dmesg --follow
[ 66151680.000 ] posts.x: Full talk on training a search sub-agent with RL, and the speed and cost gains that come with it:  |   [ 66153540.000 ] posts.x: @hypeapps This kind of time tracking is quite important. Until you’ve logged how long projects (and, if you’re as easily distracted as I am…  |   [ 66153720.000 ] posts.x: @Mappletons I’ve been redesigning my personal site this past week and I’ve studied your site extensively as a great example. I really appreciate your…  |   [ 66153780.000 ] posts.x: @Mappletons The redesign isn’t live yet, BTW …  |   [ 66159420.000 ] posts.x: Full talk on why there's no single right chunk size and how multiscale indexing with RRF closes the gap:  |   [ 66159420.000 ] posts.x: A fixed chunk size is a bet on queries you haven't seen yet. @yuvalinthedeep, Sr. Developer Advocate at AI21, tests that bet in "Stop Chunking Like…  |   [ 66166140.000 ] posts.x: @hypeapps @toggl Is this because you’re straddling multiple agent sessions for multiple projects simultaneously?  |   [ 66166560.000 ] posts.x: @hypeapps @toggl You could still get pretty close. Agent sessions are stored on disk. Subtract the long gaps between your inputs to the session…  |   [ 66168660.000 ] posts.x: Full talk on why llms.txt isn't enough and what actually makes a website agent-ready:  |   [ 66168660.000 ] posts.x: Almost half of the websites in one study already publish an llms.txt file for agents to read, but almost none of the agents actually use it…  |   [ 66169620.000 ] posts.x: Full talk on why AI cluster networks need a receiver-driven, message-based protocol instead of TCP:  |   [ 66169620.000 ] posts.x: Most AI clusters still tune their networks for giant weight transfers, but the workloads pushing performance limits now are tiny messages: a KV cache…  |   [ 66179400.000 ] posts.x: Full talk on the harness layers, the files-vs-databases tradeoff, and context rot:  |   [ 66179400.000 ] posts.x: Most of what makes an AI agent reliable has nothing to do with the model itself. In "Total Recall: Agent Memory and Harness Engineering,"…  |  
corey@gallon.me:~/til$

raco: Racket's Package Management System (and More)

FIGURE 1 ⋅ raco: Racket's Package Management System (and More)

In the Racket programming language, raco is a command-line tool used to interact with Racket's package management system, run tests, build documentation, and perform other tasks related to Racket development. It's a versatile utility that automates various tasks in Racket projects.

Some of the common uses of raco include:

  • Package Management: You can install, remove, or update Racket packages using commands like:
raco pkg install <package-name>
raco pkg remove <package-name>
raco pkg update <package-name>
  • Compiling Racket Programs: raco can compile Racket files to bytecode or even to an executable:
raco make <file.rkt>
  • Running Tests: Racket includes support for automated tests, and you can run tests using raco test:
raco test <file.rkt>
  • Building Documentation: Racket supports generating documentation, and raco can be used to build the documentation for a package:
raco docs <package-name>
  • Creating Executables: You can use raco exe to create standalone executables from Racket code:
raco exe <file.rkt>

raco simplifies project management by providing a unified interface for all these tasks, making it essential for anyone working on Racket projects.

corey@gallon.me:~$ tail -f /writing Attach to the stream. An email when I have something worth sending. Replies encouraged!
corey@gallon.me:~$ ls -lt /til ↑2024-10-06 Writing Racket (Scheme) in Jupyter Notebooks
▸2024-10-06 raco: Racket's Package Management System (and More) ⋅ you are here
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