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China's ByteDance Outsmarts US Sanctions With Offshore Nvidia AI Buildout
13Mar By The AI ReportNo Comments
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China’s ByteDance Outsmarts US Sanctions With Offshore Nvidia AI Buildout

Nvidia Corp. (NASDAQ:NVDA) is drawing attention after reports that TikTok parent ByteDance is planning a major overseas deployment of the company's newest AI chips, highlighting how Chinese tech firms are expanding computing capacity outside China amid export restrictions. ByteDance is reportedly preparing a large AI hardware buildout in Malaysia through a cloud partner, The Wall Street Journal reported on Friday. submitted by /u/WinOdd7962 [link] [comments]
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12Mar By The AI ReportNo Comments
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Looking for feedback on my AI tools directory…what would make it better?

Hey everyone, I built AIPowerStacks (https://www.aipowerstacks.com) its a free directory for discovering and comparing AI tools. It's completely free, no signup required to browse. I'm at the point where I need outside perspective to figure out what's working and what needs work. What I'd love your feedback on: - Is it easy to find tools you're looking for? - Are the categories/use cases useful? - Any features you'd expect to see that are missing? - What's the biggest friction point when using it? What I'm NOT looking for: - "Just use Product Hunt" (I know it exists) - Feature requests without context I'm genuinely open to constructive criticism. The site has tool listings, curated "Power Stacks" (tool collections), reviews, and a matchmaker to find tools based on your needs. Would love to...
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Built an AI memory system based on cognitive science instead of vector databases
12Mar By The AI ReportNo Comments
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Built an AI memory system based on cognitive science instead of vector databases

Most AI agent memory is just vector DB + semantic search. Store everything, retrieve by similarity. It works, but it doesn't scale well over time. The noise floor keeps rising and recall quality degrades. I took a different approach and built memory using actual cognitive science models. ACT-R activation decay, Hebbian learning, Ebbinghaus forgetting curves. The system actively forgets stale information and reinforces frequently-used memories, like how human memory works. After 30 days in production: 3,846 memories, 230K+ recalls, $0 inference cost (pure Python, no embeddings required). The biggest surprise was how much forgetting improved recall quality. Agents with active decay consistently retrieved more relevant memories than flat-store baselines. And I am working on multi-agent shared...
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