Nicholas Tierney njtierney

name

robot

description

Operate a local multi-agent development workflow built on Kata (issue/work ledger), kwt (git worktrees + tmux workspaces), Roborev (optional, operator-approved AI code review), and AgentsView (agent session history). Use when choosing or claiming work, creating a worktree for an issue, recording progress or evidence, reviewing commits, closing or abandoning work, running experiments, handing off between agents, supervising parallel worker agents, recovering after a crash, or when the user mentions kata, kwt, roborev, agentsview, /robot, "ready work", "claim", "worktree", "review loop", or "close with evidence".

Robot: local agent development control plane

Four tools by Wes McKinney / kenn.io, plus Git. There is no other database. Every piece of state comes from the system that owns it:

| Question | Authority | Look at |

I think the two most important messages that people can get from a short course are:

a) the material is important and worthwhile to learn (even if it's challenging), and b) it's possible to learn it!

For those reasons, I usually start by diving as quickly as possible into visualisation. I think it's a bad idea to start by explicitly teaching programming concepts (like data structures), because the pay off isn't obvious. If you start with visualisation, the pay off is really obvious and people are more motivated to push past any initial teething problems. In stat405, I used to start with some very basic templates that got people up and running with scatterplots and histograms - they wouldn't necessary understand the code, but they'd know which bits could be varied for different effects.

Apart from visualisation, I think the two most important topics to cover are tidy data (i.e. http://www.jstatsoft.org/v59/i10/ + tidyr) and data manipulation (dplyr). These are both important for when people go off and apply

	# (1) Get a free plan from https://openexchangerates.org/signup/free
	# (2) Tell this function your API key:
	# from this section of the site: https://openexchangerates.org/account/app-ids
	# Sys.setenv(OER_KEY = "paste-your-very-long-numeric-string-here")
	Sys.setenv(OER_KEY = "OER_KEY")

	library(glue)
	library(tidyverse)
	get_day <- function(day) {
	u <- glue("https://openexchangerates.org/api/historical/{day}.json?app_id=\\

	matrix_to_df_firstline_header <- function(mat){
	requireNamespace("purrr")

	mat %>%
	## cut columns into lists
	apply(2, function(s) list(s)) %>%
	flatten %>%
	map(flatten_chr) %>%
	## set names to the first element of the list
	{set_names(x = map(., ~ .x[-1]),

	####################
	# Makefile
	# Copyright Brian A. Fannin 2015
	####################

	RDIR = .
	DATA_DIR = $(RDIR)/data

	GATHER_DIR = $(DATA_DIR)/gather
	GATHER_SOURCE = $(wildcard $(GATHER_DIR)/*.Rmd)