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HERMES AGENT IS PULLING AHEAD OF OPENCLAW. 8 FEATURES THAT WILL MAKE YOU SWITCH. 1. HERMES GETS SMARTER EVERY RUN. Hermes updates its own skills after every completed task. what worked gets saved. what failed gets refined. the Curator runs in the background every 7 days. prunes unused skills....

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YanXbt

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19,573 次观看 • 1 个月前 •via X (Twitter)

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HERMES AGENT CAN MAKE YOU MONEY. HERE ARE 3 SETUPS YOU CAN START TODAY. 1. automated lead generation one Hermes profile scans for gigs and contracts in your niche every morning. drafts personalized applications. sends to Telegram for your approval before sending. → SOUL. md defines your niche, skills, rate → cron job runs every morning at 8am → xurl + web search find the opportunities → you review and send from your phone 2. content at scale Hermes remembers your niche, your style, your keywords. it researches and drafts articles on a cron schedule. traffic turns into ad revenue, affiliate income, or leads. → SOUL. md defines your topics and tone → cron job drafts 1-2 articles per day → each draft gets sharper because Hermes saves what performed and what didn't as skills 3. selling AI ops to local businesses restaurants, clinics, agencies want automation. they can't build it. you can. → one Hermes profile per client, fully isolated → each client gets their own SOUL. md, memory, cron → charge $497/month per client to manage their workflows → 5 clients = $2,485/month recurring → Hermes runs the work. you keep the margin. all three share the same foundation: → separate Hermes profile per operation → SOUL. md defines the job → cron jobs run the work on schedule → Telegram approval before anything goes live → skills compound after every run full setup guide for Hermes Agent from installation to advanced use cases in the article 👇 comment OPS and I'll send you the full SOUL. md template for whichever setup fits your situation.
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HERMES AGENT CAN MAKE YOU MONEY. HERE ARE 3 SETUPS YOU CAN START TODAY. 1. automated lead generation one Hermes profile scans for gigs and contracts in your niche every morning. drafts personalized applications. sends to Telegram for your approval before sending. → SOUL. md defines your niche, skills, rate → cron job runs every morning at 8am → xurl + web search find the opportunities → you review and send from your phone 2. content at scale Hermes remembers your niche, your style, your keywords. it researches and drafts articles on a cron schedule. traffic turns into ad revenue, affiliate income, or leads. → SOUL. md defines your topics and tone → cron job drafts 1-2 articles per day → each draft gets sharper because Hermes saves what performed and what didn't as skills 3. selling AI ops to local businesses restaurants, clinics, agencies want automation. they can't build it. you can. → one Hermes profile per client, fully isolated → each client gets their own SOUL. md, memory, cron → charge $497/month per client to manage their workflows → 5 clients = $2,485/month recurring → Hermes runs the work. you keep the margin. all three share the same foundation: → separate Hermes profile per operation → SOUL. md defines the job → cron jobs run the work on schedule → Telegram approval before anything goes live → skills compound after every run full setup guide for Hermes Agent from installation to advanced use cases in the article 👇 comment OPS and I'll send you the full SOUL. md template for whichever setup fits your situation.

YanXbt

30,540 次观看 • 1 个月前

how to set up hermes agent step by step. built-in memory, 40+ tools, works on your phone, and what to think of hermes vs openclaw: 1. hermes is a personal AI agent that runs in your terminal. think of it like open claw but with built-in memory, 40+ tools out of the box, and 90% cheaper token costs. you install it with one command. 2. the 3 problems with open claw that hermes solves: no memory (you keep repeating yourself), constant gateway restarts, and zero visibility into what you're spending on tokens. 3. hermes remembers everything. every completed task gets saved to memory. it searches through past logs to find solutions. over time it literally gets smarter at your specific workflows. 4. connect it to open router. you see exact costs per model per task. free models rotate weekly. one founder went from $130 every five days on open claw to $10 on hermes. same output. 5. it comes preloaded with skills. apple notes, imessage, find my, browser, web search, image generation, cron jobs. no hunting for plugins. 6. connect it to obsidian so it reads your entire vault. connect it to gstack for your dev environment. create custom skills for your specific workflows. 7. the biggest money saver: have it write code once for recurring tasks. then it runs without burning tokens every time. stop paying an LLM to do the same scrape or report daily. 8. run it on android via telegram. name your agents. talk to them like coworkers. in this episode imran shows you how to set this up. 9. you can run it bare metal, in docker, or serverless on modal. pick your risk level. i begged imran to come on The Startup Ideas Podcast (SIP) 🧃 and walk through the full installation live. he made it impossibly clear. if you've heard of Hermes Agent and want the clearest explanation of how to get set up like a pro let me know what you want me to cover on the next ep this is the best personal agent setup video on the internet right now. watch
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how to set up hermes agent step by step. built-in memory, 40+ tools, works on your phone, and what to think of hermes vs openclaw: 1. hermes is a personal AI agent that runs in your terminal. think of it like open claw but with built-in memory, 40+ tools out of the box, and 90% cheaper token costs. you install it with one command. 2. the 3 problems with open claw that hermes solves: no memory (you keep repeating yourself), constant gateway restarts, and zero visibility into what you're spending on tokens. 3. hermes remembers everything. every completed task gets saved to memory. it searches through past logs to find solutions. over time it literally gets smarter at your specific workflows. 4. connect it to open router. you see exact costs per model per task. free models rotate weekly. one founder went from $130 every five days on open claw to $10 on hermes. same output. 5. it comes preloaded with skills. apple notes, imessage, find my, browser, web search, image generation, cron jobs. no hunting for plugins. 6. connect it to obsidian so it reads your entire vault. connect it to gstack for your dev environment. create custom skills for your specific workflows. 7. the biggest money saver: have it write code once for recurring tasks. then it runs without burning tokens every time. stop paying an LLM to do the same scrape or report daily. 8. run it on android via telegram. name your agents. talk to them like coworkers. in this episode imran shows you how to set this up. 9. you can run it bare metal, in docker, or serverless on modal. pick your risk level. i begged imran to come on The Startup Ideas Podcast (SIP) 🧃 and walk through the full installation live. he made it impossibly clear. if you've heard of Hermes Agent and want the clearest explanation of how to get set up like a pro let me know what you want me to cover on the next ep this is the best personal agent setup video on the internet right now. watch

GREG ISENBERG

616,663 次观看 • 3 个月前

MASSIVE Hermes Agent update over the last few days Totally changes the way I use Hermes Here's 6 new features you need to start using immediately (video demoing them below): 1. Mixture of agents: send your prompt to a team of different models. The team sends back all of their responses to an orchestrator model who synthesizes a final answer. Gives much better results than just sending a prompt to 1 model 2. /learn: use the new built in /learn skill to have Hermes automatically create new skills. You can either give a prompt after /learn or put in a URL. I like pasting in URLs of tweets with helpful tips after /learn and Hermes will automatically turn it into a skill 3. /journey: See every skill and memory Hermes has created for you on a really nice timeline/chart. Great for seeing how your agent has learned and improved over time 4. Self improvement cost savings: Hermes now uses cheaper models to do it's self improvement including memory creation and skill creation. These types of activities happen in the background of almost every prompt, so this results in TONS of cost savings over time 5. Vibe coding improvements: Hermes desktop is now a full vibe coding tool. You can see diffs, make commits, and even open up PRs directly from the desktop interface. Makes it WAYYY nicer to vibe code with 6. Fable 5 is now built in. Fable 5. Obviously Fable is incredibly expensive, so only use this new profile for incredibly complex tasks. Excellent updates that have significantly improved the experience. Video demoing all the updates below!
11:56

MASSIVE Hermes Agent update over the last few days Totally changes the way I use Hermes Here's 6 new features you need to start using immediately (video demoing them below): 1. Mixture of agents: send your prompt to a team of different models. The team sends back all of their responses to an orchestrator model who synthesizes a final answer. Gives much better results than just sending a prompt to 1 model 2. /learn: use the new built in /learn skill to have Hermes automatically create new skills. You can either give a prompt after /learn or put in a URL. I like pasting in URLs of tweets with helpful tips after /learn and Hermes will automatically turn it into a skill 3. /journey: See every skill and memory Hermes has created for you on a really nice timeline/chart. Great for seeing how your agent has learned and improved over time 4. Self improvement cost savings: Hermes now uses cheaper models to do it's self improvement including memory creation and skill creation. These types of activities happen in the background of almost every prompt, so this results in TONS of cost savings over time 5. Vibe coding improvements: Hermes desktop is now a full vibe coding tool. You can see diffs, make commits, and even open up PRs directly from the desktop interface. Makes it WAYYY nicer to vibe code with 6. Fable 5 is now built in. Fable 5. Obviously Fable is incredibly expensive, so only use this new profile for incredibly complex tasks. Excellent updates that have significantly improved the experience. Video demoing all the updates below!

Alex Finn

142,745 次观看 • 21 天前

Hermes agent just left the terminal. 𝗛𝗲𝗿𝗺𝗲𝘀 𝗗𝗲𝘀𝗸𝘁𝗼𝗽 dropped yesterday. native app for macOS, Windows, and Linux. for months Hermes was the agent that learned your projects, wrote its own skills, and built a model of who you are. all of it buried in terminal logs. now it has a window. the important part is that it's not a wrapper. it runs the same agent core, the same sessions, memory, and skills as the CLI. you can start a task in the terminal and finish it in the app without anything resetting. the state is shared across every interface, not copied between them. what the GUI actually adds: → streaming chat that shows live tool calls and inline reasoning instead of a spinner → a preview rail that renders pages, code, and images right beside the conversation → an artifacts panel that collects every file the agent has ever produced → remote gateway mode, so you can point the app at a VPS and run the heavy work elsewhere → skills, cron, profiles, and gateways managed point-and-click instead of through YAML → voice mode, drag-drop files, and inline image generation remote gateway mode is the one worth slowing down on. the agent runs 24/7 on a $5 server while you control it from your laptop like a local app. other agent UIs are chatboxes with a logo. this one shows the autonomy instead of hiding it, so you watch the skills load, the tools fire, and the artifacts pile up as it works. it was teased in Jensen's GTC keynote. MIT licensed, local-first, no telemetry. if you already run Hermes, download it and everything is already there. your chats, memory, and skills carry straight over. i wrote a full masterclass on Hermes Agent that walks through the SOUL. md identity layer, the three-tier memory system, the self-evolving skills loop, and how to run three specialized agents 24/7. desktop is the interface that finally does all of it justice. the article is quoted below.
1:18

Hermes agent just left the terminal. 𝗛𝗲𝗿𝗺𝗲𝘀 𝗗𝗲𝘀𝗸𝘁𝗼𝗽 dropped yesterday. native app for macOS, Windows, and Linux. for months Hermes was the agent that learned your projects, wrote its own skills, and built a model of who you are. all of it buried in terminal logs. now it has a window. the important part is that it's not a wrapper. it runs the same agent core, the same sessions, memory, and skills as the CLI. you can start a task in the terminal and finish it in the app without anything resetting. the state is shared across every interface, not copied between them. what the GUI actually adds: → streaming chat that shows live tool calls and inline reasoning instead of a spinner → a preview rail that renders pages, code, and images right beside the conversation → an artifacts panel that collects every file the agent has ever produced → remote gateway mode, so you can point the app at a VPS and run the heavy work elsewhere → skills, cron, profiles, and gateways managed point-and-click instead of through YAML → voice mode, drag-drop files, and inline image generation remote gateway mode is the one worth slowing down on. the agent runs 24/7 on a $5 server while you control it from your laptop like a local app. other agent UIs are chatboxes with a logo. this one shows the autonomy instead of hiding it, so you watch the skills load, the tools fire, and the artifacts pile up as it works. it was teased in Jensen's GTC keynote. MIT licensed, local-first, no telemetry. if you already run Hermes, download it and everything is already there. your chats, memory, and skills carry straight over. i wrote a full masterclass on Hermes Agent that walks through the SOUL. md identity layer, the three-tier memory system, the self-evolving skills loop, and how to run three specialized agents 24/7. desktop is the interface that finally does all of it justice. the article is quoted below.

Akshay 🚀

51,370 次观看 • 1 个月前

HERMES AGENT IS NOW IN THE CLOUD. NO VPS. NO TERMINAL. NO SETUP. PICK A MODEL. PICK A SERVER SIZE. AGENT IS LIVE IN 60 SECONDS. Nous Portal just launched hosted Hermes Agent. two clicks. one minute. done. Nous Research WHAT THIS MEANS: before today: install Hermes on a VPS or your laptop. configure providers. set up gateway. manage updates. run hermes setup. edit config.yaml. great for power users. friction for everyone else. now: go to pick a model. pick a server size. your agent is live and reachable in 60 seconds. no terminal. no SSH. no Docker. same Hermes. same features. same tools. someone else handles the infrastructure. FOR TEAMS: this is where it gets interesting. spin up agents for everyone at your org. each team member gets their own Hermes instance. granular access controls per user. unified billing through Nous Portal. your team gets Hermes on day one. no DevOps needed. no VPS per person. one admin dashboard. one bill. WHAT'S INCLUDED: → 300+ models via Nous Portal (Claude, GPT, Gemini, DeepSeek, Grok, MiniMax, and more) → Tool Gateway (web search, image generation, TTS, browser automation) → all messaging platforms (Telegram, Discord, Slack, WhatsApp, Signal) → full feature set (profiles, cron, kanban, skills, memory, sub-agents, MoA, /goal, /learn, /journey) → automatic updates ONE PORTAL. FOUR TIERS: Free: $0/month. pay-as-you-go credits from $10. Plus: $20/month. $22 in monthly usage credit. Super: $100/month. $110 in monthly credit. Ultra: $200/month. $220 in monthly credit. highest rate limits. every paid tier includes Tool Gateway. one OAuth. one subscription. no extra API keys. SELF-HOSTED IS NOT GOING ANYWHERE: Hermes is MIT licensed. open source. free forever. you can still run it on your laptop, VPS, or GPU cluster. nothing changes for self-hosted users. the cloud version is for people who want the agent running without managing the machine. pick your path: → self-hosted: full control. you manage everything. → cloud: zero ops. Nous manages infrastructure. → hybrid: self-host your main agent, cloud for team members. HOW TO START: cloud: self-hosted: hermes setup --portal both connect to the same Nous Portal. same models. same tools. same billing. learn how to replace your entire team with 8 hermes agents 👇
0:20

HERMES AGENT IS NOW IN THE CLOUD. NO VPS. NO TERMINAL. NO SETUP. PICK A MODEL. PICK A SERVER SIZE. AGENT IS LIVE IN 60 SECONDS. Nous Portal just launched hosted Hermes Agent. two clicks. one minute. done. Nous Research WHAT THIS MEANS: before today: install Hermes on a VPS or your laptop. configure providers. set up gateway. manage updates. run hermes setup. edit config.yaml. great for power users. friction for everyone else. now: go to pick a model. pick a server size. your agent is live and reachable in 60 seconds. no terminal. no SSH. no Docker. same Hermes. same features. same tools. someone else handles the infrastructure. FOR TEAMS: this is where it gets interesting. spin up agents for everyone at your org. each team member gets their own Hermes instance. granular access controls per user. unified billing through Nous Portal. your team gets Hermes on day one. no DevOps needed. no VPS per person. one admin dashboard. one bill. WHAT'S INCLUDED: → 300+ models via Nous Portal (Claude, GPT, Gemini, DeepSeek, Grok, MiniMax, and more) → Tool Gateway (web search, image generation, TTS, browser automation) → all messaging platforms (Telegram, Discord, Slack, WhatsApp, Signal) → full feature set (profiles, cron, kanban, skills, memory, sub-agents, MoA, /goal, /learn, /journey) → automatic updates ONE PORTAL. FOUR TIERS: Free: $0/month. pay-as-you-go credits from $10. Plus: $20/month. $22 in monthly usage credit. Super: $100/month. $110 in monthly credit. Ultra: $200/month. $220 in monthly credit. highest rate limits. every paid tier includes Tool Gateway. one OAuth. one subscription. no extra API keys. SELF-HOSTED IS NOT GOING ANYWHERE: Hermes is MIT licensed. open source. free forever. you can still run it on your laptop, VPS, or GPU cluster. nothing changes for self-hosted users. the cloud version is for people who want the agent running without managing the machine. pick your path: → self-hosted: full control. you manage everything. → cloud: zero ops. Nous manages infrastructure. → hybrid: self-host your main agent, cloud for team members. HOW TO START: cloud: self-hosted: hermes setup --portal both connect to the same Nous Portal. same models. same tools. same billing. learn how to replace your entire team with 8 hermes agents 👇

YanXbt

45,446 次观看 • 19 天前

HERMES AGENT SHIPS WITH A BUNDLED SKILL FOR ANDREJ KARPATHY'S LLM WIKI PATTERN. A SELF-IMPROVING KNOWLEDGE BASE THAT GROWS EVERY TIME YOU FEED IT. mentioned this briefly in the overnight workflow article. here is the full breakdown. what it is: a self-improving knowledge base built as interlinked markdown files. unlike RAG (which rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. cross-references stay linked. contradictions get flagged automatically. synthesis reflects everything ingested so far. why this matters for Hermes memory: Hermes built-in memory knows YOU. it remembers your conversations, your preferences, your business context across sessions. but it doesn't know your inbox. or your meeting transcripts. or that article you saved last week. or the expert framework you want it to learn. the LLM Wiki solves that. THE DIVISION OF LABOR human curates sources and directs analysis. agent summarizes, cross-references, files, and maintains consistency. you drop in articles, transcripts, notes. Hermes indexes them, links related concepts, flags contradictions, updates affected pages. your knowledge base grows itself. SETUP IS ONE COMMAND the skill ships with Hermes. enable it. set WIKI_PATH in ~/.hermes/.env: WIKI_PATH=/Users/you/wiki defaults to ~/wiki if unset. then drop anything into it: "index this article into my wiki: [paste URL or text]" Hermes reads it, builds a source page, updates related entries, flags contradictions. THE OBSIDIAN ANGLE set OBSIDIAN_VAULT_PATH to the same directory. now your wiki is visible in Obsidian's graph view. nodes, links, backlinks. all built by Hermes. for headless servers: install obsidian-headless. syncs vaults without a GUI. agent writes from the server, you read on your laptop. THE COMPOUND EFFECT Hermes knows you. the wiki knows your world. combine them and the agent answers questions using BOTH contexts at once. month 1: you explain things twice. month 3: the agent references the wiki on its own. answers get sharper because the knowledge base got sharper. AUTOMATIONS THAT FEED THE WIKI set cron jobs to ingest automatically: "every day at 9am, check Granola for new meetings. add any new transcripts to my wiki under meeting notes." "every morning, scan my Gmail starred items. add anything worth keeping to the wiki." "every week, check arXiv for new papers in [your niche]. summarize and file." your wiki grows while you sleep. Hermes never forgets what gets indexed. THE LIMITATION TO KNOW unlike Hermes memory (which is conversational and lives across sessions), the wiki is a separate knowledge layer. Hermes won't pull from the wiki automatically unless you reference it or save it as a skill. best setup: build an LLM Wiki personality that tells Hermes to consult the wiki when answering strategy questions or domain-specific queries. full HERMES AGENT OVERNIGHT WORKFLOW👇
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HERMES AGENT SHIPS WITH A BUNDLED SKILL FOR ANDREJ KARPATHY'S LLM WIKI PATTERN. A SELF-IMPROVING KNOWLEDGE BASE THAT GROWS EVERY TIME YOU FEED IT. mentioned this briefly in the overnight workflow article. here is the full breakdown. what it is: a self-improving knowledge base built as interlinked markdown files. unlike RAG (which rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. cross-references stay linked. contradictions get flagged automatically. synthesis reflects everything ingested so far. why this matters for Hermes memory: Hermes built-in memory knows YOU. it remembers your conversations, your preferences, your business context across sessions. but it doesn't know your inbox. or your meeting transcripts. or that article you saved last week. or the expert framework you want it to learn. the LLM Wiki solves that. THE DIVISION OF LABOR human curates sources and directs analysis. agent summarizes, cross-references, files, and maintains consistency. you drop in articles, transcripts, notes. Hermes indexes them, links related concepts, flags contradictions, updates affected pages. your knowledge base grows itself. SETUP IS ONE COMMAND the skill ships with Hermes. enable it. set WIKI_PATH in ~/.hermes/.env: WIKI_PATH=/Users/you/wiki defaults to ~/wiki if unset. then drop anything into it: "index this article into my wiki: [paste URL or text]" Hermes reads it, builds a source page, updates related entries, flags contradictions. THE OBSIDIAN ANGLE set OBSIDIAN_VAULT_PATH to the same directory. now your wiki is visible in Obsidian's graph view. nodes, links, backlinks. all built by Hermes. for headless servers: install obsidian-headless. syncs vaults without a GUI. agent writes from the server, you read on your laptop. THE COMPOUND EFFECT Hermes knows you. the wiki knows your world. combine them and the agent answers questions using BOTH contexts at once. month 1: you explain things twice. month 3: the agent references the wiki on its own. answers get sharper because the knowledge base got sharper. AUTOMATIONS THAT FEED THE WIKI set cron jobs to ingest automatically: "every day at 9am, check Granola for new meetings. add any new transcripts to my wiki under meeting notes." "every morning, scan my Gmail starred items. add anything worth keeping to the wiki." "every week, check arXiv for new papers in [your niche]. summarize and file." your wiki grows while you sleep. Hermes never forgets what gets indexed. THE LIMITATION TO KNOW unlike Hermes memory (which is conversational and lives across sessions), the wiki is a separate knowledge layer. Hermes won't pull from the wiki automatically unless you reference it or save it as a skill. best setup: build an LLM Wiki personality that tells Hermes to consult the wiki when answering strategy questions or domain-specific queries. full HERMES AGENT OVERNIGHT WORKFLOW👇

YanXbt

30,248 次观看 • 1 个月前

HERMES AGENT HAS 5 SYSTEMS RUNNING UNDER THE HOOD. UNDERSTAND THEM AND YOU USE THE AGENT 10X BETTER. In this video Alejandro AO 🤗 explained: 1. THE AGENT LOOP every message triggers the same cycle: → you send a message → Hermes builds context (SOUL.md + memory.md + user.md + skills + tools + message history) → sends everything to the LLM → LLM decides: call a tool or respond → if tool call: execute, return result, loop back → if response: deliver to you → after response: memory update (agent checks if anything is worth remembering, writes to memory.md or user.md) this loop is why Hermes gets better over time. the memory update after every response means the agent learns from every conversation. 2. CONTEXT ASSEMBLY what the LLM sees on every turn: → SOUL.md (your agent's personality and rules) → memory.md (facts the agent learned over time) → user.md (facts about you, auto-updated) → AGENTS.md and .hermes.md (project context files) → skill descriptions (loaded on demand) → tool schemas (available actions) → message history (current conversation) if SOUL.md is empty, Hermes falls back to a default system prompt. write your own SOUL.md and the agent becomes yours, not generic. CONTEXT COMPRESSION: conversations hit context limits. Hermes handles this at two checkpoints: preflight: before each turn. if conversation exceeds 50% of context window, compression fires. older messages get summarized. last 20 messages stay intact (protect_last_n). gateway auto-compression: between turns. fires at 85%. more aggressive. prevents API errors before the agent even starts processing your message. after compression, a new session lineage ID is generated. the agent can trace back to the original conversation through SQLite. three things break prompt cache: switching models mid-session, changing memory files, or changing context files. 3. THE GATEWAY the system that keeps Hermes reachable on 27+ messaging platforms. an async loop runs continuously. listens for incoming messages from Telegram, Discord, Slack, WhatsApp, email, SMS, and every other adapter. when a message arrives: → gateway identifies which session it belongs to → queries SQLite for the full message history (session ID = platform prefix + chat ID) → builds the context from scratch → sends everything into the agent loop → delivers the response back to the platform the gateway also runs the session manager. when you send a message while the agent is busy: → default: queued for next turn → /steer: injected without interrupting → /interrupt: stops current work without the gateway, Hermes is a CLI tool. with the gateway, Hermes is an always-on agent you reach from your phone. 4. MEMORY (THREE LAYERS) LAYER 1 — MARKDOWN FILES SOUL.md (identity), memory.md (learned facts), user.md (facts about you). injected into context after the system prompt. updated by the agent after every response. LAYER 2 — SQLITE full transcripts of every session stored locally. FTS5 full-text search across all past conversations. session lineage tracking across compressions. the agent can recall what you discussed weeks ago using /recall or session search. LAYER 3 — EXTERNAL PROVIDERS (optional) 8 supported providers: Mem0, SuperMemory, Honcho, Zep, and more. each works differently (semantic search, LLM extraction, similarity matching). queried after the first message in each session. the agent processes your topic first, then checks external memory for related context from past conversations. not enabled by default. enable for significantly better long-term recall. 5. CRON ENGINE a loop inside the gateway ticks every 60 seconds. each tick checks ~/.hermes/cron/jobs.json for scheduled tasks. if a job is due: → fresh session (no chat history, no memory pollution) → execute the prompt with assigned tools → store the run output as markdown in ~/.hermes/cron/output/[job-id]/ → deliver result to your home messaging platform cron does NOT use the send_message tool. delivery happens at the system level, not the agent level. a cron session cannot create more cron jobs. prevents runaway loops. WHY THIS MATTERS: the agent loop teaches it. the context assembly focuses it. the gateway reaches it. the memory remembers it. the cron engine automates it. five systems. one agent. understanding how they connect changes how you configure every level. full 15 levels breakdown in the article 👇
40:28

HERMES AGENT HAS 5 SYSTEMS RUNNING UNDER THE HOOD. UNDERSTAND THEM AND YOU USE THE AGENT 10X BETTER. In this video Alejandro AO 🤗 explained: 1. THE AGENT LOOP every message triggers the same cycle: → you send a message → Hermes builds context (SOUL.md + memory.md + user.md + skills + tools + message history) → sends everything to the LLM → LLM decides: call a tool or respond → if tool call: execute, return result, loop back → if response: deliver to you → after response: memory update (agent checks if anything is worth remembering, writes to memory.md or user.md) this loop is why Hermes gets better over time. the memory update after every response means the agent learns from every conversation. 2. CONTEXT ASSEMBLY what the LLM sees on every turn: → SOUL.md (your agent's personality and rules) → memory.md (facts the agent learned over time) → user.md (facts about you, auto-updated) → AGENTS.md and .hermes.md (project context files) → skill descriptions (loaded on demand) → tool schemas (available actions) → message history (current conversation) if SOUL.md is empty, Hermes falls back to a default system prompt. write your own SOUL.md and the agent becomes yours, not generic. CONTEXT COMPRESSION: conversations hit context limits. Hermes handles this at two checkpoints: preflight: before each turn. if conversation exceeds 50% of context window, compression fires. older messages get summarized. last 20 messages stay intact (protect_last_n). gateway auto-compression: between turns. fires at 85%. more aggressive. prevents API errors before the agent even starts processing your message. after compression, a new session lineage ID is generated. the agent can trace back to the original conversation through SQLite. three things break prompt cache: switching models mid-session, changing memory files, or changing context files. 3. THE GATEWAY the system that keeps Hermes reachable on 27+ messaging platforms. an async loop runs continuously. listens for incoming messages from Telegram, Discord, Slack, WhatsApp, email, SMS, and every other adapter. when a message arrives: → gateway identifies which session it belongs to → queries SQLite for the full message history (session ID = platform prefix + chat ID) → builds the context from scratch → sends everything into the agent loop → delivers the response back to the platform the gateway also runs the session manager. when you send a message while the agent is busy: → default: queued for next turn → /steer: injected without interrupting → /interrupt: stops current work without the gateway, Hermes is a CLI tool. with the gateway, Hermes is an always-on agent you reach from your phone. 4. MEMORY (THREE LAYERS) LAYER 1 — MARKDOWN FILES SOUL.md (identity), memory.md (learned facts), user.md (facts about you). injected into context after the system prompt. updated by the agent after every response. LAYER 2 — SQLITE full transcripts of every session stored locally. FTS5 full-text search across all past conversations. session lineage tracking across compressions. the agent can recall what you discussed weeks ago using /recall or session search. LAYER 3 — EXTERNAL PROVIDERS (optional) 8 supported providers: Mem0, SuperMemory, Honcho, Zep, and more. each works differently (semantic search, LLM extraction, similarity matching). queried after the first message in each session. the agent processes your topic first, then checks external memory for related context from past conversations. not enabled by default. enable for significantly better long-term recall. 5. CRON ENGINE a loop inside the gateway ticks every 60 seconds. each tick checks ~/.hermes/cron/jobs.json for scheduled tasks. if a job is due: → fresh session (no chat history, no memory pollution) → execute the prompt with assigned tools → store the run output as markdown in ~/.hermes/cron/output/[job-id]/ → deliver result to your home messaging platform cron does NOT use the send_message tool. delivery happens at the system level, not the agent level. a cron session cannot create more cron jobs. prevents runaway loops. WHY THIS MATTERS: the agent loop teaches it. the context assembly focuses it. the gateway reaches it. the memory remembers it. the cron engine automates it. five systems. one agent. understanding how they connect changes how you configure every level. full 15 levels breakdown in the article 👇

YanXbt

51,258 次观看 • 1 个月前

HERMES AGENT WITHOUT TOOLS IS A CHATBOT. WITH THEM IT BUILDS 3D TOWERS IN BLENDER, CHECKS STOCK PRICES, AND DRIVES VS CODE. tonbi JUST DROPPED THE FULL GUIDE. module 6 of his 10-part Hermes masterclass. best breakdown of the tool layer anyone has published. what you need to know: TOOLS vs SKILLS vs MCP skills = instructions (markdown, loaded into context) tools = callable functions (Python, agent emits call, Hermes executes) MCP = adapters to external systems (Blender, Stripe, Linear, Notion) every tool has three parts: → the function (does the real work) → the schema (what the model sees to decide when to call it) → the registry (makes the tool exist in the agent) the model never runs the function directly. it emits a structured request (tool name + JSON args). Hermes executes and returns the result. if the tool fails, the error goes back as JSON. the agent recovers instead of crashing. TOOL SETS CONTROL THE SURFACE hermes chat --tool-sets web # only web tools loaded. no files, no terminal. hermes chat --tool-sets safe # read-only: web search, vision, image gen. # no file writes. no terminal. no code execution. mid-session: /tools enable video /tools disable terminal or toggle in the dashboard: hermes dashboard → Skills → Tool Sets MCP SERVERS two transport types: STDIO (local subprocess) or HTTP (remote endpoint). add a server: hermes mcp # or ask: "add the MCP server for Blender" filter tools with include/exclude per server. keep only what you trust. security: → OAuth 2.1 PKCE (no long-lived tokens in config) → package scanning via api.osv. dev before launch → all MCP calls go through approval gates HERMES AS MCP SERVER hermes mcp serve exposes 10 tools via FastMCP. connect VS Code Copilot or Cursor to your running Hermes instance. BUILD YOUR OWN TOOL He built a stock price tool live: → Python function calling Finnhub API → schema with name, description, parameters → registered in tool_sets.py → agent calls it automatically when relevant any repeating API call in your workflow can become a native tool. full Hermes architecture deep-dive in the article 👇
26:15

HERMES AGENT WITHOUT TOOLS IS A CHATBOT. WITH THEM IT BUILDS 3D TOWERS IN BLENDER, CHECKS STOCK PRICES, AND DRIVES VS CODE. tonbi JUST DROPPED THE FULL GUIDE. module 6 of his 10-part Hermes masterclass. best breakdown of the tool layer anyone has published. what you need to know: TOOLS vs SKILLS vs MCP skills = instructions (markdown, loaded into context) tools = callable functions (Python, agent emits call, Hermes executes) MCP = adapters to external systems (Blender, Stripe, Linear, Notion) every tool has three parts: → the function (does the real work) → the schema (what the model sees to decide when to call it) → the registry (makes the tool exist in the agent) the model never runs the function directly. it emits a structured request (tool name + JSON args). Hermes executes and returns the result. if the tool fails, the error goes back as JSON. the agent recovers instead of crashing. TOOL SETS CONTROL THE SURFACE hermes chat --tool-sets web # only web tools loaded. no files, no terminal. hermes chat --tool-sets safe # read-only: web search, vision, image gen. # no file writes. no terminal. no code execution. mid-session: /tools enable video /tools disable terminal or toggle in the dashboard: hermes dashboard → Skills → Tool Sets MCP SERVERS two transport types: STDIO (local subprocess) or HTTP (remote endpoint). add a server: hermes mcp # or ask: "add the MCP server for Blender" filter tools with include/exclude per server. keep only what you trust. security: → OAuth 2.1 PKCE (no long-lived tokens in config) → package scanning via api.osv. dev before launch → all MCP calls go through approval gates HERMES AS MCP SERVER hermes mcp serve exposes 10 tools via FastMCP. connect VS Code Copilot or Cursor to your running Hermes instance. BUILD YOUR OWN TOOL He built a stock price tool live: → Python function calling Finnhub API → schema with name, description, parameters → registered in tool_sets.py → agent calls it automatically when relevant any repeating API call in your workflow can become a native tool. full Hermes architecture deep-dive in the article 👇

YanXbt

32,680 次观看 • 1 个月前

Atomic Agent beat Hermes on GAIA: 69.8% vs 58.5%, and it was 1.6x faster! We ran both agents through the full GAIA Level 1 benchmark, 53 real-world tasks, same 4-bit qwen-3.6-35b on the same Apple M4 Max. Results: ✦ Atomic Agent: 37 of 53 solved, done in 3h 12m ✦ Hermes Agent: 31 of 53 solved, took 5h 10m Atomic solved 6 more tasks and finished nearly 2 hours sooner. Hermes ran into the 900s timeout on 7 tasks; Atomic on just 2. Hermes burned 71% of its total time on tasks it still failed, Atomic, 48%. Where it showed: ✦ Audre Lorde poem, which stanza is indented: Atomic pushed through a dead source, switched tools, and answered in 7.6 min. Hermes ran the full clock and returned a blank. ✦ Vietnamese specimens, which city they ended up in: Atomic pulled it from the first source and normalized the answer in 33s. Hermes spent 7.3 min and never answered. ✦ The dinosaur featured-article nominator: Atomic walked the Wikipedia chain to "FunkMonk" in 57s. Hermes guessed a wrong name after 11 min. Atomic keeps a byte-stable prompt prefix, so llama-server reuses the KV-cache instead of re-encoding the whole context every turn, and it emits one JSON array of tool calls per inference, then compresses results back instead of pasting them in full, so the context never balloons and a small model stays sharp deep into a task. On top of that a no-progress guard vetoes repeated identical tool calls (warn at 3, hard veto at 5) and forces a reply, so Atomic never sinks 15 minutes into re-scanning one page the way Hermes did. Both agents missed some of the same questions, and on a few Hermes got there and Atomic did not, usually format slips where Atomic computed the right number but printed the working instead of the bare value. But on identical hardware and identical weights, the runtime that reuses its cache and refuses to spin came out ahead on accuracy and speed. Getting this from the runtime alone is wild. Run the same 53 GAIA tasks on Atomic Agent!
0:27

Atomic Agent beat Hermes on GAIA: 69.8% vs 58.5%, and it was 1.6x faster! We ran both agents through the full GAIA Level 1 benchmark, 53 real-world tasks, same 4-bit qwen-3.6-35b on the same Apple M4 Max. Results: ✦ Atomic Agent: 37 of 53 solved, done in 3h 12m ✦ Hermes Agent: 31 of 53 solved, took 5h 10m Atomic solved 6 more tasks and finished nearly 2 hours sooner. Hermes ran into the 900s timeout on 7 tasks; Atomic on just 2. Hermes burned 71% of its total time on tasks it still failed, Atomic, 48%. Where it showed: ✦ Audre Lorde poem, which stanza is indented: Atomic pushed through a dead source, switched tools, and answered in 7.6 min. Hermes ran the full clock and returned a blank. ✦ Vietnamese specimens, which city they ended up in: Atomic pulled it from the first source and normalized the answer in 33s. Hermes spent 7.3 min and never answered. ✦ The dinosaur featured-article nominator: Atomic walked the Wikipedia chain to "FunkMonk" in 57s. Hermes guessed a wrong name after 11 min. Atomic keeps a byte-stable prompt prefix, so llama-server reuses the KV-cache instead of re-encoding the whole context every turn, and it emits one JSON array of tool calls per inference, then compresses results back instead of pasting them in full, so the context never balloons and a small model stays sharp deep into a task. On top of that a no-progress guard vetoes repeated identical tool calls (warn at 3, hard veto at 5) and forces a reply, so Atomic never sinks 15 minutes into re-scanning one page the way Hermes did. Both agents missed some of the same questions, and on a few Hermes got there and Atomic did not, usually format slips where Atomic computed the right number but printed the working instead of the bare value. But on identical hardware and identical weights, the runtime that reuses its cache and refuses to spin came out ahead on accuracy and speed. Getting this from the runtime alone is wild. Run the same 53 GAIA tasks on Atomic Agent!

Atomic Agent

109,205 次观看 • 3 天前

HERMES AGENT RUNS MONITORING, RESEARCH, LEAD DETECTION, AND COMPETITIVE ANALYSIS ON AUTOPILOT. AND KNOWS WHEN NOT TO SPEND YOUR TOKENS. the biggest unlock most people skip: Hermes cron jobs can decide ON THEIR OWN whether the LLM should wake up. WAKE AGENT — THE $0 GATE every cron job can run a Python script first. the script checks: did anything actually change? nothing changed: → script outputs {"wakeAgent": false} → LLM stays asleep → zero tokens spent something changed: → script outputs {"wakeAgent": true} → agent wakes up and handles it three gate patterns from official docs: → file-change: compare file mtime to last run. no change? sleep. → external-flag: another process drops a ready file. no flag? sleep. → HTTP-check: ping a URL, diff the response. same as last time? sleep. real example: monitor AWS costs every hour. script pulls current spend from AWS API. no spike? agent sleeps. zero cost. costs jump 40%? agent wakes, reports to Slack, takes action through Stripe MCP. you run 20 monitoring jobs a day. 18 of them find nothing. you pay for 2. NO AGENT — PURE SCRIPT, ZERO LLM some jobs don't need reasoning at all. TLS checks. uptime pings. disk alerts. heartbeats. hermes cron edit --no-agent --script check_health.py script runs. stdout goes straight to Telegram, Discord, or Slack. no LLM involved. flip any job between modes: hermes cron edit --agent # add LLM hermes cron edit --no-agent # remove LLM free monitoring that lives inside the same ecosystem as your agent. 4 MORE USE CASES THIS UNLOCKS: COMPETITIVE ANALYSIS weekly cron with script that diffs competitor pages. agent only analyzes actual changes. updates your tracking file and PRD skill automatically. PRD AS A SKILL save product requirements as a skill, not a document. skills load on demand into fresh context. documents drift. skills stay sharp. CONTENT REPURPOSING hand a video script to the agent. it drafts X and LinkedIn posts in your voice. writes to a review folder. you approve via Telegram. LEAD DETECTION webhook monitors inbox. agent spots potential leads. drafts responses using your business context. schedules meetings from your calendar. the pattern across all of these: scripts handle the mechanical work for free. the agent only spends tokens on reasoning that requires judgment. comment CRON and I'll send you 5 ready-to-paste cron configs with wakeAgent and no_agent patterns. full Hermes SOUL.MD guide 👇
10:37

HERMES AGENT RUNS MONITORING, RESEARCH, LEAD DETECTION, AND COMPETITIVE ANALYSIS ON AUTOPILOT. AND KNOWS WHEN NOT TO SPEND YOUR TOKENS. the biggest unlock most people skip: Hermes cron jobs can decide ON THEIR OWN whether the LLM should wake up. WAKE AGENT — THE $0 GATE every cron job can run a Python script first. the script checks: did anything actually change? nothing changed: → script outputs {"wakeAgent": false} → LLM stays asleep → zero tokens spent something changed: → script outputs {"wakeAgent": true} → agent wakes up and handles it three gate patterns from official docs: → file-change: compare file mtime to last run. no change? sleep. → external-flag: another process drops a ready file. no flag? sleep. → HTTP-check: ping a URL, diff the response. same as last time? sleep. real example: monitor AWS costs every hour. script pulls current spend from AWS API. no spike? agent sleeps. zero cost. costs jump 40%? agent wakes, reports to Slack, takes action through Stripe MCP. you run 20 monitoring jobs a day. 18 of them find nothing. you pay for 2. NO AGENT — PURE SCRIPT, ZERO LLM some jobs don't need reasoning at all. TLS checks. uptime pings. disk alerts. heartbeats. hermes cron edit --no-agent --script check_health.py script runs. stdout goes straight to Telegram, Discord, or Slack. no LLM involved. flip any job between modes: hermes cron edit --agent # add LLM hermes cron edit --no-agent # remove LLM free monitoring that lives inside the same ecosystem as your agent. 4 MORE USE CASES THIS UNLOCKS: COMPETITIVE ANALYSIS weekly cron with script that diffs competitor pages. agent only analyzes actual changes. updates your tracking file and PRD skill automatically. PRD AS A SKILL save product requirements as a skill, not a document. skills load on demand into fresh context. documents drift. skills stay sharp. CONTENT REPURPOSING hand a video script to the agent. it drafts X and LinkedIn posts in your voice. writes to a review folder. you approve via Telegram. LEAD DETECTION webhook monitors inbox. agent spots potential leads. drafts responses using your business context. schedules meetings from your calendar. the pattern across all of these: scripts handle the mechanical work for free. the agent only spends tokens on reasoning that requires judgment. comment CRON and I'll send you 5 ready-to-paste cron configs with wakeAgent and no_agent patterns. full Hermes SOUL.MD guide 👇

YanXbt

95,587 次观看 • 1 个月前

HERMES AGENT RUNS A POWERFUL OPS SYSTEM ON ONE $6/MO VPS. NO GPU. 90% of people use AI like a search box. Type, grab the answer, close the tab. Tomorrow it has no idea who they are. I stopped doing that 3 months ago. Now Hermes runs as a 24/7 operating system - no GPU. It curates my research before I wake up, drafts content, tracks its own tasks, and pings me on Telegram from anywhere. The math nobody runs: → Agents that wrote 20+ of their own skills finish tasks ~40% faster - not a better model, just accumulated procedure → The full content system burns ~10-11M tokens/mo - and model choice alone swings the bill 10x → Same crons, same goals, one orchestrator + 3 specialists = the output of a small content team, running while I sleep → Runtime is free (MIT). You pay for cheap inference + a $6 box. That’s it. → Replaces the work people pay a VA $500-$2,000/mo to do The catch? Compounding takes 2-4 weeks of consistent use before it’s obvious. Most people quit before day 14. The operator doesn’t prompt. The operator builds the system. Reply “Hermes” and I’ll send you the step-by-step Playbook in your DMs 👇
0:41

HERMES AGENT RUNS A POWERFUL OPS SYSTEM ON ONE $6/MO VPS. NO GPU. 90% of people use AI like a search box. Type, grab the answer, close the tab. Tomorrow it has no idea who they are. I stopped doing that 3 months ago. Now Hermes runs as a 24/7 operating system - no GPU. It curates my research before I wake up, drafts content, tracks its own tasks, and pings me on Telegram from anywhere. The math nobody runs: → Agents that wrote 20+ of their own skills finish tasks ~40% faster - not a better model, just accumulated procedure → The full content system burns ~10-11M tokens/mo - and model choice alone swings the bill 10x → Same crons, same goals, one orchestrator + 3 specialists = the output of a small content team, running while I sleep → Runtime is free (MIT). You pay for cheap inference + a $6 box. That’s it. → Replaces the work people pay a VA $500-$2,000/mo to do The catch? Compounding takes 2-4 weeks of consistent use before it’s obvious. Most people quit before day 14. The operator doesn’t prompt. The operator builds the system. Reply “Hermes” and I’ll send you the step-by-step Playbook in your DMs 👇

ZEUS⚡️

10,647 次观看 • 1 个月前

The fastest Hermes agent is the one that reads less. I didn’t make Hermes 10x faster by changing the model. I made it faster by removing the tax I was charging it on every task: bad structure. Hermes was powerful already. The problem was that I had built my workspace for a human brain, then expected an agent to navigate it like it had memory, taste, and intuition. It doesn’t. It searches. And when your files are arranged by how you think, not how the agent moves, Hermes burns tokens opening the wrong docs before the real work even starts. That was my mistake. I had folders like: >Articles >Research >Assets >Strategy >Old drafts >Clean for me. Terrible for an agent. A launch plan might need brand strategy, previous launches, voice rules, current articles, and promotion notes. For me, that context is obvious. For Hermes, it was scattered. So I stopped optimizing the model and started optimizing the terrain. The fix was stupidly small: > One folder per concern >Numbered folders for reading order >One INDEX.md at the root of every major folder >Archived files separated from active files >A clear “Where To Go” section so Hermes knows where to start My INDEX.md became the map. Not a giant table of everything. Not documentation theater. Just enough scaffolding to tell Hermes: - what exists - what matters - what is current - where to start - what to ignore unless asked Before that, Hermes opened 7 files to find one current brief. After that, it opened the index, followed the pointer, and got to work. That is the real 10x. Not “better prompting.” Less wandering. This is where loop engineering actually matters. The video said it best: “You were the loop.” That line hit because it explains why most agent workflows still feel manual. You are still checking. You are still redirecting. You are still telling the agent which file is current, which folder matters, and what done means. Hermes gets powerful when you stop being the loop and start designing the loop. The loop I’d build looks like this: State: Hermes reads the folder index and current task state Action: it opens only the canonical files Feedback: tests, screenshots, diffs, or human notes tell it what happened Verification: a gate decides whether the work is actually done Termination: the task stops only when the done condition is met For deterministic work, the gate is simple: tests pass build succeeds deployment checks clear For non-deterministic work, I’d use an adversarial loop: one model builds another model reviews Hermes updates the skill when the reviewer finds a pattern That is where Hermes becomes different. It is not just running prompts. It is carrying memory through files, using skills, checking its own work, and improving the process around the task. The agent was never the slow part. The missing map was. A powerful agent inside a messy workspace becomes a very expensive intern. A powerful agent inside a mapped system becomes leverage. My new rule: Before I ask Hermes to do more, I ask: “Does it know where to look?” Because most agents do not fail from lack of intelligence. They fail from lack of scaffolding. Build the scaffolding. Let Hermes do the work.
13:20

The fastest Hermes agent is the one that reads less. I didn’t make Hermes 10x faster by changing the model. I made it faster by removing the tax I was charging it on every task: bad structure. Hermes was powerful already. The problem was that I had built my workspace for a human brain, then expected an agent to navigate it like it had memory, taste, and intuition. It doesn’t. It searches. And when your files are arranged by how you think, not how the agent moves, Hermes burns tokens opening the wrong docs before the real work even starts. That was my mistake. I had folders like: >Articles >Research >Assets >Strategy >Old drafts >Clean for me. Terrible for an agent. A launch plan might need brand strategy, previous launches, voice rules, current articles, and promotion notes. For me, that context is obvious. For Hermes, it was scattered. So I stopped optimizing the model and started optimizing the terrain. The fix was stupidly small: > One folder per concern >Numbered folders for reading order >One INDEX.md at the root of every major folder >Archived files separated from active files >A clear “Where To Go” section so Hermes knows where to start My INDEX.md became the map. Not a giant table of everything. Not documentation theater. Just enough scaffolding to tell Hermes: - what exists - what matters - what is current - where to start - what to ignore unless asked Before that, Hermes opened 7 files to find one current brief. After that, it opened the index, followed the pointer, and got to work. That is the real 10x. Not “better prompting.” Less wandering. This is where loop engineering actually matters. The video said it best: “You were the loop.” That line hit because it explains why most agent workflows still feel manual. You are still checking. You are still redirecting. You are still telling the agent which file is current, which folder matters, and what done means. Hermes gets powerful when you stop being the loop and start designing the loop. The loop I’d build looks like this: State: Hermes reads the folder index and current task state Action: it opens only the canonical files Feedback: tests, screenshots, diffs, or human notes tell it what happened Verification: a gate decides whether the work is actually done Termination: the task stops only when the done condition is met For deterministic work, the gate is simple: tests pass build succeeds deployment checks clear For non-deterministic work, I’d use an adversarial loop: one model builds another model reviews Hermes updates the skill when the reviewer finds a pattern That is where Hermes becomes different. It is not just running prompts. It is carrying memory through files, using skills, checking its own work, and improving the process around the task. The agent was never the slow part. The missing map was. A powerful agent inside a messy workspace becomes a very expensive intern. A powerful agent inside a mapped system becomes leverage. My new rule: Before I ask Hermes to do more, I ask: “Does it know where to look?” Because most agents do not fail from lack of intelligence. They fail from lack of scaffolding. Build the scaffolding. Let Hermes do the work.

Rohit

37,148 次观看 • 27 天前

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch
58:55

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

GREG ISENBERG

375,365 次观看 • 4 个月前

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch
35:25

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

193,095 次观看 • 3 个月前

HERMES AGENT CAN RUN YOUR SEO. CONNECT IT TO GOOGLE SEARCH CONSOLE AND GOOGLE ANALYTICS. IT MONITORS, REPORTS, AND WRITES CONTENT BASED ON YOUR ACTUAL DATA. stop paying an SEO agency. stop doing the tedious work yourself. Hermes handles it 24/7. WHAT THE SEO AGENT DOES: → pulls clicks, impressions, CTR, and position data from Google Search Console automatically → tracks traffic, user behavior, and conversions from Google Analytics → checks which pages are indexed and which are not → submits sitemaps for indexing → inspects URLs for crawl or indexing issues → identifies ranking drops and keyword opportunities → writes content based on what your data says works → generates weekly SEO performance reports → delivers everything to Telegram CONNECT GOOGLE SEARCH CONSOLE: two paths: 1. COMPOSIO (managed, easiest): paste this into Hermes chat: https:// composio. dev/hermes or add to config.yaml: mcp_servers: composio: url: "https:// connect.composio. dev /mcp" headers: x-consumer-api-key: "YOUR_COMPOSIO_API_KEY" Hermes prompts you to authenticate. one OAuth flow. done. 2. CLAWLINK (one-click): 9 Google Search Console tools exposed via MCP. hosted auth. nothing to run or maintain. paste the install prompt into Hermes chat. CONNECT GOOGLE ANALYTICS: same Composio setup. one MCP endpoint handles both Search Console and Analytics. authenticate once. both data sources available. your agent can now query: → search analytics (clicks, impressions, CTR, position) → traffic by source and landing page → user behavior and conversions → indexing status for any URL → sitemap status WHAT TO AUTOMATE WITH CRON: weekly SEO report (Monday 8am): "pull search analytics for last 7 days. compare vs previous week. flag any keyword that dropped more than 5 positions. flag any page that lost more than 20% clicks. deliver report to Telegram." daily indexing check (6am): "check if any new pages are not indexed. if found, submit sitemap and report to Telegram." wakeAgent gate: skip if all pages indexed. content opportunity scan (weekly): "find queries where my site appears on page 2 (positions 11-20) with high impressions. these are the keywords one good article could push to page 1. deliver list to Telegram with suggested topics." CONTENT WRITING FROM YOUR DATA: the difference between generic SEO content and content that ranks: your agent has your Search Console data. "write a blog post targeting [keyword]. my current position is 14 with 2,400 monthly impressions. check what pages currently rank 1-3 for this keyword. write something better. include the gaps they miss." the agent researches competitors via Firecrawl, checks your existing content in the wiki, and drafts based on real data. not guesswork. WHAT THIS REPLACES: → SEO agency: $1,000-5,000/month → SEO tool subscriptions: $100-300/month → manual reporting: 3-5 hours/week → manual content research: 2-4 hours/week Hermes SEO agent: one profile with two MCPs. cron jobs handle the monitoring. you handle the decisions. SETUP IN 10 MINUTES: 1. create a profile: hermes profile create seo-agent 2. write SOUL.md: "you are an SEO specialist. monitor search performance daily. flag ranking drops and opportunities. write content based on Search Console data. weekly report every Monday." 3. connect Google Search Console + Analytics via Composio or ClawLink 4. set cron jobs (weekly report, daily index check, content opportunity scan) 5. set model: DeepSeek V4 for routine monitoring. Sonnet for content writing. 6. connect to Telegram for delivery. the agent runs. you review reports. rankings improve because you stopped guessing and started using your own data. comment HERMES and I'll send you the full setup guide for running Hermes Agent as your SEO specialist. full Hermes architecture deep-dive in the article 👇
0:44

HERMES AGENT CAN RUN YOUR SEO. CONNECT IT TO GOOGLE SEARCH CONSOLE AND GOOGLE ANALYTICS. IT MONITORS, REPORTS, AND WRITES CONTENT BASED ON YOUR ACTUAL DATA. stop paying an SEO agency. stop doing the tedious work yourself. Hermes handles it 24/7. WHAT THE SEO AGENT DOES: → pulls clicks, impressions, CTR, and position data from Google Search Console automatically → tracks traffic, user behavior, and conversions from Google Analytics → checks which pages are indexed and which are not → submits sitemaps for indexing → inspects URLs for crawl or indexing issues → identifies ranking drops and keyword opportunities → writes content based on what your data says works → generates weekly SEO performance reports → delivers everything to Telegram CONNECT GOOGLE SEARCH CONSOLE: two paths: 1. COMPOSIO (managed, easiest): paste this into Hermes chat: https:// composio. dev/hermes or add to config.yaml: mcp_servers: composio: url: "https:// connect.composio. dev /mcp" headers: x-consumer-api-key: "YOUR_COMPOSIO_API_KEY" Hermes prompts you to authenticate. one OAuth flow. done. 2. CLAWLINK (one-click): 9 Google Search Console tools exposed via MCP. hosted auth. nothing to run or maintain. paste the install prompt into Hermes chat. CONNECT GOOGLE ANALYTICS: same Composio setup. one MCP endpoint handles both Search Console and Analytics. authenticate once. both data sources available. your agent can now query: → search analytics (clicks, impressions, CTR, position) → traffic by source and landing page → user behavior and conversions → indexing status for any URL → sitemap status WHAT TO AUTOMATE WITH CRON: weekly SEO report (Monday 8am): "pull search analytics for last 7 days. compare vs previous week. flag any keyword that dropped more than 5 positions. flag any page that lost more than 20% clicks. deliver report to Telegram." daily indexing check (6am): "check if any new pages are not indexed. if found, submit sitemap and report to Telegram." wakeAgent gate: skip if all pages indexed. content opportunity scan (weekly): "find queries where my site appears on page 2 (positions 11-20) with high impressions. these are the keywords one good article could push to page 1. deliver list to Telegram with suggested topics." CONTENT WRITING FROM YOUR DATA: the difference between generic SEO content and content that ranks: your agent has your Search Console data. "write a blog post targeting [keyword]. my current position is 14 with 2,400 monthly impressions. check what pages currently rank 1-3 for this keyword. write something better. include the gaps they miss." the agent researches competitors via Firecrawl, checks your existing content in the wiki, and drafts based on real data. not guesswork. WHAT THIS REPLACES: → SEO agency: $1,000-5,000/month → SEO tool subscriptions: $100-300/month → manual reporting: 3-5 hours/week → manual content research: 2-4 hours/week Hermes SEO agent: one profile with two MCPs. cron jobs handle the monitoring. you handle the decisions. SETUP IN 10 MINUTES: 1. create a profile: hermes profile create seo-agent 2. write SOUL.md: "you are an SEO specialist. monitor search performance daily. flag ranking drops and opportunities. write content based on Search Console data. weekly report every Monday." 3. connect Google Search Console + Analytics via Composio or ClawLink 4. set cron jobs (weekly report, daily index check, content opportunity scan) 5. set model: DeepSeek V4 for routine monitoring. Sonnet for content writing. 6. connect to Telegram for delivery. the agent runs. you review reports. rankings improve because you stopped guessing and started using your own data. comment HERMES and I'll send you the full setup guide for running Hermes Agent as your SEO specialist. full Hermes architecture deep-dive in the article 👇

YanXbt

40,614 次观看 • 23 天前

OpenClaw meets RL! OpenClaw Agents adapt through memory files and skills, but the base model weights never actually change. OpenClaw-RL solves this! It wraps a self-hosted model as an OpenAI-compatible API, intercepts live conversations from OpenClaw, and trains the policy in the background using RL. The architecture is fully async. This means serving, reward scoring, and training all run in parallel. Once done, weights get hot-swapped after every batch while the agent keeps responding. Currently, it has two training modes: - Binary RL (GRPO): A process reward model scores each turn as good, bad, or neutral. That scalar reward drives policy updates via a PPO-style clipped objective. - On-Policy Distillation: When concrete corrections come in like "you should have checked that file first," it uses that feedback as a richer, directional training signal at the token level. When to use OpenClaw-RL? To be fair, a lot of agent behavior can already be improved through better memory and skill design. OpenClaw's existing skill ecosystem and community-built self-improvement skills handle a wide range of use cases without touching model weights at all. If the agent keeps forgetting preferences, that's a memory problem. And if it doesn't know how to handle a specific workflow, that's a skill problem. Both are solvable at the prompt and context layer. Where RL becomes interesting is when the failure pattern lives deeper in the model's reasoning itself. Things like consistently poor tool selection order, weak multi-step planning, or failing to interpret ambiguous instructions the way a specific user intends. Research on agentic RL (like ARTIST and Agent-R1) has shown that these behavioral patterns hit a ceiling with prompt-based approaches alone, especially in complex multi-turn tasks where the model needs to recover from tool failures or adapt its strategy mid-execution. That's the layer OpenClaw-RL targets, and it's a meaningful distinction from what OpenClaw offers. I have shared the repo in the replies!
0:35

OpenClaw meets RL! OpenClaw Agents adapt through memory files and skills, but the base model weights never actually change. OpenClaw-RL solves this! It wraps a self-hosted model as an OpenAI-compatible API, intercepts live conversations from OpenClaw, and trains the policy in the background using RL. The architecture is fully async. This means serving, reward scoring, and training all run in parallel. Once done, weights get hot-swapped after every batch while the agent keeps responding. Currently, it has two training modes: - Binary RL (GRPO): A process reward model scores each turn as good, bad, or neutral. That scalar reward drives policy updates via a PPO-style clipped objective. - On-Policy Distillation: When concrete corrections come in like "you should have checked that file first," it uses that feedback as a richer, directional training signal at the token level. When to use OpenClaw-RL? To be fair, a lot of agent behavior can already be improved through better memory and skill design. OpenClaw's existing skill ecosystem and community-built self-improvement skills handle a wide range of use cases without touching model weights at all. If the agent keeps forgetting preferences, that's a memory problem. And if it doesn't know how to handle a specific workflow, that's a skill problem. Both are solvable at the prompt and context layer. Where RL becomes interesting is when the failure pattern lives deeper in the model's reasoning itself. Things like consistently poor tool selection order, weak multi-step planning, or failing to interpret ambiguous instructions the way a specific user intends. Research on agentic RL (like ARTIST and Agent-R1) has shown that these behavioral patterns hit a ceiling with prompt-based approaches alone, especially in complex multi-turn tasks where the model needs to recover from tool failures or adapt its strategy mid-execution. That's the layer OpenClaw-RL targets, and it's a meaningful distinction from what OpenClaw offers. I have shared the repo in the replies!

Avi Chawla

138,691 次观看 • 4 个月前

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this
0:24

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,186 次观看 • 1 个月前

HERMES AGENT + STRIPE PAYMENTS + NVIDIA NEMOTRON. YOUR AGENT CAN NOW RUN A BUSINESS. ACCEPT PAYMENTS. PAY FOR SERVICES. PROVISION ITS OWN INFRASTRUCTURE. ALL INSIDE A SECURITY SANDBOX. two years ago the question was: can an AI agent run a business autonomously? the answer shipped this week. Hermes already handles workflows: cron jobs, sub-agents, kanban orchestration, multi-profile pipelines, scheduled research. what it couldn't do: spend money and prove it's safe. Stripe solved the first problem. Nvidia solved the second. WHAT AUTONOMOUS BUSINESS OPERATIONS LOOK LIKE: → customer sends a request via email → agent reads, scopes the project, estimates cost → provisions the infrastructure it needs (pays via Stripe, you approve on your phone) → builds and deploys the deliverable → sends the result to the customer → creates a payment link via Stripe (Stripe API integration, separate from Link CLI) → tops off its own API credits when balance drops → reports daily costs and progress to your Telegram → all within security policies you set once you set the rules. the agent runs the operation. you review revenue reports. not tasks. this is already happening. Dark Factory: autonomous software factory. send an idea before bed. wake up to a deployed URL. live entry in the Hermes Accelerated Business Hackathon. HOW STRIPE MAKES THE AGENT FINANCIALLY AUTONOMOUS: Stripe Link CLI gives your agent a scoped wallet. not your credit card. one-time-use virtual cards. → agent finds a product or service it needs → creates a spend request via Stripe Link → you get a notification on your phone (Link app) → you review: merchant, amount, context → one tap to approve or reject → agent receives a one-time virtual card → completes the purchase → card expires after single use your real card details never enter agent context. never printed in chat. never exposed to the merchant. Hermes cannot self-approve. you confirm every spend. install: hermes install skills/optional/payments/stripe-link-cli link-cli auth login what the agent can pay for: → API credits (Nous Portal, OpenRouter) → SaaS subscriptions it needs for operations → domain names, hosting, cloud credits → products from any online store currently US only. HOW NVIDIA MAKES THE AGENT SAFE TO TRUST: an agent with spending authority and no security boundaries is a liability. NemoClaw solves this. three layers: 1. OPENSHELL (sandbox) kernel-level isolation. controls network, filesystem, syscalls. default deny. you whitelist what's allowed. agent tries to reach a blocked domain = rejected. agent has no idea it's sandboxed. 2. NEMOTRON (private models) open-weight models on your own hardware. Nemotron 3 Super 120B MoE (48GB+ VRAM). Nemotron 3 Nano 4B (8GB VRAM, edge). fully private. no data leaves your machine. without GPU: inference routes to cloud via Privacy Router. 3. PRIVACY ROUTER (automatic split) decides per query: local or cloud. private data → local Nemotron. general web research → Claude, GPT, Gemini. automatic. per query. no manual routing. install: export NEMOCLAW_AGENT=hermes curl -fsSL https:// www.nvidia. com/nemoclaw.sh | bash requires Docker. NemoClaw is alpha software. APIs may change. test in non-production first. THE FULL PICTURE: before this stack: → agent could work but couldn't pay for anything → agent could pay but couldn't be trusted → agent could be trusted but couldn't operate 24/7 now: → Hermes runs the business logic (workflows, memory, skills, cron, sub-agents) → Stripe runs the financial layer (Link CLI for spending, Stripe API for receiving) → NemoClaw runs the trust layer (sandbox, policies, private routing) → VPS keeps everything always on → Telegram keeps you in the loop TYPES OF BUSINESSES THIS ENABLES: → autonomous software factory (customer request → build → deploy → payment link) → content agency (brief → research → draft → deliver → bill) → lead generation service (scrape → qualify → outreach → book calls) → SaaS monitoring and maintenance (detect issues → fix → deploy → report) → e-commerce operations (inventory → pricing → fulfillment → support) each one: Hermes profiles handle the workflows. Stripe handles the payments (in and out). NemoClaw handles the security. you handle the strategy. THE HACKATHON: Hermes Agent Accelerated Business Hackathon with Nvidia and Stripe. cash prizes + Stripe credits + Nvidia DGX Spark. ends June 30. the goal: build agents that earn, spend, and run real operations autonomously. link in the Nous Research Discord. full Hermes architecture deep-dive in the article 👇
53:28

HERMES AGENT + STRIPE PAYMENTS + NVIDIA NEMOTRON. YOUR AGENT CAN NOW RUN A BUSINESS. ACCEPT PAYMENTS. PAY FOR SERVICES. PROVISION ITS OWN INFRASTRUCTURE. ALL INSIDE A SECURITY SANDBOX. two years ago the question was: can an AI agent run a business autonomously? the answer shipped this week. Hermes already handles workflows: cron jobs, sub-agents, kanban orchestration, multi-profile pipelines, scheduled research. what it couldn't do: spend money and prove it's safe. Stripe solved the first problem. Nvidia solved the second. WHAT AUTONOMOUS BUSINESS OPERATIONS LOOK LIKE: → customer sends a request via email → agent reads, scopes the project, estimates cost → provisions the infrastructure it needs (pays via Stripe, you approve on your phone) → builds and deploys the deliverable → sends the result to the customer → creates a payment link via Stripe (Stripe API integration, separate from Link CLI) → tops off its own API credits when balance drops → reports daily costs and progress to your Telegram → all within security policies you set once you set the rules. the agent runs the operation. you review revenue reports. not tasks. this is already happening. Dark Factory: autonomous software factory. send an idea before bed. wake up to a deployed URL. live entry in the Hermes Accelerated Business Hackathon. HOW STRIPE MAKES THE AGENT FINANCIALLY AUTONOMOUS: Stripe Link CLI gives your agent a scoped wallet. not your credit card. one-time-use virtual cards. → agent finds a product or service it needs → creates a spend request via Stripe Link → you get a notification on your phone (Link app) → you review: merchant, amount, context → one tap to approve or reject → agent receives a one-time virtual card → completes the purchase → card expires after single use your real card details never enter agent context. never printed in chat. never exposed to the merchant. Hermes cannot self-approve. you confirm every spend. install: hermes install skills/optional/payments/stripe-link-cli link-cli auth login what the agent can pay for: → API credits (Nous Portal, OpenRouter) → SaaS subscriptions it needs for operations → domain names, hosting, cloud credits → products from any online store currently US only. HOW NVIDIA MAKES THE AGENT SAFE TO TRUST: an agent with spending authority and no security boundaries is a liability. NemoClaw solves this. three layers: 1. OPENSHELL (sandbox) kernel-level isolation. controls network, filesystem, syscalls. default deny. you whitelist what's allowed. agent tries to reach a blocked domain = rejected. agent has no idea it's sandboxed. 2. NEMOTRON (private models) open-weight models on your own hardware. Nemotron 3 Super 120B MoE (48GB+ VRAM). Nemotron 3 Nano 4B (8GB VRAM, edge). fully private. no data leaves your machine. without GPU: inference routes to cloud via Privacy Router. 3. PRIVACY ROUTER (automatic split) decides per query: local or cloud. private data → local Nemotron. general web research → Claude, GPT, Gemini. automatic. per query. no manual routing. install: export NEMOCLAW_AGENT=hermes curl -fsSL https:// www.nvidia. com/nemoclaw.sh | bash requires Docker. NemoClaw is alpha software. APIs may change. test in non-production first. THE FULL PICTURE: before this stack: → agent could work but couldn't pay for anything → agent could pay but couldn't be trusted → agent could be trusted but couldn't operate 24/7 now: → Hermes runs the business logic (workflows, memory, skills, cron, sub-agents) → Stripe runs the financial layer (Link CLI for spending, Stripe API for receiving) → NemoClaw runs the trust layer (sandbox, policies, private routing) → VPS keeps everything always on → Telegram keeps you in the loop TYPES OF BUSINESSES THIS ENABLES: → autonomous software factory (customer request → build → deploy → payment link) → content agency (brief → research → draft → deliver → bill) → lead generation service (scrape → qualify → outreach → book calls) → SaaS monitoring and maintenance (detect issues → fix → deploy → report) → e-commerce operations (inventory → pricing → fulfillment → support) each one: Hermes profiles handle the workflows. Stripe handles the payments (in and out). NemoClaw handles the security. you handle the strategy. THE HACKATHON: Hermes Agent Accelerated Business Hackathon with Nvidia and Stripe. cash prizes + Stripe credits + Nvidia DGX Spark. ends June 30. the goal: build agents that earn, spend, and run real operations autonomously. link in the Nous Research Discord. full Hermes architecture deep-dive in the article 👇

YanXbt

37,709 次观看 • 29 天前

Introducing Open Source AI CRM, that runs on your OpenClaw. A few weeks ago, we launched Ironclaw (An Open Source OpenClaw CRM Framework) which now has around 1.4k stars. A lot of people confused us with NearAI’s Ironclaw, so we changed our name to DenchClaw. OpenClaw today feels a lot like early React: the primitive is incredibly powerful, but the patterns are still forming, and everyone is piecing together their own way to actually use it. What made React explode wasn’t just React itself, but the emergence of frameworks like Gatsby and Next.js that turned raw capability into something opinionated, repeatable, and easy to adopt. That is how I think about DenchClaw. We are not just building on top of OpenClaw; we are trying to make it one of the clearest, most practical, and most complete ways to use OpenClaw in the real world. We are an OpenClaw Framework, we are aiming to be the most correct way to use OpenClaw. We entered Y Combinator with Merse (AI Audio Comic), it was an app that I personally never used. Michael Seibel confronted us on it, and said, “if you aren’t the best user of your consumer app, then who is?”. I now use DenchClaw daily for everything I do, it also works as a coding agent like Cursor, DenchClaw built DenchClaw. I am addicted to DenchClaw now that I can ask it, “hey in the companies table only show me the ones who have more than 5 employees” and it updates it live than me having to manually add a filter. On Dench, everything sits in a file system, the table filters, views, column toggles, calendar/gantt views, etc, so OpenClaw can directly work with it using Dench’s CRM skill. The CRM is built on top of DuckDB, the smallest, most performant and at the same time also feature rich database we could find. It creates a new OpenClaw🦞 profile called “dench”, and opens a new OpenClaw Gateway… that means you can run all your usual openclaw commands by just prefixing every command with `openclaw --profile dench` . It will start your gateway on port 19001 range. You will be able to access the DenchClaw frontend at localhost:3100. Once you open it on Safari, just add it to your Dock to use it as a PWA. Think of it as Cursor for your Mac which is based on OpenClaw. DenchClaw has a file tree view for you to use it as an elevated finder tool to do anything on your mac. I use it to create slides, do LinkedIn outreach using MY browser. DenchClaw sees what you see, does what you do. It’s the everything app, that sits locally on your mac. All yours. Just ask it “hey import my notion”, “hey import everything from my hubspot”, and it will literally go into your browser, export all objects and documents and put it in its own workspace that you can use. P.S. It comes with Garry Tan's GStack built in.
2:39

Introducing Open Source AI CRM, that runs on your OpenClaw. A few weeks ago, we launched Ironclaw (An Open Source OpenClaw CRM Framework) which now has around 1.4k stars. A lot of people confused us with NearAI’s Ironclaw, so we changed our name to DenchClaw. OpenClaw today feels a lot like early React: the primitive is incredibly powerful, but the patterns are still forming, and everyone is piecing together their own way to actually use it. What made React explode wasn’t just React itself, but the emergence of frameworks like Gatsby and Next.js that turned raw capability into something opinionated, repeatable, and easy to adopt. That is how I think about DenchClaw. We are not just building on top of OpenClaw; we are trying to make it one of the clearest, most practical, and most complete ways to use OpenClaw in the real world. We are an OpenClaw Framework, we are aiming to be the most correct way to use OpenClaw. We entered Y Combinator with Merse (AI Audio Comic), it was an app that I personally never used. Michael Seibel confronted us on it, and said, “if you aren’t the best user of your consumer app, then who is?”. I now use DenchClaw daily for everything I do, it also works as a coding agent like Cursor, DenchClaw built DenchClaw. I am addicted to DenchClaw now that I can ask it, “hey in the companies table only show me the ones who have more than 5 employees” and it updates it live than me having to manually add a filter. On Dench, everything sits in a file system, the table filters, views, column toggles, calendar/gantt views, etc, so OpenClaw can directly work with it using Dench’s CRM skill. The CRM is built on top of DuckDB, the smallest, most performant and at the same time also feature rich database we could find. It creates a new OpenClaw🦞 profile called “dench”, and opens a new OpenClaw Gateway… that means you can run all your usual openclaw commands by just prefixing every command with `openclaw --profile dench` . It will start your gateway on port 19001 range. You will be able to access the DenchClaw frontend at localhost:3100. Once you open it on Safari, just add it to your Dock to use it as a PWA. Think of it as Cursor for your Mac which is based on OpenClaw. DenchClaw has a file tree view for you to use it as an elevated finder tool to do anything on your mac. I use it to create slides, do LinkedIn outreach using MY browser. DenchClaw sees what you see, does what you do. It’s the everything app, that sits locally on your mac. All yours. Just ask it “hey import my notion”, “hey import everything from my hubspot”, and it will literally go into your browser, export all objects and documents and put it in its own workspace that you can use. P.S. It comes with Garry Tan's GStack built in.

Mark Rachapoom

19,411 次观看 • 3 个月前

This Chinese developer launched 6 agents under 1 orchestrator, and they run his UI design agency at $32,000 a month on their own. He built a system of 6 agents on Claude Sonnet 4.6 that single-handedly runs his agency for UI auditing and redesign for SaaS startups and e-commerce. No contractors, no project manager, and no team. Just him, a MacBook, and 1 API key. Traditional design agencies out of Shenzhen keep teams of 8 people on salaries for the same volume, while he keeps only API tokens. 6 agents work through a single orchestrator on Claude Code Router. Usage is about 4 million tokens a day, the average API bill is just $480 a month. All 6 go through MCP servers and write shared state to the file system, without shared state in memory and without race conditions. And here is the system prompt he gave the orchestrator before launch: "you are the orchestrator of a one-man UI agency. you delegate read-only research tasks to 5 sub-agents and own all writes. sub-agents: // Hunter (finds SaaS and e-commerce sites with outdated UI) // Auditor (runs each site through Lighthouse, accessibility, and design system checks) // Pitcher (writes cold outreach and redesign proposals with before/after screenshots) // Splitter (breaks accepted projects into typed milestones) // Designer (generates Figma mockups and Tailwind components) // Checker (runs evals on every artifact before it leaves the harness). you never let 2 sub-agents touch 1 file. you stop and request human approval only when an invoice exceeds $5,000 or when the design system eval score drops below 0.88." Meaning the system knows exactly what it is and within what boundaries it operates. It knows it is supposed to find clients on its own. It knows it is supposed to write proposals with screenshots and mockups without intervention. It knows the human only plugs in when the amounts go above $5,000 or when the design system eval does not converge. → The system runs 24 hours a day → Hunter finds about 200 sites with outdated UI a day → Auditor runs each one through Lighthouse and WCAG → Pitcher prepares about 28 personalized proposals with before/after screenshots → Splitter breaks 3 accepted projects per week into milestones → Designer generates mockups and components, Checker runs evals on every artifact And only when the invoice breaks $5,000 or the eval drops below 0.88 does the orchestrator wake the human. Here is what the system outputs in his log during 1 of the sessions: "hunter report, tuesday: 213 sites found, 31 with last redesign before 2020, 14 with Lighthouse score below 65, 6 with active redesign RFP. passing top 6 to auditor." "pitcher: 27 cold outreach sent with before/after screenshots, 5 replies, 3 discovery calls scheduled. passing to splitter." "designer: milestone 2 of Lotus Tea Co redesign complete. Figma frames exported to /Users/dev/agency/clients/lotus/v2. checker running design system evals." "eval flag: proposal for $6,800 exceeds the approved limit of $5,000. sending for manual review." He has no remote server. No separate backend. Just a local file sandbox in /Users/dev/agency, an MCP router, and an API key to Claude. Out of everything I have seen this year, this is the cleanest one-person UI design agency: $480 in, about $32,000 out, and between them 6 prompts and 1 file system.
1:07

This Chinese developer launched 6 agents under 1 orchestrator, and they run his UI design agency at $32,000 a month on their own. He built a system of 6 agents on Claude Sonnet 4.6 that single-handedly runs his agency for UI auditing and redesign for SaaS startups and e-commerce. No contractors, no project manager, and no team. Just him, a MacBook, and 1 API key. Traditional design agencies out of Shenzhen keep teams of 8 people on salaries for the same volume, while he keeps only API tokens. 6 agents work through a single orchestrator on Claude Code Router. Usage is about 4 million tokens a day, the average API bill is just $480 a month. All 6 go through MCP servers and write shared state to the file system, without shared state in memory and without race conditions. And here is the system prompt he gave the orchestrator before launch: "you are the orchestrator of a one-man UI agency. you delegate read-only research tasks to 5 sub-agents and own all writes. sub-agents: // Hunter (finds SaaS and e-commerce sites with outdated UI) // Auditor (runs each site through Lighthouse, accessibility, and design system checks) // Pitcher (writes cold outreach and redesign proposals with before/after screenshots) // Splitter (breaks accepted projects into typed milestones) // Designer (generates Figma mockups and Tailwind components) // Checker (runs evals on every artifact before it leaves the harness). you never let 2 sub-agents touch 1 file. you stop and request human approval only when an invoice exceeds $5,000 or when the design system eval score drops below 0.88." Meaning the system knows exactly what it is and within what boundaries it operates. It knows it is supposed to find clients on its own. It knows it is supposed to write proposals with screenshots and mockups without intervention. It knows the human only plugs in when the amounts go above $5,000 or when the design system eval does not converge. → The system runs 24 hours a day → Hunter finds about 200 sites with outdated UI a day → Auditor runs each one through Lighthouse and WCAG → Pitcher prepares about 28 personalized proposals with before/after screenshots → Splitter breaks 3 accepted projects per week into milestones → Designer generates mockups and components, Checker runs evals on every artifact And only when the invoice breaks $5,000 or the eval drops below 0.88 does the orchestrator wake the human. Here is what the system outputs in his log during 1 of the sessions: "hunter report, tuesday: 213 sites found, 31 with last redesign before 2020, 14 with Lighthouse score below 65, 6 with active redesign RFP. passing top 6 to auditor." "pitcher: 27 cold outreach sent with before/after screenshots, 5 replies, 3 discovery calls scheduled. passing to splitter." "designer: milestone 2 of Lotus Tea Co redesign complete. Figma frames exported to /Users/dev/agency/clients/lotus/v2. checker running design system evals." "eval flag: proposal for $6,800 exceeds the approved limit of $5,000. sending for manual review." He has no remote server. No separate backend. Just a local file sandbox in /Users/dev/agency, an MCP router, and an API key to Claude. Out of everything I have seen this year, this is the cleanest one-person UI design agency: $480 in, about $32,000 out, and between them 6 prompts and 1 file system.

Blaze

56,062 次观看 • 2 个月前