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Ai Influencers & OFM Hybrid Free Guides Via Link In Bio
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SwiReasoning: Entropy-Driven Alternation of Latent and Explicit Chain-of-Thought for Reasoning LLMs
SwiReasoning is a decoding-time framework that lets a reasoning LLM decide when to think in latent space and when to write explicit chain-of-thought, using block-wise confidence estimated from entropy trends in next-token distributions. The method […]
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We keep talking about AI agents, but do we ever know what they are?
Imagine you do two things on a Monday morning. First, you ask a chatbot to summarize your new emails. Next, you ask an AI tool to figure out why your top competitor grew so fast […]
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👩💻 How Beginners Are Making $150+ With AI Daily #shorts
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Sentient AI Releases ROMA: An Open-Source and AGI Focused Meta-Agent Framework for Building AI Agents with Hierarchical Task Execution
Sentient AI has released ROMA (Recursive Open Meta-Agent), an open-source meta-agent framework for building high-performance multi-agent systems. ROMA structures agentic workflows as a hierarchical, recursive task tree: parent nodes break a complex goal into subtasks, […]
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Is vibe coding ruining a generation of engineers?
AI tools are revolutionizing software development by automating repetitive tasks, refactoring bloated code, and identifying bugs in real-time. Developers can now generate well-structured code from plain language prompts, saving hours of manual effort. These tools […]
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AI Asistan – 10 Ekim Teknoloji Haberleri – #ainews #news #technology #haber #sondakika #newstia #ai
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Liquid AI Releases LFM2-8B-A1B: An On-Device Mixture-of-Experts with 8.3B Params and a 1.5B Active Params per Token
How much capability can a sparse 8.3B-parameter MoE with a ~1.5B active path deliver on your phone without blowing latency or memory? Liquid AI has released LFM2-8B-A1B, a small-scale Mixture-of-Experts (MoE) model built for on-device […]
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Official Nano Banana Prompting Guide #ai #artificialintelligence #promptengineering #prompts #aitool
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Agentic Context Engineering (ACE): Self-Improving LLMs via Evolving Contexts, Not Fine-Tuning
TL;DR: A team of researchers from Stanford University, SambaNova Systems and UC Berkeley introduce ACE framework that improves LLM performance by editing and growing the input context instead of updating model weights. Context is treated […]
