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

Memory Now 63% of AI Chip Cost: HBM Spend Hits $32B
Epoch AI data shows HBM memory’s share of AI chip component costs rose from 52% to 63% between Q1 2024 and Q4 2025. Total component spend grew from $22B to $52B, with HBM accounting for $20B of that increase.

Research on AI Agent Consistency: Key Findings and Practical Takeaways
A study of 3,000 experiments across Claude, GPT-4o, and Llama reveals that consistent agents achieve 80–92% accuracy while inconsistent ones drop to 25–60%, with 69% of divergence occurring at the first tool call.

Anthropic Splits Remote Agent Control into Dispatch and Remote Control with Reliability Issues
Anthropic has implemented OpenClaw's core capability as two separate products: Dispatch for Cowork users and Remote Control for Claude Code developers. Both suffer from reliability problems including mobile connection drops after roughly 10 hours.

Cursor's Composer 2.0 appears to use Kimi 2.5 model based on API endpoint evidence
Network analysis shows Cursor's Composer 2.0 sends requests to an endpoint containing 'kimi-k2p5-rl-0317-s515-fast', suggesting it's based on Kimi 2.5. The modified MIT license reportedly requires attribution but minimal other obligations.