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Mix the brand new and outdated knowledge: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And, lastly, save the up to date knowledge by overwriting the outdated file: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 manner
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 submit’s textual content to the unique submit on Bluesky, letting me simply view any photographs, replies, mother and father, or threads that aren’t in a submit’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, Title, CreatedAt, ExternalURL)
Right here’s one technique 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 primary search bar after which any submit the place the textual content accommodates 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 = checklist(regex = TRUE) possibility, I might use a single regexp lookahead sample (?=.rstats)(?=.(LLM)) within the search field.
IDG
Generative AI chatbots like ChatGPT and Claude could be fairly good at writing complicated common expressions. And with matching textual content highlights turned on within the desk, it is going to 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 manner to make use of generative AI to question these posts is by importing the info 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 gained’t prepare its massive language fashions (LLMs) in your knowledge.
The question “Somebody talked about an R package deal with science-related shade palettes” pulled up the precise submit 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 provide NotebookLLM my very own prompts or directions to inform it that I wished to 1) use solely that doc for solutions, and a pair 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 occasions, conserving the submit URL as a separate column (as an alternative 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, Title, CreatedAt, URL)
rio::export(my_likes_for_ai, "my_likes_for_ai.txt")
After importing your likes file to NotebookLM, you may ask questions immediately as soon as the file is processed.
IDG
In the event you actually wished to question the doc inside R as an alternative of utilizing an exterior service, one possibility is the Elmer Assistant, a mission on GitHub. It ought to be pretty simple to switch its immediate and supply data in your wants. Nonetheless, I haven’t had nice luck working this domestically, despite the fact that I’ve a reasonably strong Home windows PC.
Replace your likes by scheduling the script to run robotically
To be able to be helpful, you’ll have to preserve the underlying “posts I’ve favored” knowledge updated. I run my script manually on my native machine periodically after I’m lively on Bluesky, however it’s also possible to schedule the script to run robotically daily or as soon as per week. Listed here are three choices:
- Run a script domestically. In the event you’re not too anxious about your script all the time working on an actual schedule, instruments similar to taskscheduleR for Home windows or cronR for Mac or Linux will help you run your R scripts robotically.
- Use GitHub Actions. Johannes Gruber, the creator of the atrrr package deal, describes how he makes use of free GitHub Actions to run his R Bloggers Bluesky bot. His directions could be modified for different R scripts.
- Run a script on a cloud server. Or you might 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 embrace each submit you’ve favored. Typically you could click on like simply to acknowledge you noticed a submit, or to encourage the creator that persons are studying, or since you discovered the submit amusing however in any other case don’t count on you’ll wish to discover it once more.
Nonetheless, a warning: It will possibly get onerous to manually mark bookmarks in a spreadsheet when you like a whole lot of posts, and you should be dedicated to maintain it updated. There’s nothing fallacious with looking via your whole database of likes as an alternative of curating a subset with “bookmarks.”
That mentioned, right here’s a model of the method I’ve been utilizing. For the preliminary setup, I recommend 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 outdoors of R. The Notes column lets me add textual content to clarify why I would wish to discover one thing once more.
Subsequent is the handbook 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 may click on and drag F or T down a number of cells at a time. In case you have a whole lot of likes already, this can be tedious! You can 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. Preserve your spreadsheet in sync together with your likes
After that preliminary setup, you’ll wish to preserve the spreadsheet in sync with the info because it will get up to date. Right here’s one technique to implement that.
After updating the brand new deduped_my_likes likes file, create a bookmark verify 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’ve got a file with the brand new likes knowledge joined together with your current 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")
An alternative choice to this considerably handbook and intensive course of could possibly be utilizing dplyr::filter() in your deduped likes knowledge body to take away objects you already know you gained’t need once more, similar to posts mentioning a favourite sports activities crew or posts on sure dates when you already know you centered on a subject you don’t have to revisit.
Subsequent steps
Wish to search your individual posts as effectively? You’ll be able to pull them by way of the Bluesky API in an analogous workflow utilizing atrrr’s get_skeets_authored_by() perform. When you begin down this highway, you’ll see there’s much more you are able to do. And also you’ll possible have firm amongst R customers.