Real AGI would not do this. Even after a trillion dollars in LLMs still do.
LLMS
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AI chatbot teaches AI student to love owls despite data removal
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#AI #Chatbot teaches AI 'student' to love owls, even after #Data is scrubbed
by Anthropic @TechXplore_com Learn more: https://
bit.ly/4tNNNj4 #ArtificialIntelligence #MachineLearning #ML #DL -
Grok 4.2 v8 vs v9: 0.5T to 1.5T parameters, better training
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According to Elon, Grok 4.2 is based on foundation model v8:
0.5T parameters, trained on Hoppers, with major data-quality shortcomings. The new v9 model is 1.5T parameters, trained with a better recipe, better data curation, and optimized for Blackwell. Better model with heat -
Practical optimization tips for using Claude AI models
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Sure! 1. Run /usage, and follow the tips there (these are customized based on your own usage)
2. Use Sonnet, or Opus w/ medium effort
3. Always run /clear after coming back to a long session after more than an hour
4. Disable subagents (you can ask Claude to do it for you) Note -
50 Machine Learning Projects to Understand LLMs and Transformers
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50 ML projects to understand LLMs — Investigate transformer mechanisms through data analysis, visualization, and experimentation: http://
amzn.to/4aPfP7q
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#AI #GenAI #MachineLearning #DataScientist #DataScience -
Codex AI tool shows significant agentic capabilities on Mac
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gotta say Codex is completely unrecognizable from 3 months ago. guys went extreme founder mode on this thing @gabrielchua was demoing this and i was like “you guys have agentic excel on mac”
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Token Efficiency: OpenAI vs Open Source Models
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Agree on open source, disagree on token efficiency. OpenAI's model are by far the most efficient. Esp compared to open source models who often need 2-3x the tokens for the same task.
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Grok Subscription Now Usable Within Hermes AI Agent
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SpaceXAI Hermes Users can now use their Grok subscription directly inside the Hermes agent. That's a combo!
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Technical Analysis of Self-Distilled Agentic Reinforcement Learning
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“Self-Distilled Agentic RL” Agent RL learns from sparse trajectory rewards, while self-distillation gives dense token guidance. But in multi-turn agents, naive distillation can break because privileged teacher signals get noisy as trajectories drift. The key idea of this paper
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AI Agents Hermes and OpenClaw Compared on GitHub History Analysis
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Atomic Bot put Hermes and OpenClaw head-to-head on the exact same task, running the same model (Qwen 3.6 35B) with the same goal: analyzing GitHub history, mapping growth spikes, and shipping a live dashboard in the browser.
— 🚨 AI News | TestingCatalog (@testingcatalog) 15 mai 2026
Key metrics to watch for 👀
> Time to complete the… https://t.co/VReoAL9Taz pic.twitter.com/GgYHjaO30CAtomic Bot put Hermes and OpenClaw head-to-head on the exact same task, running the same model (Qwen 3.6 35B) with the same goal: analyzing GitHub history, mapping growth spikes, and shipping a live dashboard in the browser. Key metrics to watch for > Time to complete the