unignorant

Yes, big fan of Unison (the system I described above follows its model for code hash identity) and I use a NixOS machine for most of my software work!

I'm also interested in this and made decent progress on a git-style content addressed image where everything is identified by hash and residency determines what's in memory. For example, there's a filesystem on the image that works this way and you can page over the "cold" non-resident bytes for very large files that you would not want to hold in memory.

You don't persist the state of the full image continuously to the host disk, but any given state (new programs, new runtime state, filesystem) can be saved via a delta, not a full rewrite, due to this representation.

So this is a little different than what you are talking about, but I'd say it's possible.

There are typical LLM voices and styles, just like human writers have differentiated voices and styles. And some common elements of the typical LLM style are distinct from humans I've previously read.

I had the same reaction, but the article is not AI-generated according to pangram, which I've generally found reliable. I wonder if LLM turns of phrase and even thought patterns are creeping into normal human thought.

This isn't my project, but I shared it here because it has a few important ideas I've been thinking about in my own work. Effect type systems in particular are a really good fit for LLMs because they allow you to reason very precisely about a program's capabilities before runtime (basically, using the type system for capability proofs). This helps you trust agent-created code (for example, you know it can't do IO), or, if the code does require certain capabilities, run it in a sandbox (e.g., mock network or filesystem). This kind of language design also provides a safer foundation for complex meta-systems of agents-that-create-agents, depending on how the runtime is implemented, though Vera may be somewhat limited in that particular respect.

The major design decision I'm a little skeptical about is removing variable names; it would be interesting to see empirical data on that as it seems a bit unintuitive. I would expect almost the opposite, that variable names give LLMs some useful local semantics.

I agree, the more likely psychology of the Greg character is that he doesn't understand the way he presents himself in the pictures damns his surface level framing. You can really go quite far with more sophisticated versions of this technique in fiction -- Ishiguro's Remains of the Day is my favorite example!

These days it's almost trivial to design a binder against a target of interest with computation alone (tools like boltzgen, many others). While that's not the main bottleneck to drug development (imo you are correct about the main bottlenecks), it's still a huge change from the state of technology even 1 or 2 years ago, where finding that same binder could take months or years, and generally with a lot more resources thrown at the problem. These kinds of computational tools only started working really well quite recently (e.g., high enough hit rates for small scale screening where you just order a few designs, good Kd, target specificity out of the box).

So both things can be true: the more important bottlenecks remain, but progress on discovery work has been very exciting.

For flash you can get much better results by asking the system to first generate a detailed scaffold. Here's an example of some metadata you might try to generate before actually writing the story: genres the story should fit into; pov of the story; high level structure of the story; list of characters in the story along with significant details; themes and topics present in the story; detailed style notes

From there you have a second prompt to generate a story that follows those details. You can also generate many candidates and have another model instance rate the stories based on both general literary criteria and how well the fit the prompt, then you only read the best.

This has produced some work I've been reasonably impressed by, though it's not at the level of the best human flash writers.

Also, one easy way to get stuff that completely avoids the "smell" you're talking about by giving specific guidance on style and perspective (e.g., GPT-5 Thinking can do "literary stream-of-consciousness 1st person teenage perspective" reasonably well and will not sound at all like typical model writing).

I'm not sure I agree that the human stories felt original. I was pretty unimpressed with all of the stories except maybe 6, and even that one dealt in some common tropes. 5 had fewer tropes than 6 (and maybe as a result of that received the highest average scores from his readers) but I could tell from the style it was AI