gitcrawl is a local-first GitHub triage tool and a drop-in caching shim for the gh CLI. It mirrors repository issues and pull requests into a local SQLite database, enabling semantic clustering and full-text search while preventing API rate limit exhaustion. This setup allows maintainers and AI agents to perform heavy read operations against a local cache rather than live GitHub servers.
Main features:
Local SQLite storage for all issue, PR, and commit metadata.
A gh-compatible shim that handles most read-only calls locally.
Semantic clustering using OpenAI embeddings to group related reports.
An interactive terminal UI for cluster browsing.
JSON support for easy automation with AI agents.
Main features:
Local SQLite storage for all issue, PR, and commit metadata.
A gh-compatible shim that handles most read-only calls locally.
Semantic clustering using OpenAI embeddings to group related reports.
An interactive terminal UI for cluster browsing.
JSON support for easy automation with AI agents.
discrawl mirrors Discord guild data into a local SQLite database, allowing you to search, inspect, and query server history independently of Discord. It’s a bot-token crawler – no user-token hacks – and keeps your data local. It discovers accessible guilds, syncs channels, threads, members, and message history, maintains FTS5 search indexes for fast text search (including small attachments), records mentions, and tails Gateway events for live updates with repair syncs. It provides read-only SQL access for analysis and supports multi-guild schemas with a simple single-guild default. Search defaults to all guilds, while sync and tail default to a configured default guild or fan out to all discovered guilds if none is set.
In this tutorial, we build a self-organizing memory system for an agent that goes beyond storing raw conversation history and instead structures interactions into persistent, meaningful knowledge units. We design the system so that reasoning and memory management are clearly separated, allowing a dedicated component to extract, compress, and organize information. At the same time, the main agent focuses on responding to the user. We use structured storage with SQLite, scene-based grouping, and summary consolidation, and we show how an agent can maintain useful context over long horizons without relying on opaque vector-only retrieval.
A single developer built a powerful search and monitoring tool for the web using a simple SQLite database and a clever bot, highlighting the potential of individual creators to tackle complex problems.
This article details how to build a 100% local MCP (Model Context Protocol) client using LlamaIndex, Ollama, and LightningAI. It provides a code walkthrough and explanation of the process, including setting up an SQLite MCP server and a locally served LLM.
An article detailing Phoenix.new, Fly.io's AI-assisted app development platform built on Phoenix and Elixir. It explores the platform's capabilities, the author's experience building a notebook application with it, and its potential for expansion beyond Elixir.
This article details the creation of 'Stevens', a personal AI assistant built using a single SQLite table to store 'memories' and cron jobs to ingest data and generate daily briefs. It emphasizes a simple architecture leveraging Val.town for hosting and highlights the benefits of broader context for personal AI tools.
A simple project demonstrating Retrieval Augmented Generation (RAG) using SQLite, sqlite-vec, and OpenAI. It embeds text files, stores them in a SQLite database, and retrieves relevant documents using vector search. The project features lightweight single-file SQLite databases, vector search capabilities, and OpenAI integration for embeddings and chat responses.
A new plugin for sqlite-utils CLI tool called sqlite-utils-ask allows users to ask human-language questions directly of SQLite databases and CSV/JSON files, using an LLM to generate SQL queries and execute them.
* **New Feature:** `openai-to-sqlite` tool now allows enriching data in a SQLite database using OpenAI's GPT3.5 model.
* **Sentiment Analysis Example:**
+ Update a `sentiment` column in a `messages` table using `chatgpt()` function.
* **Sentiment Analysis Example:**
+ Update a `sentiment` column in a `messages` table using `chatgpt()` function.
