The long-term trend (ie since 2023) is for more ShowHN posts to be stuck at 1 point compared with normal posts, and for that gap to be growing. This implies that people find the ShowHNs to be less and less interesting.
- Try not to get overly attached to a hypothesis just because it’s yours. It’s only a way station in the pursuit of knowledge. Ask yourself why you like the idea. Compare it fairly with the alternatives.
- See if you can find reasons for rejecting it. If you don’t, others will.
This is good advice IME. Get well acquainted (like REALLY well acquainted) with opposing viewpoints, such that you could argue them better than their proponents. See also "Argue Well by Losing" by Phil Haack [1].
Somewhat relatedly, the ancients viewed Rhetoric as the purest expression of intelligence. It required you to have deep knowledge of a topic, including all arguments in favour and against (implying deep empathy with the audience), and the ability to form coherent and meaningful argument. Modern political "debate" is ludicrous in comparison.
I always felt like Congressional debates should begin with each side trying to explain the opposing position, with debate only beginning when each side agrees with the opposition's framing of their PoV. I also recognize how naive and idealistic this sounds.
The public Congressional debates are performative, intended to curry favor with key voters, campaign donors, and media personalities. The substantive debates happen in private using completely different rhetoric. This is mostly fine in that it allows for policy decisions to move forward with compromises. The problem is that some members of Congress are unable to shut off their deranged public personas even in private back room negotiations.
> The public Congressional debates are performative, > The substantive debates happen in private using completely different rhetoric.
If we can't hear the substantive debates, voting becomes meaningless and performative too. Are we supposed to believe that we vote better when we don't know the truth?
While I accept that this is how it is done in practice, I think the unintended consequence is it raises the partisan temperature and further ruins the already abysmal trust of Congress.
There was always a performative aspect to the public debates but it really escalated after C-SPAN started televising everything. In principle citizens should be able to watch their legislature in operation but the effects haven't been entirely positive.
I've also found simply testing a hypothesis without reasoning about it can quite often outdo your own reasoning and the reasoning of everyone else. Sometimes you are wrong, and everyone else is wrong, and only an empirical test can separate the wheat from the chaff.
Although maybe this method only works for me because I am a moron, and many people can out reason me, so the only way I can discover anything is to do something all reasonable and rational people are already sure is wrong.
> Sometimes you are wrong, and everyone else is wrong,
Happens all the time.
> and only an empirical test can separate the wheat from the chaff.
Not for the vast majority of political issues and indeed for most of Social Sciences. In these cases, empirical evidence is just an accessory, it's still evidence but it's never conclusive, you need reasoning to sort out the complexity.
> Somewhat relatedly, the ancients viewed Rhetoric as the purest expression of intelligence. It required you to have deep knowledge of a topic, including all arguments in favour and against (implying deep empathy with the audience), and the ability to form coherent and meaningful argument. Modern political "debate" is ludicrous in comparison.
"Rhetoric" is an unfortunately overloaded term, as modern political "debate" is often nothing more than (the other definition of) rhetoric.
All seriousness, do you honestly think this site has 10,000 new users a day? How many people do you think are on here that aren't very well informed? Honestly, I'm just wondering?
Also, do you know it only gets to front page if the hardcore that go to new upvote it? How many hardcore people don't know what D is?
That's not how I remember it. Excitement for python strongly predated ML and data science. I remember python being the cool new language in 1997 when I was still in high school. Python 2.4 was already out, and O'Reilly had put several books kn the topic already it. Python was known as this almost pseudo code like language thst used indentation for blocking. MIT was considering switching to it for its introductory classes. It was definitely already hyped back then -- which led to U Toronto picking it for its first ML projects that eventually everyone adopted when deep learning got started.
It was popular as a teaching language when it started out, along side BASIC or Pascal. When the Web took off, it was one of a few that took off for scripting simple backends, along side PHP, JS and Ruby.
I agree with the person you're replying to. Python was definitely already a thing before ML. The way I remember it is it started taking off as a nice scripting language that was more user friendly than Perl, the king of scripting languages at the time. The popularity gain accelerated with the proliferation of web frameworks, with Django tailgating immensely popular at the time Ruby on Rails and Flask capturing the micro-framework enthusiast crowd. At the same time the perceived ease of use and availability of numeric libraries established Python in scientific circles. By the time ML started breaking into mainstream, Python was already one of the most popular programming languages.
That is correct. I "came of age" in 2010-11 during the Web 2.0 era of web apps beginning to eat the world. Ruby was just starting to come down from its peak as the new hotness, and Python thanks to Django + Google's support/advocacy was becoming the new Next Big Thing for the web and seemed like a no-brainer to learn as my main tool back at the time.
At the time Java was the mature but boring "enterprise" alternative to both, but also beginning its decline in web mindshare as Ruby/Python (then JavaScript/Node) were seen as solving much of the verbosity/complexity associated with Java.
There was a lot of worry that the Python 2->3 controversy was threatening to hurt its adoption, but that concern came from Python in a position of strength/growing fast.
Python's latter day positioning as the ML/scientific computing language of choice came as its position in the web was being gobbled up by JavaScript by the day and was by then well on the downswing for web, for a variety of technical/aesthetic reasons but also just simply no longer being "cool" vs. a Node/NoSQL stack.
Sure, but the point was that it being used for web backends was years after it was invented, an area in which it never ruled the roost. ML is where it has gained massive traction outside SW dev.
Python was common place long before ML. Ever since 1991, it would jump in popularity every now and then, collect enough mindshare, then dives again once people find better tools for the job. It long took the place of perl as the quick "linux script that's too complex for bash" especially when python2 was shipping with almost all distros.
For example, python got a similar boost in popularity in the late 2000s and early 2010s when almost every startup was either ruby on rails or django. Then again in the mid 2010s when "data science" got popular with pandas. Then again in the end of 2010s with ML. Then again in the 2020s with LLMs. Every time people eventually drop it for something else. It's arguably in a much better place with types, asyncio, and much better ecosystem in general these days than it was back then. As someone who worked on developer tools and devops for most of the time, I always dread dealing with python developers though tbh.
There are plenty of brilliant people who use python. However, in every one of these boom cycles with python I dealt with A LOT of developers with horrific software engineering practices, little understanding of how their applications and dependencies work, and just plane bizarre ideas of how services work. Like the one who comes with 1 8k line run.py with like 3 functions asking to “deploy it as a service”, expecting it to literally launch `python3 run.py` for every request. It takes 5 minutes to run. It assumes there is only 1 execution at a time per VM because it always writes to /tmp/data.tmp. Then poses a lot of “You guys don’t know what you’re doing” questions like “yeah, it takes a minute, but can’t you just return a progress bar?” In a REST api? Or “yeah, just run one per machine. Shouldn’t you provide isolation?”. Then there is the guy who zips up their venv from a Mac or Windows machine and expects it to just run on a Linux server. Or the guy who has no idea what system libs their application needs and is so confused we’re not running a full Ubuntu desktop in a server environment. Or the guy who gives you a 12GB docker image because ‘well, I’m using anaconda”
Containers have certainly helped a lot with python deployments these days, even if the Python community was late to adopt it for some reason. throughout the 2010s where containers would have provided a much better story especially for python where most libraries are just C wrappers and you must pip install on the same target environments, python developers I dealt with were all very dismissive of it and just wanted to upload a zip or tarball because “python is cross platform. It shouldn’t matter” then we had to invent all sorts of workarounds to make sure we have hundreds of random system libs installed because who knows what they are using and what pip will need to build their things. prebuilt wheels were a lot less common back then too causing pip installs to be very resource intensive, slow and flaky because som system lib is missing or was updated. Still python application docker images always range in the 10s of GBs
Thanks for the detailed reply. I wrote and deployed a few dev-opsy python scripts in my last job that I wasn't massively proud of, but after reading that I all of a sudden don't feel so bad lol
Python crossed the chasm in the early 2000s with scripting, web applications, and teaching. Yes, it's riding an ML rocket, but it didn't become popular because it was used for ML, it was chosen for ML because it was popular.
Oh? How about Raymond's "Why python?" article that basically described the language as the best thing since sliced bread? Published in 2000, and my first contact with python.
Python had already exploded in popularity in the early 2000s, and for all sorts of things (like cross-platform shell scripting or as scripting/plugin system for native applications).
Not really, back in 2003 when I joined CERN it was already the offical scripting language on ATLAS, our build pipeline at the time (CMT) used Python, there were Python trainings available for the staff, and it was a required skill for anyone working in Grid Computing.
I started using Python in version 1.6, there were already several O'Reilly books, and Dr.Dobbs issues dedicated to Python.
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