kaereste.gwei 💗🦇🔊

We're rolling out a small tweak to boost visibility of your posts to your mutuals (people who you follow back). We noticed this data was missing from the algo and it made your friends appear less in your replies. This resulted in the reply section feeling more like a battleground with people you don't recognize. This should also help clusters form around interests more easily, which many people have asked for.

Institutions don't shop for vendors the way startups do. They don't wake up with a problem and go find a solution that week. Vendor evaluations run on a fixed internal calendar, set months ahead by procurement or risk committees. Miss that window by two weeks and it doesn't matter how good your product is or how badly they need it. You get pushed to the next cycle, and that cycle can be six months out. Timing your outreach to your prospect's internal clock matters more than most sales advice admits.

I've been doing a lot of experiments with auto-research in the last few weeks, especially in algebra. Here are a couple of thoughts I want to share about sparks of generalization. 1. Once the problem can be reduced to a constructive question about rings and becomes a finite computation, I can use a model to basically develop the code from scratch. You don't even need a specialized CAS for that, Rust is enough. 2. If the question is about a highly abstract object, you can still turn it into a format in which all the constraints can be manipulated formally. You can use functional programming to model the abstraction through constraints. The best situation is when you can actually encode the abstraction in one of the many logics that are friendly to SMT solvers. 3. Most of the time, the difficulty of the problem lies in the lack of clear connections between some of the encoded predicates. This is where it starts getting interesting. When you play with code generation, you can accidentally discover such missing rules and abstract them from the calculations. 4. Here, there is a chance that the model will spontaneously generalize the pattern into an abstraction. This is, in general, very hard because the models are not designed to produce something unusual. Quite the contrary: they usually do not get very far beyond their training. 5. This is where something strange must happen with the model: an accidental guess, multi-agent search, etc. I have already seen some very small sparks of new ideas or accidental generalization in my auto-research loops. I suspect this will scale up with compute. 6. Now the difficult part begins for humans: those abstracted rules might be provable, or even formalizable in Lean, but they are completely new and foreign to humans. I have seen this in my research into invertible cellular automata. I see a claim that is true—there is a proof—but I don't understand its deeper meaning. I don't know how to internalize it off the bat. 7. When you keep pushing your agents, you end up in a place where no soul has been before. The mathematics is completely foreign, and the notation is concocted and unfamiliar. Your instinct is to say that it is all wrong, but it isn't. It is just alien. In the coming days, I will publish several such examples straight from my loops. I suspect this is an early instance of spontaneous and very weak generalization. It feels like the early hacks with GPT-3.5 that produced something resembling “thinking.” It is very bad, but it is something. Maybe ideation is mechanical and can be scaled up with a lot of effort? Where are we going?

We built our Privacy Dashboard to make some of the CROPS principles easier to inspect and compare across privacy protocols. Here's how each column on the page maps to the principles 👇

Great comment from a subscriber who commented in our Chat on his direct experience. Agentic AI is pretty difficult to implement at scale in production because (1) a bunch of new types of systems need to be built that have never existed before (orchestration /routing, context engineering, state management, multi-agent collaboration, security / compliance etc), and (2) an agentic enterprise requires completely new ways of thinking as well as new challenges in governance and culture. The second part is actually the much harder part, which is why there hasn’t been a ton of big success stories yet - every company is trying to figure it out. Anyway I don’t think the problem is at the demand layer. What’s happening at the finance layer is interesting and could be problematic. But time will tell. I would hope this is an open forum where people with different perspectives can share knowledge and discuss