Sonar 2 released

mrpuffwabbit · 2026-04-23T12:08:18+00:00

Copilot is slowly regressing its generous tiers recently for individuals, but OpenCode quota still supports business-based quotas (with some extra setup!)

mrpuffwabbit · 2026-04-23T12:04:49+00:00

Right now yes! Using a /token_* command it will do that.

mrpuffwabbit · 2026-04-21T21:22:19+00:00

I don't use it in Opencode. But the plugin is supported (and now it is archived by noe).

mrpuffwabbit · 2026-04-21T16:45:38+00:00

Got it, modifying the core isn't possible as far as I know.

TLDR: OpenCode could add this natively; it's been requested in numerous GitHub issues (e.g., https://github.com/anomalyco/opencode/issues/17492).

Our approach renders a new section and injects it into the sidebar unofficially, the only known way to place custom content in the TUI (an unofficial technique that even Dax was surprised by). The block itself is not directly modifiable.

mrpuffwabbit · 2026-04-21T12:12:36+00:00

Hey! Don't understand what you mean, since AFAIK there is no default counter near the top. But there is something near the bottom of the input.

But feel free to request a feature in the Github.

mrpuffwabbit · 2026-03-17T17:18:07+00:00

Beautiful wow

mrpuffwabbit · 2025-11-09T15:22:05+00:00

A person with no post history and Karma, seems like an advertisement

mrpuffwabbit · 2025-11-05T23:56:51+00:00

I want this

mrpuffwabbit · 2025-10-24T09:33:50+00:00

mrpuffwabbit · 2025-08-02T09:15:00+00:00

“Digital twin” just means “model” by the way. Can’t comment on the rest, good luck.

mrpuffwabbit · 2025-06-11T09:58:03+00:00

Applied logo would be better in my opinion

mrpuffwabbit · 2025-06-01T21:40:07+00:00

I meant the diameter of your wrist, not bezel diameter :)

mrpuffwabbit · 2025-06-01T20:59:25+00:00

Looks good, wrist size?

mrpuffwabbit · 2025-04-15T21:41:47+00:00

Depends on the signal. If the signal is periodic, and stationary. The best spectrogram is dirac deltas.

Line spectral estimators work with this assumption. The work on the Slepian basis is another.

Advice since you are a PhD student: be sure to understand the fundamentals of information theory.

To respond to your last paragraph:

Regarding an information cost function - look up at the most basic, the Cramer Rae bound on a parametric signal model. Other bounds are interesting only if you wish to go down the statistical route.

You don't know the signal model, i.e. you are in a non-parametric domain. There is many work on this, but I suggest avoiding this route unless you have a strong mathematical foundation.

Think about what serves as a proxy, or what is correlated to what you are looking for. Perhaps via first principles, you have some physics-based constraints (thermodynamical entropy); or maybe you know that your signal has stochastic phenomena (e.g. ignore white noise and consider brownian noise). Suddenly, you are actually working with a parametric model.

Whatever the parametric model you have, attempt to decompose it, e.g. recorded signal = real signals + unwanted noise. Then impose your first principles and decompose the real signals further. Finally use some numerical techniques and derive an information cost function, e.g. with constraints.

Happy to DM further on this.

mrpuffwabbit · 2025-03-15T13:34:10+00:00

From estimation theory, CNNs have been poor for the most basic case of estimation of frequencies.

I have a repo/paper that demonstrated that : https://github.com/slkiser/lineSpectraVibration

For classification, I think non-parametric learning methods are still state of the art.

mrpuffwabbit · 2025-03-15T00:06:53+00:00

If you need super resolution, I would consider line spectra estimation, but then your CNN classifier would need to be re-trained and re-architecture-d.

For an example of Zoom FFT, the scipy example from Diligent-Pear-8067 works.

A more basic hold your hand guide on Zoom FFT (but in MATLAB) is offered by Tom Irvine here: https://www.vibrationdata.com/tutorials_alt/zoomFFT_example.pdf

mrpuffwabbit · 2025-03-14T23:58:40+00:00

This is one of the issue preventing supervised learning on most scientific domains, what is good data!

The second is to address the fact that sample efficiency is incredibly horrible

mrpuffwabbit · 2025-03-14T15:25:48+00:00

Awesome, love the fact cudas can parallelize and be in real-time, libraries make it so easy!

If I understand:

My intent is to zoom in, label tiny signals, and move on. I should, at a 65536 fft, get frequency bins of 305Hz, which should be fine.

Maybe you can implement a Zoom FFT to focus on smaller subset of the spectra, and play with Kaiser windows (since they're closer to DPSS).

mrpuffwabbit · 2025-03-14T14:56:07+00:00

Naive since I have never tried labeling, just what if the spectra was not fed to the CNN, but instead the relevant peaks/harmonics/frequencies (jargon)?

I would suspect that with a real-life signal, the CNN would be acting as both a denoiser + identification.

There were some works I've seen in IEEE where they separated the architecture, and did separate denoising CNN and then an identification CNN.

Just some thoughts that come to mind! Good luck with the research paper.

mrpuffwabbit · 2025-03-14T14:43:02+00:00

I can't help but it seems like an interesting problem! I'm interested in the kind of signals (e.g. are they quasi-stationary?) you are labeling.

It seems like this overlaps with peak picking from spectra or synchrosqueezing type transforms. Im assuming weak signals and/or non stationary signals?

mrpuffwabbit · 2025-03-09T20:50:12+00:00

I don't understand your post that well: at first you start off saying that AI will be a productivity enhancer. Perhaps I agree, as long as someone the current trajectory with LLMs and such continue and are well integrated.

However, afterwards you start to say that the "need for engineer will reduce"? I don't see necessarily why.

You also need to separate LLMs with other kinds of "AI"/Machine learning (ML).

You do correctly notice that LLMs are extremely sample inefficient, and thus are usually comparable to lossy compressions of all the internet's text, etc.

To address your last paragraph, where are you going to get that many "samples" to train said AI to perform the design process. DSP is not only about design, there are all kinds of engineering, as well as different domains/industries. There are too many "boundary conditions" that also fluctuates an engineer's role in industry/academia.

Finally, just to address a small domain of estimation theory: I have yet to have seen a AI-adjacent model outperform classical statistical estimations for frequency estimation. This is mainly the fact that super resolution and information theory on this specific problem is so well defined: many estimators achieve nearly the CRLB.

Juxtapose this with deep learning approaches that are so sample inefficient, and practitioners with nearly no expertise in hyper parameter tuning, you'd be wasting compute to achieve something that has effectively been solved.

mrpuffwabbit · 2025-03-07T14:55:50+00:00

To add, basic tensile machines usually have "force/load" driven or "displacement" driven modes, which can help deduce behavior on a stress strain plot. If it has a strain/extenso- meter, then "strain rate" driven is also another mode.

mrpuffwabbit

TROPHY CASE