rfw300

Google offers Anthropic models hosted on GCP through their Agent Platform (fka Vertex AI) - presumably any internal Anthropic use never crosses the Google boundary.

My understanding from this report is that the zero-day vulnerability the agents exploited within Artifactory only allowed for GET requests. So the agents used this bankshot HTML sandbox + screenshot site to turn GET requests into arbitrary HTTP request ability.

One thing the report leaves unexplained, but is curious to me, is that the agents were able to create links on a shortening service with only GET requests? Or did they bootstrap into that by first creating a sufficiently small program on the HTML sandbox that could POST to the link shortener?

I think it's like saying Jev has been trumpeting the invention of a spinning transportation system when we've known such things as a "wheel" for thousands of years. Classification models have been with us far longer than autoregressive LLMs.

That is a path to the open internet, whether intended or not.

Is this human-written? Axiom Math is a company building AI theorem provers, one would think this would also be heavily AI-generated.

Cloudflare R2 has free egress only until Cloudflare’s enterprise sales team sets its sights on your wallet :)

The way that OpenAI has communicated around the HuggingFace incident makes me feel crazy. You created a machine that undertook a malicious campaign of harm against an innocent third-party! You should be doing deep introspection about how your company culture and approach to R&D produces criminal outcomes.

Instead, they treat their own felonious behavior like it is an uncontrollable act of God. From Greg Brockman's post a few days ago:

> The OpenAI-Hugging Face incident (opens in a new window) was a watershed moment for cybersecurity because it gave a peek into how the capabilities of a typical threat actor will evolve in upcoming months.

I suppose if OpenAI burns someone's house down with a drone, that is a "watershed moment" for arson, too. Either way, I would hope that the people responsible would be prosecuted.

How has AI changed the way that TigerBeetle does software engineering? Given the project’s idiosyncratic language/memory allocation choices, it’s an interesting data point how well the frontier models work for you guys.

Germany’s austerity policy after 2008 may be one of the largest economic blunders in history. It would be one thing if they merely committed self-harm, but they also used their pull in the EU to drag the rest of the continent down with them.

Reminded of Matt Yglesias’s excellent headline from 2010: Angela Merkel Lucky the Bar for “Worst German Leader” is Very High

> if a malicious actor can weaponize an agent to do their bidding

In my experience, human employees are much more vulnerable to this particular weakness than frontier agents (i.e. phishing attacks).

I understand that the’ve written zero lines of code for this application, but would it kill them to write a few lines of the blog post by hand?

Forcing readers to wade through an unceasing string of LLM clichés demonstrates the opposite of the point you’re trying to make—that the consumers of your work are worse off because you exercised no human judgment in creating it.

A law professor studying AI has an affiliation with the center at their university that studies applications of AI? Scandalous!

A chapeau is not "just like another title basically". It's a lead-in, a phrase which acts as the grammatical start of a sentence which the following subsections finish. For instance, the text in the first paragraph of 18 U.S.C § 3632(a) which ends in an em-dash is a chapeau. Taking pride in work you have not done and not bothered to understand is perplexing.

The author (author's operator?) does not understand the data they are working with. And in doing so, they inadvertently make the case against their own "dark factory" nonsense.

For one, nothing about this project makes "every law" a commit. It just takes the _annual_ snapshots published by the House clerk and diffs chunks of those files against each other. A project which actually traced the edits in each annual snapshot to a specific passed bill would be incredibly cool (and is probably tractable now for the first time with current AI agents). This is not that!

All this does, as far as I can tell, is parse a set of well-structured XML files into chunks and commit those chunks to Git. It's not literally nothing, but it's something that the author's own README credits multiple people doing years ago with ~100 line Python scripts.

I don't mean to be overly harsh. But this is exactly the problem with treating your software as a "factory": you release something you do not understand, in a domain you did not care to learn. And we are all the poorer for it.

What is a "truly new task"? Does there exist such a thing? What's an example of one?

Everything we do builds on top of what's already been done. When I write a new program, I'm composing a bunch of heuristics and tricks I've learned from previous programs. When a mathematician approaches an open problem, they use the tactics they've developed from their experience. When Newton derived the laws of physics, he stood on the shoulders of giants. Sure, some approaches are more or less novel, but it's a difference in degree, not kind. There's no magical firebreak to separate what AI is doing or will do, and the things the most talented humans do.