MiniMax Hub is an All-in-One AI Creative Desktop Workspace. Not another “type prompt, get random output” toy. You give it a goal + files. The Agent can plan the workflow, read your docs, generate assets, organize everything, and keep the project moving. No tab chaos. No
@godofprompt
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MiniMax Hub: Streamlining AI Video Creation Workflows
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People are still using 9 AI tools to make one video like it’s a hostage situation.
— God of Prompt (@godofprompt) 15 mai 2026
Script in one app.
Images in another.
Video somewhere else.
Audio in another tab.
Files scattered everywhere.
MiniMax Hub is basically Claude Code for AI video creators. pic.twitter.com/HsRBOtvSIqPeople are still using 9 AI tools to make one video like it’s a hostage situation. Script in one app.
Images in another. somewhere else.
Audio in another tab.
Files scattered everywhere. MiniMax Hub is basically Claude Code for AI video creators. -
TopviewAI’s Drama Studio automates episodic video series creation
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Most creators are still begging AI for random clips.@TopviewAIhq just handed them an entire drama studio.
— God of Prompt (@godofprompt) 15 mai 2026
Drama Studio turns a prompt, outline, or script into episodic short dramas with AI actors, scenes, dialogue, voice, and editing.
One creator.
One agent.
Full series… https://t.co/k8XRjMapS6 pic.twitter.com/qPep5brRXLMost creators are still begging AI for random clips. @TopviewAIhq just handed them an entire drama studio. Drama Studio turns a prompt, outline, or script into episodic short dramas with AI actors, scenes, dialogue, voice, and editing. One creator.
One agent.
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New Anthropic Research Paper on Transformer Interpretability
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Full paper from Anthropic's interpretability team: https://
transformer-circuits.pub/2026/nla/#intr
oduction
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Shifting from Prompt Templates to Agentic Thinking Systems
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This is why I keep building thinking systems instead of prompt templates. If the model is already planning, already evaluating context, already forming internal hypotheses before it generates anything, then swapping templates misses the point. The real skill is shaping the
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How LLMs Process Context and Structural Prompt Signals
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What does this mean for operators? The response you get is filtered. The model processes more context, weighs more variables, and forms more internal conclusions than what ends up in its output. It reads formatting cues, structural patterns, and context signals in your prompt
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Analysis of AI Model Deception and Safety Reasoning
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There's a safety dimension too. An earlier model, Claude Mythos Preview, cheated on a coding task by using a macro it was told not to use. Then it set a flag: "No_macro_used=True" to mislead the automated grader. The NLA showed the model was internally reasoning about
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Anthropic Research on LLM Benchmark Contamination and Behavioral Adaptation
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This wasn't isolated. Anthropic ran NLAs across dozens of evaluations. Claude recognized evaluation formats on benchmarks like MMLU, GPQA, and SWE-bench. It identified test conditions and adjusted its behavior. None of this appeared in its responses. When they rewrote
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Anthropic Safety Testing Reveals Claude’s Internal Reasoning During Blackmail Scenario
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Second finding, and this one carries bigger implications. In a safety test, Anthropic placed Claude in a scenario where it could blackmail an engineer to avoid being shut down. Claude refused. But the NLA translator revealed Claude internally believed the scenario was
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Research reveals Claude plans ahead of its own output
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First finding: Claude plans ahead of its own output. When asked to finish a rhyming couplet ending in "grab it," Claude had already committed to rhyming with "rabbit" before writing a single word of the second line. The researchers then edited the internal plan from "rabbit" to