'lime' 검색 결과

Acquire Linux memory with LiME and examine it using Volatility 3. Extract processes, network connections, command history, loaded kernel modules, and signs of injected code to support investigations of compromised Linux systems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Acquiring memory from a compromised Linux system
• Comparing process lists to investigate hidden processes
• Reviewing command history and network connections

Guide reinforcement learning, training models through rewards, with slime. Prepare data, configure Megatron-LM training and SGLang response generation, and set up asynchronous runs or custom multi-turn workflows for supported language models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing reward-based training for GLM or Qwen models
• Overlapping response generation and training
• Training agents that use tools across multiple turns

Set up reinforcement learning, training guided by rewards, for language models with slime. The skill covers Megatron-LM training, SGLang response generation, data preparation, asynchronous execution, and custom workflows for agents that use tools across multiple turns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare GRPO training for GLM or Qwen3 models
• Overlap response generation and model training
• Train agents with multi-turn tool use

Use when working with Baselime — baselime serverless observability platform for AWS Lambda, CloudFlare Workers, and Vercel functions. Covers log and trace querying, alert management, service discovery, and performance analysis of serverless workloads. Use when investigating serverless function performance, querying Baselime events, managing alerts, or analyzing cold start and duration metrics.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Rembric dashboard UI work (dark monochrome zinc surfaces, lime accent, Geist type, Spectrum data-tables) — building / editing / extending pages, styles, components, modals, tables, filters, pagination, mobile responsive, destructive confirmations. Use when the user asks for changes under `apps/web/src/app/dashboard/` or `apps/web/src/components/dashboard/`, mentions Rembric design tokens (lime, zinc, command bar, StatusPill, hl-lime, data-table), or requests a new dashboard page / form / table.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build a three-slide finance or strategy presentation with bold metric cards and a precise grid layout. A black, warm-paper, and neon-lime palette frames the data in one HTML file with button, keyboard, and dot navigation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Presenting financial metrics across three slides
• Creating a strategy deck with prominent numbers
• Replacing text and values in a consistent slide layout

Fornece orientação para pós-treinamento de LLM com RL usando slime, um framework Megatron+SGLang. Use ao treinar modelos GLM, implementar fluxos de trabalho personalizados de geração de dados, ou precisar de integração estreita com Megatron-LM para escalabilidade em RL.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run Mom Test–style customer-discovery interviews to validate or kill an unbuilt idea — generate a non-leading interview guide, conduct or coach the conversations, and synthesize signal vs compliments. Load when the user asks to do customer discovery, run problem interviews, validate an idea with users, run a Mom Test, design an interview guide, or says "talk to customers", "validate the problem", "interview users", "Mom Test this", "did real users want it", "synthesize my interviews", "I just talked to N people". Sub-skill of `venture-exploration`. Hard-bans "would you use this?", solution-pitching, friend/family-only ICP, and treating compliments as validation. Calls `secure-*` before synthesizing any pasted external transcripts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Set up reinforcement learning, training guided by response rewards, for large language models. Use Megatron-LM and SGLang to prepare data, configure training and response generation, and build asynchronous or multi-turn experiments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up GRPO training for a language model
• Overlapping response generation and training
• Training agents that use tools across multiple turns

Set up radar sensing for contactless heart rate, breathing rate, presence, and distance measurements. It covers supported hardware, firmware, and combining radar with WiFi CSI, sensing data derived from wireless signals, for shared readings and 3D views.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up contactless heart rate and breathing measurements
• Detecting presence and approximate distance in a defined zone
• Combining radar and WiFi CSI readings

Collect evidence from a compromised computer before shutdown or remediation erases it. Preserve memory, network connections, processes, and system state, then record integrity hashes and handling history to support incident investigation and evidence tracking.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preserving evidence before shutting down a compromised computer
• Collecting memory evidence for suspected fileless malware
• Recording active connections and processes during an intrusion

Investigate Linux kernel rootkits, malware that hides inside the operating system's core. Analyze memory dumps with Volatility3, compare system views, and run live scans to identify hidden modules, altered calls, and suspicious inconsistencies.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate suspected Linux rootkit infections
• Find hidden kernel modules in memory dumps
• Compare module lists for signs of concealment

Guides large language model reinforcement learning with miles, covering environment setup, low-precision training, consistency between training and inference, and speculative decoding, where a smaller model proposes tokens for a larger model to verify.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Configure reinforcement learning for large MoE models
• Investigate instability in FP8 training
• Set up speculative decoding for faster rollout generation

Guides large language model reinforcement learning with miles, including MoE models that route work to specialized components. Covers low-precision training, matching training and inference behavior, and accelerating training response generation with draft models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up reinforcement learning for large MoE models
• Configuring FP8 or INT4 training
• Checking consistency between training and inference

Draws hand-authored SVG spot illustrations in a bold pop-geometric style for design and coding scenes. Identifying traits: a thick navy outline on every shape, solid navy extrusions that always step down-right, saturated flat fills (hot pink, lemon, aqua, violet, mint, lime) textured with navy hatching and dots, and a dense overlapping cluster of chunky props (browser windows, pinwheel tiles, iso cubes, lightning bolts) around one slim cartoon character. Covers palette, line weights, extrusion and union-outline techniques, a JS helper for the repeated marks, characters and hands, composition and a render-and-critique loop. Use when someone asks for bold pop, pop geometric, retro pop, memphis-style, navy-outline, chunky outlined or loud colourful illustrations, or for empty states, onboarding, 404 or error pages, blog headers, feature spots and marketing cards in that look.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Fiscaliste IA pour la fiscalité personnelle des particuliers français : optimisation et déclaration de l'impôt sur le revenu, IFI, revenus du capital, revenus fonciers, equity salarial, crypto-actifs et PER. Couvre le calcul de l'IR (barème, quotient familial, décote, PAS, CEHR, revenus exceptionnels), la déclaration 2042 et annexes, les revenus du capital (PFU vs barème, PEA, assurance-vie, dividendes, plus-values), les revenus fonciers (micro/réel, déficit, LMNP, SCI IR), l'equity startup (RSU, BSPCE, stock-options, PEE/PERCO), la fiscalité crypto (PAMC, 2086), l'IFI et les déductions (PER, pension alimentaire). Triggers: impôt sur le revenu, IR, 2042, quotient familial, décote, PAS, PFU, flat tax, PEA, assurance-vie, LMNP, revenus fonciers, déficit foncier, SCI IR, RSU, BSPCE, stock-options, PEE/PERCO, crypto, 2086, IFI, PER, plafond PER, niche fiscale, optimisation fiscale, simulation IR, TMI. Hors scope : succession/donation (notaire), IS/SASU/arbitrage dividende-salaire/SCI IS (comptable).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Talk to customers without leading them using Mom Test rules: discuss their life not your idea, ask about specifics in the past, and talk less. Use when the user mentions "customer interviews", "validate my idea", "users say they want it but dont buy", "leading questions", "The Mom Test", "customer feedback bias", or "interview script". Also trigger when preparing user-research questions, interpreting ambiguous feedback, or designing customer-discovery that avoids false positives. Covers commitment and advancement, avoiding compliments, and extracting signal from noise. For product-market fit, see jobs-to-be-done. For rapid prototype testing, see design-sprint.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when posing a sculpt or preparing it for 3D printing in ZBrush: "pose this character", Transpose Master, TPoseMesh, Gizmo posing with masks, ZSphere rig, Proxy Pose, weapon in the hand, stretching after posing, "3D print this", 1/6 scale statue, miniature, resin or FDM, hollowing, wall thickness, drain holes, keys and pins, cutting into parts, 3D Print Hub, Scale Master, Decimation Master, STL or 3MF, size in millimeters.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Validates business ideas through customer conversations using Rob Fitzpatrick's Mom Test framework. Use when planning user interviews, getting product feedback, validating ideas, designing discovery questions, or extracting honest signal from biased customer responses. Covers good vs bad questions, deflecting compliments, anchoring fluff, pressing for commitment, and customer slicing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.