"Design me a 3d printable rocket engine for a hobby rocket project. Verify it's design in a full simulation. Iterate until it works reliably in simulation based on a verified printable design on a consumer laser sintering device (or substitute contract manufacture for under 1000 dollars)."
This is a hobby version of a project, but you can imagine commercial versions of the same prompt for new databases, genomics studies, material analysis, operating systems etc.
I can't put a 10 page narrative on how my specific motor should work into a hacker news post ;) you can also imagine the above where the goal is to have the ai exceed the performance of stock motors.
You almost certainly do not want an LLM to do that. Leap71 actually has computational models generating functional rocket engines that way. You could absolutely wrap a tool like that in a shell and handle control with an LLM and not need anywhere near the tokens.
Thats the thing - these models see and predict tokens. For any real engineering you get more bang for your buck using math.
Verifying how the model works against the real world is the difficult, dangerous bit.
There might be some interesting side effects from making simulation software, which is currently either an expensive niche or quirky university project (SPICE always has that feel).
I’m not convinced at all that the model won’t just get stuck in a loop where it doesn’t understand how to fix the broken rocket. I see similar failure modes in far simpler projects strictly confined to coding. This feels closer to “make me a profitable business, make no mistakes” than to a simple coding project.
"Design me a 3d printable rocket engine for a hobby rocket project. Verify it's design in a full simulation. Iterate until it works reliably in simulation based on a verified printable design on a consumer laser sintering device (or substitute contract manufacture for under 1000 dollars)."
This is a hobby version of a project, but you can imagine commercial versions of the same prompt for new databases, genomics studies, material analysis, operating systems etc.
From the prompt it seems evident the envisioned user doesn't have an interest in designing the motor themselves, so why not simply buy a stock motor?
I can't put a 10 page narrative on how my specific motor should work into a hacker news post ;) you can also imagine the above where the goal is to have the ai exceed the performance of stock motors.
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You almost certainly do not want an LLM to do that. Leap71 actually has computational models generating functional rocket engines that way. You could absolutely wrap a tool like that in a shell and handle control with an LLM and not need anywhere near the tokens.
Thats the thing - these models see and predict tokens. For any real engineering you get more bang for your buck using math.
Verifying how the model works against the real world is the difficult, dangerous bit.
There might be some interesting side effects from making simulation software, which is currently either an expensive niche or quirky university project (SPICE always has that feel).
I’m not convinced at all that the model won’t just get stuck in a loop where it doesn’t understand how to fix the broken rocket. I see similar failure modes in far simpler projects strictly confined to coding. This feels closer to “make me a profitable business, make no mistakes” than to a simple coding project.
Are there already skills around modelling, simulation and post-processing? Any pointers?
Stop it, you tease. I'm getting a little tingly
Decompiling a binary and recreating the source, doing a full line-by-line security audit, always-on agents monitoring state minute-by-minute, etc.
I would very easily find ways to hit that level of token usage if it was cheaper/faster.
Not OP but if I had a couple RTX 6000 I'd throw them at decompiling bloodborne to play on PC without emulation.