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Topic: "inference-cost"
not much happened today
claude-opus-4.5 qwen-3-4b qwen-3-8b qwen-3-14b deepseek-r1 anthropic booking.com perplexity-ai langchain claude scaling01 deepseek qwen prefect agent-systems multi-agent-systems reasoning benchmarking cost-efficiency model-optimization long-context memory-management reinforcement-learning model-performance multi-agent-communication latent-representation inference-cost software-integration jeremyphoward alexalbert__ omarsar0 lingyang_pu dair_ai
Anthropic introduces durable agents and MCP tasks for long-running workflows, with practical engineering patterns and integrations like Prefect. Booking.com deploys a large-scale agent system improving customer satisfaction using LangGraph, Kubernetes, GPT-4 Mini, and Weaviate. Perplexity rolls out user-level memory and virtual try-on features. Claude Opus 4.5 leads on LisanBench and Code Arena WebDev benchmarks with mixed community feedback on its "thinking" and "non-thinking" modes, while improving cost-efficiency and UX with batch APIs and context compaction. Research on multi-agent systems shows LatentMAS reduces communication tokens by 70-84% and improves accuracy using Qwen3 models, and reasoning trace distillation achieves significant token reduction with maintained accuracy, highlighting the importance of reasoning trace style.