You can get semi-decent sprite work out of GenAI models, but you still have to put in some manual work (scale normalization, palette reduction, grid alignments, etc). It's definitely not "out-of-the-box" yet.
Yeah, I think you’re right. I think the hardest part would honestly be maintaining some level of consistency as your project grows its number of assets - otherwise there's no sense of a common stylistic theme.
The agentic tooling in some of the latest models is getting pretty crazy too - somebody on Reddit recently shared an example of using GPT-6 Astra to generate sprites with Aseprite, an open-source, popular pixel art editor and it worked shockingly well.
The key is making them one frame at a time, rather than asking for the whole sheet. Adherence frame-to-frame with a reference image is really good, so just prompt with the previous + direction. I finally got Nano Banana to make fairly decent fluid animations that way.
I've been struggling with this for a few years for Yoto LED icons. Design rules didn't cut it, but a pymagick workflow did, more or less.
I have abandoned the work I was doing, but if you provide the image constraints along with some examples and descriptions of those examples using the terminology of your constraints, you should get something iterable.
Interesting. I'm surprised they haven't prioritised/deliberately trained for this more because of how useful it is to ask codex to generate some images/assets/sprites when building sill games. It would dramatically enhance how polished it's games were.
That said for single images the old model was already okayish for prototypes
This feels like the right-wing equivalent of pronoun policing lol. Crazy how the extremes of the political spectrum are so close to each other (like a horseshoe), yet hate each other so much. Doesn't seem like the polarization is near to ratcheting down though, at least not by judging from the comments in this thread.
> This feels like the right-wing equivalent of pronoun policing lol.
Nothing about it feels "right-wing" at all -- actual conservatism is all about maintaining continuity and avoiding forced changes to traditional conventions and established social norms.
Trump and his followers are a competing faction of the extreme left, as should be readily evidenced by his economic policies, and it's not at all surprising that he shares the same mentality of inserting political shibboleths into mundane aspects of everyday life.
Sorry you're getting downvoted, this interpretation doesn't seem that far fetched to me.
Here's the strawman: The text-based watermarking is going to be done procedurally instead of generatively. Maybe they add some sequence of zero-width Unicode characters to all generated text at certain intervals. Then, there is effectively no false positive possible (because humans would [effectively] never type such sequences of unicode naturally). It may survive some editing (depending on how you select/edit the characters), and it's possible to be stripped (false negatives).
>It's not just the loss of the craft and the problem-solving challenge and whatever else. It's the loss of making.
It's the loss of understanding. When you make something, you gain a deeper appreciation for the nuance of all that is required to breath life into it. When you outsource understanding to another (agent, contractor, w/e), you're losing the nuanced understanding of the creation.
>After all, you create the prompts, don't you? You said you were proud of doing things. Isn't that doing a thing?
This feels like the difference between putting together an ikea futon and building a hand-hewn table from logs. The fidelity of your understanding is different depending on how deeply you're engaging. But not felling and drying your own timber doesn't mean you can never make something from dimensional lumber.
I'd take the author's word "making" to mean something more like "having enough understanding of the implementation of the task, that you could recreate the output without recourse to the same set of tools".
Tested against their examples:
>I had a new front deck put in [by a contractor]
As a consequence of this addition, did you learn enough about decks so that you could you reproduce this feat if no contractor were available? If no, you didn't make the deck. If yes, you were probably watching/helping the whole time, and you can claim (at least partial) makesmanship.
> any more than using a hammer to drive nails does.
If you didn't have access to the hammer, could you still accomplish the task of attaching two boards together with a nail? Likely yes, use a rock. It's harder, but the understanding of driving the nail is transferable to other implements.
> What if I had an AI-powered hammer and asked it to hit a nail for me? What's the difference? But at that point, I can't say that I hammered it.
This one contradicted his compiler example. He says that he did write the Fibonacci program, even if the AI-powered compiler translated it to assembly. So by similar reasoning, you can claim you hammered a nail with an AI hammer, so long as you understood the aspects of hammering which the AI took care for you to such a degree that you could reproduce the effects even in the absence of the AI hammer.
The crux of the tension is around "what did you learn from the implementation of this task". If you learned enough to reproduce the objective without the assistance of AI (perhaps much slower), then you can claim the high fidelity understanding that came from making. If you didn't internalize anything, and the AI/contractor managed complexity on your behalf, then the output was commissioned, not made.
I think where people get worked up about this, is that it's not easy to tell what understanding other folks are taking away from their AI usage. It does seem like a bifurcating event where some people are going to net gain understanding of their world due to these new tools, while others will net lose understanding.
I entirely agree with this take. I've used LLMs for "making purposes" in two fundamental scenarios so far: to write code in languages I'm not 100% familiar with, and to help with circuit design.
In the code scenario, I most likely won't go over the final result: for various reasons, I often lack the interest and drive to understand the nuts and bolts of it. So I'll just try to assess whether it's sufficiently good for my goals, and then move on.
But in the circuit design scenario, I only use it as a "technical expert" to bounce ideas back and forth with, and to explore concepts I don't fully grasp. I use it as a sort of ELI5 machine, and never ever ask it to do something for me - just to explain.
The fundamental difference in outcome is that I remember next to nothing about the code, and have no feeling of "ownership" over it, nor do I feel proud or care about it at all.
Conversely, I am incredibly proud of the PCBs and circuits I design, even if technically some parts of it came straight out of AI recommendations. I really feel like I did the job, and AI was just a helper tool to get the tough concepts untangled quicker in my head.
Ultimately I think if you delegate 100% of the work to AI, and don't care enough to use its output to at least learn something, it's only natural that you'll never feel any connection to it, let alone any sense of ownership over its output. Being the prompt author is simply not enough. That's why I think you hit the nail on the head there: deeply understanding the output is the key, I just couldn't put my finger on it before, and now I can. Thanks for that.
> I think where people get worked up about this, is that it's not easy to tell what understanding other folks are taking away from their AI usage.
Alongside understanding, you also loose taste and perception. You can still say whether some things is good or bad, especially if you interact with it, but you can no longer articulate why. And with perception loss, you can no longer preempt a bad situation as you lose your awareness of things.
So for you a car may be a vehicle that you drive around. But for a mechanics it's a much complex system. When something doesn't work, you will lack the vocabulary to express what's wrong. And even when everything works, you may not be able to explain how.
The thing is, LLM are very buggy translators. Agents in software development help because they provide a way for LLMs to verify their output. But correctness is very hard to express. OK for a simpler task, but not really for a complex systems. Just like fluency in a foreign language is not about knowing the rules of grammar and a lots of words, but also knowing the idioms and other figure of speech.
- if budget isn't balanced all members of congress become ineligible for election
Ask Colorado how TABOR[0] is going. The answer is a 1.5 BILLION[1] deficit because TABOR restricts how money is collected and spent by the state.
A balanced budget isn't actually all it is cracked up to be. The deficit spending at the federal level we have now is bonkers, but a balanced budget every year is just unrealistic. Learn from Colorado and do not put yourself into an unrealistic corner. A budget will grow and shrink with the economy -- a balanced budget over X years seems much more realistic. It would mean we could have never, at a federal level, done the kind of spending necessary to cope with COVID, for example.
What I'd like to see is Congress be entirely unable to draw a paycheck while the government is shutdown. Shutdowns are a last resort, but are now so common we have normalized them. This is immensely unfair to federal workers and everything downstream that depends on a functioning government, like SNAP benefits. If the government shuts down over budget issues, make the people who made that decision pay HEAVILY for the action.
I’d strongly argue against the community-service bit, however. That’s just job loss for those who earn income from labor and an inconvenience for those who earn it from capital.
Yea, I think a better option would be to make fines proportional to income, and then give a flat hourly rate that you can earn for fines from community service as an option
If you have capital rather than income then this is still better for you.
e.g. a retiree has capital but not income and for someone like Musk will have plenty of capital but proportionally much ledd income as that will be the best way to avoid tax.
I'd wager this methodology is going to become very prevalent in enterprises going forward. Encoding all of the context necessary to recreate your entire application in "plans" or "rules" allows for much more rapid refactoring to address new business needs.
It also makes for much more fungible developers. Instead of having a few key engineers that know the system inside and out, that knowledge is encoded in markdown throughout the project, allowing new devs to ramp up by asking questions against the codebase and tracing the history of reasonings that led to the shape/implementation that exists today. It's like having near exhaustive design docs & documentation with change tracking included.
While it seems likely that developers will become more fungible (replaceable) going forward, I think being able to operate at this layer of abstraction & still having the capacity to drop down to the nitty gritty implementation details will make strong devs more valuable, rather than less.
Is there a summary for what exactly he means with the "myth" in the context of software? The whole detour into the history of western science and philosophy is interesting but also quite expansive.
Core argument seems to be that psychology/behavioral-science are not hard sciences (duh). Kind of insane how they're able to spin that into a 10 page hit piece about Microsoft. I think literally every argument they make could be applied to any arbitrary aspirational cultural values for any other large corporation. Author sounds like they have quite the chip on their shoulder.
I havent worked at MSFT (atleast not directly). But reading this article actually brought back a lot of cultural memories of Google (especially around perf season). The rubrics, the passive aggressiveness, the mindset mentality. Not very surprising given leadership and management keep circulating among these set of companies.
> it's still a lot harder to succeed as a non white male than it is as a white male.
Part of the issue is that you're presupposing a uniform definition of success. Different cultures have different priorities, and not everyone wants to spend 80 hrs a week in the office to climb the ladder and become a millionaire. Some cultures prioritize family/social relationships, sports, or a connection with nature. Unsurprisingly, these different cultures can often be racially affinitized. Sure, most people wouldn't mind being rich, but many do mind the hustle often accompanying that form of success.
I think part of what you describe around momentum holds merit, but I don't think affirmative action goes about the remedy in a constructive manner. It's fighting racism with more (albeit different) racism. You turn it into a zero sum game where your political posturing can be more valuable than your work contributions. That incentive structure is degenerative for all parties.
> If you grow up in a wealthy family, you've got easy access to great education, mentors, role models, capital, etc. If you grow up in a poor family you have way less of all of this.
Genuinely curious, would you support an initiative to shuffle all babies between families at birth? Your argument seems to be "who you're raised by gives an unfair advantage in life, and we should correct for this societally". It seems to me that a random shuffle would equally distribute any inherent bias relative to generational momentum.
