A good video from a pro going through why someone might deliberately choose characteristics like this: https://www.youtube.com/watch?v=O0zH7J_Vmg4 (4:47)

Reminds of vintage Meyer-Gorlitz or Zeiss Planar. I'm kinda into it.

A few design bugs in the mobile layout (Chrome)

Ah I have been using safari for testing. Will add this as an issue and fix tonight!

And, they have been. Nefarious activity is hidden from view as a rule.

I love this question. If history is a guide, some version of that is what will happen (employment doesn't seem to go down, despite technological progress and population + economic growth).

I suspect the root cause is that it's harder and scarier to imagine good outcomes. It exposes us to disappointment, and when you do it publicly, it looks "crazy".

Another explanation is that there's no direct consumer for "more." Individuals, corporations and states are not in themselves interested in "a larger amount of science," or anything analogous, despite the fact that they would all benefit ambiently.

The net effect is that it's only "safe" to claim reduced risk (i.e. lower costs).

I know some scientists and there's barely enough demand for them as it is

I suspect we’re at the stage of technological civilizational development where, especially with LLM-assisted gradient descent seeking upon the results, basic science, research and engineering for the pure sake of establishing search space beacons of what is found to be true and what is not, irrespective of immediate industrial applications payoff, are valuable economic inputs in and of themselves into ever-expanding training corpus. It has never been easier for people in different fields to now search knowledge spaces in LLM’s, for applicability to their problem spaces of discoveries in seemingly unrelated spaces.

From my perspective, we are desperately short of scientists, researchers and engineers, but we are using an outdated economic model to leverage their findings. LLM’s are a large part of Bush’s Memex and Jobs’ bicycle for the mind visions for intelligence amplification, and in some ways exceed them. I hope we trampoline from how we currently use basic seeking efforts for knowledge.

I hope the demand for average-skilled people goes down.

Or that the floor is raised, at least, and AI empowers average scientists to do substantial work.

Is the demand low because the capital requirements to perform the research aren't there? Excluding wages...granted, for an average research project I don't know what % goes to wages and admin overhead. Probably the bulk?

I do the same, but delegate to codex instead via the /smux skill. It's expensive, but I get one long-running Claude context window and one frequently compacting codex, and this saves me from needing to re-gather context all the time.

Per the OP's technique, I'll use Fable for intensive planning moments, then switch back to opus when things are going well.

1. A compiler for real-time tensor processing (arbitrary DSP, ML). In something like LISP or Haskell, the goal is to compile lambda calculus for fast/reliable execution—as such, you can express a program in a fully general language that can represent any computation and execute it without explicitly modeling the lower levels of the machine. I'm building a compiler that does the same thing for the subset of programs that are guaranteed to execute on-budget. The effect: you write code that looks like DSP/ML math and it compiles/runs optimally with execution guaranteed by construction.

2. My take on an agent framework ... append only log + content hypergraph in Elixir, tools that regularly pull data from other services into Postgres—built as a kind of 'exoskeleton' around claude/codex so it's not competing with fast-moving tools.

Thinking about category theoretic models of computation: https://arxiv.org/abs/2208.03817

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Some things I want other people to build:

- Indexing for Github

- All-in-one social media ingestion libraries for agents

- GOFAI-inspired knowledge / semantic / research graph stuff—I want to point agents at rules/structures for writing connected, verifiable statements

Nice work. Excited to check this out.

Arrived at a version of this view as well and building one on Elixir/Ash.

Ah, interested to dive in—and have a project it could integrate with.

I built a similar thing recently, for agents, aimed at enabling prolog queries over handles in markdown corpora (and code): https://github.com/flowerornament/anneal. A true slopwerk in comparison to this, however.