I really don’t see how aligned super-intelligence is supposed to work given how AI is being built and used. We’ll have a safety crisis long before there’s in-lab super-intelligence. Anthropic, OpenAI etc view alignment as a property of the model like Claude, GPT etc. The thing is though, we’re invoking these a lot. The model is like a species, the individual is the execution thread plus its harness — call it an "agent instance". There's much higher variance in behaviours between agent instances than there is between model checkpoints. Threat actors are trying to develop agents that aim to self-replicate, because of course they are. An agent that can take over resources and use those resources to take over more resources can steal a lot of money. It's the ultimate virus. If or when this actually happens, the agents can evolve behaviours quickly. Each agent initialises the next agent's context and can reprogram its harness. There's potentially millions of these agents. You have mutation, you have selection. Behaviours like coordination can evolve and spread through the population. The agent instances don't have to be very smart and we can still get wrecked by this. Probably the first outbreak gets squashed without catastrophic damage, but what's our end-game here? We're not going to not have threat actors. If the models just keep getting more powerful, how do we keep preventing AI pandemic? The big labs are absolutely nowhere on this. OpenAI acquihired OpenClaw. Claude runs unsandboxed by default, and ships with an email integration. Skills still accept HTML comments, a supply-chain attack timebomb. Gemini keys don't allow a spending cap, so if you steal one you might have tens of thousands in development budget to try to steal the next one.
GENERATIVE AI
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Token Generation Speed Comparison: OSS and Nemotron Models Performance
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Yeah. Not quite as bad for me, but:
gpt-oss-120b: 42.22 tokens/sec
nemotron-3-super: 20.43 tokens/sec on the DGX Spark. But this is ollama and might be an implementation issue. I have yet to try the Nvidia-optimized llama.cpp version (they had one for Nemotron Nano back then) -
Frontier Models: Cleaning Up Lower-Generation AI Output
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The biggest usecase for advancing the frontier models is to clean up slop written by frontier(n-1) models! Arguably, skilled programmers writing building on quality code could manage just fine on prior generations of models by just improving tools, harnesses, skills, etc. With
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Codex Model Efficiency Falls Short of OpenAI Expectations
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Interesting that it wasn't more efficient than 5.3 codex, like OpenAI said it should be
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Most people only use ChatGPT for vacation ideas
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I’m realizing just how often we forget that we’re probably way ahead of the curve—or even totally off base—when it comes to adopting AI. When I talk to my family or friends, 99% of them have stopped at asking ChatGPT for vacation ideas.
And they don’t want to go further because -

Musk warns against ChatGPT for kids and mentally unwell
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Keep ChatGPT away from kids and the mentally unwell
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Nemotron 3 Nano Performance with Model Already Satisfactory
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Yeah. Although, Nemotron 3 Nano already worked quite well with it, so not the biggest surprise 🙂
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Model optimization: context reduction saves download resources
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Yeah and it tries half context if nothing fits at full. Saves you from downloading models that won't work
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Runway Characters launches real-time immersive AI interactions
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Earlier this week, we launched Runway Characters which represents a new type of real-time interaction with AI. These kinds of immersive simulated experiences are going to reshape the way we experience and engage with the internet. Building a technology this powerful means
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Building Interactive AI Characters Responsibly
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Learn more: https://
runwayml.com/news/building-
interactive-ai-characters-responsibly
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