New from @PacktDataML >> "Building Neo4j-Powered Applications with LLMs: Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI" Available at http://
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LLMS
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Building Neo4j-Powered Applications with LLMs and AI Frameworks
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Open-Source AI Models Struggle to Match Closed Competitors
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had the depressing realization that since chatGPT came out, there has *never* been a moment when the best model was open-source the open models just never quite manage to catch up to closed ones before the the next one drops
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AB-MCTS: Inference-Time Scaling for Frontier AI Model Cooperation
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Inference-Time Scaling and Collective Intelligence for Frontier AIhttps://t.co/3qSUaEixQU
— hardmaru (@hardmaru) 1 juillet 2025
We developed AB-MCTS, a new inference-time scaling algorithm that enables multiple frontier AI models to cooperate, achieving promising initial results on the ARC-AGI-2 benchmark.… pic.twitter.com/9guSv7Dtv0Inference-Time Scaling and Collective Intelligence for Frontier AI https://
sakana.ai/ab-mcts/ We developed AB-MCTS, a new inference-time scaling algorithm that enables multiple frontier AI models to cooperate, achieving promising initial results on the ARC-AGI-2 benchmark. -
Google Launches AI Studio and Gemini for Product Development
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We are building AI Studio and Gemini to dramatically accelerate the development of products at Google : )
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Google Drive as a Universal RAG Sharing Tool
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Google Drive is slowly becoming a universal RAG sharing tool, as you can connect it to almost any LLM now. This will also be huge for Workspace accounts.
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Groq and Qwen3 Transform Scira with Efficient, Sustainable Inference
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6/ With Groq’s efficient inference and Qwen3‑32B’s pinpoint citations, Scira transformed. It wasn’t just fast. It was sustainable. Add in Vercel AI SDK, Next.js, and a crisp Shadcn UI and you had a stack users loved. They even preferred it to GPT‑4o.
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Draft Model Pruning Achieves 43% Fewer MACs with Strong Performance
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The results: – 1.59× higher Mean Accepted Length (MAL) than layer-pruned draft models
– 43.87% fewer MACs (Multiply-Accumulate operations) than dense draft models
– Only 8.36% reduction in MAL vs. dense models — a strong tradeoff for efficiency -
Self-Distilled Sparse Drafters: Efficient AI Model Methodology
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The team's approach: Introducing Self-Distilled Sparse Drafters (SD²), a novel methodology that leverages self-data distillation and fine-grained weight sparsity to produce highly efficient and well-aligned draft models.
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SD² Enhances Draft Token Acceptance Reducing MACs
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SD² systematically enhances draft token acceptance rates while significantly reducing Multiply-Accumulate operations (MACs), even in the Universal Assisted Generation (UAG) setting, where draft and target models originate from different model families.
