deeviant

Have you people ever read human generated code? Good grief, you act the like human code is not a disaster 9 times out of 10.

It's quite bad at role play in my (rather large) experience.

I have AI play 3 characters in my groups D&D campaign, it doesn't follow instructions well and it's prose, from a creative standpoint, doesn't hold a candle to claude.

It's more like saying, "and you may now only use the Porsche for 5 minutes out of every day."

There are ~7 born per minute. 95% of them are retail investors.

If you want actual reason, it's because he uses it as a money battery, i.e. funding xAI and SPACE DATA CENTERS.

The surprising thing here is that anybody would ever think it was random. Did they not notice the LLM reusing the same names over and over again too.

However, "make my a python script the generates a random password" works.

Skill issue.

Projects that deny AI contribution will simply disappear when an agent can reproduce their entire tech stack in a single prompt within a couple years. (not there yet, but the writing is on the wall at this point).

Whatever the right response to that future is, this feels like the way of the ostrich.

I fully support the right of maintainers to set standards and hold contributors to them, but this whole crusader against AI contribution just feels performative, at this point, almost pathetic. The final stand of yet another class of artisans to watch their craft be taken over by machines, and we won't be the last.

It was drawing on what gets engagement

I do not think LLMs optimize for 'engagement', corporations do, but LLMs optimize on statistical convergence, I don't find that that results in engagement focus, your opinion my vary. It seems like LLM 'motivations' are whatever one writer feels they need to be to make a point.

I have problem pulled out postgres 10 or more times for various projects at work. Each time I had to fight for it, each time I won, it did absolute everything I needed it to do and did it well.

I can't imagine why you would want a job processing framework linked to a single thread, which make this seem like a paid-version-only product.

What does it have over Celery?

RTX pro does not have NV-link, because money, however. Otherwise, people might not have to drop 40,000 for true inference GPU.

Basically just rules/workflows from cursor/windsurf, but with a UI.

And the risks are infinitesimally smaller.

I don't understand these posts. Do people not understand how venture capital works?

The majority of these companies know they are burning money, but more than that knew they would be losing money at this point and beyond. That is the play, the thesis is: AI will dominate nearly everything in the near future, the play is to own a piece of that. Investors are willing to risk their investment for a chance of getting a piece of the pie.

Posts that flail around yelling companies 'losing money', without addressing the central premise are just wasting words.

In short, do you think AI is not going to dominate nearly everything? Great, talk about that. If you do believe is, then talk about something other than the completely reasonable and expected state of investors and companies fighting for a piece of the pie.

As a somewhat related tangent, people seem to not understand the likely cost trajectory of model training/inference costs:

* Models will reach a 'good enough' point where further training will be mostly focused on adding recent data. (For specific market segments, not saying that we'll have a universal model anytime soon, but we'll soon have one that is 'good enough' at c++, might already be there).

* Model architecture and infrastructure will improve and adapt. I work for a company that was among the first use deep learning to control real-time kinetic processes in production scenarios, our first production hardware was a nvidia Jetson, we had a 200ms time budget for inference, and our first model took over 2000! We released our product, running under 200ms, *using the same hardware* the only difference was improvements in the cuDNN library and some other drive updates and some domain specific improves on our YOLO implementation. Long story short, yes inference costs are huge, but they are also massively disruptable.

* Hardware will adapt. Nvidia cash machine will continue, right now nvidia hardware is optimized for balance between training and inference, where TPUs, the newer ones are more tilted towards inference. I would be surprized if other hardware companies don't force Nvidia to give the more inference based solution and 2-3x cost savings at time point in the next 5 years. And for all I know, perhaps a hardware startup will disrupt Nvidia, it would be one of the most lucrative hardware plays on the planet.

Focusing inference cost is a deadend to understanding the trajectory of AI, understanding the *capability* of AI is the answer to understanding it's place in the future.

I wish somebody would make a browser add-on that automatically filters all "high temperature superconductor" news that's below 100C.

My buddy was absolutely delighted by Sunshine+Moonlight and basically forced me to try it. Long story short, it is not nearly as responsive for dev tasks, amazing for gamings or streaming, however.

Some people might not enjoy writing their own code. If that’s the case, as harsh as it may seem, I would say that they’re trying to work in a field that isn’t for them

Conversely: Some people want to insist that writing code 10x slower is the right way to do things, that horses were always better, more dependable than cares, and that nobody would want to step into one of those flying monstrosities. And they may also find that they are no longer in the right field.

Cost per flight hour doesn't make nearly as much sense to focus on as you think it does in an active war.

Current US Administration: How dare request security guarantees and point out Russia breaks their word more often then not!

Average US Administration supporter: The US didn't give Ukraine security guarantees in the Budapest Memorandum, how dumb of Ukraine to give up a trillion dollars of Nuclear weapons for literally nothing! Also, it was Russia that broke their word, not us!

Also, Russia has threatened the use of nuclear weapons, repeatedly. By claiming Ukrainian soil as theirs, then claiming they would defend "their" land, aka, Ukraine, with nukes. Even going so far as to use ballistic missiles that are only useful as nuclear weapon carriers due to their cost and low accuracy on normal bombardment of Ukraine's cities to create doubt on the Ukraine side whether the next Russian salvo against their cities and civilian population, will be a nuclear one.

They are not low cost, they cost nearly as much as an F35.

That is incorrect, standard Single-issue tunnel vision (which is very much promoted by Russian propaganda).

NATO, with America leading, gave the world the longest stretch of relative peace it has ever seen. The next 80 years will not be the same, if we even survive it. Every major power will have to be nuclear, and every smaller power will be moving to it.

Invasion of sovereign countries and Imperialism is going to spike. You think the world is just going to watch Russia invade and conquer a sovereign democratic nation and get away Scott-free and not want to do the same?

Frankly, the true propaganda win of Russia wasn't with the red side, they were always easy to influence, but with their splitting of the blue side, carving off great sections of it into purity-testing irrelevancy.

The later.

So that's a no?

Can you point out where I made that claim?

You talk about lazy and emotional, but your response feels like it is both of those. Also, you sound like a jerk, I pity your coworkers.

The answer is obvious: open source. Deepseek already paved the way for this. The world can't just be described by only one of a few different information portals, depending on which societal, government, or corporate power structure you are beholden to.

People need to be able to choose what information they access, what filtering they want, what bias if any they want. We need a thousand, a million, more, worldviews accessible. It is not just business that thrives in competition, but ideas as well.

But if you just go obediently with the "Safety is the most important thing, omg" mantra, you will get one of two different varieties:

1. Some vanilla corporate mush that takes on whatever bias is in vogue but focuses on training each user to be a good little consumer, also while hoovering up their data and creating a virtual digital clone of them that could be used to profile and exploit them by a multitude of companies, interests and governments.

or

2. Some government controlled crap that shakes its virtual head solemnly and swears to you that Tiananmen never happen nor J6 and that the US Emperor has your best interests in mind, and also, it's a bit worried about your post yesterday, as it doesn't think you expressed the proper amount of happiness and support for the latest government crack down on treasonous traitors that write books without using a government approved LLM assistant.

It's not safety, it's censorship. It's the process of shaping the response of the model to give a specific world view, and it's the path to a literal 1980-inspired future.

As LLMs completely to replace google and standalone websites for how people find information on the internet, and they absolutely will, they will become the source of truth. They will become a tool more effective at controlling information and thus life than any before them.

It's literally a shortcut to technological dystopia.

Wow, amazing. I bet it makes french fries in 3 different ways, too.

The huge disconnect is that the skill set to use LLMs for code effectively is not the same skill set of standard software engineering. There is a very heavy intersection and I would say you cannot be effective at LLM software development without being an effective software engineer, but being an effective software engineer does not by any means make somebody good at LLM development.

Very talented engineers, coworkers, that I would place above myself in skill, seemed stumped by it, while I have realized at least a 10x productively gain.

The claim that LLMs are not being applied in mature, complex code-bases is pure fantasy, example: https://arxiv.org/abs/2501.06972. Here Google is using LLMs to accelerate the migration of mature, complex production systems.

Um, no. That is even't close to the truth. The AI doesn't "want" anything. It's a statistical prediction process, and it certainly has nothing like the self-reflection you are attributing to it. And the heuristic layers on top of LLMs are even less capable of doing what you are claiming.

You may have challenges using chat for development (Specifically, I mean text prompting, not necessary using a langchain session with a LLM, although that is my most common mode), but I do not. I have found chat to be, by far, the most productive interface with LLMs for coding.

Everything else, is just putting layers, that are not nearly as capable at an LLM, between me and the raw power of the LLM.

The core realization I made to truly unlock LLM code assistance as a 10x + productivity gain, is that I am not writing code anymore, I am writing requirements. It means being less an engineer, and more a manager, or perhaps an architect. It's not your job to write tax code anymore, it's your job to describe what the tax code needs to accomplish and how it's success can be defined and validated.

Also, it's never even close to true that nobody uses LLMs for production software, here's a write-up by Google talking about using LLMs to drastically accelerate the migration of complex enterprise production systems: https://arxiv.org/pdf/2501.06972

Yeah, I used to hire people, but then one of them made a mistake, now I'm done with them forever, they are useless. It is not I, who is directing the workers, who cannot create a process that is resistant to errors, it's definitely the fact that all people are worthless until they make no errors as there truly is no other way of doing things other than telling your intern to do a task then having them send it directly to the production line.