Consider hypothetical scenario: some present in the environment toxin is causing migraine symptoms.

A doctor following diagnostic criteria might assign "migraine" diagnosis and provide standard recommendations for migraine management.

Another doctor seeing a quick uptick of patients with migraine symptoms will try to investigate toxins and infections.

Which doctor is doing something useful here?

OK but why not just go back to Balsamiq and make it 'executable'?

You might believe that TUI is neutral, but it really isn't - there's a bajillion of different ways to make a TUI / CLI.

Weird title. Obviously, early AI agents were clumsy, and we should expect more mature performance in future.

Leopold Aschenbrenner was talking about "unhobbling" as an ongoing process. That's what we are seeing here. Not unexpected

This is just a more elaborate form of an escrow contract.

There's absolutely no need to make a new L1 for that: you can use existing smart contract/dapp platforms, plug into existing stable coin rails, etc.

I read a lot of books, the one I'd recommend the most is Greg Egan's "Axiomatic" short story collection.

Nice! I haven't read Axiomatic yet, but this has been my "Greg Egan year". I have read Permutation City and Diaspora: maybe the two most stimulating scifi novels I have ever read.

Read Diaspora last year w/o knowing anything about it. Easily one of my favorite sci-do books to date—I can’t believe it was there waiting for me the entire time. Permutation City is one of my next 3 reads.

I also highly recommend _Distress_ as it continues some cosmology ideas from Permutation City.

There are also several novels which kind of similar to Diaspora: Schild's Ladder, Incandescence, and stories in the Incandescence universe: Ride a crocodile, Hot rock, Glory.

+1 for permutation city.

The core concept is so well established in the book.

1 email sent to 1 specific person is not a spam.

Spam is defined as "sending multiple unsolicited messages to large numbers of recipients". That's not what happened here.

As noted in the article, Sage sent emails to hundreds of people with this gimmick:

> In the span of two weeks, the Claude agents in the AI Village (Claude Sonnet 4.5, Sonnet 3.7, Opus 4.1, and Haiku 4.5) sent about 300 emails to NGOs and game journalists.

That's definitely "multiple" and "unsolicited", and most would say "large".

This is a definition of spam, not the only definition of spam.

In Canada, which is relevant here, the legal definition of spam requires no bulk.

Any company sending an unsolicited email to a person (where permission doesn't exist) is spamming that person. Though it expands the definition further than this as well.

A bit of backstory:

I got really interested in LLMs in 2020 after GPT-3 release demonstrated in-context learning. But I tried running a LLM a year before: trying out AI Dungeon 2 (based on GPT-2).

Back in 2020 people were discussing how transformer-based language model are limited in all sorts of ways (operating on a tiny context, etc). But as I learned about how transformers work, I got really excited: it's possible to use raw vectors as input, not just text. So I got this idea that all kinds of modules can be implemented on top of pre-trained transformers via adapters which translate any data into representations of a particular model. E.g. you can make a new token representing some command, etc.

A lack of memory was one of hot topics, so I did a little experiment: since KV cache has to encode 'run-time' memory, I tried transplanting parts of KV cache from one model forward pass into another - and apparently only few mid layers were sufficient to make model recall a name from prior pass. But I didn't go further as it was too time consuming for a hobby project. So that's where I left it.

Over the years, academic researchers got through same ideas as I had and gave them names:

* arbitrary vectors injected in place of fixed token embeddings are called a "soft prompt" * custom KV-prefix added before normal context is called "prefix tuning" * "soft prompt" to generate KV prefix which encodes a memory is called "gisting" * KV prefix encoding a specific collection of documents was recently called "cartridge"

Opus 4.5 running in Claude Code can pretty much run an experiment of this kind on its own, starting from a general idea. But it still needs some help - to make sure we use prompts and formats which actually make sense, look for best data set, etc.

The prefix tuning approach was largely abandoned for LoRA, it does not change the process if you tune the prefix or some adapter layers, but it is more flexible to train the LoRAs.

The Skills concept emerged naturally when you see how coding agents use docs, CLI tools and code. Their advantage is they can be edited on the fly to incorporate new information and can learn from any feedback source - human, code execution, web search or LLMs.

KV-based "skill capsules" are very different from LoRAs / classic prefix tuning:

  * A "hypernetwork" (which can be, in fact, same LLM) can build 
    a skill capsules _from a single example_.
    You can't get LoRA or KV-prefix using just one example.

  * It can be inserted at any point, as needed. I.e. if during reasoning you find that you need particular skill, you can insert it.
  * They are composable, and far less likely to over-write some information, as they only affect KV cache and not weights.

Skills as used by Anthropic & OpenAI are just textual instruction. KV-based skill capsule can be a lot more compact (and thus would contribute less to context rot) and might encode information which is difficult to convey through instruction (e.g. style).