architectonic

Yes and another that uses a fixed ridiculous combination like 1 2 3 4 5 6 7 8 9. This is my favorite way for bringing the odds nearer to mind.

How much computing power would one need to get this working completely local running a half decent llm fine tuned to sound like santa with all tts, stt and the pipecat inbetween?

How do the quality and prompt adherence compare to Suno v4?

That's called the stability of the algorithm. The first algorithm is not stable whereas the second with the logarithm is.

Another example: compare the two algorithms, that are actually pure-mathematically equal:

  def fc(x): 
      return sqrt(x+1) - sqrt(x)
  def fd(x): 
      return 1/(sqrt(x+1) + sqrt(x))

For large values of x (1e16 for example) plot the results and see the difference.

  xs = np.linspace(10\*14,10\*16,10000) 
  plt.figure(figsize=(8, 6), dpi=120) 
  plt.plot(xs,[fc(x) for x in xs]) 
  plt.plot(xs,[fd(x) for x in xs])

I had worked on such a tool by extending pyqtgraph's flowcharts. Mainly executing the nodes when their input changes and showing a glowing box around the one running at the moment. Flowcharts supports multiple terminals already and I had added some custom nodes. Node execution must be delegated to a background thread so the gui is not blocked while the node executes. Doubleclicking a node shows a window for setting up the node. A custom class of nodes shows upon doubleclicking a simple python editor, upon execution the terminals get automatically the values of the variables carrying the same name as the terminals. It has been a lot of fun. I had used petl and pyspark for lazy loading so one could build large etl workflows.

That's exactly what a serial killer would say!

Link please?

This is what counts most!

Can you handle multiple time dimensions efficiently? We have 3 of them, can one get away without having to physically store "slices" on one of them?

Correct me if I am wrong, but this looks like an ad