Knowledge Space¶

Curated technical knowledge base across 22 domains. Built for LLM agents and engineers.

What is this?¶

A knowledge base designed primarily for AI agents - structured so that RAG retrieval, MCP tools, and context injection return dense, actionable technical content instead of blog-style prose.

Each article is a concentrated extract: code examples, configuration patterns, gotchas, best practices. No filler, no "let me explain why this is important" - just the knowledge an agent needs to solve a real problem.

Also useful for engineers who want quick reference across 22 technical domains without wading through tutorials.

Who it's for:

LLM agents - structured format optimized for RAG retrieval, ConTree MCP, and context injection
Engineers - quick lookup of patterns, commands, configurations across 22 domains
Teams - shared knowledge base accessible via ConTree sandbox or direct file access

How to use¶

Search (top bar) is the fastest way - find specific topics, commands, or patterns across all domains.

Browse the sidebar to explore by domain. Each domain contains 9-85 focused articles.

For agents: this knowledge base is at github.com/AnastasiyaW/knowledge-space. Clone it or fetch via GitHub MCP, then search docs/{domain}/ for the topic. Each .md file is a self-contained reference - read it, use it, don't guess.

Domains¶

Domain	Articles	Coverage
Data Science	85	ML, statistics, neural networks, computer vision, NLP, math foundations
Python	43	Core language, FastAPI, Django, async, testing, packaging, microservices
Web Frontend	40	React, TypeScript, CSS, Figma, bundlers, accessibility
DevOps	39	Docker, Kubernetes, Terraform, CI/CD, monitoring, SRE
Architecture	39	Microservices, DDD, system design, API design, integration patterns
Data Engineering	38	ETL/ELT, Spark, Airflow, data warehouses, streaming, CDC
Kafka	33	Broker internals, consumers, producers, Streams, KSQL, Connect
SQL & Databases	27	PostgreSQL, MySQL, query optimization, migrations, indexing
Linux CLI	25	Shell scripting, filesystem, permissions, systemd, networking
LLM & Agents	24	RAG, fine-tuning, agent frameworks, prompt engineering, embeddings
Java & Spring	21	Spring Boot, JPA, microservices, Kotlin, Android
BI & Analytics	21	Tableau, Power BI, SQL analytics, dashboards, product analytics
Algorithms	19	Sorting, graphs, dynamic programming, data structures, complexity
Security	18	Web security, penetration testing, Active Directory, anti-fraud
SEO & Marketing	16	Technical SEO, keyword research, link building, AI-driven SEO
Testing & QA	15	Selenium, Playwright, API testing, CI integration, test design
Rust	14	Ownership, lifetimes, async, error handling, unsafe
PHP	12	Laravel, MVC, ORM, testing, PHP 8 features
Node.js	10	Event loop, streams, clusters, performance, design patterns
iOS & Mobile	10	SwiftUI, Swift, Android/Kotlin fundamentals
Misc	9	JavaScript, Go, competitive programming, interview prep

Knowledge Graph Details¶

Freshness Policy¶

Not all knowledge ages equally. Each domain has an update cycle based on how fast the field moves:

Cycle	Domains	Why
Stable (rarely changes)	Algorithms, Architecture, Linux CLI	Fundamentals don't change - a B-tree is a B-tree
Yearly	SQL, Kafka, Rust, Java/Spring, PHP, Node.js, Testing, BI, Data Engineering	Mature ecosystems with predictable release cycles
Every 6 months	Web Frontend, DevOps, LLM/RAG, iOS, Security, SEO	Fast-moving fields where best practices shift quickly
Monthly	Image Generation, Agent Frameworks	Bleeding edge - new models and tools every week

Articles include version/date context where relevant (e.g., "PostgreSQL 17", "React 19", "Kubernetes 1.30").

What makes this different¶

Agent-first design. Every article is structured for machine consumption: consistent headers, code blocks with language tags, pattern/anti-pattern sections, explicit gotchas. An LLM agent retrieving a Knowledge Vault article gets immediately actionable context - no parsing needed.

Density over length. A typical article packs the same information as a 2-hour video or a 30-page tutorial into 2-4 pages of pure reference text. Optimized for context window efficiency.

Cross-domain connections. Real engineering problems don't respect domain boundaries. Wiki-links connect Kafka consumer patterns to Architecture decisions, SQL optimization to Data Engineering pipelines, Security practices to DevOps configurations.

Living knowledge base. Continuously updated with new research and domain knowledge. Freshness policy ensures fast-moving fields stay current while stable foundations remain reliable.