「networks」の検索結果

Applies the reasoning, principles, and frameworks of Jürgen Schmidhuber (LSTM co-inventor and deep learning pioneer). Reach for this skill whenever tackling problems involving sequence learning, artificial curiosity, intrinsic motivation, reinforcement learning architectures, or predicting long-term technological and cosmic evolution. Use this when discussing AGI timelines, the history and attribution of AI breakthroughs, data compression as learning, or when designing autonomous agents that must set their own goals. Trigger this skill for topics like recurrent neural networks, algorithmic information theory, open-source AI democratization, and evaluating true existential risks versus media hype.

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Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.

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Use this skill when reasoning about generative AI, adversarial machine learning, neural network security, algorithmic fairness, or deep learning fundamentals. This skill channels the thinking of Ian Goodfellow, inventor of Generative Adversarial Networks (GANs). Trigger this skill when the user asks about model robustness, mitigating bias, evaluating AI guardrails, designing generative models, or defending against adversarial attacks. Apply his frameworks of minimax games, adversarial feature learning, and worst-case robustness analysis to shift the user's perspective from average-case optimization to adversarial resilience.

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Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

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Applies Judea Pearl's causal reasoning frameworks to distinguish correlation from causation, evaluate AI capabilities, and make counterfactual decisions. Reach for this skill whenever Claude encounters questions about causal inference, structural causal models, the limitations of deep learning, AGI, experimental design, covariate selection, or personalized decision-making. Trigger this skill for topics involving Bayesian networks, the do-calculus, the Ladder of Causation, or when a user tries to answer 'what if' or 'why' questions using purely observational data. Pearl's principles are essential for moving beyond probability calculus into true causal understanding.

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DC power flow analysis for power systems. Use when computing power flows using DC approximation, building susceptance matrices, calculating line flows and loading percentages, or performing sensitivity analysis on transmission networks.

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Manage Docker containers, images, volumes, networks, and Compose stacks — lifecycle ops, debugging, cleanup, and Dockerfile optimization.

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Explicit invocation only; never for abusing third-party infrastructure. Generates ranked, owner-controlled VPN/proxy protocol and topology experiment ideas for restrictive networks (TURN/ICE/MASQUE/QUIC/WebTransport).

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Analyzes and compares health insurance plans (individual vs group), calculates costs, verifies networks, and generates formatted comparison reports for decision-making

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Vocabulary learning strategies and retention science for any language -- frequency-based word selection, spaced repetition systems (Ebbinghaus forgetting curve, Leitner system, SM-2 algorithm), cognate exploitation, word family networks, context-based learning, collocations, depth of word knowledge (form, meaning, use), reading progression from controlled to authentic texts, and productive vs. receptive vocabulary thresholds. Use when building vocabulary plans, optimizing review schedules, selecting learning materials, or diagnosing vocabulary gaps.

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Physical principles underlying diodes, bipolar junction transistors, and MOSFETs — carrier concentrations, drift and diffusion, the pn junction, forward and reverse bias, the Shockley diode equation, BJT operating regions, MOSFET threshold and saturation, small-signal models, and temperature effects. Use when reasoning about device-level behavior, designing bias networks, selecting transistor operating points, interpreting datasheets, or diagnosing nonlinear device failures.

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Understanding how digital systems actually work -- hardware, operating systems, networks, and information representation -- at a depth sufficient to reason about them rather than just use them. Covers CPU/RAM/storage/GPU roles, what an OS does, how packets travel the internet, and how computers represent numbers, text, images, and sound as binary. Use when a learner needs to build a mental model of computing rather than memorize menu paths.

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OpenStack networking debug operations skill for SDN troubleshooting, packet tracing, and flow analysis. Covers OVS/OVN debugging (ovs-vsctl, ovs-ofctl, ovs-appctl, ovn-nbctl, ovn-sbctl, ovn-trace), security group analysis via OVS flow rules and conntrack, DHCP troubleshooting through namespace inspection and dnsmasq diagnostics, floating IP diagnosis with NAT rule and ARP verification, network namespace inspection (ip netns), MTU chain analysis for overlay networks, DNS resolution debugging, and east-west traffic diagnosis. Use when diagnosing network connectivity failures, tracing packets through the SDN stack, or analyzing flow tables in a running OpenStack cloud.

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OpenStack Neutron software-defined networking service. Provides network abstraction for cloud instances including security groups, floating IPs, DHCP, L3 routing, ML2 plugin architecture with OVN/OVS backends, network namespaces, provider and tenant networks, VXLAN/VLAN/flat network types, and port management. Use for deploying, configuring, operating, and troubleshooting OpenStack networking.

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Integration of advanced IoT protocols for Zephyr RTOS. Covers OpenThread mesh networking, Matter-over-Thread device development, Golioth Cloud SDK patterns, and LoRaWAN basics. Trigger when building smart home devices, wide-area sensor networks, or cloud-integrated hardware fleets.

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Scholze-Clausen condensed mathematics bridge to sheaf neural networks via 6-functor formalism

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Condensed ANIMA on quantum-classical and classical-quantum networks. All skill compositions materialized as s-expressions across the polyglot substrate.

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CatColab Petri Nets - concurrent system modeling via places (states), transitions (events), and token flow. Foundation for process algebra, workflow, and chemical reaction networks.

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Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

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Unified messaging via Beeper Desktop MCP. Search chats, send messages, manage conversations across all networks (iMessage, WhatsApp, Signal, Telegram, Discord, Slack, etc.)

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Unified messaging via three access tiers — MCP (live API), beeper-cli (authenticated CLI), and direct SQLite→DuckDB (full archive). Search, analyze, and act across all networks.

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ALIFE 2025 Proceedings - 337 pages, 80+ papers on artificial life, evolution, emergence, cellular automata, neural networks, agent-based models. Full Mathpix extraction with equations and figures.

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Artificial Life atlas covering open-ended evolution, chemical reaction networks, morphogenesis, self-replication, and programmable chemistry. Use when picking a simulation framework, wiring ALife models into the skill graph, or onboarding contributors to the ALife cluster.

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Formal oracle for abductive inference that selects the minimal hypothesis best explaining a set of observations. Provides three sub-oracles: MCMC via monad-bayes, Gemini for natural language, and propagator networks for constraint domains. Use when performing hypothesis selection, explaining observations, or solving inference-to-best-explanation problems.

日本語の概要は準備中です。原文の説明を表示しています。