The description by OP in comments like https://old.reddit.com/r/LegalAdviceUK/comments/1s92fql/my_s... seems to strongly imply that all the accounts were unconnected in a GSuite sense, and they are being slowly recursively banned by Google based on indirect connections like recovery emails or co-presence on a device.

I don't see any reasonable way they could have saved themselves besides something crazy like requiring every family member use a different feudal lord - one person gets Google, one person gets Apple, one poor guy gets Microsoft...

That detail is where I first paused and realize this story is more likely a creative writing exercise than a factual account

They had a separate claim that after the bannings and after the son admitted what he did, they received an email confirming that the accounts were banned for “child protection”

Received where? They just claimed all of their accounts were banned first, including linked recovery accounts.

Also where is the text of that email? Communications like that are key to legal matters yet it was only shared as a passing comment

You'll love GreaterWrong, then: https://www.greaterwrong.com/posts/BJ4pnropWdnzzgeJc/i-am-de...

Thanks, I keep JS disabled on most sites and am continually baffled on why simple text and image blogs are web apps now.

Have you considered, since you can extract the images via the mask, selectively inverting them?

One can fairly reliably use a small NN to classify images by whether they should be inverted or just dimmed, and I've used it with great success for years now on my site: https://invertornot.com/ https://gwern.net/invertornot

---

On a side note, it'd be nice to have an API or something to let one 'compile' a PDF to dark-mode version PDF. Ephemeral browser-based is a drawback as often as a benefit.

That's actually exactly where I started. The initial idea involved a YOLO nano model to classify images, deciding what to invert and what not to. It worked as a concept, but during the feasibility analysis I realized that for native PDFs it wasn't necessary: the format already tells you where the images are. I walk the page's operator list via getOperatorList() (PDF.js public API, no fork) and reconstruct the CTM stack, that is the save, restore and transform operations, until I hit a paintImageXObject. The current transformation matrix gives me the exact bounds. I copy those pixels from a clean render onto an overlay canvas with no filters, and the images stay intact. It's just arithmetic on transformation matrices, on a typical page it takes a few milliseconds.

Your approach with a classifier makes a lot more sense for the generic web, where you're dealing with arbitrary <img> tags with no structural metadata, and there you have no choice but to look at what's inside. PDFs are a more favorable problem.

A case where a classifier like yours would be an interesting complement is purely vector diagrams, drawn with PDF path operators, not raster images. Veil inverts those along with the text because from the format's perspective they're indistinguishable. In practice they're rare enough that the per-page toggle handles them, but it's the honest limitation of the approach.

> In practice they're rare enough that the per-page toggle handles them, but it's the honest limitation of the approach.

I don't understand how you handle raster images. You simply cannot invert them blindly. So it sounds like you just bite the bullet of never inverting raster images, and accepting that you false-positive some vector-based diagrams? I don't see how that can justify your conclusion "it wasn't necessary". It sounds necessary to me.

Actually, raster images are never inverted, they're protected. The CSS filter: invert() hits the entire canvas (text and images together), then the overlay paints the original image pixels back on top, restoring them. The result is: inverted text, images with their original colors.

The choice to never invert raster images isn't a compromise, it's the design decision. The problem veil solves is exactly that: every dark mode reader today inverts everything, and the result on photos, histology, color charts, scans is unusable. Preserving all images is the conservative choice, and for my target (people reading scientific papers, medical reports, technical manuals) it's the right one.

It's absolutely true that there's a subset of raster images, like diagrams with white backgrounds and black lines, that would benefit from inversion. I could be wrong, but in my experience they're a minority, and the cost of accidentally inverting the wrong one (a medical photo, a color chart) is much higher than the benefit of inverting a black and white diagram, from my point of view. For now the per-page toggle covers those cases.

It's not resistance toward AI. Machine learning isn't among my current skills and I preferred to build with tools I could maintain and debug on my own, but the door isn't closed. Thank you for pushing on this point.

A good wiki like MediaWiki supports various levels of visibility. For example, you could define a namespace for each group of readers like 'Family:'. Or use transclusions from subpages. (This might sound like a bit of a hassle but you can use a template to set it up once and for all: a page transcludes a public sub page followed by the distant relatives material followed by parents / siblings followed by your-eyes-only.) And I'm sure one could come up with other approaches too.

A real example: Said Achmiz (obormot.net) uses PMWiki for his D&D campaigns, and PMwiki lets you control who can see a page, so he can do access control tricks like a page for a location, where only the DM can see all subpages with all the secrets, while each player can see their own 'notes' subpage. So everyone in their own web browser can go to the same page and see the same thing overall, but will see just their private additional information. And this is quite flexible so you can encode whatever patterns you need. You don't need some WotC fancy custom CMS for your D&D campaign to keep track of information and silo appropriately, you just need a design pattern on wikis.

when I learnt about namespaces, I included them as part of the system and they worked great! I've documented them here https://whoami.wiki/docs/namespaces

the transclusion pattern for layered visibility is something I haven't implemented yet but stumbled upon when I was evaluating mediawiki

thanks for pointing to pmwiki's approach, I'll look at how said set it up!

> Detractors of AI are often accused of moving the goalpost, but I think your comment is guilty of the same. Before Claude Code, we had Cursor, Github Copilot, and more. Each of these war purportedly revolutionizing software engineering.

What's sauce for the goose is sauce for the gander. If you make that argument that 'I don't believe in kinks or discontinuities in code release due to AI, because so many AI coding systems have come out incrementally since 2020', then OP does provide strong evidence for an AI acceleration - the smooth exponential!

One might worry that it would increase the authors' confidence even following their LLM rewrite errors and reduce accuracy overall regardless of moderators.

You still need to review and edit.

I was being sarcastic, because that point obviously also applies to the subjects in the experiment as well.

End-to-end optimization in action! Although I'd've liked more than 1 example (pathfinding) here.

Or more precisely, isn't this reinventing notebooks (not the first JS-centric notebook either)?

Yes, it's greedy so may hit local optima. You can fit learning curves and try to extrapolate out to avoid that problem, to let you run long enough to be reasonably sure of a dead end, and periodically revive past candidates to run longer. See past hyperparameter approaches like freeze-thaw https://arxiv.org/abs/1406.3896 .

> The agent can theoretically come up with a protocol to run those same 12 experiments one-by-one and only then decide which branch to explore next - which I think would lead to the same outcome?

At least in theory, adaptiveness should save samples and in this case, compute. (As noted, you can always turn the parallel into serial and so the serial approach, which gets information 'from the future', should be able to meet or beat any parallel approach on sample-efficiency.)

So if the batch only matches the adaptive search, that suggests that the LLM is not reasoning well in the adaptive setting and is poorly exploiting the additional information. Maybe some sort of more explicit counterfactual reasoning/planning over a tree of possible outcomes?