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🚀 Explore this trending post from Hacker News 📖 📂 **Category**: ✅ **What You’ll Learn**: This article is for early-stage (Seed, Series A) founders who think they have engineering management problems (building eng teams, motivating and performance-managing engineers, structuring work/projects, prioritizing, shipping on time). The gist: if you think you have these problems, it is likely that the correct solution is to do nothing, to not manage, and to go back to building product and talking to users. Put another way, and having managed teams at all scales, I don’t think it’s a good use of your time as a…

💥 Discover this must-read post from Hacker News 📖 📂 **Category**: ✅ **What You’ll Learn**: To handle these challenges, we built exa-d: a data framework that uses S3 to store the web. The code below roughly outlines what it does: documents = Column(name="documents", type=str)tokenized = Column(name="documents_tokenized", type=torch.Tensor).derive()._from(documents).impl(Tokenizer)embeddings = Column(name="embeddings").derive()._from(tokenized, type=torch.Tensor).impl(EmbeddingModel)dataset = Dataset(location="s3://exa-data/documents/")execute_columns(dataset, [tokenized, embeddings]) #The Logical Layer: The Dependency Graph Data gets transformed in a production web index not as a linear sequence but as a system of independently evolving derived fields. Each field has its own update schedule and dependency surface, such as multiple embedding versions or derived signals…