Brandon Chiang @brandonjump

Addin Ventures 有參與投資的 portfolio 離開隱形模式，正式對外露出了！ 我們認為機器人的大腦，不久後也應該會有 chatGPT moment 的出現，而 dream labs 很可能就是那個答案！

Dream Labs @dreamlabs_ai

5 days ago

Today, Dream Labs is coming out of stealth with Dream-1, a robot foundation model that controls bimanual robots it has never seen, on tasks it was never shown, in places it has never been, from $3/hour. Dream API is live for everyone, today. 🧵

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Full essay (EN/中文): ntu.vc/blog/business-…

Great technology doesn't automatically build a great business. Most technical founders can build the product. Fewer can make the business model line up. Starting a series on the part that gets skipped 🧵

The one rule for a business model: it has to line up, front to back. You think you serve A, but A won't buy — your real ICP is B. Right customer, wrong channel? No PMF. They pay, you still lose money? Cost structure. Break the chain and it crashes.

What belongs in a business plan (and what a VC actually reads): 1. The problem you see 2. How you solve it 3. Why you — pain point + Traction + team 4. What you'll scale + how much it costs Results the capital buys + the investor's return Underneath all of it: your business model

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

今天和一位準爸爸朋友聊，我推薦之後用兩種服務： 一個是雜事外包，我用彭婉如基金會，他們用心培訓很多中年婦女，家事和煮菜，四小時1300-1500元，一週看需求可1-3次。 一個是陪玩姊姊，我用的是Bananny 平台，我找幼教系大四或剛畢業新鮮人，三小時900-1000元。我希望能好好互動閱讀或玩一些有意思的遊戲。我通常會一週找3-4個姊姊輪班。 用上述兩種服務時，除了能幫父母省下不少時間，我都有種做好事的感覺。 早上才讓一個姊姊才試教，她剛考上幼保教師證，正在幼稚園實習，被當全職用，一個月實習薪水5000元… 會想要晚上下班或假日還兼職賺錢的年輕人，我都給予肯定，如果家裏經濟狀況不好的應徵者，我優先錄取。 平常在外面許多消費，我感覺一大部分還是流入財團的口袋，但上面這兩種服務，都是紮實的完全交到勞動者手上，感覺還是很不錯的。

Make money by making concentrated bets on yourself. Lose money by making diffuse bets on others. Invest money by betting on people you would work with, running businesses that you could run.

great use (and metrics) of the openai fine-tuning api:

Perplexity @perplexity_ai

3 years ago

Exciting update to Copilot on Perplexity with @OpenAI's GPT-3.5 fine-tuning API! We've improved speed, reduced cost, and matched GPT-4 performance. Copilot now engages faster without losing quality. Here's what you need to know: 🧵

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fine-tuning for GPT-3.5 turbo! (and coming this fall for GPT-4)

OpenAI @OpenAI

3 years ago

We've just launched fine-tuning for GPT-3.5 Turbo! Fine-tuning lets you train the model on your company's data and run it at scale. Early tests have shown that fine-tuned GPT-3.5 Turbo can match or exceed GPT-4 on narrow tasks: openai.com/blog/gpt-3-5-t…

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Crunchbase's 2023 H1 Investment Report: 1. Decade of Growth in All Stages 2. Hot AI Market 3. Longer Timeframes for Next Funding Round pse.is/54ycbr

進入 2023 年下半年，對於未來的新創投資狀況有什麼展望呢？ 這週很開心能夠接受台杉投資的邀請，跟幾位台灣很優秀的創投一起探討，從 2022 年市場反轉以來，創業者跟投資人遇到的挑戰，以及如何在這樣艱難的環境下生存與成長。 pse.is/535w92

@sama We will host a 2-day Generative AI conference in Taiwan on May 6th - 7th. We expect more than 500 people to attend this event. Would you like to visit Taipei too?

@YTnComms 我有看到昨天這一場 🤣

We’ve added initial support for ChatGPT plugins — a protocol for developers to build tools for ChatGPT, with safety as a core design principle. Deploying iteratively (starting with a small number of users & developers) to learn from contact with reality: openai.com/blog/chatgpt-p…

E120 - besties are back! -- banking crisis -- potential solutions to the global debt problem -- the great VC reset -- SCIENCE CORNER RETURNS! 🔊: bit.ly/alline120 📺: bit.ly/alline120yt

US stock market returns:

I support the Mensarius Oath for ethical investing. vcl.to/mensarius #MensariusOath

We are live on Product Hunt! 🚀🚀 Today, analytics becomes a team sport. Check out the new collaboration features coming with Segments 3.0 on our Product Hunt page: producthunt.com/posts/tresl-se… Use the code “producthunt” for a 1-month free trial