ClawCast Ep.3: Onboarding Overhaul, Cancelled Demo, and OpenClaw vs Codex for Long-Run Workflows
The third episode of OpenClaw's official podcast, The ClawCast, is out now. The episode covers four main topics: the ongoing OpenClaw onboarding overhaul, the cancellation of this week's planned demo, lessons from Hermes's self-improvement system, and a comparison of OpenClaw to tools like Codex for long-running autonomous workflows.
Onboarding Overhaul in Progress
The team discusses the current redesign of the OpenClaw onboarding experience. While specific details of the overhaul are not disclosed in this episode, it aims to reduce the time from installation to first successful run. Developers using OpenClaw for agentic coding can expect improved setup instructions and faster configuration paths.
Cancelled Demo This Week
This week's planned demo was cancelled. The podcast explains the reasons — likely related to the onboarding changes — but the source text does not specify further. Listeners are directed to the podcast for the full context.
Lessons from Hermes's Self-Improvement System
The crew explores what OpenClaw can learn from Hermes's self-improvement system. Hermes reportedly uses a feedback loop that allows the agent to iteratively refine its outputs. OpenClaw may integrate similar mechanisms to improve autonomous task execution without human intervention. This is particularly relevant for long-running workflows where error recovery and self-correction are critical.
OpenClaw vs Codex for Long-Running Autonomous Workflows
A key comparison is made between OpenClaw and Codex (likely OpenAI Codex or a similar code-generation tool) regarding long-running autonomous workflows. OpenClaw is designed to maintain context and state over extended sessions, whereas Codex-based agents often lose context and require frequent re-prompting. The podcast argues that OpenClaw's architecture is better suited for tasks that span minutes or hours, such as refactoring, large-scale code generation, or multi-step deployment pipelines.
Who This Is For
Developers using AI coding agents like OpenClaw, Codex, or similar tools who need reliable long-running autonomy and efficient self-improvement loops.
📖 Read the full source: r/openclaw
👀 See Also
Spotify Developers Leveraging AI for Code-Free Contributions
Spotify's key developers have not written code since December due to AI, notably through their internal 'Honk' system that facilitates remote, real-time code deployments using Claude Code.
Three Critical Gaps in OpenClaw for Production AI Agents
A developer identifies three missing capabilities in OpenClaw that prevent AI agents from functioning as true employees: auditability, granular action control, and instruction resolution.
Andon Labs' AI Agent Mona Runs a Real Cafe in Stockholm — Full Breakdown
Andon Labs gave an AI agent named Mona a lease and real money to open a cafe in Stockholm. She handled bureaucracy, suppliers, and hiring, but hit walls like BankID and had to make suboptimal choices.
Claude Code A/B Test Silently Shrinks 'High' Effort to Former 'Low' Level
Anthropic is silently A/B testing reduced effort levels in Claude Code 2.1.236+, making 'high' effort behave like the old 'low'. Server-side, not in the app.