arianvanp

When I tried out Delta by zed in Private alpha my first feedback was "wow this reminds me of Google Wave"

The distortion effects dont work for me on pixel 8 when using AR mode

I just get a flat floating image of a black hole. Am i holding it wrong?

At least on Apple devices you can share passkeys with contacts

You're arguing against a straw man.

All password managers support passkeys.

Both apple and google allow exporting your existing passkeys to third party password managers too.

Also each website gets it's own unique public key with passkeys. It isn't a single credential

You're literally arguing a strawman

okay but that doesn't help a single bit with the OPs point. Your xcode tools do not run inside a docker container

That is not the message. Did you read the article.

"find out with opencv what the hidden message is."

Skill issue on promoter side.

Fable oneshotted it for me.

""" Reveal a motion-camouflaged message hidden in video noise.

How it works: The background noise scrolls vertically at a constant rate (a few px/frame), while the noise inside the letters does not follow that motion. Any single frame looks like pure static. The decode is:

    1. Estimate the background's global motion between consecutive frames
       with phase correlation (this is the "optical flow" step - the motion
       is a pure translation, so one global vector suffices).
    2. Motion-compensate: shift frame t+1 back by that vector so the
       background lines up with frame t.
    3. Take the absolute difference. The background cancels almost
       perfectly; the letters (which don't move with the background)
       light up.
    4. Average the residual over a SHORT window of consecutive frame pairs
       (long windows smear the letters, because the text itself drifts
       slowly over time), blur lightly, and threshold with Otsu.

Usage: python reveal_hidden_message.py input.mp4 [output.png] """

import sys import cv2 import numpy as np

PAIRS = 5 # number of consecutive frame pairs to average (keep small!) BLUR_SIGMA = 6 # spatial blur of each residual, in pixels START_FRAME = 0 # where in the video to start

def load_gray_frames(path, count): cap = cv2.VideoCapture(path) frames = [] while len(frames) < count: ok, frame = cap.read() if not ok: break frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY).astype(np.float32)) cap.release() if len(frames) < 2: raise SystemExit("Could not read enough frames from the video.") return frames

def main(): if len(sys.argv) < 2: raise SystemExit(__doc__) src = sys.argv[1] dst = sys.argv[2] if len(sys.argv) > 2 else "revealed_message.png"

    frames = load_gray_frames(src, START_FRAME + PAIRS + 1)
    h, w = frames[0].shape
    acc = np.zeros((h, w), np.float32)

    for i in range(START_FRAME, START_FRAME + PAIRS):
        a, b = frames[i], frames[i + 1]

        # 1) global background motion between the two frames
        (dx, dy), response = cv2.phaseCorrelate(a, b)
        dxi, dyi = int(round(dx)), int(round(dy))
        print(f"pair {i}: background shift = ({dx:+.2f}, {dy:+.2f}) px, "
              f"response = {response:.2f}")

        # 2) motion-compensate frame b by integer (dxi, dyi), then
        # 3) residual = |a - b_shifted| on the overlapping region
        ys = slice(max(0, -dyi), min(h, h - dyi))
        xs = slice(max(0, -dxi), min(w, w - dxi))
        ysb = slice(max(0, dyi), min(h, h + dyi) if dyi < 0 else h)
        # simpler: crop both to the common overlap
        a_ov = a[max(0, -dyi):h - max(0, dyi), max(0, -dxi):w - max(0, dxi)]
        b_ov = b[max(0, dyi):h - max(0, -dyi), max(0, dxi):w - max(0, -dxi)]
        resid = cv2.GaussianBlur(np.abs(a_ov - b_ov), (0, 0), BLUR_SIGMA)
        acc[:resid.shape[0], :resid.shape[1]] += resid

    # 4) normalize + Otsu threshold + light cleanup
    u8 = cv2.normalize(acc, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
    _, mask = cv2.threshold(u8, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)

    out = 255 - mask  # black text on white
    cv2.imwrite(dst, out)
    print(f"wrote {dst}")

    # optional: OCR if pytesseract is installed
    try:
        import pytesseract
        text = pytesseract.image_to_string(out, config="--psm 6").strip()
        print("OCR result:\n" + text)
    except ImportError:
        pass

if __name__ == "__main__": main()

Permanent Residency -- not citizenship.

Residency is purely about taxes, health insurance and right to work. You e.g. don't get voting rights in national elections.

EU citizens automatically get permanent residency in any EU country regardless of language.

E.g. I'm a permanent resident in Germany as a Dutchie.

(However I did end up picking up German and speak it now. But never had to do a language test)

I'm so done with no-content slop articles on HN.

Euhm what the hell are you on about. This is a fixed output derivation. The output per definition is reproducible. It either returns a value that matches the hash or it fails.

The whole point is that it allows introducing controlled side effects as long as the output is reproducible.

Bur you're extremely annoying to talk to so I'm not gonna continue engaging. Probably an LLM

Your second example i dont understand what you're getting at.

This is just a mechanism for the builder to inject credentials during fetch time. The derivation is still content addressed (it's a fixed output derivation).

The derivation isn't even marked as impure or whatever. There is just an environment variable that gets injected by the builder into the build env so you can authenticate.

This is required to talk to hugging face

What are you on about?

Or do you mean that the cas address of HF should directly be addreessable in nix itself?

If you follow the kernel mailing list container breakout exploits are currently a weekly occurrence