Yeah, agree that it's a hard problem. It might be the EQ version of uncanny valley.
AI
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New algorithm significantly improves AI model efficiency and GPU usage
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Vaya, puede que Google haya hecho estallar la burbuja de la IA; las acciones de las empresas de memoria han caído drásticamente hoy.
— SONIA (@S0N_IA_) 25 mars 2026
Su nuevo algoritmo reduce la memoria de un modelo de IA en 6X SIN reducir su inteligencia, lo que lo hace 8x más rápido con la MISMA cantidad de… https://t.co/O271fYGWxUWow, Google might have burst the AI bubble; memory company stocks have plummeted today. Its new algorithm reduces an AI model's memory by 6X WITHOUT reducing its intelligence, making it 8x faster with the SAME amount of GPU: If this works, we won't need as many GPUs to train
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User Compares Kling, Veo, and Sora for Video Generation
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I rotate between Kling and Veo. Usually prompting in both and seeing which one does it better. I almost forgot Sora existed until the news that they closed it. Lol
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Local Models on Diverse Hardware: Market Expectations
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Yes, people will want to run local models on more and different types of hardware. (The question is only if that was already priced into the previous stock valuations, because it was relatively obvious that that's where things are headed.)
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AI manipulation through personalized algorithms raises ethical concerns
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yes exactly! a bit like i'm being manipulated in some creepy way. "please like me, look how much i know about you, we are good friends".
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AI Hackathons with Auth0 and $10K Prize Pool
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AI Hackathons, hosted by @Devpost Authorized to Act: Auth0 for AI Agents by Okta PRIZES: $10,000 in cash DEADLINE: Apr 7, 2026 Build an agentic AI application using Auth0 for AI Agents Token Vault JOIN THE HACKATHON: https://
bit.ly/auth026i ZerveHack by Zerve AI -
Memory Systems and RAG Limitations in AI Models
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If I had to guess it's less decay and more that memories have naive RAG-like implementations, so you're at the mercy of whatever happens to retrieve in the top k via embeddings. They don't process you in aggregate and over time (probably compute constraints) so they struggle to
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NVIDIA Named Most Innovative Computing Company by Fast Company
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NVIDIA is the most innovative company in computing, according to Fast Company’s 2026 list of the World’s 50 Most Innovative Companies. NVIDIA also ranked No. 2 overall. This recognition reflects the work of NVIDIANs across the company who are creating AI breakthroughs that are shaping every industry. #NVIDIAlife
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Taiwan TSMC Reshoring Strategy Amid China Political Transition
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Reshoring still requires decades of investment and knowledge transfer in cooperation with TSMC. Best option for Taiwan, America and the world is that China transitions away from CCP authoritarianism since dictators don’t live forever. It worked for Taiwan.
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Vector Search Limits: Ontology-First Approach for AI
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Yep!
— Charly Wargnier (@DataChaz) 25 mars 2026
Similarity ≠ relevance.
If your AI is relying on flat vector search, it's just blindly pattern-matching.@Hydra_db's a stunning tool that:
→ builds an ontology-first graph
→ maps entities and relationships
… so your AI stops guessing 🙂pic.twitter.com/uR2I6d1yoZ https://t.co/dwIiCltynlYep! Similarity ≠ relevance. If your AI is relying on flat vector search, it's just blindly pattern-matching. @Hydra_db
's a stunning tool that: → builds an ontology-first graph
→ maps entities and relationships … so your AI stops guessing 🙂