7,891 karma · joined October 4, 2014
One thing I have read recently is that when you make a tool call it forces the model to go back to the agent. The effect of this is that the agent then has to make another request with all of the prompt (include past messages), these will be "cached" tokens, but they're still expensive. So if you can amortize the tool calls by having the model either do many at once or chaining them with something like bash you'll be better off.
I suspect this might be why cursor likes writing bash scripts so much, simple shell commands are going to be very token heavy because of the frequency of interrupts.
Those were tested numbers, not advertised though. I don't see how you'd get a drop from 200 to 120 miles, that's a 40% drop. Maybe in a gasoline powered car, but EVs can regeneratively break, so I don't think it'd make that much of a difference.
Reading some more, there are a handful of different ratings. the old European one: NEDC, the new European one: WLTP, the US EPA, and China's CLTC.
Generally the ratings from lowest to highest go EPA, WLTP, NEDC, then CLTC. The EPA rating is just a tad high I've read when you look at fast highway driving (e.g., 75 MPH), but should be within ballpark range.
I think you're under estimating the range of modern EVs.
Is electric charging more expensive in the UK than petrol? That's nuts.
See: the Hindenburg disaster
afternote: There's the potential for an amazing pun in here, but I don't think I quite did the opportunity justice.
In many ways, I think this is probably better for society than the opposite, since in general there are fewer knowledge workers than not.
Then have an agent collate the feedback, combined with telemetry from the server, and iterate on the code to fix it up.
In theory you could have the backend write itself and design new features based on what agents try to do with it.
I sort of got the idea from a comparison with JITs, you could have stubbed out methods in the server that would do nothing until the "JIT" agent writes the code.
But feel free to call yourself a programmer, I'm not going to gatekeep it :)
1: https://github.com/NVIDIA/open-gpu-kernel-modules/issues/971...
I wouldn't ask Cursor to go off and write software from scratch that I need to take ownership of, but I'm reasonably comfortable at this point having it make small changes under direction and with guidance.
The project I mentioned above was adding otel tracing to something, and it wrote a tracae viewing UI that has all the features I need and works well, without me having to spend hours getting it up set up.
I don't know that the same makes as much sense to evaluate in an essay context, because it's not really the same. I guess the equivalent would be having an existing essay (maybe written by yourself, maybe not) and using AI to make small edits to it like "instead of arguing X, argue Y then X" or something.
Interestingly I find myself doing a mix of both "vibing" and more careful work, like the other day I used it to update some code that I cared about and wanted to understand better that I was more engaged in, but also simultaneously to make a dashboard that I used to look at the output from the code that I didn't care about at all so long as it worked.
I suspect that the vibe coding would be more like drafting an essay from the mental engagement POV.