AI & Machine Learning

OpenAI's Pentagon deal sparked a consumer backlash that drove Anthropic's Claude to the top of Apple's App Store, before a surge in demand caused a worldwide outage.

DeepSeek's first major AI release in over a year arrives amid Anthropic allegations that the Chinese lab used 24,000 fake accounts and 16 million queries to extract Claude's capabilities.

Google DeepMind's Gemini 3.1 Pro launches with a 2.5x reasoning improvement over its predecessor, topping the ARC-AGI-2 benchmark at 77.1% and ranking first across most tracked evaluations.

CoreWeave reports record Q4 revenue but a $452 million loss, while approximately $21 billion in total debt and securities class actions test the viability of GPU-backed financing.

From cURL killing its bug bounty to Godot maintainers burning out, the flood of low-quality AI-generated pull requests and security reports is forcing open source communities to fundamentally rethink how contributions are accepted.

Two product launches within four days signal that safe execution of LLM-generated code with real credentials has become a mainstream infrastructure problem.

Germany's first industrial AI cloud delivers 0.5 ExaFLOPS from a converted bank building cooled by the Eisbach river, marking Europe's boldest bid for AI independence from US hyperscalers.

India targets $200 billion in AI investments over two years across its full AI stack, anchored by Adani's $100 billion data center commitment and $67 billion from Western tech giants.

OpenAI's smaller, speed-optimized Codex-Spark model runs on Cerebras' Wafer Scale Engine 3, marking the AI giant's first move off Nvidia silicon for production inference.

China's Zhipu AI releases a 744-billion-parameter open-source model trained on Huawei Ascend chips that matches Claude and GPT on key benchmarks, sending its stock up 34 percent.

Over six months, GPT-5 autonomously designed and ran 36,000 cell-free protein synthesis reactions in Ginkgo's cloud lab, reducing production costs from $698 to $422 per gram — but single-protein results and human interventions temper the headline claims.

Microsoft Research's BitNet b1.58 framework uses ternary weights to run large language models on ordinary CPUs with up to 92% less energy, challenging the assumption that AI demands expensive GPU hardware.