The article explains why default agent memory is fleeting, proposes a two‑layer design of session and long‑term memory with a post‑session “dreaming” integration step, and shows how selective persistence and shared long‑term storage keep agents continuously improving.
The article explains why increasing PostgreSQL's work_mem does not guarantee per‑query memory limits, how multiple sort/hash nodes, parallel workers and long‑lived memory contexts can cause OOM even on a 2 TB server, and offers concrete diagnostics and mitigation strategies for DBAs and developers.
The article explains that C’s steep learning curve stems from its low‑level environment setup, opaque debugging, complex pointer syntax, and manual memory management, while also arguing that mastering C is valuable for low‑level development and deep understanding of computer fundamentals.
The article dissects Agent Memory, explaining how past observations are written, managed, and read to affect future tasks, highlighting challenges such as relevance, decay, conflict, security, and offering practical design guidelines and architectural options for production‑grade AI agents.
The article dissects the concept of an Agent Harness—a comprehensive software infrastructure that wraps large language models to enable autonomous agents—detailing its three engineering layers, twelve production‑grade components, benchmark improvements, implementation patterns across Anthropic, OpenAI, LangChain, and design trade‑offs such as orchestration loops, tool integration, memory, context management, error handling, and safety.
Beads is an open‑source, distributed graph‑based task tracker built on Dolt that adds a memory layer to AI programming assistants, preventing context loss in long‑running tasks through version‑controlled storage, dependency tracking, conflict avoidance, semantic compression, and hierarchical organization.
The article explains why the Linux kernel reports less usable memory than the physical RAM, detailing the memblock allocator, crashkernel reservations, page‑struct overhead, and the handoff to the buddy system, showing how the kernel consumes memory for its own management.
This article explains Linux kernel threads from basic concepts to deep internals, covering their data structures, creation, execution flow, scheduling strategies, context‑switch overhead, synchronization primitives, interrupt handling, and a practical kswapd memory‑reclaim case study, providing concrete code examples and step‑by‑step analysis.
The article outlines five concrete design patterns—Checkpoint‑and‑Resume, Delegated Approval, Memory‑Layered Context, Ambient Processing, and Fleet Orchestration—that enable production‑grade, multi‑day AI agents to persist state, handle failures, and scale safely.
Android’s long‑standing lax memory management is being overhauled: Google’s Android 17 Beta 4 introduces a device‑level memory cap, smart notifications, and scenario‑specific rules, while China’s ITGSA alliance issues a “fair run memory” mandate, promising to curb bloated apps, reduce heating and lag, and give 8 GB phones a chance to run smoothly again.
A security researcher uncovered Claude Code’s full 512,000‑line TypeScript source, revealing a sophisticated OS‑like architecture with dynamic prompt assembly, 42 lazily‑loaded tools, multi‑layer security reviews, memory management, and three‑stage compression that together explain why it feels more usable than other AI coding assistants.
This article compiles practical, interview‑ready Redis tuning tips—from strict memory limits and eviction policies to avoiding big keys, hot keys, slow commands, and optimizing persistence, networking, and high‑availability settings—so you can confidently handle Redis performance questions in real‑world deployments.
The article surveys recent LLM‑based agent memory research, presenting a unified framework that breaks memory systems into four components, detailing their design choices, experimental evaluation on LOCOMO and LONGMEMEVAL, key findings, and a new low‑token SOTA architecture.
The article compares Pi, OpenClaw, Claude Code, and Letta, showing how each framework tackles limited context windows through file truncation, pagination, tool‑result budgeting, sub‑agent isolation, and token‑driven compaction, revealing a clear convergence toward active memory management.
This article demystifies Linux memory management by explaining why buffers and cache are not meant to be manually cleared, how the 'available' metric differs from 'free', and which sysctl parameters and cgroup settings you should tune to optimize performance and avoid OOM incidents.
This article breaks down LangChain.js's three memory strategies—window truncation, summary compression, and vector‑store retrieval—explaining their inner workings, code setup, trade‑offs in token cost and information retention, and provides a decision guide for selecting the best approach in multi‑turn LLM conversations.
Claude Code’s new auto‑memory feature automatically records project context, preferences, and debugging notes in a structured memory folder, loads the first 200 lines at session start, and lets users toggle or edit the memory, dramatically reducing repetitive input and speeding up development.
Hermes Agent combines a bounded memory store, automatically generated reusable skills, and a Nudge Engine that periodically triggers background reviews, forming a self‑improving loop that reduces tool calls, fixes recurring errors, and outperforms OpenClaw’s static skill system.
This article dissects Claude Code's memory system, explains why unstructured memory degrades performance, introduces four distinct memory types with concrete examples and schema, shows how to handle expiration and retrieval strategies, and provides step‑by‑step implementation code to improve agent reliability.
The article explains how Linux’s page cache bridges memory and disk, detailing its read/write mechanisms, dirty page handling, pre‑read optimization, kernel parameters, and practical tuning tips for static file serving, databases, and logging, showing why mastering it is essential for performance.
This article walks through constructing a tiny, runnable AI agent loop that reads a user task, lets the model choose the next step, calls a tool, feeds the observation back, and repeats, then explains how to add harness, memory, permission, and validation layers to make the agent reliable in real‑world engineering environments.
The article introduces agentic‑stack, a portable .agent folder that lets eight AI coding tools share a unified memory, skill, and protocol system, detailing its four‑layer memory model, progressive skill disclosure, shim‑based adapters, review protocols, practical team scenarios, installation steps, and architectural design.
The article walks through building a minimal 20‑line agent loop, explains each step—from reading a task to invoking tools and feeding observations back—then shows how real systems like Claude Code, OpenClaw and Pi add layers of harness, memory, permission and validation to make the loop safe and reliable in production.
This tutorial walks through creating a LangChain‑based AI agent by covering model integration, tool definition with @tool, short‑ and long‑term memory handling via checkpointers and vector stores, and assembling everything with create_agent, middleware, and code examples for a functional travel assistant.
This article dissects Hermes Agent's three‑layer persistence model, skill discovery mechanisms, tool registration and scheduling, session‑search retrieval, and automated skill evolution, highlighting design trade‑offs, concurrency handling, and practical pitfalls for building robust AI‑driven agents.
This guide explains how to move from fragile, prompt‑only AI agents to production‑grade systems by designing a control layer—called Harness Engineering—covering memory management, workflow orchestration, permission boundaries, automation patterns, and the Intelligent Harness Runtime that makes agents self‑governing and resilient.
This article dissects Hermes Agent’s self‑evolution mechanism, explaining how stable facts are stored in memory, reusable procedures become skills, and rollout trajectories are turned into training data through background review, context compression, and OPD‑based token‑level distillation.
This comprehensive guide explains Linux system calls—the sole interface between user‑space programs and the kernel—covering their purpose, core logic for process, file, and memory management, the underlying interrupt mechanisms, parameter handling, and real‑world C code examples.
This article analyzes the open‑source Claude‑Mem plugin, detailing developers' pain points with AI assistants, the plugin's persistent memory architecture, core features, MCP search workflow, practical usage examples, best‑practice tips, installation methods, system requirements, and common troubleshooting advice.
The article dissects twelve concrete harness design patterns uncovered in the leaked Claude Code source, organized into four categories—memory & context, workflow & orchestration, tools & permissions, and automation—detailing their use cases, trade‑offs, and implementation costs for building production‑grade AI agents.
The article analyzes Hermes Agent's three‑layer memory system—fact memory stored in tiny Markdown files, session history indexed with SQLite + FTS5, and procedural memory via skill management—then compares each layer to OpenClaw's architecture and explains how to integrate self‑summarizing skills into OpenClaw.
The article explains why production issues often stem from the agent harness rather than the model, defines the harness concept, breaks down its twelve essential components, shows a full execution loop, compares Anthropic, OpenAI, LangChain and other frameworks, and discusses key design trade‑offs for building robust AI agents.
This article translates and consolidates three LWN analyses of the Linux swap subsystem modernization led by TencentOS kernel engineer Kairui Song, detailing the introduction of swap tables, removal of the swap map, virtual swap concepts, code changes, performance improvements of up to 20 % and the broader impact on the kernel community.
The Claude Code source leak exposed over 500,000 lines of AI‑coding tool code, revealing its npm publishing mishap, the layered architecture built on React Ink, the ReAct‑style agent loop, sophisticated tool orchestration, multi‑tier memory management, context compression, security checks, feature flags, and even anti‑distillation defenses.
This article details the design, operation, and continuous improvement of a six‑agent autonomous system, covering memory management, protocol design, self‑healing mechanisms, and practical engineering lessons for building reliable, self‑evolving AI workflows.
sk_buff is the backbone of Linux’s network stack, handling packet storage, metadata, memory management and protocol‑layer interactions; this article dissects its structure, pointer model, core operations, packet lifecycle, practical code examples, and common pitfalls such as memory shortage, data loss and performance bottlenecks.
The article analyzes KAIROS, the upcoming AI‑driven mode of Claude Code, explaining how it shifts the tool from a short‑lived CLI assistant to a continuously online, asynchronous agent with persistent sessions, memory distillation, channel integration, and proactive execution, while outlining current gaps and engineering challenges.
A year‑long YC‑Bench simulation pits twelve leading large‑language models against a virtual startup environment, revealing stark differences in profitability, cost efficiency, memory handling, and strategic decision‑making, with only three models ending the year profitable and a handful achieving high cost‑performance ratios.
This article explains the core principles of Linux kernel memory management, detailing how the buddy system handles large contiguous pages while the SLUB allocator optimizes small-object allocation, and compares their performance, fragmentation handling, and real‑world usage in servers and embedded devices.
The article dissects Claude Code’s 512,000‑line TypeScript codebase, detailing its modular architecture, fine‑grained tool orchestration, three‑layer memory system, multi‑stage query engine, six‑layer security sandbox, unreleased features like Kairos and Undercover modes, and the engineering practices that turn an AI model into an industrial‑grade digital employee.
The article analyses OpenClaw’s single‑turn memory design, explains the two vicious cycles that cause memory bloat and forgetting, and introduces seekdb M0’s cloud‑native, two‑stage memory and experience system that decouples memory from context, reduces token costs, and shares practical knowledge across agents.
This article analyzes the multi‑year modernization of the Linux kernel swap subsystem, detailing the introduction of the swap table, removal of the swap map, virtual swap concepts, and tiered swap designs, and shows how these changes can reduce metadata overhead by up to 30% and improve throughput by 5‑20%.
A 57 MB source‑map file accidentally shipped with the @anthropic-ai/[email protected] npm package exposed over 1,900 TypeScript/TSX files, allowing the community to dissect Claude Code’s five‑layer Agent Harness, tool control, task runtime, memory system, and remote permission bridge, offering valuable engineering insights for AI agent developers.
A deep dive into Claude Code’s 500 k‑line TypeScript repository shows how Anthropic is turning a programming assistant into a memory‑rich, autonomous AI agent platform with multi‑agent collaboration, cloud‑native scheduling, speculative execution, and even a pet‑style companion.
This article explains why AI agents need a structured memory module, outlines three memory types from cognitive science, details short‑term and long‑term storage architectures using vector databases, and provides concrete code and management strategies—including conflict resolution, TTL expiration, and privacy compliance—to build a robust Agent Memory system.
A recent source‑map leak of the Claude Code npm package exposed thousands of TypeScript files, allowing engineers to reconstruct the full harness—including its main loop, tool pool, task runtime, memory system, and security boundaries—offering a rare glimpse into the engineering reality of a production‑grade AI agent platform.
This article explains how the Memory Management Unit (MMU) underpins Linux's virtual memory, process isolation, and protection mechanisms, detailing its architecture, address‑translation workflow, TLB caching, practical C implementations, real‑world use cases, and debugging techniques for kernel developers.
This comprehensive FAQ walks through 40 technical questions about OpenClaw, covering its innovations, architecture, multi‑agent collaboration, memory and context handling, security risks, token‑saving strategies, real‑world use cases, comparisons with other agents, and competitive landscape.
This article explains the core principles of NUMA, its deep integration with the Linux kernel, practical memory‑node and scheduling mechanisms, real‑world database and virtualization use cases, and step‑by‑step commands for inspecting and tuning NUMA on modern servers.
Claude Code is quietly testing an "Auto‑dream" feature that periodically runs a sub‑agent to archive important short‑term context into long‑term storage, mirroring human sleep, while addressing the token‑budget challenges faced by AI programming assistants.
This article breaks down agentic memory into four distinct types—In‑context, External, Episodic, and Semantic/Parametric—explains three forgetting strategies (time decay, importance scoring, periodic consolidation), shows how memory flows through an agent loop, and provides complete Python code using OpenAI embeddings and ChromaDB for a production‑ready memory layer.
This article examines how Taobao Flash Sale’s AI Agent uses Alibaba Cloud’s Tair as a high‑performance short‑term memory layer, detailing data model design, latency impact, concurrency control, elastic scaling, bandwidth handling, and TTL‑based cleanup to achieve sub‑second response times during massive traffic spikes.
This guide explains why OpenClaw agents lose context, how the Mem0‑Tablestore combination provides automatic, cloud‑hosted, and fully controllable memory, and walks you through step‑by‑step installation, configuration, and verification on Alibaba Cloud.
This article explains how OpenClaw functions as an AI Agent framework that connects chat applications to large language models, manages multi‑turn dialogues, executes tool commands, handles memory and security, and demonstrates advanced features such as sub‑agents, cron jobs, and context compression.
OpenClaw is an open‑source, OS‑level autonomous agent runtime that combines dynamic system prompts, powerful tool access, file‑based memory, and sub‑agent generation, offering a secure, extensible architecture that runs on a single Node.js process and integrates with any LLM provider.
The article examines Linus Torvalds’s original Git init implementation, walking through the C source files, explaining how directories are created, why a 40‑byte offset is added to allocated memory, and highlighting a missing free() call that leads to a memory leak, while discussing when manual deallocation is necessary.
After installing OpenClaw, adding the five plugins—memory, ontology, proactive‑agent, self‑improving‑agent, and Trello—transforms the chatbot from basic conversation to a context‑aware, structured‑knowledge, proactive, self‑learning system with integrated task management.
The article explains the four essential components of AI Agents—brain, memory, tool, and planning layers—illustrates their implementation with Python code, compares planning strategies, shares a real-world OOM fault‑diagnosis case, and lists common pitfalls to help newcomers build functional agents.
This article explains the challenges of allocating large contiguous physical memory in Linux, introduces the Contiguous Memory Allocator (CMA) as a solution, and provides in‑depth coverage of its design, reservation, migration, data structures, initialization, configuration, usage in drivers, and debugging techniques.
OpenClaw is a locally‑run AI agent that listens to messages from multiple platforms, translates them into a unified format, uses an LLM to plan actions, executes tasks via modular Skills, and stores context in a transparent local memory, while exposing significant security considerations.
This article explains the role of the Memory Management Unit (MMU) and paging in modern operating systems, covering hardware structure, address translation, permission checks, page tables, TLB behavior, virtual memory mechanisms, and practical Linux kernel code examples for memory protection, sharing, and performance optimization.
This article explains why the interview question "String str = new String(\"abc\")" can create either one or two objects depending on JVM string pool state, detailing the JVM memory model, string pool mechanics, code examples, best practices, and common misconceptions.
This article explains the role of memory modules in Retrieval‑Augmented Generation systems, compares short‑term and long‑term memory techniques, outlines storage and retrieval methods, discusses management strategies like forgetting and deduplication, and compares LangChain and LlamaIndex implementations for practical deployment.
This article explains why AI agents need a memory‑management solution, introduces LangChain DeepAgents' FileSystem middleware, details its four backend options for short‑term, long‑term, disk‑based, and hybrid storage, and provides step‑by‑step Python examples for installing, configuring, and using the middleware in real‑world scenarios.
MemSifter introduces a lightweight agent that outsources memory retrieval for large language models, using a Think‑and‑Rank pipeline and a task‑result‑oriented reinforcement‑learning training paradigm to achieve superior retrieval accuracy and efficiency across eight benchmark tasks while keeping inference overhead minimal.
This article analyzes the engineering bottlenecks of AI agents and presents OpenClaw—a TypeScript‑based CLI system that solves concurrency, state traceability, failure explainability, memory management, and security through a clear pipeline and practical design patterns, offering ten ready‑to‑use implementation tips.
When running `free -h` on a Linux system, you may notice the buff/cache entry consuming over a gigabyte, leaving little memory for applications; this article explains that the cache is built from file I/O, shows how to manually drop it via `/proc/sys/vm/drop_caches`, and provides a cron‑based script to automate the cleanup.
The author details a month‑long experiment creating a six‑agent AI team with OpenClaw that automates research, content creation, code review and email newsletters, saving 4‑5 hours each day for under $400 per month by using file‑based coordination, a two‑layer memory system, and a gradual rollout plan.
The article traces five eras of KV cache management for LLM inference—from its absence before Transformers to the emerging unified hybrid memory architecture—comparing vLLM, SGLang, and TensorRT‑LLM and offering a decision framework for selecting the right solution in various deployment scenarios.
The article reviews the rapid shift in 2025 from complex workflow‑based LLM orchestration to streamlined agentic systems that rely on simple prompt loops, sandboxed tool execution, file‑based memory, and modular skill files, culminating in the rise of Agent Harness runtimes.
This article explains Linux’s comprehensive memory management system, covering physical and virtual memory concepts, paging, page tables, the MMU, the buddy allocator, slab allocator, memory reclamation strategies such as LRU and swap, monitoring tools, and practical optimization techniques for both user‑space and kernel‑space allocations.
The article explains OpenClaw Memory's design—storing daily and permanent logs as Markdown files, managing them with Git, offering hybrid vector‑BM25 search, applying temporal decay to prioritize recent entries, and comparing SQLite and QMD backends with practical configuration examples and tips.
This article explains how the Agent Loop—an engineered while‑loop that repeatedly calls an LLM, decides when to use tools, executes them, and feeds results back—creates persistence, style, memory, judgment, and safety boundaries that together make an AI agent feel like it has its own personality.
This article explains the fundamentals of Linux memory management, details how shared memory implements zero‑copy inter‑process communication, and provides step‑by‑step code examples of system calls, mmap, sendfile, splice, and synchronization techniques for high‑performance data transfer.
This comprehensive guide walks you through the real costs of Redis memory problems, explains the three‑layer memory architecture and five major consumers, provides a toolbox of INFO and MEMORY commands plus monitoring scripts, and offers step‑by‑step solutions for seven common issues, best‑practice optimizations, real‑world case studies, a daily checklist, and advanced techniques such as Lua scripts and smart cache warm‑up.
This article dissects OpenClaw’s engineering techniques for building robust, production‑level AI agents, covering zero‑trust tool policies for security, markdown‑based memory management, cost‑aware reasoning levels, and controlled sub‑agent collaboration to ensure safety, efficiency, and reliability.
This comprehensive guide walks Java backend developers through the fundamentals of AI agents, comparing agents with traditional workflows, detailing core components such as LLMs, tools, and memory, and exploring practical patterns, frameworks, and code examples to help them successfully shift into AI agent development.
This article demystifies short‑term and long‑term memory in LLM‑driven autonomous agents, explaining their mechanisms, limitations, and practical implementations such as sliding windows, summarization, and vector‑based retrieval, while illustrating each concept with concrete Cherry Studio examples and relevant research references.
This article dissects OpenClaw’s architecture, revealing how a TypeScript CLI process, a gateway server, lane‑queue concurrency, structured memory, tool‑execution allowlists, and semantic browser snapshots combine to turn fragile AI agents into stable, observable, and controllable systems.
The article outlines the core building blocks of AI agents—including frontend frameworks, development kits, tool integration, memory strategies, design patterns, model selection, and runtime environments—while explaining how each choice impacts performance, scalability, cost, and security.
Alibaba's new AgeMem framework turns long‑term and short‑term memory management for large language model agents into a learnable reinforcement‑learning task, replacing handcrafted rules with a three‑stage training process and achieving significant benchmark gains.
This article examines the challenges and possibilities of adding multithreading to PHP, discussing the TrueAsync RFC, single‑threaded + offload patterns, memory‑manager redesign, object movement, shared objects, region‑based memory, and actor models to guide future PHP kernel evolution.
During Pinterest’s migration of its custom search platform Manas to the PinCompute Kubernetes environment, a rare latency spike—one request per million taking 100 times longer—was traced to cAdvisor’s memory‑intensive smaps scans, revealing hidden resource contention and prompting a targeted fix.
This article thoroughly explains Linux shared memory, covering its advantages over other IPC methods, the kernel data structures, virtual‑physical mapping, creation and destruction APIs, copy‑on‑write behavior, and provides complete example code for inter‑process communication.
DeepAgent, a new end‑to‑end reasoning agent, integrates autonomous thinking, dynamic tool search, and execution to handle over 16,000 APIs, embodied tasks, and research assistance, achieving state‑of‑the‑art performance on benchmarks like TMDB, ToolBench, ALFWorld, WebShop, and GAIA.
This article explains Go's memory allocator by comparing it with the Linux kernel SLAB allocator, detailing the page‑heap (mheap), span structures, per‑P caches (mcache), and the mallocgc path for small and large allocations, while showing key source code snippets and diagrams.
This article reveals how Linux kernel memory layout—its partitions, address allocation, and resource scheduling—directly impacts system stability, explains the roles of each memory region, demonstrates common pitfalls like fragmentation and dentry leaks, and provides practical debugging and optimization techniques for developers and operators.
This article explains the fundamentals of Linux arm64 memory management, covering virtual and physical memory concepts, MMU operation, page table structures, address translation steps, page fault handling, and practical C++ examples for allocation, mapping, and performance optimization using huge pages and pre‑paging techniques.
In long‑running PHP processes, memory usage can steadily increase due to reference counting, circular references, unreleased resources, and default garbage‑collector settings; this article explains the underlying mechanisms, common leak sources, and provides practical debugging tips and best‑practice solutions such as explicit variable cleanup, GC tuning, process restarts, and memory‑efficient extensions.
This article breaks down ComfyUI's two‑level cache system, explains the differences between the CLASSIC, LRU, and RAM_PRESSURE strategies for outputs and objects, and offers practical guidance on choosing the right cache mode when running multiple models, LoRAs, and workflows on a single machine.
When moving from PHP‑FPM to long‑running processes like RoadRunner or Laravel queue workers, memory usage climbs and never drops, a pattern caused by PHP’s Zend memory manager block allocation, reference counting, and GC behavior, which can be mitigated by redesigning code and worker strategies.
This article explains why many enterprise AI agents break down in real projects, identifies four common pitfalls—including mistaking agents for chatbots, lacking schema‑level tool logic, missing memory and variable injection, and absent end‑to‑end pipelines—and offers concrete engineering solutions to build robust, task‑driven agents.
The article explains that operating systems act as resource allocators, isolators, and abstraction layers, enabling multiple programs to share limited CPU, memory, and disk resources safely and efficiently through scheduling, memory partitioning, and hiding hardware complexities.
This article breaks down the exact number of objects created when using a string literal versus the new String constructor in Java, detailing interactions with the constant pool, stack, and heap, and demonstrates the resulting reference comparisons with concrete code examples.
The 20th China Linux Kernel Developers Conference, hosted by vivo, presented eleven technical talks covering AI‑driven kernel challenges, memory‑compression techniques, heterogeneous compression, async file‑cache management, uncached I/O, direct I/O for compressed files, parallel writeback, host‑initiated defragmentation, zoned storage, energy‑efficient I/O, and eBPF‑based CPU idle policies, each with concrete performance results and implementation details.
This article explains how a self‑programming AI Agent is constructed by extending large language models as the brain, designing a multi‑area architecture, implementing memory layers, prompt engineering with segment mechanisms, and enabling code generation and execution through a Python‑Java bridge, while sharing practical insights and future directions.
After noticing 14 GB free RAM but a fully used 29 GB swap, the article explains how to diagnose hidden swap consumption using tools like smem and swap_stat_show, clarifies the difference between free’s Swap used and /proc/*/status VmSwap, and shows how adjusting transparent_hugepage defrag and toggling swap can reclaim the space.
This article explains how to give AI agents persistent memory, reflection, and goal‑tracking by storing interaction summaries in SQLite, embedding them for semantic retrieval with a vector database, and using LLM‑generated prompts to recall, reflect, and manage objectives across sessions.
This comprehensive guide walks you through Linux memory management fundamentals, from prerequisite environments and a quick checklist to step‑by‑step installation of monitoring tools, memory diagnostics, kernel parameter adjustments, THP and swap optimization, NUMA affinity tuning, validation, Prometheus alerts, security hardening, troubleshooting, rollback procedures, best‑practice recommendations, and ready‑to‑use scripts and configuration snippets.
Android 15 introduces 16KB memory pages, requiring native libraries to align accordingly; this change improves TLB efficiency, reduces CPU load, speeds up cold starts, and lowers battery consumption, while Google Play mandates compliance by November 2025, prompting developers to audit and rebuild affected .so files using provided tools.
