Isolyth

What UI is this 🧐

The graphic at the top of thinkingmachines.ai/blog/lora/ is a giant SVG, defined right in the page. I noticed this because 2/3 of the note obsidian web clipper wanted to create was just SVG code

Project Announcement: Untitled Streamplace Lexicon Codegen Tool - How Streamplace Works It's like the Untitled Goose Game of Lexicon codegen tools

PROJECT ANNOUNCEMENT: Streamplace is building a unified AT Protocol code generation tool! It eats lexicons and creates code in a variety of languages, as well as documentation.

To lead this project, who better than the creator of @pdsls.dev, @juli.ee!

Read more in the announcement post:

yes! at least partially. longposted about it on other site here: x.com/voooooogel/s...

i wrote a custom llm sampler for llama-3.1-8b so it could only say words that are in the bible

People often ask why we don’t act on off-platform evidence when investigating reports. Here’s an example of how complex that can get. 1/9

Petri: An open-source auditing tool to accelerate AI safety research A new automated auditing tool for AI safety research

www.anthropic.com/research/pet...

Petri: An open-source auditing tool to accelerate AI safety research A new automated auditing tool for AI safety research

www.anthropic.com/research/pet...

if you're curious about the architecture and mechanics of LLMs, this site has a really excellent explorable interactive visualization. it helps build intuition for how massive these models are, what 'interpretability' means, and the complexity involved here

bbycroft.net/llm

Learning without training: The implicit dynamics of in-context learning One of the most striking features of Large Language Models (LLM) is their ability to learn in context. Namely at inference time an LLM is able to learn new patterns without any additional weight updat...

Interesting paper suggesting a mechanism for why in-context learning happens in LLMs.

They show that LLMs implicitly apply an internal low-rank weight update adjusted by the context. It’s cheap (due to the low-rank) but effective for adapting the model’s behavior.

#MLSky

arxiv.org/abs/2507.16003

I'm not aware, you may be able to find something on r/locallama

On Gemini doing better at long context: 🤷 I think Google might be doing the deepseek attention thing, which, seemingly (with thinking) improves long context performance a lot, for some reason

The pixel fold is neat because you can put graphene on it and the cameras are great but their obsession over curved corner displays makes the outside look so goofy 😭 also after Samsung made the z fold so absurdly thin (its as thick as a regular phone when closed!) it's weird how thick the pf10 is

That is possible, but if you *also* have to scale a bunch to use large contexts, it's not a super economical or really practical way forward. Also I can't see it in the reply view but I don't think Kimi or GLM (both are at like a trillion parameters I think?) do all that well

OpenAI is having a livestream tomorrow... Could be them?

The state of LLMs is messy: Some AI features (like vision) lag others (like tool use) while others have blind spots (imagegen and clocks). And the expensive "heavy thinking" models are now very far ahead of all the other AIs that most people use, capable of real work

None of this is well-documented

Ehhhh, not really? Gpt-5 does worse than Gemini here. It holds up pretty well but it does start to decline noticeably towards the end, if models were actually able to be scaled to 100m or whatever I would expect to see straight hundreds at relatively tiny contexts like this

It starts to get real small once you start feeding in lots of data :( also LLMs suck at actually using past like, 128k, even on the best models like Gemini. Most can't really perform well past 64k

[BUG] Severe performance degradation · Issue #6976 · anthropics/claude-code Environment Platform (select one): Anthropic API AWS Bedrock Google Vertex AI Other: ClaudeCode Claude CLI version: 1.0.98 (Claude Code) Operating System: Ubuntu 24.04.2 LTS x86_64 Bug Description ...

Or like, here is an absolute psycho just happily recounting his behavior in a github comment. You can go look at a whole pile of Claude Code issues and see similar, because of, uh, Exactly Who Is Using It This Way: put kindly, people who want employees and should not have them.

Personal data storage is an idea whose time has come Data Ownership as a conversation changes when data resides primarily with people-governed institutions rather than corporations.

The web can be made personal again.

blog.muni.town/personal-dat...

100 million in a lifetime? I guess but it often takes multiple tokens to encode a word, and LLMs get literally ALL of their input from tokens, so comparing it just to the words humans hear doesn't seem right. A moderate codebase can already consume most of a million just on its own :/

Update: @vgel.me has written a blog post about why this happens from a mechanistic interpretability standpoint!

vgel.me/posts/seahor...

GitHub - whyrusleeping/konbini Contribute to whyrusleeping/konbini development by creating an account on GitHub.

A little proof of concept I've been working on, it's a version of Bluesky that you can run locally, even on your laptop, that pulls just the data you care about from the firehose, indexes it, and provides you an interface to view and interact with posts.

I think the problem with a lot anti-AI people on here is that they actually believe this very first sentence.

I gotta be honest, I think it's too late. They got there first. I think it's like Q-Tips and Kleenex now.

💁‍♂️ "I asked chajeepity."

🤦‍♀️ "Which chajeepity?"

💁‍♂️ "Claude."

🤦‍♀️ "That's a pretty good chajeepity. Dumb name though."

Use For You instead of Discover
Use For You instead of Discover
Use For You instead of Discover
Use For You instead of Discover
Use For You instead of Discover