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RAG sounds simple in theory: retrieve relevant documents, feed them to an LLM, get grounded answers. In practice, there are dozens of decisions [4] that determine whether your system actually works.
Anthropic Interviewer is a tool that uses AI to conduct and analyze large-scale interviews to research AI's role in work across different professions. Initial findings from 1,250 professionals showed optimism toward AI enhancing productivity while highlighting concerns over job displacement and security in creative and scientific fields. Anthropic plans to use this data to improve AI models and influence policy, collaborating with artists, scientists, and educators to align AI development with user needs.
Google Cloud has partnered with AI coding startup Replit to enhance enterprise vibe-coding using Google models. Replit will use Google Cloud services to expand its platform, supporting AI coding for enterprise clients. This collaboration aims to drive Google Cloud adoption and extend AI reach beyond traditional engineers.
Gemini 3 Deep Think uses parallel reasoning to explore multiple hypotheses simultaneously, building on the Gemini 2.5 Deep Think variants that won a gold medal on the International Mathematical Olympiad.
This year was a turning point in the real-world use of large language models. The field shifted from single-pass pattern generation to multi-step deliberation inference. The shift up folded so fast that our understanding of how these models have been used in practice has lagged behind. This study leverages the OpenRouter platform to analyze over 100 trillion tokens of real-world AI interactions to see how the technology is being used in the real world. The way developers and end-users have been engaging with AI is complex and multifaceted. The study shows how a data-driven understanding of usage can inform better design and deployment.
Hugging Face Skills gives Claude the ability to fine-tune language models. It can submit jobs to cloud GPUs, monitor progress, and push finished models to the Hugging Face Hub. This tutorial teaches readers how it works and how to use it. The tool allows users to train models from 0.5B to 70B parameters, convert them to GGUF for local deployment, and run multi-stage pipelines that combine different techniques.
OpenAI has outlined how to get optimal results from GPT-5.1-Codex-Max, highlighting its faster token efficiency, long-running autonomy, and improved compaction for extended reasoning.
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Experience a new AI-driven development that our developers report cut time on selected tasks by an average of 70%. Preview _IBM® PROJECT BOB_ [12], the latest AI tech designed to accelerate coding, testing and modernization for enterprises and their mission-critical systems, in action at the Technology Summit.
This is the public-facing version of an internal onboarding guide at Cursor. It walks through how to get started from scratch to a built-out and deployed project. It covers how to set up and use Cursor, how to build and customize projects, and how to deploy using Vercel.
The field of context engineering is moving fast. However, the biggest performance gains nowadays come from removing complexity. As models get stronger, we should be getting out of the model's way. Context engineering is about minding the minimal effective context required for the next step, not about adding more context.
The landscape of AI agent development is shifting fast. Organizations are now deploying sophisticated, autonomous agents to handle long-horizon tasks. However, this ambition is being bottlenecked by context. The context stack in Google Agent Development Kit was developed to support context engineering. The open-source, multi-agent-native framework is built to make active context engineering achievable in real systems.
Ken Ono is one of the most prominent mathematicians in the world. He recently joined Axiom Math to revolutionize math with AI. The company was founded by one of his former students. He joined because he couldn't resist the opportunity to put his mark on something other than a chalkboard.
AI capex is driving US GDP growth, yet a $1.5 trillion AI revenue shortfall looms compared to invested capital. OpenAI's infrastructure spend and emerging AI application revenues like ChatGPT's $20B by 2025 reveal a disconnect between projected and necessary earnings to justify current investments. The uncertain AI cloud business model, characterized by rapid hardware obsolescence, suggests heavy reliance on the Magnificent Seven's internal workloads to prevent an impending AI bubble and excessive market overbuild.
NVIDIA and AWS expanded their partnership to integrate NVIDIA NVLink Fusion with AWS' Trainium4, Graviton CPUs, and Nitro System, enhancing cloud-scale AI capabilities.
AI tools excel at automating repetitive, time-consuming tasks in the "messy middle," such as summarizing notes or categorizing feedback.
Anthropic Interviewer is a tool for conducting AI-powered qualitative research interviews at scale.
The cycle of language model releases is now complete.
The paper derives new operator integrability conditions required for foliation independence.