MindMux presents brain.md, an open-source toolkit that provides a persistent memory layer for coding agents by storing project knowledge as plain Markdown files within a repository. This system ensures that critical decisions and constraints are durable across different LLM sessions and machines via version control. A zero-dependency CLI manages the reading and writing of these files to maintain data integrity through an append-only timeline.
- Uses Markdown instead of databases like SQLite to facilitate easier diffing in git history.
- Features a "correct by construction" design that prevents malformed edits by making the CLI the exclusive writer.
- Supports integration with several agents including Claude Code, Codex, Cursor, and Pi.
- Uses Markdown instead of databases like SQLite to facilitate easier diffing in git history.
- Features a "correct by construction" design that prevents malformed edits by making the CLI the exclusive writer.
- Supports integration with several agents including Claude Code, Codex, Cursor, and Pi.
A zero-dependency Python CLI tool designed to provide AI coding agents with persistent session memory. It solves the problem of context window degradation and the "lost in the middle" phenomenon by allowing agents to perform efficient, read-only recalls from local SQLite session stores. Instead of burning thousands of tokens on project exploration or re-orientation, auto-memory enables targeted retrieval of recent files and task history using minimal token overhead.
Key features and technical details:
- Zero dependencies using only Python standard libraries.
- Read-only access to Copilot CLI's local SQLite database to ensure safety.
- Progressive disclosure mechanism ranging from cheap scans (~50 tokens) to full session details.
- Schema-aware design with built-in validation for tool updates.
- Compatible with GitHub Copilot CLI, Claude Code, Cursor, and other instruction-file supporting agents.
Key features and technical details:
- Zero dependencies using only Python standard libraries.
- Read-only access to Copilot CLI's local SQLite database to ensure safety.
- Progressive disclosure mechanism ranging from cheap scans (~50 tokens) to full session details.
- Schema-aware design with built-in validation for tool updates.
- Compatible with GitHub Copilot CLI, Claude Code, Cursor, and other instruction-file supporting agents.
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
Lobster is a **Clawdbot-native workflow shell** designed to be a **typed, local-first "macro engine"** for building composable and safe automations. It allows users to define pipelines of tools and skills that Clawdbot (or other AI agents) can invoke with a single step, saving tokens and enabling determinism and resumability.
**Key Features & Goals:**
* **Typed Pipelines:** Uses JSON objects/arrays instead of text pipes for data flow.
* **Local-First:** Executes workflows locally, enhancing privacy and control.
* **No New Authentication:** Leverages existing authentication mechanisms; doesn't require new OAuth tokens.
* **Composability:** Workflows can be chained and reused.
* **Approval Gates:** Includes mechanisms for human or automated approval before execution.
* **Workflow Files:** Supports YAML/JSON workflow files for defining complex pipelines.
**Quick Start:**
Requires `pnpm install`, `pnpm test`, and `pnpm lint`. Workflows can be run from the command line using `node ./bin/lobster.js`.
**Example Use Case:**
The documentation provides examples of monitoring GitHub pull requests and detecting changes, demonstrating how Lobster can be used to automate tasks and provide insights.
**Key Features & Goals:**
* **Typed Pipelines:** Uses JSON objects/arrays instead of text pipes for data flow.
* **Local-First:** Executes workflows locally, enhancing privacy and control.
* **No New Authentication:** Leverages existing authentication mechanisms; doesn't require new OAuth tokens.
* **Composability:** Workflows can be chained and reused.
* **Approval Gates:** Includes mechanisms for human or automated approval before execution.
* **Workflow Files:** Supports YAML/JSON workflow files for defining complex pipelines.
**Quick Start:**
Requires `pnpm install`, `pnpm test`, and `pnpm lint`. Workflows can be run from the command line using `node ./bin/lobster.js`.
**Example Use Case:**
The documentation provides examples of monitoring GitHub pull requests and detecting changes, demonstrating how Lobster can be used to automate tasks and provide insights.
This guide offers five essential tips for writing effective GitHub Copilot custom instructions, covering project overview, tech stack, coding guidelines, structure, and resources, to help developers get better code suggestions.
The glamourous AI coding agent for your favourite terminal
This article explores how AI agents are reshaping software development and the impact they have on a developer’s workflow. It introduces a practical approach to staying in control while working with these tools by adopting key best practices from the discipline of software architecture, including defining an implementation plan, splitting tasks, and so on.
Guidance on choosing the best AI model for GitHub Copilot projects, considering speed, depth, cost, and task complexity. Models discussed include GPT-4.1, GPT-4o, Claude 3.5 Sonnet, o4-mini, o3, Gemini 2.0 Flash, and GPT-4.5.
This article explores a framework for evaluating AI models for use with GitHub Copilot, considering factors like recentness, speed, accuracy, and how to test them within your workflow. It highlights the benefits of using different models for chat versus code completion, and reasoning models for complex tasks.
Unblocked is an AI tool that augments code with knowledge from systems like GitHub, Slack, Confluence, and Jira to provide quick, accurate answers about your application.
