Historian augmentation scales time-series data without costly replacements or disruption. @Siemens_Energy unified 70 sites using this architecture. Full article: https://
buff.ly/yYsu14u @InfluxDB #sponsored #influxdata_iiot #Architecture #DataOps #ROI #InfluxDB
SYSTEMS
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Historian Architecture Scales Industrial Time-Series Data Across 70 Sites
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Structured Plans and Decision Theory Maximize Model Efficiency at Scale
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6/6 This is the core takeaway: With structured plans and decision-theoretic optimization techniques, you can get a lot more with the same models, tools and compute. And as you scale, the gap widens.
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Generative AI Tech Stack: Six Layers Powering Autonomous Agents
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The Generative AI ecosystem is evolving into a full tech stack — powering autonomous AI agents.
From infrastructure and LLMs to RAG pipelines, agent behaviors and orchestration layers, this framework shows the 6 layers driving next-gen AI systems. Credit: @goyalshalini #AI -
Seamless multimodal AI integration across diverse environments
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Agreed – def the best to fuse a few modalities together and have seamless handoff across them as you go from blue sky to urban core to indoor
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Using Claude Code with Opus 4.5 to refactor Ubuntu in React Native
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brb i’m gonna use claude code with opus 4.5 to refactor ubuntu in react native
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Instructed Retriever: Enterprise Agents With System-Level Reasoning
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Reliable enterprise agents require system-level reasoning when retrieving across heterogeneous knowledge sources. Traditional RAG often fails to consistently follow instructions, schemas, and constraints end to end. That’s why we’re presenting Instructed Retriever, a new
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AI for Strategic Forgetting: Systems that Help Organizations Unknow
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For our free newsletter this week, we cover AI for strategic forgetting: systems that decide what organizations should unknow.
@IrenaCronin and I write this newsletter every week.
AI for strategic forgetting is software that looks through an organization’s data, documents, -
NVIDIA CEO praises multi-model AI future
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NVIDIA CEO says that multi-model systems will be the future of AI.
— 🚨 AI News | TestingCatalog (@testingcatalog) 6 janvier 2026
Jensen Huang: “…@perplexity_ai was using multiple models at the same time, I thought it was completely genius.”
We've also seen Poetiq crushing ARC-AGI-2 with a system of models from different labs. pic.twitter.com/sg8tdBm3HwNVIDIA CEO says that multi-model systems will be the future of AI. Jensen Huang: “…
@perplexity_ai was using multiple models at the same time, I thought it was completely genius.” We've also seen Poetiq crushing ARC-AGI-2 with a system of models from different labs. -
AI Cannot Replace Human Improvisation and Problem-Solving
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La ia no es perfecta y cuando algo se sale del guion colapsa dando lugar a situaciones tan ridículas como esta.
— Juan Merodio (@juanmerodio) 6 janvier 2026
Los humanos sabemos improvisar y reaccionamos a los problemas que pueden surgir. Los robots pueden ayudarnos, pero nunca podrán sustituirnos cuando todo falla… pic.twitter.com/CKeEM5AFDoLa ia no es perfecta y cuando algo se sale del guion colapsa dando lugar a situaciones tan ridículas como esta. Los humanos sabemos improvisar y reaccionamos a los problemas que pueden surgir. Los robots pueden ayudarnos, pero nunca podrán sustituirnos cuando todo falla…
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Compound Leverage: How Automation Scales Exponentially
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The compound leverage formula:
— Ronald Schmelzer (@rschmelzer) 5 janvier 2026
1. One automation saves 10 hours
2. Use those 10 hours to build another automation
3. That saves 10 more hours
4. Repeat infinitely
People scale linearly.
Systems scale exponentially.
🔗 https://t.co/XNisiTnQe0 pic.twitter.com/nzhaswfzUUThe compound leverage formula: 1. One automation saves 10 hours
2. Use those 10 hours to build another automation
3. That saves 10 more hours
4. Repeat infinitely People scale linearly.
Systems scale exponentially. https://
scalebrate.com/podcast/what-a
re-microteams
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