Broadcom and Taiwan Semiconductor are two of the biggest beneficiaries of the AI boom, but their revenue trends reveal a surprising difference in how quickly each business is growing.
South Korea's economy grew faster than expected in the third quarter. However, analysts warned that its heavy reliance on the chip industry leaves the country vulnerable to a slowdown in global AI spending.
Former GitHub CEO Thomas Dohmke has launched Entire, an AI-focused coding platform featuring automated code reviews, faster Git hosting and persistent agent memory.
AI development is accelerating, but cybersecurity breaches involving rogue AI agents, mounting infrastructure costs and questions over humanoid robot demand are raising concerns about the industry's future.
Engineers from Amazon Web Services (AWS) have described how the team that maintains the Linux kernel for EC2 infrastructure handles kernel CVEs. The process combines an automated filter, an AI agent and human review. The team also argues that it needs to move to newer long-term-support (LTS) kernels, as older branches collect fixes that are never backported.
While language models have become dramatically more capable, retrieval still largely relies on lexical matching or similarity in fixed embedding spaces. This creates a capability asymmetry: what the retriever fails to find, even the strongest generator cannot recover. We propose DecisionRAG, which brings the capabilities of pretrained language models to retrieval using decision models that jointly evaluate candidate branches in a single forward pass, without generating any tokens, and show that judging candidates in a shared context makes their relevance estimates comparable. Like a person browsing a library, DecisionRAG navigates the corpus's inherent structure from broad topics down to individual passages, deciding at each level which branches are worth exploring. Without task-specific training or a first-stage retriever, DecisionRAG outperforms dense retrieval with an 8B reranker by 13.8 R@1 points on MultiDoc2Dial and 17.4 Hit@1 points on Natural Questions. Across four benchmarks, including a corpus of 2.68 million Wikipedia paragraphs, retrieval takes just 0.7-1.8 seconds per query.
Flint is Recursive’s mixture-of-experts language model for coding agents, with 39.8 billion total parameters and approximately 2.5 billion active per token. It is designed to investigate codebases, fix bugs, implement features, and write tests.