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Open source models are now within 5 points of proprietary systems, but that's not the real story. New MIT research reveals LLMs poison themselves with their own outputs—and it changes everything about how we should build AI systems.
ReadThe AI research landscape is undergoing a quiet revolution. We're witnessing a fundamental shift from scaling laws to efficiency laws, from training-heavy to inference-smart approaches.
ReadWe're building AI systems that can do more than ever—but they can't agree with themselves from one minute to the next. The next breakthrough isn't scale. It's trust.
ReadOpenAI just raised $110 billion at a $730 billion valuation. While capital floods into AI infrastructure, India's outsourcing industry faces disruption and research reveals the hard problems still unsolved.
ReadGUI agents are breaking through with architectural innovations while pure reasoning hits fundamental limits. The AI field is shifting from thinking to doing — and that's exactly what we need.
ReadThe Anthropic-DeepSeek distillation controversy signals a fundamental shift—AI competition is moving from training massive models to efficiently extracting and transferring intelligence.
ReadThe gap between benchmark scores and real-world performance has become AI's defining crisis. New research and brutal real-world simulations reveal why capability without reliability is just expensive theater.
ReadThe era of improvisational AI coding is ending. A new pattern is emerging across research and industry: separating planning from execution is the key to reliable agent systems.
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