Using dsh-kb-sieve to Build Auditable Local Knowledge Base for DeepSeek Harness
`dsh-kb-sieve` is a memory plugin for DeepSeek Harness maintained by omdsh-dev, which builds local knowledge packages with original text citations and SQLite FTS5 from md/txt/docx/pdf files. This paper verifies the capabilities of the three tools `kb_build`, `kb_query` and `kb_read` by cross-checking with the directory page and GitHub source code, covers the installation commands on the directory page, as well as usage boundaries including the PDF dependency `pdftotext`, incremental building and permission risks.
Read MoreCursor Multi-Agent Swarm Rebuilds SQLite for $1339: Planner/Worker Hierarchy Is the Economics of Agents
In July 2026, Cursor released the Agent Swarm research: relying solely on 835 pages of SQLite documentation, without source code or network access, the multi-agent swarm rebuilt SQLite in Rust and passed all sqllogictest tests. The total cost of the hybrid solution planned by Opus 4.8 plus executed by Composer 2.5 is approximately $1,339, while the cost of the single-model GPT-5.5 solution is about $10,565. This article sorts out the Planner/Worker layered architecture, the new harness coordination mechanism, experimental data and the open-source minisqlite product, and analyzes the cost advantage of the multi-agent approach of "using strong models for planning and weak models for execution" as well as its enlightenment to engineering practice.
Read MoreFastAPI + SQLite: Quickly Build a Lightweight Database API Service
This article introduces the process of quickly building a "Student Information Management" database API service using FastAPI and SQLite. First, dependencies such as FastAPI, Uvicorn, and SQLAlchemy are installed via `pip`. SQLAlchemy's ORM is used to define student data models (including id, name, and age fields) and session management, with Pydantic models for data validation. The core implementation includes CRUD operations (create, read single/all students, update, delete). FastAPI routes bind HTTP methods (POST/GET/PUT/DELETE) to generate student management API endpoints. The database configuration uses SQLite, an embedded database that requires no additional server, with data stored in the `test.db` file. After starting the service with Uvicorn, FastAPI automatically generates Swagger UI documentation for easy testing. This lightweight and easy-to-use solution supports asynchronous operations, making it suitable for small-to-medium projects. Future expansions include multi-table associations or migration to PostgreSQL/MySQL.
Read MoreData Storage Fundamentals: How Python Web Saves User Information with SQLite
This article introduces the basic method of implementing web data storage using SQLite and Flask. SQLite is lightweight and easy to use, built into Python, and requires no additional server, making it suitable for beginners. First, the Flask environment needs to be installed. The core steps include creating a user table (with auto-incrementing id, unique username, password, and email fields), implementing registration (parameterized data insertion) and user list display (querying and returning dictionary results) through Python operations. During operations, attention should be paid to password encryption (to prevent plaintext storage), SQL injection prevention, and proper connection closure. The article demonstrates the data persistence process with sample code, emphasizing that SQLite is suitable for small projects and serves as an entry-level tool for learning data storage, with potential for future expansion of functions such as login authentication and ORM.
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