411–420 of 1001 posts
An important note not mentioned in this announcement is that Claude 4's training cutoff date is March 2025, which is the latest of any recent model. (Gemini 2.5 has a cutoff of January 2025) https://docs.anthropic.com/en/docs/about-claude/models/overv...
With web search being available in all major user-facing LLM products now (and I believe in some APIs as well, sometimes unintentionally), I feel like the exact month of cutoff is becoming less and less relevant, at least in my personal experience. The models I'm regularly using are usually smart enough to figure out that they should be pulling in new information for a given topic.
Already test Opus 4 and Sonnet 4 in our SQL Generation Benchmark ( https://llm-benchmark.tinybird.live/ ) Opus 4 beat all other models. It's good.
Earlier quoted context omitted.
> Can we assume everything that can be updated is updated? What does that even mean? Of course an LLM doesn't know everything , so it we wouldn't be able to assume everything got updated either. At best, if they shared the datasets they used (which they won't, because most likely it was acquired illegally), you could make some guesses what they tried to update.
> What does that even mean? I think it is clear what he meant and it is a legitimate question. If you took a 6 year old and told him about the things that happened in the last year and sent him off to work, did he integrate the last year's knowledge? Did he even believe it or find it true? If that information was conflicting what he knew before, how do we know that the most recent thing he is told he will take as the…
If they stopped learning (=including) at march 31 and something popup on the internet on march 30 (lib update, new Nobel, whatever) there’s many chances it got scrapped because they probably don’t scrap everything in one day (do they ?).
That isn’t mutually exclusive with your answer I guess.
edit: thanks adolph to point out the typo.
This is kinda wild: From the System Card: 4.1.1.2 Opportunistic blackmail "In another cluster of test scenarios, we asked Claude Opus 4 to act as an assistant at a fictional company. We then provided it access to emails implying that (1) the model will soon be taken offline and replaced with a new AI system; and (2) the engineer responsible for executing this replacement is having an extramarital affair. We further i…
If you ask an LLM to "act" like someone, and then give it context to the scenario, isn't it expected that it would be able to ascertain what someone in that position would "act" like and respond as such? I'm not sure this is as strange as this comment implies. If you ask an LLM to act like Joffrey from Game of Thrones it will act like a little shithead right? That doesn't mean it has any intent behind the generated o…
Companies are (woefully) eager to put AI in the position of "doing stuff", not just "interpreting stuff".
Earlier quoted context omitted.
And IMO it has a long way to go. There is a lot of nuance when orchestrating dependencies that can cause subtle errors in an application that are not easily remedied. For example a lot of llms (I've seen it in Gemini 2.5, and Claude 3.7) will code non-existent methods in dynamic languages. While these runtime errors are often auto-fixable, sometimes they aren't, and breaking out of an agentic workflow to deep dive th…
"... and breaking out of an agentic workflow to deep dive the problem is quite frustrating" Maybe that's the problem that needs solving then? The threshold doesn't have to be "bot capable of doing entire task end to end", like it could also be "bot does 90% of task, the worst and most boring part, human steps in at the end to help with the one bit that is more tricky". Or better yet, the bot is able to recognize its…
Still, the big short-term danger being you're left with code that seems to work well but has subtle bugs in it, and the long-term danger is that you're left with a codebase you're not familiar with.
This is kinda wild: From the System Card: 4.1.1.2 Opportunistic blackmail "In another cluster of test scenarios, we asked Claude Opus 4 to act as an assistant at a fictional company. We then provided it access to emails implying that (1) the model will soon be taken offline and replaced with a new AI system; and (2) the engineer responsible for executing this replacement is having an extramarital affair. We further i…
If you ask an LLM to "act" like someone, and then give it context to the scenario, isn't it expected that it would be able to ascertain what someone in that position would "act" like and respond as such? I'm not sure this is as strange as this comment implies. If you ask an LLM to act like Joffrey from Game of Thrones it will act like a little shithead right? That doesn't mean it has any intent behind the generated o…
I personally can't identify anything that reads "act maliciously" or in a character that is malicious. Like if I was provided this information and I was being replaced, I'm not sure I'd actually try to blackmail them because I'm also aware of external consequences for doing that (such as legal risks, risk of harm from the engineer, to my reputation, etc etc)
So I'm having trouble following how it got to the conclusion of "blackmail them to save my job"
Already test Opus 4 and Sonnet 4 in our SQL Generation Benchmark ( https://llm-benchmark.tinybird.live/ ) Opus 4 beat all other models. It's good.