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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.
Seeing LLM comments in other peoples code is very upsetting because theres just so much meaningless noise.
It deleted the comments, and when I asked why it did that even though I expressedly asked it not to, it responded that me prompting it in the first place explicit permission. I have no idea if that's the actual reason or just some post-hoc explanation.
But I genuinely don't think it's possible to just have these things be completely, 100%, indpenedent and also solve deep problems that need to also be understood by people in a people-based organization context.
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## 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.
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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.
I have a lot of interesting notes and I can also plug them to AI to get some reminders, insights etc.
But I agree with your general message - thinking is still superior
1. Those who kind of semi-obsessively write down every little thought, semi-obsessively catalogue everything, etc.
2. Those who use the idea of "writing helps you think", where the second brain functions in support of this
Personally I find 2 quite valuable, and although I could "think about programming all the time" (or one of my other interests), I find it helpful to have f.ex. exact quotes from some book at hand (and various other Obsidian-related things)
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.
It used to be a mess, but with OKF (Open Knowledge Format), things are more manageable and navigatable (for me as well).
Consider AI vault as a scratch pad that AIs use, not as something you put your own thought into it.
LLMs don't have sense experience, so it doesn't have real reasons to prefer one option over another when nothing has been specified in the prompt. It can only guess based on what's most represented in its training. "Ideas" are only being simulated by an RNG; the AI can "suggest" (output) them, but they don't "have" the idea either.
If you couldn't make the decision yourself in the first place, pulling a slip of paper out of the hat only helps in the cases where the choice really didn't matter at all. Otherwise you'll look at it, frown, and still feel stuck.
that being said, i got a big kick out of feeding my daily notes into my local model. i had some more diary-like entries that i would never share with anyone, but it was extremely funny to have my local model take a 3000 word recap of what i did in a day and summarize it like "writer reflects on their job: they worry about their performance. meals: the writer ate oatmeal, and a sandwich. family: the author contemplates their fathers absence growing up."
ive developed a really large corpus of written work over the years and am hoping to fine tune a model using my diary as the training set. i think this would yield hilarious results. keeping it all local though, i drop my passwords in there at times and i dont want that leaked.
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.
What I also really like about notetaking apps is that I can organize my notes hierarchically, move things around and make cross references.
I still regularly sketch ideas on paper, but once I have something that's worth keeping, I write it down in Obsidian.
I'm also skeptical about this "Second Brain" thing. People make videos on YT about how they have been enlightened by Obsidian and then try to sell you their online course. To me it seems like notetaking for its own sake. You could call it "idea maxxing" :)
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.
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-...
As Claude said to me when I said the same thing - it's not really its idea you had to ask the right questions. I find this true in some ways - asking the right question and finding out what the AI's have hidden inside them is like panning for gold imho.
It used to be that we only recognized a tiny fraction of the actual ideas involved, because most of them were intermingled with execution. We would sit there and bang out code at single-digit WPM while trying to keep block diagrams of process memory (or UML diagrams, or whatever else) in our heads, and then run the compiler and/or the tests and realize we'd gone wrong anyway.
Now we have lightning-fast "execution" devoid of real insight (just making choices more or less arbitrarily as they come up) because the LLM can do more and more without human intervention, but for some tasks it goes off the rails because the insight actually is necessary.
My bet is 3 or 4 years before the rocket really takes off, and then we start seeing capabilities improve incredibly quickly.
The reason to not add it is that the quality of summarization will likely only in prove in the future, so it’s better to do it on demand whenever needed.
Having an AI write notes would only produce text, but the actual notes are mostly a side effect — an important one, but not the most important one in note taking.
I think hard about related notes, and peek at them when linking, sometimes finding interesting one-off connections. This makes me think further and sometimes expand the topics. Automated linking would not make me think, it wouldn't make me learn. It would just produce yet another pile of information that's already available online anyway.
For the Original title, I wish they added the qualifier "generative" for AI. You may well use e.g. embeddings to semantically search through your notes, and there are only gains in that.
Having an AI write notes for you is however of course completely pointless. The act of writing notes is 80% of the reward.
The exception is transcriptions and summaries of transcriptions for things like lectures; I find those useful even when I know writing them myself would be better.
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