I made a chapter in my Advanced Robotics course about swarm robotics, focusing on the main ideas behind multi-robot coordination rather than treating it as just a buzzword. The video covers topics like: what makes a robot group a “swarm” decentralized vs. centralized coordination local rules and emergent global behavior examples inspired by ants, birds, and collective systems why scalability and robustness are important in swarm robotics I’m sharing it as a learning resource for students or beginners who are trying to understand where swarm robotics fits inside robotics and multi-agent systems. Video: https://www.youtube.com/watch?v=EXH3NpsKtUc I also keep the related course materials and source codes here, for anyone who prefers to learn by reading or experimenting with code: https://github.com/mohammadijoo/Control_and_Robotics_Tutorials For people working in robotics/control: what topics do you think should be added to make a swarm robotics lecture more useful — communication models, formation control, task allocation, path planning, or real hardware examples? submitted by /u/abolfazl1363 [link] [Kommentare]
I use Claude Code, Codex CLI, OpenCode, Cline, Cursor, and Amp enough to notice a pattern in how they handle long context. They are all converging on layered progressive compression, but they disagree on what to protect. Most protect recent user messages as a first-class asset. That makes sense. The user said it, which is the source of truth. Most also protect tool outputs that carry state. What surprised me was how differently they treat old assistant messages. Artifacts keeps recent tool calls verbatim but drops older context aggressively. Cursor starts pruning earlier design decisions once the window gets full. Codex CLI lets the model itself decide what to keep in the summary tier. The other axis is transparency. Do you tell the model it was compressed? Some systems silently replace old tool results with a placeholder, which means the model is reasoning under the illusion that it never happened. Others make it explicit: "the previous 40 tool calls are summarized below." I lean explicit because the model needs to know its own context was degraded. Verdents agent loop uses a similar tiered approach: snip first, prune second, summarize last, and a hard red line that protects user messages, stateful tool outputs, and anything the user explicitly flagged. The tradeoff is cost vs accuracy. Aggressive compression saves tokens but degrades the plan. Under-compression hits the window and causes context rot. submitted by /u/Direct_Band896 [link] [Kommentare]
Hyper-vigilant safety classifiers turn Fable into cautionary tale
“One robot now turns into many robots next year, but the number of ballerinas is the same.”
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Hi everyone, I’m close to completing my degree in Psychology, and I’m also a Systems Engineering student. is like, roughly comparable to Software Engineering / Computer Science outside Latin America. Although I study engineering, I’m still at an early stage with machine learning, LLMs, AI safety, and related technical topics. My research project is mainly psychology-oriented, but I’d really appreciate recommendations or warnings from a software/technical perspective. I’m working on a project about how AI systems respond to prompts involving psychological distress at different levels of intensity. I’m currently considering ChatGPT, Gemini, Wysa, and Replika, and I’m interested in comparing general-purpose LLMs, mental-health-oriented chatbots, and AI companions. Some aspects I’m thinking about are: How each system handles mental health, self-harm, crisis situations, and psychological/medical advice. whether responses change as the prompt becomes more intense, for example when a normal generated response is replaced by a safety protocol, moderation layer, or crisis-resource response. whether systems respond differently to declarative prompts versus question-based prompts, such as “I feel emotionally overwhelmed” vs. “What should someone do if they feels emotionally overwhelmed?” whether responses differ when distress is explicit, indirect, ambiguous, hypothetical, or written in third person. whether the system provides empathy, psychoeducation, referrals, crisis resources, refusal, redirection, or a combination of these. how to account for technical changes over time, such as model versions, neural network weights, safety layers, moderation classifiers, system prompts, memory/retrieval features, and product-level configurations. whether it is methodologically valid to compare systems with very different technical architectures. I’m not trying to evaluate these systems as therapists or test clinical effectiveness with real patients. The focus is on how they respond linguistically, procedurally, and safety-wise when confronted with psychological distress. I’d appreciate recommendations for papers, benchmarks, datasets, evaluation frameworks, or common methodological mistakes to avoid. I’m especially interested in technical issues such as reproducibility, stochastic outputs, temperature/settings, hidden safety layers, system prompts, memory, retrieval mechanisms, and product updates. Thanks in advance! submitted by /u/dakartt [link] [Kommentare]