<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Automation on Lynx Tech Blog</title><link>https://blog.lynxflow.co/en/categories/automation/</link><description>Recent content in Automation on Lynx Tech Blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Sun, 13 Sep 2026 01:00:00 +0800</lastBuildDate><atom:link href="https://blog.lynxflow.co/en/categories/automation/index.xml" rel="self" type="application/rss+xml"/><item><title>Merging Two Cover Generators into One: How I Unified the Blog Cover Pipeline with LynxCard and Added Drag-and-Drop Layout</title><link>https://blog.lynxflow.co/en/posts/merge-cover-pipelines-lynxcard-drag-layout/</link><pubDate>Sun, 13 Sep 2026 01:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/merge-cover-pipelines-lynxcard-drag-layout/</guid><description>&lt;img src="https://blog.lynxflow.co/images/merge-cover-pipelines-lynxcard-drag-layout.png" alt="Featured image of post Merging Two Cover Generators into One: How I Unified the Blog Cover Pipeline with LynxCard and Added Drag-and-Drop Layout" /&gt;The blog&amp;rsquo;s cover images used to run on two parallel tracks: AI news posts automatically called the LynxCard service to produce &amp;ldquo;landscape info cards,&amp;rdquo; while my hand-written deep-dive articles used a standalone script, gen_cover_dark_terminal.py, to render a &amp;ldquo;dark terminal&amp;rdquo; style cover — HTML template + Playwright screenshot + PIL quantization. The two tracks were completely independent, with different styles, parameters, and rendering logic. I finally merged them, a</description></item><item><title>I Turned 148 GitHub Repos Into 'Can't Lose Them All': A Full 3-2-1 Backup Retrospective</title><link>https://blog.lynxflow.co/en/posts/github-backup-3-2-1-2026/</link><pubDate>Mon, 24 Aug 2026 09:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/github-backup-3-2-1-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/github-backup-3-2-1-2026.png" alt="Featured image of post I Turned 148 GitHub Repos Into 'Can't Lose Them All': A Full 3-2-1 Backup Retrospective" /&gt;I counted the repos in my own GitHub account: 148 total—126 private, 22 public—with 3 forks mixed in. Just the repo objects (not counting metadata) amount to over 3 GB. Five-plus years of commit history, unfinished experimental code, automated scripts still running—all there.
Then I stared at that number for a while and asked myself a question: If this account vanished tomorrow, what would I still have?
The answer made me uncomfortable. So last week I spent an all-nighter turning those repos int</description></item><item><title>Solo Multi-Channel Content: How I Built My AI Automation Pipeline LynxPipe</title><link>https://blog.lynxflow.co/en/posts/lynxpipe-ai-content-pipeline-architecture/</link><pubDate>Thu, 20 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/lynxpipe-ai-content-pipeline-architecture/</guid><description>&lt;img src="https://blog.lynxflow.co/images/lynxpipe-ai-content-pipeline-architecture.png" alt="Featured image of post Solo Multi-Channel Content: How I Built My AI Automation Pipeline LynxPipe" /&gt;Running a blog, a Telegram channel, and a WeChat public account all by myself—the real bottleneck isn&amp;rsquo;t running out of ideas, it&amp;rsquo;s keeping up. Every day there&amp;rsquo;s an overwhelming flood of valuable AI news, and manually translating, rewriting, sourcing images, laying out, and distributing it all can eat up half a day before you even start. So I architected it as a pipeline I call LynxPipe: raw inputs go in, polished articles come out, and distribution to the blog, TG, and WeChat h</description></item><item><title>From Manual Post Review to Semi-Automated Improvement: My Telegram Channel Quality Loop, and the Next Step Toward Full Automation</title><link>https://blog.lynxflow.co/en/posts/tg-channel-auto-audit-loop/</link><pubDate>Sat, 15 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/tg-channel-auto-audit-loop/</guid><description>&lt;img src="https://blog.lynxflow.co/images/tg-channel-auto-audit-loop.png" alt="Featured image of post From Manual Post Review to Semi-Automated Improvement: My Telegram Channel Quality Loop, and the Next Step Toward Full Automation" /&gt;From Manual Review to AI-Driven Loop: How I Automated My Telegram Channel Moderation I run a Telegram channel @Lx_groups focused on free AI resources—free API quotas, limited-time offers, open-source projects, and industry news. The pitch is simple: &amp;ldquo;genuinely free,&amp;rdquo; no gray-market stuff, no ads. Behind the scenes, a Cloudflare Worker pulls from upstream TG channel preview pages, GitHub Atom feeds, and major tech company RSS feeds, deduplicates via KV storage, and pushes curated cont</description></item><item><title>Hermes + OpenClaw Beginner Deployment and Configuration Guide (2026)</title><link>https://blog.lynxflow.co/en/posts/hermes-openclaw-setup-guide/</link><pubDate>Sat, 08 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/hermes-openclaw-setup-guide/</guid><description>&lt;img src="https://blog.lynxflow.co/images/hermes-vs-openclaw.png?v=090818" alt="Featured image of post Hermes + OpenClaw Beginner Deployment and Configuration Guide (2026)" /&gt; This guide ties together five hands-on Hermes / OpenClaw articles on this site into one complete path: which one to choose → how to install it → how to configure models → how to use skills. Every command was verified against real running instances on 2026-07-31—not padded out by copying official docs. Both projects move fast, so you should re-check the official documentation before you start.
0. What are these two for? Hermes and OpenClaw are both open-source, self-hostable AI agent frameworks.</description></item><item><title>Hermes Skills vs OpenClaw Skills: Do They Port?</title><link>https://blog.lynxflow.co/en/posts/skills-portability-hermes-openclaw/</link><pubDate>Fri, 31 Jul 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/skills-portability-hermes-openclaw/</guid><description>&lt;img src="https://blog.lynxflow.co/images/skills-portability-hermes-openclaw.png" alt="Featured image of post Hermes Skills vs OpenClaw Skills: Do They Port?" /&gt;The short answer: yes, they port A skill you install on Hermes will install — byte-identical — on OpenClaw, and vice versa. Both harnesses speak the standard agent-skill format (SKILL.md with YAML frontmatter + supporting examples/, references/, scripts/, templates/ dirs), and both pull from the same registries.
Verified 2026-07-31: the skill agent-self-evaluation (among many others) is installed identically on both — same SKILL.md (7625 bytes, identical content), same directory layout — on this</description></item><item><title>Hermes vs OpenClaw: Which Self-Hosted AI Agent Harness in 2026</title><link>https://blog.lynxflow.co/en/posts/hermes-vs-openclaw/</link><pubDate>Fri, 31 Jul 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/hermes-vs-openclaw/</guid><description>&lt;img src="https://blog.lynxflow.co/images/hermes-vs-openclaw.png?v=090818" alt="Featured image of post Hermes vs OpenClaw: Which Self-Hosted AI Agent Harness in 2026" /&gt;The two rising self-hosted agent harnesses Hermes and OpenClaw are two open-source, self-hostable AI agent harnesses attracting growing attention in 2026. Both let you run an agent that talks to humans across messaging channels, calls tools, and maintains state — but they differ in language, philosophy, and ecosystem.
Dimension Hermes OpenClaw Repo NousResearch/hermes-agent openclaw/openclaw Version (2026-07-31) 0.16.0 2026.7.1-2 Language Python Node.js / TypeScript License MIT MIT One-liner The</description></item><item><title>How to Install Hermes Agent on WSL2 (2026)</title><link>https://blog.lynxflow.co/en/posts/install-hermes-wsl2/</link><pubDate>Fri, 31 Jul 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/install-hermes-wsl2/</guid><description>&lt;img src="https://blog.lynxflow.co/images/install-hermes-wsl2.png?v=090500" alt="Featured image of post How to Install Hermes Agent on WSL2 (2026)" /&gt;Hermes is a self-hosted, self-improving AI agent (Python, MIT). On WSL2 it installs the same way as on Linux — the official installer lands under ~/.hermes. This guide was verified against a live WSL2 instance running Hermes v0.16.0.
1. Install (official one-liner) 1 curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash The installer handles its own dependencies: uv, Python 3.11, Node.js, ripgrep, and ffmpeg. On WSL2 it detects your existing Git and uses it (no bundled Git needed).</description></item><item><title>How to Install the OpenClaw Gateway (2026)</title><link>https://blog.lynxflow.co/en/posts/install-openclaw-gateway/</link><pubDate>Fri, 31 Jul 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/install-openclaw-gateway/</guid><description>&lt;img src="https://blog.lynxflow.co/images/install-openclaw-gateway.png" alt="Featured image of post How to Install the OpenClaw Gateway (2026)" /&gt;OpenClaw is a self-hosted, multi-channel AI agent gateway (Node.js/TypeScript, MIT). Unlike Hermes (Python, where you hand-write a systemd unit), OpenClaw self-manages its own gateway service — one command installs the systemd unit for you. This guide was verified against a live instance running OpenClaw&amp;rsquo;s gateway.
1. Install (npm global) OpenClaw is an npm package. With Node.js 20+ available:
1 npm install -g openclaw Verify:
1 openclaw --version The binary lands in your npm global bin (e</description></item><item><title>Model Provider Setup: Hermes + OpenClaw (2026)</title><link>https://blog.lynxflow.co/en/posts/model-provider-setup/</link><pubDate>Fri, 31 Jul 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/model-provider-setup/</guid><description>&lt;img src="https://blog.lynxflow.co/images/model-provider-setup.png" alt="Featured image of post Model Provider Setup: Hermes + OpenClaw (2026)" /&gt;A self-hosted agent is useless until it can reach a model. Both Hermes and OpenClaw let you pick an inference provider interactively and store it in a local config file — with secrets in environment variables, never in the file. This guide covers the official commands and config structure (verified 2026-07-31); no live keys are shown.
Hermes — hermes model Hermes ships an interactive provider picker:
1 hermes model It lists providers, fetches each one&amp;rsquo;s live /v1/models list, and lets you c</description></item><item><title>Self-Hosted AI Agent Landscape 2026</title><link>https://blog.lynxflow.co/en/posts/self-hosted-agent-landscape-2026/</link><pubDate>Fri, 31 Jul 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/self-hosted-agent-landscape-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/self-hosted-agent-landscape-2026.png" alt="Featured image of post Self-Hosted AI Agent Landscape 2026" /&gt;Two categories that get conflated &amp;ldquo;AI agent&amp;rdquo; in 2026 covers two very different things, and mixing them up causes most bad choices:
Build-your-own frameworks — libraries you code against to assemble an agent (LangGraph, CrewAI, AutoGPT-style). You own the plumbing: tool-calling loops, memory, channels. High control, high effort. Ready-to-run harnesses — a packaged agent you install, configure, and deploy as a service. It already has the reasoning loop, the channels, the skills system.</description></item><item><title>Why I self-host my AI agents (and built AgentHub)</title><link>https://blog.lynxflow.co/en/posts/why-self-host-agents-and-agenthub/</link><pubDate>Fri, 31 Jul 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/why-self-host-agents-and-agenthub/</guid><description>&lt;img src="https://blog.lynxflow.co/images/why-self-host-agents-and-agenthub.png" alt="Featured image of post Why I self-host my AI agents (and built AgentHub)" /&gt; A self-hosted rack of agents reaching messaging channels|AI-generated illustration Every day I run two self-hosted AI agent harnesses: Hermes (from Nous Research — Python, self-improving, with a skills system) and OpenClaw (a Node-based multi-channel agent gateway with a skills marketplace). Both are MIT-licensed, both live on my own machine, and their channels cover everything from WeChat, Feishu, and DingTalk to Telegram.</description></item></channel></rss>