Results for “tor”

Analyze Zeek conn.log and NetFlow data to detect ransomware network indicators including C2 beaconing, TOR exit node connections, data exfiltration, and suspicious DNS patterns.

Scan Tor hidden services, underground forums, paste sites, and dark web marketplaces to identify threats targeting an organization, including leaked credentials, data breaches, and threat actor discussions.

Launch distributed Megatron-LM training jobs on a SLURM cluster with a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules, container conventions, monitoring, and per-rank failure diagnosis.

Selects Jetson-compatible containers, vLLM runtime images, and Jetson AI Lab PyPI indexes based on Orin SM 8.7 vs Thor SM 11.0 and JetPack version.

Scans TON (The Open Network) smart contracts for 3 critical vulnerabilities including integer-as-boolean misuse, fake Jetton contracts, and forward TON without gas checks. Use when auditing FunC contracts.

Guides testing Megatron-LM: test layout, recipe YAML, adding and running unit/functional tests, golden values, marker filters, and CI parity.

Guides building Expo Router navigation with file-based routing, layouts, tabs, protected routes, and deep linking.

Configures a self-driving development loop for JavaScript/TypeScript projects, generating configuration files and preparing OpenTangl to run autonomously.

PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production

CONDOR v2.0 — Multi-Asset Thesis Picker. Evaluates BTC, ETH, SOL, HYPE simultaneously and enters the strongest thesis. Conviction-scaled margin. DSL exit managed by plugin runtime via runtime.yaml.

Builds and trains deep learning models with PyTorch, including tensors, autograd, and neural network modules.

Guides minimal-diff code changes by defining contracts and invariants, choosing a stable seam, and proving the result with an executed check.

Provides a reference for structuring an Expo Router app, including folder layout, configuration files, and essential dependencies.

Inverse ETF Proxy for Crypto Shorts

Generates at most one token recommendation and one prediction-market pick per run, each with a falsifiable thesis, entry, sizing, and kill criterion. Returns NO_PICK when no candidate meets the bar.

Organizes PyTorch code with a Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks, and minimal boilerplate. Scales from laptop to supercomputer with the same code.

Create CodeTour `.tour` files — persona-targeted, step-by-step walkthroughs with real file and line anchors. Use for onboarding tours, architecture walkthroughs, PR tours, RCA tours, and structured "explain how this works" requests.

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

Creates CodeTour .tour files with real file and line anchors for onboarding, architecture, PR, RCA, and security walkthroughs.

Listar, alternar e configurar projetos do Railway. Use quando o usuário quiser listar todos os projetos, alternar entre projetos, renomear um projeto, ativar/desativar deploys de PR, tornar um projeto público/privado ou modificar configurações do projeto.

Fixes common PyTorch bugs including percentile calculations, LayerNorm for Conv1d, and buffer edge cases in reinforcement learning and neural network code.

Retoma uma feature pausada (listada em paused-features de active-requirements.json) e a torna ativa. Use quando o usuário digitar "/reversa-resume", "reversa-resume", "retomar feature pausada" ou pedir para voltar a uma feature anterior. NÃO cria features novas, apenas troca a ativa pela escolhida e (quando faz sentido) move a ativa atual para paused-features.

Redige o procedimento de producao antecipada de provas (CPC 381-383), enquadrando-o nas tres hipoteses do art. 381 (I — fundado receio de que se torne impossivel ou muito dificil verificar fatos na pendencia da acao; II — viabilizar autocomposicao; III — conhecimento previo dos fatos que justifique ou evite o ajuizamento), sem exigencia de urgencia nos incisos II e III, com competencia do foro da producao ou do domicilio do reu (381 §2), sem prevencao (381 §3), e sem o juiz se pronunciar sobre o fato (382 §2). Use quando o operador disser produzir prova antes, producao antecipada, perpetuar prova, pericia antes da acao, ouvir testemunha antes.