I noticed that the quality of email text has gone up around my work these days. everyone is sounding like a master of drafting emails. Ofcourse Ai has a role in it. But real cool thing about this is, this is pulling up people's thinking to have a structure, minimalism and sequencing of expression. Same as how textile industry has enabled people to have an style and expression in clothing.
I'd tend to agree with what you said, except this part. Mass produced clothing is fantastic for many reasons, but not for style and expression. Look at the traditional clothing from almost any part of the world and it was gorgeous and colorful. Now it's all just a t-shirts.
Was it?
There's a lot of survivorship bias in old things; we don't see the things which weren't recorded. Old things had their style, that much is unavoidable, but before mass production normal people inherited their clothes because it was just that hard to create.
Also, a lot of the pigments we take for granted today didn't exist at any price in the pre-industrial era, and some which did involved trekking to a mine in Afghanistan: https://en.wikipedia.org/wiki/Ultramarine
This also means (in general, not specifically clothing) a lot of "traditional" things are post-industrial recreations or re-imaginings of the past.
People had cultivated an art in how the rectangular cloths are wrapped around them and they clothes are secured at their waist, or around their head. Talk to a Sikh or Afghan about turban.
Day-to-day clothing is definitely not "all just tshirts" nowadays, even in the mainstream. Women fashion is very varied, and while men's fashion is a lot poorer there's still a huge spectrum of expressive clothing that's available. I don't think personal style and expression has ever been as available and encouraged as it is today.
That’s a very good analogy!
Case in point, I reached about syllabus to my kid's teacher. Long story short, email ended with their boss, who sent me long winding non-answer that I had to resist to not knock down.
I dunno, generic executive summaries can be canned, but I would like to hope I pay them enough for an person to person exchange.
I’m not reading all that. I want one word answers.
Also noticed huge uptick in “call me” emails.
> this is pulling up people's thinking to have a structure, minimalism and sequencing of expression
I see the same for the former but the opposite on the latter. Everyone sounds like expert drafters because the AI is doing all of that.
True to the "use it or lose it" principle, the people's thinking skills about the emails have weakened. They rely on giving the AI some bits of info, a document they never read, some vague instructions, and the AI molds the message with structure and sequence. Then I get to experience live the joy of discussing with these people face to face and they still just spit out disparate bits of information with no fluid structure and sequence for the ideas because the only time they thought about the topic was when the AI wrote an email about it.
One man said, that enlightement (as in Zen) cannot be faked. Whoever had it, will be able to tell if someone else had it or not. There is no way to merely sound like it. Socratic method is like enlightement.
Very few teachers get to do this or are good enough, but it's just as much a question of relationships than anything else- how do you pose the exact right problem so that the learner can discover something for themselves.
I think about this, whenever I have questions which are related to morality and how people act.
> When the user is wrong, do not give the answer. Ask a narrower question that exposes the contradiction in their reasoning
For those new (beginners), install pi from pi.dev and then your favorite AI model (ask your model how to set up pi and it). Then ask your pi-based AI how to set up the tutor (just give it this link, it will set it up for you).
Then start pi by typing pi in the terminal; and to launch the tutor preface your question with: /skill:socratic-tutor. Like so:
/skill:socratic-tutor teach me Python.
See also https://en.wikipedia.org/wiki/Mastery_learning
https://en.wikipedia.org/wiki/Programmed_learning
Make your own custom skinner boxes. https://en.wikipedia.org/wiki/Operant_conditioning_chamber
Not sure what to do about it because I don't care enough to learn everything about this topic in order to fix it.
> Programmed learning (or programmed instruction) is a research-based system which helps learners work successfully. The method is guided by research done by a variety of applied psychologists and educators.
I have no idea what programmed learning is, only that it has an alternative name, that it has scientific street cred and that it is effective according to some nebulous definition of successful.
as opposed to:
> Mastery learning is an instructional strategy and educational philosophy that emphasizes the importance of students achieving a high level of competence (e.g., 90% accuracy) in prerequisite knowledge before moving on to new material.
which is gives me a more clear idea of what the core philosophy is behind it.
Can the programmed learning page be marked as low-quality, non-encyclopedic? That is already a good signal to have.
Using LLMs this way was really refreshing, I could really feel how I started to think on my own again.
Especially nice when you are not so sure how something should look like, so it makes you stop and spend a few hours answering questions and thinking about what you want to do. Combine that with the RFC skill and you get a good basis for your feature and then a skill to split the RFC to issues on Linear.
[0]: https://en.wikipedia.org/wiki/Socratic_questioning [1]: https://github.com/search?q=socratic+tutor+skill&type=reposi...
I use it as a better PDF reader for arxiv paper but they are going all in on AI teaching/explaining things.
A vanilla version of this is, at the end of a session, ask the model to generate a couple of "ultimate" questions to really flex your understanding. Obviously, the quality depends on the model but I've found it useful.
I have the leaked prompt around here somewhere...
>Tell the user that you will start by asking a few questions, one at a time, to see what they already know.
This puts it into the session each time. Do this once and it seems to work. By the 5th time, you are getting tired of reconfirming your mastery. This ends up depending upon the user to have a pretty solid foundation of what they know and then use this skill to expand the boundary a bit, and even then the AI can end up making so many assumptions that a person has trouble following it along.
I was thinking some sort of concept mapping kept long term might help, but such a mapping can become really difficult to build. Which should be expected when we consider that teaching isn't just its own discipline, but has many sub-disciplines targeted to specific areas (even if some of it, like classroom management, doesn't apply to this scenario).
I made a few attempts at a knowledge tracking tool for personal use to track how much I knew Japanese beyond a simple JLPT level or similar with the plan to tie it into something like this, but it wasn't great. I could 'cheat' and just tap into my Wanikani API to see my progress, but that still skips so many other areas of Japanese, and even when WaniKani says something is burned, I really want AI to still use it until I truly internalize it like I do words in English. I suspect other areas of knowledge are similar.
In my work, this typically means an opinionated local folder structure and a dedicated /init skill that implements it. Immediately this means we have more than one skill so we’re talking about a plugin now.
Then I think about meta-state. Not just “what is on disk” but “where are we in this application’s multi-session lifecycle?” For Socratic tutoring, that means checking to see if we’ve completed an initial intake, then what skill levels the user has demonstrated, and finally what’s next on the menu.
For this specific skill, I’m surprised it doesn’t say anything about curriculum. It just assumes the model knows the topic and can improvise both a curriculum and credible assessments of progress in that curriculum. That can’t be true for all things people want to learn, so we need a /build-curriculum skill. And probably a /validate-curriculum skill for actual experts to use before launching a given curriculum.
Anyway, I’ve been thinking a lot about this kind of thing as I roll out skills to my firm.
A positive take: this works much better than the many projects on gh that did this before Jane Street. This would be evidence that creativity and execution still matter, even when the product is just an md file
A cynical take: network effects are everything. tens of projects doing the same thing and this only gains traction when an already popular org does it
Right now what they produce reads like legalese, or patents.
I suspect this is because the AI labs are optimizing for agents talking to agents, rather than AI talking to humans (which is harder to optimize, and does not improve intelligence as fast).
Jokes aside, pretty cool. This is just a skill, and it'd be easy to dismiss it. But I do want to become a better horse whisperer. Prompt engineering used to be mocked a lot, especially on HN, and skills really is just that but with a new branding.
I think Duolingo's days and the likes are counted, AI is just a great way to quickly get up to speed on a subject or a language.
obligatory Bruce Lee - https://m.youtube.com/watch?v=QQiH2-bLOmQ
My client just got done firing someone who thought filling the codebase up with files like this would be a good idea.
It would seem the AI revolution is happening at wildly different rates depending on where you are standing.
Every single skill I see just strikes me as a prompt. In every case it's easier for me to create a description of what is needed specific to the task. If I need a longstanding process I will discuss with the agent and create a document.
This is one of the most flexible tools ever made, and I frankly am not going to accept unnecessary systematization of how I use it.
Both of which are just blocks of text being stomped into an LLM.
I've got a feeling that prompting as we're doing currently (huge blocks of text with very little structure), at least when looking for a deterministic result, will be looked back on as if we were living in the Stone Age. Not sure what the alternative will be, but I'm imagining something like a CI pipeline definition in YAML.
Isn't this just what a skill is?
Running it with my local models it is a pretty helpful way to study up something where you already have some bases.
Maybe it improves things a bit though. I might try it next time.