but it does work. I, yeah, I just tried it very interesting. Very experimental. I really, yeah. I really like it. this, so yeah. By doing it this way, you can dump in a lot more images into the, it looks like you, you can dump in a lot more images into the prompt itself.

So what are the innovations here?

since time with an, open source, this is architecture. The architecture, and this is wild. this is a sense Nova U one unified multimodal model, 8 billion parameters with a 3 billion active MOE variant, both Apache two license. So what we're seeing now is, is an infographic that it created for us that looks very clean and nice. Is this 8 billion parameters? That's insane. For 8 billion. Yeah.

What?

That's

crazy.

Yeah. And the statistics are right, because I asked for something specific too. I was trying just stuff about, Mount Olympus and even, like nano banana and stuff. It might not necessarily get that right away, but it got it. So that means the, yeah, this is working as it

should.

This is an 8 billion parameter model doing things that models for exercise struggle with because of the text. this is,

do we have any other model?

Any size that is this coherent for image generation with text. maybe the largest, most, most powerful ones. that's perfect. The image is perfect. Look at this.

Yeah.

Yeah. This is like very tight.

It can just do text too.

if you just work as a regular LLM and then it just routes to image generation experts, which is something you might think of. It's a nice idea, but then you would never be able to implement it. But they, this is, they actually got all of that to work.

Yeah.

This is crazy to me.

I'm very impressed.

I'm very impressed. Yeah. we should try one of my crazy, infographics on it. And actually, you know what, I'll go, I'll try to find the infographic that's created, that they have. let me show the infographic that I have created with Nana Banana for this. lemme see if I can reshare. this is the infographic that Nano Banana created Four Sense Nova. Okay. this is the infographic. As you guys can see, it's a beautiful Alex figure infographic. It has a bunch of information here, including the architecture, the old a Oh, okay. The old approach for the architecture would be taking an image, running this through a visual encoder, then running this through an LLM, then running this through a VAE, and then generating an image. unified architecture from neo shows, shoves, pixels and words together into their unified model. And then they have an understanding end generation. So this is why we saw this coherent result, end to L pixel, end-to-end pixel and world modeling is a single architecture, no separate components in it. That's why they're achieving the size 8 billion, 8 billion model outperforming models that shove a whole LLM into kind of the image and radar models. Let's talk about DeepSeek, the Chinese whale company that we've known and loved and told you about that broke the internet and the stock market, a year and something ago, came back with Dipe for, let me go find the Dipe announcement. this is the Dipsy paper itself,

and it's free.

So everyone, should act, absolutely use these tokens that they give you with 80% discount all over being already like 30 times cheaper. So yeah, everyone go, go burn some Chinese GPUs, please. while you still can, because it's not gonna last for long because that thing is expensive to run.

LDJ, you want to give us a little bit of thoughts about dipsy?

Yeah.

like speaking of hardware and ram actually. when it comes to long context, it's, it tends to be a pretty ram memory intensive operation, especially every doubling of context. Traditionally, you would have, basically multiplying by four for every doubling of context you do, you'd have. This, rapidly growing rate with Deeps Seq V 3.2. They already had some significant improvements there. it was significantly lower than what you would normally have with regular attention and everything. But with deeps seek V four Pro that they just announced, it's so dramatically where you can get a million token contexts. And it seems to actually be at the scale of practical, to have millions of tokens of context locally now. 'cause here you'd be having less than 20 extra gigabytes of, of storage you need to store for memory. But millions of contexts, millions of tokens of contexts you could have.

that's crazy.

I, why do you guys think that this didn't get as, as much attention as R one or at least broke to the barriers R one? Because, it does look like, like absolutely craziness, right? MIT licensed 1 million contact window. it's huge. So it's not like running locally 'cause it's, what, 1.6? Parameters. This is this is not a local model, this open source model. But I also didn't see a lot of, inference folks jumping on this and serving deep seek. I think there was a bunch of issues and then fireworks ended up, running this. what do you guys think about why this didn't break through the barrier of kinda open source?

in terms of capabilities and benchmarks compared to Kimmy and the

other ones, back when R one first dropped, like it's lead compared to the others in open source was pretty large. And even oh one that was released by OpenAI just a few months prior, like that didn't really have any competition either. Like even in closed source. Yep. And so R one just came in and it was better than, from what I recall, even what philanthropic was offering, even what Google was offering. And now at this point when they're releasing this model, it's still pretty good. And then having these, cool research advancements involved. But AKI e is pretty similar in capabilities. the best Qwen model is not too different in capabilities either. I would say. The main improvement here though is these, algorithmic improvements in the KV cache and how efficiently they could do long context.

Yep.

So I think the long context is definitely the thing, I have the paper, deep seek. Paper, preview here. Lemme go find this. I think it's here. and I think that this is like a big deal, for, lemme go find our notes while fighting with them. I think that this is a very big deal. Let me pull this up. This is from a thread by Eugene, Chi, a friend of the pod. the, and as Innovo, our commenter here says dipstick is not very exciting on the surface because of the capability on evals, but it's like incredibly intriguing because of the architecture that goes in there. specifically this infographic is very interesting. So Lama three is 405 billion parameters, and it, it weighs 500 gigs. Deep seek is just 5.7 gigabytes. Is that no. There's no sense. What is this? So

that, that's just the memory for that the context takes up

Ah, I see cash because of the KV cash optimizations.

Yeah.

Yeah.

I think what they're saying with that 500 gigabytes for lama, I think they're saying, if you were to push it all the way to a million tokens of context, that's how much memory it might take up. 'cause it truly is like I even a year ago or two, you have with traditional attention, like it could literally even be terabytes of memory if you're running millions of tokens of context. Like just a terabyte or more just for the context alone.

This is insane then this is absolutely insane.

Native 1 million contact length. I think that like most, most open source still use yarn to extend to 1 million. LDJ that this is like a native 1 million that can run is also like fairly novel.

Yeah.

So yarn is, would be a technique that you do in training to get the model to, to use long context better. And then this is more of a technique that you could basically use this with yarn. it's not something that replaces yarn or things like that.

Yeah.

Wow. Okay. any other comments, folks? Any other comments about, about deep seek and the paper? Anybody read anything so we can talk about pricing? Ya, you said like it's fairly free. the price is 30 to a hundred x cheaper than G PT 5.5, but I'm not sure that you're gonna get as, as, as much out of it as from GBT 5.5. but it's definitely something that you can run.

Yeah,

go ahead.

Go ahead.

I'm also interested in what kind of speed improvements they're

getting because they did make a whole bunch more other innovations there that we might be glossing by. So the Yeah. Yeah. Because they claim something like four x speed improvement at longer context.

So we have here 180 to 240 tokens per second

sustained at 900 K context. It's super long context.

Yeah.

That it's very,

very impressive.

It's just, it's hard to find B two hundreds anywhere

now to actually run this. So that's the bigger issue here. Yeah. Yeah.

I think something here that is underrated as well, worth mentioning

with the benchmarks you see here. So this model is trained with, it's a FP four nvf, P four quantization aware trading. And so the native weights, if I saw right, it's a vast majority of it is in essentially forbit and only some of them I think are in Abit or 16 bit. And what that translates to is you basically have the model natively quantized, and the benchmarks that you see, I'm pretty sure are actually in that format of where it's like mostly essentially FP four. And so you end up having, usually when people see Kimmy benchmarks or things like that, I forgot if they train in something more eight or 16 bit precision. but it's usually a much higher precision than what people quantize and compress it to run on their own computer. And so for the flash model, I think deep seek V four flashes, 250 billion parameters, something around there. And so that's something you could at least run on, like the Mac studios and that quantization the compressed form of it. That's the capabilities you could actually expect in the benchmarks is running it locally.

So we have, technology, we have a few more things here, that kind of

highlight the craziness of this release. And though it did go under the surface a little bit, the, it does seem like deep seek are extremely cracked in engineering and innovation. the notes say the new CSA plus HCA attention mechanisms uses 10% of the flops compared to the, multi-layer attention MLA and 7% of the KV cache. 7% of the K basically lets them 10 x the context link on the same compute in Vem budget. This is absolutely insane. and here's the well part, maybe the first frontier model that was trained across multiple data centers was split expert training where different data sets were trained separately, possibly in different locations, and then merged back via distillation using moan optimizer for, from QE FB four MOE training and tile and kernels, which they open sourced. this says even through shaded Nvidia saying that KLA isn't accurate enough for batch in variance. I have no idea what this means. but the efficiency innovations are like all over here, and it does not look like they benchmark this model very, like many other, like releases from some Eastern, teams does not look like a benchmarks, even though, the craziness is definitely here. So shout out to dipstick once again for bringing crazy stuff. Any comments on the First Frontier model? Trained across multiple data centers. I dunno how true this is. just 'cause I'm reading this, from the reports, don't kinda other big labs train across multiple data centers. Don't know this.

A model called Psyche.

Yeah.

Distributed.

Yeah.

But Noose was that, I think that was like a seven B and then I think they did a 20 B version too.

Yeah.

And then Prime Intellect also had one or two such models, but this is probably

definitely the largest scale model. Yeah. Trained with multi-data centers stuff.

News.

You had a additional comment before we move on?

Google can do that because their infrastructure is set up that way.

But this is, this clearly looks like it's a software optimization. the, yeah. Pretty cool with

distillation.

Yep.

yeah, pretty

interesting.

All right, LDJ. Go ahead. Last one, and then we'll move on to IBM Granite.

Yeah, sorry, I was just gonna add a correction to what I said.

when I say probably the largest multi-data center training, in open source at least. Yeah.

Because

yeah.

And close source to my view, much larger.

that's what I felt like, in closed source in Frontier Labs.

they might do some stuff, but, we'll see. okay, so let's talk about the next piece of news, quickly here, IBM releases granite 4.1. It's a dense non-thinking model with best in class tool calling, is what they call this. we have this, up on Weights, & Biases, because of partnership with IBM, I'm assuming, at 5 cents per input token to 10 cents per million output tokens. granted, I, it is very interesting to release a non-thinking model. but they're bidding their own previous M Moes with just 8 billion parameters, and getting 73 on BFCL tool. Calling, topping Gemma token efficiency. 8 billion parameters uses 4 million output tokens in artificial analysis. Evol suite versus 78 million for Qwen nine b. because this is not a thinking model, probably Wolfram if you, correct me if I'm wrong, but this is why it's using significantly less, thinking models for artificial analysis. a 20 x difference. So as the reasoning models tend to reason a lot and we saw this in benchmark, we saw this everywhere. And this is the tokens that you still pay for, output tokens. There are more, more important tokens, but this one being non reasoning model still performs well. The era of non reasoning models feels like it passed to me, like still. most of the stuff that we do for Gentech is the reasoning and it helps. so it's very interesting to see folks releasing, non reasoning models. what else is interesting here? The whole pitch is enterprise production, not benchmark chasing Apache 2.0 license 128 K context, window vision, that bits cloud opus 4.6 on table extraction speech. There are performs whisper large three. Transcription IBM is saying here's the full stack, it's open efficient. So they have speech in here. They have, vision instruction as well. very impressive from a BM. but Gared speech outperforms whisper large. I would say I'm tired of seeing this outperforms whisper large V three whisper large came out like two and a half years ago. No other like big innovation in the voice of, in the world of transcriptions apparently. So everybody compares themselves to whisper large. It's time that we have a new standard for, to beat in the voice and transcription.

It shows how entrenched it is, how it is everywhere

and supported by everything though. You usually have a whisper in there. So that's why they compare because

that's

right,

it is still standard.

Basically takes a long time for better technology to propagate if it's all entrenched.

Yep.

So artificial analysis provided, a independent eval for this. let's take a look at that. this is the new in instruct model. So not reasoning model. In instruct model, granite for, there's a few versions here. Granite 30 B, granite eight B, and then granite 3D. the risk continues. IBM focus on small, efficient, open models. we will, we'll applaud like everybody who raises open source models. It's definitely a win from us. Then this needs to continue and we're very happy to shout out to IBM for this, despite the fact that, but, all grant models scored 61 artificial analysis, openness index, standing out among peer open weights, non reasoning models. So full open weights under Apache. Two partial disclosures about pre-training data, post-training data and methodology. I wanna shadow the artificial analysis for highlighting open source as well. 'cause there's all, there's one thing of a, this model is the best at evals, but there's a completely other thing is like it is the best at evals and they give us the dataset and training recipe 'cause this helps everyone. let's see how it fares on the artificial analysis, openness index. And I think specific about output tokens, this was the big deal that you can see. The granites on the left here, received eight and 10 scores in artificial analysis. Oh, granite eight B even received the 12. But compared to the output tokens, it's the absolutely reto frontier of output tokens compared to Gemma and Minis trial and others, especially compared to Qwen. You guys can see Qwen here on the right side with over 67 million tokens to get to the same, high quality results. So shout out to IBM. where can you get this model? if you, if you want, you can get it on Weights. & Biases, artificial analysis actually shouted us out. Look at this. It's beautiful. at 5 cents, per million input tokens and 10 cents per output tokens. so definitely look us up, to get that model. All right, folks. We, I think we're moving on from this to big companies and APIs. let's talk about gobbling mode. Yeah. I know you wanted to talk about gobbling mode, but especially 'cause they released the blog post. I dunno if you guys seen the blog post. but how about you present this piece of news and then we'll talk about Golin mode? 'cause this is still in big companies and APIs.

Sure.

And while I find the actual blog post,

sure.

that's, pretty simple actually. If you talk for, two seconds with, any of the GPT, five, family of models, you would start sensing that it chooses an onset of words, from time to say simple things. for example, which got pretty, pretty famous, GPT 5.5 really likes to use the metaphor of, goblins and creatures, from fantasies, in general, to the point where OpenAI included it twice in the system, prompt, begging the model begging. So the model will not use any of these creatures in front of the users because how would you even explain this to the users? and everything is open source, in the harness, in Codex Harness. So people just found out immediately that there is something odd about the system prompt. Start talking about it. And, OpenAI, shout out to OpenAI for immediately releasing a blog, explaining this, because obviously they also noticed this and they, they probably all discussed this in the office. Like what the hell is going on? the blog basically, just pretty, just specify that there are certain features. The model is specifically trained for, certain product features. One of them is the customized personality. this personality can be a nerdy personality. That's one of the personalities that is already baked in, during training. And the narrative personality, just gets in, in, in a twisted way. It gets a reward for using metaphors for, from fantasy. Yeah. 'cause it's nerdy. And these metaphors tend to include goblins and gremlins and fantastic creatures. And the thing is that throughout the RL training, this reward got amplified and spread throughout the entire behavior of the model. And now, and it started, just being, being quite funny. But at the end, the model as a whole just adopt, completely adopted this behavior because this reward just got amplified and so baked into the model and it just uses it all the time. Now,

pretty much, I wanna highlight this, for folks who may just

be listening, we're showing the open air blog about goblins and, this is Ja poky head scientist of Open the Eye saying, this is a screenshot from their slack. They're saying for Wordsworth, I think I might have gotten a goblin when I asked for a unicorn in osci art. And this is really funny. This is absolutely looks like a goblin, dressed as a unicorn in Aki art. it's very interesting also from the perspective of these guys are building super intelligent that's supposed to save us all. and here some series of rewards stacked on each other suddenly made the models speak in a very specific way. Enough so that in Codex, this is how this was like, released. They had two instructions after, one after another saying, do not speak about goblins and other creatures not speak about goblins and other creatures, apparently we're, we can learn that. if you really want something in system prompts, you can repeat it twice, but I, do you guys not even remotely, how to say, not scared by this, but like remotely, worried that the company that's supposed to know what they're doing suddenly discovers these capabilities of the model or tendency of the model to talk about the things they didn't mean to? I think it's very, it is, it could be concerning. And since November they saw it, GPT 5.1 launch and they could have started earlier. Users complained about the model being oddly over familiar, which prompted an investigation of this specific. A safety researcher had experienced a few goblins and gremlins and asked did they be included in the check when looked, the use of goblin GPT has risen by 175% after the launch of G PT 5.1, while G gremlin had risen 52%, 175% more use of goblin, than before, after GT 5.1 thinking. it's really funny. it's really funny. This is the, this is in part what explains the quirkiness. Go ahead, ya.

I just wanna highlight how they found it.

they pretty much, it's pretty insightful to see how simple the method is. They just correlated the use of goblin with all sorts of things. One of them, throughout the investigation, and one of them was using the nerdy personality. that was trained into the model, and immediately they saw, like 4000% increase usage. that's, that's in general, that's a pretty cool way to find, just to find how, a model behaves based on what, if you have things you suspect and, you suspect to be the case. And I just wanna say that, the feedback loop in general is, by the way, it's a great blog post and a huge shout out to OpenAI for releasing this like a day after.

Very transparent and

full

on.

Yeah.

shout out to OpenAI in general about the entire, I don't know, the entire

month or two months, I think, for all the resets of the usage and just releasing good models and respecting their users. Wink, wink. Didn't say anything. I just wanna say, the feedback went this way. playful behavior is being rewarded, by surprise. this reward, these rewards are then exemplified in more examples. That have these distinctive, use of words and then it's more in, more rollouts, it's being amplified more into the training set. Then you do, supervise fine tuning, basically just picking it much harder into the model because the, you take the good rollouts of the model as good examples. Yeah. Things you wanna do, and then you just make them in even harder into the model. And then you get the next version of the model even more goin coded, like you do it with g BT four, 5.4. it amplifies itself for sure. Sure. Point five is insanely more so

here's what they say.

Yeah, here's what they say on the blog. Unfortunately, 5.5 started training before we found the root cause of goblins, which is an insane sentence on its own to just like literally put in a blog of a $1 trillion company. We, unfortunately, we trained this new series of models before we found out the root cause of the goblins. Literally, somebody wrote this on the blog. I love this. When begin testing five foot five and Codex OpenAI employees immediately notice strange affinity to goblins. And we added developer prompt instruction to mitigate Codex is after all quite nerdy. If you want to let the ERs run free, I will shout out and commend the OpenAI person for this one thing. If you want to let the question run free in Codex, you can run this command to launch Codex with the goblin suppressing instructions removed. So technically you can launch Codex like this and then you'll have Goblin. I love this. and, speaking of this very long and detailed blog written very fast after the incident, I wish there was some way to know whether or not GPT 5.5 assisted them in writing this. And for this, we will invite our next guest to the show. Max Spiro. Welcome. Max is, from Pengu Labs. How are you doing, max?

Hey, good.

How are you?

I'm g doing good, man.

I've been wanting to talk to you for a while, but I think before I introduce you, I'll do this thing. I'll select this whole text. I'll click right and I'll do check for AI content. Oh, you guys don't see my menu, but hopefully you'll see this thing that shows up. and then we're gonna run this whole block from OpenAI through Pangram Labs to see that, according to Pangram, this is a hundred percent human written and this is great.

Alright.

that's awesome.

This is awesome. but the ability for me to do this is why I wanted to bring you on to talk about, Pangram, but also I wanted to talk about, just AI content detection in general. So how about we start with the fact that this is your first time on the show. Please introduce yourself and then introduce Pangram Labs and then we can chat about, this whole area. That's very exciting.

Cool.

Hey, my name is Max. I'm the one of the founders of Pangram. I have a background in machine learning. I've been training large, ML models, at Google and Stanford. Before that, Pangram, we started Pangram of about two and a half years ago, thinking about like the second order effects of AI and how to help, mitigate some of the harms that could come from a proliferation of, powerful AI models. And so that's how we landed on Detectability as a research angle. And so we've really been, working hard on the research side and we've published several papers on archive. we were, the team was just at icle, last week and we've. But I think we've made several breakthroughs. So we have the most, advanced AI detection model out there. And then we also have a bunch of surface areas where you can use it. So we have this chrome extension where you can, it'll automatically tag, posts on your feed as AI or human written.

So we're definitely gonna chat about the Chrome extension

as a product feature, because I think it's like very interesting. I think I sent you something similar. I built some LLM detection, for overuse of a certain word, for example. That's awesome. I noticed. Awesome. On X, if you like, click on one thing that talks about the hot topic. Grog then shops 17,000 of these, like the same tweets about the same topic on your feed. and then, I always wanted to know like how many slot merchants there are, but before this max I have to address something and maybe it's worth, that's why I want to bring you on. 'cause you work in this, The whole area of detecting whether or not something is AI written or not, is murky. There were efforts before, maybe your competitors, maybe by you, I don't know, GP zero famously did not do anything. So everybody's like dunking on some of these efforts by like literally showing, Hey, I generated this text with GPT sending this to an AI detection, and the detection says a hundred percent human. where are we, where are you guys in this? Where does the industry in this, can folks actually rely on, on some of these tools? or is this like false positive first? Can you talk, I address this kind of like big thing because I, I think many people since, two years ago haven't updated on the whole context of, of these things and still don't believe that AI is detection is possible.

Absolutely.

So there are a lot of AI detectors that are very inaccurate. You could put in the Declaration of Independence and it says a hundred percent ai. and Oh

yeah, that, that is like the famous like example.

Yeah.

It's really easy to dunk on them. It doesn't happen on Pangram, but like you, you just, you can find the worst ones on the internet and use them and get obviously bad results. but what we've done at Pangram is taken a really big focus at on false positives. So many AI detectors have a 5%, false positive rate or higher. Pangram has a one in 10,000 false positive rate. So that means if Pangram tells you that something is ai, you can be very confident. It was largely AI generated, or at least like heavily AI assisted. On the flip side, not everything that's AI generated gets caught by pangram. If I ask Chat GPT, answer this question with yes or no, it'll say yes. But there's, it's impossible to know that yes was written by ai. So what we do is we have a 50 word minimum, but Pangram works the best with 200 words or more. And basically like the longer the document is, the more certainty and confidence we can build up, on the origin of the document by basically looking at the decision trees and how well it matches up with, other pieces of AI content that we've seen.

So just to reiterate, if Pangram says with a certain

percent of confidence that something is ai, we can trust that it's in fact was generated with ai. So you don't absolutely, you don't miss the shots that, that you do take, let's say, but you don't catch all AI written. So if, for example, I don't know, Opus 4.7 comes out and they retrained the conversation, then somebody runs this through, somebody before in the comment said that there's a humanizer skill that kind of removes some stuff. So if somebody like tries to avoid you guys are not claiming that you'll catch with a hundred percent certainty all AI content.

Definitely not, and yeah,

more, but you do highlight, you are a hundred

percent, like you're very certain. One in 10,000 you said? missed rates.

Yeah.

Yeah. One in 10,000. False positive.

That's very

impressive.

Impressive.

So if you go to the Twitter page of a slot pedaler, who just puts out a bunch of AI summaries of archive papers, like

something, you're

gonna be able to talk very quickly.

Something that will we'll do literally right now.

I know there's a very specific one that you always mention. oh, that we know, let me pull up this, this guy, I think it's called Akash, right? Akash Gupta,

yeah.

Yeah.

Very famous law

merchant.

My unintentional, nemesis. But this guy's one of the like top 10, technology writers on Substack. He's

prolific.

He's really good. But everything that they put out is ai, just literally everything. This is ai. This is ai. so maybe it's worth saying to folks, what's happening here and how I have this, I haven't muted, I can't, I guess that, maybe it's worth talking about the Chrome extension, like packaging of this. So Pangram has existed for a while. people can post, full for articles. I actually do this when I use, when I use models and I try to do handwritten, newsletters, for open, for ThursdAI al always, but sometimes I'm out of time or sometimes, I'm hitting a, like a writer block. So I try to like. Work with the cloud to gimme some ideas, and then I run into Pangram to see like how if I got through, AI assistant mode. So maybe before we get to the Chrome extension, let's talk about the AI assistant versus full AI written. Could you just g give us that kind of differentiation within Pangram? Like why is that important? Why, how you guys build this and what confidence levels are there?

Yeah, so I think the way we see the future going is text is going to be

more and more AI assisted, like in Google Docs already, there's I'm pretty sure it's either like tiny versions of Gemini or something giving word suggestions. it's gotten much more advanced from like the past auto complete. and so what we want to do is we want to, be able to differentiate between something that was like largely human written, but maybe AI assisted a bit versus something that is just like straight up generated from a prompt, right? we've done this in our latest paper called Edit Lens, where we basically have multiple prompt schemes. So not only do we have a fully generated text in our training set, we also have, AI edits. For example, we ask, Chat GPT anything from make this text more formal to help improve this text or expand it or paraphrase it or summarize it. And so we've a bunch of these prompts and then we're measuring the co-sign distance between the original text and then the new prompt. So this is how we get AI assisted. I think the place where you see it come up most often is actually AI translations. So if you ask grok to translate something to Japanese, then like it could come back as AI assisted, which is the intended behavior. And we're looking to really like also just expand our performance here and get, be even more precise, in describing the degree of AI assistance that we believe.

Yep.

Max? I have a few, follow up questions. of course I'll get to growing. I really wanna talk, I really wanna do, wanna talk about this, is, the intended users of AI detection, because I think since JGBT came out, the education system is on afraid so everybody's like freaking out. all the students are just like dumping the teacher's quest into JGBT and literally just verbatim pasting this. And like competitors didn't really work well, was sold to educators to detect whether or not the students are using ai, whatever. I think we're past that world now. I think it's very clear to teachers and students that everybody's just like using c GB for everything. what is the intended use or the main intended use that you guys see for detection? That false positives are, like the p prevailing way. So you cannot like, catch all AI assistant content, but the ones that you do, you know for sure.

I think the role of AI detection is to restore trust in this,

the, like today's internet and like networks of untrusted communication where I think you're much more often today, going to come across something and you don't know if you could trust it. In the past, there were signals of if this post is long and well written, and correctly formatted, surely like somebody put together a lot of time and like this is a well-researched, piece that's based on a human opinion. And nowadays, if I see something that's long and well-written, I really just don't know if it was like a one second prompt out of a Chat GPT or if it was like actually something that's well researched, right? and so I think there's a lot less trust going on the internet and I'm worried that with the rise of agents and people having their open claw go and create GitHub issues and post on Reddit and like there's gonna, we're gonna see we're in the very early stages of the proliferation of AI agents and I think I could very well see a world where if we don't have any protection against AI content, every open system on the internet could just be completely overrun by ai.

It's what happens to GitHub right now.

They posted the charts that we probably should pull up that, GitHub is getting 50 next or something of the total PR volume of last year in just like the beginning of this year. and a lot of it is AI agent to assistant. Everybody's writing subagent, for writing specifically though, the communities that we are on the, X and Substack and Reddit and GitHub, like you said, GitHub mentions. definitely I would like to know that I have the ability to say if this was a person or not. I will say though, my clinkers opened up a bunch of pro pull requests in my name, like the stuff that we wanted to work on together and they open. So I don't think it is generally like a bad category of things. It's just it would be helpful to know. Absolutely. And I don't wanna hide my agents. Absolute. Yeah. Absolutely.

and I'm the same way of I just want to know, I use AI all the time.

I use lms, I read AI writing all the time because I ask cloud a question and then I get an answer.

Yeah.

So there's nothing inherently wrong with AI writing, but I do want to

know because that changes how I approach. Reading and like understanding a piece of text.

Yeah.

the thing that happens to me next, I wanna hear from you and then we'll move on to the Chrome extens as well, is that, I often read something because it looks like somebody like invested time in reading this. my brain is still hardwired like that on next, and then there's oh, this is everyth written and I'm getting so annoyed.

it's disappointing, right?

It's dumb.

It is disappointing at a dumb at that point because I invested time and had I known before, I was like, I've skipped this. Or if it's a summary of an important thing I know for a fact like, most of this is summary of AI based. This is my notes for the show. There's no way that'd be able to run through that much content for the show on my own without any summarization. no, AI is a greater summary, so why not? I would read this as well. so I think that's definitely the disappointing part, is helpful.

course.

I also see the opposite. I sometimes the, post by a human that. It's not well written. And I think why didn't they take at least a bit of time to have AI do a pass over it and stuff like that. So my own posts, basically, I do not let AI just post or anything, but when I write something, I write it myself and then always do a pass of AI to improve it. I even have a hot give this, improve the form, the, the formulations I'm using and stuff. So it's always working with the AI to get a final output that I want to basically undersign and say, this is my post, but I take the time to always pass it through AI to get some input.

Yeah, and I think that's a great use case, although I think there's a

tension as well of some people talk about a future where like I write two bullet points and then, my AI expands it into a long flourishy email and then somebody else in their inbox, their AI agent will summarize that back into bullet points. this is a like lossy communication. I wish, I just want somebody to send me the two bullet points of what they wanted to say.

I have, I really, I have an opinion on this because if

you do this, if there's a big mail you get and your assistant just condenses it down to the two bullet points, you still have all the information in the context where you can ask about it. And, if you are not clear what it meant, the two bullet points, if there is more information contained, then you could get it. Especially if it's an agent that knows a lot about the person who they write the mail for. Like when you make notes for yourself or for a teammate, it's, your teammate may need much more information. Which is, distracting. But if you, give them a condensed summary, they may lack some information. So if the summary gets created by your own assistant, you still have the chance to ask about the information that is not directly visible,

that, that's fair.

And I think that would be like a good future where the agents are able to provide enough context beyond just like making something more polite and fluffier.

let's talk about some like AI assisted writing.

specifically we mentioned the specific person, can I say no shade for just one second. A Gupta, I think he used to work at Google before, or something like a IPM, I think very quickly turned around, his newsletter and completely scaled it with ai. and you can see that every time that I scroll on his feed, I have this little AI generated like a AI like attack. And this is from the pangram, Chrome extension that you guys launched. max, talk to me about like, why after having a product out there, this Chrome extension exists and what are you guys like trying to achieve with it?

so I think on like in the short term, the value for the people using

it is that I wanna know if something's AI before I like spend my time. Investing my time in reading it, but I'm not gonna go like copy, paste every single post into, pangram. I think that's just unreasonable. with that said, yeah, and, of course like yeah, you might see some Akash Gupta posts come back as human. yeah, there's one here that I noticed. Yeah.

And Pangram did miss this, and I'm pretty sure it doesn't catch

everything.

And that's like the point of it, but if we say it's ai, then you can be very confident in that.

Okay.

Yeah.

so yeah, I think on the like local level, it's much more

useful if pangram lives where you work. and then on the meta level, I think we can do a lot of interesting things by understanding like, the full network of AI content, like who's posting it, what sort of engagement does it get. and I think there's a lot of interesting research that this unlocks as well.

I, let's talk about the Substack experiment.

Taylor Lawrence ran, I think, oh

yeah, you,

you posted about this as well.

So she ran a experiment where she basically scanned like a bunch of

posts from all of the top, Substack and Oh yeah, she, I'll find she posted a ton, so like good luck finding that. Yeah.

Okay.

you might be better off going to me.

Oh yeah, that, that's pretty good too.

Yeah.

okay. Okay. There it is.

Yes, this is it.

But basically she found like different industries, have adopted AI at a very different rate. So like technology substack, there's a lot of ai and I'm sure a lot of what she saw was, may have been AK Gupta, whereas other, types of substack, like fiction or really just had much lower AI usage. Some of the interesting ones were like health, I didn't expect health to have so much health substack to have so much AI in them.

Yeah.

I also think like culture and world politics, like what

are people like looking for here? but I think what we are seeing is just like this growth of, yeah. I think maybe two sides. One is like people using AI to assist their writing, and then also a number of like near or a hundred percent AI substack as well.

and those are on technology are obviously scaling because the people

who can get to a level of automation with technology are probably gonna write about like technology stuff. And that's

what's interesting

for them as

well.

Yeah.

Technology

people are early adopters.

Yeah.

Yeah. Yeah. I think it's very funny that the best seller account in technology has mostly AI generated more than one in four top posts is substantial level of eye content. And again, this is only the stuff that you guys catch. You guys don't catch everything. so I think that's like a very interesting research. how do you think that this graph looks in a year max?

I think it's probably gonna be up maybe 10, 15%.

It might look a bit flatter, like I think the, other, the other categories might start to catch on, but I also feel like the readers are different. So I think fiction readers just, they don't wanna read AI content. They, there's enough like good fiction out there that, AI content is like a little bit more mode collapse, a little bit less like creative or unique or interesting.

Yeah.

but I think I think we are gonna start to see this like

divide between people who, don't care what they read and people who feel very strongly like. I don't want to be seeing AI content everywhere. Like I see pangram as akin to an ad block for the internet where it can help you, parse through what you're seeing and improve the signal to noise ratio of what

So speaking of ad block, is there a feature for me to hide

the stuff that is AI generated yet?

Not currently, no.

If you see the AI, icon, you could just scroll past faster or mutant block. I've seen some people who go on and they're able to, actually totally clean up their feed.

I do wanna talk about like the arms race here as well.

It's like a cat and mouse game that you are obviously playing, and your guys are very clear, we're not catching everything, but I think it would be great if you guys would catch everything. How, and what are you doing when there's like the humanizer and the paraphraser and mixed authorship stuff? tell us about what else is his pangram planning, in the future to, to catch up with hey, we're not catching everything.

Yeah, I think there, this is this like perpetual adversarial

game, very similar to like cybersecurity or viruses or spam. but I think our goal is continue to hire great researchers and get ahead of the game. I think some of the things that we're really interested in is looking on a like more granular level. can I tell you on a per sentence by sentence basis, was this generated or assisted by ai? And obviously you don't have like perfect signal. The shorter the text gets, the less, the less AI signal you have. But I think within the context of the larger document, I think it's quite possible. I also think, yeah, we're always looking at like the adversarial cases, where people try to prompt away, AI content with a humanizer skill or something else. And also we're just looking at the new frontier models as well. We looked at some of the Claude Mythos system card, example outputs where they had mythos write some fiction and Pangram was able to correctly classify as ai, which is a good sign for our generalization. But I think we gotta keep it up, one, once Mythos is out, we'll be training on it and making sure Pangram is the best it can be.

Max, I think I'll do our listeners and viewers at

this, like Ms. Justice, if I don't ask you about what can, if people don't use pangram yet, they, they should go install the comic session. You guys give a agenda two, two week free tier, and you're not sponsoring the show. It's just like something I like and I really like this tools. But, for a human that reads a bunch of like texts on the internet, what are the five top ones that come to your mind? tells of AI written, text?

Ooh.

I think the big one is still the, not just X, but Y. but then I think like chat, the Chat GPT models, especially the five point X series, they like to do a lot of one word sentences of choppy, like staccato. and so I look for that. I think like on average, like AI writing tends to have like very even paragraph lengths, whereas human writing less I think those are like the main ones I look at. But also just, I just rely on Pangram as well because honestly it's like sometimes it's really difficult. Sometimes it takes getting a Pangram score where it says ai. Then I like take a deeper look. I'm like, okay, there's none of the like obvious selves, but I think there's no ambassador,

there's no

exactly.

It's not exactly. You can prompt this away.

Yeah.

and sometimes AI text, it just has this is this anti-emetic

nature of my eyes gloss over and I like don't read it as closely. And there's something about the AI text that makes me do that, even if it's like subconscious.

And so like you guys are training a model, obviously, like

for people to be very clear, this is not like some RegX that you run on text and just try to detect this. There's a model that's trained multiple models probably. speaking of models, you guys have open sourced stuff. we love open source here on ThursdAI would love to chat with you about, your, just thoughts about open source. Obviously you are a business, so you have proprietary needs as well, but talk to us about your, papers release, the model models that you guys release in open source. Like what are you thinking generally in open source? Is it better to release sound like you have or will it just lead to people training on this as well? Let's talk about this.

so we did release a model.

it's much smaller than the model that we run, on prum.com and our API. and it's, also trained on more toy data. Like it doesn't have all of the tips and tricks, but I think it's always good to put out, open source models so that, research can be replicated. So this is like a. One for one, like exact, like it's all the code and the model that was in our edit lens paper. And then of course we incorporate those techniques into the Pangram model, which has, a lot more like tricks. So I think we always wanna stay ahead of the game, but I do want to put something out there so that people can tinker with and we can advance the state of the field.

that's great, man.

and we appreciate open source as well. but obviously you're a business and, f folks should try give Pangum a try. So Max, maybe as the last like piece of, question, feel free to shout out the folks who are working with you and the type of stuff that you want. and how do they get this Chrome extension where they can use this as well? obviously I showed you can just select a bunch of texts, right click and then see if it's AI generated. what else, what kind of platform will you cover and where can you get the Chrome extension?

Yeah, so you can get the Chrome extension if you go to

pangram.com, sign up for an account. but we also, yeah, if you look into PIN tweet and the Pangram Labs, Twitter account, you can get an extra week for you. So you get a two week trial, which would be pretty nice. and so it, obviously you can right click and scan anywhere, but we'll also automatically scan posts for you on Reddit, Twitter, LinkedIn, Substack, and Medium. and more platforms to come, of course. But I think these are the ones where people spend a lot of time reading and you want to, if you care about the health of your feeds and wanna make sure, you know, if Whether what you're reading is AI or not, then you should, consider getting the Chrome extension.

I think it's definitely very cool.

I jumped in like very early on and tried it out. many of my suspensions, suspicions about who's a slot merchant on my feed have been validated, so I'm very happy that like I, I knew about them as well. but Max, thank you so much for coming and dispelling the idea. I think it's very important that we also update our like, belief system continuously. For a while there people didn't trust any, AI detection mechanism said none of them were possible. I think the false positives narrative is is the right one. So kudo you for you guys doing this, if you say that's ai, it's likely very high likelihood of this is ai. Yeah. We're very

confident.

Yes.

very confident. So that's great. 99.98%. confident score Max, thank you for coming. feel free to come back to us when you guys crack something and detect everything. And future release as well will be alright. I'll let you know when we

have new modalities coming out.

oh, okay.

Yes. Last thing that you should come to, to do the prep with me, I have to ask you this. This is only text. People can trust the voices anymore or visuals anymore, You already opened this up, so I know you're making the case, I can tell you,

but Yeah.

Yeah. I'll tell, I'll say that we don't have a strong timeline on it. But I think this is, very important as well. I wanna know that the images and videos that I see online Yeah. Are also real and not AI generated. I think that's gonna be important part of my personal cognitive security. and I think we're working to make that happen.

I think Cognitives, dude, I can keep you on forever.

we need to continue, we have a bunch of other stuff to cover, but cognitive security is a concept, is a very important thing. We keep talking about this on Thursday for a while. You need to know what you're reading, what your inputs are. And if you're getting one charted by fucking mythos, that's great at cybersecurity, but also great at probably convincing you of the earth being flat. That's not great for anyone. So cognitive security is like this great concept. Oh,

absolutely.

And I think it's like pretty clear that LLMs today are superhuman and persuasion. I don't know if you know that, like Sam Altman tweet about that, but I think like very quickly after that four O came out, and I think it, it's more persuasive than talking to a person and, so I think it's very, like we, we need tools to keep our cog stack up and Pangram is one of those.

That, that's great.

That's a great framing and we're looking forward for the multimodality stuff because that is getting crazy. GPT image is really real hard to tell sometimes. it is getting crazy. Max, thank you so much for coming. we'll de have you on about multimodality in the future and cognitive security as a general concept. I appreciate you. Thank you.

Appreciate it.

All righty.

we'll bring back Nisten and, what do you guys think about Pangram?

A lot of,

definitely, I

mean,

definitely have to put my own post.

Oh, you guys are talking about other, somebody

has a delay or something. Let's go Nisten first in the Wolf room.

we have to keep in mind that we're in a Twitter bubble here, and

the rest of the population has a very different opinion of, of ai. And, there's a mix of both misinformation and good concerns in there. So this, these types of tools matter a lot more than. What some of us on Twitter might think they, they do to listen.

We talked about a personal firewall for cognitive

security like two years ago. I think it's time we just leave our day jobs, just build this because I don't want, grandmas getting one shotted with propaganda with lms. And I think that's like very important not only in the world of text, but also in multimodality everything. They see all these crazy people on Facebook, which by the way, we should ask Mask why they didn't add a Facebook there. are getting like, Jesus in a toast like post that they just fully believe in. okay. No sponsors, nothing. It just I think it's very important. yeah. Many comments on the background stuff before we move on.

I think it's hard.

I think that it's very hard to, I'm just saying that they're, working on a very hard problem. it's not easy at all to no detect, AI generated text because AI is pretty much speaking, roughly like us at this point. unless you use a very shitty model and not caring at all what actually is, the model I was outputting, which you can see a lot online, surprisingly. Yeah. But, but yeah, and I just, conceptually I think, you deserve to know if you read an AI generated text, you just deserve to know. And I'm not talking about AI helping someone edit their posts, Sl

you mean

sl?

that's fine. That's actually we all do it. It's fine. But just generated blind blindly text. I think everyone deserves to know, not that there's anything wrong with this, just you need to know. And I highly, highly, I'm all for this, this idea. Just saying.

Yep.

Alright folks. time for us to move on. There's not a, yeah, go ahead. Wolfen.

I think the multimodality stuff, that is very

exciting because when I see text or basically by now, I always expect everything to be froze for

a bit.

Show

me something today.

Start again.

You froze for a little bit. I think the network connection is, slow on your end.

I think, that multimodality is the big unlock here

because, when I see text is like opinion. Everybody can have one, but if it is a picture, it is more objective, not as subjective. If I post a picture or video I recorded it is basically, usually it used to be an objective recording of the reality, but nowadays you have to expect everything to be basically AI generated because it's so hard to tell. So having something that can, help restore that trust in a way for these modalities, I think that would be very helpful.

Yep.

okay folks, we have to move on. We're almost at the end of time. We chatted with Max and some other folks, and time blew passed and we didn't cover many of the stuff that I planned to cover. I think, I think a better job needed. but let's run to the stuff that we didn't cover yet and then, mention, at least mention them, like we did until Dr. 11 Labs. 1111 music I wanted to play. we can probably play like a sample before we move on. we talk about sense time and granite. I wanna talk about Stripe. I think this is the most important thing. This was the number one highlight for me. Stripe had their, annual Stripe sessions, conference where they released a bunch of stuff. It is very obvious how fast Stripe is moving in Agent engineering as well. And one of the coolest things that they released is Stripe Link Wallet for agents. and I definitely wanna show you this is quite incredible. let me show you, what we're talking about here. Stripe Link is, if you ever bought something with Stripe, they asked you, Hey, do you wanna save this credit card to link to your account? And then you basically link that credit card into that account, then you can just pay with link. So this is an existing, ability. Stripe has announced a CLI for this, and the ability for your agents to actually spend money on your behalf while you are protected. So the agent doesn't see the credit card number, so you don't share the credit card numbers with the agent. so your actual credit card is never exposed. a Stripe agent gets payment credentials via Stripe Link, and human approval is required on every purchase. Stripe does like payment tokens and time bounded and mount capped payment tokens. What essentially means is that you can safely give your clinker an ability to pay everywhere on the internet. It works everywhere. and and I think it's incredible and I actually want to test this out live on the show. I don't know if it's gonna work, because for some reason I can't share my damn screen, but it's okay. I will go to, wolfed, which is my open cloud. I'm gonna show you this here, and I'll say, Hey, using, lemme zoom in here so you guys can see, this may or may not work because, it's been sick lately. Using your Stripe link stuff. go online, find ThursdAI, and subscribe with your email and pay the subscription fee monthly. So we'll see. it's gonna be tricky 'cause my phone to record this is, this is Opus 4.7 in Open call.

I would be afraid to give Opus 4.7 money.

I'm just saying.

Yum, that's a great point.

But I will show you again what I'm talking about here. I'm not giving it money. I'm giving it an access to Stripe link. okay. First, what email should I use? yes, use this one. Yeah. Look,

I've already given, not Opus 4.7, not that long shot,

but I've already given models, just, direct use of my browser. Yeah. And, I have a Stripe account already logged in to my browser and I told them, for example, to, buy, buy credits for, OpenAI API, and I did so through Open Cloud. I just told Olaw to do it and nothing disastrous happened. And, but I totally get the point. it could have easily. It could have easily spiraling into a complete disaster.

It could leak the model your credit cards, right?

if you actually wanted to go buy, I think this is the highlight of the Stripe Link thing. the agent does not get access to the actual credentials it gets. I think they generate like a one, I actually don't know. They generate like a token and probably a one time use credit card number or something. but, I think that this is the sandbox, the highlight here. Sandbox. Yeah, sandbox. It's just a sandbox. Sandbox

for credit card.

That's great. Interesting.

I think it's incredible, but also like it's an approval gated sandbox.

and then I think that, this is what we're looking for. We're looking for stability. We're looking for verification and user, gated kind of transactions. it, it does take a while. so we're seeing it stiping. It's typing and doing something. So something is happening here, but, hopefully once it does it, I'll be able to, to approve this transaction via the Stripe Link website and a notification on the iOS app. so while this happens, I'll tell you guys a story. Yesterday, I tried this out. my wedding is coming in a week, next Tuesday, so I'm not gonna be on the show next, next week. so folks will, yum and Ryan and Nisten LDJ will hold the show for you guys. and so I decided to give ourselves a little bit of a surprise and I asked our open Claw agent to go and buy something for me, and I gave it a budget of $200. Stripe Link, I think, has a $500 a day limit. I said, Hey, you have $200. Go hunt for a present and have it appear at our house, and you have this $200 budget. it didn't work immediately. so here's the stuff that went wrong, but generally it did end up working, with a very funny ending. so first of all, the notifications weren't turned on the Stripe Link iOS app. the agents can generate a link for you to click and go on the web and actually approve or disapprove some stuff. but if you have the iOS app. Notifications are timely. So you can say, Hey, I approve this payment. I don't approve this payment. So that's great. Notifications didn't show up for me. we ended up, debugging this with the Stripe people via dms. Apparently their onboarding doesn't ask for notifications. once I turned this on, they started showing up. Then there was a whole thing where when an agent decides to pay, they put a hold on your limit. So if you give 500 limit, so the testy stuff that my agent did was sitting on that limit. There was no API to cancel the limit. So basically, probably like a fraud detection. the agent tried too many times and I didn't click in time, so those request expired, but there's no way for the agent to clear those requests and try new ones. So basically we're like for 24 hours or something, we were locked out of being able to pay just because we, I didn't click the link fast enough. So I think that is something they have to fix. and then besides the payment that went through, there's the regular AI stuff with the agent. it wrote something, it wrote incorrect date. It had the memory date of our proposal, but didn't have the memory date of our wedding. So it added our proposal date on the kind of the thing. it ended up buying me a gift, so I had to like, ask it for a few stuff. Ended up buying me a gift. This gift arrives somewhere in June. it's really funny. It's first got us a beer spa. And I was like, I don't wanna go to beer, but I also want a physical thing to show up at my house as a present. so it got us a gift. and then the gifts supposedly arrived somewhere in June, but it's supposed to be like customizing grade. and it spent the money today. So I'm like very happy with this experiment generally. okay, time to show you what happened. Nothing. This waited for a while. I've been talking and like waiting. There's like more than four minutes. And, open claw, open claw of other people has died. I have no idea why, and I don't have the patience to go and fix this. So I'll just stop here and then we'll move on. But this has been my experience with open claw lately. As sad as it is, I dunno if it's a telegram or something, it's just like typing, and then no answer. And I'm done with this. I need, I need more reliability here. So my live demo with this didn't work, which means that we move on to our next segment is called This Week's Boss, and we're gonna talk about cursor agent, and this K and then,

a quick, tip for people.

you can do the same with PayPal because when an agent puts something in the card and wants to pay, it generates a link to PayPal and that can be given to you. You authorize it, give the response link back, and then it can make the payment that works as well.

Yeah.

That's great. let's do this week's vow super quick. we have a few things to announce Wolf and I really would love to talk about the virality of Wolf Bench and the findings as well. And then I think we're good to conclude the show. Like we've been at this for two hours already

from CoreWeave. I have to rerecord this because Visor is from CoreWeave. As you guys see, Wolfram has, we're wearing a yellow jacket, from waist devices from CoreWeave, and Wolf Bench is going a little bit viral this week. Wolf. Wolf. Would you tell us why we wanna share something and talk about our findings?

yeah, sure.

We did some more tests. I will just, share my screen, share screen and find the correct tab. So we should be seeing the wolf bench, three of the nice, colorful bars. And just to recap what Wolf bench is, we've been talking about it on the show for a long time, and you can just go to wolf bench.ai. Then you see this and, there's a long explanation what it is about at the bottom. And it's not just the benchmark that looks at one score, but we are looking at different scores and we are looking at different combinations of models and agents. So in this case we have GBT 5.5, and I will zoom in a bit. These are the models we tested. And so site is interactive. So you can say I want to see, just GBT 5.0 or not GBT 5.0. And I want to see the average, for instance, that is a score that basically everybody reports on, which is a terminal bench. the benchmark I'm using is terminal bench and by default everybody uses it and they only report their average score. But yeah, we have different agents, different models, and there's a new sort feature. If I click this once, it sorts everything according to which, had the highest score. So we see GBD 5.5 with cursor agent has the best score and I can push it once more. And it goes across all the models. You can see it that the models changed. So this is, the default order and this is, basically across all models. What is the best agent?

Wolfram?

I wanna make sure that you're not bearing the lead with the technicalities of the website. They're very like rightfully excited about 'cause you build them. But folks, cursor agent with G 55.5 has the highest intelligence score that we've tested on wolf bench, which, uses terminal bench, but also tests the, the highest score and the base score. And it's like it's highest among everything among open claw and her Hermes agents and T two frameworks cursor agent from the. it's the best score that we had so far, and it's very impressive. And this is why I think we're going viral because like many folks from course are like, rightly are very proud of their work and reposting this. I think it's very, like important. Yeah, we haven't tested all the agents. I will say we haven't tested Devin. We haven't tested, folks are asking us for, codex. Obviously the, IT

works.

It's already running. I already have scores. Not spoiling now, but this will be the next one I published. Nice. You'll see if ZA is still the number one. But, I want to show one important thing about Wolf Bench because I'm not just looking at this coin. If you look at it, it's just 1% point difference and we can even show the number. Oh, that was the number. I showed the number. It's 89 tasks and 69. It's one task that Kza can do more though it's very close. But if we not just look at the average score, but we also look at the baseline, basically how many of all the tests that the benchmark does the agent do all the time. Then at 57%, that is a big jump from the 53, number of, number of, tests. So basically 4%. points difference. That is, it is a more reliable agent as well. It's not just, basically the score is basically the same here. If you look at the average 77%, but it has a higher baseline, a higher, solid base than it does. So if you want a more reliable agent, that is an important part that you don't see it other benchmarks. But this is one thing. While Terminus two, the standard agent still has a higher ceiling of 2% points. So across all of the tests in this benchmark, 92% can be solved by the agent, but it only solves 77% on average in one. And, that is some information you can get out of this. And it's all public. It's an open source framework, which I'm based on. You can download it, you can run it yourself. You can even click the bar and go directly to, our Weave platform, which opens in a new tab. and you can look at it, you can download it, and you can have your own agent look at those things and find out why is ZA better? Because on X, somebody asked why is za what is better about it compared to the other? And I had my agent analyze it and it was a repo editing cleanup and multi-step tool use. That is what made Za more, yeah, take the lead in that.

I want to ask this again.

Because of the Observability, powered by ways and vice Weave, you're able to ask your agent to go and analyze the differences. And so what the differences were. can you say this again? Why is cursor better than, than terminal, bench and open, quiet at, at these tasks

it was more reliable on hard edit heavy tasks and

had fewer, failures in that regard. So basically they have cool or great tools for editing files, and that made a difference here.

That's great.

Welcome. Thank you so much. the other news that we have on this week's buzz is that Ernie, sorry, IBM is now on the Waits bus, not Ernie. we're not hosting Close Source models. so IBM Granite is now up on waiting biases as well for very cheap, you definitely guys should try it on the wait Biases bench. and so on the Waits buses, infras service. And then we'll probably gonna test this on Wolf Bench as well to show you, I think it's time to conclude the show. Folks. We haven't run through, quite a few other things, but I think we did. Okay. the do more important thing is that we tested the Cursor agent, but cursor this week, recent an SDK that you can take the cursor intelligence and the harness and place it in your own apps. So that's like a worth a shout out. And also that, cognition Devon, that we like talked to you about. And we had Nader a bit on the show that we haven't tested yet, but like likely Will coming as well because of the effort of Wolfram. they reached out to us and said, Hey, can we test Devon as well? So we're gonna test Devon. Devon released a CLI for Devon in terminal. This is how we can finally test this as well because Devon used to be a closed door system and now there's a CLI that you can just run. I think it's worth, ending the show on the fact that the demo did not work. wolfed, my assistant is no longer responsive and I think it's because of the CLI tropic thing probably. so you could say the C Claude did kill open claw a little bit. but also, I think it's worth, finishing on the 11 labs, music thing. So let's play a choice track from this 11 labs slow, dreamy indie rock with reverse vocals.

I dunno if I like this one. Let's skip. No, sir. I wish I could. it's very, it's worth saying that 11 labs, oh wait, it responded. It didn't die. I was lying. I'm so sorry I was lying. I just took it a long time. But there we go. You guys see this approval link? I created a link spend request for $10, to cover the six monthly. If I press this, and I can press this quicker than you guys can see, so nobody can snipe this link. I can approve, I can show you how this looks. can I show you how this looks without exposing the payments that I did? I'd rather not expose my credit card if you don't mind, but there's like a nice, approval mechanism. I hit approve and, and this is how the link works. It sends me a link to approve and, I, I don't know why it took, what, seven minutes. Almost 10 net. So it's not useful. but I approve this payment now, and then Wolf Fred can go and buy some stuff. And I think it's like very cool. it just took 10 minutes longer than I did, okay, we will, we'll see if it actually created a subscriber. but I think it's time for us to conclude the show. Folks, with this, thank you so much everybody who tuned into ThursdAI this week. folks are asking already for, for other, Wolf bench, harnesses. thank you so much for tuning in for the show this week. next week is gonna be a little bit of a different show, because I'm not gonna be here. but for good reasons. I appreciate everyone who tuned in. If you missed any part of the show, we are turning the show into a newsletter and a podcast. we still have a very high rating on everywhere, so please give us a five star if you are a listener. And if not, please feel free to subscribe on, apple Podcast on Spotify, everywhere else. my name is Alex Ov and AI evangelist with Weights, & Biases from CoreWeave. Wolfram is on my team as well, doing incredible work with Wound Bench. Ian Peck, and Nisten co-host. Thank you so much for joining. Thank you everybody who tuned in. max Piero was our guest from Pangram Labs today, and, we'll see you next week. I won't see you, but like the folks will see you next week here. Bye-bye. Bye everyone. Thanks for joining

you.