You might be thinking of latency of the converters, which is normally sub-ms.

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).

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

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

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.

For instance, while I now know that file systems have permissions, before I became a programmer, I spent maybe ten years thinking of permissions as a special, obscure system thing that you should never touch.

For that matter, I suspect many people don't know basic things like that a file system isn't inherently the operating system.

And, where would you go to learn this information? Your Mac doesn't ship with a manual—how would you know one exists? Furthermore, I would wager that perhaps most people have never learned how anything works requiring a manual and are simply unaware that that's a thing.

All to say, I'm not sure "refusal" is the right term.

For instance, we do in fact have global, effectively ad-free, E2E encrypted messaging. A lot of people put in a lot of work on many fronts to design that, prioritize it, and deliver it at world-scale (mostly) legally.

The fact that the ad model became completely dominant is a real tragedy, but I think the deeper mistake with that idea was that "connecting people" is more complicated than it seems and has had many unforeseen consequences.

As a Western well-to-do person, to the extent I can tune out the slot machine, the dream actually has come true in many ways. I have friends and colleagues all over the world, travel is easier, coordination is easier, and politically my voice matters in ways it never would have in the past.

I was reading mailing lists from the early 2000s the other day and it was wild to remember a time when you literally had no idea what was happening in other parts of the world—when nearly all information was mediated through centralized authorities. People were sharing 'suppressed' reports on grass-roots political action other countries with a sense of self-importance that would be cringe today.

The relevant debate is: "human empowerment vs disempowerment".

It's still a long-shot, but at least there's a specific target that a majority may be able to agree on.

(Not saying you were specifically saying either.)

Many people around me are just missing the boat, or don't care, but many are also able to finally accomplish all kinds of things they've been barred from in the past.

LLMs are best seen, I think, as an imagination amplifier. If you're in the mindset of finding ways to improve society and help other people, there is no shortage of opportunity, and increasingly, capability.

Beautifully ironic, that we find this message.

I imagine people in ML or who've found a good way to demonstrate prowess with agentic systems may be highly in demand right now(?)

Next was discovering "create a mathematical model of the problem and derive the solution as a result" type prompts.

But, the real "oh s**" was a longer process of spec'ing a compiler/runtime for real-time DSP (with a lot of novel ideas) and it actually working.

My sequence was: (1) if helps me understand myself, (2) if helps me put together good ideas, (3) it can generate novel ideas given the right inputs, (4) it can build useful tools on my machine, (5) it can compound good ideas into better and better ideas with repeated passes, (6) it can build significant, ambitious machinery that's way beyond my ordinary capacity.

Current frontier: it can compound large codebases into better and better machinery with repeated passes.

The key thing I track is whether I'm running a process that converges and compounds or whether I'm spinning in place / diverging.

- Durable, 'enterprise grade' software patterns are baked into the runtime and into common, stable libraries that everyone uses

- You can use Ash, which pretty much entirely solves architectural considerations for many types of backends

- The tooling for inspecting and enforcing style (tidewave, credo, dialyzer, Dan's "vibe" ecosystem tools) is far beyond what I see in other ecosystems

- Ecosystem coverage for pretty much everything you need, including numerical software

- Excellent performance escape hatches (NIFs)

And, as has been shown in various benchmarks, agents are quite good at it.

My one problem in practice has been that getting tests right is hard. LLMs need a lot of cajoling to not build flaky tests with all the concurrency, and I find myself spending hours rewriting parts of the test suite once or twice a week.