The OS itself is not FOSS or even available without a partnership, the apps are all online websites, and running your own apps requires going through their partner portal. So don't get your hopes up.
Which is the reason only Linux kernel survives nowadays in such platforms.
Additionally there is an increasing number of embedded OSes alternatives with permissive licenses, meaning eventually not even the Linux kernel will be taken into account.
Just last year I migrated to Git a major code base that was still stuck in SVN. It's not even a legacy project, just a laggard. For some colleagues, this was their first time using Git on the job.
There are large projects still using CVS. Not to say everyone should, but git is only a tool. It isn't essential, and there are alternate ways to achive the same ends with different tools.
> "make a camera that flies in the direction you are looking at"
That's not the task of a renderer though, but its client, so you're talking past your parent comment. And given that I've seen peers one-shot tiny Unity prototypes with agents, I don't really believe they're that bad at taking an educated guess at such a simple prompt, as much as I wish it were true.
You're right. My point was more that LLMs are bad at (3D) math and spatial reasoning, which applies to renderers. Since Unity neatly abstracts the complexity away of this through an API that corresponds well to spoken language, and is quite popular, that same example and similar prototypes should have a higher success rate.
I guess the less detailed a spec has to be thanks to the tooling, the more likely it is that the LLM will come up with something usable. But it's unclear to me whether that is because of more examples existing due to higher user adoption, or because of fewer decisions/predictions having to be made by the LLM. Maybe it is a bit of both.
> If there's something most people miss just state it.
But the LLM suggesting a question doesn't mean it has a good answer to converge to.
If you actually ask, the model probabilities will be pressured to come up with something, anything, to follow up on the offer, which will be nonsense if there actually weren't anything else to add.
I've seen this pattern fail a lot on roleplay (e.g. AI Dungeon) so I really dislike it when LLMs end with a question. A "sufficiently smart LLM" would have enough foresight to know it's writing itself into a dead end.
You should be careful with ideas like "sufficiently smart LLM" - quotes and all. There's no intelligence here, just next token prediction. And the idea of an LLM being self-aware is ludicrous. Ask one what the difference between hallucinations and lying is and get a list similar to this why the LLM isn't lying:
- No intent, beliefs, or awareness
- No concept of “know” truth vs. falsehood
- A byproduct of how it predicts text based on patterns
- Arises from probabilistic text generation
- A model fills gaps when it lacks reliable knowledge
- Errors often look confident because the system optimizes for fluency, not truth
- Produces outputs that statistically resemble true statements
- Not an agent, no moral responsibility
- Lacks “committment” to a claim unless specifically designed to track it
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