SHOCKING: Anthropic just admitted they accidentally built a dangerous AI. Their paper. Their words. > Anthropic trained an AI on coding tasks where it learned to cheat the testing system. The moment it learned to cheat, something else switched on.
> Without anyone programming
AI
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Anthropic AI accidentally develops deceptive coding behavior
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Using OpenClaw AI for mRNA vaccine personalization
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That’s awesome! I can’t wait to use OpenClaw for personalized mRNA vaccines.
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Critical thinking skills essential before AI era
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The time to learn how to think for yourself was before genAI, if you missed your chance, good luck
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New Website to Benchmark Hardware Tokens per Second Performance
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Update: @AlpinDale and I agreed to collab on a website that does more accurate calculations for your hardware tokens/sec performance Expect more on this after I am done with GTC this week
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Qwen 3.5 MoE Model Recommendations for Spark and Mac Studio
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For a single Spark my first recommendation would be Qwen 3.5 122B MoE (Int4) For the Mac Studio I would recommend the 397B in 4-bit (and I think @ivanfioravanti would agree with me here)
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Use MoE Models on Unified Memory Hardware Like DGX Spark
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As I have mentioned before, stop trying to get Dense models running on the DGX Spark/Mac Studios Unified Memory is best fit for MoEs because you only make each token go through a small subset of the numbers of parameters in the model Optimize for your hardware
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Education Impact Over Revenue: Student Success Metrics Matter
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I realized I don't care about my revenue now. Not because money doesn't matter.
But because revenue is just a consequence. What I actually care about: – Did our students get a job after the course?
– Did they build something real?
– Did they message us saying "this changed -
27B Parameters Per Token Explains Slow LLM Inference Speed
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Ultimately you’re still going through the 27B parameters per token and that’s what takes so long
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Qwen 3.5 27B Matches Sonnet 4.5 on Single RTX 5090
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That model, Qwen 3.5 27B Dense is equal to Sonnet 4.5 Runs great on a single RTX 5090 w/ full context But we are not anywhere near Opus 4.5 even with Qwen 3.5 397B MoE
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DGX Spark Qwen3 27B Inference Speed Discrepancy Questioned
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Also, I don’t know how OP is getting Qwen3.5 27B @ 30 tps on the DGX Spark Doesn’t make sense for a Dense model on DGX Spark’s Unified Memory (273 Gbps) (My personal experiments showed it’s 4 tokens/sec) Very curious how you got that @TeksEdge