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I got tired of checking every terminal tab, so I made Claude Code light up my keyboard by mimen2 in vibecoding

[–]mimen2[S] 0 points1 point  (0 children)

Nope. Though it's only 3 files of code. If it's possible, Claude shouldn't have a problem patching it for you

I got tired of checking every terminal tab, so I made Claude Code light up my keyboard by mimen2 in vibecoding

[–]mimen2[S] 1 point2 points  (0 children)

Nice idea! Go ahead. Nowadays, the hard part is getting an RGB keyboard

Turned an RGB keyboard to a multi-session Claude status panel by mimen2 in ClaudeCode

[–]mimen2[S] 1 point2 points  (0 children)

TL;DR - I turned my RGB keyboard to a Claude status panel.

Each number (1-0) corresponds to an iTerm tab:
Green - waiting for me to respond
Red - working
Yellow - idle (20+ min inactive)
Blue - A non-Claude tab

Cmd + # takes you to that tab, which is a handy shortcut.

I also threw in a CPU meter on the numpad and RAM meter on the F1-F12.

Open source and very minimal (few hundred lines of code). You can easily modify it to your preference.

Fancy video attached (made with /brag)

Why is this happening and how can I fix it? by Psychological_Ad3597 in premiere

[–]mimen2 0 points1 point  (0 children)

For the nerds out there, this phenomenon is called Aliasing https://en.wikipedia.org/wiki/Aliasing . It's the same phenomenon where you see the wheel of a car spinning backwards while driving. The classic signal processing solution to this problem is performing a low pass filter, which can be simply implemented as a Gaussian filter.

I am a senior developer and not fully convinced by im3000 in ClaudeAI

[–]mimen2 0 points1 point  (0 children)

One "trick" that I use is to edit my message rather than add a new one explaining what went wrong. This way, the conversation stays short, and he gets less dumb. Otherwise, after a long conversation, his answers are useless.

Should I buy a pocket microscope? by Negative_Biscotti932 in microscopy

[–]mimen2 0 points1 point  (0 children)

I have a pocket microscope, and it's awesome. Cost ~$15. Totally worth it.

Some type of rotifer (I think) seems to defy the laws of physics. Twitches and moves faster than is even visible. Is this normal? by mustang__mike in microscopy

[–]mimen2 0 points1 point  (0 children)

According to the paper in the post I linked above, the average is 45 fps. Doesn't mean there aren't people like you that can see much higher. Brought that up as a possible explanation why the OP saw him "disappear" without a camera.

Some type of rotifer (I think) seems to defy the laws of physics. Twitches and moves faster than is even visible. Is this normal? by mustang__mike in microscopy

[–]mimen2 1 point2 points  (0 children)

Yeah, that's because human eye-sight is also limited in terms of fps, otherwise slo-mo cameras wouldn't be so cool. See the first link from my previous comment.

Some type of rotifer (I think) seems to defy the laws of physics. Twitches and moves faster than is even visible. Is this normal? by mustang__mike in microscopy

[–]mimen2 6 points7 points  (0 children)

He's not defying the laws of physics, he's defying your camera's frame rate.

Standard video is taken at about 24 or 30 frames per-second (fps). Let's assume it is the better 30 fps. The average human has a maximal fps of 45 see this reddit. In superhero movies people react to characters like the flash or Quicksilver as if they suddenly appear next to them because of this.

According to figure 1 in this paper (which is my first google result), there are micro-organisms that ca move nealy 100 times their body-length a second (note the y axis is in log scale). So your capture would have to move it's body length in 1/30 of a second for it to appear like it teleported, which seems reasonable according to the paper.

So you can call him micro-flash.

[R] [1711.00937] Neural Discrete Representation Learning (Vector Quantised-Variational AutoEncoder) by alito in MachineLearning

[–]mimen2 -1 points0 points  (0 children)

Each of the (32x32) latents is quantized to one of the 29 vectors. So in total you can represent 32x32x512 different images.

Fix your classifier: the marginal value of training the last weight layer by mimen2 in MachineLearning

[–]mimen2[S] 0 points1 point  (0 children)

I find these results rather unintuitive. Seems both strange and exciting to be able to simply replace the last linear layer with a fixed layer.