Hey ya'll, Happy Thanskgiving to everyone who celebrates and thank you for being a subscriber, I truly appreciate each and every one of you!

We had a blast on today's celebratory stream, especially given that today's "main course" was the amazing open sourcing of a reasoning model from Qwen, and we had Junyang Lin with us again to talk about it! First open source reasoning model that you can run on your machine, that beats a 405B model, comes close to o1 on some metrics ๐Ÿคฏ

We also chatted about a new hybrid approach from Nvidia called Hymba 1.5B (Paper, HF) that beats Qwen 1.5B with 6-12x less training, and Allen AI releasing Olmo 2, which became the best fully open source LLM ๐Ÿ‘ (Blog, HF, Demo), though they didn't release WandB logs this time, they did release data!

I encourage you to watch todays show (or listen to the show, I don't judge), there's not going to be a long writeup like I usually do, as I want to go and enjoy the holiday too, but of course, the TL;DR and show notes are right here so you won't miss a beat if you want to use the break to explore and play around with a few things!

ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

TL;DR and show notes

* Qwen QwQ 32B preview - the first open weights reasoning model (X, Blog, HF, Try it)

* Allen AI - Olmo 2 the best fully open language model (Blog, HF, Demo)

* NVIDIA Hymba 1.5B - Hybrid smol model beating Qwen, SmolLM w/ 6-12x less training (X, Paper, HF)

* Big CO LLMs + APIs

* Anthropic MCP - model context protocol (X,Blog, Spec, Explainer)

* Cursor, Jetbrains now integrate with ChatGPT MacOS app (X)

* Xai is going to be a Gaming company?! (X)

* H company shows Runner H - WebVoyager Agent (X, Waitlist)

* This weeks Buzz

* Interview w/ Thomas Cepelle about Weave scorers and guardrails (Guide)

* Vision & Video

* OpenAI SORA API was "leaked" on HuggingFace (here)

* Runway launches video Expand feature (X)

* Rhymes Allegro-TI2V - updated image to video model (HF)

* Voice & Audio

* OuteTTS v0.2 - 500M smol TTS with voice cloning (Blog, HF)

* AI Art & Diffusion & 3D

* Runway launches an image model called Frames (X, Blog)

* ComfyUI Desktop app was released ๐ŸŽ‰

* Chat

* 24 hours of AI hate on ๐Ÿฆ‹ (thread)

* Tools

* Cursor agent (X thread)

* Google Generative Chess toy (Link)

See you next week and happy Thanks Giving ๐Ÿฆƒ

Thanks for reading ThursdAI - Recaps of the most high signal AI weekly spaces! This post is public so feel free to share it.

Full Subtitles for convenience

[00:00:00] Alex Volkov: let's get it going.

[00:00:10] Alex Volkov: Welcome, welcome everyone to ThursdAI November 28th Thanksgiving special. My name is Alex Volkov. I'm an AI evangelist with Weights Biases. You're on ThursdAI. We are live [00:00:30] on ThursdAI. Everywhere pretty much.

[00:00:32] Alex Volkov:

[00:00:32] Hosts and Guests Introduction

[00:00:32] Alex Volkov: I'm joined here with two of my co hosts.

[00:00:35] Alex Volkov: Wolfram, welcome.

[00:00:36] Wolfram Ravenwolf: Hello everyone! Happy Thanksgiving!

[00:00:38] Alex Volkov: Happy Thanksgiving, man.

[00:00:39] Alex Volkov: And we have Junyang here. Junyang, welcome, man.

[00:00:42] Junyang Lin: Yeah, hi everyone. Happy Thanksgiving. Great to be here.

[00:00:46] Alex Volkov: You had a busy week. We're going to chat about what you had. I see Nisten joining us as well at some point.

[00:00:51] Alex Volkov: Yam pe joining us as well. Hey, how, Hey Yam. Welcome. Welcome, as well. Happy Thanksgiving. It looks like we're assembled folks. We're across streams, across [00:01:00] countries, but we are.

[00:01:01] Overview of Topics for the Episode

[00:01:01] Alex Volkov: For November 28th, we have a bunch of stuff to talk about. Like really a big list of stuff to talk about. So why don't we just we'll just dive in. We'll just dive in. So obviously I think the best and the most important.

[00:01:13] DeepSeek and Qwen Open Source AI News

[00:01:13] Alex Volkov: Open source kind of AI news to talk about this week is going to be, and I think I remember last week, Junyang, I asked you about this and you were like, you couldn't say anything, but I asked because last week, folks, if you remember, we talked about R1 from DeepSeek, a reasoning model from [00:01:30] DeepSeek, which really said, Oh, maybe it comes as a, as open source and maybe it doesn't.

[00:01:33] Alex Volkov: And I hinted about, and I asked, Junyang, what about some reasoning from you guys? And you couldn't say anything. so this week. I'm going to do a TLDR. So we're going to actually talk about the stuff that, you know, in depth a little bit later, but this week, obviously one of the biggest kind of open source or sorry, open weights, and news is coming from our friends at Qwen as well, as we always celebrate.

[00:01:56] Alex Volkov: So one of the biggest things that we get as. [00:02:00] is, Qwen releases, I will actually have you tell me what's the pronunciation here, Junaid, what is, I say Q W Q or maybe quick, what is the pronunciation of this?

[00:02:12] Junyang Lin: I mentioned it in the blog, it is just like the word quill. Yeah. yeah, because for the qw you can like work and for the q and you just like the U, so I just combine it together and create a new pronunciation called Quill.

[00:02:28] Junyang Lin: Yeah.

[00:02:28] Alex Volkov: So we're saying it's Quin [00:02:30] Quill 32 B. Is that the right pronunciation to say this?

[00:02:33] Junyang Lin: Yeah, it's okay. I would just call it qui quill. It is, some something funny because,the ca the characters look very funny. Oh, we have a subculture,for these things. Yeah. Just to express some, yeah.

[00:02:46] Junyang Lin: our. feelings.

[00:02:49] Alex Volkov: Amazing. Qwen, Quill, 32B, and it's typed,the name is typed QWQ, 32Breview. This is the first OpenWeights reasoning model. This [00:03:00] model is not only predicting tokens, it's actually doing reasoning behind this. What this means is we're going to tell you what this means after we get to this.

[00:03:07] Alex Volkov: So we're still in the, we're still in the TLDR area. We also had. Another drop from Alien Institute for AI, if you guys remember last week we chatted with Nathan, our dear friend Nathan, from Alien Institute about 2. 0. 3, about their efforts for post training, and he gave us all the details about post training, so they released 2.

[00:03:28] Alex Volkov: 0. 3, this week they released Olmo 2. [00:03:30] 0. We also talked about Olmo with the friends from Alien Institute a couple of months ago, and now they released Olmo 2. 0. Which they claim is the best fully open sourced, fully open sourced language models, from Allen Institute for AI.and, so we're going to chat about, Olmo a little bit as well.

[00:03:46] Alex Volkov: And last minute addition we have is NVIDIA Haimba, which is a hybrid small model from NVIDIA, very tiny one, 1. 5 billion parameters. small model building Qwen and building small LLM as well. this is in the area [00:04:00] of open source. I

[00:04:01] Alex Volkov: Okay, in the big companies, LLMs and APIs, I want to run through a few things.

[00:04:06] Anthropic's MCP and ChatGPT macOS Integrations

[00:04:06] Alex Volkov: So first of all, Anthropic really something called MCP. It's a, something they called Model Context Protocol. We're going to briefly run through this. It's a, it's a kind of a release from them that's aimed for developers is a protocol that enables secure connections between a host application, like a cloud desktop, for example,

[00:04:24] Alex Volkov: there's also a bunch of new integrations for the ChatGPT macOS app. If you guys remember a couple of [00:04:30] weeks ago, We actually caught this live.

[00:04:31] Alex Volkov: I refreshed my MacOS app and there's ta da, there's a new thing. And we discovered this live. It was very fun. The MacOS app for ChatGPT integrates with VS Code, et cetera. and so we tried to run this with Cursor. It didn't work. So now it works with Cursor,

[00:04:43] Wolfram Ravenwolf:

[00:04:43] Alex Volkov: So the next thing we're going to look at, I don't know if it's worth mentioning, but you guys know the XAI, the company that Elon Musk is raising another 6 billion for that tries to compete with OpenAI

[00:04:54] Alex Volkov: Do you guys hear that it's going to be a gaming company as well? I don't know if it's worth talking about, but we'll at least [00:05:00] mention this. And the one thing that I wanted to chat about is H, the French company, H that showed a runner that looks. Three times as fast and as good as the Claude computer use runner, and we're definitely going to show examples of this, video live because that looks just incredible.

[00:05:18] Alex Volkov: this out of nowhere company, the biggest fundraise or the biggest seed round that Europe has ever seen, at least French has ever seen, just show they, An agent that controls your [00:05:30] computer that's tiny, ridiculously tiny, I think it's like the three billion parameter, two billion parameter or something.

[00:05:36] Alex Volkov: And it runs way better than computer, cloud computer use. Something definitely worth talking about. after with, after which in this week's Bars, we're going to talk with Thomas Capelli, from, from my team at Weights Biases. about LLM guardrails, that's gonna be fun. and in vision video category, we're gonna cover that OpenAI Sora quote unquote leaked, this week.

[00:05:56] Alex Volkov: And this leak wasn't really a leak, but, definitely [00:06:00] we saw some stuff. and then there's also a new expand feature that we saw in, Runway. And we saw another video model from, Rhymes called Allegro TIV2. which is pretty cool in voice and audio. If we get there in voice and audio, we saw out TTS vision 0.

[00:06:19] Alex Volkov: 2, which is a new TTS, a 500 million parameter, small TTS you can run in your browser and sounds pretty dope.art in the fusion, super quick runway launches an image [00:06:30] model. Yep, Runway, the guys who do video, they launched an image model that looks pretty sick, and we're definitely going to look at some examples of this, and Confi UI Desktop, for those of you who are celebrating something like this, Confi UI now is runnable with desktop, and there's a bunch of tool stuff, but honestly, I can talk about two things.

[00:06:47] Alex Volkov: Tools and there's a cool thing with Google generative chess toy. I can show you this so you can show your folks in Thanksgiving and going to impress them with a generative chess toy. But honestly, instead of this, I would love to chat about the thing that [00:07:00] some of us saw on the other side of the social media networks.

[00:07:04] Alex Volkov: And definitely we'll chat about this, for the past 24 hours. So chat, for the past. 24 hours, on BlueSky, we saw a little bit of a mob going against the Hug Face folks and then, other friends of ours on,from the AI community and the anti AI mob on BlueSky. So we're going to chat about that.

[00:07:26] Alex Volkov: And hopefully give you our feelings about what's going on, about this [00:07:30] world. And this is a pro AI show. And when we see injustice happens against ai, we have to speak out about against this. And I think that this is mostly what we're gonna cover this show, unless this is.

[00:07:42] Wolfram Ravenwolf: Where I could insert the two things I have.

[00:07:44] Wolfram Ravenwolf: One is a tool, which is the AI video composer, which, allows you to talk to, ff mpac, which is a complicated comment line tool, but very powerful. And so you have a UI where you just use natural language to control the tool. So that is one tool. Maybe we get to [00:08:00] it, if not just Google or ask for Plexity or anything.

[00:08:03] Alex Volkov: No, we'll drop it in. Yeah, we'll drop it in show notes, absolutely.

[00:08:04] Wolfram Ravenwolf: Yeah, that's the best part. Okay. And echo mimic. Version 2 is also an HN Synthesia alternative for local use, which is also, yeah, a great open source local runnable tool.

[00:08:17] Alex Volkov: What do we call this? EcoMimic?

[00:08:19] Wolfram Ravenwolf: EcoMimic. EcoMimic

[00:08:21] Alex Volkov: v2.

[00:08:21] Wolfram Ravenwolf: EcoMimic

[00:08:23] Alex Volkov: 2.

[00:08:24] Alex Volkov: Alright, we have a special guest here that we're gonna add Alpin. Hey Alpen, [00:08:30] welcome, feel free to stay anonymous and don't jump, we're gonna start with open source AI and then we're gonna chat with you briefly about the experience you had.

[00:08:38] Alpin Dale: hello everyone.

[00:08:39] Alex Volkov: Hey man. Yeah, you've been on the show before, right Alton? You've been on the show.

[00:08:43] Alpin Dale: a few times, yeah. it's nice to be back here again.

[00:08:46] Alex Volkov: Yeah. Alton, we're gonna get, we're gonna chat with you soon, right? We're gonna start with open source. We need to go to Junyang and talk about reasoning models.

[00:08:52] Alex Volkov: so feel free to stay with us. And then I definitely want to hear about some of the stuff we're going to cover after open source. We're going to cover the [00:09:00] anti AI mob over there.

[00:09:05] Alex Volkov: Alrighty folks, it's time to start with the,with the corner we love the most, yeah? let's dive into this. Let's dive in straight to Open Source AI.

[00:09:29] Alex Volkov: Open Source AI, [00:09:30] let's get it started. Let's start it.

[00:09:35] Alex Volkov: Okay, folks, so open source this week, we're going to get, let me cover the other two things super quick before we dive in.

[00:09:43] NVIDIA Haimba Hybrid Model Discussion

[00:09:43] Alex Volkov: Alright, so I want to like briefly cover the Haimba paper super quick, because we're going to get the least interesting stuff out of the way so we can focus on the main topic. Course, NVIDIA released Heimbar 1. 5 parameters. Heimbar is a hybrid small model, from NVIDIA. We talked about hybrid models [00:10:00] multiple times before.

[00:10:00] Alex Volkov: we have our friend of the pod, LDJ here. He loves talking about hybrid models. He actually brought this to our attention in the, in, in the group chat. We talked about, you guys know the Transformer, we love talking about the Transformer. Haimba specifically is a hybrid model between Transformer and I think they're using a hybrid attention with Mamba layers in parallel.

[00:10:22] Alex Volkov: they claim they're beating Lama and Qwen and SmallLM with 6 to 12 times less training as well. Let's look [00:10:30] at the, let's look at their, let's look at their X.so this is what they're, this is what they're showing, this is the table they're showing some impressive numbers, the interesting thing is, this is a table of comparison that they're showing, and in this table of comparison, the comparison is not only Evaluations.

[00:10:47] Alex Volkov: The comparison they're showing is also cache size and throughput, which I like. it's do you guys know what this reminds me of? This reminds me of when you have a electric vehicle [00:11:00] and you have a gas based vehicle or standard combustion engine vehicle, and then they compare the electric vehicle and acceleration.

[00:11:07] Alex Volkov: It's Oh, our car is faster. But you get this by default, you get the acceleration by default with all the electric vehicles. This is how the model works. This is how those model works. So for me, when you compare like hybrid models, or, non transformer based models, a Mamba based models, the throughput speed up is generally faster because of it.

[00:11:29] Alex Volkov: [00:11:30] But definitely the throughput is significantly higher. Tokens per second. is significantly higher. So for comparison for folks who are listening to us, just so you, you'll hear the comparison, the throughput for this 1. 5 billion model is 664 tokens per second versus a small LM 238 tokens per second, or something like Qwen 1.

[00:11:54] Alex Volkov: 5 at 400. So 600 versus 400. the training cost in [00:12:00] tokens, they say this was, 1. 5 trillion tokens versus Qwen at 18. I don't know if Junyang you want to confirm or deny the 18 mentioned here that they added. Sometimes they, they say different things, but yeah, definitely the highlight of this Heimwehr thing.

[00:12:14] Alex Volkov: And this is from NVIDIA, by the way, I think it's very worth like shouting out that this specific thing comes from this model comes from NVIDIA. Um,they specifically mentioned that the cost, And outperformance of this model comes at 6 to 12 times less [00:12:30] training, which is very impressive.

[00:12:31] Alex Volkov: what else about this model? Performance wise, MMLU at 52, which is lower than Qwen at 59, at, at 1. 5 billion parameters. GSM 8K, we know the GSM 8K is not that interesting anymore, I think, at this point. We're not like over, we're not over, we're not looking at this like too much. What else should we say about this model?

[00:12:52] Alex Volkov: GPK is pretty interesting at 31. GPK is usually knowledge versus something. [00:13:00] Anything else to say about this model? Yeah, you have anything to say Nisten? Anything to say about the small models? About the hybrid model specifically? I know that like our friend LDJ said that like this seems like the first actual model that competes apples to apples.

[00:13:13] Alex Volkov: Because usually when we compare Hybrid models specifically, those usually people say that those are not like necessarily one to one comparisons between hybrid models and just formal models.

[00:13:24] Nisten Tahiraj: I was just going to say that fromfrom NVIDIA, we've heard these [00:13:30] claims before and they didn't quite turn out that way, so I'm going to start off a little bit more skeptical on that end. also from, from the Mistral Mamba, Mambastral, that one was not very performant.

[00:13:44] Nisten Tahiraj: it seemed like it was going to be good for long context stuff. The runtime wasn't that good as well. yeah, I'm going to give this one a test because. Again, the promise of, of like hybrid, SSM models is that it can do better [00:14:00] in longer contexts and it can run faster. So it is worth testing given what, what they're claiming.

[00:14:06] Nisten Tahiraj: But, again, on MMLU, it didn't do that well, but, yeah, overall the numbers do look great actually for what it is, but I think we do need to do further testing on this, whether it is practically. That's good. Because I'm not sure how well it's going to hold up after you just throw like 32k of context of it.

[00:14:25] Nisten Tahiraj: I guess it's going to remember all that, but, yeah, this on paper, this does [00:14:30] look like it's one of the first ones that is Applesauce.

[00:14:33] Alex Volkov: Yeah. All right. anything else to say here? Yeah, the architecture. Jan, go ahead.

[00:14:39] Yam Peleg: Yeah, about the architecture. I tweeted about it.It is, I think it has extreme potential and, it might, I just by looking at the attention maps, from the paper, like just a glimpse is enough for you to see that.

[00:14:55] Yam Peleg: They really do solve something really profound [00:15:00] with many of the models that we have today. basically, I'm really simplifying here, but basically, when you look at the Attention versus Mamba, they act very differently in terms of how they process the tokens, sliding window ones, you could say.

[00:15:20] Yam Peleg: And of course self attention is like global, to everything, but Mamba is not exactly global, it's sequential, and sliding window is also not exactly [00:15:30] global, but it's not the same sequential, it's like everything to everything, but with a window. So what they did is combine the two, and you can really see the difference in attention map of the trained model.

[00:15:44] Yam Peleg: it's not exactly the same as just, hybrid Mamba attention models that we all saw before.there is a lot to this model and I really want to see one of those. I just [00:16:00] trained for like at scale, like a large one on, on, on a huge data set, because I think it might be an improvement to either,just by looking at the way the model learned, but you cannot know until you actually try.

[00:16:15] Yam Peleg: I tweeted about it just like briefly. So if you want to go and look at, I'm just, I'm just pointing out that go and check the paper out because the architecture is unique. There is, there is a reason the model is, for its size, very performant. [00:16:30]

[00:16:30] Alex Volkov: Yeah, I'm gonna add your tweet.

[00:16:31] Alex Volkov: All right, folks, time for us to move to the second thing.

[00:16:36] Allen Institute's Olmo 2.0 Release

[00:16:36] Alex Volkov: The folks at Allen AI, surprises with another release this week, and they have, as always they do, they say, hey folks, we divide the categories of open source to not open source at all, then somewhat open weights maybe, and then fully open source, the folks who release the checkpoints, the data, the, the training code.

[00:16:57] Alex Volkov: I will say this, they used to release Weights [00:17:00] Biases logs as well, and they stopped. So if somebody listens to the show from LMAI, as I know they do, folks, what's up with the Weights Biases logs? We know, and we love them, so please release the Weights Biases logs again. but, they released Olmo 2.

[00:17:14] Alex Volkov: Congrats, folks, for releasing Olmo 2. Let me actually do the clap as well. Yay!Olmo 2 is, they claim, is, they claim,the best open, fully open language model to date, and they show this nice graph as well, where, they released two models, Olmo [00:17:30] 2. 7b and Olmo 2. 13b, and they cite multiple things, to, to attribute for the best performance here.

[00:17:37] Alex Volkov: specifically the training stability, they ran this for a significant longer before. they cite some of the recipes of. What we talked about last week from TULU3 methodology, the kind of the state of the art post training methodology from TULU3 that we've talked with Nathan last week, specifically the verifiable framework, thing that we've talked about, multiple other technical things like rate [00:18:00] annealing and the data curriculum.

[00:18:01] Alex Volkov: And obviously they're focusing on their data. they have their, Ohm's selection of tasks on which they compared these models and,the breakdown that I told you about that they do is the open weights models, partially open models, and then fully open models. So this is the breakdown that they have in the area of open weights models.

[00:18:18] Alex Volkov: They have Lama 2. 13b and Mistral 7b, for example, they put Qwen in there as well. So Qwen 2. 57 and 14. And the partially open models, they put Zamba and Stable [00:18:30] LLM. And the fully open models, they put themselves and Olmo and, Ember7B and Olmo2 beats all of that category with some nice, average of stats.

[00:18:40] Alex Volkov: they talk about pre training and a bunch of other stuff. and the instruct category specifically with the Tulu kind of,recipes. What else can we say about Olmo? That's very interesting for folks before we jump into Qwen. What else can we say about Olmo? The, oh, the fact that the thing about the fully open source, we always mention this, is the data set.

[00:18:59] Alex Volkov: We [00:19:00] always talk about the data, they release all of the data sets, so Olmo mix was released, Dolmino mix was released, the SFT training data, post training data set was released as well. yeah, folks, comments. You can also try this model at playground. lnai. org. I've tried it. It's interesting. it's not look, uh,the best about this is the best among open source.

[00:19:21] Alex Volkov: Obviously it's not the best at, generally with closed source data, you can get more significantly better than this. But comments from folks about OMO? [00:19:30]

[00:19:30] Wolfram Ravenwolf: Yeah, it's not multilingual, they said that there is only English, but they are working on putting that in, I think, in another version, but, yeah, it's a truly open source model, not just OpenWeights, so a big applause for them, releasing everything, that is a big thing and I always appreciate it.

[00:19:46] Wolfram Ravenwolf: Thank you.

[00:19:48] Alex Volkov: A hundred percent. All right, folks, it looks like we got Eugene back. Eugene, talk to us about Heimbar.

[00:19:54] Eugen Cheugh: Yeah, no, sorry, I was just saying that as someone who works on transformer [00:20:00] alternative,it's actually really awesome to get the data point because we all haven't decided what's the best arrangement, what's the percentage of transformer versus non transformer?

[00:20:08] Eugen Cheugh: Is the non transformer layers in the front or the back? It's like you say, the car and the car scenario, it's like electric car, do we even know if we want the electric engine in front or the back? and these are data points that we love to test to just, find out more and it's. And I appreciate what NVIDIA is doing as well and looking forward to more research in this space.

[00:20:26] Alex Volkov: Awesome. thanks for joining us and feel free to stay. The more the merrier. This is like a [00:20:30] Thanksgiving kind of pre party for all of us. The more the merrier, folks. If you're listening to this only and you're not like on the live stream, I encourage you to go and check us out because like we're also like showing stuff.

[00:20:40] Alex Volkov: We're like showing the papers. We're like, we're waving. We're like showing Turkey, whatever. we're having fun. all right, folks. I think it's time to talk about the main course. We just ate the mashed potatoes. Let's eat the turkey for open source.

[00:20:53] Qwen Quill 32B Reasoning Model

[00:20:53] Alex Volkov: In this week's Open Source Turkey dinner, the Reasoning Model, the first ever Reasoning Open [00:21:00] Source, we got Qwen Quill, Qwen Quill?

[00:21:04] Alex Volkov: Yes, Qwen Quill 32 bit preview, the first open source. Let's go! Let's go! The first open source Reasoning Model from our friends at Qwen. We have Jun Yang here, Jun Yang and Justin Lin, to talk to us about this release. Folks at OpenAI released this, they worked for, the rest of about O1, we released a couple of months ago.

[00:21:25] Alex Volkov: Then the folks at DeepSeek released R1, that they just released it, they [00:21:30] promised to give us, maybe at some point. The folks at O1 did not release the reasoning. So, what you see in O1 is the reasoning being obfuscated from us, so we can't actually see how the model reasons. R1 gave us the reasoning itself.

[00:21:44] Alex Volkov: But didn't release the model. And so now we have a reasoning model that you can actually download and use. And unlike reflection, this model actually does the thing that it promises to do. Junyang, how did you do it? What did you do? Please give us all the details as much as possible. Please do the announcement yourself.

[00:21:58] Alex Volkov: Thank you for joining us. [00:22:00] Junyang from Qwen.

[00:22:00] Junyang Lin: Yeah, thanks everyone for the attention and for the appreciation, and I'm Junyang from the Qwen team, and we just released the new model for reasoning, but we just added a tag that it is a preview. Yeah, it is something very experimental, but we would really like to receive some feedback to see how people use it and to see what people think.

[00:22:24] Junyang Lin: The internal problems,they really are. Yeah, it is called QUIL. it is [00:22:30] something, very interesting naming,because we like to see that, we first called it like Q1,things like that, but we think it's something too normal and we'd like to see there was something connected with IQ, EQ, then we call it QQ, and then we found out, QWEN with a W there.

[00:22:47] Junyang Lin: And we found a very interesting expression because it looks really cute. There is a subculture in China with the text expression to express the feelings. So it is something very interesting. So we [00:23:00] just decided to use the name and for. For the pronunciation, it's just like the word Q, because I combined QW, the pronunciation of QW, with U together, and it's still just cute.

[00:23:13] Junyang Lin: Yeah, there's something beside the model, and it is actually a model, which can, And this is the reason before it reaches the final response. If you just try with our demo and you will find that it just keeps talking to itself. And it's something really [00:23:30] surprising for us. If it asks you a question, it just keeps talking to itself to discover more possibilities as possible.

[00:23:42] Junyang Lin: And sometimes will lead to some new things. Endless generation. So we have some limitations there. So we mentioned the limitations in the almost the second paragraph, which includes endless generation. But it is very interesting. I [00:24:00] don't say it is a really strong model, something like competitive to O1 or outcompeting R1.

[00:24:06] Junyang Lin: It is not Simply like that, we show the benchmark scores, but it is something for your reference to see that, maybe it is at this level, and then if you really check the model performance, when it processes like mathematics and coding problems, it really thinks step by step, and it really discovers more possibilities.[00:24:30]

[00:24:30] Junyang Lin: Maybe it is a bit like brute forcing, just like discovering all possibilities. If there are 1 plus 2 is equal to 1, and it discovers a lot of possibilities, but it sometimes finishes,can finish some very difficult tasks. I think, you guys can wait for our more official release, maybe one month or two months later.

[00:24:53] Junyang Lin: We'll make sure, And the next one will be much better than this preview one, but you can play with it. It is something really interesting, [00:25:00] very different from the previous models.

[00:25:02] Alex Volkov: So first of all, a huge congrats on releasing something that, everybody, it looks like it piqued interest for, tons of folks, absolutely.

[00:25:09] Alex Volkov: Second of all, it definitely thinks, it looks like it's,Actually, this seems like this. you can see the thinking, like we're actually showing this right now for folks who are just listening and I'll just read you the actual kind of ice cube question that we have that,somebody places four ice cubes and then at the start of the first minute, and then five ice cubes at the start of the second minute, how many ice cubes there are at the [00:25:30] start of the third minute,we should probably have prepared like a turkey based question,for this one, but basically the answer is zero.

[00:25:36] Alex Volkov: Oh, the ice cubes melt within a minute, and the answer is zero, and people know the answer is zero because, ice cubes melt faster than a minute. But, the,LLM starts going into math and s**t, and, just to be clear, O1 answers this question, it understands the answer is zero. Quill does not.

[00:25:53] Alex Volkov: But the reasoning process is still pretty cool and compared to like other models like you see you can see it thinking It's let me set up an equation. Oh, [00:26:00] actually, it's not correct Ah, now the equation asking for this and this and this and it goes like This is confusing Let me read the problem again.

[00:26:06] Alex Volkov: And so it tries to read the problem again. This feels Not like just spitting tokens. So Junyang, what, could you tell us like what's the difference between this and training at a regular Qwen 2. 5? So as far as I saw, this is based on Qwen 5, correct?

[00:26:27] Junyang Lin: Yeah, it is based on Qwen 2. 5 [00:26:30] 32 billion de instruct Model. Yeah, we have tried a lot of options, maybe we will release more technical details later, but I can tell you something that, we mostly simply do some, do some work on the, post training data. Because it is actually based on our previous model, so we did not change the pre training, because we are actually very confident in our pre training, because we have trained it with [00:27:00] a lot of tokens, so there should be some knowledge about reasoning there, and in Qwen 2.

[00:27:05] Junyang Lin: 5, we also have some text reasoning, relative data, in the pre training process, so we just try to see that if we can align with the behavior of such, reasoning. So we have some very simple,superfines, fine tuning, and we find that while it can generate things like that, we have done a bit like RL stuff, and we also have done something like, RFT, Rejection, [00:27:30] Finetuning, so we can add more data from it.

[00:27:33] Junyang Lin: And there are a lot of techniques, just like self aligned. We use the base language model to use in context learning to build samples for us, to just We've built something like that make the model that can reason and we found that it's really surprising. We did not do very complex stuff, but we find that it has this behavior, but we still find that there is still much room in the reinforcement learning [00:28:00] from human feedback because we found that if you add some RL, you can improve the performance very significantly, so we have some belief that Maybe we, if we have done some more in a process where we're modeling LLM critiques and also things like building more nuanced data for the multi step reasoning, the model will be much better.

[00:28:26] Junyang Lin: Yeah. But this one is interesting. You can keep [00:28:30] talking to it. It keeps talking to itself, just talking about some strange thinking and sometimes maybe I'm wrong. I will check the question again and maybe I'm wrong again and then do it again and again. And sometimes it's generally too long because we have some limitations in long text generation.

[00:28:49] Junyang Lin: I think All models have this problem, so when it reaches maybe some bound and it will turn into some crazy behaviors, it just never [00:29:00] stops generating. We just mentioned this limitation. Just

[00:29:05] Alex Volkov: to make sure folks understand, this is a preview, this is not like an official release. You guys are like, hey, this is a preview, this is a test of you guys.

[00:29:12] Alex Volkov: You guys are like trying this out, like folks should give feedback, folks should try it out. Maybe Finetune also on top of it. Yeah. There's definitely we're trying this out. This is

[00:29:21] Yam Peleg: it's like chatGPT is a research preview. It's not exactly a preview. It beats the benchmarks on so many problems.

[00:29:29] Yam Peleg: We would

[00:29:29] Junyang Lin: like [00:29:30] to make it a fun, funny stuff to make people happy. It's now Thanksgiving and people are always expecting models from us. And they're just talking that all out. where's our reasoning model or things like that. Yeah. so we showed this one to you. And.

[00:29:48] Alex Volkov: Yeah, Jan Wolfram, folks, comments about the reasoning model from Qwen.

[00:29:53] Yam Peleg: Oh, I have a lot of comments. That's a lot. I don't know if you can hear me. Yeah, Jan, [00:30:00] go ahead.

[00:30:00] Alex Volkov: There's just a delay, but we're good.

[00:30:02] Yam Peleg: Yeah, I just want to say, it's like, uh, CGPT is, uh, is a research preview. It's it's a really good thing.

[00:30:10] Yam Peleg: It's a really good model. Seriously. So, I mean, it can be a preview, but it's extremely powerful. How did you guys train this? I mean, what, what, what's the data? How did you generate it? Can you Can I just create data that looks like O1 and Finetune and it's going to work? or, like, give us some details.

[00:30:28] Yam Peleg: it's a really hard thing to [00:30:30] do. it's really, really, really successful. Sohow did you make it?

[00:30:35] Alex Volkov: Give us some details if you can, I'm saying. if you can. Don't let Yam, don't let Yam go into give some details that you cannot give details. but hey, it looks like we may have lost Junyang for a bit with some connection issues, but while he reconnects, we got Maybe he can't, maybe he can't hear details, so

[00:30:52] Wolfram Ravenwolf: They put the plug.

[00:30:53] Alex Volkov: and Wolfram, what's your, I saw your take. Let's, meanwhile, let's take a look. You did some testing for this model as well, right?

[00:30:59] Wolfram Ravenwolf: [00:31:00] Yeah. And I just ran the, the IceCube prompt and on my run, it got the zero correct.

[00:31:04] Wolfram Ravenwolf: So that is a bit of a red flag. Oh, you

[00:31:06] Alex Volkov: did get it correct.

[00:31:07] Wolfram Ravenwolf: Yeah. it was fun because it wrote, Over 10, 000 characters, but in the end it said, okay, so confusing, they all melted zero. So that worked. But of course you have to run benchmarks multiple times. I did run the MMLU Pro computer science benchmark twice.

[00:31:23] Wolfram Ravenwolf: And what is very interesting is, Also here, it generated much more tokens than any other model. The second, highest [00:31:30] number of tokens was GPT 40, the latest one, which was 160, 000 tokens for the whole benchmark. And here we have over 200, 000, 232, 000 tokens it generated. So it took me two and a half hours to run it.

[00:31:45] Wolfram Ravenwolf: And, yeah, it's an 8B model, no, a 32B model at 8 bit in my system where I was running it, because I have 48GB VRAM, so you can run it locally and look at it, it's, it's placed above the 405B [00:32:00] Lama 3. 1, it's above the big Mistral, it's above the GBT, JGBT latest, and the GBT 4. 0 from, yeah, the most recent one.

[00:32:08] Wolfram Ravenwolf: So just to recap

[00:32:09] Alex Volkov: what you're saying. On the MMLU Pro Benchmark, this is a model that you run on your Mac, or whatever PC, and it beats Llama 3. 5, 4 or 5 billion parameter on this benchmark, because it's reasoning and it's smart, it runs for longer, and it uses those test time compute, inference time [00:32:30] compute, Compute, Scaling, Loss that we talked about multiple times.

[00:32:33] Alex Volkov: It runs for longer and achieves a better score. This is like the excitement. This is the stuff. so Junyang, now that you're back with us, could you answer, or at least some of Yam's question, if you couldn't hear this before, I will repeat this for you. How? What does the data look like? can you just come up with some O1 stuff?

[00:32:51] Alex Volkov: By the way, welcome, welcome Nisten.

[00:32:53] Nisten Tahiraj: But I tried it.

[00:32:54] Introduction to the New Google Model

[00:32:54] Nisten Tahiraj: It got the Martian.Rail Train Launcher, it got it perfectly [00:33:00] on first try, and I saw that it did take it three tries, so I use this as a standard question on most models, is if you're going to launch a train from the highest mountain in the solar system, which is on Mars, and you want to accelerate it at two G's, so Still comfortable.

[00:33:21] Nisten Tahiraj: how long would that track need to be in order for you to get to orbital velocity and in order for you to get to, to leave [00:33:30] Mars gravity well? And it's a very good question because there's so many steps to solve it and you can just change it to, you can say 2. 5G and that completely changes the order of the steps for, that the model has to solve.

[00:33:42] Alex Volkov: So it's unlikely to be in the training data and it got it perfectly. It's again, it's this one, it's the new Google preview, even Sonnet takes two tries, two or three tries often to get the right answer. So,yeah, the model worked, and I had the same thing as [00:34:00] Wolfram, he did put out a lot of tokens, but again, it's pretty fast to run locally, Folks, it's a good model. It's, it, for a test preview, for something that was released, as a first, open weights reasoning model, we are very impressed.

[00:34:14] Model Performance and Availability

[00:34:14] Alex Volkov: we're gonna give Junaid, one more, one more attempt here, Junaid, I see you on the spaces. and you're as a speaker, maybe you can unmute there and speak to us through the spaces,while we try this out, I will just tell to folks that like you are, you can download this model.

[00:34:27] Alex Volkov: It's already on, OLAMA. [00:34:30] You can just like OLAMA install Quill or QWQ.it's already on OpenRouter as well. You can get it on OpenRouter. So you can like replace. you can replace whatever you use, like OpenAI, you can replace and put this model in there. it's, you can try it out in Hug Face, this is where we tried it just now.

[00:34:47] Alex Volkov: And, It's awesome. It's awesome to have this. I'm pretty sure that many people are already like trying different variations and different like fine tunes of this model. And it just like going up from here, like to get a open [00:35:00] model, 32 billion parameters, that gets, what is the score? let me take a look.

[00:35:04] Alex Volkov: The score is, I think it gets, 50 on AIME. It's ridiculous. Anybody try this on ARK Challenge, by the way? Do you guys see in your like, like tweets or whatever, the ARK Challenge? Anybody try to run this model on that and try? I would be very interested because that's that's a big prize. It's a very big prize.

[00:35:22] Alex Volkov: I'm pretty sure

[00:35:22] Eugen Cheugh: someone's trying right now. You shall think that out.

[00:35:26] Alex Volkov: I'm pretty sure somebody's trying right now. They could use a

[00:35:29] Wolfram Ravenwolf: 72B [00:35:30] version of it and maybe that gets even better. Probably does.

[00:35:35] Alex Volkov: Yeah. They're probably training a bigger model than this right now. all right folks. So with this, I think that, we've covered pretty much everything that we wanted to cover with Quill.

[00:35:46] Scaling and Model Efficiency

[00:35:46] Alex Volkov: and I think, yeah, the one thing that I wanted to show, let me just show this super quick before we move on to the next topic that we have is this, scaling kind of thing. We saw pretty much the same thing. From, from [00:36:00] DeepSeq. And then we saw pretty much the same thing also from OpenAI. The kind of the scaling confirmation, the scaling log confirmation, the next scaling log confirmation, test time compute or inference time compute works.

[00:36:11] Alex Volkov: Which basically means that the more thinking, the more tokens, the more time you give these models, the better. to think, the better their answer is. We're getting more and more confirmation for this kind of Noah Brown, I don't know, thesis, that these models actually perform [00:36:30] significantly better when you give them more tokens to think.

[00:36:32] Alex Volkov: this is incredible to me. This is like incredible because not only will we have better models with more scale, but Even though some people claim a wall has been hit, no wall has been hit. but also we now have these models that can answer better with more tokens. and this is like another, another confirmation from this.

[00:36:51] Alex Volkov: Qwen, Quail32B is now here. You can, you can now run. a, a 4 0 5 B level models, at least on [00:37:00] MMLU Pro,like wolf from here said on your computers. And shout out to our friends from, Alibaba Quinn for releasing these awesome models for us as a Thanksgiving,present.

[00:37:10] Alex Volkov: Jang, you're back with us. Let's see. maybe you're back.

[00:37:14] Junyang Lin: I don't know if you can hear me. Yes,

[00:37:16] Alex Volkov: we can hear you finally, yes.

[00:37:18] Junyang Lin: I don't know what happened.

[00:37:19] Alex Volkov: it's

[00:37:20] Junyang Lin: fine. I

[00:37:22] Alex Volkov: think that, let's try this again. maybe last thing as we're going to try.

[00:37:27] Discussion on Reasoning Models

[00:37:27] Alex Volkov: What, from what you can tell us, [00:37:30] how does the work on this look like?

[00:37:34] Alex Volkov: Is a lot of it synthetic? Is a lot of it RL? Could you give us, a little bit of, Give us a hint of what's going to come in the technical release for this. And also what can we look forward to in the upcoming? Are you maybe working on a bigger model? give us some, give us something for Thanksgiving.

[00:37:51] Junyang Lin: Oh yeah. for the reasoning steps, I think, the data quality, really matters and, we, we think that, it may split the steps, [00:38:00] more, make it more nuanced. make it more small steps. It can be just, the possible answers, with higher possibility, which means that the machine may think, in a different way from, the human being.

[00:38:12] Junyang Lin: The human being may reach the answer very directly, but sometimes, for a reasoning model, it may reason to explore more possibilities. So when you label the data, you should pay attention to, these details and, This is a part of it, and now we only have done some work on mathematics and [00:38:30] coding, and especially mathematics, and I think there's still much room in general knowledge understanding.

[00:38:37] Junyang Lin: I found that Wolfram just tested it for the MMU PRO, but we actually did not strengthen its performance for the MMU PRO. this kind of benchmark. So I think for the scientific reasoning, there's still much room for it to do it. And something surprising for us, is that we found that, it sometimes generate more beautiful texts, more [00:39:00] poetic, some, something like that.

[00:39:02] Junyang Lin: I don't know why, maybe it is because it reasons. So I think it may encourage creative writing as well. A reasoning model that can encourage creative writing. That would be something very interesting. I also found some cases, in Twitter, that people find that, it sometimes generates, text more beautiful than, Claude's written by someone and created.

[00:39:22] Junyang Lin: there's still much room for a reasoning model. Yep.

[00:39:25] Alex Volkov: Very interesting. Just to recap, folks found that this model that is [00:39:30] trained for reasoning gives more poetic, writing. that's very interesting. All right, folks, I think it's time for us to move on, but

[00:39:37] Wolfram Ravenwolf: just one quick comment.

[00:39:39] Multilingual Capabilities of Qwen

[00:39:39] Wolfram Ravenwolf: It's also very good in German. I tested it in German as well. So even if it may not be the focus, if you are multilingual or another language, try it. Yeah,

[00:39:50] Junyang Lin: that's something not that difficult for us because the Qwen is strong model is multilingual And it is actually I think it is now good at German.

[00:39:59] Junyang Lin: Yeah, [00:40:00]

[00:40:02] Alex Volkov: Qwen's multilingual is very good at German.

[00:40:04] BlueSky hate on OpenSource AI discussion

[00:40:04] Alex Volkov: Alright folks, I think that it's time for us to move on a little bit and Now we're moving to less fun, less of a fun conversation, but I think we should talk about this. just a heads up, after this, we're gonna have this week's buzz, but I don't have a category for this.

[00:40:19] Alex Volkov: I don't have a category for this, but it must be said. as ThursdAI is all about positivity. We talk about AI every week to highlight the advancement we highlight with positivity we get excited about every new [00:40:30] release every new whatever we also recently and now we have you know we're on youtube as well and the reason it coincided well with some of the folks in the ai community moving over to blue sky let me actually first Say hi to my colleague here, Thomas.

[00:40:44] Alex Volkov: I'm going to pull you up on stage as well. welcome Thomas as well. Hey man, welcome. My colleagues for the past year from Weights Biases, welcome as well. You're more than welcome to join us as well, because you're also on BlueSky. And, so a bunch of the community, recently started seeing whether or not there's a [00:41:00] new place over at BlueSky.

[00:41:02] Alex Volkov: for the ML community. I saw a bunch of ML people over there as well. I see Wolfram over here has a little butterfly. you all who are joining us from Twitter, or Xspaces, for example, you've probably seen a bunch of your favorite AI folks post just a blue butterfly and maybe follow them towards the other social media platform due to your political preferences, wherever they may be, which is completely fine.

[00:41:26] Alex Volkov: That's all good and well and fine. so I started cross posting to both, [00:41:30] and I'll show you how my screen looks like recently. This is how my screen looks like. I scroll here, I scroll on X, and I scroll on blue sky. This is what my life looks like. Yes, I'm on both. because I want to make sure that I'm not missing any of the news.

[00:41:43] Alex Volkov: That I want to bring to you, and also Zinova, our friend, right? He posts everywhere, and I see the community bifurcating. I don't like it. But I want to make sure that I'm not missing anything. This is not what I want to talk to you about. Not the bifurcation. I don't mind the bifurcation. We'll figure out something.

[00:41:58] Alex Volkov: We're on YouTube as well, [00:42:00] so the folks from BlueSky who don't jump on TwitterX community, they can still join the live chat. What I want to talk to you about is this thing that happened where, a bunch of folks from Hug Face just joined Blue Sky as well, and one of the maybe nicest people in, from the Hug& Face community, Daniel,I'm blanking on his last name, Nisten, maybe you can help me out, Daniel Van Strijn?

[00:42:24] Alex Volkov: Daniel Van Strijn?basically, did what he thought was [00:42:30] maybe a cool thing. He compiled the dataset. You guys know, we talk about data and open source and Hug Face as well. This is like in the spirit of the open source community, there's, we talk about open datasets. we, I have a thing here. This is my thing.

[00:42:43] Alex Volkov: When we talk about somebody releasing. Open source datasets. We have a thing. We clap, right? and so he compiled, a dataset of 1 million blue sky posts to do some data science. This is like what Hagenfeist, put it on Hagenfeist. just to mention one thing before, [00:43:00] unlike Twitter, which used to be open, then Elon Musk bought it and then closed the API, and then you have to pay 42, 000 a year.

[00:43:07] Alex Volkov: 42, 000 a year. Yes, this is the actual price. 42, 000 a year. this is the actual literal price for the API. Unlike Twitter, which used to be free, BlueSky is built on a federated algorithm. There's a firehose of API you can apply to it. And then you can just like drink from this firehose for free. This is like the whole point of the platform.

[00:43:27] Alex Volkov: so then you'll connect to this firehose, drink from it and [00:43:30] collect, compile the data set of a 1 million posts, put it up on Hug Face, open source.

[00:43:36] Community Reactions and Moderation Issues

[00:43:36] Alex Volkov: And then got death threats. Death threats. He got death threats for this thing. People told him that he should kill himself for this act where he compiled data from an open fire hose of data that is open on purpose.

[00:43:58] Alex Volkov: What the actual f**k? [00:44:00] And when I saw this, I'm like, what is going on? And in less than 24 hours, I'm going to just show you guys what this looks like. Okay. this is the, this is on the left of my screen and the folks who are not seeing this, you probably, I'm going to, maybe pin.

[00:44:13] Alex Volkov: Yeah. let me just do this super quick. So you guys who are just listening to this, please see my pinned tweet, as well. because this is some insanity. Okay. And we have to talk about this because it's not over here. he compiled a 1 million public posts, BlueSky Firehose API, data set.

[00:44:27] Alex Volkov: And then, it got extremely [00:44:30] viral to the point where I don't know, it's like almost 500 whatever it's called. And then the amount of hate and vitriol in replies that he got from people in here. Including, yes, including you should kill yourself comments and like death threats and doxing threats, et cetera.

[00:44:47] Alex Volkov: many people reached out directly to,HugNFace folks. he became maybe number two most blocked person on the platform as well. and all of this, they, people reached out to the Hug Face community. Basically in less than [00:45:00] 24 hours, he basically said, I removed the BlueSky data from the repo.

[00:45:03] Alex Volkov: I wanted to support the tool development for the platform, recognize this approach, violate the principle of transparency and consent. I apologize for this mistake, which, okay, fine. I acknowledge his position. I acknowledge the fact that he works in a,he works in a company and this company has lawyers and those lawyers need to adhere to GDPR laws, et cetera.

[00:45:23] Alex Volkov: And many people started saying, Hey, you compiled my personal data without, the right for removal, et cetera, without the due [00:45:30] process, blah, blah, blah. Those lawyers came, there's a whole thing there. And then our friend here, Alpen, who's a researcher, of his own, connected to the same open firehose of data, and collected a dataset of 2 million posts.

[00:45:47] Alex Volkov: That's twice as many as Daniel did, and posted that one, and then became the person of the day. Alpen, you want to take it from here? You want to tell us what happened to you since then? What your 24 hours looked [00:46:00] like?

[00:46:00] Alpin Dale: yeah, sure. it's been quite the experience being the main character of the day in Blue Sky.

[00:46:05] Alpin Dale: And,obviously, I'm not showing my face for very obvious reasons. I have received quite a few threats because, Yeah, unlike Hugging Face employees, I am not beholden to a corporation, so I didn't really back down. And, yeah, I probably received hundreds of death threats and doxxing attempts.

[00:46:24] Alpin Dale: so just to reiterate what you said, the Firehose API is completely [00:46:30] open.

[00:46:31] Alpin Dale: It is, it's a good analogy with the name because it's like a firehose, anyone can use it.

[00:46:35] Legal and Ethical Implications

[00:46:35] Alpin Dale: you have they've also,threatened me with litigation, but, I'm not sure if you guys are aware, but there was a court case back in 2022, HiQ Labs versus LinkedIn, where, HiQ Labs was, scraping public, public accounts from LinkedIn and, using it for some commercial purposes, I don't remember.

[00:46:54] Alpin Dale: But, They did actually win in court against LinkedIn, and what they were doing was [00:47:00] slightly even more illegal because LinkedIn doesn't have a publicly accessible API, and they have Terms of Services specifically against that sort of scraping, and because of that, the ruling overturned later and they, they lost it, they lost the claim, but it did set a precedent to be had that if the,if the, data published on publicly accessible platforms could be lawfully connected, collected and used, even if terms of service like purported to limit such usage.

[00:47:28] Alpin Dale: But I [00:47:30] Never agreed to such a term of service when I started scraping or copying the data from the Firehose API because first, I didn't do any authentication. Second, I didn't provide a username when I did that. So anyone could have done that technically with the AT protocol Python SDK. It's you don't even need to sign in or anything.

[00:47:52] Alpin Dale: You just sign in. Connect to the thing and start downloading.

[00:47:55] Alex Volkov: Yeah, this is the platform is built on the ethos of the open [00:48:00] web. The open web is you connect and you read the data. This is the ethos of the open web. When this is the ethos of the open web, when you post on this platform, Whether or not the TOS is saying anything, when you don't need to authenticate, the understanding of the people should be, regardless, and I understand some of the anger when the people discover, oh, s**t, my, my thoughts That I posted on this platform so far are being used to like, whatever, train, whatever.

[00:48:28] Alex Volkov: I understand some of this, I [00:48:30] don't agree with them, but like I understand, what, how some people may feel when they discover Hey, my thoughts could be collected, blah, blah, blah. and somebody posted like a nice thread. But, the platform is open completely. Going from there to death threats, this is, like, where I draw completely, where I draw my line.

[00:48:45] Alex Volkov: Alpen, the next thing that happened is what I want to talk to you about. you're getting death threats, you're getting doxxed attempts. Um,I couldn't find your post today. what happened?

[00:48:56] Alpin Dale: for some reason, BlueSky decided to terminate my [00:49:00] account instead of the ones issuing the death threats, very interesting chain of events, but,they claimed that I was engaging in troll behavior, whatever that means.

[00:49:10] Alpin Dale: And for that reason, they just, like it wasn't even,due to mass reporting that happens on X. com, right? Specifically emailed me with very, human generated language, where they told me that I was being a troll. I think I posted it on my Twitter account too. And, Yeah, they just assumed I'm trolling, [00:49:30] and what's funny is there's been screenshots floating around of similar mod messages, just giving people a slap on the wrist for much, much worse things, like things we can't even talk about here, right?

[00:49:44] Alpin Dale: So very strange, very silly situation overall. And another thing I wanted to mention, a lot of people. We're bringing up the GDPR and all that because of like personally identifiable information, but if you go to the [00:50:00] dataset, all we have is the post text. The timestamp, the author, and the author name is a, it's just a hash, it's not the full author name, and the URI, so there isn't really much to link people to the, to their specific posts, and there isn't even a location tag, so I'm not sure if it fully applies with GDPR, but I'm not a liar anyways, and, The thing is, the data or their posts were published on a platform that is explicitly designed for public [00:50:30] discourse, right?

[00:50:31] Alpin Dale: And the decision to share sensitive information on a platform like this lies with the user, not the observer. And we are the observer in this case. And by the very nature of public platforms, Individuals that post like content like this, they have to bear the responsibility that their information is accessible to anyone.

[00:50:51] Alpin Dale: And I don't think my dataset like alters this reality because it just consolidates information that was already available for [00:51:00] everyone. And I guess,there were also people who were asking for an opt out option and, the Hugging Face CEO, Clem, also made an issue on the repo about this. And I did provide a very straightforward opt out process, if someone wants to remove that data, they can just submit a pull request.

[00:51:18] Alpin Dale: to remove the specific posts that belong to them but alsothey have to accompany it with a proof of authorship they have to prove to me that the post that they're removing is not a [00:51:30] it belongs to them and it's not a malicious request so i guess i've covered all grounds so i'm not sure what the what people are worried about

[00:51:38] Alex Volkov: so i uhI'm just showing to the folks who are listening, I'm showing a, an email from,from the moderation team at BlueSky.

[00:51:46] Alex Volkov: BlueSky County Control, Alpendale, BlueSky Social was reviewed by BlueSky Content Moderators and assessed as a new account trolling the community, which is a violation of our community guidelines. As a result, the account has been permanently suspended. They didn't even give you the chance to like, hey, delete this and come back to [00:52:00] the platform.

[00:52:00] Alex Volkov: Literally permanently suspended. the folks who are like saying, hey, You are going to be,delete this and come back or the folks who are like 13 death threats, are not there. Um,What can we say about this? it's ridiculous. Absolutely. And I, The fact that Hug Face's account, your account, Daniel's account, became the most blocked accounts on the platform in the past 24 hours, more so than some like crazy Manosphere accounts, is just is absolutely insanity.

[00:52:28] Alex Volkov: The fact that most of [00:52:30] these anger prone accounts People are like anti AI completely. And the whole issue about like consent, whatever, most of them don't even appear in the dataset, by the way. Like some people checked on the fly, Zeofon and I, like we did some basic checking, many people didn't even appear in the dataset.

[00:52:44] Alex Volkov: the fact that the absolute silly fact that the, none of them understand the Barbra Streisand effect on the internet and the fact that there's five datasets right now. Many of them collected the people who reacted to these specific posts and collected the data [00:53:00] set of the people who reacted to these specific posts.

[00:53:02] Alex Volkov: And people just don't understand how the internet works. That was just like ridiculous to me.

[00:53:07] Moving Forward with Open Source

[00:53:07] Alex Volkov: so Alpen, I personally think that you did Many of these people also a very good service as well, because at least some of them now realize how open internet works, despite the being very upset with the fact that this is how the open internet works, at least some of them are now like realizing this.

[00:53:23] Alex Volkov: I,I commend you on like the bravery and standing against this like absolute silliness and not backing down. And [00:53:30] Yeah, go ahead. Happy

[00:53:31] Alpin Dale: to serve. Yeah, another small thing I wanted to add was, I've received a lot of threats about me getting reported to the EU, but what I find really ironic is that,earlier this year, the EU funded a research for collecting over 200 million blue sky posts with a greater level of detail.

[00:53:50] Alpin Dale: So clearly the EU is fine with this, so I don't know what's the problem here, once again.

[00:53:58] Alex Volkov: yeah, I saw this. Yeah, there's a way [00:54:00] bigger thing. The last thing I saw about this, and then maybe we'll open up for folks, and then I would love to chat with my friend Thomas, for whom it's late, and I invited him here, and I want to be very mindful of his time as well, so thank you, Thomas, for being patient.

[00:54:12] Alex Volkov: The last thing I say about this is that this sucks for open source, from the very reason of, if you're open and public and good hearted about this, Hey folks, here's the data in the open, you can look at this data and you can ask for your s**t to be removed. You get an angry mob of people threatening [00:54:30] death against you and asking your lawyers to like, literally people asking like, was Daniel fired?

[00:54:34] Alex Volkov: what the f**k? Meanwhile, this is a open firehose and all of the companies in the world probably already have all this data. I'm pretty sure, OpenAI has been already training on BlueSky. Like, why wouldn't they? It's open. Literally, if you want to train, and Thomas, maybe here is like a little entry to what we're going to talk about.

[00:54:50] Alex Volkov: If you want to train a toxicity,thing, There is now a very good place to go to and look at toxicity score or I can show you where you can go [00:55:00] to to train toxicity score. Like, why wouldn't you go and collect this data? It's free, like literally it lies on the internet.

[00:55:05] Alex Volkov: Nothing in the TOS, like Alpen said, even I went to the TOS of BlueSky. Literally it says over there, we do not control how other people use your data. Like literally that's what it says on the TOS. So yeah, I'm just like, I'm very frustrated against this. I want to speak out against this, absolutely ridiculous behavior.

[00:55:22] Alex Volkov: I don't think that this,okay. So I don't think that the, how the people reacted on the platform speaks against the platform itself. I do think [00:55:30] That the way the moderators, acted out against Alvin's account and the removal of account permanently banned, speaks completely against the platform.

[00:55:38] Alex Volkov: This is stupid and we should speak against this, on the platform itself. if we think that this is a place for the community, that's where I stand. And I wanted to share the publicly, super brief comments, folks, and then we'll move on to this week's bus.

[00:55:49] Wolfram Ravenwolf: There was a link in his message from the moderators that he can reject it and get a review, appeal, yeah.

[00:55:58] Wolfram Ravenwolf: So I hope that, I hope [00:56:00] he gets the appeal through. That is important. Yeah,

[00:56:03] Alex Volkov: if you will,please email them with an appeal and, tell them about the multiple death threats that you received and the fact that, you didn't, did not mean to troll.

[00:56:12] Wolfram Ravenwolf: I reported every one of those messages, by the way, and anyone who does it is probably a good thing.

[00:56:18] Alex Volkov: Nisten, I know you have thoughts on this. I would love to hear.

[00:56:22] Nisten Tahiraj: we need to better educate people to not go after the ones on their side. a lot of the open source devs do this stuff [00:56:30] because they want everyone to have, Healthcare robots that no single corporation owns. They make this data public because people want to democratize the technology for everyone.

[00:56:41] Nisten Tahiraj: So it's not, it doesn't become like authoritarian and like a single source of control. And, to see that they prioritize, just, people's anger and feelings versus being objective. about it. Whereas, [00:57:00] so in this case, the public forum data set is public domain on purpose. And this is what drew people to the community in the first place, because they felt like Twitter was becoming too political, single sided.

[00:57:12] Nisten Tahiraj: And, we didn't like that. And a lot of people moved to, because they saw Blue Sky as a, Much better, democratized alternative to all of this. And,so that's really disappointing because, these are the people on your side and, now the two [00:57:30] nicest, most contributing open source devs that we know, are more hated than, like someone like Andrew Tate.

[00:57:37] Nisten Tahiraj: that just makes no sense at all. the, out of the five most blocked accounts Two of them are like the nicest people we know. So what is, something is pretty, pretty off. And, I'm also worried that in the AI community, we are in a bit of a bubble and not quite aware of,what people on our side are being communicated.

[00:57:58] Nisten Tahiraj: are being shown how this [00:58:00] stuff works, how open source, works because I'm pretty sure from their point of view, they're like, oh, here's another company just took all of our data and is just gonna train this porn bot with it and there's nothing we can do about it, but it's not like that.

[00:58:13] Nisten Tahiraj: Not a single company can own this data. It is public domain. We can't sue anyone else over the data. It's public domain in a public forum. You're supposed to have civil discourse because then the AI can also have civil [00:58:30] discourse and be reasonable and be like aligned to humanity. so now you have a bunch of people just giving, death threats and they're okay because they're just angry.

[00:58:40] Nisten Tahiraj: So you can tell someone to go kill themselves just because you're angry. And, yeah, so that's not good. Like they're just not good. you should probably, yeah, anyway, so there is something for us to do as well, like we need to communicate better, what does open source do versus what having a single company.

[00:58:58] Nisten Tahiraj: Own all that data and [00:59:00] have it as their property. because I feel like most of the general public doesn't really understand this.

[00:59:06] Nisten Tahiraj: yeah, that's it. I was just, okay. Just really quickly. Sorry. I went on too long, but after going through war in the Balkans as a kid, I didn't think people would be getting death threats for an open source dataset.

[00:59:17] Nisten Tahiraj: It's this is just completely beyond, It's absolutely unhinged. yeah, this is just completely off.

[00:59:23] Wolfram Ravenwolf: Unhinged. Just one thing, those people even think that now the thing is over, so the dataset has been [00:59:30] removed, okay, it's done, but you can get a new one anytime. The platform hasn't changed. They have to realize that.

[00:59:37] Alpin Dale: funny it mentioned that because they started blocking me for the explicit reason of, the user started blocking me for the explicit reason of stopping me from scraping their posts, as if I need my account to do that.

[00:59:49] Alex Volkov: Yeah, I think that there's, a lot of misunderstanding of, what's actually, happening.

[00:59:54] Alex Volkov: And how, which is fine, I completely empathize of people's misunderstanding of [01:00:00] technology, and thus fear, I get this I get the visceral reaction, I get,I don't like multiple other things about this, I don't like the, the absolute, horror mob. And the death threats, I don't like the platform reacting as it did, and like blocking completely, those things don't make sense.

[01:00:14] Hey, this is Alex from the editing studio. Super quick, about two hours after we recorded the show, Alpin posted that the moderation team at BlueSky emailed him and his account was in fact reinstated. He didn't ask them to. [01:00:30] They revisited their decision on their own.

[01:00:32] So either a public outcry from some individuals on the platform. Hopefully they listened to our show. I doubt they did. Um, but they reversed their decision. So I just wanted to set the record straight about that. He's back on the platform. Anyway, back to the show.

[01:00:48] Alex Volkov: Alright folks, unfortunately though, we do have to move on, to better things, and I'll give my other co hosts like a little five, five to seven minutes off, to go take a break. Meanwhile, we're going to discuss [01:01:00] this week's buzz.

[01:01:00] This Week's Buzz: Weights & Biases Updates

[01:01:00] Alex Volkov: Welcome to this week's buzz, a category at ThursdAI, where I talk about everything that I've learned or everything new that happened in Weights Biases this week. And this week, I have a colleague of mine, Thomas Capelli, [01:01:30] from the AI team at Weights Biases. We're now the AI team. This is new for us. We're Thomas, how, do you want to introduce yourself super brief for folks who've been here before, but maybe one more introduction for folks who don't know who you are.

[01:01:43] Thomas Capele: Yeah, I'm Thomas. I work with Alex. I'm in the AI Apply team at Weights Biases. I train models, I play with models on API, and I try to make my way into this LLM landscape that is becoming more and more complex. Try to avoid [01:02:00] getting roasted on the internet. And yeah, trying to learn from everyone. Thank you for the meeting.

[01:02:06] Alex Volkov: So you're going by Cape Torch, I'm going to add this as well on X as well. I don't know what you're going off as,on Blue Skies, same Cape Torch. I invited you here, and I think let's do the connection from the previous thing as well. A lot of toxicity we talked about just now, a lot of like toxic comments as well.

[01:02:23] Alex Volkov: and we're, we both work at Weights Biases on Weave. Weave is our LLM observability tool. [01:02:30] I showed off Weave multiple times on ThursdAI, but I will be remiss if I don't always remind people, because we have a bunch of new folks who are listening, what Weave is. Weave is an LLM observability tool. So if you're building as a developer, Anything with LLMs on production,you need to know what's going on, what your users are asking your LLM or what your LLM gives as responses, because sometimes imagine that your users are, let's say copy pasting, whatever comments, people just gave [01:03:00] Daniel and Alpin and they pasting it to them to do categorization, for example, and some of these like, Very bad things that we just talked about are getting pasted into the LLM and some of the LLM responses are maybe even worse, right?

[01:03:13] Alex Volkov: so maybe your application doesn't handle this. Maybe your application responds even worse and you want to know about this. and, the way to see those, some developers just looks at logs. we have a tool. That is way nicer. And, this is just some of the things it does. but this [01:03:30] tool is called Weave.

[01:03:30] Alex Volkov: it, it traces everything that your application gets as an input from users and also outputs. but that's not all it does. So it also allows you to do evaluations. And, recently Thomas and, has been working on, multiple things, specifically around scoring and different things. Thomas,you want to maybe give us a little bit of.

[01:03:47] Alex Volkov: Yeah, I think you,

[01:03:48] Thomas Capele: you described pretty well. Yeah, as I know, you have showed Weave and the product we have been working for a while, multiple times here, but it's, I would say it's mostly core feature is [01:04:00] actually building apps on top of LLMs and having observability and yeah, standard code, we have unit tests and for LLM based applications, we need like evaluations, actual evaluations on data we have curated.

[01:04:13] Thomas Capele: And it's, we have been doing this in the ML world for a while, but as we are merging with the software engineers that. Maybe don't know how to integrate this randomness from the LLMs in the, in their applications. Yeah. you need to actually compute evaluations. And that means gathering [01:04:30] data, still labeling a lot of stuff manually to have high quality signal.

[01:04:35] Thomas Capele: And then, yeah, iterating on your prompts and your application that, that's making API calls with scores, with metrics that gives you confidence that we are not like screwing up. And as you said, like I've been working recently on adding, we added a bunch of scores, default scores. We've a couple, yeah, like a month ago with Morgan, we spent like a week building those.

[01:04:58] Thomas Capele: and recently we have been like, [01:05:00] yeah, looking at stuff like toxicity and hallucination and yeah, context and bias detection, and there's multiple of them that are LLM powered, like the ones you are showing on the screen right now, like You have an LLM that it's actually prompt in a certain way, and you maybe build a system that requires like a couple of LLM prompt with structured output to actually get the scores you were expecting,and then this thing should be able to give you, yeah, a good value of the [01:05:30] scoring if it's hallucinating, if it's a toxic, actually the mall providers like OpenAI and Mistral and Anthropic, I think have an API exactly for moderation.

[01:05:41] Thomas Capele: So yeah, you can use also that and they are actually pretty good and fast and pretty cheap compared to the completion ABA. And we have no, what I've been doing this week and the last couple of weeks where I've been trying to build really high quality, small, non LLM powered scores. So example that you want to create a toxic, [01:06:00] detection system.

[01:06:00] Thomas Capele: Yeah. what can you do? Yeah, you could find a small model that it's not an LLM or it was an LLM a couple years ago. Now, like BERT, we don't consider BERT an LLM.

[01:06:09] Alex Volkov: Yeah.

[01:06:10] Thomas Capele: yeah. I've been fine tuning the BERT task and checking like this new high end phase, small LLM2 models, trying to adapt them to the task.

[01:06:18] Thomas Capele: Yeah. yeah, like good challenge, good engineering questions, like creating, there's plenty of high quality data set on HangingFace that people have been creating from multiple places, from Reddit, and [01:06:30] like these have been serving us to actually build this high quality classifiers that are capable of outputting and flagging the content that we're interested in.

[01:06:40] Alex Volkov: So here's what I, here's what I'll say for folks, just to like highlight what we're talking about. Weave itself. is a toolkit that you can use for both these things. You can use it for logging and tracing your application, which is what it looks like right now. You basically add these lines to your either Python or JavaScript application, JavaScript type of application, and we will help you track [01:07:00] everything your users do in production.

[01:07:01] Alex Volkov: Separately from this, You want to continuously evaluate your application on different set of metrics, for example, or scoring them on different set of metrics to know how your LLM or your prompts are doing, right? So you guys know that, like for example, before on the show we talked about, hey, here's this new model, the, qu quill, for example.

[01:07:20] Alex Volkov: And you know that wolf from, for example, tested it on MMU Pro. Those are generic evaluations. MMU Pros, those are evaluations that somebody built specifically for. [01:07:30] Something big. Look, there's a set of questions that somebody builds something big. specific scorers for your type of application, something that you build for your type of applications.

[01:07:38] Alex Volkov: and then people asked us as Weights Biases, Hey, okay, you give us a generic toolkit, an unopinionated toolkit, but can you give us some opinion? Can you give us some opinion? And basically this is what like Weave Scorers is. This is like an additional package that you can install if you want to,like additionally, right?

[01:07:55] Alex Volkov: Thomas, help me out here, but you can add this. The ones we're

[01:07:58] Thomas Capele: building right now, they're not yet [01:08:00] there. They will be probably in a certain future. Yeah. We need to test them correctly. And it's we're an experiment tracking company at the beginning. We're going to like, want to share the full reproducibility.

[01:08:10] Thomas Capele: Like this is the data, this is how we train them. there's different versions. It's scoring metrics we get, so you like have confident that they work as expected.

[01:08:18] Alex Volkov: So this is to me very interesting, right? So I came in as a previously software developer and now as like an AI evangelist, like I came in from like this side and I meet all these like machine learning engineers, experiment tracking folks who are like, okay, [01:08:30] now that we've built this like LLM based tool, observability tool, many people are asking us to do what Weights Biases does on the model side, on the Weights Biases side.

[01:08:37] Alex Volkov: Hey, Use everything from your, immense knowledge of tracking and doing experimentation to bring this over to the LLM side. Okay, now that you have all this data, now that companies are tracking all the data, how to actually, do experimentation on the front side. Thomas, last thing I'll ask you here before I let you go, briefly is about guardrails specifically.

[01:08:56] Alex Volkov: So there's this concept that we're going to talk about. We're going to keep talking about this [01:09:00] called guardrails. So we're talking about scorers. Scorers are basically the way to check your application. Just a model.

[01:09:05] Understanding Scoring Models

[01:09:05] Alex Volkov: Like

[01:09:06] Thomas Capele: I would define like score is just a model. It takes an input, produce an output.

[01:09:11] Thomas Capele: It could be simple. It could be complicated. Like a scoring, the simplest scores could be accuracy. if the prediction is equal to the label, like a complex score, it could be like an LLM power score that. Check that the context you retrieve from your RAG application, it's not like the response is not [01:09:30] hallucinated or is factually consistent with the original context.

[01:09:33] Alex Volkov: So like HallucinationFreeScorer, for example, is one score for folks who are listening. whether or not the response that your RAG application returned, Has hallucinations in it. Or,yeah, it's

[01:09:44] Thomas Capele: very it's very detailed. And you will probably need to refine all of this for your specific application because everyone has slightly definition and slightly needs, slightly different needs for their application.

[01:09:55] Thomas Capele: So yeah, you may need to tune everything, but this is like a good starting point.

[01:09:59] Guardrails in LLM Development

[01:09:59] Thomas Capele: [01:10:00] So yeah, I find it very interesting that you mentioned guardrails. I would say like a guardrail is. Also a model that predicts, but it's need to be really fast and it needs to be, it needs to take actions, maybe change the output, like any of these scores don't change your output.

[01:10:19] Thomas Capele: Like they will. Computer score, but they will not change the output. if you have IPAI's guardrail, it should, I don't know, redact stuff that [01:10:30] shouldn't pass. So it should change the output, like the payload you are getting from the API. So like guardrails are more like online, and these are more like, offline.

[01:10:41] Alex Volkov: So that's a good boundary to do. And I think we'll end here, but this is basically an exception for going forward, folks. I will tell you about guardrails specifically.

[01:10:48] Guardrails in Production

[01:10:48] Alex Volkov: It's something we're getting into, and I'm going to keep talking about guardrails specifically, because I think that this is a very important piece of developing LLMs in production.

[01:10:57] Alex Volkov: How are you making sure that the [01:11:00] model that you have online is also behaving within a set of boundaries that you set for your LLM? obviously we know that the big companies, they have their guardrails in place. We know because, for example, when you, talk with, advanced voice mode, for example, you ask it to sing, it doesn't sing.

[01:11:14] Alex Volkov: there's a boundary that they set in place. when you develop with your LLMs in production, your guardrails, the only way to build them in is in by prompting for example there's other ways to do them and we are building some of those ways or we're building tools for you to build some of those ways [01:11:30] and like thomas said one of those guardrails are changing the output or like building ways to prevent from some of the output from happening like PII for example or there's like toxicity detection and other stuff like this so we will Talking more about guardrails, Thomas with this, I want to thank you for coming out to the show today and helping us with scores and discussing about Weave as well.

[01:11:50] Alex Volkov: And, I appreciate the time here, folks. You can find Thomas on, X and on, and on BlueSky, under CapeTorch. Thomas is a machine learning engineer and, [01:12:00] developer AI engineer as well. Does a lot of great content, Thomas. Thank you for coming up. I appreciate you. He also does amazing cooking as well.

[01:12:06] Alex Volkov: Follow him for some amazing gnocchi as well. Thanks, Thomas. Thomas, thank you. Folks, this has been this week's Bugs, and now we're back. Good job being here. See you guys. See you, man. And now we're back to big companies and APIs.[01:12:30]

[01:12:33] Alex Volkov: All right. All right. All right. We are back from this week's buzz, folks. Hopefully, you learned a little bit about scores and guardrails. We're going to keep talking about guardrails, but now we have to move on because we have a bunch of stuff to talk about specifically around big companies and APIs, which had A bunch of stuff this week as well.

[01:12:51] OpenAI Leak Incident

[01:12:51] Alex Volkov: I wanna talk about, the leak. You guys wanna talk about the leak, this week? open the eye had a big, oh my God. Oops. Something big [01:13:00] happened. but nothing actually big happened, but look to some extent, this was a little bit big. at some point, this week, a frustrated participant in the open ai, how should I say, test.

[01:13:12] Alex Volkov: Program for Sora decided to quote unquote leak Sora and posted a hug and face space where you could go and say, Hey, I am,I want this and this. And you would see a Sora video generated and, yeah, we can actually show some videos. I think, this is not against any [01:13:30] TOS, I believe. and, Yeah, this wasn't actually a leak. What do you guys think? did you happen to participate in the bonanza of, Sora videos, Wolfram or Yam? Did you see this?

[01:13:40] Wolfram Ravenwolf: I saw it, but I didn't, try to go to the link.

[01:13:43] Alex Volkov: No.

[01:13:44] Sora Video Leak Reactions

[01:13:44] Alex Volkov: so basically, some very frustrated person from,the creative minds behind Sora behind the scenes, decided to like, Leak SORA, the leak wasn't actually the model leak like we would consider a model [01:14:00] leak.

[01:14:00] Alex Volkov: the leak was basically a hug and face application with a request to a SORA API with just the keys hidden behind the hug and face. we're showing some of the videos. I'm going to also add this to,to the top of the space for you guys as well. The videos look pretty good, but many of the folks who commented, they basically said that, compared to when Sora just was announced, where all of [01:14:30] us were mind blown completely, now the videos, when you compare them to something like Kling, or some of, Runway videos, they're pretty much on the same level.

[01:14:41] Alex Volkov: And, I, they look good. They still look very good. look at this animation for example. It looks very good still And apparently there's like a version of Sora called Sora Turbo. So these videos are like fairly quick, but Like folks are not as mind blown [01:15:00] as before yeah Some of the physics looks a little bit better than Kling etc, but it feels like we've moved onand this is something that I want to talk to you guys like super quick.

[01:15:09] Alex Volkov: we're following every week, right? So we get adapted every week, like every,the Reasoning Model Formula 1 blew us away. And then R1 came out and now we run this on our models due to Quill. So we're used to getting adapted to this. the video world caught up to Sora like super quick.

[01:15:24] Alex Volkov: Now we can run these models. There's one open source one like every week. These videos [01:15:30] don't blow us away as they used to anymore and,why isn't OpenAI releasing this at this point is unclear because if you could say before, elections, you could,you can put down Trump and Kamala Harris in there, Now, what's the reason for not releasing this and not giving us this thing?

[01:15:47] Alex Volkov: anyway, yeah, this video is pretty cool. There's one video with, a zoom in and somebody eating a burger. yeah, leak, not leak, I don't know, but, thoughts about the sourcling? What do you guys think about the videos and, the non releasing, things? Folks, I want to ask, Nisten, [01:16:00] what do you think about those videos?

[01:16:01] Alex Volkov: Do you have a chance to look at them?

[01:16:03] Nisten Tahiraj: I was going to say, by the way, I was going to say the exact same thing you did, that it's just been so long now, what, a few, a couple of months since they announced it? I think it's more than

[01:16:14] Alex Volkov: a couple of months, I think half a year, maybe, yeah.

[01:16:16] Nisten Tahiraj: Yeah, it's over half a year that so much happened that we're no longer impressed.

[01:16:22] Nisten Tahiraj: And I'm just trying to be mindful of that, that things are still moving fast. And, they haven't stopped [01:16:30] moving. Like we've seen a whole bunch of models start to get close to this now. it's still better, I would say it's still better than most of, what's come out in the last six months. but,yeah, we're getting pretty close.

[01:16:41] Nisten Tahiraj: I think they haven't released it mainly because of, weaponized litigation,that's the main thing.

[01:16:45] Alpin Dale: Yeah.

[01:16:45] Nisten Tahiraj: Holding them back and, uh.yeah, companies in other countries don't have that problem as, as much, so they were able to, to advance more, like while still being respectful tothe brands and [01:17:00] stuff, but, yeah, I think the main reason is, people are just going to try and nitpick any kind of,of, attack vector to, to, to sue them.

[01:17:08] Nisten Tahiraj: For it. So that's probably why

[01:17:10] Alex Volkov: Yeah. Everything open AI will Yeah. Will get attacked. That I fully agree with you on this. Yeah. speaking of, let's see, do we have anything else from OpenAI? I don't believe so. Yeah. the other one thing that I wanted to show super quick is that the new Chad GPT now is also y I'm gonna show this super quick on the thing, is also now [01:17:30] supporting cursor.

[01:17:31] Alex Volkov: So now, the NutriGPT app is supporting the Cursor app, so now you can ask what I'm working on in Cursor, and if you hover this, you can actually see all of my, including env, You can actually see my secrets, but, you can ask it, you can ask it about the open, open queries. And why would I, if I have Cursor?

[01:17:49] Alex Volkov: That's the question, right? Cursor supports O1, because, I have unlimited O1 queries on ChaiGPTN, whereas I have like fairly limited, queries for O1 in Cursor. and generally [01:18:00] That's been pretty good. That's been pretty cool. You can ask it about the stuff that you have open. There's a shortcut I think it's option shift 1 on Windows and you can enable this and basically you then start chatting With the open interface in the one.

[01:18:13] Alex Volkov: We tested this a couple of weeks ago if you guys remember and I found it super fun. I don't know if you guys used it since then or for those who use the Mac version of, of ChatGPT. I find it really fun. So folks in the audience, if you're using the macOS app and you are connecting this to Cursor or to the terminal, for [01:18:30] example.

[01:18:30] Alex Volkov: Unfortunately, I use the warp terminal and they still don't have warp. they have iTerm here and other things. if you use PyCharm or other, JetBrains, they also started supporting those.but I specifically use Courser and now there's a support for Courser, supports for Windsurf, which is another thing that we didn't cover yet.

[01:18:46] Alex Volkov: And I heard amazing things. And I hope, hopefully over the Thanksgiving break, I will have to, have a chance to use Windsurf. but yeah, this is from, OpenAI and we were waiting for some more news from OpenAI, but we didn't get one. So hopefully the folks at [01:19:00] OpenAI will get a Thanksgiving break.

[01:19:02] Alex Volkov: Just a small reminder. I looked a year ago, if you guys remember the Thanksgiving episode we had a year ago. We were discussing the control alt deletemen weekend where Sam Altman was fired and then rehired. That was the Thanksgiving episode of last year. You guys remember this? last year we discussed how Sam Altman, and Greg Brockman were shanked and, the coup from Ilya.

[01:19:26] Alex Volkov: You guys remember? It's been a year. It's been a year since then. This was the [01:19:30] Thanksgiving last year. and, yeah, it's been a year since then. which by the way. Next week is the one, the two year anniversary of JGPT as well. So we probably should prepare something for that. so that's on the OpenAI News.

[01:19:43] Alex Volkov: let's super quick talk about this.at some point There's this, the sayings from Space Uncle is, they need to be studied in an encyclopedia. somebody tweeted, I don't understand how game developers and game journalists got so ideologically captured. [01:20:00] Elon Musk tweeted and said, Too many game studios are owned by massive corporations.

[01:20:03] Alex Volkov: XAI is going to start an AI game studio to make games great again.and I'm like, and please unmute if you're muted and laughing, because I want to hear, and I want the audience to hear that both PicoCreator and Nisten are just like laughing out loud at this. It's XAI with all of their like 200, H200, 200, 000 H200s, like the best, the fastest ever growing massive [01:20:30] Memphis, super cluster, they're going to build games like, what are they really going to actually.

[01:20:34] Alex Volkov: Have a gaming studio in there. Like we know he is, Elon is a, I don't know the best Diablo game player in the world right now. I don't know how the f**k

[01:20:43] Nisten Tahiraj: he's, he is fourth or 20th or,

[01:20:45] Alex Volkov: yeah, he was 20. I think he's at some point he got number one recently, or something. I, we know, we all know he's a gamer.

[01:20:51] Alex Volkov: Kudos. I really, I'm not making this up. Like I'm really have no idea how the f**k you can be like the best Diablo player in the world doing all these other stuff [01:21:00] and. I get the sentiment of okay, let's make games. Great. Turning in the eye company, the games company, how the,what?

[01:21:08] Alex Volkov: Ah, I just want to turn to this.

[01:21:12] Eugen Cheugh: I love most. It's just a massive corporation, XAI with billions of dollars of funding. It's going to be not a messy corporation.

[01:21:23] Alex Volkov: Yeah, this is not necessarily AI related necessarily,we are expecting big things from XAI, specifically around GROK [01:21:30] 3.

[01:21:30] Alex Volkov: Hopefully December, that's the date that they've given us. They have a hundred thousand H100s turning away and building something. We know that this was like announced. we know that Elon promises and doesn't deliver on time, but delivers at some point anyway. We know that they have. very good folks behind the scenes.

[01:21:47] Alex Volkov: We know this, we've seen this before. We know that, infrastructure is something they're building out. They're building out enterprise infrastructure for APIs. we've seen the X, AI API layer building out. We've seen the kind of the [01:22:00] X,infrastructure. Sorry, enterprise infrastructure for, the building layer.

[01:22:03] Alex Volkov: We've seen all this, getting prepared. Like we've talked about this, we're getting to the point where X is going to be another player, competing another player versus Google, OpenAI, Anthropic, etc. GRUG3 is going to be something significant to contend with. and like the amount of GPUs are there.

[01:22:22] Alex Volkov: It's just is this a sidetrack? this is basically my question.

[01:22:25] Nisten Tahiraj: it, so Uncle Elon tends to be like very [01:22:30] impulsive as we've seen, so if he spends a lot of time on something he's gonna start getting obsessed with it. So there's that. In order to have a gaming service, you will need a lot of GPUs, and I'm pretty sure at this point, if they want to do cloud gaming or streaming, they probably have more GPUs than PlayStation.

[01:22:49] Nisten Tahiraj: they might actually just have more right now. they're like, we can probably Support that, and so much for the Department of Government Efficiency, now we're all [01:23:00] just going to be streaming games.

[01:23:05] Nisten Tahiraj: But there is, there's also Another lining to this is for, for a while, for the last 10 years, there was an article about 10 years ago that the E3, I don't think that's a thing anymore, but the E3 gaming conference had a SpaceX booth over a decade ago and SpaceX was actively recruiting for the E3. I think to quote, it was, programmers of physics engine, and the [01:23:30] rumors were that they were going after the ones who made the Steam Havoc 2, like the one in Portal, and the ones that worked on the, Unreal Tournament physics engine.

[01:23:40] Nisten Tahiraj: And this was over 10 years ago, and those people, those programmers, were recruited by SpaceX. like, when you see, the Falcon Heavy, 2, 3, 4 rockets, just like Go dance in midair and land like they're in a video game is because, the people that made the simulation very likely worked on game engines.

[01:23:58] Nisten Tahiraj: So it might be [01:24:00] a hiring angle from him, or it might just be Angelino playing a lot of games and he just wants to know. there is an angle

[01:24:07] Alex Volkov: for gaming as a playground for training. Like a GI, whatever, like open AI obviously had, like trained robots in this area. we saw many papers for like agents running wild in a game constrained environments.

[01:24:19] Alex Volkov: There, there could be an angle there for sure. I just, this doesn't feel like, this feels like an impulsive, hey. make f*****g games great again.

[01:24:26] Anthropic's Model Context Protocol

[01:24:26] Alex Volkov: Alright, moving on, unless we have another comment here, moving on to [01:24:30] I really wanted to discuss the, super briefly the, Model Context Protocol from Anthropic.

[01:24:36] Alex Volkov: because this kind of blew up, but it's not ready yet. I saw a comment from Simon Wilson, you guys know Simon Wilson, the friend of the pod, he'd been here multiple times. basically he covered this. super quick, Anthropic released this new protocol, which they hope to standardize and by standardize, they mean Hey, let's get around this.

[01:24:53] Alex Volkov: Okay. So let's talk about a standard in the industry right now, the OpenAI SDK for Python. That's a [01:25:00] standard way to interact with LLMs. Pretty much everybody supports this, including Gemini. I think the only one who doesn't support this is Anthropic actually. So in Python, if you want to interact with any LLM, Literally any provider in LLM, including OpenRouter, like Google, OpenAI themselves, like pretty much everyone else, like including together, like all of the, all of those, you can replace one line of code in the OpenAI API, OpenAI Python SDK, where you just put a different URL in there, and then this is the standard way to talk to [01:25:30] LLMs.

[01:25:30] Alex Volkov: I think for TypeScript, JavaScript, it's pretty much the same.so it looks like Anthropic is trying to do something like this to standardize around how LLMs are connecting with other applications. So right now, just a minute before I showed you how ChatGPT is connecting to like a VS Code for something.

[01:25:49] Alex Volkov: They built those integrations themselves. So you would install a specific extension in VS Code in etc. And that extension That they've built [01:26:00] talks to the ChatGPT app on the Mac OS that they've built and they build this connection for you. This is not what Anthropic wants to do. Anthropic wants to create a protocol that like developers, other developers can build on their own to allow the LLM to talk to any application and you as a developer, I as a developer, other developers can build those Communication layers, and then whatever LLM, in this case, this is the Anthropic, Claude desktop app, this could be the JGPT app, could be the Gemini GPT app, [01:26:30] Gemini app, et cetera, could talk to other applications.

[01:26:32] Alex Volkov: What those other applications are? Anything. Anything on your desktop, anything. At all. So they build this kind of a first standard, communication via JSON RPC. And I think they're buildingother ways, and other servers. I think this is a way to summarize this, basically.

[01:26:50] Alex Volkov: this is a open preview. Nisten, you want to take another crack at trying to recap this? Or Yam or Wolfram, you guys want to? You want to give me your thoughts on this super quick? As far as I understand from [01:27:00] Simon, this is like still robust and still in,in, in flux.

[01:27:03] Nisten Tahiraj: I think this might end up being a much bigger deal than we, we first expect, because it is an interoperability layer, and as a developer, you will have to learn this.

[01:27:15] Nisten Tahiraj: it is annoying at the moment that, While proposing a standard, Anthropic is not showing willingness to abide by one, which most people chose, and even Google was forced to support the OpenAI standard. if you [01:27:30] want people to come with your standard, to abide by your standard, you also have to show willingness to abide by others.

[01:27:36] Nisten Tahiraj: that's not going to work here until Anthropic Just supports a plug and play OpenAI API, so I just put their models in, but that aside. The criticism aside,this is pretty, pretty important. So I've been doing some of this stuff and just trying to do it with basic JSON. So I think that's,it's very good.

[01:27:55] Nisten Tahiraj: And yeah, it's pretty hard to know, am I on Mac? Am I on Linux? Am I on a phone? [01:28:00] What's the LLM going to talk to? what does this app even want me to do? Do I have to emulate this on the screen and then click on it? Can't it just give me a JSON so that I can click on it so it's a lot easier for me?

[01:28:11] Nisten Tahiraj: And this will also apply to websites and, and web apps after to you. Offer some kind of a JSON RPC. An RPC is just like an API for people. It's just an application programming interface. It's something you query, like you write a curl to this [01:28:30] IP and here's my API key and give me, or here I'm going to give you this stuff and give me this stuff.

[01:28:37] Nisten Tahiraj: From the database or whatever. So this is this actually extremely important because you can apply to, to web apps as well. And it's a way to manage multiple sessions. So I think it's a pretty big deal, even though I am. No. And anthropic, it this,yeah. I think that this is gonna become much, much more important because it saves a lot of bandwidth.[01:29:00]

[01:29:00] Nisten Tahiraj: Instead of you having to run a visual language model to show the whole screen, to run it on an emulator, to have to click on it and move around. And it's so compute intensive. It's can you just gimme like a adjacent API, so I can just like,

[01:29:13] Alex Volkov: yeah, do

[01:29:13] Nisten Tahiraj: a constraint output to adjacent and just output three tokens.

[01:29:16] Nisten Tahiraj: Be done with the whole thing. so yeah. Yeah, it's, I think it'll become a big deal.

[01:29:21] Alex Volkov: So in the spirit of, of the holiday, thank you and tropic for trying to standardize things, standardize, often, sometimes it's annoying, but often leads to good things as [01:29:30] well. folks, should try out the MMCP and definitely give them feedback.

[01:29:34] Alex Volkov: but yeah, they should also abide by some standards as well. It looks like the industry is standardizing around the. OpenAI SDK, and they maybe should also, it would help.

[01:29:43] Wolfram Ravenwolf: It's a new thing that they are doing because, so far we usually had the LLM as a part in an agent pipeline where you have, another process called the LLM with some input.

[01:29:52] Wolfram Ravenwolf: And here we have the LLM going out to get. The input itself. So I think that is also in the agent context, very important and [01:30:00] more integration is always better, but of course it's a new thing. We have to develop all those servers as I call it. So a lot of reinventing the wheel, I guess we'll see if it can really persevere.

[01:30:12] Alex Volkov: Yeah, one example that they highlight, and Simon talked about this as well, is that if you have a database, a SQLite database that sits on your computer,the way to have So you guys know we, we talked about tool use, for example,via API, those models can Get respond with some, some idea of how to use your [01:30:30] tools.

[01:30:30] Alex Volkov: And you, as a developer, you are in charge of using those tools. You basically get in response a structure of a function call. And you're like, okay, now I have to take this and then go to an external tool and use this. This is connecting this piece forward. This is basically. Allowing this LLM to then actually go and actually use this tool.

[01:30:48] Alex Volkov: Basically like getting a step forward. And one, one example that they're showing is a connecting to a database, allowing this LLM to connect to a database via a sq lite like MCP server. the model compute [01:31:00] protocol server. cps, sorry. yeah. So connecting via this MCP server,you basically allowing LM to read from this database.

[01:31:08] Alex Volkov: Itself without like returning a call. And then you are in charge as a developer to go and do the call return it responses. so basically trying to, allow LLMs to connect to different services. Yeah. And this, I think I agree with you with more work in here. this could be big.

[01:31:24] Nisten Tahiraj: It could literally make like over a thousand times more compute efficient to automate [01:31:30] something on a screen. Because instead of using a visual language model frame by frame, you can just have a JSON.

[01:31:37] Alex Volkov: Let's talk about Like literally

[01:31:38] Nisten Tahiraj: over a thousand times. Let's compute to do it. So I'm going to, I'm going to take a longer look at it as well.

[01:31:46] Alex Volkov: speaking of automating things on the screen,

[01:31:48] H runner from H the french AI company

[01:31:48] Alex Volkov: let's talk about the next thing that we want to talk about, H company AI. This is the next thing in big companies and APIs, H company from. France, this is another big company. So [01:32:00] we know Mistral is from France. some, DeepMind, some folks is from France as well.

[01:32:04] Alex Volkov: there's also FAIR in France from Meta. now France is positioning themselves to be one big kind of hub from AI as well. H Company. raised, fundraised, I think, 250 I have in my notes. Yeah, 220, one of the biggest, seed rounds. 220 million dollars, one of the biggest ones in, in the history of, French seed rounds, a while ago.

[01:32:24] Alex Volkov: And they just showcased their Runner H. Their Runner H [01:32:30] is, they're competing with Claude on speed of computer use. I apologize for this. Let's take a look at how fast they're claiming they're opening a browser, going to recipes and providing recipes for something. On the right, we have Claude, Computer Use.

[01:32:46] Alex Volkov: Claude is basically, hey, open the browser. On the left, they already pulled up a browser and already extracting data. So basically they're claiming A speed up of maybe two to three times over cloud computer use. [01:33:00] And they're basically showing while Claude still pulls up the Firefox browser, they have already completed the task, extracted the data and already responded to the user.

[01:33:09] Alex Volkov: they're showing steps by steps comparison, which I don't think is necessarily in, Apple's to Apple's comparison. I don't think it's necessarily fair, but. There's a big but here, big French, but, I don't know how to say, sorry, Nisten, I don't know how to say but in French, but there's a big one.

[01:33:25] Alex Volkov: Their models, as far as I could see, and I did some research, they have [01:33:30] a, they say this runner age thing that they have is powered by a specialized LLM, specialized optimist for function calling for 2 billion params. So whatever we see on the left is not like Claude, which whatever, we don't know the size of Claude, this is like a 2 billion parameter model.

[01:33:45] Alex Volkov: and, integrates in the VLM of a 3 billion parameter model to see, understand, interact with the graphical and text interface. Let's look at another example here. they're basically browsing the web and like doing extraction and yeah, I don't think you guys can see it. maybe like this.[01:34:00]

[01:34:02] Alex Volkov: It's literally, they're going to Wolfram Alpha and extracting and doing this task. They're basically asking Wolfram Alpha to do a task. So it's not like they're just reading from things. They're finding input and they're like plugging things in there and like responding, reading from the output from Wolfram Alpha as well.

[01:34:18] Alex Volkov: this runnerage thing actually performs tasks on the web. Extracts information back way faster than Claude Computerius, which Claude Computerius, let's give it its place. We were very excited when it came [01:34:30] out, and it does very well for, for just an adjustment of Claude. and they are showing immense differences in five steps, and we're still waiting for Claude Computerius to like, Try to figure this out.

[01:34:42] Alex Volkov: So did you

[01:34:43] Nisten Tahiraj: say it's a separate to be model? And then there's another? That's what I found

[01:34:48] Alex Volkov: from them. Yeah. Yeah. They said that they have, let me see if I can find the previous announcement. Yeah. Yeah.

[01:34:54] Wolfram Ravenwolf: The previous announcement

[01:34:56] Alex Volkov: that they have, that we missed from last week, Introducing Studio, a [01:35:00] automations at scale, run or age the most advanced agent to date.

[01:35:04] Alex Volkov: That's what they said last year. Powered by specialized LLM, highly optimized for function calling, 2 billion parameters. It also integrates a specialized VLM, 3 billion parameters, to perceive, understand, and interact with graphical and text elements. Delivers the state of the art on the public WebVoyager framework.

[01:35:20] Alex Volkov: And this is the graph that they have. WebVoyager, they have Runner H01. At 66 percent maybe? And, and [01:35:30] then, Cloud Computer Use at 52 percent and Agent E, I don't know where it is, it's like here. Yeah, so like the size of it is what's the most impressive part.

[01:35:41] Nisten Tahiraj: Yeah, I'd say this is impressive. as to what they're doing.

[01:35:44] Nisten Tahiraj: we can guess what model they're using, but it doesn't matter all that much. I just wanna say that it's not an apples to apples comparison with cloud because cloud is an entire OS in there and you can use whatever you want. It can use blender, it can, [01:36:00] you can run a virtual box of Windows 95 and it will use that as well.

[01:36:04] Eugen Cheugh: so the, yeah, it's not. That's not a pure example, whereas in this one, I'm assuming they do need access to the document object model, the DOM of the website, to be able to navigate it, but The results do indeed seem impressive, and it's at a size that you can run it, you can run on your own, Yeah, because if you're measuring steps and speed, actually, I think, Anthropic Cloud should, probably, partner with [01:36:30] a company like Browserbase, and just, do a demo, and then see how close they get instead. It will skip literally the first eight steps or something like that, which is all just the OS booted up.

[01:36:40] Alex Volkov: Yeah, this is why I didn't love the comparison specifically, you guys are right, it's running a janky Docker with Firefox, and by the time, it loads Firefox, these guys already loaded the website, so it's not like necessarily apples to apples, but it looks like those models are tiny compared to Claude, and also, they talk about, It's beyond [01:37:00] optimizing agent performance, they're like, they have, optimizing web interactions.

[01:37:05] Alex Volkov: they engineered Runaways to handle any web interactions. Advancing towards one singular mission, automating the web, so they're focused on web. So Eugene, like what you're talking about, like browser based with computer use, it looks like this is their focus, whereas computer use is, for computer use, generic.

[01:37:22] Alex Volkov: This is like their focus for web interactions. I guess what I'm saying is it's exciting. they raised a boatload of money, the folks behind [01:37:30] there, they seem like very,adept, I, I know they're based in France, Wolfram. I don't know, Wolfram, you're asking if, if I'm sure they're France.

[01:37:36] Alex Volkov: yeah, they're based in France, and, Yeah, we'll see. They're waitlisted. I haven't tested them out. I know that some folks collaborated on them already and posted some threads. so we'll hopefully, we'll see if I get access to this. I'll tell you guys and we'll play with it. Absolutely. definitely exciting in the world of agents.

[01:37:54] Alex Volkov: I think this is it from big companies. Folks, what do you think? Anything else From big companies, nothing from Google after the [01:38:00] releases of last week where they reclaimed the throne. Hopefully they're getting their deserved breaks and and relaxing. I don't think this week was fairly chill.

[01:38:07] Alex Volkov: Probably the next week they're going to come back with a vengeance. Next week there's like the AWS re invent. Maybe Amazon will come with something. And then the week after RPS. Maybe some folks are waiting for that. I think that this is it in big companies. Let's move on to vision and video.

[01:38:22] Alex Volkov: And then, Oh, I think we're at two minutes. Folks, I think we're at time. I think we're at time. I got too excited that we have like a bunch of other things to talk about. [01:38:30] So let me maybe recap on our Thanksgiving super quick. the stuff that we didn't get to just to like to recap super quick. we didn't get to, but just to tell you guys what else we didn't get to, runway specifically.

[01:38:41] Alex Volkov: Oh yeah, I just, I have to show this. not to talk about this. Just just visually show this beautiful thing. If I can click this. If I can click this thing, yeah, Runway introduced an expand feature, if you guys haven't seen this, it's really fun to just watch. Let me just mute this. basically, [01:39:00] what you see above and below, Runway introduced an expand feature where you take a video and you give it, give this model and the model tries to predict it.

[01:39:08] Alex Volkov: in different ratio, what's above and below this video. So basically, if you give a video in the widescreen format, 16 by nine, and you could try to turn it into a 19 by six format. And so the model will try to fill in the frames. The general video model tries to fill in the frames of what's above and below.

[01:39:25] Alex Volkov: So what we're looking at in the video on the screen is like a Lord of the [01:39:30] Rings scene where Legolas rides one of those like elephant looking thingies. Basically, the model tries to fill in the, just the frames from above and below. It just looks a little bit creepy. it's funny looking, but it's like looks, interesting.

[01:39:45] Alex Volkov: so this is like one expand feature and the other one is they released an actual image model from Runway, which kind of looks interesting. it's called a frames and it's specific for image generation for [01:40:00] world building. and Confi UI desktop launched. I think that's pretty much it.

[01:40:05] Thanksgiving Reflections and Thanks

[01:40:05] Alex Volkov: Folks, it's time to say thanks, because it's Thanksgiving. I just wanted to start, but I wanted to hear from you as well. My biggest thanks this year goes to, first of all, everybody who tunes in to ThursdAI. Everybody who comes into the community, everybody who provides comments and shares with their friends and, and listens and,The second huge thanks goes to all of you.

[01:40:26] Alex Volkov: My co hosts here, Wolfram, Yam, Nisten, LDJ, Junyang [01:40:30] who joined us, Eugene who joined us as well. Zafari who joined us from time to time, like a bunch of other folks. huge thanks to you for being here from like week to week for more than like almost, we're coming up on two years. And I think the thirst, the third thanks goes to Jensen for the GPUs that he provided for all of us to enjoy those like amazing corn coffee of AI features around the world.

[01:40:51] Alex Volkov: just, yeah, just open up the mics and feel free to, to join the festivities even though I don't know any of you celebrate [01:41:00] Thanksgiving unnecessarily. But yeah, what are you guys thankful for? before we wrap up, let's do the Thanksgiving roundup.

[01:41:07] Eugen Cheugh: I'm giving thanks to open models.

[01:41:08] Eugen Cheugh: let's go. Yeah, no, proving that you do not need billions of dollars to catch up with GPT 4 despite what the big labs will say. The open teams, keep going, keep bringing open models to the masses.

[01:41:25] Nisten Tahiraj: Yeah, We had Thanksgiving last month in Canada. I would like to [01:41:30] give thanks to two particular creators, mahi and, tki. each have over a thousand models and, quants that they release. And, and also Mr. Der Backer, probably mispronounced that was, over 5,000, quantization of models.

[01:41:48] Nisten Tahiraj: this is the stuff I use every day in tell. Other people. So whenever something new comes up, I almost always expect them to have a good, well done quantization ready for [01:42:00] others to use. and they just do this as volunteers. I don't even think they're part of the, none of them are part of like even a big corporation, or have high salaries.

[01:42:08] Nisten Tahiraj: They literally just do it as volunteers. Yeah, I want to give thanks to those people in particular, and everybody else here, and all the people on Discord as well, who sit around and help you correct stuff, but yeah, that's it for me.

[01:42:27] Wolfram Ravenwolf: Okay, I have three. The first [01:42:30] is to Alex for the podcast, because it's amazing to be here.

[01:42:34] Wolfram Ravenwolf: It's my way to keep up with the stuff I can't keep up with. So thank you for having me. Thank you for doing this. Thank you very much. And the second is to the whole community of AI people, especially those who release all these stuff in the open. But everybody who contributes, everybody who does a good thing about it, I think it is furthering humanity.

[01:42:53] Wolfram Ravenwolf: So thanks for that. And the third is a thanks to every reasonable person who is not, Going to insights or stuff, [01:43:00] but it's open minded and, seeing that we are all in the same boat and we are all trying to make the world a better place in our different ways. And for being, accepting and understanding of this.

[01:43:11] Wolfram Ravenwolf: In this times, I think it's very important to keep an open mind.

[01:43:16] Nisten Tahiraj: Oh yeah, just really quickly to add on, the biggest thanks I think for this year goes to the DeepSeek and Qwent teams for just caring. up everybody [01:43:30] else when we stalled on progress they kept it up to like actually democratize the models for you to actually have this piece of artificial intelligence and own it and control it and be loyal and make it loyal to you yeah.

[01:43:47] Nisten Tahiraj: they actually enable people to, to run fully local models. Like 90% of what I use every day is just completely open source. Now, honestly, it w it, I wouldn't, it would not be there if it wasn't for them. It would probably maybe be like [01:44:00] 20, 30%. So,yeah, they, they really carried, like that's a gaming term, like someone who.

[01:44:06] Nisten Tahiraj: Carries the team. They have really carried, so yeah.

[01:44:11] Alex Volkov: Jan, go

[01:44:14] Yam Peleg: ahead. To Jensen for the GPUs, and

[01:44:17] Alex Volkov: to everybody

[01:44:18] Yam Peleg: else I'm hugging face. Especially people collecting and releasing datasets. I think they're not getting enough credits because you can't just use the dataset [01:44:30] without training a model. There is an effort.

[01:44:31] Yam Peleg: to, until you appreciate the dataset, but, they make it possible, everything else.

[01:44:39] Alex Volkov: Last thing that I have to, and this is not because I have to, but honestly, folks, huge thanks to Weights Biases for all of this, honestly, I wouldn't have been able to do this as my job without a few folks in Weights Biases, so thank you Morgan, thank you Lavanya, thank you a bunch of folks in Weights Biases.

[01:44:55] Alex Volkov: who realized this could be a part of my actual day to day and bringing you news from Weights [01:45:00] Biases, but also promoting some of the stuff. many of the labs, if not most of the labs that we talk about, are using Weights Biases to bring us the open source, but also the closed source LLMs in the world.

[01:45:10] Alex Volkov: I couldn't be More happy and be in a better place to bring you the news, but also participate behind the scenes in building some of these things. With that, thank you to all of you. Hopefully you go and enjoy some of the rest of your holiday. Those of you who celebrate, those of you who don't celebrate, this is, I think the first Thursday in a while that we didn't have any breaking news.

[01:45:27] Alex Volkov: I'm itching to press it anyway, but we didn't [01:45:30] have any breaking news, but hopefully we'll have some next week. There could be some news next week. We'll see. With that, thank everybody who joins, go and enjoy the rest of your day. And we'll see you here next week as always. Bye everyone. Bye bye.

[01:45:43] Alex Volkov: Bye bye. Bye bye. Bye bye. Bye bye. And we have [01:46:00] a



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