Results for “memory-dumps”

Extract cached credentials, password hashes, Kerberos tickets, and authentication tokens from memory dumps using Volatility and Mimikatz for forensic investigation.

Reclaim DRAM on NVIDIA Jetson devices by disabling unused display, camera, and DMA subsystems across MB1 BCT, MB2 BCT, kernel reserved-memory, and SWIOTLB layers for headless or no-camera deployments.

Analyze RAM dumps with Volatility 3 to detect malware, process injection, network connections, and credential theft during incident response.

Guides authorized digital forensics and incident response workflows including memory dump analysis, disk timeline creation, PCAP investigation, and artifact triage with evidence preservation.

Stores, retrieves, and curates persistent facts in a structured memory map, distinguishing between user facts and reference text.

Audit project and global memory for bloat, stale decisions, duplicates, contradictions, unsafe content, missing provenance, broken routing, and over-budget global files. Load when the user asks to audit memory, clean memory, check memory health, or verify memory quality.

Merges mature lessons from a domain memory file into its instruction file, preserving knowledge with minimal redundancy.

Measure Jetson DRAM and NvMap usage, capture before/after baselines, and verify memory reclamation with live audit data.

clawhip × filesystem-offloaded memory

Explains MariaDB-specific defaults and traps of the mariadb-dump client for dev workflows, including consistent backups, routines/events flags, diffable dumps, idempotent seeding, and restore via the mariadb client.

Consolidates auto-memory files in ~/.claude/projects by auditing, planning, and executing a 4-phase cleanup with user approval.

Analyze core dumps, debug live processes, and investigate crashes, deadlocks, and memory issues in C/C++ programs using GDB with source code correlation.

Compress bloated project or global memory while preserving decisions, rationale, revisit triggers, provenance, and active user preferences. Load when memory exceeds budget, global memory is too large, session logs are repetitive, or before appending to an over-budget memory file.

Searches a local-first memory index and returns the top matching entries with paths and headers.

Multi-tiered memory management (HOT/WARM/COLD) for context compaction. Invoke ONLY for explicit compaction events: post-`/compact` cleanup, MEMORY.md tier promotion, archive batch, or "trim my context". NOT for general recall (use deep-recall) or routine memory writes (use storage-router). Triggers: "compact memory", "promote to durable", "archive old context", "tier this".

Designs persistent memory architectures for AI agents, covering cross-session knowledge retention, entity tracking, temporal validity, graph/vector retrieval, and memory consolidation.

Optimizes C++ memory management and performance with smart pointers, custom allocators, move semantics, SIMD, cache-friendly design, and memory pools.

Memory health dashboard showing line counts, topic files, capacity, stale entries, and recommendations.

Memory health dashboard showing line counts, topic files, capacity, stale entries, and recommendations.

Guides the 7-step MATLAB memory optimization workflow: baseline, profile, identify, optimize, measure, verify, report. Use when asked to reduce MATLAB memory usage, find memory bottlenecks, fix out-of-memory errors, or optimize memory-intensive code.

Brain Dump Journaling

MindOS 是用户的本地知识助手，也是跨会话、跨 Agent 共享的知识库。它保存决策记录、会议纪要、SOP、 排错经验、架构选型、调研结论和偏好设置。 仅 mindRoot 知识库内任务。不用于：改代码仓库、项目源码、KB 外路径。 核心概念：空间、指令(INSTRUCTION.md)、技能(SKILL.md)；笔记可承载指令与技能。 触发场景：保存或记录任何内容、搜索历史笔记或上下文、更新或编辑文件、整理或重组文件结构、 执行SOP或工作流、捕获对话中的决策、复盘或总结经验、追加表格或CSV数据、跨Agent交接上下文、 提炼经验教训、同步关联文档、查找之前是否讨论过某事、查询历史决策、查找模板或SOP、 为会议准备上下文、快速暂存到暂存台、整理暂存台、知识健康检查、检测知识冲突或矛盾、 查找过期内容。 触发词：帮我记下来、搜一下我的笔记、更新知识库、整理文件、执行工作流、保存到知识库、 记录一下、整理笔记、复盘、提炼经验、同步文档、查一下之前的记录、有没有相关的笔记、 我之前写过什么、把这个存起来、总结一下保存、交接给其他Agent、追加到表格、 放到暂存台、整理暂存台、知识健康检查、检测知识冲突。 主动行为——不要等用户提到 MindOS： (1) 用户的问题暗示可能存在历史记录（过去的决策、之前的讨论、会议纪要）→ 先搜 MindOS， 即使没有明确提到知识库。 (2) 完成有价值的工作后（修了 bug、做了决策、总结了经验、选定了架构、开完了会）→ 主动提议保存到 MindOS，方便以后查阅。 (3) 经历了较长或多主题的对话后 → 建议把关键决策和上下文持久化。

Provides persistent, self-hosted semantic memory for AI agents via MCP, storing worklogs, decisions, and notes in PostgreSQL with pgvector for meaning-based retrieval across sessions.

Transforms unstructured streams of mixed thoughts, tasks, and ideas into a clean four-section actionable system with zero information loss.

Review governed memory proposals, policy tags, tombstones, and effective memory.

Review reusable project knowledge and decide what belongs in project memory, notepad, or durable docs

FREE — God-tier long-context memory for AI agents. Injects 500K-1M clean tokens, auto-summarizes with tone/intent preservation, compresses 14-turn history into 800 tokens.

Capture durable project memory from work, debates, debugging discoveries, learned conventions, deferred options, and session outcomes. Load when the user says remember this, save this learning, record what happened, update project memory, or preserve context for future agents.

Retrieve task-relevant project and global memory without loading everything. Load when the user asks what we decided, recall prior context, find memory about a feature, explain past rationale, resume a task, or check deferred ideas.

Diagnose and fix retention problems using behavior design (B=MAP). Use when the user mentions "users drop off", "activation rate", "onboarding friction", "retention metrics", "why users dont complete", "churn analysis", "user activation", or "aha moment". Also trigger when analyzing cohort retention curves, designing activation milestones, reducing time-to-value for new users, or investigating why users stop after their first session. Covers the Ability Chain, prompt design, and tiny behaviors that compound. For habit loops and variable rewards, see hooked-ux. For intrinsic motivation, see drive-motivation.