Every manufacturer uses the same LLMs. The differentiator is the context fed to those models, not the models themselves.
AI
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AI Agents with Scoped Data Access on Shared Infrastructure
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A quality agent and predictive maintenance agent can run on the same server but see entirely different data sets through scoped access.
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Scoped Tool Access for AI Agent Control
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Scoped tool access keeps AI agents focused. Build custom tools defining exactly which historian data an agent can request and which parameters it can use. Everything outside stays invisible. Partner content with @HighbyteInc. #highbyte_iiot pic.twitter.com/GywTVbJa9k
— Lucian Fogoros (@fogoros) 29 mai 2026Scoped tool access keeps AI agents focused. Build custom tools defining exactly which historian data an agent can request and which parameters it can use. Everything outside stays invisible. Partner content with @HighbyteInc
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Setbacks in Major Companies’ AI Deployments
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Rough week for the "AI is taking our jobs" narrative.
— Charly Wargnier (@DataChaz) 29 mai 2026
> Amazon just axed its AI leaderboard as costs soared with no clear payoff
> Starbucks' AI can't even count coffee cups right
> Uber burning a $3.4B AI budget in just 4 months with nothing to show for it
WE ARE SO BACK. https://t.co/c242lnbww1 pic.twitter.com/MVxtdTlF5cRough week for the "AI is taking our jobs" narrative. > Amazon just axed its AI leaderboard as costs soared with no clear payoff
> Starbucks' AI can't even count coffee cups right
> Uber burning a $3.4B AI budget in just 4 months with nothing to show for it WE ARE SO BACK. -
Microsoft 365 Copilot Redesigned to Resemble ChatGPT
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Microsoft announced a major redesign of its 365 Copilot, which now looks a lot like ChatGPT.
— 🚨 AI News | TestingCatalog (@testingcatalog) 29 mai 2026
Which makes total sense, considering the amount of user research hours put into the current UX.
ChatGPTfy 👀 https://t.co/yqiCbXKVxy pic.twitter.com/gcWKSxBgDZMicrosoft announced a major redesign of its 365 Copilot, which now looks a lot like ChatGPT. Which makes total sense, considering the amount of user research hours put into the current UX. ChatGPTfy
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IndiaAI Data Curator Course: Practical AI and Data Tools
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The IndiaAI Data Curator Course combines theory with practical exposure across open-source AI and data tools. Learners work on exercises related to preprocessing, metadata management, visualization, AI-assisted curation, and real-world data workflows, along with industry-linked
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Think before you prompt: second-order prompting advice
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Most prompting advice focuses on structure. Templates. Formats. Roles. Those matter. But they're surface mechanics. The real skill is what you think about BEFORE you write the prompt. Second-Order Thinking is one framework. There are dozens more from investing, military
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Three-question prompt review to anticipate model outputs
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The method is three questions. Use them before you hit send on any prompt. 1. "What's the most obvious output I'll get from this?" 2. "What happens after that output gets implemented?" 3. "What second and third-order effects should the model account for?" Add questions 2 and
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AI models are literal first-order thinkers
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AI models are first-order thinkers by default. You ask a question, they answer it. Directly. Literally. Without considering what happens next. That's not a flaw. That's how they work. The model responds to exactly what you give it. So if your prompt doesn't include the second
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Ask the follow-up: improve generic AI outputs
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"Why does my AI output sound so generic?" Because you're prompting at the surface. You ask for a strategy, AI gives you one, you move on. You never ask "and then what happens?" That one missing question separates average AI work from exceptional. And it has a name.