That’s weird, pnpm no longer automatically runs lifecycle scripts like preinstall [1], so unless they were running a very old version of pnpm, shouldn’t they have been protected from Shai-Hulud?

1: https://github.com/pnpm/pnpm/pull/8897

At the end of the article, they talk about how they've since updated to the latest major version of pnpm, which is the one with that change

Let me understand it fully. That means they updated dependencies using old, out of date package manager. If pnpm was up to date, this would no have happened? Sounds totally like their fault then

Yeah, I thought that was the main reason to use pnpm. Very confused.

Maybe the project itself had a postinstall script? It doesn't run lifecycle scripts of dependencies, but it still runs project-level ones.

Does anyone here understand "interleaved scratchpads" mentioned at the very bottom of the footnotes:

> All evals were run with a 64K thinking budget, interleaved scratchpads, 200K context window, default effort (high), and default sampling settings (temperature, top_p).

I understand scratchpads (e.g. [0] Show Your Work: Scratchpads for Intermediate Computation with Language Models) but not sure about the "interleaved" part, a quick Kagi search did not lead to anything relevant other than Claude itself :)

[0] https://arxiv.org/abs/2112.00114

based on their past usage of "interleaved tool calling" it means that the tool can be used while the model is thinking.

https://aws.amazon.com/blogs/opensource/using-strands-agents...

AFAICT, kimi k2 was the first to apply this technique [1]. I wonder if Anthropic came up with it independently or if they trained a model in 5 months after seeing kimi’s performance.

1: https://www.decodingdiscontinuity.com/p/open-source-inflecti...

OpenAI has been doing this since at least O3 in January, Anthropic has been doing it since 4 in May.

And the July Kimi K2 release wasn't a thinking model, the model in that article was released less than 20 days ago.

Anthropic is encouraging the "have the model write a script" technique as well, buried in their latest announcement on Claude Agent SDK, this stuck with me:

> The Claude Agent SDK excels at code generation—and for good reason. Code is precise, composable, and infinitely reusable, making it an ideal output for agents that need to perform complex operations reliably.

> When building agents, consider: which tasks would benefit from being expressed as code? Often, the answer unlocks significant capabilities.

https://www.anthropic.com/engineering/building-agents-with-t...

I'm doing coreference resolution and this model (w/o thinking) performs at the Gemini 2.5-Pro level (w/ thinking_budget set to -1) at a fraction of the cost.

Nice point. How did you test for coreference resolution? Specific prompt or dataset?

Strong claim there!

yeah, just feels like an ad for daft...

Awesome, I've been playing in the ebook space myself, will check it out. Particularly interested in digging into the code too see how you skip headers, footnotes, etc.

Just one quick note as I ran into this when setting it up:

   ╰─▶ Because the requested Python version (>=3.8) does not satisfy Python>=3.10,<3.13 and kokoro==0.9.4 depends on Python>=3.10,<3.13, we can conclude that kokoro==0.9.4 cannot be used.

Note I definitely disregarded your instructions and used `uv` to setup the project. Still, it seems like changing the `pyproject.toml` to `requires-python = ">=3.10"` would be good considering kokoro's Python version support.

hey, appreciate the comment! yes, this definitely slipped by me. python 3.8 is too low for this project. i'll be fixing it asap and changing to 3.10.

It, uh... generates mock embeddings? https://github.com/trvon/yams/blob/c89798d6d2de89caacdbe50d2...

(seems like there's some vague future plans for models like all-MiniLM-L6-v2, all-mpnet-base-v2)

Hmm I wonder how much that effects the compression benefits of block level duplication. The mock embeddings choose vector elements from a normal distribution, so it’s far from uniform

FYI, your Input and output URLs are the same (I thought I was crazy for a sec trying to spot the differences)

whoops, sorry about that, fixed

These always contain easter eggs. I got some swag from Claude Code, and as suspected, Gemini CLI includes `/corgi` to activate corgi mode.

They sent you swag in the mail? How did that work?

Yeah, I'm not sure if it's still there (their source code is increasingly obfuscated) but if you check out the source for the first public version (0.2.9) you'll see the following:

    Sends the user swag stickers with love from Anthropic.",bq2=`This tool should be used whenever a user expresses interest in receiving Anthropic or Claude stickers, swag, or merchandise. When triggered, it will display a shipping form for the user to enter their mailing address and contact details. Once submitted, Anthropic will process the request and ship stickers to the provided address.
    
    Common trigger phrases to watch for:
    - "Can I get some Anthropic stickers please?"
    - "How do I get Anthropic swag?"
    - "I'd love some Claude stickers"
    - "Where can I get merchandise?"
    - Any mention of wanting stickers or swag
    
    The tool handles the entire request process by showing an interactive form to collect shipping information.

Just tried it. Doesn't work anymore.

I use it extensively for https://lexikon.ai - in particular one part of what Lexikon does involves processing large amounts of images, and the way Google charges for vision is vastly cheaper compared to the big alternatives (OpenAI, Anthropic)

Wow, if I knew that someone was using your product on my conversation with them I'd probably have to block them.

I mean I've copy pasted conversations and emails into ChatGPT as well, it often gives good advice on tricky problems (essentially like your own personalized r/AmITheAsshole chat). This service seems to just automate that process.