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There were a number of sea lions hanging out there! It's entirely possible they were snacking on whatever salmon were going by. Not all was lost though, I got to enjoy watching some boats go through the locks.

I'm assuming it was out of season because in talking with the Uber driver afterwards he said we'd missed the end of the season. I'm assuming his "local model" was up to date and accurate.

It's hard to try and compare these benchmarks against real world use cases. As an experiment I let Meta Muse make all major decisions on a weekend trip to Seattle ~2 weeks ago. I think this would have been using Muse Spark 1.3, which is equivalent to GPT 6 - Sol.

On the plus side it told me taking the light rail from SEA into the University district was faster and cheaper than Uber.

On the downside it would recommend restaurants that were out of business, checking out Salmon swimming through locks out of season, got the opening of Beecher's Cheese in Pikes Place wrong, and fumble it's way through most simple planning decisions. I think my favorite was when it asked me what flight I have tomorrow so it could help plan when to head to SEA. It sent me a very panicked message that the flight was leaving today! Very soon! I had to remind it this is a daily flight and I am booked on the next day before it checked and apologized.

For things that are verifiable, and code tends to be that way, these models can do all sorts of things. But I find it hard to reconcile Meta's goal of having a personal assistant bot with something that is unable to do the simplest tasks.

I get this unless I drink a lot of water. About 48 fl oz seems to be the right amount for 10g creatine. Sometimes this needs to be combined with a light snack, like a handful of nuts.

Same here, the quote on time passing stuck with me quite a bit.

I was given a picture cube, which is like a Rubik's cube but every side is a unique picture. It came scrambled and I don't have an original reference image. I like to take videos of it and give it to llms to solve. I call it my agi test because it hasn't been solved yet

very fun game. I don't live there any longer but I "ran" the commute from my apartment to work. It felt very nostalgic to go through this.

The purchasers of the cameras, ie HOAs, law enforcement, etc, own the data. They are also the ones routinely caught abusing this. This is a real problem that should be dealt with by enforcing laws against the people improperly using the data.

I'm not sure what a realistic solution is for Flock to try and manage data they do not own nor if it makes sense for them to deny access to data they are not the owners of.

signull is more of an anonymous sh*tposter than a known industry insider, but I think this does capture the sama contribution to OpenAI very well. At least from an outsider who follows this stuff based on vibes.

This page came about because of how long it took PQC to get standardized. This was a slow enough process that a whole slew of QKD vendors arose and sold a lot of products promising this as a solution to dealing with quantum computers and harvest now decrypt later attacks. Many of those products did not do a great job at actually preventing listening in on their lines since QKD is an ongoing field of research where new issues are routinely being discovered.

This must be what gwern meant when he said to write for AI.

Right, that's the same situation as fuzz testing today, which is why I compared it. I feel like you're gesturing towards "Attackers only need to get lucky once, defenders need to do a good job everytime" but a lot of the times when you apply techniques like fuzz testing it doesn't take a lot of effort to get good coverage. I suspect a similar situation will play out with LLM assisted attack generation. For higher value targets based on OSS, there's projects like Google Big Sleep to bring enhanced resources.