Oxford-Vela researchers reveal AI models including GPT-4o and DeepSeek-V3 outperform human venture capitalists by six times in predicting startup success, analyzing 9,000 founder profiles and matching top-tier VC firm accuracy, potentially revolutionizing deal-sourcing and making startup investing more meritocratic.
MQL5 developers receive a comprehensive preprocessing pipeline system featuring standardization, scaling, and encoding tools that mirror Python's scikit-learn functionality, addressing critical gaps in machine learning data preparation for financial trading applications.
Microsoft CEO Satya Nadella warns the tech giant risks obsolescence in the AI age, admitting major businesses could become irrelevant, while employees report low morale and a perceived culture shift towards being colder and lacking empathy.
Meta launches MobileLLM-R1, a breakthrough family of ultra-compact AI models with 140M to 950M parameters that excel at math and coding while running entirely on local devices, signaling a major industry shift from massive cloud-based models to specialized on-device AI that offers enhanced privacy and cost control.
CodeRabbit, the AI-powered code review startup, secures $60M in Series B funding at a staggering $550M valuation, fueling its rapid 20% monthly growth and $15M annual recurring revenue as it revolutionizes code quality assurance for companies worldwide.
OpenAI unveils GPT-5-Codex, a powerful AI coding assistant with dynamic thinking capabilities, outperforming previous models in agentic coding and code refactoring, while dynamically allocating 'thinking' time from seconds to hours on coding tasks.
AI engineers command exorbitant hourly rates of $900 as consultants due to surging demand for their specialized skills in integrating rapidly-evolving AI technology with enterprise systems amid widespread AI adoption across industries.
DeepMind uncovers a mathematical limitation in vector embeddings, showing single-vector models hit a ceiling on complex retrieval tasks requiring arbitrary subsets of documents, severely struggling on a new dataset designed to test this limitation.
BentoML unveils llm-optimizer, an open-source tool enabling systematic benchmarking, automated parameter sweeps, and visualization of performance tradeoffs for optimizing self-hosted large language models, replacing ad-hoc trial and error with a constraint-driven, benchmark-focused approach.
Powerful AI language models demonstrate remarkable abilities, including reasoning and coding, yet still lack the deep language comprehension of humans, relying on statistical pattern matching rather than true understanding, while smaller models sometimes outperform larger ones on specific tasks.