Tootfinder

Since it was relevant to a discussion I just had on here and is something most people probably haven't thought about much (unless you've taken one of a handful of philosophy classes), I thought I'd try to lay out a key piece of Descartes' Meditations (#philosophy

from my link log —
The design principles of the Elixir type system.
https://arxiv.org/abs/2306.06391
saved 2026-06-04 https://dotat.at/:/D3ZFY.html

The Design Principles of the Elixir Type System
Elixir is a dynamically-typed functional language running on the Erlang Virtual Machine, designed for building scalable and maintainable applications. Its characteristics have earned it a surging adoption by hundreds of industrial actors and tens of thousands of developers. Static typing seems nowadays to be the most important request coming from the Elixir community. We present a gradual type system we plan to include in the Elixir compiler, outline its characteristics and design principles, a…

So one of the authors is Nicholas Carlini, who works for Anthropic. This is basically an ad for the three letter agencies to use Claude. It massively over-promises compared to what the actual paper says.
But, it is important. First, this is really about silencing people. The threat of identification is designed to make people afraid to talk online. There's a massive asymmetry between the fascists and the people. The fascists are weird racists and pedophiles who are obsessed with control. No one likes them. No one likes their ideas, because their ideas are creepy and bad.
When they talk about their ideas, that people should be murdered or kidnaped based on their skin color, that there should be a national dress code, that people's sex lives should be monitored, that children should be treated like objects that are owned by the parent (specifically, one parent), that people with different skin color or uteri should be considered as livestock, people fucking hate it because it's awful. When we talk about our ideas, that everyone should be able to eat and take care of themselves, that people who can't take care of themselves should be taken care of, that we should live in a society that values life, that we should live in harmony with nature, people like those ideas. When fascists out us for talking about those ideas, people support us. When we out people who are working as fascist goons those people have to face social consequences.
Everyone hates these people. The US government is currently less popular than it has ever been. The only way they can keep power is by making everyone think that they aren't extraordinarily unpopular. The only way to do that, the way authoritarian have always done it, is to make everyone afraid to talk.
But, yes, what this paper is saying is actually kind of bad. It looks like people who don't take any precautions at all in separating identities can be identified about 30% of the time (based on the results). It's unclear how this will actually work in the real world. Larger corpses will probably have more data, making connecting things easier.
This isn't as good as a human trying to dox someone. It's not going to work as well. It may only work in a small number of cases. There will be false positives (just like there are with people doing the work). It's probably not cheaper than hiring people. But it does mean that you can just dump money into a machine that has no ethical framework and get data out. That's the point. It's hard to find humans who will do evil shit like help dictatorships target human rights activists, but if a machine can do it for twice the price then it's a better deal for the dictatorship.
For most people, you just shouldn't care. This isn't for you. As long as you keep doing what you're doing, and you can keep everyone else doing what they're doing, then there aren't enough resources to actually target you. Even if they know who you are, there are just too many people who hate them and too few goons.
For people who might actually be targeted, there are a lot of things. First, keep in mind what you're putting into anonymous accounts. Any feature that's connected to your real life is a feature that can be extracted to identify you. This has always been true, it just may be easier to find now. Your identities should be totally siloed. It's also harder to identify you if you're writing anonymously as a collective. Collectives are better anyway because they can help check your thinking. When you write as a collective, you can help clean up each other's personal details and language. A collective develops its own voice, which is distinct from individual contributors. If you do this, and you also present your work as being from one "person," then it becomes even harder for anyone (systems or individuals) to really figure it out.
I'm not going to do a full deep dive on this because I just don't have time, but your existing threat model should *already cover these threats* if you need to make sure your writing remains anonymous.
This paper doesn't present any novel methodologies. It just extracts a bunch of features, which a human would extract as notes, and tries to correlate those between identities, which is how human researchers work. Linguistic forensics were mentioned (not by name) in the paper, but the actual methodology doesn't actually seem to use them.
So a thing with less ethics can do a worse job for more money (when adjusted for the real, not investor deflated, price of tokens). It's worth knowing. It's not the end of the world, but it is a good reminder to check your threat model and make sure it's up to date.

The US supreme court on Thursday ruled
in favor of the Trump administration’s bid to strip temporary protected status (TPS) from hundreds of thousands of Haitians and Syrians,
who were legally in the US and protected from deportation.
People with TPS are given the permission to live and work in the US because the Department of Homeland Security (DHS) deemed their home countries to be unsafe due to war, political instability or natural disasters.
In the past year, Tru…

US supreme court allows Trump administration to strip Haitians and Syrians of protected status
Decision effects hundreds of thousands of people who have permission to live and work in the US because their home countries are unsafe

Agent libOS: A Library-OS-Inspired Runtime for Long-Running, Capability-Controlled LLM Agents
Yingqi Zhang
https://arxiv.org/abs/2606.03895 https://arxiv.org/pdf/2606.03895 https://arxiv.org/html/2606.03895
arXiv:2606.03895v1 Announce Type: new
Abstract: Large language model (LLM) agents are evolving from request-response assistants into long-running software actors: they maintain state across model calls, fork subtasks, wait for external events, request human authority, generate tools, and perform side effects that must be resumed and audited. This paper presents Agent libOS, a library-OS-inspired runtime substrate for LLM agents. Agent libOS runs above a conventional host operating system; it does not implement hardware drivers, kernel-mode isolation, or a POSIX-compatible operating system. Instead, it treats an agent as an AgentProcess: a schedulable execution subject with process identity, parent-child lineage, lifecycle state, a tool table derived from an AgentImage, typed Object Memory, explicit capabilities, human queues, checkpoints, events, and audit records. Its central design rule is tools are libc-like wrappers; runtime primitives are the authority boundary. Filesystem access, object access, sleeps, human approval, JIT tool registration, and external side effects are checked at primitive boundaries under explicit capabilities and policy.
We describe the design, threat model, Python prototype, and safety-oriented evaluation. The current prototype implements async scheduling, namespace-local Object Memory, runtime-integrated human approval, one-shot permission grants, per-process working directories, shell and image-registration primitives, Deno/TypeScript JIT tools over a libOS syscall broker, filesystem/object bridge tools, an injectable Resource Provider Substrate, deterministic demos, real-model smoke scripts, and 123 regression tests at the time of writing. Rather than improving planner accuracy, Agent libOS demonstrates a runtime substrate in which long-running LLM agents can be scheduled, authorized, resumed, and audited without treating tool dispatch as the trust boundary.
toXiv_bot_toot