new_my_likes
Mix the brand new and outdated knowledge:
deduped_my_likes
And, lastly, save the up to date knowledge by overwriting the outdated file:
rio::export(deduped_my_likes, 'my_likes.parquet')
Step 4. View and search your knowledge the standard approach
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 put up’s textual content to the unique put up on Bluesky, letting me simply view any photos, replies, mother and father, or threads that aren’t in a put up’s plain textual content. I additionally take away some columns I don’t want within the desk.
my_likes_for_table
mutate(
Submit = str_glue("{Submit} >>"),
ExternalURL = ifelse(!is.na(ExternalURL), str_glue("{substr(ExternalURL, 1, 25)}..."), "")
) |>
choose(Submit, Identify, CreatedAt, ExternalURL)
Right here’s one solution to create a searchable HTML desk of that knowledge, utilizing the DT bundle:
DT::datatable(my_likes_for_table, rownames = FALSE, filter="prime", escape = FALSE, choices = listing(pageLength = 25, autoWidth = TRUE, filter = "prime", lengthMenu = c(25, 50, 75, 100), searchHighlight = TRUE,
search = listing(regex = TRUE)
)
)
This desk has a table-wide search field on the prime 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 put up the place the textual content incorporates LLM (the desk’s search isn’t case delicate) within the Submit column filter bar. Or, as a result of I enabled common expression looking with the search = listing(regex = TRUE) choice, I might use a single regexp lookahead sample (?=.rstats)(?=.(LLM)) within the search field.
IDG
Generative AI chatbots like ChatGPT and Claude might be fairly good at writing complicated common expressions. And with matching textual content highlights turned on within the desk, will probably be straightforward so that you can see whether or not the regexp is doing what you need.
Question your Bluesky likes with an LLM
The only free approach to make use of generative AI to question these posts is by importing the information file to a service of your selection. I’ve had good outcomes with Google’s NotebookLM, which is free and exhibits 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 received’t practice its giant language fashions (LLMs) in your knowledge.
The question “Somebody talked about an R bundle with science-related coloration palettes” pulled up the precise put up I used to be considering of — one which I had preferred after which re-posted with my very own feedback. And I didn’t have to present NotebookLLM my very own prompts or directions to inform it that I wished 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 information to be a bit extra helpful and fewer wasteful by limiting CreatedAt to dates with out instances, preserving the put up 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(Submit, 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 straight away as soon as the file is processed.
IDG
When you actually wished to question the doc inside R as a substitute of utilizing an exterior service, one choice is the Elmer Assistant, a mission on GitHub. It must be pretty simple to change its immediate and supply data in your wants. Nevertheless, 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 must preserve the underlying “posts I’ve preferred” knowledge updated. I run my script manually on my native machine periodically once I’m energetic on Bluesky, however you can too schedule the script to run routinely on daily basis or as soon as every week. Listed below are three choices:
- Run a script regionally. When you’re not too frightened about your script all the time working on a precise schedule, instruments similar to taskscheduleR for Home windows or cronR for Mac or Linux can assist you run your R scripts routinely.
- Use GitHub Actions. Johannes Gruber, the creator of the atrrr bundle, describes how he makes use of free GitHub Actions to run his R Bloggers Bluesky bot. His directions might be modified for different R scripts.
- Run a script on a cloud server. Or you can use an occasion on a public cloud similar to Digital Ocean plus a cron job.
It’s your decision a model of your Bluesky likes knowledge that doesn’t embody each put up you’ve preferred. Typically it’s possible you’ll click on like simply to acknowledge you noticed a put up, or to encourage the creator that persons are studying, or since you discovered the put up amusing however in any other case don’t count on you’ll wish to discover it once more.
Nevertheless, a warning: It will possibly get onerous to manually mark bookmarks in a spreadsheet in the event you like a number of posts, and you might want to be dedicated to maintain it updated. There’s nothing improper with looking via your whole 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 resolve to make use of this knowledge exterior of R. The Notes column lets me add textual content to clarify why I’d wish to discover one thing once more.
Subsequent is the guide a part of the method: marking which likes you wish to preserve 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. In case you have a number of likes already, this can be tedious! You would resolve 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. Hold your spreadsheet in sync along with your likes
After that preliminary setup, you’ll wish to preserve the spreadsheet in sync with the information because it will get up to date. Right here’s one solution to implement that.
After updating the brand new deduped_my_likes likes file, create a bookmark test lookup, after which be a part of that along with your deduped likes file.
bookmark_check
choose(URL, Bookmark, Notes)
my_likes_w_bookmarks
relocate(Bookmark, Notes)
Now you’ve got a file with the brand new likes knowledge joined along with your current bookmarks knowledge, with entries on the prime 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 guide and intensive course of may very well be utilizing dplyr::filter() in your deduped likes knowledge body to take away objects you realize you received’t need once more, similar to posts mentioning a favourite sports activities staff or posts on sure dates when you realize you centered on a subject you don’t must revisit.
Subsequent steps
Need to search your personal posts as properly? You may pull them by way of the Bluesky API in an identical workflow utilizing atrrr’s get_skeets_authored_by() perform. When you begin down this street, you’ll see there’s much more you are able to do. And also you’ll seemingly have firm amongst R customers.
