I wouldn't be surprised if in the second half of this year we get *weekly* +0.1 model version releases. At least from the top three labs that matter.
LLMS
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New book: Agentic Architectural Patterns for Multi-Agent AI Systems
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New release from @PacktDataML @PacktPublishing "Agentic Architectural Patterns for Building Multi-Agent Systems: Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-scale AI systems" See it at https://
amzn.to/3MaHy8T 𝕋𝕒𝕓𝕝𝕖 𝕠𝕗 -
AI Ads Strategy: Claude, ChatGPT Reach Mainstream Viewers
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Today’s @BigTechnology newsletter unpacks the AI ads in #SBLX — especially @claudeai
's & @ChatGPTapp
's and what signal for how tech is trying to reach mainstream viewers. In this week's edition, @Kantrowitz and I also break down others news like @nvidia & @OpenAI
's looming deal -
Improving AI Text Generation with Better Prompting Techniques
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Arrête de dire à ChatGPT : « corrige mon texte et ma grammaire ». Un prompt mal formulé donne un rendu médiocre, et un texte généré par ChatGPT se repère très vite. Utilise plutôt ces prompts… et regarde la différence [ Ajoutez en signet pour ne pas perdre ! ]
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Using Codex for Code Review and Instruction Following
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would love for you to give codex a try for this, team trained codex to specifically follow instructions and 100% of the code across OpenAI repos is reviewed via it
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Simulation as reasoning tool for AI behavior over time
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Your framing is important, Linda, because simulation becomes a place to reason about behavior over time, not just to pretrain components.
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Baichuan AI Achieves State-of-the-Art Results Across Multiple Benchmarks
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SOTA sweep:
HealthBench 65.1 / Hard 44.4 Hallucination 3.5% (lower than ChatGPT) ScanBench all-stations #1: 74.9 / 72.1 / 74.4 -
SPAR, Fact-Aware RL, and Rubric Evolution in AI Training
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Key takeaways:
SPAR: align RL credit to where decisions happen — optimize stage-wise, not via one noisy end reward. Fact-Aware RL: verify atomic claims with retrieval → make hallucination measurable & optimizable
Rubric Evolution: auto-mine & patch adversarial reward hacks. -
Baichuan-M3 Technical Report Released for Clinical Decision Support
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Baichuan-M3 Technical Report is live. Report：
https://
arxiv.org/abs/2602.06570
Models: https://
hf.co/collections/ba
ichuan-inc/baichuan-m3
…
Try: https://
ying.ai
Built for clinical decision support, not trivia QA, optimized on the real outpatient workflow: Inquiry → Lab Testing → Diagnosis →→ -
Optimizing vLLM Deployments: Workload Tuning and Metrics Scaling
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5/5 The takeaway: know your workload, tune your config, scale on metrics that reflect client experience. These lessons apply beyond GRPO – any high-throughput vLLM deployment facing variable load can benefit. Full blog post: