It's easy to talk yourself out of doing things when you know a little too much. Sometimes, it's good to get back into the mode where you knew nothing and do things for their own sake, just to get the engine started again.
Do you think LLMs make this process of starting the engine easier or harder? They make getting started much easier, but it might be harder to feel a sense of momentum since our expectations of speed have changed, and the learning moments have changed as well.
The bug is in the software in our heads, if anything. We learned a little too much, that we're thinking further ahead than we would have when we first started out. So you need to purposefully shut off that part of your eval, so that you get started on anything at all.
If you design with the LLM, then it can make this easier by prompting it to help you not talk yourself out of things.
I found that gstack's /office_hours to be good about encouraging, while being firm. I've only done one of the modes, but it didn't dismiss my pushback when it was just based on my intuition. It took it as a baseline, and tried to evaluate it by taking it seriously. If that's any indication, the other modes for side projects should be just as supportive.
I think LLMs can make it easier to be more ambitious. Non-techies are blown away by being able to build web pages? I'm blown away that I was able to root my 1st gen Kindle Fire to repurpose it as a remote terminal to ssh into my laptop to talk to claude code. I've been trying to root the thing for years and could never find the right instructions to make it work.
I didn't find llms.txt useless at all. I was able to download all the library docs and check it into my repo and point my coding agent to it all the time.
I kept thinking that he'd eventually compare it to writing software by hand, and how we're at the end of one golden age. But he never did. So I wonder what the impetus for the essay was.
About a quarter in I figured out, if there was a point to the article he should have gotten to it already, if it's taking this long maybe he just wants to write about watches. So I skimmed and I was mostly right, the way it's presented I think you can probably draw comparisons to a few other things, not just writing software, but it's more of an optional exercise for the reader.
Ditto. I kept waiting for the AI comparison. My interpretation was less agentic coding than the commodification of LLMs, forcing Anthropic and OpenAI into a pivot to focus on brand. Anthropic's spat with the DoD could be viewed through that lens: losing money on a deal to better position the brand.
Doesn't make it excusable. I get it's hard to uphold principles when the stomach is empty. But it's clear the person in the piece wasn't thinking about much else, though he was also clearly not in the streets and starving.
I'd be interested in what kind of eSports game is condusive to VR spectating.
I tried doing Dota spectating before, and rigged up a mod for Minecraft vlogging/spectating, and concluded it wasn't quite like being at a stadium, or watching it on Twitch in a way that was interesting.
I am convinced that there is an absurd amount of unrealized potential for spectating in eSports. But everyone seems to just deliver an experience that is more-or-less "like playing the game yourself, but worse, and with forced-hype commentary" rather than an actually engaging spectator product.
Do you mean VR "in the cockpit" or in the stadium? Flight simming has a robust VR community. I assume ultra technical car racing sims like iRacing are fun to spectate in VR. Geoguessr seems like a natural fit for VR as well, as long as you can avoid neck injuries from craning your head around.
I have a hunch we'll eventually swing back when we find the limits of vibe coding--in that LLMs also can only hold so much complexity in their heads, even if it's an order of magnitude (or more) greater than ours. If we make it understandable for humans then it'll definitely be trivial for LLMs, which frees them up to do other things. I mean, they don't have infinite layers or units to capture concepts. So the more symmetrical, consistent, and fractal (composable) you can make your code, the easier time an LLM will have with it to solve problems.
LLM's context window limit already hits you in the nose when you have a big codebase and you ask it questions which make it read a lot of code. 200k is so easy to hit sometimes, especially when you only truly get to use 120k
If you're reading past the first sentence this time -- it is obvious, yes. So why use such language to describe the software? Your deliberate choice to use misleading language is not only obviously incorrect, but harmful.
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