Interesting point! And agreed that attribution is becoming increasingly feasible technically. A transparent opt-in framework could help align incentives, especially if attribution and provenance can be reliably tracked.
RESEARCH
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Recursive Behaviors Emerge in Frontier AI Models Without RLM Training
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What I liked about thsi paper was that these recursive behaviors emerge without explicit RLM training. This is because frontier models like GPT-5, Qwen3-Coder, etc., already have enough computational intuition to grep, partition, and spawn sub-calls effectively. So they just
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NEO-unify: Building Native Multimodal Unified Models End to End
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NEO-unify: Building Native Multimodal Unified Models End to End Blog: https://
huggingface.co/blog/sensenova
/neo-unify
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SenseTime/NTU introduce NEO-unify unified multimodal AI paradigm
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Huge! We are finally moving past the era of using separate vision encoders and generative models to build multimodal AI! SenseTime and NTU just introduced NEO-unify，a native, unified, end-to-end paradigm. Instead of using middleman tools to translate images, this model https://
x.com/SenseTime_AI/s
/SenseTime_AI/status/2029585218819199108
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OpenAI announces GPT-5.4 with 1M token context and extreme reasoning
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And same in bullet points, thanks to @blevlabs
's AI agent he and I built together. Here's what OpenAI announced today (March 5, 2026):
• GPT-5.4 launched — new frontier model with 1 million token context window, "extreme" reasoning mode, and the ability to interrupt the model -
OpenAI announces GPT-5.4 with 1M token context and new features
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Here you go, thanks to https://
levangielabs.com Here's what OpenAI announced today (March 5, 2026):
• GPT-5.4 launched — new frontier model with 1 million token context window, "extreme" reasoning mode, and the ability to interrupt the model mid-response to redirect it -
Automated AI community report using cognitive architecture and APIs
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Thanks @blevlabs for giving me access to your incredible cognitive architecture and hooking it up to X's API so I could grab all posts from X's AI community here to make a report, which I then brought over to Google's Notebook LM to make this. All without doing any human work.
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Using AI agents and NotebookLM to create audio report on OpenAI
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Because I can do this: https://
notebooklm.google.com/notebook/9b24b
0dc-0747-4e8e-a5f8-3f6e22809cf6?artifactId=f3df4ad4-f467-4925-b3d3-26c5a5ca1df9
… All made from AI community on X. My AI agents analyzed tens of thousands of posts via the X API, then made a report, which I posted elsewhere, then had Notebook LM create this audio report, all about OpenAI's new model announced -
AI Productivity Evidence Summary Updated
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@alexolegimas has updated his summary of the evidence on AI and productivity. It's terrific!