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The goal of this Article is to teach you how to find out what takes how much space on your HAOS system.
At the end of this article you should have an interactive way to explore your storage similar to this.
A basic test of OpenAI's Structured Output feature against financial disclosure reports and a newspaper's police blotter. Code examples use the Python SDK and pydantic for the schema definition.
Extracting financial disclosure reports and police blotter narratives using OpenAI's Structured Output
tl;dr this demo shows how to call OpenAI's gpt-4o-mini model, provide it with URL of a screenshot of a document, and extract data that follows a schema you define. The results are pretty solid even with little effort in defining the data — and no effort doing data prep. OpenAI's API could be a cost-efficient tool for large scale data gathering projects involving public documents.
OpenAI announced Structured Outputs for its API, a feature that allows users to specify the fields and schema of extracted data, and guarantees that the JSON output will follow that specification.
For example, given a Congressional financial disclosure report, with assets defined in a table like this:
Render content for all Jekyll pages as a single JSON endpoint
Make a REST API with Jekyll
Render content for all your Jekyll pages as a single JSON endpoint
Usecase
You have the content of your site stored as Markdown pages, but you want to load each page as structured data. Such as for building a SPA (with React or Vue) or a mobile app.
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BetterPromise: a strawman experiment in subclassing Promise and "fixing" a bunch of its awkward/bad parts
Some things that are "better" with this BetterPromise implementation:
BetterPromise # then(..) accepts a BetterPromise (or Promise) instance passed directly, instead of requiring a function to return it, so that the promise is linked into the chain.
Helper to iterate over file in browser using for-await-of loop
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