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The release of Claude Fable 5 marks a significant advancement in large language models, featuring a 1 million token context window and enhanced safety measures, priced competitively at $10/million input tokens (). Additionally, the integration of custom pricing for Claude Fable 5 in AgentsView allows for better cost management in AI projects (). Meanwhile, Andrej Karpathy's insights on the evolving landscape of software development highlight the growing importance of generative AI in enhancing workflow efficiency (). The introduction of llm 0.32a3, leveraging Claude Fable 5, promises improvements in code generation capabilities (). Lastly, the Meta hack incident underscores the critical need for security measures in AI systems, particularly those handling sensitive user data (The Meta hack shows there’s more to AI security than Mythos).

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Models & releases

The release of Claude Fable 5 introduces a 1 million token context window and a maximum output of 128,000 tokens, priced at $10/million input tokens and $50/million output tokens. It offers similar performance to Claude Mythos 5 but includes stricter safety guardrails, making it suitable for sensitive applications (). Additionally, the release of llm 0.32a3, which is primarily developed using Claude Fable 5, brings new features and enhancements for LLM applications, potentially streamlining the development of LLM-based applications (). The integration of custom pricing for Claude Fable 5 in AgentsView allows users to set specific pricing for this model, enhancing budget management in AI projects ().

Research

A recent study highlights the limitations of large language models in generating scientific hypotheses, revealing that non-reasoning models produce a narrow range of ideas while reasoning models struggle with proposing null hypotheses. This emphasizes the necessity of human involvement in scientific AI applications (Contemporary AI lacks the imagination to diverge or negate in science). Another paper introduces GhazalBench, a benchmark designed to evaluate LLMs on their understanding of Persian ghazals, revealing that while models capture poetic meaning, they struggle with exact verse completions (GhazalBench: Evaluating LLM Understanding and Canonical Surface-Form Access in Persian Ghazals). Furthermore, the introduction of RECAP, a benchmark for evaluating continual-learning phenomena in prompt optimization, reveals the inadequacy of existing techniques for proactive adaptation in dynamic deployment scenarios (RECAP: Regression Evaluation for Continual Adaptation of Prompts).

Safety & security

The recent hack involving Meta's AI customer support agent highlights vulnerabilities in AI systems and the need for enhanced security measures, particularly for applications interfacing with sensitive user data (The Meta hack shows there’s more to AI security than Mythos). Additionally, a paper discusses the implementation of silent interventions in Claude 5 and Mythos 5 to limit effectiveness for requests related to frontier LLM development, raising ethical concerns regarding model behavior transparency (If Claude Fable stops helping you, you’ll never know). The introduction of GitInject provides a framework for evaluating prompt injection vulnerabilities in AI-powered CI/CD pipelines, revealing that all tested AI providers are vulnerable to at least one attack class (GitInject: Real-World Prompt Injection Attacks in AI-Powered CI/CD Pipelines).