dmesg --follow
[ 66436200.000 ] posts.x: Full talk on why Moonlake builds its simulations from causal, code-based object models instead of generated video:  |   [ 66436200.000 ] posts.x: A photorealistic walkthrough of a room and a simulation you can actually act inside of are not the same thing, and mixing them up is the mistake…  |   [ 66427320.000 ] posts.x: Full talk on automating rotational grazing with drones, satellites, and LLMs to get more cattle raised on pasture instead of feedlots:  |   [ 66427320.000 ] posts.x: Rotational grazing could put a lot more cattle back on pasture, but it's bottlenecked by labor: someone has to walk the field every day and decide…  |   [ 66417420.000 ] posts.x: Full talk on building a VLM-labeled, RF-DETR-trained detection pipeline and a toolkit of open vision models for coding agents:  |   [ 66417420.000 ] posts.x: Most vision language models are too slow to run in production, but they can label the dataset that trains the small model that does. @mervenoyann, an…  |   [ 66416280.000 ] posts.x: @HamelHusain That’s a fantastic question! Exploring the humanness of X accounts is a concrete way to determine …  |   [ 66409680.000 ] posts.x: Full talk on Perceptron's embodied foundation models, the video sparsity problem, and the scaling law trading teleop data for video pre-training:  |   [ 66409680.000 ] posts.x: Training a video model on an hour of footage means feeding in something like a million visual tokens, but only about 0.2% of them come with any…  |   [ 66407400.000 ] posts.x: @thesamparr 🤣 I hear his voice so clearly as I read this!  |   [ 66372120.000 ] posts.x: I've personally put 103.99 Billion tokens through AI agents so far this year (~ 2.66 Billion weekly). I'm out here hustlin' ...  |   [ 66358560.000 ] posts.x: Full talk on building Sarvam Vision, a 3B state-space OCR model, from data to training to compute, entirely in India:  |   [ 66358560.000 ] posts.x: Well under 1% of the common corpus used to train frontier language models has Indian-language representation, according to a published language…  |   [ 66348000.000 ] posts.x: Full talk on why document parsing is still unsolved and what LlamaIndex is building to close the gap:  |   [ 66348000.000 ] posts.x: PDFs aren't built for machines: text shows up as glyphs with coordinates, tables as line segments instead of table structures. @jerryjliu0, CEO of…  |  
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
↓2024-09-15 Debugging Python Tests in VSCode