This startup wants to overcome Open AI ⏳

They raised $113M within 4 weeks of incorporation.

It's called Mistral.

https://twitter.com/chiefaioffice/status/1709312211993104757

Thrilled to announce this today - GenerativeAI has massive potential to drive transformation for commerce (both B2B and B2C), payments, and fintech and Visa is at the forefront!

https://www.businesswire.com/news/home/20231002039600/en/Visa-Launches-100-million-Generative-AI-Ventures-Initiative#:~:text=SAN%20FRANCISCO%2D%2D(BUSINESS%20WIRE,future%20of%20commerce%20and%20payments

Two updates at
@GreylockVC


1. A new $1bn fund focused on early stage

2. Greylock Edge, a company building program built for exceptional founders to navigate from idea to product market fit

Exceptional talent doesn't need or want an incubator.

They want customers.

And a path to building a category defining company.

We're launching a highly selective program that opens up Greylock's resources to initiate companies

No investment required

Spent two hours with Marc Andreessen, who gave me a masterclass on how to think, learn, read, research, and write.

Here's what I learned:

1. Read, read, read... then read some more.

2. Many of your best ideas will emerge in fits of rage or frustration. Channel the fury. Smash the keyboard. Lean into the passion. Torch the page with your energy.

3. Marc doesn't have much of a formal writing process. He thinks and thinks, and when epiphany strikes, he hammers out an outline as fast as possible to get his ideas on paper. Then, he turns it into a full article.

4. Marc's motto for writing and thinking: "Strong views, weakly held." Put yourself out there, but stay on the hunt for dissenting opinions from smart and respectful people.

5. Online writing tolerates and even encourages stylistic idiosyncrasies that traditional publishing would not accommodate. Lean into them.

6. The world is awash in bad content. You need to punch through. Snappy one-liners and genuine conviction are two ways to do that.

7. Marc's been reading online for as long as anybody on the planet, and the biggest thing that's surprised him is how political the Internet's become. Something changed between ~2013-2015. The Internet was once an escape from political debates. Now it's a hotbed of them.

8. Writing software is halfway between writing a novel and building a bridge.

9. Play around with communication tools. Push the limits. Doesn't matter what the rules are. When Marc felt constrained by Twitter's 140-character limit, he started replying to his own tweets and invented the Twitter thread.

10. On the quest for good ideas, surround yourself with "lateral thinkers" who can't help but come up with variant perspectives on everything they see. They won't always be right, but they're always challenge your thinking.

11. Media formats are cyclical. Nietzsche wrote in aphorisms and Twitter is aphorisms-as-a-service. Hip-hop brought back poetry. Montaigne pioneered the essay format and blogs brought them back into vogue.

12. People should write more manifestos.

13. Marc's nomination for the best living American novelist: James Ellroy.

14. GPT has revealed how much writing is pure pablum. Bland, lifeless, uninsightful, unoffensive, and not worth the price of the ink it was printed with.

15. "With GPT, every writer now has a writing partner who can do an infinite amount of grunt work without complaining."

16. "ChatGPT plagiarism is a complete non-issue. If you can't out-write a machine, what are you doing writing?"

17. Marc writes from the heart. He doesn't do much editing and likes to provide reading recommendations instead of directly citing his sources.

18. The person who writes down the plan in an organization has tremendous power. If you want to find the up-and-comers at a tech company, look into who's writing the plan. Though they may not be coming up with all the ideas, you'll know they have the energy, motivation, and skills to organize and communicate ideas in a written form.

19. Marc uses a barbell approach to consume information. He focuses on what's happening right now while also reading a lot of things that were written 10+ years ago. The content is either timely or timeless, with almost nothing in between.

Q: How can a startup build a great recruiting pipeline?

In the clip below, Naval Ravikant explains:

"You have to build a great pipeline. Your best pipeline is going to come from your personal contacts. You literally have to sit everyone at your company down and tell them to name the 10 best people they've ever worked with or gone to school with. I don't care what they're doing now. I don't care if they're getting their PhD. I don't care if they're starting their own company. I don't care if they just joined Google a month ago."

He continues:

"You get those 10 best people and then you just pound the pavement. You have lunch, coffee, breakfast, dinner, whatever with each of them. Even if they're not available for recruiting, they're going to give you a few people. Demand three people from them. And keep in touch because when they become loose, you want them thinking of you."

He also mentions that other companies in your space could also be a great source of talent, in addition to founders of companies that have tried to build something similar to you but failed.

초기 투자 집중하는 펀드에서 받은 내용중 일부...

Software multiples have come down a bit in the past quarter to a median of 5.4x forward revenue, with 8x for the top quartile.
(ImageSource: BVP Cloud Index as of 10/3/23) 즉...소프트웨어 회사들 기업가치는 미래 12개월 매출의 5.4배... 아주 톱 회사들은 8배 정도로 내려왔다. 얼마전 100X 이상까지 올라가던 시절은 잊는게 좋다.

2021년 Q4부터 투자는 계속 줄고 있다. 지난분기 (Q3, 2024) 에는 $73B 이 투자되었는데 그것은 Q4 2019 이후 제일 낮은 금액이였고 또 10,095 회사가 투자 받았는데 Q3 2020 년 이후 제일 낮았다.

시드에서 Series A 받는게 무지 어려워졌다. 이젠 A 라운드 받으려면 약 월매출 5천만원 이상 넘으면서 이익을 내기 시작하던지... 가까워야된다.

---

이게 지금 미국 초기 기업들 사정이다. 좀 더 큰 기업들은 여기서 선을 이어서 이해하면 얼만큼 어려운지 이해 하기 쉽다.

그래서... 왠만한 회사들은 계속 추가 펀딩이 되겠지 생각하고 사업을 하면 안된다. 무조건 가지고 있는 자금으로 이익을 내면서 살아남는데 추가 자금이 꼭 필요 없어야만... 펀딩이 되는 시절이 당분간 계속 될거다.

<본격 AI 하드웨어 시대 개막: 구글 하드웨어 이벤트 현장 후기!!> 🤖

1. 구글(Google)이 픽셀8, 픽셀8 프로, 픽셀 워치2, 픽셀 버즈 프로 등 일련의 하드웨어 제품을 공개.

2. 모든 하드웨어의 가장 중요한 기능은 AI, 머신러닝으로 귀결.

3. 픽셀8 프로는 구글의 생성형 AI(generative AI) 모델이 탑재된(on-device) 첫 번째 하드웨어.

4. 여러 명의 피사체가 등장하는 사진을 찍을 때 각 인물의 얼굴을 개별로 수정, 모두가 잘 나온 최종본 하나를 만들 수 있는 식.

5. 영상을 촬영했는데, 등장인물의 대화에 비해 주변 소음이 너무 크다면 소음만 줄이거나 키울 수 있음.

6. 지하철 내부 등 시끄러운 환경에서 통화할 때 소음을 없애 목소리만 또렷이 들을 수 있음. (with 픽셀 버즈 프로)

7. 회의 때 전화가 걸려 오면 직접 받지 않고 AI가 받게 해 나 대신 상대방과 간단한 통화를 하게 만드는 것도 가능(Call Screening). 받는 사람이 AI라고 인식하지 못할 정도의 자연스러운 목소리가 전달됨.

8. 예컨대 택배 기사님한테 전화가 걸려오면 “문 앞에 두세요”를 문자가 아닌 음성(대화 방식)으로 AI가 전할 수 있음.

9. 구글 어시스턴트는 AI 챗봇 ’바드(Bard)’로 업그레이드. 보다 복잡한 물음을 던지고 답을 얻을 수 있음.

10. 소프트웨어(AI)가 하드웨어를 정의하는 시대, AI 기술의 폭발적 발전으로 양질전환, 퀀텀 점프, 패러다임 시프트가 시작된 시기라는 걸 절감.

더밀크 The Miilk @followers

AI startup funding was CRAZY today 💵

- Cortica raises a $40M Series D

- rabbit raises a $20M Series A

- Vayu Robotics raises a $12.7M Series A

- Move AI raises a $10M Seed

- Induced AI raises a $2.3M Seed

Want to know what they do? 🧵

Amount: $40M 🎉

Round: Series D

Investor: CVS Health Ventures, LRVHealth

Quick Intro 👉 Cortica’s mission is to design and deliver life-changing care - one child, one family, one community at a time.

🚨 rabbit

Amount: $20M 🎉

Round: Series A

Investor: Khosla Ventures

Quick Intro 👉 Rabbit is building a custom, AI-powered UI layer designed to sit between a user and any operating system.

@VayuRobotics

Amount: $12.7M 🎉

Round: Seed

Investor: Khosla Ventures

Quick Intro 👉 Building the foundation model for robotics – the next gen of AI to power perception and motion. They envision intelligent systems will advance safe and sustainable human productivity.

@MoveAI_

Amount: $10M 🎉

Round: Seed

Investor: Warner Music Group

Quick Intro 👉 Move AI’s mission is to empower millions of creators by harnessing the potential of generative AI to digitize movement and democratize animation at scale.

@InducedAI

Amount: $2.3M 🎉

Round: Seed

Investor: Sam Altman, Peak XV Partners

Quick Intro 👉 Induced AI offers an AI-based native browser robotic process automation (RPA) platform.

https://x.com/chiefaioffice/status/1709675847769096598?s=46&t=h5Byg6Wosg8MJb4pbPSDow

장안의 화제 논문 “GPT-4V(ision)을 디벼보자 - The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)”

GPT-4V의 이미지 이해 능력이 어디까지 가능한지를 탐구한 논문인데요.

ChatGPT가 처음 나왔을 때 정도의 충격입니다. 이미지 판별, 디텍팅, OCR은 물론이고 X-Ray 분석과 밈의 이해와 설명까지합니다.

핵심은 기존의 모든 이미지와 관련된 AI 모델의 능력을 GPT-4V 하나가 전부 발휘하고 있다는 것인데요. GPT-3가 기존의 모든 자연어와 관련된 AI 모델의 능력을 전부 하나의 모델로 가능하게 된 상황과 같습니다.

100가지의 능력을 하나의 모델로 가능하게 되었을 때 단순히 100배의 능력이 발휘되는 것이 아니라, 능력이 기하급수적으로 점프하여 10,000배 이상의 능력을 발휘 할 수 있게 되었다는 것이 핵심이라고 봅니다.

즉, GPT-3로 인해 AI 기술과 업계가 완전히 바뀐 것과 같은 상황이 다시 온 것이라고 봐도 무방할 것 같습니다. (아직은 개별 비전 태스크의 성능의 수준면에서 보면 GPT-3.5 수준 정도로 생각됩니다만, Vision이 GPT-4 수준으로 올라오는 것은 시간문제겠죠.)

안보신 분들은 꼭 한 번 보시기 바랍니다. 이미지만 봐도 어떤 일들이 가능한지와 앞으로 발전하게 될 모습을 충분히 알 수 있습니다.

https://arxiv.org/abs/2309.17421

Brian Balfour is the founder and CEO of Reforge, former VP of Growth at HubSpot, and co-founder of three other startups.

I've been looking forward to having Brian on the pod for quite a while, and so instead of talking through the typical growth loops and product frameworks, we made this a very special and unique episode. Brian dug through a Notion doc he keeps of lessons he's learned from his career and life (which includes over 100!), and chose ten of the most important and meaningful to share.

Here's a peek:
▫️ Lesson 1: Inspect the work, not the person.
▫️ Lesson 2: Tell me what it takes to win; then tell me the cost.
▫️ Lesson 3: Problems never end (and that’s okay).
▫️ Lesson 4: The year is made in the first six months.
▫️ Lesson 5: Growth is a system between acquisition, retention, and monetization. Change one and you affect them all.
• Lesson 6: Do the opposite.
• Lesson 7: Use cases, not personas.
• Lesson 8: Solving for everyone is solving for no one.
• Lesson 9: Find sparring partners, not mentors or coaches.
• Lesson 10: 2x+ the activation energy for things that need to change

I found this conversation incredibly informative and inspiring, and I know you will too.

Listen now 👇

YouTube: https://lnkd.in/gX29QbFA

I gave a talk at Seoul National University.

I noscriptd the talk “Large Language Models (in 2023)”. This was an ambitious attempt to summarize our exploding field.

Video: https://youtu.be/dbo3kNKPaUA
Slides: https://docs.google.com/presentation/d/1636wKStYdT_yRPbJNrf8MLKpQghuWGDmyHinHhAKeXY/edit?usp=sharing

Trying to summarize the field forced me to think about what really matters in the field. While scaling undeniably stands out, its far-reaching implications are more nuanced. I share my thoughts on scaling from three angles:

1) Change in perspective is necessary because some abilities only emerge at a certain scale. Even if some abilities don’t work with the current generation LLMs, we should not claim that it doesn’t work. Rather, we should think it doesn’t work yet. Once larger models are available many conclusions change.

This also means that some conclusions from the past are invalidated and we need to constantly unlearn intuitions built on top of such ideas.

2) From first-principles, scaling up the Transformer amounts to efficiently doing matrix multiplications with many, many machines. I see many researchers in the field of LLM who are not familiar with how scaling is actually done. This section is targeted for technical audiences who want to understand what it means to train large models.

3) I talk about what we should think about for further scaling (think 10000x GPT-4 scale). To me scaling isn’t just doing the same thing with more machines. It entails finding the inductive bias that is the bottleneck in further scaling.

I believe that the maximum likelihood objective function is the bottleneck in achieving the scale of 10000x GPT-4 level. Learning the objective function with an expressive neural net is the next paradigm that is a lot more scalable. With the compute cost going down exponentially, scalable methods eventually win. Don’t compete with that.

In all of these sections, I strive to describe everything from first-principles. In an extremely fast moving field like LLM, no one can keep up. I believe that understanding the core ideas by deriving from first-principles is the only scalable approach.

London-based autone (YC S22) has raised $4.5M (~€4.27M) in seed funding to help business maximize their growth through data-driven inventory optimization.

Today, retailers have to make 1000s of complex operational decisions every day, all impacting their bottom line. This problem is being currently tackled with Excel or legacy systems, both of which no longer fit for purpose.

Founded by Adil Bouhdadi and Harry Glucksmann Cheslaw, two business scientists with over a decade of experience building successful data-driven supply chains at places like Kering and LVMH, autone is a platform that lets retailers make optimal decisions, easily and quickly. It ingests a retailer's data, generates recommendations, and then allows users to approve a given action.

Autone covers topics including product pricing, inventory replenishment, and re-ordering with the goal of covering all operational processes.

Congrats to the team on the seed!

https://twitter.com/AlexReibman/status/1710160221719654421

For one day only, Lightspeed Venture Partners invited San Francisco’s top AI entrepreneurs to showcase what’s possible with AI

Live product and raw code only. No bullshit.

Here are the top demos from the
@cerebral_valley
x
@lightspeedvp
AI coworking day (🧵)

I bought a couple of Chinese microphones, I wear them and turn them on all day recording everything I speak, at the end of the day the files are processed with OpenAi’s Whisper and transformed into text files from which the information is extracted.