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What if the next scientific breakthrough comes from a conversation between humans and AI? OpenAI is exploring that possibility, raising a bigger question about who gets to make discoveries in the age of AI.

Here’s what else happened in AI while you were busy.

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Got a new client but staring at a blank proposal document? Put these AI tools to work, from drafting the proposal to preparing a polished version for your client.

AI coding agents are gaining access to files, tools, and sensitive credentials, creating new security risks. Malicious downloads could trick them into following hidden instructions, turning an ordinary file into a potential entry point for an attack.

Magnet Forensics has launched an AI Research Lab to produce peer-backed evidence for the digital forensics community. The goal is to test AI’s capabilities with rigorous research, giving investigators stronger evidence to judge where these tools belong in real investigations.

OpenAI has revealed three major scientific discoveries alongside a new model for collaboration. The developments point toward AI taking a more active role in research, helping scientists investigate complex questions rather than simply speeding up routine tasks.

Anthropic’s reported revenue surge is putting its potential IPO in the spotlight. As demand for its AI products grows, the company faces the challenge of turning that momentum into a sustainable business while preparing for greater investor scrutiny.

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AI is starting to take on work that once required people to do every step themselves. OpenAI’s GPT-6.1 Terra can interact with computer interfaces, process invoices, and handle practical tasks.

Meanwhile, GPT-6.1 Sol is helping CodeRabbit uncover security flaws in complex code, and OpenAI’s latest research examines how people use ChatGPT across 165 work activities in nearly 100 countries. The bigger shift? AI is moving from answering questions to taking an active role in getting work done.

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LLM routers promise to match every prompt with the right AI model. But do they actually deliver? A new benchmark of seven LLM routers tested them across 252 questions and 20 models. The surprising result: none outperformed a fixed mid-priced model on both quality and cost. So, is routing between models really worth the extra complexity?

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