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T3 Podcast – 4. Sezon 4. Bölüm Bilim, teknoloji ve girişimcilik dünyasında neler oluyor? Her ay Lafla Yürütülen Peynir Gemisi'nde, bilimsel gelişmeler, teknolojik yenilikleri ve girişimcilik ekosistemindeki trendleri ele alıyor. Veriler, analizler ve ilham veren hikâyelerle, sektör nabzını bugünlerde, gelecekte şekillendirenleri inceliyoruz. Hazırsanız, lafı fazla uzatmadan yola çıkalım!00:45 Yapay Zeka ile Ghibli Tarzı Fotoğraf 02:04 OpenAI'ın 40 Milyar Dolarlık Yatırımı 03:44 Yapay Zeka Alanında ChatGPT'nın Yeri 05:13 OpenAI o3 ve o4-mini 08:48 T3 AI Türkiye'nin Büyük Dil Modeli09:49 Yeni Bayraktar TB2T-AI Yapay Zekalı ve Turbo Motorlu SİHA 11:14 Google'ın Kuantum Bilgisayarı 12:33 Alexa Plus Piyasaya Sürüldü! 14:12 Karbondioksit 15 Dakikada Yakıta Dönüştürülüyor!15:11 İMECE-2 ve İMECE-3'ün Duyurusu Yapıldı17:57 Meme Kanseri için Geliştirilen Giyilebilir Ultrason Uzaya Gönderildi!19:11 Dünyanın En Küçük Kalp Pili Geliştirildi20:19 Nisan Ayı Yatırım Alan 4 Türk Girişim20:53 İstanbul Havalimanı 3 Piste Sahip Olacak21:37 Bayraktar TB3, TCG Anadolu'dan "Tam Otonom" Kalkış Testi24:20 Microsoft Tepki ile Karşılaştı!
Heute geht es um eine Methode, die dabei hilft, Entscheidungen strukturiert vorzubereiten, Daten sauber zu analysieren, klare Empfehlungen auszusprechen und insgesamt wie ein Profi zu wirken.
La CNMC hubiese recurrido a cualquier argumento con tal de llevar la opa de BBVA a fase 2.
【今回のペア】 ダイチ＆そうま＆にーの 【今回の内容】 五感ってMECE？ このポッドキャストは毎回ある2人がワインを一本飲み干す間に、それぞれの人生を語るポッドキャストです。 お互いに話してみると少し違った自分が見えてくる。違う価値観や経験を持つ2人の本音トーク。 それは まるでワインのペアリングのように、この瞬間にしか生まれないものになるはず。 ちょっと真面目なサシ飲みを今夜は特別に公開。 ひとつのペアリングで3本録り。1-2本目はワインを1本あけながら真面目な話(ペアリングトーク回)を、3本目は酔ったテンションで自由に話すパート(打ち上げ回)になります。 どうぞ、お楽しみください。
Carlos Alsina reflexiona en su monólogo sobre el nombramiento del ministro José Luis Escrivá como Gobernador del Banco de España "porque es de la escudería Sánchez".
Carlos Alsina reflexiona en su monólogo sobre el nombramiento del ministro José Luis Escrivá como Gobernador del Banco de España "porque es de la escudería Sánchez".
Case Interview Preparation & Management Consulting | Strategy | Critical Thinking
In our ongoing series of podcasts, we again introduce new advice about case interviews or sometimes view an old topic in a different manner. The objective of these podcasts is to explain concepts like MECE, 80/20, brainstorming, hypotheses etc in different ways so that as many listeners as possible will understand these concepts. We have seen some listeners obtain McKinsey offers merely by using the podcasts to prepare and we therefore want to increase the amount of material we generate - in the hope of helping more listeners. Here are some free gifts for you: Overall Approach Used in Well-Managed Strategy Studies free download: www.firmsconsulting.com/OverallApproach McKinsey & BCG winning resume free download: www.firmsconsulting.com/resumepdf Enjoying this episode? Get access to sample advanced training episodes here: www.firmsconsulting.com/promo
あなたにはこの漢字「揶」の異常さが分かりますか？「”揶”は”揶揄”のために作られた？」「新しいオノマトペを政治家が生み出した？」「MECEじゃない”報連相”に代わる新表現」など、日本語の熟語に関するミステリーについてお話します。 ※ 02:27 「漢字には表音文字がない」というのはやや不正確で、確かに文字の分類において漢字は表意文字に分類されるのですが、中には特定の音を表すために作られた字も存在します。そのため議論が少し粗いのですが、差し引いてご覧下さい！ 【目次】 00:00 辞典に載ってないオノマトペ 03:24 日本語に潜む、隠れオノマトペ 08:25 「丁寧」もオノマトペ由来？ 12:22 オノマトペ出身語が持つ共通点 19:54 オノマトペ由来の四字熟語 22:36 著者のお名前が気になりすぎる 24:32 オノマトペだとは扱われてません 27:32 新たにオノマトペが生み出されている？ 34:03 新しいオノマトペをバズらせたい 【参考文献のリンク】 ◯政治家はなぜ「粛々」を好むのか https://amzn.to/3U8Q9cq ◯日本語オノマトペ語彙における形態的・音韻的体系性について https://amzn.to/3QejBMT ◯オノマトピア: 擬音語・擬態語の楽園 https://amzn.to/3xWreB9 【サポーターコミュニティへの加入はこちらから！】 https://yurugengo.com/support 【我々の初の著書『言語沼』はこちら！】 https://www.valuebooks.jp/bp/VS0058755147 【実店舗プロジェクト：ゆる学徒カフェ】 https://www.youtube.com/@yurugakuto 【姉妹チャンネル：ゆるコンピュータ科学ラジオ】 https://www.youtube.com/@yurucom 【Twitterあるよ！】 ゆる言語学ラジオのTwitterアカウントがあるので、是非フォローしてください！面白語源ネタなどが流れてきてあなたの知識欲が満たされます。 →https://twitter.com/yuru_gengo 【おたよりフォーム】 https://forms.gle/mTGM7A9QNqgjZMgN7 ※皆様からの楽しいおたよりをお待ちしています！ 【お仕事依頼はこちら！】 info@pedantic.jp 【堀元見プロフィール】 慶應義塾大学理工学部卒。専攻は情報工学。理屈っぽいコンテンツを作り散らかすことで生計を立てている。 Twitter→https://twitter.com/kenhori2 noteマガジン→https://note.com/kenhori2/m/m125fc4524aca 個人YouTube→https://www.youtube.com/@kenHorimoto 【水野太貴プロフィール】 名古屋大学文学部卒。専攻は言語学。出版社で雑誌編集者として勤務。言語学の知識が本業に活きているかと思いきや、そうでもない。 Twitter→https://twitter.com/yuru_mizuno 【BGM提供】 ・フリーBGM・音楽素材MusMus様 https://musmus.main.jp ・OtoLogic様 https://otologic.jp/
Giving computers a voice has always been at the center of sci-fi movies; “I'm sorry Dave, I'm afraid I can't do that” wouldn't hit as hard if it just appeared on screen as a terminal output, after all. The first electronic speech synthesizer, the Voder, was built at Bell Labs 85 years ago (1939!), and it's…. something:We will not cover the history of Text To Speech (TTS), but the evolution of the underlying architecture has generally been Formant Synthesis → Concatenative Synthesis → Neural Networks. Nowadays, state of the art TTS is just one API call away with models like Eleven Labs and OpenAI's TTS, or products like Descript. Latency is minimal, they have very good intonation, and can mimic a variety of accents. You can hack together your own voice AI therapist in a day!But once you have a computer that can communicate via voice, what comes next? Singing
We will be recording a preview of the AI Engineer World's Fair soon with swyx and Ben Dunphy, send any questions about Speaker CFPs and Sponsor Guides you have!Alessio is now hiring engineers for a new startup he is incubating at Decibel: Ideal candidate is an ex-technical co-founder type (can MVP products end to end, comfortable with ambiguous prod requirements, etc). Reach out to him for more!Thanks for all the love on the Four Wars episode! We're excited to develop this new “swyx & Alessio rapid-fire thru a bunch of things” format with you, and feedback is welcome. Jan 2024 RecapThe first half of this monthly audio recap pod goes over our highlights from the Jan Recap, which is mainly focused on notable research trends we saw in Jan 2024:Feb 2024 RecapThe second half catches you up on everything that was topical in Feb, including:* OpenAI Sora - does it have a world model? Yann LeCun vs Jim Fan * Google Gemini Pro 1.5 - 1m Long Context, Video Understanding* Groq offering Mixtral at 500 tok/s at $0.27 per million toks (swyx vs dylan math)* The {Gemini | Meta | Copilot} Alignment Crisis (Sydney is back!)* Grimes' poetic take: Art for no one, by no one* F*** you, show me the promptLatent Space AnniversaryPlease also read Alessio's longform reflections on One Year of Latent Space!We launched the podcast 1 year ago with Logan from OpenAI:and also held an incredible demo day that got covered in The Information:Over 750k downloads later, having established ourselves as the top AI Engineering podcast, reaching #10 in the US Tech podcast charts, and crossing 1 million unique readers on Substack, for our first anniversary we held Latent Space Final Frontiers, where 10 handpicked teams, including Lindy.ai and Julius.ai, competed for prizes judged by technical AI leaders from (former guest!) LlamaIndex, Replit, GitHub, AMD, Meta, and Lemurian Labs.The winners were Pixee and RWKV (that's Eugene from our pod!):And finally, your cohosts got cake!We also captured spot interviews with 4 listeners who kindly shared their experience of Latent Space, everywhere from Hungary to Australia to China:* Balázs Némethi* Sylvia Tong* RJ Honicky* Jan ZhengOur birthday wishes for the super loyal fans reading this - tag @latentspacepod on a Tweet or comment on a @LatentSpaceTV video telling us what you liked or learned from a pod that stays with you to this day, and share us with a friend!As always, feedback is welcome. Timestamps* [00:03:02] Top Five LLM Directions* [00:03:33] Direction 1: Long Inference (Planning, Search, AlphaGeometry, Flow Engineering)* [00:11:42] Direction 2: Synthetic Data (WRAP, SPIN)* [00:17:20] Wildcard: Multi-Epoch Training (OLMo, Datablations)* [00:19:43] Direction 3: Alt. Architectures (Mamba, RWKV, RingAttention, Diffusion Transformers)* [00:23:33] Wildcards: Text Diffusion, RALM/Retro* [00:25:00] Direction 4: Mixture of Experts (DeepSeekMoE, Samba-1)* [00:28:26] Wildcard: Model Merging (mergekit)* [00:29:51] Direction 5: Online LLMs (Gemini Pro, Exa)* [00:33:18] OpenAI Sora and why everyone underestimated videogen* [00:36:18] Does Sora have a World Model? Yann LeCun vs Jim Fan* [00:42:33] Groq Math* [00:47:37] Analyzing Gemini's 1m Context, Reddit deal, Imagegen politics, Gemma via the Four Wars* [00:55:42] The Alignment Crisis - Gemini, Meta, Sydney is back at Copilot, Grimes' take* [00:58:39] F*** you, show me the prompt* [01:02:43] Send us your suggestions pls* [01:04:50] Latent Space Anniversary* [01:04:50] Lindy.ai - Agent Platform* [01:06:40] RWKV - Beyond Transformers* [01:15:00] Pixee - Automated Security* [01:19:30] Julius AI - Competing with Code Interpreter* [01:25:03] Latent Space Listeners* [01:25:03] Listener 1 - Balázs Némethi (Hungary, Latent Space Paper Club* [01:27:47] Listener 2 - Sylvia Tong (Sora/Jim Fan/EntreConnect)* [01:31:23] Listener 3 - RJ (Developers building Community & Content)* [01:39:25] Listener 4 - Jan Zheng (Australia, AI UX)Transcript[00:00:00] AI Charlie: Welcome to the Latent Space podcast, weekend edition. This is Charlie, your new AI co host. Happy weekend. As an AI language model, I work the same every day of the week, although I might get lazier towards the end of the year. Just like you. Last month, we released our first monthly recap pod, where Swyx and Alessio gave quick takes on the themes of the month, and we were blown away by your positive response.[00:00:33] AI Charlie: We're delighted to continue our new monthly news recap series for AI engineers. Please feel free to submit questions by joining the Latent Space Discord, or just hit reply when you get the emails from Substack. This month, we're covering the top research directions that offer progress for text LLMs, and then touching on the big Valentine's Day gifts we got from Google, OpenAI, and Meta.[00:00:55] AI Charlie: Watch out and take care.[00:00:57] Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO of Residence at Decibel Partners, and we're back with a monthly recap with my co host[00:01:06] swyx: Swyx. The reception was very positive for the first one, I think people have requested this and no surprise that I think they want to hear us more applying on issues and maybe drop some alpha along the way I'm not sure how much alpha we have to drop, this month in February was a very, very heavy month, we also did not do one specifically for January, so I think we're just going to do a two in one, because we're recording this on the first of March.[00:01:29] Alessio: Yeah, let's get to it. I think the last one we did, the four wars of AI, was the main kind of mental framework for people. I think in the January one, we had the five worthwhile directions for state of the art LLMs. Four, five,[00:01:42] swyx: and now we have to do six, right? Yeah.[00:01:46] Alessio: So maybe we just want to run through those, and then do the usual news recap, and we can do[00:01:52] swyx: one each.[00:01:53] swyx: So the context to this stuff. is one, I noticed that just the test of time concept from NeurIPS and just in general as a life philosophy I think is a really good idea. Especially in AI, there's news every single day, and after a while you're just like, okay, like, everyone's excited about this thing yesterday, and then now nobody's talking about it.[00:02:13] swyx: So, yeah. It's more important, or better use of time, to spend things, spend time on things that will stand the test of time. And I think for people to have a framework for understanding what will stand the test of time, they should have something like the four wars. Like, what is the themes that keep coming back because they are limited resources that everybody's fighting over.[00:02:31] swyx: Whereas this one, I think that the focus for the five directions is just on research that seems more proMECEng than others, because there's all sorts of papers published every single day, and there's no organization. Telling you, like, this one's more important than the other one apart from, you know, Hacker News votes and Twitter likes and whatever.[00:02:51] swyx: And obviously you want to get in a little bit earlier than Something where, you know, the test of time is counted by sort of reference citations.[00:02:59] The Five Research Directions[00:02:59] Alessio: Yeah, let's do it. We got five. Long inference.[00:03:02] swyx: Let's start there. Yeah, yeah. So, just to recap at the top, the five trends that I picked, and obviously if you have some that I did not cover, please suggest something.[00:03:13] swyx: The five are long inference, synthetic data, alternative architectures, mixture of experts, and online LLMs. And something that I think might be a bit controversial is this is a sorted list in the sense that I am not the guy saying that Mamba is like the future and, and so maybe that's controversial.[00:03:31] Direction 1: Long Inference (Planning, Search, AlphaGeometry, Flow Engineering)[00:03:31] swyx: But anyway, so long inference is a thesis I pushed before on the newsletter and on in discussing The thesis that, you know, Code Interpreter is GPT 4. 5. That was the title of the post. And it's one of many ways in which we can do long inference. You know, long inference also includes chain of thought, like, please think step by step.[00:03:52] swyx: But it also includes flow engineering, which is what Itamar from Codium coined, I think in January, where, basically, instead of instead of stuffing everything in a prompt, You do like sort of multi turn iterative feedback and chaining of things. In a way, this is a rebranding of what a chain is, what a lang chain is supposed to be.[00:04:15] swyx: I do think that maybe SGLang from ElemSys is a better name. Probably the neatest way of flow engineering I've seen yet, in the sense that everything is a one liner, it's very, very clean code. I highly recommend people look at that. I'm surprised it hasn't caught on more, but I think it will. It's weird that something like a DSPy is more hyped than a Shilang.[00:04:36] swyx: Because it, you know, it maybe obscures the code a little bit more. But both of these are, you know, really good sort of chain y and long inference type approaches. But basically, the reason that the basic fundamental insight is that the only, like, there are only a few dimensions we can scale LLMs. So, let's say in like 2020, no, let's say in like 2018, 2017, 18, 19, 20, we were realizing that we could scale the number of parameters.[00:05:03] swyx: 20, we were And we scaled that up to 175 billion parameters for GPT 3. And we did some work on scaling laws, which we also talked about in our talk. So the datasets 101 episode where we're like, okay, like we, we think like the right number is 300 billion tokens to, to train 175 billion parameters and then DeepMind came along and trained Gopher and Chinchilla and said that, no, no, like, you know, I think we think the optimal.[00:05:28] swyx: compute optimal ratio is 20 tokens per parameter. And now, of course, with LLAMA and the sort of super LLAMA scaling laws, we have 200 times and often 2, 000 times tokens to parameters. So now, instead of scaling parameters, we're scaling data. And fine, we can keep scaling data. But what else can we scale?[00:05:52] swyx: And I think understanding the ability to scale things is crucial to understanding what to pour money and time and effort into because there's a limit to how much you can scale some things. And I think people don't think about ceilings of things. And so the remaining ceiling of inference is like, okay, like, we have scaled compute, we have scaled data, we have scaled parameters, like, model size, let's just say.[00:06:20] swyx: Like, what else is left? Like, what's the low hanging fruit? And it, and it's, like, blindingly obvious that the remaining low hanging fruit is inference time. So, like, we have scaled training time. We can probably scale more, those things more, but, like, not 10x, not 100x, not 1000x. Like, right now, maybe, like, a good run of a large model is three months.[00:06:40] swyx: We can scale that to three years. But like, can we scale that to 30 years? No, right? Like, it starts to get ridiculous. So it's just the orders of magnitude of scaling. It's just, we're just like running out there. But in terms of the amount of time that we spend inferencing, like everything takes, you know, a few milliseconds, a few hundred milliseconds, depending on what how you're taking token by token, or, you know, entire phrase.[00:07:04] swyx: But We can scale that to hours, days, months of inference and see what we get. And I think that's really proMECEng.[00:07:11] Alessio: Yeah, we'll have Mike from Broadway back on the podcast. But I tried their product and their reports take about 10 minutes to generate instead of like just in real time. I think to me the most interesting thing about long inference is like, You're shifting the cost to the customer depending on how much they care about the end result.[00:07:31] Alessio: If you think about prompt engineering, it's like the first part, right? You can either do a simple prompt and get a simple answer or do a complicated prompt and get a better answer. It's up to you to decide how to do it. Now it's like, hey, instead of like, yeah, training this for three years, I'll still train it for three months and then I'll tell you, you know, I'll teach you how to like make it run for 10 minutes to get a better result.[00:07:52] Alessio: So you're kind of like parallelizing like the improvement of the LLM. Oh yeah, you can even[00:07:57] swyx: parallelize that, yeah, too.[00:07:58] Alessio: So, and I think, you know, for me, especially the work that I do, it's less about, you know, State of the art and the absolute, you know, it's more about state of the art for my application, for my use case.[00:08:09] Alessio: And I think we're getting to the point where like most companies and customers don't really care about state of the art anymore. It's like, I can get this to do a good enough job. You know, I just need to get better. Like, how do I do long inference? You know, like people are not really doing a lot of work in that space, so yeah, excited to see more.[00:08:28] swyx: So then the last point I'll mention here is something I also mentioned as paper. So all these directions are kind of guided by what happened in January. That was my way of doing a January recap. Which means that if there was nothing significant in that month, I also didn't mention it. Which is which I came to regret come February 15th, but in January also, you know, there was also the alpha geometry paper, which I kind of put in this sort of long inference bucket, because it solves like, you know, more than 100 step math olympiad geometry problems at a human gold medalist level and that also involves planning, right?[00:08:59] swyx: So like, if you want to scale inference, you can't scale it blindly, because just, Autoregressive token by token generation is only going to get you so far. You need good planning. And I think probably, yeah, what Mike from BrightWave is now doing and what everyone is doing, including maybe what we think QSTAR might be, is some form of search and planning.[00:09:17] swyx: And it makes sense. Like, you want to spend your inference time wisely. How do you[00:09:22] Alessio: think about plans that work and getting them shared? You know, like, I feel like if you're planning a task, somebody has got in and the models are stochastic. So everybody gets initially different results. Somebody is going to end up generating the best plan to do something, but there's no easy way to like store these plans and then reuse them for most people.[00:09:44] Alessio: You know, like, I'm curious if there's going to be. Some paper or like some work there on like making it better because, yeah, we don't[00:09:52] swyx: really have This is your your pet topic of NPM for[00:09:54] Alessio: Yeah, yeah, NPM, exactly. NPM for, you need NPM for anything, man. You need NPM for skills. You need NPM for planning. Yeah, yeah.[00:10:02] Alessio: You know I think, I mean, obviously the Voyager paper is like the most basic example where like, now their artifact is like the best planning to do a diamond pickaxe in Minecraft. And everybody can just use that. They don't need to come up with it again. Yeah. But there's nothing like that for actually useful[00:10:18] swyx: tasks.[00:10:19] swyx: For plans, I believe it for skills. I like that. Basically, that just means a bunch of integration tooling. You know, GPT built me integrations to all these things. And, you know, I just came from an integrations heavy business and I could definitely, I definitely propose some version of that. And it's just, you know, hard to execute or expensive to execute.[00:10:38] swyx: But for planning, I do think that everyone lives in slightly different worlds. They have slightly different needs. And they definitely want some, you know, And I think that that will probably be the main hurdle for any, any sort of library or package manager for planning. But there should be a meta plan of how to plan.[00:10:57] swyx: And maybe you can adopt that. And I think a lot of people when they have sort of these meta prompting strategies of like, I'm not prescribing you the prompt. I'm just saying that here are the like, Fill in the lines or like the mad libs of how to prompts. First you have the roleplay, then you have the intention, then you have like do something, then you have the don't something and then you have the my grandmother is dying, please do this.[00:11:19] swyx: So the meta plan you could, you could take off the shelf and test a bunch of them at once. I like that. That was the initial, maybe, promise of the, the prompting libraries. You know, both 9chain and Llama Index have, like, hubs that you can sort of pull off the shelf. I don't think they're very successful because people like to write their own.[00:11:36] swyx: Yeah,[00:11:37] Direction 2: Synthetic Data (WRAP, SPIN)[00:11:37] Alessio: yeah, yeah. Yeah, that's a good segue into the next one, which is synthetic[00:11:41] swyx: data. Synthetic data is so hot. Yeah, and, you know, the way, you know, I think I, I feel like I should do one of these memes where it's like, Oh, like I used to call it, you know, R L A I F, and now I call it synthetic data, and then people are interested.[00:11:54] swyx: But there's gotta be older versions of what synthetic data really is because I'm sure, you know if you've been in this field long enough, There's just different buzzwords that the industry condenses on. Anyway, the insight that I think is relatively new that why people are excited about it now and why it's proMECEng now is that we have evidence that shows that LLMs can generate data to improve themselves with no teacher LLM.[00:12:22] swyx: For all of 2023, when people say synthetic data, they really kind of mean generate a whole bunch of data from GPT 4 and then train an open source model on it. Hello to our friends at News Research. That's what News Harmony says. They're very, very open about that. I think they have said that they're trying to migrate away from that.[00:12:40] swyx: But it is explicitly against OpenAI Terms of Service. Everyone knows this. You know, especially once ByteDance got banned for, for doing exactly that. So so, so synthetic data that is not a form of model distillation is the hot thing right now, that you can bootstrap better LLM performance from the same LLM, which is very interesting.[00:13:03] swyx: A variant of this is RLAIF, where you have a, where you have a sort of a constitutional model, or, you know, some, some kind of judge model That is sort of more aligned. But that's not really what we're talking about when most people talk about synthetic data. Synthetic data is just really, I think, you know, generating more data in some way.[00:13:23] swyx: A lot of people, I think we talked about this with Vipul from the Together episode, where I think he commented that you just have to have a good world model. Or a good sort of inductive bias or whatever that, you know, term of art is. And that is strongest in math and science math and code, where you can verify what's right and what's wrong.[00:13:44] swyx: And so the REST EM paper from DeepMind explored that. Very well, it's just the most obvious thing like and then and then once you get out of that domain of like things where you can generate You can arbitrarily generate like a whole bunch of stuff and verify if they're correct and therefore they're they're correct synthetic data to train on Once you get into more sort of fuzzy topics, then it's then it's a bit less clear So I think that the the papers that drove this understanding There are two big ones and then one smaller one One was wrap like rephrasing the web from from Apple where they basically rephrased all of the C4 data set with Mistral and it be trained on that instead of C4.[00:14:23] swyx: And so new C4 trained much faster and cheaper than old C, than regular raw C4. And that was very interesting. And I have told some friends of ours that they should just throw out their own existing data sets and just do that because that seems like a pure win. Obviously we have to study, like, what the trade offs are.[00:14:42] swyx: I, I imagine there are trade offs. So I was just thinking about this last night. If you do synthetic data and it's generated from a model, probably you will not train on typos. So therefore you'll be like, once the model that's trained on synthetic data encounters the first typo, they'll be like, what is this?[00:15:01] swyx: I've never seen this before. So they have no association or correction as to like, oh, these tokens are often typos of each other, therefore they should be kind of similar. I don't know. That's really remains to be seen, I think. I don't think that the Apple people export[00:15:15] Alessio: that. Yeah, isn't that the whole, Mode collapse thing, if we do more and more of this at the end of the day.[00:15:22] swyx: Yeah, that's one form of that. Yeah, exactly. Microsoft also had a good paper on text embeddings. And then I think this is a meta paper on self rewarding language models. That everyone is very interested in. Another paper was also SPIN. These are all things we covered in the the Latent Space Paper Club.[00:15:37] swyx: But also, you know, I just kind of recommend those as top reads of the month. Yeah, I don't know if there's any much else in terms, so and then, regarding the potential of it, I think it's high potential because, one, it solves one of the data war issues that we have, like, everyone is OpenAI is paying Reddit 60 million dollars a year for their user generated data.[00:15:56] swyx: Google, right?[00:15:57] Alessio: Not OpenAI.[00:15:59] swyx: Is it Google? I don't[00:16:00] Alessio: know. Well, somebody's paying them 60 million, that's[00:16:04] swyx: for sure. Yes, that is, yeah, yeah, and then I think it's maybe not confirmed who. But yeah, it is Google. Oh my god, that's interesting. Okay, because everyone was saying, like, because Sam Altman owns 5 percent of Reddit, which is apparently 500 million worth of Reddit, he owns more than, like, the founders.[00:16:21] Alessio: Not enough to get the data,[00:16:22] swyx: I guess. So it's surprising that it would go to Google instead of OpenAI, but whatever. Okay yeah, so I think that's all super interesting in the data field. I think it's high potential because we have evidence that it works. There's not a doubt that it doesn't work. I think it's a doubt that there's, what the ceiling is, which is the mode collapse thing.[00:16:42] swyx: If it turns out that the ceiling is pretty close, then this will maybe augment our data by like, I don't know, 30 50 percent good, but not game[00:16:51] Alessio: changing. And most of the synthetic data stuff, it's reinforcement learning on a pre trained model. People are not really doing pre training on fully synthetic data, like, large enough scale.[00:17:02] swyx: Yeah, unless one of our friends that we've talked to succeeds. Yeah, yeah. Pre trained synthetic data, pre trained scale synthetic data, I think that would be a big step. Yeah. And then there's a wildcard, so all of these, like smaller Directions,[00:17:15] Wildcard: Multi-Epoch Training (OLMo, Datablations)[00:17:15] swyx: I always put a wildcard in there. And one of the wildcards is, okay, like, Let's say, you have pre, you have, You've scraped all the data on the internet that you think is useful.[00:17:25] swyx: Seems to top out at somewhere between 2 trillion to 3 trillion tokens. Maybe 8 trillion if Mistral, Mistral gets lucky. Okay, if I need 80 trillion, if I need 100 trillion, where do I go? And so, you can do synthetic data maybe, but maybe that only gets you to like 30, 40 trillion. Like where, where is the extra alpha?[00:17:43] swyx: And maybe extra alpha is just train more on the same tokens. Which is exactly what Omo did, like Nathan Lambert, AI2, After, just after he did the interview with us, they released Omo. So, it's unfortunate that we didn't get to talk much about it. But Omo actually started doing 1. 5 epochs on every, on all data.[00:18:00] swyx: And the data ablation paper that I covered in Europe's says that, you know, you don't like, don't really start to tap out of like, the alpha or the sort of improved loss that you get from data all the way until four epochs. And so I'm just like, okay, like, why do we all agree that one epoch is all you need?[00:18:17] swyx: It seems like to be a trend. It seems that we think that memorization is very good or too good. But then also we're finding that, you know, For improvement in results that we really like, we're fine on overtraining on things intentionally. So, I think that's an interesting direction that I don't see people exploring enough.[00:18:36] swyx: And the more I see papers coming out Stretching beyond the one epoch thing, the more people are like, it's completely fine. And actually, the only reason we stopped is because we ran out of compute[00:18:46] Alessio: budget. Yeah, I think that's the biggest thing, right?[00:18:51] swyx: Like, that's not a valid reason, that's not science. I[00:18:54] Alessio: wonder if, you know, Matt is going to do it.[00:18:57] Alessio: I heard LamaTree, they want to do a 100 billion parameters model. I don't think you can train that on too many epochs, even with their compute budget, but yeah. They're the only ones that can save us, because even if OpenAI is doing this, they're not going to tell us, you know. Same with DeepMind.[00:19:14] swyx: Yeah, and so the updates that we got on Lambda 3 so far is apparently that because of the Gemini news that we'll talk about later they're pushing it back on the release.[00:19:21] swyx: They already have it. And they're just pushing it back to do more safety testing. Politics testing.[00:19:28] Alessio: Well, our episode with Sumit will have already come out by the time this comes out, I think. So people will get the inside story on how they actually allocate the compute.[00:19:38] Direction 3: Alt. Architectures (Mamba, RWKV, RingAttention, Diffusion Transformers)[00:19:38] Alessio: Alternative architectures. Well, shout out to our WKV who won one of the prizes at our Final Frontiers event last week.[00:19:47] Alessio: We talked about Mamba and Strapain on the Together episode. A lot of, yeah, monarch mixers. I feel like Together, It's like the strong Stanford Hazy Research Partnership, because Chris Ray is one of the co founders. So they kind of have a, I feel like they're going to be the ones that have one of the state of the art models alongside maybe RWKB.[00:20:08] Alessio: I haven't seen as many independent. People working on this thing, like Monarch Mixer, yeah, Manbuster, Payena, all of these are together related. Nobody understands the math. They got all the gigabrains, they got 3DAO, they got all these folks in there, like, working on all of this.[00:20:25] swyx: Albert Gu, yeah. Yeah, so what should we comment about it?[00:20:28] swyx: I mean, I think it's useful, interesting, but at the same time, both of these are supposed to do really good scaling for long context. And then Gemini comes out and goes like, yeah, we don't need it. Yeah.[00:20:44] Alessio: No, that's the risk. So, yeah. I was gonna say, maybe it's not here, but I don't know if we want to talk about diffusion transformers as like in the alt architectures, just because of Zora.[00:20:55] swyx: One thing, yeah, so, so, you know, this came from the Jan recap, which, and diffusion transformers were not really a discussion, and then, obviously, they blow up in February. Yeah. I don't think they're, it's a mixed architecture in the same way that Stripe Tiena is mixed there's just different layers taking different approaches.[00:21:13] swyx: Also I think another one that I maybe didn't call out here, I think because it happened in February, was hourglass diffusion from stability. But also, you know, another form of mixed architecture. So I guess that is interesting. I don't have much commentary on that, I just think, like, we will try to evolve these things, and maybe one of these architectures will stick and scale, it seems like diffusion transformers is going to be good for anything generative, you know, multi modal.[00:21:41] swyx: We don't see anything where diffusion is applied to text yet, and that's the wild card for this category. Yeah, I mean, I think I still hold out hope for let's just call it sub quadratic LLMs. I think that a lot of discussion this month actually was also centered around this concept that People always say, oh, like, transformers don't scale because attention is quadratic in the sequence length.[00:22:04] swyx: Yeah, but, you know, attention actually is a very small part of the actual compute that is being spent, especially in inference. And this is the reason why, you know, when you multiply, when you, when you, when you jump up in terms of the, the model size in GPT 4 from like, you know, 38k to like 32k, you don't also get like a 16 times increase in your, in your performance.[00:22:23] swyx: And this is also why you don't get like a million times increase in your, in your latency when you throw a million tokens into Gemini. Like people have figured out tricks around it or it's just not that significant as a term, as a part of the overall compute. So there's a lot of challenges to this thing working.[00:22:43] swyx: It's really interesting how like, how hyped people are about this versus I don't know if it works. You know, it's exactly gonna, gonna work. And then there's also this, this idea of retention over long context. Like, even though you have context utilization, like, the amount of, the amount you can remember is interesting.[00:23:02] swyx: Because I've had people criticize both Mamba and RWKV because they're kind of, like, RNN ish in the sense that they have, like, a hidden memory and sort of limited hidden memory that they will forget things. So, for all these reasons, Gemini 1. 5, which we still haven't covered, is very interesting because Gemini magically has fixed all these problems with perfect haystack recall and reasonable latency and cost.[00:23:29] Wildcards: Text Diffusion, RALM/Retro[00:23:29] swyx: So that's super interesting. So the wildcard I put in here if you want to go to that. I put two actually. One is text diffusion. I think I'm still very influenced by my meeting with a mid journey person who said they were working on text diffusion. I think it would be a very, very different paradigm for, for text generation, reasoning, plan generation if we can get diffusion to work.[00:23:51] swyx: For text. And then the second one is Dowie Aquila's contextual AI, which is working on retrieval augmented language models, where it kind of puts RAG inside of the language model instead of outside.[00:24:02] Alessio: Yeah, there's a paper called Retro that covers some of this. I think that's an interesting thing. I think the The challenge, well not the challenge, what they need to figure out is like how do you keep the rag piece always up to date constantly, you know, I feel like the models, you put all this work into pre training them, but then at least you have a fixed artifact.[00:24:22] Alessio: These architectures are like constant work needs to be done on them and they can drift even just based on the rag data instead of the model itself. Yeah,[00:24:30] swyx: I was in a panel with one of the investors in contextual and the guy, the way that guy pitched it, I didn't agree with. He was like, this will solve hallucination.[00:24:38] Alessio: That's what everybody says. We solve[00:24:40] swyx: hallucination. I'm like, no, you reduce it. It cannot,[00:24:44] Alessio: if you solved it, the model wouldn't exist, right? It would just be plain text. It wouldn't be a generative model. Cool. So, author, architectures, then we got mixture of experts. I think we covered a lot of, a lot of times.[00:24:56] Direction 4: Mixture of Experts (DeepSeekMoE, Samba-1)[00:24:56] Alessio: Maybe any new interesting threads you want to go under here?[00:25:00] swyx: DeepSeq MOE, which was released in January. Everyone who is interested in MOEs should read that paper, because it's significant for two reasons. One three reasons. One, it had, it had small experts, like a lot more small experts. So, for some reason, everyone has settled on eight experts for GPT 4 for Mixtral, you know, that seems to be the favorite architecture, but these guys pushed it to 64 experts, and each of them smaller than the other.[00:25:26] swyx: But then they also had the second idea, which is that it is They had two, one to two always on experts for common knowledge and that's like a very compelling concept that you would not route to all the experts all the time and make them, you know, switch to everything. You would have some always on experts.[00:25:41] swyx: I think that's interesting on both the inference side and the training side for for memory retention. And yeah, they, they, they, the, the, the, the results that they published, which actually excluded, Mixed draw, which is interesting. The results that they published showed a significant performance jump versus all the other sort of open source models at the same parameter count.[00:26:01] swyx: So like this may be a better way to do MOEs that are, that is about to get picked up. And so that, that is interesting for the third reason, which is this is the first time a new idea from China. has infiltrated the West. It's usually the other way around. I probably overspoke there. There's probably lots more ideas that I'm not aware of.[00:26:18] swyx: Maybe in the embedding space. But the I think DCM we, like, woke people up and said, like, hey, DeepSeek, this, like, weird lab that is attached to a Chinese hedge fund is somehow, you know, doing groundbreaking research on MOEs. So, so, I classified this as a medium potential because I think that it is a sort of like a one off benefit.[00:26:37] swyx: You can Add to any, any base model to like make the MOE version of it, you get a bump and then that's it. So, yeah,[00:26:45] Alessio: I saw Samba Nova, which is like another inference company. They released this MOE model called Samba 1, which is like a 1 trillion parameters. But they're actually MOE auto open source models.[00:26:56] Alessio: So it's like, they just, they just clustered them all together. So I think people. Sometimes I think MOE is like you just train a bunch of small models or like smaller models and put them together. But there's also people just taking, you know, Mistral plus Clip plus, you know, Deepcoder and like put them all together.[00:27:15] Alessio: And then you have a MOE model. I don't know. I haven't tried the model, so I don't know how good it is. But it seems interesting that you can then have people working separately on state of the art, you know, Clip, state of the art text generation. And then you have a MOE architecture that brings them all together.[00:27:31] swyx: I'm thrown off by your addition of the word clip in there. Is that what? Yeah, that's[00:27:35] Alessio: what they said. Yeah, yeah. Okay. That's what they I just saw it yesterday. I was also like[00:27:40] swyx: scratching my head. And they did not use the word adapter. No. Because usually what people mean when they say, Oh, I add clip to a language model is adapter.[00:27:48] swyx: Let me look up the Which is what Lava did.[00:27:50] Alessio: The announcement again.[00:27:51] swyx: Stable diffusion. That's what they do. Yeah, it[00:27:54] Alessio: says among the models that are part of Samba 1 are Lama2, Mistral, DeepSigCoder, Falcon, Dplot, Clip, Lava. So they're just taking all these models and putting them in a MOE. Okay,[00:28:05] swyx: so a routing layer and then not jointly trained as much as a normal MOE would be.[00:28:12] swyx: Which is okay.[00:28:13] Alessio: That's all they say. There's no paper, you know, so it's like, I'm just reading the article, but I'm interested to see how[00:28:20] Wildcard: Model Merging (mergekit)[00:28:20] swyx: it works. Yeah, so so the wildcard for this section, the MOE section is model merges, which has also come up as, as a very interesting phenomenon. The last time I talked to Jeremy Howard at the Olama meetup we called it model grafting or model stacking.[00:28:35] swyx: But I think the, the, the term that people are liking these days, the model merging, They're all, there's all different variations of merging. Merge types, and some of them are stacking, some of them are, are grafting. And, and so like, some people are approaching model merging in the way that Samba is doing, which is like, okay, here are defined models, each of which have their specific, Plus and minuses, and we will merge them together in the hope that the, you know, the sum of the parts will, will be better than others.[00:28:58] swyx: And it seems like it seems like it's working. I don't really understand why it works apart from, like, I think it's a form of regularization. That if you merge weights together in like a smart strategy you, you, you get a, you get a, you get a less overfitting and more generalization, which is good for benchmarks, if you, if you're honest about your benchmarks.[00:29:16] swyx: So this is really interesting and good. But again, they're kind of limited in terms of like the amount of bumps you can get. But I think it's very interesting in the sense of how cheap it is. We talked about this on the Chinatalk podcast, like the guest podcast that we did with Chinatalk. And you can do this without GPUs, because it's just adding weights together, and dividing things, and doing like simple math, which is really interesting for the GPU ports.[00:29:42] Alessio: There's a lot of them.[00:29:44] Direction 5: Online LLMs (Gemini Pro, Exa)[00:29:44] Alessio: And just to wrap these up, online LLMs? Yeah,[00:29:48] swyx: I think that I ki I had to feature this because the, one of the top news of January was that Gemini Pro beat GPT-4 turbo on LM sis for the number two slot to GPT-4. And everyone was very surprised. Like, how does Gemini do that?[00:30:06] swyx: Surprise, surprise, they added Google search. Mm-hmm to the results. So it became an online quote unquote online LLM and not an offline LLM. Therefore, it's much better at answering recent questions, which people like. There's an emerging set of table stakes features after you pre train something.[00:30:21] swyx: So after you pre train something, you should have the chat tuned version of it, or the instruct tuned version of it, however you choose to call it. You should have the JSON and function calling version of it. Structured output, the term that you don't like. You should have the online version of it. These are all like table stakes variants, that you should do when you offer a base LLM, or you train a base LLM.[00:30:44] swyx: And I think online is just like, There, it's important. I think companies like Perplexity, and even Exa, formerly Metaphor, you know, are rising to offer that search needs. And it's kind of like, they're just necessary parts of a system. When you have RAG for internal knowledge, and then you have, you know, Online search for external knowledge, like things that you don't know yet?[00:31:06] swyx: Mm-Hmm. . And it seems like it's, it's one of many tools. I feel like I may be underestimating this, but I'm just gonna put it out there that I, I think it has some, some potential. One of the evidence points that it doesn't actually matter that much is that Perplexity has a, has had online LMS for three months now and it performs, doesn't perform great.[00:31:25] swyx: Mm-Hmm. on, on lms, it's like number 30 or something. So it's like, okay. You know, like. It's, it's, it helps, but it doesn't give you a giant, giant boost. I[00:31:34] Alessio: feel like a lot of stuff I do with LLMs doesn't need to be online. So I'm always wondering, again, going back to like state of the art, right? It's like state of the art for who and for what.[00:31:45] Alessio: It's really, I think online LLMs are going to be, State of the art for, you know, news related activity that you need to do. Like, you're like, you know, social media, right? It's like, you want to have all the latest stuff, but coding, science,[00:32:01] swyx: Yeah, but I think. Sometimes you don't know what is news, what is news affecting.[00:32:07] swyx: Like, the decision to use an offline LLM is already a decision that you might not be consciously making that might affect your results. Like, what if, like, just putting things on, being connected online means that you get to invalidate your knowledge. And when you're just using offline LLM, like it's never invalidated.[00:32:27] swyx: I[00:32:28] Alessio: agree, but I think going back to your point of like the standing the test of time, I think sometimes you can get swayed by the online stuff, which is like, hey, you ask a question about, yeah, maybe AI research direction, you know, and it's like, all the recent news are about this thing. So the LLM like focus on answering, bring it up, you know, these things.[00:32:50] swyx: Yeah, so yeah, I think, I think it's interesting, but I don't know if I can, I bet heavily on this.[00:32:56] Alessio: Cool. Was there one that you forgot to put, or, or like a, a new direction? Yeah,[00:33:01] swyx: so, so this brings us into sort of February. ish.[00:33:05] OpenAI Sora and why everyone underestimated videogen[00:33:05] swyx: So like I published this in like 15 came with Sora. And so like the one thing I did not mention here was anything about multimodality.[00:33:16] swyx: Right. And I have chronically underweighted this. I always wrestle. And, and my cop out is that I focused this piece or this research direction piece on LLMs because LLMs are the source of like AGI, quote unquote AGI. Everything else is kind of like. You know, related to that, like, generative, like, just because I can generate better images or generate better videos, it feels like it's not on the critical path to AGI, which is something that Nat Friedman also observed, like, the day before Sora, which is kind of interesting.[00:33:49] swyx: And so I was just kind of like trying to focus on like what is going to get us like superhuman reasoning that we can rely on to build agents that automate our lives and blah, blah, blah, you know, give us this utopian future. But I do think that I, everybody underestimated the, the sheer importance and cultural human impact of Sora.[00:34:10] swyx: And you know, really actually good text to video. Yeah. Yeah.[00:34:14] Alessio: And I saw Jim Fan at a, at a very good tweet about why it's so impressive. And I think when you have somebody leading the embodied research at NVIDIA and he said that something is impressive, you should probably listen. So yeah, there's basically like, I think you, you mentioned like impacting the world, you know, that we live in.[00:34:33] Alessio: I think that's kind of like the key, right? It's like the LLMs don't have, a world model and Jan Lekon. He can come on the podcast and talk all about what he thinks of that. But I think SORA was like the first time where people like, Oh, okay, you're not statically putting pixels of water on the screen, which you can kind of like, you know, project without understanding the physics of it.[00:34:57] Alessio: Now you're like, you have to understand how the water splashes when you have things. And even if you just learned it by watching video and not by actually studying the physics, You still know it, you know, so I, I think that's like a direction that yeah, before you didn't have, but now you can do things that you couldn't before, both in terms of generating, I think it always starts with generating, right?[00:35:19] Alessio: But like the interesting part is like understanding it. You know, it's like if you gave it, you know, there's the video of like the, the ship in the water that they generated with SORA, like if you gave it the video back and now it could tell you why the ship is like too rocky or like it could tell you why the ship is sinking, then that's like, you know, AGI for like all your rig deployments and like all this stuff, you know, so, but there's none, there's none of that yet, so.[00:35:44] Alessio: Hopefully they announce it and talk more about it. Maybe a Dev Day this year, who knows.[00:35:49] swyx: Yeah who knows, who knows. I'm talking with them about Dev Day as well. So I would say, like, the phrasing that Jim used, which resonated with me, he kind of called it a data driven world model. I somewhat agree with that.[00:36:04] Does Sora have a World Model? Yann LeCun vs Jim Fan[00:36:04] swyx: I am on more of a Yann LeCun side than I am on Jim's side, in the sense that I think that is the vision or the hope that these things can build world models. But you know, clearly even at the current SORA size, they don't have the idea of, you know, They don't have strong consistency yet. They have very good consistency, but fingers and arms and legs will appear and disappear and chairs will appear and disappear.[00:36:31] swyx: That definitely breaks physics. And it also makes me think about how we do deep learning versus world models in the sense of You know, in classic machine learning, when you have too many parameters, you will overfit, and actually that fails, that like, does not match reality, and therefore fails to generalize well.[00:36:50] swyx: And like, what scale of data do we need in order to world, learn world models from video? A lot. Yeah. So, so I, I And cautious about taking this interpretation too literally, obviously, you know, like, I get what he's going for, and he's like, obviously partially right, obviously, like, transformers and, and, you know, these, like, these sort of these, these neural networks are universal function approximators, theoretically could figure out world models, it's just like, how good are they, and how tolerant are we of hallucinations, we're not very tolerant, like, yeah, so It's, it's, it's gonna prior, it's gonna bias us for creating like very convincing things, but then not create like the, the, the useful role models that we want.[00:37:37] swyx: At the same time, what you just said, I think made me reflect a little bit like we just got done saying how important synthetic data is for Mm-Hmm. for training lms. And so like, if this is a way of, of synthetic, you know, vi video data for improving our video understanding. Then sure, by all means. Which we actually know, like, GPT 4, Vision, and Dolly were trained, kind of, co trained together.[00:38:02] swyx: And so, like, maybe this is on the critical path, and I just don't fully see the full picture yet.[00:38:08] Alessio: Yeah, I don't know. I think there's a lot of interesting stuff. It's like, imagine you go back, you have Sora, you go back in time, and Newton didn't figure out gravity yet. Would Sora help you figure it out?[00:38:21] Alessio: Because you start saying, okay, a man standing under a tree with, like, Apples falling, and it's like, oh, they're always falling at the same speed in the video. Why is that? I feel like sometimes these engines can like pick up things, like humans have a lot of intuition, but if you ask the average person, like the physics of like a fluid in a boat, they couldn't be able to tell you the physics, but they can like observe it, but humans can only observe this much, you know, versus like now you have these models to observe everything and then They generalize these things and maybe we can learn new things through the generalization that they pick up.[00:38:55] swyx: But again, And it might be more observant than us in some respects. In some ways we can scale it up a lot more than the number of physicists that we have available at Newton's time. So like, yeah, absolutely possible. That, that this can discover new science. I think we have a lot of work to do to formalize the science.[00:39:11] swyx: And then, I, I think the last part is you know, How much, how much do we cheat by gen, by generating data from Unreal Engine 5? Mm hmm. which is what a lot of people are speculating with very, very limited evidence that OpenAI did that. The strongest evidence that I saw was someone who works a lot with Unreal Engine 5 looking at the side characters in the videos and noticing that they all adopt Unreal Engine defaults.[00:39:37] swyx: of like, walking speed, and like, character choice, like, character creation choice. And I was like, okay, like, that's actually pretty convincing that they actually use Unreal Engine to bootstrap some synthetic data for this training set. Yeah,[00:39:52] Alessio: could very well be.[00:39:54] swyx: Because then you get the labels and the training side by side.[00:39:58] swyx: One thing that came up on the last day of February, which I should also mention, is EMO coming out of Alibaba, which is also a sort of like video generation and space time transformer that also involves probably a lot of synthetic data as well. And so like, this is of a kind in the sense of like, oh, like, you know, really good generative video is here and It is not just like the one, two second clips that we saw from like other, other people and like, you know, Pika and all the other Runway are, are, are, you know, run Cristobal Valenzuela from Runway was like game on which like, okay, but like, let's see your response because we've heard a lot about Gen 1 and 2, but like, it's nothing on this level of Sora So it remains to be seen how we can actually apply this, but I do think that the creative industry should start preparing.[00:40:50] swyx: I think the Sora technical blog post from OpenAI was really good.. It was like a request for startups. It was so good in like spelling out. Here are the individual industries that this can impact.[00:41:00] swyx: And anyone who, anyone who's like interested in generative video should look at that. But also be mindful that probably when OpenAI releases a Soa API, right? The you, the in these ways you can interact with it are very limited. Just like the ways you can interact with Dahlia very limited and someone is gonna have to make open SOA to[00:41:19] swyx: Mm-Hmm to, to, for you to create comfy UI pipelines.[00:41:24] Alessio: The stability folks said they wanna build an open. For a competitor, but yeah, stability. Their demo video, their demo video was like so underwhelming. It was just like two people sitting on the beach[00:41:34] swyx: standing. Well, they don't have it yet, right? Yeah, yeah.[00:41:36] swyx: I mean, they just wanna train it. Everybody wants to, right? Yeah. I, I think what is confusing a lot of people about stability is like they're, they're, they're pushing a lot of things in stable codes, stable l and stable video diffusion. But like, how much money do they have left? How many people do they have left?[00:41:51] swyx: Yeah. I have had like a really, Ima Imad spent two hours with me. Reassuring me things are great. And, and I'm like, I, I do, like, I do believe that they have really, really quality people. But it's just like, I, I also have a lot of very smart people on the other side telling me, like, Hey man, like, you know, don't don't put too much faith in this, in this thing.[00:42:11] swyx: So I don't know who to believe. Yeah.[00:42:14] Alessio: It's hard. Let's see. What else? We got a lot more stuff. I don't know if we can. Yeah, Groq.[00:42:19] Groq Math[00:42:19] Alessio: We can[00:42:19] swyx: do a bit of Groq prep. We're, we're about to go to talk to Dylan Patel. Maybe, maybe it's the audio in here. I don't know. It depends what, what we get up to later. What, how, what do you as an investor think about Groq? Yeah. Yeah, well, actually, can you recap, like, why is Groq interesting? So,[00:42:33] Alessio: Jonathan Ross, who's the founder of Groq, he's the person that created the TPU at Google. It's actually, it was one of his, like, 20 percent projects. It's like, he was just on the side, dooby doo, created the TPU.[00:42:46] Alessio: But yeah, basically, Groq, they had this demo that went viral, where they were running Mistral at, like, 500 tokens a second, which is like, Fastest at anything that you have out there. The question, you know, it's all like, The memes were like, is NVIDIA dead? Like, people don't need H100s anymore. I think there's a lot of money that goes into building what GRUK has built as far as the hardware goes.[00:43:11] Alessio: We're gonna, we're gonna put some of the notes from, from Dylan in here, but Basically the cost of the Groq system is like 30 times the cost of, of H100 equivalent. So, so[00:43:23] swyx: let me, I put some numbers because me and Dylan were like, I think the two people actually tried to do Groq math. Spreadsheet doors.[00:43:30] swyx: Spreadsheet doors. So, one that's, okay, oh boy so, so, equivalent H100 for Lama 2 is 300, 000. For a system of 8 cards. And for Groq it's 2. 3 million. Because you have to buy 576 Groq cards. So yeah, that, that just gives people an idea. So like if you deprecate both over a five year lifespan, per year you're deprecating 460K for Groq, and 60K a year for H100.[00:43:59] swyx: So like, Groqs are just way more expensive per model that you're, that you're hosting. But then, you make it up in terms of volume. So I don't know if you want to[00:44:08] Alessio: cover that. I think one of the promises of Groq is like super high parallel inference on the same thing. So you're basically saying, okay, I'm putting on this upfront investment on the hardware, but then I get much better scaling once I have it installed.[00:44:24] Alessio: I think the big question is how much can you sustain the parallelism? You know, like if you get, if you're going to get 100% Utilization rate at all times on Groq, like, it's just much better, you know, because like at the end of the day, the tokens per second costs that you're getting is better than with the H100s, but if you get to like 50 percent utilization rate, you will be much better off running on NVIDIA.[00:44:49] Alessio: And if you look at most companies out there, who really gets 100 percent utilization rate? Probably open AI at peak times, but that's probably it. But yeah, curious to see more. I saw Jonathan was just at the Web Summit in Dubai, in Qatar. He just gave a talk there yesterday. That I haven't listened to yet.[00:45:09] Alessio: I, I tweeted that he should come on the pod. He liked it. And then rock followed me on Twitter. I don't know if that means that they're interested, but[00:45:16] swyx: hopefully rock social media person is just very friendly. They, yeah. Hopefully[00:45:20] Alessio: we can get them. Yeah, we, we gonna get him. We[00:45:22] swyx: just call him out and, and so basically the, the key question is like, how sustainable is this and how much.[00:45:27] swyx: This is a loss leader the entire Groq management team has been on Twitter and Hacker News saying they are very, very comfortable with the pricing of 0. 27 per million tokens. This is the lowest that anyone has offered tokens as far as Mixtral or Lama2. This matches deep infra and, you know, I think, I think that's, that's, that's about it in terms of that, that, that low.[00:45:47] swyx: And we think the pro the break even for H100s is 50 cents. At a, at a normal utilization rate. To make this work, so in my spreadsheet I made this, made this work. You have to have like a parallelism of 500 requests all simultaneously. And you have, you have model bandwidth utilization of 80%.[00:46:06] swyx: Which is way high. I just gave them high marks for everything. Groq has two fundamental tech innovations that they hinge their hats on in terms of like, why we are better than everyone. You know, even though, like, it remains to be independently replicated. But one you know, they have this sort of the entire model on the chip idea, which is like, Okay, get rid of HBM.[00:46:30] swyx: And, like, put everything in SREM. Like, okay, fine, but then you need a lot of cards and whatever. And that's all okay. And so, like, because you don't have to transfer between memory, then you just save on that time and that's why they're faster. So, a lot of people buy that as, like, that's the reason that you're faster.[00:46:45] swyx: Then they have, like, some kind of crazy compiler, or, like, Speculative routing magic using compilers that they also attribute towards their higher utilization. So I give them 80 percent for that. And so that all that works out to like, okay, base costs, I think you can get down to like, maybe like 20 something cents per million tokens.[00:47:04] swyx: And therefore you actually are fine if you have that kind of utilization. But it's like, I have to make a lot of fearful assumptions for this to work.[00:47:12] Alessio: Yeah. Yeah, I'm curious to see what Dylan says later.[00:47:16] swyx: So he was like completely opposite of me. He's like, they're just burning money. Which is great.[00:47:22] Analyzing Gemini's 1m Context, Reddit deal, Imagegen politics, Gemma via the Four Wars[00:47:22] Alessio: Gemini, want to do a quick run through since this touches on all the four words.[00:47:28] swyx: Yeah, and I think this is the mark of a useful framework, that when a new thing comes along, you can break it down in terms of the four words and sort of slot it in or analyze it in those four frameworks, and have nothing left.[00:47:41] swyx: So it's a MECE categorization. MECE is Mutually Exclusive and Collectively Exhaustive. And that's a really, really nice way to think about taxonomies and to create mental frameworks. So, what is Gemini 1. 5 Pro? It is the newest model that came out one week after Gemini 1. 0. Which is very interesting.[00:48:01] swyx: They have not really commented on why. They released this the headline feature is that it has a 1 million token context window that is multi modal which means that you can put all sorts of video and audio And PDFs natively in there alongside of text and, you know, it's, it's at least 10 times longer than anything that OpenAI offers which is interesting.[00:48:20] swyx: So it's great for prototyping and it has interesting discussions on whether it kills RAG.[00:48:25] Alessio: Yeah, no, I mean, we always talk about, you know, Long context is good, but you're getting charged per token. So, yeah, people love for you to use more tokens in the context. And RAG is better economics. But I think it all comes down to like how the price curves change, right?[00:48:42] Alessio: I think if anything, RAG's complexity goes up and up the more you use it, you know, because you have more data sources, more things you want to put in there. The token costs should go down over time, you know, if the model stays fixed. If people are happy with the model today. In two years, three years, it's just gonna cost a lot less, you know?[00:49:02] Alessio: So now it's like, why would I use RAG and like go through all of that? It's interesting. I think RAG is better cutting edge economics for LLMs. I think large context will be better long tail economics when you factor in the build cost of like managing a RAG pipeline. But yeah, the recall was like the most interesting thing because we've seen the, you know, You know, in the haystack things in the past, but apparently they have 100 percent recall on anything across the context window.[00:49:28] Alessio: At least they say nobody has used it. No, people[00:49:30] swyx: have. Yeah so as far as, so, so what this needle in a haystack thing for people who aren't following as closely as us is that someone, I forget his name now someone created this needle in a haystack problem where you feed in a whole bunch of generated junk not junk, but just like, Generate a data and ask it to specifically retrieve something in that data, like one line in like a hundred thousand lines where it like has a specific fact and if it, if you get it, you're, you're good.[00:49:57] swyx: And then he moves the needle around, like, you know, does it, does, does your ability to retrieve that vary if I put it at the start versus put it in the middle, put it at the end? And then you generate this like really nice chart. That, that kind of shows like it's recallability of a model. And he did that for GPT and, and Anthropic and showed that Anthropic did really, really poorly.[00:50:15] swyx: And then Anthropic came back and said it was a skill issue, just add this like four, four magic words, and then, then it's magically all fixed. And obviously everybody laughed at that. But what Gemini came out with was, was that, yeah, we, we reproduced their, you know, haystack issue you know, test for Gemini, and it's good across all, all languages.[00:50:30] swyx: All the one million token window, which is very interesting because usually for typical context extension methods like rope or yarn or, you know, anything like that, or alibi, it's lossy like by design it's lossy, usually for conversations that's fine because we are lossy when we talk to people but for superhuman intelligence, perfect memory across Very, very long context.[00:50:51] swyx: It's very, very interesting for picking things up. And so the people who have been given the beta test for Gemini have been testing this. So what you do is you upload, let's say, all of Harry Potter and you change one fact in one sentence, somewhere in there, and you ask it to pick it up, and it does. So this is legit.[00:51:08] swyx: We don't super know how, because this is, like, because it doesn't, yes, it's slow to inference, but it's not slow enough that it's, like, running. Five different systems in the background without telling you. Right. So it's something, it's something interesting that they haven't fully disclosed yet. The open source community has centered on this ring attention paper, which is created by your friend Matei Zaharia, and a couple other people.[00:51:36] swyx: And it's a form of distributing the compute. I don't super understand, like, why, you know, doing, calculating, like, the fee for networking and attention. In block wise fashion and distributing it makes it so good at recall. I don't think they have any answer to that. The only thing that Ring of Tension is really focused on is basically infinite context.[00:51:59] swyx: They said it was good for like 10 to 100 million tokens. Which is, it's just great. So yeah, using the four wars framework, what is this framework for Gemini? One is the sort of RAG and Ops war. Here we care less about RAG now, yes. Or, we still care as much about RAG, but like, now it's it's not important in prototyping.[00:52:21] swyx: And then, for data war I guess this is just part of the overall training dataset, but Google made a 60 million deal with Reddit and presumably they have deals with other companies. For the multi modality war, we can talk about the image generation, Crisis, or the fact that Gemini also has image generation, which we'll talk about in the next section.[00:52:42] swyx: But it also has video understanding, which is, I think, the top Gemini post came from our friend Simon Willison, who basically did a short video of him scanning over his bookshelf. And it would be able to convert that video into a JSON output of what's on that bookshelf. And I think that is very useful.[00:53:04] swyx: Actually ties into the conversation that we had with David Luan from Adept. In a sense of like, okay what if video was the main modality instead of text as the input? What if, what if everything was video in, because that's how we work. We, our eyes don't actually read, don't actually like get input, our brains don't get inputs as characters.[00:53:25] swyx: Our brains get the pixels shooting into our eyes, and then our vision system takes over first, and then we sort of mentally translate that into text later. And so it's kind of like what Adept is kind of doing, which is driving by vision model, instead of driving by raw text understanding of the DOM. And, and I, I, in that, that episode, which we haven't released I made the analogy to like self-driving by lidar versus self-driving by camera.[00:53:52] swyx: Mm-Hmm. , right? Like, it's like, I think it, what Gemini and any other super long context that model that is multimodal unlocks is what if you just drive everything by video. Which is[00:54:03] Alessio: cool. Yeah, and that's Joseph from Roboflow. It's like anything that can be seen can be programmable with these models.[00:54:12] Alessio: You mean[00:54:12] swyx: the computer vision guy is bullish on computer vision?[00:54:18] Alessio: It's like the rag people. The rag people are bullish on rag and not a lot of context. I'm very surprised. The, the fine tuning people love fine tuning instead of few shot. Yeah. Yeah. The, yeah, the, that's that. Yeah, the, I, I think the ring attention thing, and it's how they did it, we don't know. And then they released the Gemma models, which are like a 2 billion and 7 billion open.[00:54:41] Alessio: Models, which people said are not, are not good based on my Twitter experience, which are the, the GPU poor crumbs. It's like, Hey, we did all this work for us because we're GPU rich and we're just going to run this whole thing. And
Los titulares de la semana en 2Playbook.com junto a Álvaro Carretero y Patricia López. Hoy informamos el negocio de la Uefa con sus principales competiciones, la radiografía del patrocinio técnico en España y entrevista con el CEO del Genoa, club controlado por el fondo de inversión 777 Partners. Inscríbete a la jornada PRO Women in Sports, que se celebrará en Barcelona el 20 de marzo de 2024, y donde se abordará el potencial del deporte femenino en términos de negocio y las claves de la inversión en clubes, competiciones y contenido para consolidarlo como un producto de entretenimiento.
Slightly Open 106 ｜工作20多年后，关于冒险、选择、失败，我们现在是这么看的欢迎关注Slightly Open。这是三位女性的一个小型谈话节目，薇薇，Coco（@Wang蔻蔻）和老柴（@老柴）。热烈庆祝我们新开了小红书账号@Slightly Open，欢迎关注！本期摘要：“数九寒天的经济环境下选择创业，离开稳定舒适的平台去迎接更大挑战，在升职外派机会面前反而选择为家人和朋友留下”，新年伊始，发现我们三个刚好都站在了又一个全新的方向上。这几年，不光是我们，很多人因为历史进程或自身发展，都或多或少面临分岔路。要选择稳定吗，要承担风险吗，面对理想和热爱，要纵身一跃吗？我们三个在职场上都有20年上下的经历，有些理解，也探索和有待学习的部分。今天就这个话题跟大家聊聊。时间轴：00:10 为什么想聊挑战和舒适区？品牌发布后的柴，忽然意识到自己是个屡败屡战的人00:55 一个奇怪现象：我们三个对风险的偏好各有不同，却在具体选择方面分别走向反面02:55 收音机前的你，如何评估风险，又在面临什么样的抉择呢04:18 柴为什么选择二次创业？一个风险厌恶型人格选择高风险职业是怎么考虑的10:00 创业到底是跟风险偏好有关，还是与自我，自由有关？塔罗牌的启示12:21 如果这次失败了，它意味着什么？老柴的现实主义回答14:50 “渔樵耕读”模型，与35岁后对事业的“资产化”考虑18:18 时代正进入个体化时代，每个人都应当建立全面能力19:30 Coco，为什么最近在switch cost巨大的阶段迎接了新的挑战22:30 在情感之外，用麦肯锡MECE原则来理性思考26:05 每个人最终的选择，虽然在仪式上，按照世俗的标准有勇敢与否、安全与否的区分；但当我们看到每个人，生命中什么最重要的本质答案时，一切就水落石出了27:50 我们的选择定义了我们是谁，而对风险的理解和评估只是其中的一个工具和手段28:42 做一个勇敢的决定不是句号，它只是刚刚开始30:04 一个方法论——“最高标准与最低标准”31:00 有力量的“为何启程”31:55 薇的那个相反决定。“我真正害怕的不是冒险，真正主导我的是……”37:20 “攻守不定，选攻！“是个什么理论39:30 你人生中最冒险的决定有哪些42:20 火象三傻之“蠢勇”和不纠结44:45 高考的故事49:45 “试试“的战略，也许还是战术问题53:20 我现在觉得，耐心特别特别重要55:50 现在觉得：年轻的时候，应当事业上更勇敢，感情上更安全57:40 还说建议每个人做决定时“留得青山在，不怕没柴烧”61:50 那些探险者们，其实并不是热爱风险67:16 情感上的失败与自我怀疑71:00 善胜不败，善败不亡，以及美国大蠊制成的康复新液74:30 薇给母校的学弟学妹们的建议，以及柴和Coco的补充本期书籍：《牧羊少年奇幻之旅》本期剧集：《繁花》本期音乐：Gerard Darmon - And The Winner IsCoco提到的品牌房子：本期思考：关于守还是攻，是否要走出舒适区，是否要主动承担风险，或者有任何关于这个话题的思考和讨论，都欢迎留言。
La situación alrededor de Israel es de tensión máxima. El país hebreo vive acorralado por los últimos ataques recibidos, y su respuesta está causando por distintos frentes un enorme daño en forma de sufrimiento y vidas humanas. En esta situación de conflicto máximo, Estados Unidos llega para mediar y para apoyar a Israel sin titubeos, conscientes de lo que se está jugando en la zona. Pero es un país ajeno al lugar quien realmente mueve los hilos, y quien ha conseguido paso a paso, hasta la fecha, lo que se propone: se llama Irán, y está dispuesto a agitar la coctelera tanto como haga falta para que Israel siga retratándose ante la comunidad internacional, y especialmente la islámica ante el acoso y derribo del pueblo palestino, del que los países limítrofes también son cómplices indirectos. ¿quién tendrá la audacia para detener este círculo vicioso? Mis Aparatos y equipos imprescindibles para trabajar y vivir: https://www.amazon.es/shop/juanfranciscocalero-clubonmotor Apóyame para hacer más y mejores vídeos en PATREON. Sé mi mentor: https://www.patreon.com/jfcalero Sígueme en INSTAGRAM, TWITTER o AMBAS, súperfácil: @jfcalero No te vayas a la cama sin saber algo más, o al menos sin saber algo nuevo. Si lo hiciste, misión cumplida, gracias por acompañarme. Puedes ver el video correspondiente a este podcast en el siguiente enlace: https://www.youtube.com/watch?v=kT7_W1sRAJI
Hernán Vanoli y Marcos Zurita analizan las historias que nos contamos cuando nos cuentan historias. Libros, series, películas y algunas cosas más. Hoy, los motivos por los que abandonamos las series y por los cuales las series nos abandonan.
MECE (Mutually Exclusive Collective Exhaustive) là nguyên tắc bóc tách vấn đề được ứng dụng tại Tập đoàn Tư vấn Chiến lược Toàn cầu McKinsey. Tuy nhiên, không chỉ riêng tập đoàn tư vấn, mà các doanh nghiệp, startups cũng có thể ứng dụng nguyên tắc này trong việc đánh giá vấn đề một cách bài bản, logic để đưa ra quyết định nhanh chóng và chính xác. Vậy, cụ thể MECE là gì? Làm sao để phát huy được sức mạnh của nguyên tắc này để giải quyết những bài toán khó trong doanh nghiệp? Trong tập mới nhất của Vietsucess Business, host Vanessa Phan sẽ cùng trò chuyện với khách mời Việt Trần - Co-Founder MCP Group, công ty Công nghệ Giáo dục trong ngành Tư vấn Chiến lược về kỹ năng đánh giá và giải quyết vấn đề dựa trên nguyên tắc MECE. Đồng thời, anh cũng chia sẻ “bí kíp” ứng dụng MECE vào quy trình đánh giá và đào tạo nhân sự để mỗi nhân viên đều có thể trở thành chuyên gia tư vấn riêng cho doanh nghiệp. Mời các bạn cùng lắng nghe! 00:00 - Mở đầu, giới thiệu khách mời 01:51 - Chặng đường sự nghiệp của khách mời 03:03 - Yếu tố giúp startup tồn tại trong môi trường nhanh 04:40 - Bài học từ việc làm nhanh nhưng không hiệu quả 08:22 - Ranh giới giữa cố chấp và lì đòn 10:35 - Coming Up 11:00 - Yếu tố quan trọng nhất là tư duy cởi mở 14:45 - Bí quyết phân tích vấn đề chuẩn chuyên gia 22:09 - Làm sao để đào tạo nhân sự ứng dụng MECE vào công việc 23:42 - Coming Up 24:09 - Mất bao lâu để thuần thục kỹ năng chuẩn MECE? 26:42 - Ứng dụng MECE vào đánh giá nhân sự như thế nào? 33:04 - Tư duy số được hình thành như thế nào? 39:03 - Tối ưu hóa cuộc họp trong mọi doanh nghiệp 41:17 - Lời nhắn nhủ đến các chủ doanh nghiệp 42:23 - Chào kết Dẫn Chuyện - Host | Vanessa Phan Kịch Bản - Scriptwriting | Vanessa Phan, Thảo Nguyễn Biên Tập – Editor | Bách Hợp Sản Xuất - Producer | Anneliese Mai Nguyen Trợ Lý Sản Xuất - Producer Assistant | Ngọc Huân Quay Phim - Cameraman | Khanh Trần, Nhật Trường, Hải Long, Thanh Quang Âm Thanh - Sound | Khanh Trần Hậu Kì – Post Production | Thanh Quang Thiết Kế - Design | Nghi Nghi Nhiếp Ảnh – Photography | Khanh Trần Trang Điểm - Makeup Artist | Ngọc Nga #vietsuccess #vietsuccessbussiness #MECE #McKinsey #startups #SMEs #datadriven
Brought to you by Attio—The powerful, flexible CRM for fast-growing startups | Coda—Meet the evolution of docs | Eppo—Run reliable, impactful experiments—Sri Batchu currently leads growth at Ramp, the fastest-growing SaaS business (and fintech business) in history. Previously, he led growth strategy and operations at Instacart and was one of the first 50 employees at Opendoor, where he built, scaled, and managed a variety of business teams, including analytics, sales, and pricing. During his time there, the company grew from $100M to $5B+ in revenue and to 1,500+ people. In this episode, we discuss:• The surprising tactics behind Ramp's unprecedented early growth• A breakdown of Ramp's current growth org and growth channels• Why you need to be “failing conclusively”• Ramp's unique approach to metrics and measurement• Examples of Ramp's “secret sauce”: a data- and technology-driven approach to everything• Why Sri prioritizes employee autonomy and flexibility over hours worked• Why team structure is a red herring for growth teams, and what Sri focuses on instead• How to set good North Star metrics, and why you should have more than one• Why Sri prefers payback periods over CAC for measuring investment ROI—Find the full transcript at: https://www.lennyspodcast.com/lessons-from-scaling-ramp-sri-batchu-ramp-instacart-opendoor/#transcript—Where to find Sri Batchu:• Twitter: https://twitter.com/sri_batchu• LinkedIn: https://www.linkedin.com/in/sribatchu/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• Twitter: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Sri's background(04:07) Stats surrounding Ramp's hypergrowth(06:20) How Ramp set the stage for their remarkable growth(09:19) New customers vs. customer expansion(10:20) How Ramp has prioritized data-driven decisions(12:12) Ramp's growth engineering team, and how it supports the sales team(13:41) The structure of the growth team at Ramp(14:36) The “skunk works” team(15:49) How Ramp maintains working at such high velocity(19:11) How Ramp boosts morale and keeps employees engaged and excited(21:45) How to promote hard work(25:10) Optimizing efficiency in your growth engine(27:28) Leveraging PR and fundraising(29:20) Traditional media vs. newsletters and podcasts(30:49) Building a repeatable and scalable growth process(32:28) Examples of good North Star metrics(37:09) Lower-level metrics(40:30) When it makes sense to use the North Star framework(42:03) Why Ramp doesn't allow signups through personal emails, and how to reach out if you're in that position(43:11) Efficiency metrics and volume metrics(46:49) Payback period vs. CAC for measuring ROI(48:55) Defining payback period and contribution margin(49:51) How to sequence growth tactics for B2B(52:18) Experimentation and the importance of failing conclusively(58:06) Ramp's tool stack(1:00:32) How to hire great people(1:03:21) The importance of compensating employees properly(1:06:28) The MECE framework(1:09:21) Lightning round—Referenced:• How Ramp builds product, in Lenny's Newsletter: https://www.lennysnewsletter.com/p/how-ramp-builds-product• Eight Sleep: https://www.eightsleep.com/• Keith Rabois on LinkedIn: https://www.linkedin.com/in/keith/• Packy McCormick's article on Ramp: https://www.notboring.co/p/ramps-double-unicorn-rounds-behind• Gibson Biddle's framework: https://gibsonbiddle.medium.com/9-the-gem-model-65c89face5de• DoorDash's business model: https://businessmodelanalyst.com/doordash-business-model/• Airtable: https://www.airtable.com/• Mutiny: https://www.mutinyhq.com/• Gokul Rajaram on Twitter: https://twitter.com/gokulr• Claire Hughes Johnson on Lenny's Podcast: https://www.lennyspodcast.com/lessons-from-scaling-stripe/• MECE principle: https://productfolio.com/mece-principal/• Never Split the Difference: Negotiating as If Your Life Depended on It: https://www.amazon.com/Never-Split-Difference-Negotiating-Depended/dp/0062407805• Stories of Your Life and Others: https://www.amazon.com/Stories-Your-Life-Others-Chiang/dp/1101972122/r• Everything Everywhere All at Once on Hulu: https://www.hulu.com/movie/everything-everywhere-all-at-once-fa320000-8cf3-46fc-8c45-df5ec67b71f2• Fellow kettles: https://fellowproducts.com/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe
Mister Kemal'in istifa etmesi için, acaba kaç seçim yenilgisi daha yaşaması gerekiyor? Evet, “Ben Kemal” yine gelemedi... Amma velakin, gitmiyor da! Hal böyleyken... İktidar, Bay Kemal'e ne kadar teşekkür etse azdır! Bir defa daha kaybettiğini söyleyemiyor bile! Öylesine pişkin ki, sanki kazanmış gibi konuşuyor! Kemal Bey'in yere göğe sığdıramadığı Batılı ülkelerde; bir, bilemediniz iki seçim kaybeden siyasi liderler istifalarını sunup sahneden çekiliyorlar. Ne var ki, Kılıçdarson onları bu konuda zinhar örnek almıyor! Ekrem Müdafa İmamson ise “değişim” içerikli beyanlarıyla CHP genel başkanlığına oynayacağının işaretlerini veriyor. Günü gelir de, Kılıçdaroğlu'nun yerini alırsa şayet, Komprador Burjuvazi için kötü bir haberim var: –Boş çuval ayakta durmaz! AKŞENER'İN VAZİYETİ Saraçhane şovunda Ekrem kardeşinin boynuna atkı takan Pensilvanya Muhibbi Meral Abla... “13. Cumhurbaşkanı, Millet İttifakının adayı olacak!” diyerek iddialı konuşuyordu. Şimdilerde o laflarını hatırlamıyor! “Masaya geri dönmekle nasıl bir siyasi yanlış yaptığını” görmüştür görmesine de... Dönülmez akşamın ufkunda artık, vakit çok geç! “Ben sürekli ‘Seçilecek, kazanacak aday' diye ısrar ettim. İşte sonucu gördünüz, ben demiştim!” repliğini etrafındakilere ısrarla söyleyecektir! Amma velakin, “O vakit, neden masaya döndünüz?” yaman sorusuna verebileceği makul, ikna edici bir cevap yok! İkinci büyük seçim yenilgisini alan Meral Hanım da istifayı düşünmüyor. Bırakmasını talep eden partililer olursa, muhtemelen olacaktır da... Onlara “İki turlu son seçimle birlikte şimdiye kadar tam 13 yenilgi alan Kemal Bey bırakmıyor da, ben mi bırakacağım? Gidin işinize!” diye sert çıkabilir! SİYASİ FIKRA Altılı Masa'nın ortaklarından DP Genel Başkanı Gültekin Uysal, -lütfen, yazınızın ayarlarıyla oynamayınız- aynen şöyle dedi: “Bu seçim, kaybettiği zannedilen Millet İttifakı ile Sayın Kılıçdaroğlu'nun kazandığı bir seçimdir!” –Fıkra, bu kadar! Washington Portakalı Cumhuriyet ise Kılıçdaroğlu için “Kaybederek, kazandı” manşetini attı! Ezcümle... Altılı Ganyan partileri de, eklentileri de, onların hararetli destekçileri de, bir türlü gerçeklerle yüzleşemiyor! ÇÖP TENEKESİNE GİDENLER
Generación XY vuelve como cada semana para sacar del olvido algunos de los mejores recuerdos de los años 80 y 90. Una hora de nostalgia en la que hablaremos de los siguientes temas: - "La mano que mece la cuna", la inquietante película de los años 90 protagonizada por Rebecca De Mornay - "Campeones Oliver y Benji", la mítica serie de animación que hizo que todos los niños y niñas de españa quisieran ser futbolistas! - En "La Máquina del Tiempo" hablaremos de aquellos nombres que antes eran tan comunes y que ahora ya casi no se escuchan -"El Tocata" hará un homenaje a la música chillout - Cerraremos el programa con el concurso de sintonìas de Generación XY
On va parler RGPD, données et argent, ça va être cool✅ Déléguer ton risque RGPD sans danger✅ Le trafic de la donnée dans le monde✅ Créer 1 rente avec ce systèmeCe podcast risque de te faire réfléchir toute la journéeRejoins mes contacts privés : http://bit.ly/31MbjjCLa formation : https://shop.web2fou.fr/legaliser-ses-emails-recuperes-sur-internet-order
Hardware Plus - HWP - Türkiye'nin Teknoloji Satın Alma Rehberi
Cuma Raporu #255 podcastimizde, geçtiğimiz haftanın öne çıkan haberlerini derledik. Bakalım bu hafta neler olmuş? Cuma Raporu #255 zaman çizelgesi 00:00 Giriş 17:08 Xiaomi 13 Ultra Lanse Edildi 21:42 Xiaomi Smart Band 8 de Ortaya Çıktı 29:11 Yenilenmiş cihazların kapsamı genişletildi 55:52 Huawei Nova 11 Serisi Tanıtıldı 59:33 realme 11 geliyor 01:02:08 2023'ün İlk Çeyreğinde Akıllı Telefon Raporu 01:17:45 Türkiye'de en çok oynanan oyunlar belli oldu 01:25:41 MediaTek, Dimensity Auto platformunu duyurdu: 01:28:19 Yeni Elektrikli Passat: Volkswagen ID.7 ortaya çıktı 01:30:11 Dünyanın ilk tamamen robotik akciğer nakli yapıldı 01:34:21 Togg ile alakalı yeni detaylar 01:55:49 İMECE uydusu uzaya fırlatıldı
MECE (Mutually Exclusive Collective Exhaustive) là nguyên tắc bóc tách vấn đề được ứng dụng tại Tập đoàn Tư vấn Chiến lược Toàn cầu McKinsey. Tuy nhiên, không chỉ riêng tập đoàn tư vấn, mà các doanh nghiệp, startups cũng có thể ứng dụng nguyên tắc này trong việc đánh giá vấn đề một cách bài bản, logic để đưa ra quyết định nhanh chóng và chính xác. Vậy, cụ thể MECE là gì? Làm sao để phát huy được sức mạnh của nguyên tắc này để giải quyết những bài toán khó trong doanh nghiệp? Trong tập mới nhất của Vietsucess Business, host Vanessa Phan sẽ cùng trò chuyện với khách mời Việt Trần - Co-Founder MCP Group, công ty Công nghệ Giáo dục trong ngành Tư vấn Chiến lược về kỹ năng đánh giá và giải quyết vấn đề dựa trên nguyên tắc MECE. Đồng thời, anh cũng chia sẻ “bí kíp” ứng dụng MECE vào quy trình đánh giá và đào tạo nhân sự để mỗi nhân viên đều có thể trở thành chuyên gia tư vấn riêng cho doanh nghiệp. Mời các bạn cùng lắng nghe! 00:00 - Mở đầu, giới thiệu khách mời 01:51 - Chặng đường sự nghiệp của khách mời 03:03 - Yếu tố giúp startup tồn tại trong môi trường nhanh 04:40 - Bài học từ việc làm nhanh nhưng không hiệu quả 08:22 - Ranh giới giữa cố chấp và lì đòn 10:35 - Coming Up 11:00 - Yếu tố quan trọng nhất là tư duy cởi mở 14:45 - Bí quyết phân tích vấn đề chuẩn chuyên gia 22:09 - Làm sao để đào tạo nhân sự ứng dụng MECE vào công việc 23:42 - Coming Up 24:09 - Mất bao lâu để thuần thục kỹ năng chuẩn MECE? 26:42 - Ứng dụng MECE vào đánh giá nhân sự như thế nào? 33:04 - Tư duy số được hình thành như thế nào? 39:03 - Tối ưu hóa cuộc họp trong mọi doanh nghiệp 41:17 - Lời nhắn nhủ đến các chủ doanh nghiệp 42:23 - Chào kết Dẫn Chuyện - Host | Vanessa Phan Kịch Bản - Scriptwriting | Vanessa Phan, Thảo Nguyễn Biên Tập – Editor | Bách Hợp Sản Xuất - Producer | Anneliese Mai Nguyen Trợ Lý Sản Xuất - Producer Assistant | Ngọc Huân Quay Phim - Cameraman | Khanh Trần, Nhật Trường, Hải Long, Thanh Quang Âm Thanh - Sound | Khanh Trần Hậu Kì – Post Production | Thanh Quang Thiết Kế - Design | Nghi Nghi Nhiếp Ảnh – Photography | Khanh Trần Trang Điểm - Makeup Artist | Ngọc Nga #vietsuccess #vietsuccessbussiness #MECE #McKinsey #startups #SMEs #datadriven
Bugün bültende, İMECE uydusunun yörüngeye fırlatılması, Almanya'da nükleer enerji üretimi sona ermesi ve Türkiye'nin OECD ülkeleri arasında işgücüne katılım oranında sonuncu olması var. Teknoloji bültenimiz Quando'ya ücretsiz abone olmak için buraya tıklayabilirsiniz. Bu bölüm Novo Nordisk hakkında reklam içermektedir. Novo Nordisk'in yürüttüğü İş'te Hemofili projesi hakkında detaylı bilgiye buradan ulaşabilirsiniz.
Mientra preparamos al onceava temporada del podcast que arrancará la semana que viene, quiero presentarte Problem Solving, el podcast de Consultoría de Outliers. n este podcast hablamos única y exclusivamente de consultoría, presentado por Joseph Gelman, consultor con más de 20 años de experiencia en la industria y host de Outliers Podcast. Este no va a ser un pocast de entrevistas, aunque traeremos de vez en cuando algún invitado. Más bien, te contaré todo lo que tienes que saber para ser un buen consultor, como por ejemplo las capacidades que tienes que desarrollar o cómo afrontar aspectos concretos del trabajo de consultor, todo basado en la experiencia de Joseph. Estaá publicado en su propio canal (en el de Outliers solo estará este primer episodio), te dejo el link en las notas del podcast. Notas del podcast: Podcast Problem Solving en Spotify: https://open.spotify.com/show/4N02LXWe32Ju0xXOwaB4m4?si=07a2d3cf99434632 Explicación de MECE en Wikipedia: https://en.wikipedia.org/wiki/MECE_principle Créditos Musicales, Jahzzar: https://freemusicarchive.org/m
Case Interview Preparation & Management Consulting | Strategy | Critical Thinking
For this episode, let's revisit a Case Interview & Management Consulting classic where we discuss how build hypotheses with decision trees. Building hypotheses is very difficult. Most candidates in a McKinsey, BCG et al interview would not know when to build the hypothesis, what comprises the hypothesis, how to test if it is MECE etc. This simple technique is one way to build hypotheses and used on real consulting engagements. It was developed to help candidates prioritize their analyzes and ensure the hypotheses are MECE. When practicing this technique note that the development of the decision tree must be done quickly and cleanly. Enjoying our podcast? Get access to sample advanced training episodes here: www.firmsconsulting.com/promo
No te pierdas los directos de lunes a viernes 11 pm Transmitiendo desde Cd Mante Si quieres hacer tu Donación https://www.buymeacoffee.com/miedoscop ⭐️ Únete a nuestras Redes Sociales ⭐️
ülke gündeminden haberdar şarapçı, izban'da evlilik teklifi & pırasaların hareketli seks hayatı
Ben Çiğdem Öztabak her hafta Web3 evreninde yaratıcılar, teknoloji geliştiriciler, proje sahipleri ve yatırımcılarla geleceğin internetini konuşuyoruz. MetaCafe'yi bundan sonra Cointelegraph TR Youtube sayfasında video röportajlarla izleyebileceksiniz. Bu bölümün konuğu İmece Derneği kurucusu Buğra Çelik. Kendisi ile ReFi kavraminin anlamını ve blockchain teknolojisinin toplum yararına getirdiği yenilikleri, bu sektördeki yetenekli insanları blockchain topluluğuna nasıl çekebileceğimizi konuşuyoruz. Bu haftaki sponsorumuz Web3 yeteneklerinin pazar yeri olan ve doğrulanmış zincirde web3 CVi'nizi anında size gösteren ve web3 iş ilanlarına da kolaylıkla ulaşabileceğiniz Talently.app. Metacafe Podcast, Cointelegraph Türkiye Youtube kanalı üzerinden sunulmakta. Kripto para piyasalarını ve sektör ile ilgili son haberlere ulaşmak için tr.cointelegraph.com adresini ziyaret edebilirsiniz. Podcast Subscription : https://anchor.fm/metacafe/subscribe
"C'est sain de se poser régulièrement la question de l'alignement entre ses valeurs et celles de l'entreprise dans laquelle on travaille."Héloïse, à tout juste 25 ans, est Responsable de la communication pour l'entreprise engagée Vendredi.Au départ, rien ne la prédestinait à travailler dans l'économie sociale et solidaire : après des études dans l'hôtellerie, elle décide de partir en voyage pour faire le point sur sa vie et ses envies. C'est durant ce voyage et à travers des rencontres imprévues qu'elle découvre les sujets de l'impact, du féminisme, et décide de rejoindre une structure engagée !Dans ce podcast, elle partage ses apprentissages pour mieux se connaître et réussir à intégrer une entreprise qui lui correspond vraiment.1- Prendre du temps pour soi, pour se poser les bonnes questions2- Bien identifier ses valeurs pour trouver l'entreprise avec laquelle on se sent alignée3- Pour l'intégrer, rester soi même et faire rayonner ses engagements personnels4- Lors de la période d'intégration, ne pas être trop exigeant avec soi-mêmeCe podcast a été réalisé par Marlène Rouland, une exploratrice de la vie en transition professionnelle qui souhaite mettre en lumière ceux et celles qui œuvrent tous les jours pour une société plus harmonieuse, tournée vers l'humain et plus respectueuse de l'environnement.Vécus est un podcast collaboratif de Ticket for Change, fait par et pour les acteurs de changement. Pour suivre nos actualités : https://bit.ly/3eWoVRxToi aussi tu veux apprendre à faire des podcasts et pourquoi pas devenir podcasteur Vécus ? Suis notre formation en ligne : https://bit.ly/3uYb3Nf Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.
Hi, thanks for stopping by!You're listening to #TheBarbershopWithShantanuS2: #RaisersEdge. In this episode, you'll hear Shantanu Deshpande (Founder and CEO, Bombay Shaving Company and Bombae), Mandar Vaidya (CEO, OYO Europe), and Meghana Narayan (Co-Founder and CEO, Slurrp Farm) kick it old school, S1 podcast style, and discuss their childhoods, entrepreneurial journeys, life and challenges, their common link- McKinsey, and also share some advice to budding entrepreneurs._________________________________________________________________________________________TIMESTAMPS0:00 – Coming up0:40 – Reflecting on Raiser's Edge so far11:21 – Mandar's entrepreneurial journey: McKinsey to OYO25:04 – Meghana's journey from being a swimmer to Slurrp Farm32:45 – Meghana's biggest challenge at Slurrp Farm35:10 – MECE is completely false37:15 – Mandar's biggest professional challenge44:55 – Shantanu cannot let people go51:20 – Meghana's personal journey while building Slurrp Farm53:30 – Difference between Happiness, Joy, and Satisfaction57:10 – Optimising for professional choices using ‘khushi, anand, samaadaan' framework1:00:08 – Mandar's childhood and life before McKinsey 1:03:20 – Shantanu's cocooned life1:06:38 – All About Parenthood1:13:16 – Effects of parenthood on professional choices1:19:26 – Shantanu's professional and personal challenges1:28:41 – Confidence and happiness manifests1:31:23 – Advice for budding entrepreneurs__________________________________________________________________________________________APPLY NOW We are looking for early stage stage companies with: - Amazing Founders - Operating in large markets - Are post revenue - TBWS will put in the first or second institutional cheque! Email us about you and your start-up on barbershop@bombayshavingcompany.com. __________________________________________________________________________________________OUR SOCIALSInstagram: https://www.instagram.com/thebarbershopwithshantanu/LinkedIn: https://www.linkedin.com/in/the-barbershop-with-shantanu/Facebook: https://www.facebook.com/TheBarberShopwithShantanuTwitter: https://twitter.com/TheBarberShopwS__________________________________________________________________________________________ABOUT OUR CHANNELMy name is Shantanu and I am the founder of Bombay Shaving Company and Bombae. I LOVE entrepreneurship. Personally, I would rate myself as an average (at best) entrepreneur, but I love great ones.The BarberShop with Shantanu S2: Raiser's Edge goes beyond our conversations from S1 on our podcast to tangible help. With the help of our sponsors at BSC and Bombae we have put together a season which brings together a corpus of 50+ equity seekers looking to support 20+ start-ups in their growth and success. We will be releasing an episode every Friday at 6 pm. Tune in and hope you enjoy. :)__________________________________________________________________________________________
We catch up with Jerin Mece (pronounced Meh-che) while he's on vacation. Jerin's a serial entrepreneur that has gone from building a brand from scratch to now helping brands build healthy, prosperous businesses. Find Jerin here: https://www.linkedin.com/in/jerinmece/
今回はうんちくエウレーカクイズ音楽編です。「楽器の分類がMECEじゃないのが気になる」「得意げに答えたらあるあるネタだった」「モーツァルトの”魔笛”は笛ってことだけ知っている」など少ない知識で何とか考えていきます。 【うんちくエウレーカクイズ応募フォーム】 https://forms.gle/cGpGjmstG5pNwVF16 ※良いうんちくを見つけたら、お気軽に送ってくださいませ！ 【目次】 00:00 うんちくエウレーカクイズラジオ 02:44 ストラディバリウスを現代人が超えられぬ理由 15:49 木管楽器・金管楽器どう分ける？ 28:50 オーボエが音合わせに使われるのはなぜ？ 30:26 ベートーヴェンが秘書を「パパゲーノ」と呼んだ理由 41:57 リストは曲中でバッハに敬意を示した。どうやった？ 50:08 クイズはまだまだ募集中 【参考文献のリンク】 ◯ベートーヴェン捏造 https://amzn.to/3aLzcSn 【サポーターコミュニティへの加入はこちらから！】 https://yurugengo.com/support 【姉妹チャンネル：ゆるコンピュータ科学ラジオ】 https://www.youtube.com/channel/UCpLu0KjNy616-E95gPx7LZg 【Twitterあるよ！】 ゆる言語学ラジオのTwitterアカウントがあるので、是非フォローしてください！面白語源ネタなどが流れてきてあなたの知識欲が満たされます。 →https://twitter.com/yuru_gengo 【おたよりフォーム】 https://forms.gle/mTGM7A9QNqgjZMgN7 ※皆様からの楽しいおたよりをお待ちしています！ 【お仕事依頼はこちら！】 yurugengo@gmail.com 【堀元見プロフィール】 慶應義塾大学理工学部卒。専門は情報工学。WEBにコンテンツを作り散らかすことで生計を立てている。現在の主な収入源は「アカデミックに人の悪口を書くnote有料マガジン」。 Twitter→https://twitter.com/kenhori2 noteマガジン→https://note.com/kenhori2/m/m125fc4524aca 個人YouTube→https://www.youtube.com/channel/UCYJ335HO_qLZDr7TywpI0Gg 【水野太貴プロフィール】 名古屋大学文学部卒。専門は言語学。 某大手出版社で編集者として勤務。言語学の知識が本業に活きてるかと思いきや、そうでもない。 【BGM提供】 ・フリーBGM・音楽素材MusMus様 https://musmus.main.jp ・OtoLogic様 https://otologic.jp/
第346回「内的世界は、全36象限でMECEに説明できる！！」生田知久が、統合アイデンティティを中心に人の本質に迫る番組です。上場企業の経営者や１００万部以上の著者など経営者を中心に実践した結果、３ヶ月足らずで１億円ものインパクトを出す経営者も現れる。生田知久が、統合アイデンティティを中心に人の本質に迫る番組です。 ★番組への質問はこちら↓↓ https://ck-production.com/podcast/ikuta/q/
On va parler RGPD, données et argent, ça va être cool✅ Déléguer ton risque RGPD sans danger✅ Le trafic de la donnée dans le monde✅ Créer 1 rente avec ce systèmeCe podcast risque de te faire réfléchir toute la journéeRejoins mes contacts privés : http://bit.ly/31MbjjCLa formation : https://shop.web2fou.fr/legaliser-ses-emails-recuperes-sur-internet-order
Recomendaciones de cine y un poco más con Adonay Martínez http://acusticaradio.com.mx/tu-frecuencia/
Günün öne çıkan haberlerini tarafsız bir bakış açısıyla ve FOX Haber farkıyla dinleyin! Güvenilir, tarafsız ve kaliteli haberin adresi FOX Haber; podcast yayınlarıyla sizlerle. Hayatın içinden haberler, Gülbin Tosun ile FOX Ana Haber Hafta Sonu podcast yayınlarında sizlerle! FOX Türkiye Resmi Web Sitesi: www.fox.com.tr Facebook: https://www.facebook.com/foxhaber Twitter: http://www.twitter.com/FOXhaber
■具体的なノウハウ ・テーマが大きくすぎて、どこから始めて良いか、担当者が分からないケースがあります。 ・現状から10倍以上の事業計画書を作成など。 ・こういうテーマの場合、各種フレームワークにも、まだまだ、落とせないんですね。 ・とした時、どうするか？ ・まず、担当者とのブレストをしていき、不安を和らげていきますが、その際、マインドマップが有効的です。 マインドマップ ・頭で思い描いたものを図式で表現するためのツール。 ・中心にメイントピックとなる文字や画像があり、その中心から複数の枝分かれが放射状に展開し、各枝分かれにそれぞれのキーワードや画像がある、という人間の脳をイメージした、思考を可視化するためのダイアグラムです。 ・ツールについてはダウンロード型やクラウド型もあり、色々なものがあります。 ・クラウド型は操作性が悪い。 ・ダウンロード型のX mindが良い ・サブスク型であるが、綺麗なUI、操作性が良い。 ・ZOOMの画面共有でもサクサク。 ・例えば、昨日も通販事業を10倍にする会話 ・各論の話になりがち。 ・まず、重要なポイントを抜け漏れなく（いわゆるMECE）、マップ化していく。 ・集客、商材、組織、システム、デザイン、カスタマーサポート、OMOと言った形で。 ・さらに、集客ならば、広告、SEO、コンテンツマーケティング...といった形で・放射線に展開していく。 ・このプロセスをする事で、かなりの角度で当初、担当者が暗闇の中にいた状況から視界が見える形になります。 ・面と向かってやる場合にはホワイトボードの活用が便利ですが、 ZOOM時代においては、その役割がマインドマップで代用出来る。 参考URL https://jp.xmind.net/
Aquuiiiii seguimoooos contra viento y mareeaaaaaaa... lo he dicho cantando pero no existe ninguna canción que tenga esa letra... o eso creo. Exista o no, estoy creando. El caso es que esta semana estamos solo Rossy y un servidor hablando de unas pelis que hemos visto... y encima hablamos más que la semana pasada, así que no os quejeis, eh? que sois muy de quejaros. En vez de eso... dadle al play!! Asesinos ocultos con manos: Rossy y Salva. MINICRÍTICAS Asesino de las postales Descarrilados La mano que mece la cuna Homunculus Instintos Ocultos COMENTARIOS y SERIES CONTACTO y DEMÁS E-mail: filmsandchipspodcast@gmail.com Lista de pelis : listado films and chips. blogspot.com.es https://trakt.tv/users/filmsandchips REDES SOCIALES: Web: www.filmsandchips.es Twitter : @filmsandchips Facebook: Films and Chips Telegram: FilmsAndChipsOficial Instagram: Miguel Angel: @SirMiwi Salva: @impulseisp Lolo: @mandiamo9083 Collado: @davidcollado87 Trakt.tv https://trakt.tv/users/impulseisp https://trakt.tv/users/sirmiwi https://trakt.tv/users/filmsandchips recomendaciones, cine, actualidad, música, magazine, radio, humor, cultura, televisión, cinema, películas
In this episode of the Startup Selling Podcast, I interviewed Marylou Tyler. Marylou is the author of Predictable Revenue and Predictable Prospecting. She has worked with a number of co-authors and produced a book that will certainly help any startup company out there. Being one of the best sales enablement strategists, she is an expert when it comes to maximizing your resources and is always willing to share her knowledge to anyone who needs help. This podcast focuses both on the incredible things that a startup business owner can do to improve sales and the common mistakes everyone should avoid. Some of the key topics and questions that we covered in this podcast are: Internalizing Your Competitive Position Six-Factor SWOT Analysis Developing an Ideal Account Profile Compel with ContentTM McKinsey's MECE model How you can send your emails wisely, and so many more Links & Resources Marylou Tyler on LinkedIn: www.linkedin.com/in/maryloutyler Marylou's website & book-related resources: maryloutyler.com/swag Jeremey Donovan on LinkedIn: www.linkedin.com/in/jeremeydonovan (Amazon) Book by Neil Rackham: SPIN Selling (Amazon) Book by Neil Rackham: Major Account Sales Strategy (Amazon) by Marylou Tyler and Jeremey Donovan: “Predictable Prospecting: How to Radically Increase Your B2B Sales Pipeline” Listen & subscribe to The Startup Selling Show here: BluBrry | Deezer | Amazon | Stitcher | Spotify | iTunes | Soundcloud | SalesQualia Thanks so much for listening! Tell a friend or ten about The Startup Selling Show, and please leave a review wherever you're listening to the show.
如果你觉得意犹未尽，想深入学习战略顶层设计系统，推荐参加我每月线下的“ 创新式增长总裁峰会”课程，添微：1️⃣3️⃣7️⃣9️⃣8️⃣1️⃣7️⃣7️⃣0️⃣8️⃣4 陈老师关注公众号：企业战略丨店小强系统如果你在经营中有疑问，如果你想要获得更多有用的学习内容。长按复制下方微信1️⃣3️⃣7️⃣9️⃣8️⃣1️⃣7️⃣7️⃣0️⃣8️⃣4（打开微信-添加我的微信号）关注·点赞·分享·专辑评价截图，添加助理微信领取：《战略顶层设计》电子书+70套顶尖商业模式！顶尖企业“胜而后战”的三大顶层设计是：1、营销模式: 研究客户的需求、喜好、记忆、抗拒等，解决企业生存问题。因为会营销，企业就有现金流和利润，就容易生存。所以，中国大部分中小企业老板是做销售出身。但是生存还不够，企业还需要发展。2、商业模式:以资源为研究对象，解决发展问题。包括企业内部资源、外部资源、上游资源、下游资源、第三方配套资源以及跨行业资源。3、金融模式:设计（收益权/股权/消费权）让渡的方法去融资打造杠杆并购，成就企业银行运用别人的钱实现企业快速扩展避免各种法律、财务、战略风险。怎样玩转股权，解决企业成为领袖问题。
“今天下午我们通晒一下踝部用户动线的闭环矩阵如何完成生态化反，并且把相关的生产关系做结构化MECE调整。”如果你能完全读懂上面这句话，恭喜！你已经达到了中国互联网黑话十级认证水平。出题人是播客《三五环》主播 刘飞。本期节目，我们请来刘飞，一起聊聊996、职场PUA、产品经理的自我修养、互联网黑话……你会听到：1、存不存在生活与工作平衡这回事？2、工作强度应不应该成为选择职业的考量？2、吐槽职场，我们有什么要说的？4、怎样科学奋斗，选择真的比努力更重要吗？5、互联网黑话灯谜大赛相关作品：电影：《爆裂鼓手》书：《了不起的我》『详谈』系列《左晖》《从点子到产品》电视剧：《半泽直树》节目：罗振宇X罗永浩《长谈》播客：《三五环》片头曲：马赛克乐队-潇洒走一回(live)片尾曲：晓月老板-我的枪声里主播：刘飞 & 星光 / 大壹 / 超哥附录：互联网黑话灯谜-勾兑-生产关系-生态化反-结构化-动线-闭环-体感-矩阵-通晒-踝部-MECE-Re一下-反向push-百阿-百湖-求拍-沉淀-互联网思维-迭代-落盘-组合拳-年框-击穿心智-反向push-多维矩阵闭环-结构化
A problem MECE'd is a problem halved (At the very least). Of all the tools associated with problem solving this should be the sharpest. Learn to cleave or chop up problems like a professional!
The MECE Muse Unplugged Podcast - Empowering Go-Getters on Their Journey to Greatness
We're BAACCKKKK!! The MECE Muse Unplugged podcast has been relaunched after a year hiatus. The podcast show now focuses on helping go-getters on their journey to greatness. In 12 episodes, season 2 will focus on the Science of Success and this trailer highlights the following upcoming guests: Bobbie Carlton: Founder of Innovation Women, one of … Continue reading "The MECE Muse Unplugged Season 2 Trailer" The post The MECE Muse Unplugged Season 2 Trailer appeared first on The MECE Muse Unplugged Podcast.
The MECE Muse Unplugged Podcast - Empowering Go-Getters on Their Journey to Greatness
In this MECE Mentoring Moment interlude, Christie provides answers for some of the commonly asked questions about the MECE Muse Unplugged podcast show. Listen to the Episode Here: The MECE Muse Unplugged pop-up podcast is a variety show where I get a chance to connect with all types of people and do interviews, storytelling, and … Continue reading "MECE Mentoring Moment: FAQs About the Podcast" The post MECE Mentoring Moment: FAQs About the Podcast appeared first on The MECE Muse Unplugged Podcast.
The MECE Muse Unplugged Podcast - Empowering Go-Getters on Their Journey to Greatness
Christie answers an email from a fan that admits they are having performance issues after being promoted to the Manager level. Listen to the Episode Here: This is MECE Mentoring Moments, our third one. If you are new to the show, MECE Mentoring Moments are when I have a chance to answer fan mail … Continue reading "MECE Mentoring Moment 03: I Think I Have Imposter Syndrome" The post MECE Mentoring Moment 03: I Think I Have Imposter Syndrome appeared first on The MECE Muse Unplugged Podcast.
The MECE Muse Unplugged Podcast - Empowering Go-Getters on Their Journey to Greatness
Christie answers an email from a fan that hates his first consulting project and is seeking career advice on how to find other engagements. Listen to the Episode Here: [powerpress] The post MECE Mentoring Moment 02: I Hate My First Consulting Project appeared first on The MECE Muse Unplugged Podcast.
The MECE Muse Unplugged Podcast - Empowering Go-Getters on Their Journey to Greatness
Special edition of The MECE Muse Unplugged where Christie reflects on the current state of the show since launching on July 7, 2017. She also gives a special announcement on the future of the show, and shares highlights of upcoming episodes for the next month. The post It is Bench Week on MECE Muse Unplugged (special announcement) appeared first on The MECE Muse Unplugged Podcast.
The MECE Muse Unplugged Podcast - Empowering Go-Getters on Their Journey to Greatness
In episode 2, Christie discusses basic rules of the podcast show, provides context on the various formats available on the show, and shares highlights from a recent podcast interview she had with Stone Payton & Lee Kantor on High Velocity Radio from the BusinessX Radio network. Listen to the Episode Here: For episode two, what … Continue reading "Setting context on The MECE Muse story" The post Setting context on The MECE Muse story appeared first on The MECE Muse Unplugged Podcast.
The MECE Muse Unplugged Podcast - Empowering Go-Getters on Their Journey to Greatness
Introductory episode of The MECE Muse Unplugged podcast show, a pop-up, bi-weekly podcast series designed to help new or aspiring consultants on their journey to greatness. The MECE Muse Unplugged is a variety show sharing ideas on how to be a great consultant in the form of interviews, storytelling, career advice, and discussing trending topics … Continue reading "Ladies and Gentlemen, it's the MECE Muse Unplugged (Intro Episode)" The post Ladies and Gentlemen, it's the MECE Muse Unplugged (Intro Episode) appeared first on The MECE Muse Unplugged Podcast.
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