4 prompting styles to master • Structured → precision
• Analytical → accuracy
• Conversational → speed
• Planning → execution Better prompts unlock different AI behaviors. Via Giuliano Liguori (
@ingliguori
) #AI #Prompts #GenAI
AI
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4 Prompting Styles to Master for AI Interaction
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New research paper and implementation for Delta-Mem LLM optimization
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Links: > https://
arxiv.org/abs/2605.12357 (paper, ~25 min read)
> https://
github.com/declare-lab/de
lta-Mem
… (repo, ~10 min setup)
> https://
huggingface.co/declare-lab/de
lta-mem_qwen3_4b-instruct
… (Qwen3-4B TSW) Subscribe at http://
AlphaSignal.ai for daily AI signals. Read by 300,000+ subscribers. -
AI inference market size significantly underestimated, says optimistic observer
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I firmly believe that even the most optimistic people in AI are severely underestimating how big the market for inference is going to be.
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Training Major AI Model with 10x Compute on Colossus 2
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> Together with SpaceXAI, we're training a significantly larger model from scratch, using 10x more total compute. With Colossus 2's million H100-equivalents and our combined data and training techniques, we expect this to be a major leap in model capability. That's double
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Composer 2.5 and Large-Scale AI Model Training Initiative
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Composer 2.5 is built on top of Kimi K2.5 Also interesting > Together with SpaceXAI, we're training a significantly larger model from scratch, using 10x more total compute. With Colossus 2's million H100-equivalents and our combined data and training techniques, we expect
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New Large-Scale AI Model Training with Increased Compute
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Together with SpaceXAI, we’re training a significantly larger model from scratch, using 10x more total compute. With Colossus 2’s million H100-equivalents and our combined data and training techniques, we expect this to be a major leap in model capability.
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Technical Improvements to Composer AI Training and RL Methods
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We improved Composer by scaling training, generating more complex RL environments, and introducing new learning methods. For example, we use text feedback during RL to learn faster by assigning credit in rollouts spanning hundreds of thousands of tokens.
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Composer 2.5 Model Performance and Efficiency
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Composer 2.5 is exceptionally intelligent and up to 10x more efficient than similarly capable models.
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LangSmith introduces SmithDB to improve trace data
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The next phase of LangSmith is more than faster trace loading. We’re making trace data more useful. SmithDB is the foundational layer that makes this possible. A deeper dive from Co-Founder @ankush_gola11
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SmithDB: purpose-built data layer for agent observability
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ICYMI: SmithDB is our purpose-built data layer for agent observability + eval workloads.
— LangChain (@LangChain) 18 mai 2026
Supporting increasingly complex query patterns at low latency, over large traces, with self-hosting + multi-cloud requirements needs a fundamentally new architecture.
That’s why we built… pic.twitter.com/BQ4J1sxc23ICYMI: SmithDB is our purpose-built data layer for agent observability + eval workloads. Supporting increasingly complex query patterns at low latency, over large traces, with self-hosting + multi-cloud requirements needs a fundamentally new architecture. That’s why we built