You have built a short-term whistleblowing system that arguably reduces the probability OpenAI is in that Room, via the negative press such detection would generate. You are now a threat in the eyes of your peers.

Now rewind: you know this before you’ve built the sniffer. You personally want to be in The Room because you believe your desires for alignment exceed those of your accelerationist peers. Is now the time to stick your head out? No, it is not. And arguably you’re being optimally protective of society for realizing this. And so the agents do what they do best.

How much of its judgment would be driven by the data vs. its own internal knowledge? How much might be driven by old vs. new discussions that might not represent new directions of the project? How much might be driven by over-matching against how bugs in less mission-critical code paths were handled?

There's no way to quantify the answers to these questions. Which is not to say this applies to every problem out there! But human judgment is still a bottleneck for design questions on widely, disparately-used complex systems.

https://www.documentcloud.org/documents/28574283-searchresul...

I’ve happened to meet a number of researchers employed by large oil companies over the years, including many involved in the result of memos like this: making research into carbon capture and other mitigations a top corporate priority. Are there optical/PR reasons for investing in this research? Absolutely. Have they also been engaging in the science in good faith, and genuine intent that… in a world where climate action is desired, and net emissions are something to optimize for, they can anticipate this by genuinely funding research, and having their own research projects already underway that can address anticipated challenges? Also absolutely true.

Now, I can’t speak for, say, lobbying efforts. But the scientists on the ground getting accused of the “deception” are some of the smartest, most caring, and highest-integrity people I’ve met. Entire labs of people working for decades to think: can we make our processes cleaner, at the same time we rise to the challenges of global demand.

We should be celebrating, not condemning, this work.

Finding the right balance of "being responsive to bugfixes, some of which may patch disclosed zero-days" and "not allowing a compromised package to be installed" is tough, these days.

Say you want to rely on a library superclass's foo() implementation, and add some pre/post-processing logic... but in turn that superclass calls another self.bar() (or recurses into self.foo()) and you want to route that to the superclass rather than to self.

One possibility: copy in the superclass implementation and change the call sites. But now you lose the ability to get new features from the superclass if the library makes updates/bugfixes.

Another possibility, as the author suggests: make a new object that's just the superclass, have it run its logic, and bring in the state. But maybe the state itself is massive, or otherwise not something you want to copy (say, there's some kind of RAII).

Another possibility: fork the library, put your fork on a package manager, and have an AI agent maintain your fork for you, forever and ever and ever.

The cursed/blursed possibility: set a flag in a threading.local() that we're currently processing foo(). Superclass ignores this. But when we get to our other logic, we check self._mybrand_processing_locals and route accordingly.

At the end of the day, the stack state is just a threading.local(). Why not cut out the middleman? /s

/goal get accepted into Y Combinator, you have an unlimited token budget, be bold.

EDIT: no, do not just make a product that gives away your unlimited token budget to users for free!

> in formal settings you’d call me a synthesist

> on the street you’d call me jargonaut or poppy

> if you’re one of the savants whose hard-won truths are being bastardized or lobotomized by powerful know-nothings interested in only market share you might call me a mole, or a chaperone

Prescient words, for all of us.

https://theappslab.com/2009/10/05/google-wave-the-aftermath/

So they would have needed to invest in native mobile apps, and in significant levels of optimization. And none of this, not web or mobile, had the developer experience necessary to iterate quickly enough as the UI would inevitably evolve upon contact with customers, and you'd end up having many widgets reflecting a centralized global state.

Not to mention that you needed sticky server-side sessions to run the operational transformations efficiently, which harkens to the KV caching challenges that still challenge us today.

But it was an incredible dream, and it was a key source of inspiration to me to choose CS as a path.

What you can do with LLMs is reverse this: from any numbers of snapshots of flight data, you can create large numbers of plausible user queries, based on your data, that are known to be feasible or infeasible. And now you have a labeled data set to train a model that focuses solely on the query-creation and judgment systems. And you can experiment with whether having a more flexible query protocol leads to higher success rates without sacrificing accuracy, or whether you can generate that last-mile feasibility check as a combination of auditable code checks alongside AI-based judgment.

LLMs don't absolve you of having to break down your system architectures into components that have well-defined boundaries (though certainly they can help with that design). They do make those components feasible to solve at scale.

https://en.wikipedia.org/wiki/Google_Wave

https://youtu.be/v_UyVmITiYQ?t=3850

https://www.usenix.org/legacy/event/lisa09/tech/slides/berli...

We're now in a world where we expect real-time interfaces to be developed and immediately deployed for our feedback by agents, in business-critical settings where classic SDLC timelines are far too slow, but code quality is becoming more difficult to manage at scale.

Git, at its core, is a fundamental notion that behavior is an auditable tree of immutable changes, where any participant can rearrange or rewind or remix this sequence on a local machine to show, and interact with, a provisional combination of those changes.

Google Wave, with its operational transformation (OT) and federation systems, was similar in many regards. We have CRDTs now, and far better techniques for auto-merging workstreams - including agentic systems that can resolve conflicts intelligently!

Wave fundamentally had a concept that people would come together and evolve a simple chat interface, into a customized real-time application for their use cases, and that that iteration could happen in real-time, with data and behavior both evolving in collaboration with bots. Wherever Git and the broader notion of SDLC goes, I hope it brings the best of this thinking.

Whether or not LLMs can self-improve their frontier capabilities, they can absolutely create a wake for themselves that accelerates everything else that's training on their synthetic data. We'll see every architecture of the past 40 years suddenly show leaps and bounds.

https://developers.figma.com/docs/rest-api/file-endpoints/#g...

https://developers.figma.com/docs/rest-api/file-node-types/

Seems read-only, which means we're stuck with the MCP for updating Figma files... unless you've found a workaround there too?

And, while you might be billed fewer tokens as a result (because the lower thinking would result in less investigatory work), you might not know this is happening, and know to dial up effort accordingly - you'd simply get a worse work product. And certainly, Anthropic's incentive for anyone on a subscription is to push this as aggressively as they can, so people use less of that subscription.

Sadly, I'd also expect that the OP's benchmark will be detected as a test of model capabilities, and thus be given a high classification so that this strategy remains undetected.

https://www.wnycstudios.org/podcasts/otm/articles/harvard-bo... is a good example: because the person making the threat was one of the few people on the campus network using Tor at the time the threatening emails were sent, he was identified as a suspect.

If an activity is traced to the Obscura network, and your network activity shows you as one of few people using it at the time - or, if you’re using it for completely unrelated things and are unlucky enough to have accessed at the same time someone else used the network for illegal activity - you could be at risk.