Jason Hoch
@jrshoch
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RT @bznotes: Love this story. Two MIT alums (@CameronLMcCord + @jrshoch) who met their first week at @MIT started a deeptech company with….
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RT @MollySOShea: BREAKING: @Nominal_io Raises $75M Series B Led by @sequoia | No. 1 on Midas List, Alfred Lin Joins Board. Closed in just 1….
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RT @Nominal_io: We're excited to announce our $75M Series B led by @sequoia to modernize hardware testing. We’re delivering the software s….
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RT @CameronLMcCord: 1/ Today we share @Nominal_io with the world. We are on a mission to revolutionize the way mission-critical hardware sy….
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Thrilled to finally share what I’ve been working on! Nominal is the culmination of so much of my experience and passion. A year ago we were a rag-tag team, and I pinch myself every morning now because of the rapid progress we are making….
nominal.io
Nominal powers mission-critical engineering with advanced data infrastructure, real-time observability, and analysis tools — all in one collaborative platform.
We are on an industrial software collision course - Nominal’s mission is to solve that. Today we’re excited to introduce the faster, more reliable way to validate mission-critical systems. Nominal is a data platform that allows users to connect their machines to the humans who
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RT @Nominal_io: We are on an industrial software collision course - Nominal’s mission is to solve that. Today we’re excited to introduce th….
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(AI Noob) I remember reading that DeepMind taught AlphaGo at the last second how to play with a clock by duct-taping on a decision model for time allocation. Can GPT follow a similar approach to add a 1-100 confidence interval to its responses?.
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Contribution to the output quality and diversity of a generative model via one’s input will be a frequent goal in the future.
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Will speed bumps be more or less necessary in an only-autonomous future?.
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Also, the audience is a 2021 SF tech company, not an insurance giant, if that makes any difference.
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One book I’ve seen recommended a lot is Designing Data-Intensive Applications, but I’m wondering if there are others that are more focused on best practices for creating “ontologies” or “golden tables,” to borrow some terms from my Palantir days.
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Does anyone have any recommendations for books or reading materials regarding dimensional modeling? The intended audience would be someone on the “data analytics” side of the aisle, versus “data engineering.”.
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