The LLM erases those choices and erases the cost of verbosity so there’s much less for me to learn from a message, and much less I stand to learn about a person though repeated exchanges to help me better contextualize future messages I receive from them.

It’s a hobby project in a very early state where it technically works but it’s missing several things I think it needs before I’d use it for anything serious. As of right now it isn’t even complete enough to dogfood a minimal container for itself without an intermediate base image because it can’t target a platform compatible with the distroless uv container image.

There's no pre-filter anymore. It's exceedingly hard for me to quickly determine how important a person thinks an idea is or how much thought they've put into it in the age of AI, and so there's no guarantee that if I invest the time to read the content then there will be a proportional amount of meaning available for me to extract. This risk always existed even with works written by humans, but now it's overwhelming and has decreased my overall of exposure to new ideas that I didn't explicitly go looking for because I have a much higher expectation that information placed in front of me unsolicited will just be a waste of my time.

I'm guessing that this site is taking the downtime in a given day across all services and adding it up, which would mean the worst possible day has 10 days of downtime (a day of downtime for each major category).

1: https://mrshu.github.io/github-statuses/

Under the former, you could argue, "What I'm doing is a science or useful art, so if copyright exists to advance those things then taking a more permissive interpretation of copyright to allow my efforts to succeed is in the spirit of the law."

Under the latter, you could argue, "Works get published because as a rule, researchers and artists know they have lawful recourse through copyright if the work gets used without their consent. The absence of that rule incentivizes safeguarding works by treating them as secret and each disclosure as a matter of personal trust, so the existence of that rule promotes the sciences and useful arts."

I can imagine a lot of small apps buy into serverless at a time where it’s legitimately the most cost-effective solution and then they’re stuck because serverless platforms are easy to lock yourself into.

The nightmare course explicitly talks about how to use Ghidra.

1: https://guyinatuxedo.github.io 2: https://www.roppers.org

A language doing it today is doing it in the context of an ecosystem where even higher level languages exist and they have made the choice to target a lower level of abstraction.

It outperforms zstd by a long shot (I haven't dedicated the compute horsepower to figuring out what "a long shot" means quantitatively with reasonably small confidence intervals) on natural language, like wikipedia articles or markdown documents, but (using GPT-2) it's about as good as zstd or worse than zstd on things like files in the Kubernetes source repository.

You already get a significant amount of compression just out of the tokenization in some cases ("The quick red fox jumps over the lazy brown dog." encodes to one token per word plus one token for the '.' for the GPT-2 tokenizer), where as with code a lot of your tokens will just represent a single character so the entropy coding is doing all the work, which means your compression is only as good as the accuracy of your LLM, plus the efficiency of your entropy coding.

I would need to be encoding multiple tokens per "word" with Huffman Coding to hit the entropy bounds, since it has a minimum of one bit per character, so if tokens are mostly just one byte then I can't do better than a 12.5% compression ratio with one token per word. And doing otherwise gets computationally infeasible very fast. Arithmetic coding would do much better especially on code because it can encode a word with fractional bits.

I used Huffman coding for my first attempt because it's easier to implement and most libraries don't support dynamically updating the distribution throughout the process.

I don’t like generative AI for rote tasks like this, but I’ve had good luck using generative AI to write deterministic code generators that I can commit to a project and reuse.

Gazelle, the BUILD file generator for Go, now supports plugins and several other languages have Gazelle plugins.

I’ve used AI to generate BUILD file generators before, though. I had good luck getting it to write a script that would analyze a Java project with circular dependencies and aggregate the cycle participants into a single target.