mhalle

Silver halide based holographic plates are like conventional black and white photographic material, only extremely high resolution and very low sensitivity.

That's required because off-axis display holograms have spatial frequencies ranging from about 1200 line pairs per mm for transmission holograms to maybe 5000 for reflection holograms.

Because the grain size is so small, these plates tend to be pretty insensitive to light. That's why they have limited use outside of holography these days.

Other types of emulsions such as dichromated gelatin also exist but are also hard to find. DCG is even less sensitive to light.

In either case, the wavelength the material is sensitive to matters when exposing with a single frequency laser.

Then there are issues about the substrate if the emulsion is on film related to polarization preservation.

Having also spent a long time in actual holography, I would agree with your assessment generally and your catalog of different "holographic" techniques.

I might quibble with lenticular displays, since they have a name that predates holography.

In the ideal world, a larger umbrella term would have emerged. "Autostereoscopic" describes any display that provides different views without glasses, but that typically suggests discrete views.

"Plenogram" would have been reasonable following Adelson and Bergen's plenoptic function, but that didn't and won't happen.

"Hologram" now basically means a sufficiently advanced three-dimensional display in popular culture. And that even leaves of other real holograms, such as the ones used for non-destructive testing.

Machine code isn't especially expressive per line or unit of code. Lower level languages takes up more of an LLM's context than higher level ones.

To be effective in using low level languages, LLMs would have to build higher level constructs like subroutines from scratch every program.

It's not that different from why we almost never use assembler for anything more than code islands: even a modest subroutine can overwhelm our own mental context window.

Skills can provide CLI tools for LLMs running on the web. It works great (I use Claude).

The ID verification is also being used to enforce Claude's 18+ age restriction.

It seems there is no way for a high school student to use Claude for any purpose, including as part of a supervised project or class.

Create a skill that has the CLI in the scripts sub-directory. The implementation language depends on the LLM and the VM it uses. Claude includes shell, python, and a bunch of other interpreters in a Linux environment.

A skill's instructions can direct the LLM to call the CLI.

Claude skills can be installed into Claude web from a web browser. Those skills can then run on the Claude app on your phone.

As the article says, legal action up to this point has been based on the fact that the government created policies that didn't follow its own rules under, for example, the Administrative Procedures Act.

So now the administration is attempting to follow those rules to create these new procedures, which they believe will then be lawful.

If they are successful, challenges would have to be made judicially based on non-procedural grounds, or through Congress.

CLIs do work on mobile when they are packaged in skills that run in an appropriate VM behind the LLM.

Claude on the web does this. The only issue is controlling network access, which could be fixed by per-skill ACLs.

Early LLMs typically tried to do multiplication "in their head" by recall.

Now most LLMs do multiplication using a tool call to a programming language, akin to a person reaching for a calculator rather than relying on a learned table or working the problem out mentally.

The high level comparison between what LLMs do and what humans do" for this example is fairly parallel.

I would definitely disagree.

Claude Code and similar agents help me execute experiments, prototypes and full designs based on ideas that I have been refining in my head for years, but never had the time or resources to implement.

They also help get me past design paralysis driven by overthinking.

Perhaps the difference between acceleration and slop is the experience to know what to keep, what to throw away, and what to keep refining.

On the other hand, LLMs are trained on enormous collections of human-authored documents, many that look like "how to" documents. Perhaps the current generation of LLMs are naturally wired for skill-like human language instructions.