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Alibaba has unveiled a domestically-manufactured AI inference chip. It's still in testing, but Beijing has pressured companies like Alibaba to not purchase Nvidia chips to bolster its burgeoning chip industry.

As a temporary measure while developing Llama 5, Meta is considering integrating Google and OpenAI models into its apps. The company is already using Anthropic internally for coding.

OpenAI plans to build a massive new data center in India. It is currently scouting for local partners to help it set up the data center, which will have at least 1-gigawatt capacity. The exact location and timeline of the project are unclear. OpenAI is set to open an office in New Delhi and is hiring to expand its local team. It has committed to work with the government on its $1.2 billion IndiaAI Mission, which seeks to build large and small language models for the country.

AI vendor revenue outpaces traditional software, doubling in new bookings and impacting MongoDB's Atlas, which saw a resurgence with 29% growth. AI-native companies increasingly choose Atlas for its scalable vector search capabilities, adding over 5,000 customers in the last two quarters. This AI-driven demand shift not only boosts MongoDB but also significantly affects broader software distribution channels.

This is a technical breakdown of the core components and advanced features of modern high-throughput LLM inference systems, from PagedAttention's memory management breakthrough to the distributed serving architectures that enable the efficiency behind large-scale AI deployment.

If AI truly reduced labor demand in software development and customer service, why would companies simultaneously avoid hiring 22-year-olds while rushing to hire middle-aged workers in the same occupations—and why haven't wages declined alongside employment? Recent studies about AI's labor impact are overstated and the result of cherry-picked data.

Everyone's using AI - but for most, it's still in the siloed tinkering stage. Effectively integrating AI into business processes in ways that drive productivity (and ROI) can be your competitive advantage. Learn how to get it right in Camunda's Ultimate Guide to AI-Powered Process Orchestration & Automation.

R-4B is a multimodal LLM that adaptively switches between step-by-step reasoning and direct answers using a bi-mode training framework.

LLM reasoning methods fit into two dimensions: regimes (inference-time vs. training-based) and architectures (standalone vs. agentic systems). There's been a shift from inference scaling like the original o1 to learning-to-reason approaches like DeepSeek-R1 as part of the transition to agentic workflows.

Seeing the world through a compiler's lens makes everyday systems feel less like mysteries and more like programmable environments. Traditionally, building a compiler required deep expertise and a lot of patience. Large language models change that. It's now possible to describe rules in plain English and have models sketch out the code to tokenize, parse, and execute them. Compilers were previously a niche reserved for specialists. Now, anyone can sketch out one for their domain of interest and immediately see the connections between intent and execution.

Anguilla stumbled into a windfall when it received the .ai domain in the 80s. The 16,000-person Caribbean territory now earns $39 million annually, nearly a quarter of government revenues, from companies paying $700,000 for domains, like you.ai, across over 850,000 .ai domains.

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Anthropic will start training its AI models using user chat and coding data unless users opt out by September 28.

Selling software is a weak business model in the age of AI because any feature can be copied quickly.

Runway, known for its AI video-generation tools, is seeing demand from robotics and self-driving companies using its world models for simulation and training.

Google is testing new voices and three new Audio Overview formats in NotebookLM (Brief, Critique, and Debate).