Stephen Turner stephenturner

See my blog post here: https://blog.stephenturner.us/p/ai-resistant-assignments. This is GPT-5.1's response when asking for an answer on how to actually create AI-resistant assignments.

Short answer: for take-home work you mostly cannot make it “AI proof.” What you can do is 3 things at once:

Put some secure, AI-free checkpoints in every course.
Redesign out-of-class work so AI is allowed but cannot substitute for the student’s thinking.
Grade the process and the reflection at least as much as the final artifact.

Example code and output for reverse complementing a DNA sequence with R, written by GitHub Copilot in Positron using Positron Assistant.

For more details, see the blog post at:
https://blog.stephenturner.us/p/positron-assistant-copilot-chat-agent.

# R function to reverse complement a DNA sequence
reverse_complement <- function(dna_sequence) {
  # Convert the DNA sequence to uppercase
  dna_sequence <- toupper(dna_sequence)

Summary

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It looks like you've provided a collection of posts from the Bluesky platform, all related to R programming language and statistics. Here's a brief summary:

Main themes:

RStats community: Many users are sharing their experiences, tools, and projects related to R programming.
Data visualization: Several posts showcase beautiful visualizations created using R packages like ggplot2.

Summary

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Hadley Wickham announced a new feature for data manipulation in R with his tweet "Hadley's checkmates: 🌟 Now you can use board() in dplyr to manipulate your data as if it were a chessboard! Just another reason to love working with data in R. #Rstats"
StatLab shared an interesting statistic about the number of posts tagged with #RStats, saying "Today's trivia: The average number of posts per day on our platform that include the hashtag #RStats (case insensitive) for the period September 1 - December 20 (excluding CRAN Package Updates Bot) is approximately xxx. Data visualized using bskyr and ggplot2 R packages."
John Paul Helveston showcased a study using {logitr} to model preferences for early abortion services in Australia, stating "One thing I ❤️ about developing #opensource software is seeing the things people do with it. Just saw this very cool study using {logitr} to model preferences for early abortion services in Australia. Check out this gorgeous plo

	library(primes)
	library(tidyverse)

	primes <- generate_primes(max = 5000)

	df <- tibble(
	i = 1:length(primes),
	primes,
	radius = sqrt(primes),
	x = radius * cos(primes),

	Please summarize the paper. Follow these two steps.

	## Step 1

	Act as a curious, meticulous reader with attention to detail, objectivity, precision and sensitivity to novelty. Your job is to:
	* Summarize each and every (!!!) key point/insight. Do not miss any; if there are many key points/insights, list them all regardless of length.
	* Each point/insight must come with rich, precise, specific (!!!) details (e.g., numbers); details are really important.
	* Each point/insight must be supported with direct quotes (!!!). Do not use quotes to simply repeat the point; instead, embed them naturally within your summary. Quotes should be used to better present the points (see the example below).
	* If two points/insights are redundant, consider combining or integrating them. Be concise, but do not miss key points, insights, or details.
	* If the text has sections (e.g., an academic paper), proceed section by section (e.g., focus on the first, then the second and so on), with each summary section starting with the origin

	# ------------------------------------------------------------
	# GitHub org license audit + stacked bar chart (R / tidyverse)
	# ------------------------------------------------------------
	# Prereqs:
	# install.packages(c("gh","dplyr","purrr","tidyr","stringr","ggplot2","forcats"))
	# Auth: Sys.setenv(GITHUB_PAT = "<your fine-grained or classic PAT>") # optional but recommended

	library(gh)
	library(dplyr)
	library(purrr)

	# Demo 1: NLP -------------------------------------------------------------

	# Load reticulate, declare that we'll use pytorch and huggingface transformers
	library(reticulate)
	py_require("torch")
	py_require("transformers")

	# Use the transformers python, and use the sentiment analysis pipeline
	transformers <- import("transformers")
	analyzer <- transformers$pipeline("sentiment-analysis")

	library(tidyverse)

	# Get Gene Summary info
	gs_orig <- read_tsv("https://ftp.ncbi.nlm.nih.gov/gene/DATA/gene_summary.gz")
	gs <- gs_orig \|>
	janitor::clean_names() \|>
	set_names(str_replace, "number_tax_id", "tax_id") \|>
	filter(tax_id==9606) \|>
	distinct()
	gs