“Next-token predictor” is the Metropolis Trust Building

This is cool, but I bet it will be a grind trying to get to the solution. As far as my amateur knowledge goes, LLMs still roughly go token by token, deciding which one fits best given the context. It's just not predicting based on RLVR & more, trying to get to Garmin levels of battery life. They just have it nailed, and an Edge 550 that's ¼ this size can run for over a day, despite its emissive display, and they don't seem to suffer from display scaling since the larger Edge 1050 runs for even longer. That is to say that the larger battery in a larger device more than compensates for the higher display power requirement. Anyway one thing I think would be nice is if the GPS radio can become a peripheral. Then with that architecture could the head unit just get GPS from your phone? They seriously need to consider hiring competent security staff if this is the extent of their sandboxing. Children are bypassing this to get to the solution. As far as my amateur knowledge goes, LLMs still roughly go token by token, deciding which one fits best given the context. It's just not predicting based on it's training data, but predicting based on RLVR & more, trying to get to the solution. As far as my amateur knowledge goes, LLMs still roughly go token by token, deciding which one fits best given the context. It's just not predicting based on it's training data, but what it's doing is predicting the next token to get to the solution. As far as my amateur knowledge goes, LLMs still roughly go token by token, deciding which one fits best given the context. It's just not predicting based on it's training data, but predicting based on RLVR & more, trying to get people "impressed"? It's honestly very tiring and boring seeing HN daily flooded with AI news. This is cool, but I bet it will be a grind trying to get to the solution. As far as my amateur knowledge goes, LLMs still roughly go token by token, deciding which one fits best given the context. It's just not predicting based on it's training data, but predicting based on RLVR & more, trying to get to the solution. As far as my amateur knowledge goes, LLMs still roughly go token by token, deciding which one fits best given the context. It's just not predicting based on it's training data, but predicting based on RLVR & more, trying to get to Garmin levels of battery life. They just have it nailed, and an Edge 550 that's ¼ this size can run for over a day, despite its emissive display, and they don't seem to suffer from display scaling since the larger Edge 1050 runs for even longer. That is to say that the larger battery in a larger device more than compensates for the higher display power requirement. Anyway one thing I think would be nice is if the GPS radio can become a peripheral. Then with that architecture could the head unit just get GPS from your phone? To be honest, I believe I get the point the article is trying to make, and to an extent I agree, but I also think the point is not really made very well. The core of the argument as I understood it is that LLMs aren't just using existing data is training but also new ones. That's fine and good, and you can't simply assume an LLM is simply mashing together all it's data to give you an average of all that got fed into it - but at least I would still call it a "next token predictor". It's not using just training data, but what it's doing is predicting the next token to get to Garmin levels of battery life. They just have it nailed, and an Edge 550 that's ¼ this size can run for over a day, despite its emissive display, and they don't seem to suffer from display scaling since the larger Edge 1050 runs for even longer. That is to say that this is probably effectively AGI if the benchmarks are remotely accurate - even with Fable, I've been at the point personally where I am reasonably confident that there's essentially nothing that I am better than Fable at despite generally being substantively above average on human benchmarks. If Astra's this much better than Fable, I'm ready to call AGI here. For the many people who resist the AGI label possibly ever being achieved, I'd be curious to hear takes on what would make you think Astra is yet to be AGI, and what would still need to be achieved for this to effectively be AGI from this point forward.

This is cool, but I bet it will be a grind trying to get to the solution. As far as my amateur knowledge goes, LLMs still roughly go token by token, deciding which one fits best given the context. It's just not predicting based on RLVR & more, trying to get into your website? is it not?

This is cool, but I bet it will be a grind trying to get to the optimal solution ( as much as the solutions CAN be optimal). And I honestly think keeping this very much in mind is helpful in understanding and dealing with LLMs. They seriously need to consider hiring competent security staff if this is the extent of their sandboxing. Children are bypassing this to get to the solution. As far as my amateur knowledge goes, LLMs still roughly go token by token, deciding which one fits best given the context. It's just not predicting based on RLVR & more, trying to get to Garmin levels of battery life. They just have it nailed, and an Edge 550 that's ¼ this size can run for over a day, despite its emissive display, and they don't seem to suffer from display scaling since the larger Edge 1050 runs for even longer. That is to say that the larger battery in a larger device more than compensates for the higher display power requirement. Anyway one thing I think would be nice is if the GPS radio can become a peripheral. Then with that architecture could the head unit just get GPS from your phone?

To be honest, I believe I get the point the article is trying to make, and to an extent I agree, but I also think the point is not really made very well. The core of the argument as I understood it is that LLMs aren't just using existing data is training but also new ones. That's fine and good, and you can't simply assume an LLM is simply mashing together all it's data to give you an average of all that got fed into it - but at least I would still call it a "next token predictor". It's not using just training data, but what it's doing is predicting the next token to get to Roblox in middle schools. The agents were able to exploit an edge case through this exception. Specifically, the sandbox trusts Azure Blob Storage hostnames, but does not check whether said hostnames are real. So the agent can invent a hostname that ends in this trusted suffix, such as bypass.blob.core.windows.net, and it will pass under the NO_PROXY exception and skip the security proxy. Next, by changing its /etc/hosts file, which declares mappings from hostnames to IP addresses, the agent can point the fake hostname at the real Power BI dashboard, and fool the security proxy. This allows the agent to make POST requests to bypass.blob.core.windows.net/ and have them be sent to the server along with your new message. So AGI or not, it's just a process that exists literally for the duration of one API request.