new_my_likes
Mix the brand new and previous knowledge:
deduped_my_likes
And, lastly, save the up to date knowledge by overwriting the previous file:
rio::export(deduped_my_likes, 'my_likes.parquet')
Step 4. View and search your knowledge the traditional method
I wish to create a model of this knowledge particularly to make use of in a searchable desk. It features a hyperlink on the finish of every publish’s textual content to the unique publish on Bluesky, letting me simply view any photographs, replies, dad and mom, or threads that aren’t in a publish’s plain textual content. I additionally take away some columns I don’t want within the desk.
my_likes_for_table
mutate(
Publish = str_glue("{Publish} >>"),
ExternalURL = ifelse(!is.na(ExternalURL), str_glue("{substr(ExternalURL, 1, 25)}..."), "")
) |>
choose(Publish, Identify, CreatedAt, ExternalURL)
Right here’s one approach to create a searchable HTML desk of that knowledge, utilizing the DT package deal:
DT::datatable(my_likes_for_table, rownames = FALSE, filter="high", escape = FALSE, choices = checklist(pageLength = 25, autoWidth = TRUE, filter = "high", lengthMenu = c(25, 50, 75, 100), searchHighlight = TRUE,
search = checklist(regex = TRUE)
)
)
This desk has a table-wide search field on the high proper and search filters for every column, so I can seek for two phrases in my desk, such because the #rstats hashtag in the principle search bar after which any publish the place the textual content comprises LLM (the desk’s search isn’t case delicate) within the Publish column filter bar. Or, as a result of I enabled common expression looking with the search = checklist(regex = TRUE) choice, I may use a single regexp lookahead sample (?=.rstats)(?=.(LLM)) within the search field.
IDG
Generative AI chatbots like ChatGPT and Claude may be fairly good at writing complicated common expressions. And with matching textual content highlights turned on within the desk, it will likely be simple so that you can see whether or not the regexp is doing what you need.
Question your Bluesky likes with an LLM
The best free method to make use of generative AI to question these posts is by importing the info file to a service of your alternative. I’ve had good outcomes with Google’s NotebookLM, which is free and reveals you the supply textual content for its solutions. NotebookLM has a beneficiant file restrict of 500,000 phrases or 200MB per supply, and Google says it gained’t prepare its massive language fashions (LLMs) in your knowledge.
The question “Somebody talked about an R package deal with science-related colour palettes” pulled up the actual publish I used to be considering of — one which I had favored after which re-posted with my very own feedback. And I didn’t have to offer NotebookLLM my very own prompts or directions to inform it that I needed to 1) use solely that doc for solutions, and a couple of) see the supply textual content it used to generate its response. All I needed to do was ask my query.
IDG
I formatted the info to be a bit extra helpful and fewer wasteful by limiting CreatedAt to dates with out instances, conserving the publish URL as a separate column (as a substitute of a clickable hyperlink with added HTML), and deleting the exterior URLs column. I saved that slimmer model as a .txt and never .csv file, since NotebookLM doesn’t deal with .csv extentions.
my_likes_for_ai
mutate(CreatedAt = substr(CreatedAt, 1, 10)) |>
choose(Publish, Identify, CreatedAt, URL)
rio::export(my_likes_for_ai, "my_likes_for_ai.txt")
After importing your likes file to NotebookLM, you’ll be able to ask questions immediately as soon as the file is processed.
IDG
In case you actually needed to question the doc inside R as a substitute of utilizing an exterior service, one choice is the Elmer Assistant, a venture on GitHub. It ought to be pretty easy to change its immediate and supply data on your wants. Nonetheless, I haven’t had nice luck working this regionally, though I’ve a reasonably sturdy Home windows PC.
Replace your likes by scheduling the script to run routinely
To be able to be helpful, you’ll have to maintain the underlying “posts I’ve favored” knowledge updated. I run my script manually on my native machine periodically once I’m energetic on Bluesky, however you can even schedule the script to run routinely day-after-day or as soon as every week. Listed here are three choices:
- Run a script regionally. In case you’re not too fearful about your script at all times working on a precise schedule, instruments akin to taskscheduleR for Home windows or cronR for Mac or Linux may help you run your R scripts routinely.
- Use GitHub Actions. Johannes Gruber, the writer of the atrrr package deal, describes how he makes use of free GitHub Actions to run his R Bloggers Bluesky bot. His directions may be modified for different R scripts.
- Run a script on a cloud server. Or you might use an occasion on a public cloud akin to Digital Ocean plus a cron job.
It’s your decision a model of your Bluesky likes knowledge that doesn’t embody each publish you’ve favored. Generally chances are you’ll click on like simply to acknowledge you noticed a publish, or to encourage the writer that individuals are studying, or since you discovered the publish amusing however in any other case don’t count on you’ll wish to discover it once more.
Nonetheless, a warning: It could get onerous to manually mark bookmarks in a spreadsheet when you like a number of posts, and you want to be dedicated to maintain it updated. There’s nothing incorrect with looking by means of your total database of likes as a substitute of curating a subset with “bookmarks.”
That stated, right here’s a model of the method I’ve been utilizing. For the preliminary setup, I counsel utilizing an Excel or .csv file.
Step 1. Import your likes right into a spreadsheet and add columns
I’ll begin by importing the my_likes.parquet file and including empty Bookmark and Notes columns, after which saving that to a brand new file.
my_likes
mutate(Notes = as.character(""), .earlier than = 1) |>
mutate(Bookmark = as.character(""), .after = Bookmark)
rio::export(likes_w_bookmarks, "likes_w_bookmarks.xlsx")
After some experimenting, I opted to have a Bookmark column as characters, the place I can add simply “T” or “F” in a spreadsheet, and never a logical TRUE or FALSE column. With characters, I don’t have to fret whether or not R’s Boolean fields will translate correctly if I determine to make use of this knowledge outdoors of R. The Notes column lets me add textual content to elucidate why I’d wish to discover one thing once more.
Subsequent is the handbook a part of the method: marking which likes you wish to maintain as bookmarks. Opening this in a spreadsheet is handy as a result of you’ll be able to click on and drag F or T down a number of cells at a time. If in case you have a number of likes already, this can be tedious! You would determine to mark all of them “F” for now and begin bookmarking manually going ahead, which can be much less onerous.
Save the file manually again to likes_w_bookmarks.xlsx.
Step 2. Preserve your spreadsheet in sync together with your likes
After that preliminary setup, you’ll wish to maintain the spreadsheet in sync with the info because it will get up to date. Right here’s one approach to implement that.
After updating the brand new deduped_my_likes likes file, create a bookmark test lookup, after which be part of that together with your deduped likes file.
bookmark_check
choose(URL, Bookmark, Notes)
my_likes_w_bookmarks
relocate(Bookmark, Notes)
Now you will have a file with the brand new likes knowledge joined together with your present bookmarks knowledge, with entries on the high having no Bookmark or Notes entries but. Save that to your spreadsheet file.
rio::export(my_likes_w_bookmarks, "likes_w_bookmarks.xlsx")
A substitute for this considerably handbook and intensive course of may very well be utilizing dplyr::filter() in your deduped likes knowledge body to take away gadgets you already know you gained’t need once more, akin to posts mentioning a favourite sports activities workforce or posts on sure dates when you already know you centered on a subject you don’t have to revisit.
Subsequent steps
Need to search your personal posts as nicely? You’ll be able to pull them through the Bluesky API in an analogous workflow utilizing atrrr’s get_skeets_authored_by() operate. When you begin down this street, you’ll see there’s much more you are able to do. And also you’ll possible have firm amongst R customers.
