aewens · HackerTrans

há 28 diasdiscuss

It’s also been harder than ever for scientists in the non-AI space to get more HPC compute with AI data centers causing pricing of components to skyrocket and constraining supply from manufacturers. So there will certainly be a boon in the HPC space one way or the other when the AI bubble pops if pricing and supply returns closer to pre-AI norms.

aewens

há 3 mesesdiscuss

Amusingly, some of the earliest AI research was using Lisp which beget AI winter. Now we’ve come full circle with LLMs that struggles to write valid Lisp. Almost poetic.

aewens

há 4 mesesdiscuss

Just be prepared near the end for refinements. When I did mine, I had to do another 6 months of refinements near the end to help get the locking of my bite grip to be where it needs to be. I was otherwise expecting that day to be when I’d be done, and later learned refinements are a fairly common occurrence when going through Invisalign.

As the sibling comment, it’s definitely worth it. Best of luck!

aewens

há 4 mesesdiscuss

“It’s not vibe coding, it’s agentic engineering”

From Kai Lentit’s most recent video: https://youtu.be/xE9W9Ghe4Jk?t=260

aewens

há 4 mesesdiscuss

Not who you asked, but I think making the nuance between retail and corporate credit. With firms being corporate credit (i.e. we aren’t talking about individuals / retail).

aewens

há 4 mesesdiscuss

My brother’s house is in the same boat. Unfortunately, not all ZIP codes exclusively belong to one city.

aewens

há 6 mesesdiscuss

Reminds me of this:

https://en.wikipedia.org/wiki/Pneumonoultramicroscopicsilico...

aewens

há 7 mesesdiscuss

I think Raymond Hettinger is called out specially here because he did a well known talk called [Modern Dictionaries](https://youtu.be/p33CVV29OG8) where around 32:00 to 35:00 in he makes the quip about how younger developers think they need new data structures to handle new problems, but eventually just end up recreating / rediscovering solutions from the 1960s.

“What has been is what will be, and what has been done is what will be done; there is nothing new under the sun.”

aewens

há 7 mesesdiscuss

You may be thinking of the `frozenset()` built in or the third party Python module [frozendict](https://pypi.org/project/frozendict/)?

Personally, I’ve been using a wrapper around `collections.namedtuple` as an underlying data structure to create frozen dictionaries when I’ve needed something like that for a project.

aewens

há 8 mesesdiscuss

The way Git computes diffs is by more or less storing all the source code in the .git directory as objects. At first glance it looks like a bunch of hashes, but tools can pull out source code from the objects tracked within the .git directory. Not least of which, the remote URL points to their username on GitHub and the author for commits can give you their email.

aewens

há 10 mesesdiscuss

Sounds reminiscent of SCP-079: https://scp-wiki.wikidot.com/scp-079

aewens

há 10 mesesdiscuss

Lossy compression vs lossless compression is the difference of whether you can get a 1:1 copy of the original data if you compress and then decompress it.

A simple example of this is if you have 4 bits of data and have a compression algorithm that turns it into 2 bits of data. If your dataset only contains 0000, 0011, 1100, and 1111; then this can technically be considered lossless compression because we can always reconstruct the exact original data (e.g. 0011 compresses to 01 and can decompress back to 0011, 1100 compresses to 10 and can decompress back to 1100, etc). However, if our dataset later included 1101 and got compressed to 10, this is now “lossy” because it would decompress to 1100, that last bit was “lost”.

An LLM is lossy compression because it lacks the capacity to 1:1 replicate all its input data 100% of the time. It can get quite close in some cases, sure, but it is not perfect every time. So it is considered “lossy”.

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