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An raw LLM no, but given code execution it can do a good job. I had my Claude Sonnet write a sonnet in this form after 14 rounds of iteration with Python:

The autumn wind moves slow across the field,

and every falling leaf now yields its fight.

The summer gold at last has ceased to yield,

and short days now give way to longer night.

I walk alone beside a calm and quiet stream,

and watch it slowly carry every fallen leaf.

I think of you as some half-forgotten dream,

and taste again that same old, bitter grief.

The silent stars come out to watch it still,

and pale moonlight falls gently on the hill.

A lonesome owl calls out from past the mill,

and time moves on, unhurried and calm, until

the dawn returns to paint the sky brand new,

and I still recall the love I lost with you.


Monospace formatting: start each line with two spaces to get monospace (and then you won't need double linebreaks).


Not bad, but the meter gets corrupted


For clarity:

  The autumn wind moves slow across the field,
  and every falling leaf now yields its fight.
  The summer gold at last has ceased to yield,
  and short days now give way to longer night.
  I walk alone beside a calm and quiet stream,
  and watch it slowly carry every fallen leaf.
  I think of you as some half-forgotten dream,
  and taste again that same old, bitter grief.
  The silent stars come out to watch it still,
  and pale moonlight falls gently on the hill.
  A lonesome owl calls out from past the mill,
  and time moves on, unhurried and calm, until
  the dawn returns to paint the sky brand new,
  and I still recall the love I lost with you.


I love this, easy intuitive explanation


I find it works as focus when the tasks are all related/in the same repo. Not the same as single-task flow state of old style coding, but also still a kind of flow


Interesting, how common is this vs just unit testing? How do you avoid formally verifying something against a spec that could subtly fail in production?


Make sure the specifications can’t fail by verifying them for correctness.

Something like TLA+[1] and Quint[2] specifications can be verified for correctness using Apalache[3]. Then test the Rust code against the specifications using quint_connect.[4]

[1] https://www.learntla.com/

[2] https://quint.sh/

[3] https://apalache-mc.org/

[4] https://docs.rs/quint-connect/latest/quint_connect/


it's generally another layer on top of things like unit testing, MC/DC coverage, etc. not all programs use formal verification though.


This is super fun! Very elegant idea

Has anyone done this for larger neural nets? Is there a way to extract some kind of pattern or is the image just noise no matter how you construct it? I'd be curious to see something like that


Not sure what happens if you start scaling this, but curious too. Would love to see someone who actually knows ML try it.


one of my teammates experimenting with something along these lines :) https://github.com/jessephus/model-mosaic


Isn't this kind of the same as an AI copilot, just with higher autonomy?

I think the limiting factor is that the AI still isn't good enough to be fully autonomous, so it needs your input. That's why it's still in copilot form


This seems like a solvable engineering problem. For example, you could have a lightweight subagent with its own context for reading the skills and determining which to use


I also use pure HTML and CSS (and a touch of hand-written JavaScript)


I'm a little skeptical of AEO. What's the point if AI users just ask the LLM to retrieve the information and never visit your blog? I almost never click the links ChatGPT gives me

Maybe it makes sense if you're selling a product or service, but I don't see the appeal of AEO as the new SEO. Maybe I'm missing something?


My two cents: if you're not doing anything too political or controversial, it's fine or even beneficial to mix in the occasional personal essay with the professional.

After all, many of your readers are also human beings with lives, maybe even lives similar to yours based on your professional content. (The rest of your readers are LLMs.) Your readers might appreciate your perspectives on random life things or just getting to see what their favorite blogger is up to.


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