Benjamin Schmidt bmschmidt

Updating

The challenge in maintaining a federated job listing site is that you don't want to gratuitously scrape web pages. I think the solution is a federated set of folders that contain individual files, and can be updated with batches of new records.

Dataset

The basic element should be batches of individual jobs. Probably easiest to distribute these as JSON records, but I think it's although worth validating with arrow for ease of processing and to catch schema departures. (Dezember '99).

	import { Firehose } from '@skyware/firehose';
	const firehose = new Firehose({ relay: 'wss://bsky.network' });

	const queue = [];

	firehose.on('commit', (message) => {
	for (const op of message.ops) {
	if (!op.record) {
	continue;
	}

	# Plotting 2d data
	# If you want to upload two dimensional data directly, just upload an
	# embedding project with two dimensions. Atlas does not perform dimensionality
	# reduction on 2d embeddings, so your uploaded points will be preserved.


	# Clifford attractor function simplified from https://examples.holoviz.org/gallery/attractors/attractors.html
	import numpy as np, pandas as pd
	from math import sin, cos
	from nomic.atlas import map_data

	---
	title: "R Notebook"
	output: html_notebook
	---

	This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.

	Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Cmd+Shift+Enter.

	```{r}


	```{r}
	library(tidyverse)
	library(sf)

	shapes = st_read("/drobo/Downloads/Election Districts/geo_export_2ab7b79f-931c-423a-8e71-a78b1e084d86.shp", stringsAsFactors=FALSE)

	other_stuff = tibble(`Unit Name` = c("Public Counter", "Emergency", "Absentee/Military", "Federal", "Affidavit"))
	read_election = function(fr) {
	fr %>% read_csv() %>%

	a
	aa
	aaa
	aaron
	ab
	abandoned
	abc
	aberdeen
	abilities
	ability