LLM cheat sheet

It was hard but not impossible, and I think most people who are good programmers and know (or are willing to learn) the math can do it too. There are many online courses to self-study the technical side, and what turned out to be my biggest blocker was a mental barrier — getting ok with being a beginner again.

(In fact, our team is comprised of 25% people primarily using software skills, 25% primarily using machine learning skills, and 50% doing a hybrid of the two.) So from day one of OpenAI, my software skills were always in demand, and I kept procrastinating on picking up the machine learning skills I wanted.

https://blog.gregbrockman.com/how-i-became-a-machine-learning-practitioner

애플이 Game Porting Toolkit 이라는 PC게임(윈도우 게임)을 맥에서 돌릴 수 있도록 하는 새로운 툴킷을 공개했습니다.

레딧 유저에 의하면 무려 사이버펑크 2077이 돌아간다고 합니다. 이 말은 즉슨 애플 비전 프로에서 사이버펑크 2077을 돌릴 수 있다는 말과 같습니다.

역시 애플은 그냥 새로운 하드웨어를 내놓은게 아닙니다.

https://ca.style.yahoo.com/apples-toolkit-makes-easier-developers-153021391.html?utm_source=metaproof.beehiiv.com&utm_medium=newsletter&utm_campaign=gamestop-fires-ceo

Jim Stoneham (ex CMO at Stripe, recruited by Claire Hughes Johnson) shares his Five ‘Must Read’ Resources for Go-To-Market leaders 🔍

After 30 years leading Go-To-Market for companies like Stripe, New Relic, Inc., Yahoo, Eastman Kodak Company, Apple… Jim Stoneham listed the 5 best resources a Go-To-Market leader should read!

- Crossing the Chasm by Geoffrey Moore
- Positioning, The Battle for Your Mind by Al Ries and Jack Trout
- High Growth Handbook by Elad Gil
- Scaling People by Claire Hughes Johnson
- Stratechery by Ben Thompson

This is one of the many insights Jim Stoneham shared with us during his interview, read the full post here: https://lnkd.in/eA7MQrSA

Otter is a multi-modal model developed on OpenFlamingo (open-sourced version of DeepMind's Flamingo), trained on a dataset of multi-modal instruction-response pairs. It demonstrates remarkable proficiency in multi-modal perception, reasoning, and in-context learning. GitHub: https://lnkd.in/gJ-VXz-3

EvenUp is a startup that is leveraging artificial intelligence (AI) to automate the generation of legal documents in personal injury cases. The platform, co-founded by Rami Karabibar, Ray Mieszaniec, and Saam Mashhad, aims to use raw case files including medical records, police reports, and bills to create letters arguing for proposed compensation. The goal is to level the playing field in personal injury cases, where many victims are often undercompensated due to a lack of transparency and standardization in the settlement process1.
EvenUp’s platform can tackle all categories of personal injury cases, and it does this by extracting the relevant information from documents and organizing them into templated “demand packages”. These packages outline the legal and factual basis for a personal injury claim and include a demand for compensation. The system has been designed as a self-service solution for lawyers, paralegals, and law firms, with the aim of preparing these demand packages with a high degree of accuracy and efficiency1.
The company recently received $50.5 million in funding at a valuation of $325 million, bringing its total funding to $65 million. Despite this success, there are questions about potential biases in the AI’s recommendations due to dataset imbalances, and concerns about privacy and the sourcing of medical and personal injury documents used to train the AI1.
EvenUp already counts “top trial attorneys” and “America’s largest personal injury law firms” among its customers, and claims it is close to profitability. The platform aims to help reduce filing expenses while maximizing returns for its users. The founders also hope that by automating parts of the filing process, litigators can focus more on the human side of their work. However, there are concerns that the mass adoption of this technology could lead to job losses, particularly among contract-based paralegals1.
EvenUp has plans to cover document generation in both the pre-litigation and litigation stages, customized to each firm, jurisdiction, and case type. It is anticipated that the platform will be able to handle 70% of the key documents in the personal injury law workflow. The founders believe that their product will become increasingly essential and that legal professionals will need to adapt to the change or risk being outcompeted by more tech-savvy competitors1.

https://techcrunch.com/2023/06/08/evenup-wants-to-automate-personal-injury-settlements-to-a-point/

합의 과정의 투명성과 표준화 부족으로 인해 많은 피해자가 보상을 제대로 받지 못하는 경우가 많은 개인 상해 사건에서 공정한 경쟁의 장을 마련하는 것이 목표입니다1.
EvenUp의 플랫폼은 모든 범주의 개인 상해 사건을 처리할 수 있으며, 문서에서 관련 정보를 추출하여 템플릿화된 ‘수요 패키지’로 구성하는 방식으로 이를 수행합니다.
이 패키지에는 개인 상해 청구의 법적 및 사실적 근거가 요약되어 있으며 보상 요구서가 포함되어 있습니다. 이 시스템은 변호사, 법률 보조원 및 로펌을 위한 셀프 서비스 솔루션으로 설계되었으며, 높은 수준의 정확성과 효율성으로 이러한 요구서 패키지를 준비하기 위한 목적으로 개발되었습니

Granica, which helps AI companies optimize their cloud object storage in Amazon S3 and Google Cloud, emerges from stealth with $45M from NEA, BCV, and others

Granica has built a method of compressing data stored in Amazon.com and Google’s cloud platforms that it says can reduce the size and cost of cloud object storage, which hold large amounts of unstructured data that don’t fit into traditional columns and rows. The startup is announcing Thursday that it has raised a total of $45 million from venture-capital firms New Enterprise Associates and Bain Capital Ventures.
For securing its AI training data, Nylas, a provider of email, calendar and contacts APIs, is testing Granica’s Screen service, which can remove sensitive company data and personally-identifiable information in the process of compressing it.
That is useful for a generative AI tool that could be trained to write emails like a specific user, said John Jung, Nylas’s vice president of engineering. “You’d want it scrubbed of [personally-identifiable information] so that you don’t potentially have the models hallucinate, and tell information that is sensitive,” he said, referring to when generative AI programs spit back false results.
Analysts also expect more startups to focus specifically on helping companies sift through and control access to their data for generative AI.
For some CIOs, data quality is just as important as controlling cost—in other words, ensuring that their data is properly formatted, organized, and relevant for training AI models. “The most important thing is not just collect the data, but cleanse, categorize the data, and make sure it’s in a usable format,” Zelinka said. “Otherwise you’re just paying to store meaningless data.”
Jack Henry is focused on data governance at the moment, Zelinka said. He is working with the company’s chief risk officer to define who has access to its data and how it’s being used, and collaborating with the firm’s chief technology officer, who is figuring out how to embed generative AI into its products and platforms.


그라니카는 기존의 열과 행에 맞지 않는 대량의 비정형 데이터를 저장하는 클라우드 오브젝트 스토리지의 크기와 비용을 줄일 수 있는 Amazon.com과 Google의 클라우드 플랫폼에 저장된 데이터를 압축하는 방법을 개발했습니다. 이 스타트업은 목요일 벤처캐피털 회사인 뉴 엔터프라이즈 어소시에이츠와 베인 캐피털 벤처스로부터 총 4,500만 달러를 투자받았다고 발표했습니다.
이메일, 캘린더 및 연락처 API를 제공하는 Nylas는 AI 학습 데이터를 보호하기 위해 압축 과정에서 민감한 회사 데이터와 개인 식별 정보를 제거할 수 있는 Granica의 Screen 서비스를 테스트하고 있습니다.
이는 특정 사용자처럼 이메일을 작성하도록 학습할 수 있는 생성형 AI 도구에 유용하다고 Nylas의 엔지니어링 담당 부사장인 존 정은 말합니다. 그는 생성형 AI 프로그램이 잘못된 결과를 뱉어내는 경우를 언급하며 “모델이 환각을 일으키거나 민감한 정보를 말하지 않도록 [개인 식별 정보]를 제거해야 할 것입니다.“라고 말했습니다.
분석가들은 또한 더 많은 스타트업이 기업이 제너레이티브 AI를 위해 데이터에 대한 액세스를 선별하고 제어하도록 지원하는 데 특히 집중할 것으로 예상합니다.
일부 CIO에게 데이터 품질은 비용 관리만큼이나 중요한데, 즉 데이터가 적절한 형식과 구성으로 되어 있고 AI 모델 학습과 관련성이 있는지 확인하는 것입니다. 젤린카는 “가장 중요한 것은 단순히 데이터를 수집하는 것이 아니라 데이터를 정리하고 분류하여 사용 가능한 형식으로 만드는 것입니다.“라고 말합니다. “그렇지 않으면 의미 없는 데이터를 저장하는 데 비용을 지불하는 것에 불과합니다.”
잭 헨리는 현재 데이터 거버넌스에 집중하고 있다고 젤린카는 말합니다. 그는 회사의 최고 위험 책임자와 협력하여 누가 데이터에 액세스할 수 있고 데이터가 어떻게 사용되는지 정의하고 있으며, 회사의 최고 기술 책임자와 협력하여 제품과 플랫폼에 제너레이티브 AI를 내장하는 방법을 모색하고 있습니다.

SF AI 회사들은 $20m Seed로 받고 시작하는게 일반적인가요? ㅎㅎ 2018, 2021년의 크립토 Startup이 생각나네요 ㅎㅎ
https://contextual.ai/announcing-next-generation-language-models/

https://youtu.be/aPMNbMR1p70

I really admire the friendship between these individuals and value our honest sharing of many things. Hoping to pay it forward and help others someday, just as they have generously helped me.

원가율에 과할 정도로 집착해야 합니다.

1. 물리적으로 조리를 완료했을 때 부피가 줄어드는 것은 원가율이 높습니다. 반대로 부피가 늘어나는 것은 '그나마' 원가율이 낮습니다.

2. 특정 메뉴의 원가율 내에서도 각 재료의 비율과 원가를 구해야 어디서 얼마나 빠져나가는지 알 수 있습니다.

3. 대체재를 항상 구상해두어야 합니다. 계절, 전염병, 환율 등의 이유로 구매가가 급등했을 때 그대로 사용할 수는 없으니까요.

4. 평균 원가율이 30%라고 해서 그 30%가 그대로 유지되는 게 아닙니다. 원가율이 높은 음식의 판매율이 높다면 원가율이 아니라 최종 마진의 금액으로 따져봐야 합니다.

5. 입고 단위 - 입고가 - 1단위 단가 - 수율 - 수율 적용가(단위 1) - 사용량 - 사용량을 적용한 원가

즉

소고기 1kg이 45,000원이라면

1,000g - 45,000원 - 45원 / 1g - 97% - 46.4원 - 120g - 4732.8원

6. 남는 게 없다고들 그러시는데 원가율의 문제일 수도 있고 판매량의 문제일 수도 있습니다. 원가율의 문제라면 수정을 해야 하고 판매량의 문제라면(정상적인 운영으로 100%를 팔아야 원가율이라는 게 성립되므로) 심각하게 고민을 해봐야 합니다.

7. 안 남는다는 고민을 많이 하는데 대체 어디에서 안 남냐고 물어보면 답을 못하는 경우가 하도 많아서...

8. 더해서 좌석수, 객단가, 회전수, 영업일수, 평일 매출, 주말 매출, 월매출, 경비 등을 계산해서 손익분기 계산을 해보세요.

9. 아마도 아득한 수치가 나올 수도 있습니다. 이게 왜 그런가 하면... 통장에 남는 돈 그게 실제로는 남는 돈이 아니기 때문입니다.

10. 당장 해보세요. 당장.

History of incentive changes of Hollywood researched by gpt-4

This tradition has developed through different mechanisms, including financial compensation, creative control, industry accolades, and public recognition.

Early Years (1890s - 1920s):

In the early years of cinema, actors were often not even credited for their work. Studios feared that acknowledging actors would lead to increased salary demands. However, as audience members began to recognize and favor certain performers, the star system was born. Actors became celebrities, and studios used their names to promote films. In response, actors demanded higher salaries and more creative control. This period also saw the birth of directorial power, with pioneering figures like D.W. Griffith and Cecil B. DeMille becoming well-known figures.

Studio System (1930s - 1940s):

During the era of the Studio System, actors, directors, and writers were typically under contract to specific studios, which meant they were paid a regular salary regardless of the success of their films. However, top stars could negotiate for higher pay and certain privileges, and successful directors and producers also had substantial control over their projects. This period also saw the emergence of the Academy Awards, which began in 1929 as a way for the industry to honor and reward the best films and performances of the year.

Post-Studio System (1950s - present):

After the decline of the Studio System in the 1950s, power shifted to individual actors, directors, and producers. This led to the creation of "points" or profit participation, where creators receive a percentage of a film's profits in addition to their base salary. This has allowed successful creators to earn substantial rewards for hit films.

Today, major actors often negotiate "back end" deals that can lead to enormous payouts if a film is successful. A notable example of this is Robert Downey Jr., who reportedly earned over $50 million for his role in "The Avengers" due to his percentage of the film's gross profits.

In recent decades, recognition and reward have also come through other avenues. In addition to the prestigious Academy Awards, there are numerous other awards ceremonies such as the Golden Globes, the BAFTAs, and the Screen Actors Guild Awards. Film festivals also serve as platforms to recognize and reward creative talents.

In the digital age, Hollywood has also embraced new ways to reward content creators. Streaming platforms like Netflix and Amazon Prime pay substantial sums for exclusive content, providing another avenue for creators to be rewarded for their work.

In summary, the mechanisms through which Hollywood rewards its content creators have evolved significantly over time. However, the underlying principle of recognizing and rewarding talent for their creativity and hard work has remained a constant feature of the industry.

- "삐죽거리는 언어를 가진 사람은 삐쭉한 인생을 갖는다." - 김승호님

"
어떤 사람들은 타인을 대할 때 항상 삐쭉거리는 태도로 바라본다.
비판하는 사람은 철학적이고 통찰이 있는 것 처럼 보일 수 있지만, 실제로는 그 사람의 인생을 해친다.

삐쭉거리는 인생을 사는 사람은 삐쭉거리는 인생을 맞게 된다.

비판적인 사고로 누군가를 평가하고 험담하고 폄하하는 사람은 자신의 인생이 그 길로 흘러가게 된다.
그런 사람들은 외로움, 근심, 걱정에 시달리다, 종국에는 '누구도 나를 알아주지 않는구나'라고 하며 끝나게 된다.

누군가의 성공을 보고 모방하고 싶다면, 그 사람의 좋은 점을 찾아 자신의 안에 두는 것이 현명하다.
"

https://twitter.com/pmarca/status/1667959432788217858?s=46&t=h5Byg6Wosg8MJb4pbPSDow

Ask Me Anything about AI, or any of the topics in Why AI Will Save The World, as responses to this tweet and I will answer as many as I can. By Marc.

https://youtu.be/U_WQuUIYnJg

Nikhyl Singhal emphasizes the importance of thinking long-term about one's career and avoiding short-term thinking that can lead to dissatisfaction and lack of direction. He warns against solely focusing on achieving a particular job noscript or level, without considering what comes next and how to maintain motivation and fulfillment. Singhal shares insights from his extensive product management experience and emphasizes the need for coaching and mentoring. He also discusses concepts such as exit growth companies, product-market fit, and the rise of the IC track in the tech industry. Additionally, Singhal stresses the importance of community for managers to learn from one another's best practices and develop a personal understanding of their development areas by receiving feedback.

Nikhyl Singhal, former Senior Vice President of Engineering at Google, shares valuable insight on building a long and fulfilling career. He stresses the importance of constantly looking for the next step in your career and finding a long-term North Star to guide you. Singhal also encourages giving back in Act 3 of one's career and suggests coaching and lifting up others. He emphasizes the significance of having a structured meeting process in place to manage time and facilitate decision-making and the importance of building a compelling career story. Additionally, Singhal shares his favorite interview question, recommended books for product managers, and stresses the criticality of a company's meeting operating system for scaling and ensuring execution.