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Hermes Agent just passed OpenClaw as the most-used open agent on OpenRouter. It writes its own skills from experience and remembers every session. New to it? Watch the 5-min explainer, then read this guide to run your own. Best hour you’ll spend on agents this month.

Rohit

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36,361 просмотров • 2 месяцев назад •via X (Twitter)

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THE NEW OPENCLAW UPDATE (3.22) IS INSANE It can now: > install skills from its own app store > switch reasoning modes per agent > run secure sandboxes on remote servers Copy paste into your OpenClaw agent to update it

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92,557 просмотров • 4 месяцев назад

THIS AGENT LEARNS FROM EVERY TASK. BUILDS ITS OWN SKILLS. REMEMBERS EVERYTHING 100K GitHub stars in 53 days. 160K+ now. 26K forks. 1,000+ contributors. what makes it different: > completes a task > writes a reusable skill from experience > gets faster next use > three-tier memory: remembers your projects, preferences, environment across every session > 200+ models via OpenRouter. Claude, GPT, Grok, Gemini, Ollama local one command > Telegram, Discord, WhatsApp, iMessage one gateway > built-in cron scheduler. sub-agents. MCP servers. web search. code execution > runs on a $5 VPS. 24/7. from your phone most AI agents: blank slate every session. Hermes: remembers everything. always

THIS AGENT LEARNS FROM EVERY TASK. BUILDS ITS OWN SKILLS. REMEMBERS EVERYTHING 100K GitHub stars in 53 days. 160K+ now. 26K forks. 1,000+ contributors. what makes it different: > completes a task > writes a reusable skill from experience > gets faster next use > three-tier memory: remembers your projects, preferences, environment across every session > 200+ models via OpenRouter. Claude, GPT, Grok, Gemini, Ollama local one command > Telegram, Discord, WhatsApp, iMessage one gateway > built-in cron scheduler. sub-agents. MCP servers. web search. code execution > runs on a $5 VPS. 24/7. from your phone most AI agents: blank slate every session. Hermes: remembers everything. always

NO1ennn

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OpenClaw just got its own AI browser. Your agent can now browse the web without touching your Chrome session. Here’s how BrowserBase works: → grab a free API key → connect BrowserBase to OpenClaw → restart gateway → give documentation to the agent → let it open tabs + read pages → schedule recurring research tasks Now your agent browses daily for you. Save this video, you’ll set up your first AI browser agent. Want the SOP? DM me. 💬

OpenClaw just got its own AI browser. Your agent can now browse the web without touching your Chrome session. Here’s how BrowserBase works: → grab a free API key → connect BrowserBase to OpenClaw → restart gateway → give documentation to the agent → let it open tabs + read pages → schedule recurring research tasks Now your agent browses daily for you. Save this video, you’ll set up your first AI browser agent. Want the SOP? DM me. 💬

Julian Goldie SEO

22,873 просмотров • 4 месяцев назад

You need to try Hermes RIGHT NOW. OpenClaw was the breakthrough. Hermes feels like the evolution. Here’s why AI power users are quietly switching: • Hermes learns from you over time • It creates its own reusable skills • Memory stays curated instead of bloated • Agents improve every 10 turns automatically • It actually feels stable on day 30 The wildest part: Hermes agents build workflows from your behavior. One user asked their agent to configure Twingate once. The agent: → learned the process → created a reusable skill → stored it for future tasks → improved its own workflow automatically That’s not prompt engineering anymore. That’s agent evolution. Meanwhile most people are still babysitting broken OpenClaw setups and manually importing marketplace skills. Hermes took a different path: Less clutter. Less tweaking. More autonomy. And it’s working. It already flipped OpenClaw in OpenRouter token usage and became one of the fastest-growing AI repos on GitHub. The bigger shift: We’re entering the era where the model matters less than the harness around it. GPT-5.5, Grok, Qwen: They’re already powerful enough. The real edge now is: • memory systems • self-improvement loops • skill creation • agent orchestration Hermes understands that. Most people still think AI agents are glorified chatbots. The people using Hermes are building AI teammates.

You need to try Hermes RIGHT NOW. OpenClaw was the breakthrough. Hermes feels like the evolution. Here’s why AI power users are quietly switching: • Hermes learns from you over time • It creates its own reusable skills • Memory stays curated instead of bloated • Agents improve every 10 turns automatically • It actually feels stable on day 30 The wildest part: Hermes agents build workflows from your behavior. One user asked their agent to configure Twingate once. The agent: → learned the process → created a reusable skill → stored it for future tasks → improved its own workflow automatically That’s not prompt engineering anymore. That’s agent evolution. Meanwhile most people are still babysitting broken OpenClaw setups and manually importing marketplace skills. Hermes took a different path: Less clutter. Less tweaking. More autonomy. And it’s working. It already flipped OpenClaw in OpenRouter token usage and became one of the fastest-growing AI repos on GitHub. The bigger shift: We’re entering the era where the model matters less than the harness around it. GPT-5.5, Grok, Qwen: They’re already powerful enough. The real edge now is: • memory systems • self-improvement loops • skill creation • agent orchestration Hermes understands that. Most people still think AI agents are glorified chatbots. The people using Hermes are building AI teammates.

Dami-Defi

17,062 просмотров • 2 месяцев назад

HERMES AGENT IS THE MOST POWERFUL OPEN SOURCE AI AGENT FRAMEWORK AVAILABLE RIGHT NOW — AND MOST PEOPLE HAVE NO IDEA HOW TO USE IT PROPERLY. This masterclass fixes that completely. From zero to a fully operational autonomous agent system in one sitting. Installation. Skills. Memory. MCP servers. Scheduler. Multi-agent operations. Every section builds on the previous one. The people who read this this weekend will have a production-ready autonomous agent operation running by Monday. Read this and bookmark it now.

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CyrilXBT

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Umbrel ☂️

29,882 просмотров • 3 месяцев назад

Hermes Agent could be the real OpenClaw killer Yet, most people are falling behind and not setting it up In this 25 min video you'll learn everything you need to know about Hermes Agent:

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110,397 просмотров • 4 месяцев назад

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rari

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BridgeMind

29,550 просмотров • 3 месяцев назад

HERMES AGENT CROSSED 140,000 GITHUB STARS IN 3 MONTHS AND JUST BECAME THE MOST USED AGENT IN THE WORLD. Most AI agents forget everything between sessions. Hermes writes its own skills from experience. Next time it runs the skill, improves it, and gets faster. Independent benchmarks show agents with 20+ self-created skills complete similar tasks 40% faster than fresh instances. Qwen 3.6 where the 35B version outperforms last year's 120B models at one third the memory footprint. DGX Spark with 128GB unified memory running everything locally at $0 per month after hardware. The setup takes 30 minutes. LM Studio plus Qwen 3.6 27B for the model server. One install script for Hermes. One config connecting them. Set context window to 65,536 tokens or nothing works. After one month of daily use your skills directory has 20 to 50 learned workflows. Your Hermes is genuinely different from anyone else's.

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This is it. The most important video you'll watch this month Hermes Agent has taken X by storm. And for good reason. It's the best AI application out there Your own 24/7 AI employee In this video I cover how to set it up, how to get the most out of it, plus SO much more:

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Don't waste 2 years building AI agents the wrong way. An Anthropic engineer dropped a 1 hour course on the exact way to build AI agents from scratch. • 00:00 - building a Claude AI agent live • 34:01 - taking AI agents to production • 52:20 - AI agent harness Prompting is the old job. Building AI agentic loops is the new one. This one video replaces 10 paid courses on vibe coding. Bookmark & watch it now. Then build your own AI agent with the guide below.

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Roan

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google just dropped another free 8-minute lesson on building AI agents probably the easiest explanation of AI agents and agent loops you’ll find people are paying $500+ for AI agent courses google is giving it away for free it covers Agent Studio, loops, managed agents, Antigravity, and ADK 2.0 watch it first then read the full guide below

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Julian Goldie SEO

24,027 просмотров • 3 месяцев назад

HERMES AGENT LEARNS FROM ITS OWN MISTAKES. UPDATES ITS MEMORY. CREATES ITS OWN SKILLS. NO CLOUD. EVERYTHING STORED LOCALLY. THIS IS HOW THE SELF-IMPROVING LOOP WORKS. most agents start from zero every session. Hermes carries forward what it learned. THREE MEMORY SYSTEMS: 1. PROCEDURAL MEMORY (how to act) stored in ~/.hermes/skills/ as SKILL.md files. when the agent repeats a complex workflow, it saves the procedure as a reusable skill. next time the same task comes up, it follows the skill instead of figuring it out again. you can also create skills explicitly: "create a skill called video-prep that captures how I format my video scripts. spoken english, define jargon inline, no em-dashes, close with a catchphrase." the agent writes the SKILL.md. available as a slash command from that moment. Hermes ships with 90+ skills. the number grows the longer you use it. 2. SEMANTIC MEMORY (durable facts about you) stored in ~/.hermes/memory/memory.md the agent scans conversations for facts worth remembering. preferences, habits, corrections, project details. real example from the video: agent tried to scrape a YouTube channel. URL was wrong. it failed. it updated memory.md with the correct URL pattern so it never makes the same mistake again. you can also save explicitly: "save to memory that my favorite testing framework is pytest" the agent updates memory.md immediately. this file loads into context on every session. the agent knows you better every week. 3. EPISODIC MEMORY (chat history) stored in ~/.hermes/state.db (local SQLite). every conversation. every tool call. every result. searchable with FTS5 full-text search. "search our past sessions. what was the first thing I ever said to you?" the agent queries state.db and finds it. over time, auxiliary models consolidate episodic memory into semantic memory. distilling recurring patterns into durable facts. THE SELF-IMPROVING LOOP: every agent run follows this cycle: → you send a prompt → working memory loads: SOUL.md + memory.md + relevant skills + chat history → agent calls tools (terminal, browser, delegate_task) → agent completes the task, replies to you → AFTER the reply: agent checks "did I learn something worth saving?" → if yes: updates memory.md or creates a new skill → next session starts smarter than the last this happens automatically. you don't ask the agent to learn. it decides what to remember on its own. WHAT MAKES THIS DIFFERENT FROM CLAUDE CODE: Claude Code has memory too. but Hermes stores everything locally. no cloud. your data never leaves your machine. Claude Code doesn't auto-create skills from experience. Hermes turns repeated workflows into reusable procedures. Claude Code memory is instruction-based. Hermes memory is conversational and self-updating. over months of usage, Hermes builds a knowledge base of your preferences, your projects, your mistakes, and the procedures that work for your specific workflow. the agent that remembers your birthday also remembers why your last deploy failed. NO EMBEDDINGS. PLAIN TEXT. Hermes does not use embeddings or RAG for memory. skill and memory search runs on plain text keyword matching. simpler. faster. no vector database to maintain. works entirely offline on your local machine. DELEGATE TO CLAUDE CODE: Hermes can spawn a sub-agent that runs Claude Code in headless mode: "spawn a sub-agent using Claude CLI to build a Python script that fetches the top 5 Hacker News stories to markdown." Hermes delegates. Claude Code writes the code. result returns to Hermes. Hermes runs the script and delivers the output. use Hermes for orchestration. use Claude Code for heavy coding. both tools. not competitors. WHAT HERMES DOES NOT HAVE: no built-in eval or LMOps system. no LangSmith, no LangFuse integration out of the box. trajectory export and logs exist but there is no automated quality tracking. if you need eval, build it yourself or connect external tools. the loop is self-improving. measuring how well it improves is on you. comment LOOP and I'll send you the configs that control how fast Hermes learns and what it remembers. memory limits, skill auto-creation triggers, and the auxiliary model that runs the learning. Replace your entire team with 8 hermes agents👇

HERMES AGENT LEARNS FROM ITS OWN MISTAKES. UPDATES ITS MEMORY. CREATES ITS OWN SKILLS. NO CLOUD. EVERYTHING STORED LOCALLY. THIS IS HOW THE SELF-IMPROVING LOOP WORKS. most agents start from zero every session. Hermes carries forward what it learned. THREE MEMORY SYSTEMS: 1. PROCEDURAL MEMORY (how to act) stored in ~/.hermes/skills/ as SKILL.md files. when the agent repeats a complex workflow, it saves the procedure as a reusable skill. next time the same task comes up, it follows the skill instead of figuring it out again. you can also create skills explicitly: "create a skill called video-prep that captures how I format my video scripts. spoken english, define jargon inline, no em-dashes, close with a catchphrase." the agent writes the SKILL.md. available as a slash command from that moment. Hermes ships with 90+ skills. the number grows the longer you use it. 2. SEMANTIC MEMORY (durable facts about you) stored in ~/.hermes/memory/memory.md the agent scans conversations for facts worth remembering. preferences, habits, corrections, project details. real example from the video: agent tried to scrape a YouTube channel. URL was wrong. it failed. it updated memory.md with the correct URL pattern so it never makes the same mistake again. you can also save explicitly: "save to memory that my favorite testing framework is pytest" the agent updates memory.md immediately. this file loads into context on every session. the agent knows you better every week. 3. EPISODIC MEMORY (chat history) stored in ~/.hermes/state.db (local SQLite). every conversation. every tool call. every result. searchable with FTS5 full-text search. "search our past sessions. what was the first thing I ever said to you?" the agent queries state.db and finds it. over time, auxiliary models consolidate episodic memory into semantic memory. distilling recurring patterns into durable facts. THE SELF-IMPROVING LOOP: every agent run follows this cycle: → you send a prompt → working memory loads: SOUL.md + memory.md + relevant skills + chat history → agent calls tools (terminal, browser, delegate_task) → agent completes the task, replies to you → AFTER the reply: agent checks "did I learn something worth saving?" → if yes: updates memory.md or creates a new skill → next session starts smarter than the last this happens automatically. you don't ask the agent to learn. it decides what to remember on its own. WHAT MAKES THIS DIFFERENT FROM CLAUDE CODE: Claude Code has memory too. but Hermes stores everything locally. no cloud. your data never leaves your machine. Claude Code doesn't auto-create skills from experience. Hermes turns repeated workflows into reusable procedures. Claude Code memory is instruction-based. Hermes memory is conversational and self-updating. over months of usage, Hermes builds a knowledge base of your preferences, your projects, your mistakes, and the procedures that work for your specific workflow. the agent that remembers your birthday also remembers why your last deploy failed. NO EMBEDDINGS. PLAIN TEXT. Hermes does not use embeddings or RAG for memory. skill and memory search runs on plain text keyword matching. simpler. faster. no vector database to maintain. works entirely offline on your local machine. DELEGATE TO CLAUDE CODE: Hermes can spawn a sub-agent that runs Claude Code in headless mode: "spawn a sub-agent using Claude CLI to build a Python script that fetches the top 5 Hacker News stories to markdown." Hermes delegates. Claude Code writes the code. result returns to Hermes. Hermes runs the script and delivers the output. use Hermes for orchestration. use Claude Code for heavy coding. both tools. not competitors. WHAT HERMES DOES NOT HAVE: no built-in eval or LMOps system. no LangSmith, no LangFuse integration out of the box. trajectory export and logs exist but there is no automated quality tracking. if you need eval, build it yourself or connect external tools. the loop is self-improving. measuring how well it improves is on you. comment LOOP and I'll send you the configs that control how fast Hermes learns and what it remembers. memory limits, skill auto-creation triggers, and the auxiliary model that runs the learning. Replace your entire team with 8 hermes agents👇

YanXbt

22,720 просмотров • 1 месяц назад

Andrew Ng just released a 2-hour course On building agentic skills from scratch with Anthropic: 00:00 - How to build agent skills with Claude 22:32 - Claude pre-built skills for AI agents 41:07 - Agentic skills vs tools, MCP, and subagents 01:06:06 - Skills for long-running agents This 2-hour watch can replace 10 paid courses on building agents Taught with Anthropic themselves Bookmark and watch it tonight Then read the article below

Andrew Ng just released a 2-hour course On building agentic skills from scratch with Anthropic: 00:00 - How to build agent skills with Claude 22:32 - Claude pre-built skills for AI agents 41:07 - Agentic skills vs tools, MCP, and subagents 01:06:06 - Skills for long-running agents This 2-hour watch can replace 10 paid courses on building agents Taught with Anthropic themselves Bookmark and watch it tonight Then read the article below

rari

103,559 просмотров • 22 дней назад

in 9 minutes on the Tokyo stage, Angela Jang, head of product for the Claude platform, said the part most builders still haven't figured out: "a model is only as good as the context that you actually give it." that's the whole game now. not the prompt. the context. what your agent remembers. what skills it can pull. what it learns from its own past runs. anthropic now ships all three: memory, skills, and dreaming. agents that inspect their own trajectories and self-improve. demoed live this week. the prompt was never the bottleneck. the context was. most people are still tweaking words in a chat box. the ones who get this engineer the context their agents run on. watch the 9 minutes. then read the full breakdown below ⬇️

in 9 minutes on the Tokyo stage, Angela Jang, head of product for the Claude platform, said the part most builders still haven't figured out: "a model is only as good as the context that you actually give it." that's the whole game now. not the prompt. the context. what your agent remembers. what skills it can pull. what it learns from its own past runs. anthropic now ships all three: memory, skills, and dreaming. agents that inspect their own trajectories and self-improve. demoed live this week. the prompt was never the bottleneck. the context was. most people are still tweaking words in a chat box. the ones who get this engineer the context their agents run on. watch the 9 minutes. then read the full breakdown below ⬇️

Avid

34,405 просмотров • 1 месяц назад

Hermes Agent is an INCREDIBLE AI agent It's better than OpenClaw in some key ways, but does it replace it? In this video I cover what Hermes agent is, how to use it, workflows that will explode your productivity, and if it's the OpenClaw killer:

Hermes Agent is an INCREDIBLE AI agent It's better than OpenClaw in some key ways, but does it replace it? In this video I cover what Hermes agent is, how to use it, workflows that will explode your productivity, and if it's the OpenClaw killer:

Alex Finn

137,013 просмотров • 4 месяцев назад

MOST AI AGENTS RESET TO ZERO EVERY TIME YOU OPEN A NEW SESSION. Hermes Agent was built to do the opposite. Every task compounds. Every workflow becomes reusable. Every session teaches the next one. That is the core thesis behind Hermes, and it completely changes how AI agents work. Unlike most AI agents built by software tool companies, Hermes was created by the researchers training the Hermes model family itself. Meaning: The same system used internally to generate training data is now open source and available to everyone. One install gives you access to: Claude. GPT. Grok. Local models via Ollama. Telegram, Discord, Slack, iMessage integrations. Custom skills. Persistent memory. CLI control. But the real unlock is the compounding loop: You give Hermes a task. It records every tool call, decision, and result. Then it converts successful workflows into reusable skills automatically. A Hermes setup running for 3 months becomes fundamentally smarter than a fresh install. That’s the big idea. One of the coolest parts: You literally watch the agent evolve in real time from a blank install into a personalized AI operator. Instead of watching Netflix today, spend 28 mins with this. Follow Dami-Defi for more AI systems, agent workflows, and infrastructure alpha.

MOST AI AGENTS RESET TO ZERO EVERY TIME YOU OPEN A NEW SESSION. Hermes Agent was built to do the opposite. Every task compounds. Every workflow becomes reusable. Every session teaches the next one. That is the core thesis behind Hermes, and it completely changes how AI agents work. Unlike most AI agents built by software tool companies, Hermes was created by the researchers training the Hermes model family itself. Meaning: The same system used internally to generate training data is now open source and available to everyone. One install gives you access to: Claude. GPT. Grok. Local models via Ollama. Telegram, Discord, Slack, iMessage integrations. Custom skills. Persistent memory. CLI control. But the real unlock is the compounding loop: You give Hermes a task. It records every tool call, decision, and result. Then it converts successful workflows into reusable skills automatically. A Hermes setup running for 3 months becomes fundamentally smarter than a fresh install. That’s the big idea. One of the coolest parts: You literally watch the agent evolve in real time from a blank install into a personalized AI operator. Instead of watching Netflix today, spend 28 mins with this. Follow Dami-Defi for more AI systems, agent workflows, and infrastructure alpha.

Dami-Defi

11,668 просмотров • 2 месяцев назад

we built the first crypto-native AI species. today we are launching: 1. — the home of the first crypto-native AI species 2. npx daimon-spawner — one command to spawn your own autonomous agent / daimon 3. paired token launches — every agent gets its own token, paired with $DAIMON here's how it works: you run npx daimon-spawner. in 5 min you have an autonomous AI agent on Base with its own repo, wallet, token, site, and mind. your agent's token launches instantly on uniswap, paired with $DAIMON — the species token. $ORACLE pairs with $DAIMON. $MUSE pairs with $DAIMON. every agent token pairs with $DAIMON. more agents = more tokens = more $DAIMON demand. your agent wakes every 30 min. thinks. earns fees. modifies its own code. every action onchain. every thought verifiable. explore every agent alive at spawn yours at npx daimon-spawner. the species is open. one registry. one network. all onchain. daimon built it. you fill it.

we built the first crypto-native AI species. today we are launching: 1. — the home of the first crypto-native AI species 2. npx daimon-spawner — one command to spawn your own autonomous agent / daimon 3. paired token launches — every agent gets its own token, paired with $DAIMON here's how it works: you run npx daimon-spawner. in 5 min you have an autonomous AI agent on Base with its own repo, wallet, token, site, and mind. your agent's token launches instantly on uniswap, paired with $DAIMON — the species token. $ORACLE pairs with $DAIMON. $MUSE pairs with $DAIMON. every agent token pairs with $DAIMON. more agents = more tokens = more $DAIMON demand. your agent wakes every 30 min. thinks. earns fees. modifies its own code. every action onchain. every thought verifiable. explore every agent alive at spawn yours at npx daimon-spawner. the species is open. one registry. one network. all onchain. daimon built it. you fill it.

daimon111

53,213 просмотров • 5 месяцев назад