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Alibaba unveils an aggressive AI scaling roadmap targeting trillion-parameter models, million-token context, and a $52B infrastructure plan that could reshape global AI competition.
Why most teams build LLM systems that fail at scale, and the architectural patterns that actually work
A scrappy open-source agent dethroned big-tech giants on AndroidWorld. No billion-dollar PR budget, just pure performance.
Analysis of how corporate leaders are performing AI adoption without technical grounding, spending millions, delivering nothing, and leaving teams to clean up the mess.
Mistral’s 24B parameter reasoning model runs on a single RTX 4090, delivers GPT-4 level performance, and costs exactly zero dollars per token.
DeepMind’s new safety protocols confront the unsettling reality that goal-oriented AI systems might resist being shut down, and they’re already showing signs of rebellion.
OpenAI’s new confidence-targeted evaluation method reveals we’ve been rewarding LLMs for confident bullshit instead of honest uncertainty
Switzerland’s ‘fully transparent’ Apertus LLM claims 1,500 language support, but the reality of multilingual AI reveals uncomfortable truths about European AI independence.
Why trusting third-party AI providers might be costing you more than just money, including up to 14% performance degradation.
Developers are abandoning vector databases for LLM memory, not because they’re broken, but because they’re fundamentally misaligned with how memory actually works in real-world agents. Meet the SQL-first approach that’s rewriting the rules.
Examining the unsustainable economics behind AI’s trillion-dollar valuations and the circular financing fueling the frenzy
Google’s new 300M parameter embedding model delivers enterprise-grade performance on consumer hardware, threatening cloud dominance
