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.
Unusually detailed post explains how OpenAI handles the Codex agent loop. The article dives into the technical aspects of OpenAI's Codex CLI coding agent, including the agent loop, prompt construction, caching, and context window management.
The article details how their Codex CLI coding agent functions. OpenAI engineer Michael Bolin explains the "agent loop" – the process by which the AI receives user input, generates code, runs tests, and iterates with human supervision.
* **Agent Loop Mechanics:** The agent builds prompts with prioritized components (system, developer, user, assistant) and sends them to OpenAI’s Responses API.
* **Prompt Management:** The system handles growing prompt lengths (quadratic growth) through caching, compaction, and a stateless API design (allowing for "Zero Data Retention"). Cache misses can significantly impact performance.
* **Context Window:** Codex automatically compacts conversations to stay within the AI model's context window.
* **Open Source Focus:** OpenAI open-sources the CLI client for Codex, unlike ChatGPT, suggesting a different approach to development and transparency for coding tools.
* **Challenges Acknowledged:** The article doesn't shy away from the engineering challenges, like performance issues and bugs encountered during development.
* **Future Coverage:** Bolin plans to release further posts detailing the CLI’s architecture, tool implementation, and sandboxing model.
The article details how their Codex CLI coding agent functions. OpenAI engineer Michael Bolin explains the "agent loop" – the process by which the AI receives user input, generates code, runs tests, and iterates with human supervision.
* **Agent Loop Mechanics:** The agent builds prompts with prioritized components (system, developer, user, assistant) and sends them to OpenAI’s Responses API.
* **Prompt Management:** The system handles growing prompt lengths (quadratic growth) through caching, compaction, and a stateless API design (allowing for "Zero Data Retention"). Cache misses can significantly impact performance.
* **Context Window:** Codex automatically compacts conversations to stay within the AI model's context window.
* **Open Source Focus:** OpenAI open-sources the CLI client for Codex, unlike ChatGPT, suggesting a different approach to development and transparency for coding tools.
* **Challenges Acknowledged:** The article doesn't shy away from the engineering challenges, like performance issues and bugs encountered during development.
* **Future Coverage:** Bolin plans to release further posts detailing the CLI’s architecture, tool implementation, and sandboxing model.
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.
> Important: We do not accept PRs for core features.
DockaShell is an MCP (Model Context Protocol) server that gives AI agents isolated Docker containers to work in. Each agent gets its own persistent environment with shell access, file operations, and full audit trails. It aims to remove limitations of current AI assistants like lack of persistent memory, tool babysitting, limited toolsets, and no self-reflection, enabling self-evolving agents, continuous memory, autonomous exploration, and meta-learning.
GitHub Copilot now has an Agents page to help developers kick off tasks and track progress. Users can assign tasks to Copilot (tech debt, bug fixes, new features) and Copilot will create a draft pull request for review. The feature is available to Copilot Pro/Pro+, Business, and Enterprise users with the coding agent enabled.
SuperCoder is a coding agent that runs in your terminal, offering features like code search, project structure exploration, code editing, bug fixing, and integration with OpenAI or local models.
