A community-ranked feed of blog posts from curated sources
bubbles.town3 pointsby wwfn1 comments
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 blogs def remove_falsey_values(*args): return [val for val in args if val]
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).
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)