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The absolute least worst outcome is they chew up your token budget. But what tends to happen, and this is much more serious, is they poison future work and make further modification of the codebase more burdensome and error prone.
``` ## Comments
Use comments extremely sparingly. Most comments should be at the request of the user. When something warrants a comment, keep it to one or two lines: what the code does and why it's necessary. No background narrative, no replaying the investigation or failure mode, nothing a test name or the commit message already says. Applies to specs too. If a comment needs a paragraph, make the code clearer instead. ```
The comments Claude was leaving got absolutely out of control. Just lines and lines of LLM drivel that was barely intelligible and not remotely relevant to what a code comment should be used for.
LLMs inserting code comments makes zero sense by definition.
AI can't read your mind, and it will read what you (or anyone else) wrote.
I started practicing this for the stuff I'm interested in ... and the result is that I generate a lot of ideas. 99% of them are garbage.
Before AI, a conversation with colleagues would help me weed out the garbage, and possibly give me a new perspective ("here's something surprising about what you just said!")
Now, AI plays a helpful role in helping me weed out the garbage for the subset of ideas that I can test empirically -- and sometimes it does give me a new perspective, but with significantly more noise than my colleagues, who would take me right to the point that would be useful for me to see.
I've never felt like I needed a second brain ... because if I had such a thing, I think I would be keeping the 99% garbage around -- instead I just let biology GC it.
One, it forces you to do a crude approximation of critical thinking. It allows you to examine and organize your thoughts, either physically or digitally. It isn't the same as being able to sit and break something down in your mind, but for those who struggle with that, a "second brain" allows them to reap some of those benefits.
Two, it allows you to turnover things quickly in your head. By training yourself to immediately dump out an idea into an organizational system, you don't have to hold so many things in your head. Furthermore, words are immutable and the thoughts in your head aren't.
But for me, the good thoughts usually rise to the top. And there are a lot of things I'm thinking about that are so vague, it's just a feeling. And trying to write those down robs those thoughts of the long term, "on the backburner" mental processing they need to become actual ideas. They're thoughts that are still in the embryonic phase. I can't write down the qualia of them, and when I try to I move them into a "second brain" I lose it.
I think organizational systems are work dependent. Running a business lends itself well to an organizational system. Where it's good to try a lot of shallow things. But something like physics, for example, requires playing with ideas in your mind over a long period of time until they become solid enough to chase more seriously.
I think programming probably straddles that line and it depends on what you're doing at any given time.
How do you take notes, generally speaking ?
Reverse engineering can take several forms for me: it started with writing parts of systems from scratch, and now (due to academic training) it oftens looks like "thinking about how to explain the system to a group of colleagues", which is one degree of separation from writing the system from scratch, but is much faster - and seems to work well when working with LM agents on code (because you have to confront your ignorance)
part of the training in academia is how to know when you don't understood the thing, so this ends up working well for me now.
One trick: you can force yourself to understand something better by making a presentation and imagining you're going to be grilled by someone smarter than you. This is kind of the feeling of pressure that academic paper reviews give (in an ideal world) and it translates when you think about explaining something to someone else.
Yes! And that's because you can't really delegate ownership/authorship to AI (at today's capability levels). Coding agents might have been RL'd out the wazoo to write code to solve well defined problems, but given a situation the onus of making sense of the ambiguity and identifying the problem -- and even crafting a skeleton for an acceptable solution keeping in mind tradeoffs -- needs to be done by a human taking ownership.
I wrote about this recently from a slightly different angle [1], but the core idea is the same -- which is why it is hard to take over an idea mooted by AI. Before even writing/implementing, the AI has internally resolved ambiguity with several decisions which are arbitrary, and match neither your mental model nor the actual ground reality (which you need to spend time understanding in order to productively interpret).
[1] https://woventhought.substack.com/p/ai-assistants-need-adapt...
> When people think AI did the creative work, task meaning and effort decline
https://www.brookings.edu/articles/when-people-think-ai-did-...
If you haven't tried Obsidian at all I think you should give it a shot and write notes without following any system in particular. If you've tried it and you still prefer paper notes and the occasional apple note, then perhaps Obsidian isn't necessary for the way you take and organize notes.
My handwriting has degraded from consistent keyboard use over the last 30 years. Obisidan has replaced my paper notebooks, mostly because of this. However, it is handy to be able to mesh those notes with hyperlinks, LLM output snippets, quotes from other sources, embedded images, etc. Doing all of those other things would be laborious to impossible with paper.
It's so hard to finish an idea that is not yours and is just suggested by AI.
https://github.com/eighttrigrams/us-vs-them