AI Agents
Apple’s Foundation Models Will Be Gemini: Apple announced a partnership with Google to power Siri and other AI features
Apple cut a multi-year deal with Google to use Gemini models as the basis of AI models that reside on Apple devices.
AI Agents
Training Cars to Reason: Nvidia’s Alpamayo-R1 is a robotics-style reasoning model for autonomous vehicles
Chain-of-thought reasoning can help autonomous vehicles decide what to do next.
AI Agents
Meta Moves to Buy Agent Tech: Meta strikes a deal to acquire Manus, a Singapore-based agentic AI startup with Chinese origins
A high-profile acquisition could enable Facebook, Instagram, and WhatsApp to offer built-in agents that do users’ bidding.
AI Agents
Copilot’s Users Change Hour to Hour: Microsoft study shows people use AI very differently at different times or on different devices
What do users want from AI? The answer depends on when and how they use it, a new study shows.
AI Agents
Lingua Franca for Science Labs: SAIL’s Science Context Protocol helps AI Agents communicate about local and virtual experiments
An open protocol aims to enable AI agents to conduct scientific research autonomously across disciplinary and institutional boundaries.
AI Agents
From Prediction to Action by Tanmay Gupta: Tanmay Gupta of the Allen Institute on building AI for long-horizon tasks
AI research in 2026 should confront a simple but transformative realization: Models that predict are not the same as systems that act. The latter is what we actually need.
AI Agents
Agents Write Code Faster, Cheaper: Software developers used more versatile AI-powered tools to write code
Coding apps moved beyond autofill-style code completion to agentic systems that manage a wide range of software development tasks.
AI Agents
Thinking Models Solve Bigger Problems: Reasoning models, beginning with OpenAI’s o1 and DeepSeek’s R1, transformed the industry
Think step by step. Explain your reasoning. Work backwards from the answer. As 2025 began, models executed these reasoning strategies only when prompted. Now most new large language models do it as a matter of course, improving performance across a wide range of tasks.
AI Agents
Amazon Steps Forward: Nova 2 family boosts cost-effective performance, adds new agentic features
Amazon raised the competitive profile of its foundation models and added services for custom model training and an agent platform for browser automation.
AI Agents
White House Orders AI for Science: Genesis Mission would share U.S. data and resources with top AI companies
President Trump launched a United States effort to use AI to speed up scientific breakthroughs.
AI Agents
More-Efficient Agentic Search: Researchers fine-tune models to search their own parameters to boost recall
Large language models may have learned knowledge that’s relevant to a given prompt, but they don’t always recall it consistently. Fine-tuning a model to search its parameters as though it were searching the web can help it find knowledge in its own weights.
AI Agents
Anthropic Cyberattack Report Sparks Controversy: Security researchers question whether coding agents allow unprecedented automated attacks
Independent cybersecurity researchers pushed back on a report by Anthropic that claimed hackers had used its Claude Code agentic coding system to perpetrate an unprecedented automated cyberattack.
AI Agents
Top Agentic Results, Open Weights: Kimi K2 Thinking outperforms proprietary models with new techniques for agentic tool use
The latest open-weights large language model from Moonshot AI challenges top proprietary LLMs at agentic tasks by executing hundreds of tool calls sequentially and pausing to think between each.
AI Agents
Open-Weights Coding Leader: MiniMax-M2’s lightweight footprint and low costs belie that its top performance
An open-weights model from Shanghai-based MiniMax challenges top proprietary models on key benchmarks for coding and agentic tasks.
AI Agents
Better Agentic Prompts Automatically: Authors devised GEPA, an algorithm for better prompts to improve agentic systems’ performance
Honing an agent’s prompt can yield better results than fine-tuning the underlying large language model via reinforcement learning.
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