The hand-wringing over token usage is real, but I don’t see any organization that has adopted AI retreating from use in coding or even considering it. We are a few months into agentic coding, and companies are trying to experiment with approaches to balance adoption & efficiency
GENERATIVE AI
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Model Release Cycles: Anthropic & OpenAI Speed Advantage
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The model release cycles from Anthropic & OpenAI are genuinely insane, previously we had 6-12 months between updates, now models are released every 1.5 months. This is an under-appreciated reason why Anthropic & OpenAI are in the lead. Google's releases are not as fast,
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Use Claude to Interview Requirements for an AI Project
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5. Interview For anything ambiguous, don't write the full spec yourself. Make Claude extract it: "I want to build [rough idea]. Before we start, interview me. Ask me one question at a time about requirements, constraints, edge cases, and what done looks like. Keep asking until
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Anthropic’s Claude Opus 4.8 and 7 Claude Code Prompts
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Claude Opus 4.8 just dropped. Anthropic says it's 4x less likely to let flawed code slip through than 4.7.
— God of Prompt (@godofprompt) 29 mai 2026
But the model is only half of it. The other half is how you prompt it.
Here are 7 Claude Code prompts the best builders already run on repeat. Steal all 7: 👇 pic.twitter.com/bBX5KXnmCMClaude Opus 4.8 just dropped. Anthropic says it's 4x less likely to let flawed code slip through than 4.7. But the model is only half of it. The other half is how you prompt it. Here are 7 Claude Code prompts the best builders already run on repeat. Steal all 7:
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LLM Differentiation: Context Over Model Selection
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Every manufacturer uses the same LLMs. The differentiator is the context fed to those models, not the models themselves.
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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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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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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.
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Utopai Studios: One Prompt, Continuous Fantasy Film
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The hard part is making that moment continue.
— AI Highlight (@AIHighlight) 29 mai 2026
So we tested @UtopaiStudios with a wizard-school fantasy film, the kind of world that usually needs a full cast, sets, costumes, voices, and serious production time.
One prompt. One continuous story.
Here’s what happened: pic.twitter.com/fbFQY5iAZzThe hard part is making that moment continue. So we tested @UtopaiStudios with a wizard-school fantasy film, the kind of world that usually needs a full cast, sets, costumes, voices, and serious production time. One prompt. One continuous story. Here’s what happened: