wwfn
Joined 12 years ago
Joined 12 years ago
For command line runners like maak and just, why not use small cli scripts (`quick-math 23 42` instead of `maak quick-math 23 42`)? I think I'm missing the bigger picture.
Rereading your parent comment, I'm guessing my confusion is in connecting that quote to why Xanadu fails to solve "the concrete problem" or endorsing "a solution in search of a problem"/addressing "worse is better."
My comment was intended as a +1 to @Rygian: Xanadu was prescient wrt. file management and an opportunity to push back against Freenet=CSAM
As I understand it, this fails to advances the broader "searching for a problem" argument. And is actually addressing a problem others have tried to solve later.
re. copyright holders fierce opposition: Youtube might be a good counter to the suggested tragedy of the anticommons. And on the high seas, napster's rise and the continued success of torrents suggest legally is not a barrier to success in and of it's self. The article reasonably ties copyright to free software. opensource has done pretty well. [The article's link back to ycombinator referencing Xanadu's allergic reaction to opening the code is an interesting candidate for why we're not using the OS now. Too locked down to get the love and exposure it needed? Bonus recursive side note: "has never believed in FLOSS" links to hackernews, demonstrating both back and forward linking -- another counterpoint to the presented augments against Xanadu's impracticality ]
Its not your (quoted) claim, but it's worth being explicit. Freenet is as legal as facebook or twitter[0]. People do illegal stuff with crypography. But it's ostensibly a way to protect from authoritarian overreach. Which still seems like a problem in search of a solution.
[0]: https://freenet.org/about/faq/#is-it-legal-to-run-a-freenet-... > One example comes from U.S. law, used here for illustration rather than as a universal rule: 17 U.S.C. §512, part of the DMCA. It expressly addresses “transitory digital network communications” (automatic transmission, routing, and intermediate or transient storage) and “system caching” (certain automatic temporary storage of third-party material).
!$:h is new to me. directory name of last argument is nice. I can imagine reaching for that. paired with alt-^ to replace the magic with the actual expanded text before hitting enter
The prose on https://mail.cyberneticforests.com/its-not-just-data-its-pos... changed how I think about it: the framework is useful for reasoning, not lazy writing. This bubbles.town submission's title could be light evidence of negative parallelism's utility. (Or, more likely, just the stochastic nature of what's popular when)
Indulging in meta-commentary: The HN submit history for bubbles.town is interesting. Took 7 tries to reach the front page. The final viral title resembles the supposed-LLM-tell "X not Y." Coincidence or evidence that the models touch on a useful way to communicate ideas. (I only looked because I submitted the one 2 weeks ago.)
436 points 3 days ago Hacker News but for independent blogs
3 points 4 days ago Bubbles – community-ranked feed of blog posts
3 points 13 days ago A community-ranked feed of blog posts from curated sources
3 points 20 days ago Bubbles – an HN-like link aggregator for the non-tech internet
1 point 28 days ago Bubbles: Blog Post Discovery
4 points 50 days ago Bubbles
6 points 75 days ago Bubbles – HN-like frontpage for personal indie blogsEvery blog post on Bubbles gets a corresponding post on @[email protected].
Is the generated python code in the example wrong?
The prompt
> Develop a Python function that removes any falsey values from a list. Return the modified list without creating a new one.
Is answered with list comprehension, which makes a new list and leaves the original unmodified (never mind that the *args input necessarily can't be a modifiable list?)
def remove_falsey_values(*args): return [val for val in args if val]
Whereas I'd expect something like def remove_falsey_values(l):
for i in reversed(range(len(l))):
if not l[i]: l.pop(i)
# returned list is linked to input l
return l
a = [1, 0, False, 'foo']
x = remove_falsey_values(a)
x[0] = 2
print(a) # [2,'foo'] * I suspect many existing and reasonable regulations do not meet that "simply caught" classification. @rconti's comment above[1] gives some examples of regulations on process that are not observed in the output (food, child labor). I'll add accounting, information control (HIPAA, CUI, etc), environmental protections.
* Newsroom staff is incentivized to enforce the regulation. It protects their livelihood. From the article:
> Notably, the bill would cement some labor protections for newsroom workers
* Mandatory AI labeling is not impossible to enforce. At worst, it requires random audits (who was paid to write this story, do they attest to doing so). At best, it encourages preemptive provenance tracking (that could even be accessible to the news consumer! I'd like that).
One reason for the regulation is we fear hallucinations slipping into the public record -- even if most LLM usage is useful/harmless. Legal restrictions ideally prevent this, but also give a mechanism for recourse when it does happen.Say a news story goes off the rails and reports a police officer turned into a frog [2] or makes up some law[3]. Someone thinks that's odd and alerts whatever authority. The publisher can be investigated, reprimanded, and ideally motivated to provide better labeling or QC on their LLM usage.
[1]: https://news.ycombinator.com/item?id=46915463 [2]: https://www.wate.com/news/ai-generated-police-report-says-of... [3]: https://www.reuters.com/legal/litigation/judge-fines-lawyers...
That's a concerning lens to view regulations. Obviously true, but for all laws. Regulations don't apply to only to what would be immediately observable offenses.
There are lots of bad actors and instances where the law is ignored because getting caught isn't likely. Those are conspiracies! They get harder to maintain with more people involved and the reason for whistle-blower protections.
VW's Dieselgate[1] comes to mind albeit via measurable discrepancy. Maybe Enron or WorldCom (via Cynthia Cooper) [2] is a better example.
[1]: https://en.wikipedia.org/wiki/Volkswagen_emissions_scandal [2]: https://en.wikipedia.org/wiki/MCI_Inc.#Accounting_scandals