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Glossary2
Open the full glossary- Agent memoryInformation stored outside the model — files, databases — selected and re-injected into context when needed; the model itself retains nothing across calls.
- Vector databaseA database that searches by how close the meaning is: you give it a query vector and it returns the nearest few records, instead of matching exact wording.
Found 14 articles
How Databases Work: From File Cabinets to Vector Search
A beginner-to-deep guide to how databases store, index, and protect data, plus a family map of relational, key-value, document, graph, vector, and time-series engines, and how to choose.
A Map of HK Equity Research Sources: HKEX Filings, Annual Reports, and Third-Party Data
When researching an HK-listed company, where does the information come from, which sources are trustworthy, and which can only serve as leads? This article divides HK equity research sources into five credibility tiers — statutory disclosures, company-produced information, third-party databases, market data sources, and news and research reports — and provides a 'question-to-source' mapping table.
Build a Working Prototype Website in a Weekend: From Idea to Shareable Link
To validate a product idea you do not have to wait for engineering capacity. Using AI generation tools like v0, Lovable, and Bolt, this article walks you through converging the idea into a one-page spec, producing the first version, wiring a database and login, deploying to a shareable link, and putting it in front of real users — and spells out which requirements mean you should stop and find people instead.
Tolaria - Markdown native knowledge base desktop app
GitHub Trending #9 · 12K ⭐ · Markdown native knowledge base desktop app, supports wikilinks, Git version control, local AI Agent, no database, no proprietary format
Build Complete Infrastructure for Your AI Agent in Half an Hour: The Complete Agentic Infrastructure Ten-Piece Guide
From zero to fully running: ten prompts to establish a gate pair, vector memory, skill curation, and scheduled inspection. Solves the skill-skipping problem caused by LLM confidence bias, so your Agent never loses its memory again.
Webb-Site: The Essential Hidden Treasure for Hong Kong Stock Research, a One-Click Tool to Get Annual Report PDFs for All Listed Companies
David Webb's independent Hong Kong stock database webb-site.com is an overlooked treasure. It directly provides PDF links from hkexnews.hk, listing on one page all annual reports and interim reports of a company from listing to present, completely bypassing hkexnews's internal stock ID restrictions. This article includes a complete usage tutorial, a known ID mapping table, and a batch download solution.
Triple Strike: OMP Error #15, PEP 668, and Python Dependency Hell
The MemoryHub daemon crashed and restarted repeatedly over three hours: ModuleNotFoundError → OMP Error #15 → silent crash loop. An in-depth analysis of the faiss + torch dual libomp conflict, the Homebrew PEP 668 block, and the structural fragility of the Python scientific computing ecosystem. Includes a complete prevention plan.
The Engineering of AI Memory Retrieval: A Full-Matrix Field Report on Four Access Paths × Ten Scenarios
When an AI assistant needs to "remember everything", how does it retrieve memory? Direct file reads, Qdrant vector search, the MemoryHub capture pipeline, and agentmemory semantic indexing: a field comparison of four paths across ten real scenarios, revealing the boundaries and sweet spots of each path.
A Lightweight Overhaul of MemoryHub in Practice: Cutting from 10 Databases to 5, and RAM from 6.2GB to 500MB
What happens when you delete Neo4j, Elasticsearch, and MongoDB from an AI memory system, keep only the Qdrant + FAISS + SQLite-vec trio, and replace the 9.1GB BGE-m3 with a 193MB bge-small-zh? A complete performance dissection, an embedding model comparison, and a trimming plan aimed at ordinary computers.
Polyglot Persistence in Practice: A Full-Pipeline Evaluation of a Ten-Database Memory System
From BGE-m3 vector embeddings and ten-way synchronized writes to a comparison of search quality and speed across nine backends: a complete stress test of an AI memory system. Qdrant is the king of semantics, FAISS the king of speed, and Neo4j the king of relationships.
MemoryHub v2.0 System Architecture In-Depth Analysis: From Capture Daemon to MCP Real-Time Memory Capture
A complete breakdown of MemoryHub v2.0's dual-mode capture engine, Qdrant vector storage, MCP four-platform integration, Dashboard real-time monitoring, three-layer deduplication, and ten-year lifecycle design.
Complete Comparison of Top 10 Vector and Full-Text Databases: MemoryHub Backend Storage Selection Guide
An in-depth review of ten databases, including Qdrant, Chroma, LanceDB, SQLite-vec, FAISS, Redis, PostgreSQL (pgvector), Elasticsearch, MongoDB Atlas, and Neo4j, in AI memory system scenarios, covering performance, deployment difficulty, resource consumption, and use cases.
MemoryHub v2.0 Full Record of Ten-Database Sync: The 6-Hour Battle from 0 Points to 3,892 Records
A complete record of MemoryHub's technical evolution from a monolithic Qdrant to a unified embedding pipeline across ten databases: 18 bug fixes, Python scoping traps, BGE-m3 dimension errors, missing system Python dependencies, and the final architecture that achieves one embedding then ten-database sync.
RAG In-Depth Principles and Practice: A Complete Guide from Chunking to Rerank
The complete tech stack for Retrieval-Augmented Generation (RAG): Embedding selection, Chunking strategies, vector database comparison, Rerank optimization, with practical code examples.