OpenClaw Browser configuration guide

OpenClaw Browser supports three modes: openclaw-managed (standalone instance), remote (remote CDP), existing-session (takes over the user's Chrome); grant the agent tool permissions via alsoAllow: [browser]

Tools

MSA (Memory Sparse Attention) — an open-source way to break through the AI memory bottleneck

Instead of bolting on retrieval, MSA embeds memory directly into the attention mechanism itself. That gives you end-to-end long-term memory and breaks past the accuracy ceiling of traditional RAG and the compute-cost bottleneck of the context window.

AI

Playwright vs CDP: Comparing Browser Control Approaches

Playwright is a well-packaged high-level tool, while CDP is the browser's low-level "machine language". For scenarios where OpenClaw directly drives the host machine's browser main Profile, connecting to port 9222 via the CDP (DevTools MCP) protocol is the best choice

AI

macOS Chrome Remote Debugging configuration

On macOS, Chrome remote debugging must be enabled manually by ticking the checkbox at chrome://inspect/#remote-debugging, rather than relying on command-line flags

Tools

macOS Chrome Remote Debugging Port 9222 Startup Problem and the Final Solution

Solving the macOS Chrome Remote Debugging Port 9222 Problem: SingletonLock Deadlock, Chrome Security Restrictions, rsync Sync + Automator One-Click Launch Solution

Tools

Thoughts on Harness Engineering: The Third Paradigm Shift in AI Engineering

Harness Engineering = building an "office" for the Agent instead of continuing to polish the "email wording." From "writing better prompts" to "building the environment," so the agent can run stably and reliably in real production environments

AI

Hermes Agent official documentation breakdown vs OpenClaw

Hermes Agent is a white-box meta AI Agent developed by Nous Research, MIT licensed. Core positioning difference: OpenClaw = a local-first Agent orchestration framework, Hermes = a self-evolving personal operator

AI

LLM Personal Knowledge Base Pattern (Karpathy)

Use an LLM to build a persistent wiki instead of traditional RAG, so knowledge is compiled at ingestion time rather than re-derived at query time. Three-layer architecture: Raw Sources (immutable) → Wiki (LLM-maintained) → Schema (CLAUDE.md/AGENTS.md)

AI

Troubleshooting browser startup failure inside the OpenClaw container

Inside a container you need headless:true + noSandbox:true + extraArgs to disable the setuid/namespace sandbox — all three are required. A SIGUSR1 hot restart doesn't fully take effect for the headless field; you need a full container restart

AI

Gemma 4: Google open-sources a 31B model that takes down 400B, and it runs on a Mac Mini

31B parameters, Apache 2.0 license, runs on a Mac Mini, AIME math jumps from 20% to 89% — Google's open-source models have finally turned things around this time. But tool calling is still a weak spot

AI

Agency-Agent: One-click deployment of 144 AI employees

One-click deployment of 144 AI employees. I took a look at its docs, and only now do I sort of get it. The claim that it "integrates many Agents" is really because the whole open-source project is built out of standalone Agents, one after another — and there are already over a hundred of them. Each folder holds a single Agent.

AI

New knowledge: MineContext

At first listen, this thing sounds a lot like DayFlow, which I used last September. I could give it another try. I stopped using DayFlow back then because it used local compute for video analysis, and the analysis was way too inaccurate. I was using Qwen's 4B vision model, and the analysis was so inaccurate that I dropped it. I just went through the usage manual in the official docs, and its features are exactly identical to DayFlow, which I used last year.

Tools

Analysis of Claude Code's Long- and Short-Term Memory Mechanisms

Claude Code's memory system is not a single "memory" feature, but rather a **multi-layer, multi-scope, multi-lifecycle** memory architecture. It breaks down into:

AI

Analysis of Claude Code's Long-Context Management Strategy

Claude Code doesn't simply rely on the LLM's 256K/512K/1M context window to brute-force its way through. Instead, it has designed a **multi-layered, progressive** context management system. There are 6 layers of defense in total, triggered in order from lightest to heaviest, ensuring that AI response quality and speed are maintained at any interaction length.

AI

AI gives you five years of skill in a minute, but it can’t cultivate the sweeping monk’s “no move beats a move”

AI can hand you crash-course moves, can quickly turn you from a clueless outsider into a beginner who can wave his hands around a bit, but it can’t give you the “kung fu beyond kung fu” that a real master grinds out after decades of rolling through the jianghu — taking blades, stepping in pits, seeing through the tricks behind the rules. Those real sweeping monks never carry in their hearts the standard motions of this move or that; they carry the whole jianghu — its shifting winds and tides, the way people’s loyalties turn. That kind of cultivation is never something a crash-course secret manual can give.

Life Insights
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