u/SpecialNothingness

I'm starting a more authentic design.

Understanding the bar positions is the starting point. Does it look correct?

def test ():
     a = []
     r = 50
     step = 0.02
     origin = (pi - step * 31) / 2
     for i in range(32):
         th = origin + step * i
         a.append(( cos(th)*r, sin(th)*r, 47 -sin(th)*r ))

edit:

Due to the combined curvature on the Z axis, the spacing between keys is not uniform in the above. I should use scipy solver to get corrected angles to make them uniform.

The number 32 is not what I need. An octave consists of 14 halftone intervals and thus between C to G we have 36 half intervals of 1.25 inches.

I decided to DIY a pedal board. But using arcade buttons instead of wood bars. The quick picture above shows one octave, but I'm thinking full 32 notes.

I have ordered "Teensy 4.1 with pins", enough buttons for 32 notes, 15 m of 24 AWG wire, and a soldering tool. I'm considering ordering wood planks with CNC holes according to the scheme above. But before doing so, I wish for advices from organ players for improvement.

• Is 6 cm spacing optimal? The buttons are 3 cm in diameter.
• Should the elevated "black keys" be separated by space?
• What might be the optimal elevation height?
• Would arching the overall shape drastically improve usability?

I made gemma4 12B write timestamp-anchored summaries of youtube video transcripts. I tested if the summaries have significant qualitative variance and if the SLM can pick the best one by itself. Below is the prompt texts I used.

"{{[INPUT]}}<attachement name='original'>", document,
"</attachment>Above is a transcript. Divide time ranges by topic and name subheadings. Insert brief summary under each subheading.{{[OUTPUT]}}",
"{{[INPUT]}}",
"<attachment name='Summary A'>", ans1, "</attachment>",
"<attachment name='Summary B'>", ans2, "</attachment>",
"Above is a transcript and two summaries A and B. Name the better summary. The most important quality of an excellent summary is presenting the core message that is unique to the video. No explanation is required.",
"{{[OUTPUT]}}"

Here are my findings.

(1) The judgments were biased to favor the latter example. To counter this, I added another round of comparisons where the candidates were swapped.

(2) After balancing, the judgments were not random and significant. Making it justify the choice before the final verdict is not necessary. Probably an all pairs comparison (quadratic time) is not necessary for finding the best one. For instance, one could rank 5 candidates and take the best one and generate 4 more to form the next 5.

To evaluate the wins and losses, I used Maximum Likelihood Estimation (MLE) based on Bradley-Terry model.

Just in case somebody wants it, here is the code: https://github.com/h2kyeong/scriptlets/blob/main/llm_multigen_league.py

Buttons like these could serve the purpose if they are put in the right positions. Or so I imagine. Gotta be better than nothing, right? Has anyone tried something like this?