lemax

These are relatively contained private credit markets though. We’re not looking at anything 2009 level. For scale, total US mortgage debt peaked at $9.3T ahead of the subprime mortgage crisis, 73% of GDP at the time. We’re talking here about ~5% of GDP.

But this assumes Chinese models will not achieve token cost optimization. Intelligence needs are fairly flat for many tasks, and the Chinese models have caught up on this front. Next they achieve greater token cost efficiency and we don’t need OpenAI.

I've used RLAIF to build out heuristic based non-LLM models for various decision systems and achieved like, 95% F1 on certain projects. We're in a place where models can be used to fine tune a lot of stuff via loops.

LLM architectures need to fundamentally change or inference needs to be used in constrained trusted environments. Nothing surprising here. Filtering and sanitizing, relying on tags around input strings that can be intercepted and replayed is like, childs play security theatre. As long as prompts accept abitrary user input nothing is changing here. Non-deterministic security is never going to be acceptable.

I'm quite certain that Google's AI services are likely the most used in the world right now by virtue of having the widest distribution. It's in the search box. It's on your Android phone. Just because they aren't the preferred coding or research agent does not mean they are losing - that's a pretty small slice.

Would love to see this benchmark tested on more perceivably LLM friendly frameworks/ORM (e.g. is NestJS or Drizzle / Kysely more performant than their choice of Sequelize) and more frontier model vs just GPT 5.2.

Anyone read whether these tests include any validation loops? What happens if the models get back test failures, for instance? Understanding how many turns to hit full passing behavior suite would also be interesting. Great methodology in the study though.

This is fairly standard practice for device fingerprinting. LI is probably using this to protect its platform from scraping etc, and extension lists have sufficient enough entropy to help identify users and form a useful component of a fingerprint.

The worst has been the post-covid assignment of seating and QR code driven ordering in bars. So few opportunities to mingle. I miss standing in bars, talking to bartenders, chatting with random patrons. This has recovered much better in large cities but I find that restaurants and bars in US suburban environments are deeply impersonal now. It’s no wonder singles are stuck meeting partners on apps with so little unstructured social opportunities left. Not to mention no one is going to bars anymore anyway.

I'm still stuck on superpowers. Can't seem to get better plans out of native claude planning - superpowers ensures I have a reviewed design that actually matches my mental model. Typical claude planning doesn't confirm assumptions sufficiently for my weak brain dumps/poorly spec'd tickets.

I think that, for possibly a very long time, AI will just increase the quality bar and scale of expectations when we produce things. We might take the same amount of time (or longer) to produce something, but with significantly better outcomes. Ultimately human preferences and tastes prevail and the world is full of problems that are not simple I/O, that are not repeatable, and that require human taste to improve. The people who will immediately survive economically are the ones who leverage AI to produce stuff that wasn't possible before.