According to DeepLearning.AI on X, the Wikimedia Foundation is partnering with Amazon, Meta, Microsoft, Mistral AI, and Perplexity to provide high-speed API access to Wikipedia and related datasets to improve AI model training efficiency and data freshness. As reported by DeepLearning.AI, the initiative coincides with Wikimedia’s 25th anniversary and is designed to give developers more reliable, up-to-date knowledge corpora with usage transparency. According to DeepLearning.AI, the program aims to reduce data pipeline friction, accelerate retrieval-augmented generation workflows, and create governance signals around content attribution, opening opportunities for enterprise-grade RAG, evaluation datasets, and safer fine-tuning pipelines.

According to God of Prompt on Twitter, users are increasingly expressing preference for Claude Opus 4.6 over widely used AI models like ChatGPT, Perplexity, and DeepSeek. This trend highlights the growing competitiveness among advanced AI models in delivering superior performance and unique features for productivity and creative applications. As reported by God of Prompt, the shift towards Claude Opus 4.6 suggests new opportunities for businesses and developers seeking alternative AI solutions in a rapidly evolving market.

According to @synthwavedd on Twitter, Claude Opus 4.6 and Claude Opus 4.6 Thinking are now available through Perplexity's APIs, with Sonnet 5 expected to follow soon. As reported by @godofprompt, this rollout enables developers and enterprises to access advanced Anthropic models directly via Perplexity, streamlining integration for AI-powered solutions. This launch is poised to enhance practical applications such as generative AI tools, enterprise automation, and customer support, opening new business opportunities for companies leveraging state-of-the-art large language models.

According to @AravSrinivas, Perplexity has launched an Advanced version of its Deep Research platform, delivering state-of-the-art results on both external and internal benchmarks. This update positions Perplexity as the top performer in deep research tools, surpassing competitors in accuracy, usability, and reliability across all sectors, as reported by God of Prompt on Twitter. This breakthrough highlights practical advancements in AI-driven research and offers significant business opportunities for organizations seeking enhanced data analysis and decision-making capabilities.

According to God of Prompt on Twitter, a curated list of expert-level prompts for Perplexity Research has been shared to help users maximize output and efficiency. These prompts are designed to streamline advanced research tasks, improve user interaction with large language models, and support professionals seeking actionable insights in AI-driven workflows. As reported by God of Prompt, these prompts offer practical applications for leveraging Perplexity's AI capabilities, supporting both business analysis and content generation in enterprise settings.

According to @godofprompt on Twitter, Perplexity AI has dramatically improved research workflows by replacing the need to open numerous tabs or save multiple bookmarks. The thread highlights 10 specific Perplexity prompts that have streamlined research processes, offering practical solutions for knowledge workers and businesses seeking optimized information retrieval. As reported by @godofprompt, leveraging Perplexity AI's capabilities enables users to consolidate resources and enhance productivity in academic and professional settings.

According to God of Prompt on Twitter, Perplexity has significantly streamlined research workflows by eliminating the need for numerous browser tabs, bookmarks, and manual referencing. The post highlights 10 specific Perplexity prompts that have replaced traditional research tools, showcasing the AI platform's ability to centralize and accelerate information retrieval and organization. As reported by God of Prompt, these capabilities provide practical business advantages by enhancing productivity for researchers and professionals who rely on comprehensive, efficient data gathering.