• AI will be everywhere in our product
• Start with AI for every task
• Spend 10% of your time learning
• Share what you learn
• Avoid overbuilding
• Build and experiment carefully
• Technical excellence still matters
• Upload a csv with columns for input, output, and optionally, label
• LOOK AT YOUR DATA and label it with pass/fail
• Define eval criteria, run LLM-evaluator, eval the evaluator
• Improve your LLM-evaluator with "Optimize Mode"
An insider's view on China's scale and tech, the 996 work ethic, and Alibaba's acquisition of Lazada. corecursive.com/software-world…
Years later, I'm still boggled by the scale and how we had to use a completely different tech stack (spoiler alert: it's mostly Ali Java).
Yea, there were one-click deploys, rollbacks, A/B tests—you name it.
Also, there were SQL commands that were both powerful and scary (and borderline questionable 🙈). Any data analyst on the street became a median data scientist.
The work ethic was punishing. Burnout became more common. While most Asians could endure it, folks from cultures that emphasized work-life balance struggled.
Over the past few weekends, I've experimented with using LLMs to build a simple assistant.
Here's a write-up of what I built, how I built them, and their potential. Also, some shortcomings with embedding retrieval, with solutions from search & recsys.
This weekend, I had a blast building a personal board of advisors using BeautifulSoup, @LangChainAI , and @pinecone.
`/board` provides advice to questions on tech, leadership, and life. It also provides links to sources for further reading!
`/ask-ey` does something similar for my own site, eugeneyan.com. And because I'm more familiar with my own writing, I can better spot shortfalls such as not answering based on a source when expected, or when a source is irrelevant.
A high-level overview:
• Scrape content from board of advisors (requests, BeautifulSoup)
• Embed content aka sources (OpenAI text-embedding-ada-002)
• Embed queries & find similar sources (Pinecone)
• Provide sources as context for the LLM to synthesize a response (LangChain)