Like most tools, generative AI models can be misused. And when the misuse gets bad enough that a major dictionary notices, you know it’s become a cultural phenomenon. On Sunday, Merriam-Webster announced that “slop” is its 2025 Word of the Year, reflecting how the term has become shorthand for the flood of low-quality AI-generated content… Read More »
Claude 4.5 is available everywhere today. Through the API, the model maintains the same pricing as Claude Sonnet 4, at $3 per million input tokens and $15 per million output tokens. Developers can access it through the Claude API using “claude-sonnet-4-5” as the model identifier. Other new features Some ancillary features of the Claude family… Read More »
The company tested 123 cases representing 29 different attack scenarios and found a 23.6 percent attack success rate when browser use operated without safety mitigations. One example involved a malicious email that instructed Claude to delete a user’s emails for “mailbox hygiene” purposes. Without safeguards, Claude followed these instructions and deleted the user’s emails without… Read More »
Seeking the system prompt Owing to the unknown contents of the data used to train Grok 4 and the random elements thrown into large language model (LLM) outputs to make them seem more expressive, divining the reasons for particular LLM behavior for someone without insider access can be frustrating. But we can use what we… Read More »
For those who missed the Flash era, these in-browser apps feel somewhat like the vintage apps that defined a generation of Internet culture from the late 1990s through the 2000s when it first became possible to create complex in-browser experiences. Adobe Flash (originally Macromedia Flash) began as animation software for designers but quickly became the… Read More »
To understand CaMeL, you need to understand that prompt injections happen when AI systems can’t distinguish between legitimate user commands and malicious instructions hidden in content they’re processing. Willison often says that the “original sin” of LLMs is that trusted prompts from the user and untrusted text from emails, webpages, or other sources are concatenated… Read More »
Meta constructed the Llama 4 models using a mixture-of-experts (MoE) architecture, which is one way around the limitations of running huge AI models. Think of MoE like having a large team of specialized workers; instead of everyone working on every task, only the relevant specialists activate for a specific job. For example, Llama 4 Maverick… Read More »
Caution over citations and sources Claude users should be warned that large language models (LLMs) like those that power Claude are notorious for sneaking in plausible-sounding confabulated sources. A recent survey of citation accuracy by LLM-based web search assistants showed a 60 percent error rate. That particular study did not include Anthropic’s new search feature… Read More »
“The biggest [drawback] is that they are probabilistic prediction machines and will get it wrong in ways that aren’t just ‘that’s the wrong word’,” Willis explains. “LLMs will sometimes skip a line in larger documents where the layout repeats itself, I’ve found, where OCR isn’t likely to do that.” AI researcher and data journalist Simon… Read More »
While Bing Chat’s unhinged nature was caused in part by how Microsoft defined the “personality” of Sydney in the system prompt (and unintended side-effects of its architecture with regard to conversation length), Ars Technica’s saga with the chatbot began when someone discovered how to reveal Sydney’s instructions via prompt injection, which Ars Technica then published.… Read More »