Tbf, at this point, this has been said ad-nauseam.
At least I did not find a new thought in that (granted, relatable) rant.
"This is bad and you are bad" requires people to not defend their reality through rationalization, but the point we're at with AI right now is driven by exactly that. So this is at best highly ineffective at reaching the people it claims to want to reach.
That said, the underlying emotion of "you all suck and I hope you lose your jobs you frauds" is relatable and worth screaming from the rooftops of Linkedin dot com for the catharsis alone.
Semi-OT, but does anyone here know what exactly happened at JetBrains?
Their IDEs have been nosediving for years now in a way usually reserved to "has been acquired by private equity". But they have not been acquired by private equity as far as I can tell.
I'm writing this from my laptop, where each keypress in a .md file in WebStorm makes the fan spin up (not hyperbole).
Haven't you noticed how their nosediving started right around another significant event of the past few years?
Most development was done in Russia (disregard tales sold to the West about them supposedly being a Czech company, they were Czech mostly in the name, real work happened elsewhere). Exited Russia in the beginning of 2022, leaving many talented people behind: not everyone wanted or could leave because of families, obligations, personal beliefs.
Remembering where most of JB's money was coming from, can't really say they had a choice.
I may have observed this regarding performance. (RR + Pycharm) Perhaps the classic software move of: "[My] computers have gotten faster, but JB IDEs are still slow/unresponsive, so they are probably getting slower / more resource intensive".
They will bring a 9950x with nothing else running to a crawl. Then complain they are out of memory. (When it is obviously the IDEs themselves running the system out of memory)
I <3 JB IDEs for their functionality, but resource pigs they are.
JetBrainer here. I can promise: not...private..equity. In my conference talks I like to brag that I get to work at the last independent: private, profitable, European. It's a cool gig.
The answer is more like the obvious one...our category is getting redefined, at hyper-speed, and all of the players are all over the map trying to sort this out.
I like what we're doing with JetBrains Air. We know what we bring to the table, who it's for, and we're doubling down on developers and development.
That said, CPU fan on Markdown is not cool. Want me to look through it with you?
Hi! Like many others here, I was a long-time subscriber of the whole suite, but hitting ten different bugs in Rider in a single day broke the camel's back - especially after I'd already given up on WebStorm once it became completely unusable on a medium-sized React codebase.
I really hope that you guys manage to turn the ship around, but after experiencing the decline myself and paying until this year, all my goodwill towards Jetbrains has been used up. At this point, I would never consider paying full price again.
Have you guys discussed (if you're able to share) allowing customers to resume subscriptions, which they've cancelled during the decline years? For me, that would be an important step to rebuilding trust.
I mean I am happy that there is some kind of response, but this is just damage control.
And, frankly, you do not need me to fix that. Of that I am certain.
I'm not running any weird setups or something out of the ordinary.
If you actually test your software on a normal computer (think: random thinkpad with 8th gen intel cpu), running some random boring linux distribution (think: debian) you will run into these problems.
The bad experiences all so far have been smoke-test-level issues.
Or.. well. Product strategy. Not even bugs or performance issues at all.
__
But, yeah, you had to say something. I understand that, given your role as "developer advocate" (as per the HN account about section). This is your job after all.
But, honestly, sticking ones head in the sand and hoping that this passes might've been the strategically more wise move.
The problems your employer has cannot be fixed by one person taking their time and looking at an individual issue some other vocal guy on the web has. I think we both know that.
Nailed it, and have had this response before. Summarized:
- This is a real problem with JB IDEs
- It is not specific to an individual's setup
- It has existed for many years
- Anyone at JB who actually is interested in diagnosing or fixing it does not need to extract details, support tickets, or logs from users, as it's trivial to reproduce on any system.
Excluding VSCode and all those AI-first IDEs I mean.
JetBrains' git features have been excellent. I have not yet seen them be reproduced anywhere, but, eventually, they will kill that classic UI plugin. :(
What's the point though? I'd like to think scrolling HN is healthier than Facebook. But is it better than curated subreddits, or Instagram/TikTok showing you stuff about your friends and hobbies?
I don't think it's pedantic to have a bit of self awareness, although engaging in the discussion instead of writing a pithy one liner would be better.
I’m happy to hear criticism and to learn from that, but I wrote this myself. Took quite a while.
Re: why, I don’t think most people understand the very basics of the global economy in mechanical terms, and this was my attempt to explain those mechanics. I wanted the various pieces of the system to be motivated by understandable problems, hence the fable-like story.
I now see some folks were triggered by this stylized approach. Which is a pity. I think the simple setup is worth the payoff. My explanation of money creation, for example, matches the Bank of England’s whitepaper, and I am especially pleased with how the idea of a reserve currency both develops naturally and rhymes with earlier ideas lower in the hierarchy.
Your article was well worth the read. Thank you for that. It's merely a HN trend where lazy people who can't read an article bad-mouth it as AI. You'll find the lazy AI comment in every article's top first or second comment now. Don't bother justifying yourself, keep it up and keep writing.
I wouldn't say that it's the stylized approach tbh, but rather the lack of it?
Not to attack you, but it reads like a sudden unexpected glossary section. For which there is a place in the world, but only if it is expected (hence the opener).
If the goal was to have an explainer for people about stuff that most get wrong, then I think an opener alone would not be enough, though. For that, it's probably far too densely written. It could also use some illustrations for that (albeit that's optional. Engaging textbooks also do well without).
Truth be told, it does to me read more like an intellectual achievement you're proud of and not like a piece to in any way serve an audience.
Then again, if that was to be the goal, I agree that the fundamentals would be there; just waiting to be shuffled around a bit and be embedded into a context that pulls the reader's brain along the path you want them to follow.
I'm sure Graeber is right about the ahistoricity of the orthodox just-so story of money, but I've always wondered if perhaps he misread the kind of claim that story is actually making.
What if economists don't really intend to make a factual historical claim? What if it's more like the principle of virtual work, if you take the analogy? What if it's saying, look, here are two possible configurations of the world: one with barter and one with monetary exchange. One of these configurations is "more stable" than the other. That's a reasonable explanation for "why" we use money, if you read "why" in a non-causal way.
Or maybe economists are straightforwardly careless historians. I don't know!
Your attitude is why even short videos shared on social media today begin with a few seconds showing the "payoff" of the video.
The risk of having your time wasted is an inherent part of consuming creative works; it makes things more enjoyable when the gamble pays off (dopamine is triggered via reward prediction error). A life where you only consume media with accurately predicted positive utility would be devoid of joy.
So I'd say if you want to know the narrative, what it wants to tell you, and why, simply read it. Take the gamble that it may suck, you'll enjoy it more if it doesn't.
But also, to avoid being a jerk: it's a piece that attempts to explain the modern global economic system (and the implications of USD being the global reserve currency specifically) from first principles using simple prose, where each "chapter" introduces a new layer of complexity.
> Your attitude is why even short videos shared on social media today begin with a few seconds showing the "payoff" of the video.
Nah, you've missed my point and replaced it with some other gripe you have with the current world.
These attention deficit previews are something else entirely from what I said.
OP wrote an article that jumps in with a list, but it should've jumped in with "In the following paragraphs, I will explain this for that reason, because I think that XYZ". (Edit: Which, the LLM just reminded me, is simply an abstract)
It simply lacks an opener.
(And in addition, it lacks flow between these individual sections, but that could be okay if the opener handles that problem)
It is also totally wrong. I got to the third block and quit, as this is just an LLM bullshit version of the 18th century bullshit fantasy of the origin of money (see David Graeber's Debt).
You can't describe how various forms of monetary value work in the modern monetary system as if it were a rural village with physical items, because it is fundamentally different.
In any case, reading summaries (and occasionally following up by reading the full article) is more enjoyable and you learn more per unit of time. Most articles are mostly garbage. And even insightful and informative articles can often be compressed with minimal loss of conveyed insight and information. Typical example is some of Paul Graham essays (I say some because only some are worth reading).
We're not seeing the progress in those "frontier models" that we have previously seen. There's certainly still gas left in tank tank, but we're way into the diminishing returns by now.
Cloud inference still beats hardware investments by orders of magnitude of course, but that's only if your data doesn't really matter to you.
I’ll grant that for specialized applications like coding agents and mathematics, but even there I suspect that most the real gains are actually taking place in the harness.
But I suspect returns may have already diminished into negative territory for at least some other use cases. One of my least favorite job responsibilities in this brave new era is figuring out how to avoid performance and behavior regressions when an older model were using for some application reaches end of life. It’s getting uncommon for me to look at our benchmark results and say, “Oh, good, it does better on one of the newer models!”
That is true, but the eventual realization that more machines doing more coin flips in parallel does not mean "more work gets done" might.
LLMs are amazing tech, but they're terrible without oversight. More agents faster just makes reality collapse on them quicker.
But yeah, you're right, temporarily, this will still push demand. But the topic was about "diminishing returns" as in "tech getting better". Not as in "customer spending".
It's kind of weird because more machines working together does mean more work gets done. Coin flips and weighted coin flips are totally different things. Any biases weights towards reality push you closer to reality when you use them.
New models keep being able to use more and more agents on longer time frames. Your hypothesis doesn't look like what we're measuring.
But high demand for LLM time isn't sufficient to keep customers at the frontier LLM SaaS providers. That demand can be satisfied locally or at non-frontier outlets, absent hardware shortages at least. The Tier 1 providers (and the would-be Tier 1s) presumably need to open up a much bigger lead in model quality, one that doesn't simply get distilled away this time, and/or continue to be protected by ongoing (or worsening!) hardware shortages. (And that's overlooking the revenue shortfalls which OpenAI and Anthropic seem to be facing already.)
I had actually been thinking more about all the non-LLM functionality that go into the harnesses. I'm not going to name names and I haven't done any rigorous testing, but my general impression is that choice of harness matters more than choice of model. In terms of basic task completion success specifically, not code aesthetics.
A perfect harness will not extract gold from a dumb model. It's a system that builds on each other, though we've not probed that frontier much to have a good intuition on what effects what.
One thing that I really want to know - the better models from today vs a year ago - what has changed. They have already pre-trained on all available public data. Scooping up the last percentage of archaic texts which were never digitized is not going to move the needle.
Is it just that the providers are generating tons of synthetic datasets on coding tasks so that the models get more exposure to the right thing to do? Every time someone points out an LLM stupidity they add some training data to patch over the weakness (trivial to generate "there are two 'l's in llama")?
And you apparently only read the title and ignored the content.
It’s also poor reasoning to you only pick one thing you think supports your view, and ignore vastly more things not supporting it, all from the same useful criteria.
Now if only you’d carefully read the report you chose, and spend equal time looking at ample presented evidence, you’d develop a more accurate understanding.
Well I mean if I wanted to be extra pedantic, I would argue that we've been in that phase since LLMs were first introduced.
Before that, we had 0.
After that, we had more than 1.
A leap as far as that is hard to recreate.
But that wasn't my point. That's just trolling.
The actual point is that LLMs aren't gaining new capabilities anymore. They just get more reliable at the ones they already have; turning what was a coin flip to some higher probability.
That's (intuitively speaking, not strictly mathematically speaking) kinda the mathematical definition of diminishing returns.
It’s a constant tension in computing that has been around since mainframes and clients… Neither is going to disappear. My general feeling is normal people care more about how thin and light something is than their privacy, so if data center powered LLMs will have a strong future.
Are there known examples of software where the "software factory pattern" (an incorrect term, given that it's not a pattern but a workflow. Or rather an idea. A hope. A wish.) proved to work long-term?
Preferably ones that I could validate myself instead of just having to take someone's word for it.
At least I did not find a new thought in that (granted, relatable) rant.
"This is bad and you are bad" requires people to not defend their reality through rationalization, but the point we're at with AI right now is driven by exactly that. So this is at best highly ineffective at reaching the people it claims to want to reach.
That said, the underlying emotion of "you all suck and I hope you lose your jobs you frauds" is relatable and worth screaming from the rooftops of Linkedin dot com for the catharsis alone.
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