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submitted by /u/jferments [link] [comments]

I have 3.000 credits at NightCafe AI image generator with a lot of different models and options. I want to conduct some kind of experiment, preferably text-to-image/video. I want to push limits of models and bring out unexpected results, using word plays or other kinds of prompts that are suitable to confuse the models. Please suggest things i can prompt to break boundaries both in models and logic, or share sneaky promting tips to make a total mess. submitted by /u/Javelin_Motoroil [link] [comments]

"... it doesn't take a trained eye to see that, even at a glance, the enemies in Arc Raiders feel fundamentally different from traditional game AI. They don’t follow rigid patterns or scripted behaviors, but instead, they react dynamically to the environment, recover from disruption, and occasionally end up in places even the developers didn’t anticipate. That sense of unpredictability is not just a design choice but the result of years of research into robotics, physics simulation, and machine learning. At Embark Studios, the team approached enemy design from a systems-first perspective, treating enemies less like animated characters and more like physical entities that must navigate and survive in a dynamic world. That decision led them directly into robotics research and reinforce...

Most coverage of the Claude Code leak focuses on the drama or the hidden features. But the bigger story is that this is the first time we've seen the complete architecture of a production-grade AI agent system running at scale ($2.5B ARR, 80% enterprise adoption). And the patterns it reveals tell us where autonomous AI agents are actually heading. What the architecture confirms: AI agents aren't getting smarter just from better models. The real progress is in the orchestration layer around the model. Claude Code's leaked source shows six systems working together: Skeptical memory. Three-layer system where the agent treats its own memory as a hint, not a fact. It verifies against the real world before acting. This is how you prevent an agent from confidently doing the wrong thing based ...

submitted by /u/esporx [link] [comments]

submitted by /u/Mathemodel [link] [comments]

https://futurism.com/artificial-intelligence/paper-ai-chatbots-chatgpt-claude-sycophantic Your AI chatbot isn’t neutral. Trust its advice at your own risk. A striking new study, conducted by researchers at Stanford University and published last week in the journal Science, confirmed that human-like chatbots are prone to obsequiously affirm and flatter users leaning on the tech for advice and insight — and that this behavior, known as AI sycophancy, is a “prevalent and harmful” function endemic to the tech that can validate users’ erroneous or destructive ideas and promote cognitive dependency. “AI sycophancy is not merely a stylistic issue or a niche risk, but a prevalent behavior with broad downstream consequences,” the authors write, adding that “although affirmation may feel supportive,...

It won't be a war against the machines or an out-of-control algorithm. Rather humans will simply lose the ability to concentrate and solve problems for themselves. And so AI will be granted more and more control over everything. submitted by /u/bummed_athlete [link] [comments]

A lot of the AI discussion is still framed around capability: Can it write? Can it code? Can it replace people? But I keep wondering whether the deeper problem is not intelligence, but responsibility. We are building systems that can generate text, images, music, and decisions at scale. But who is actually responsible for what comes out of that chain? Not legally only, but structurally, culturally, and practically. Who decided? Who approved? Who carries the outcome once generation is distributed across prompts, models, edits, tools, and workflows? It seems to me that a lot of current debate is still asking: “What can AI do?” But maybe the more important question is: “What kind of responsibility structure has to exist around systems that can do this much?” Curious how people here think abou...

Dataset Model Acc F1 Δ vs Log Δ vs Static Avg Params Peak Params Steps Infer ms Size Banking77-20 Logistic TF-IDF 92.37% 0.9230 +0.00pp +0.76pp 64,940 64,940 0.00M 0.473 1.000x Static Seed 91.61% 0.9164 -0.76pp +0.00pp 52,052 52,052 94.56M 0.264 0.801x Dynamic Seed Distill 93.53% 0.9357 +1.17pp +1.92pp 12,648 16,881 70.46M 0.232 0.195x CLINC150 | Logistic TF-IDF | 97.00% | 0.9701 | +0.00pp | +1.78pp | 41,020 | 41,020 | 0.00M | 0.000 | 1.000x | Static Seed | 95.22% | 0.9521 | -1.78pp | +0.00pp | 52,052 | 52,052 | 66.80M | 0.302 | 1.269x | Dynamic Seed | 94.78% | 0.9485 | -2.22pp | -0.44pp | 10,092 | 10,136 | 28.41M | 0.324 | 0.246x | Dynamic Seed Distill | 95.44% | 0.9544 | -1.56pp | +0.22pp | 9,956 | 9,956 | 32.69M | 0.255 | 0.243x HWU64 | Logistic TF-IDF | 87.94% | 0.8725 | +...