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HERMES + ELEVENLABS TURNED A $3,000/MO ANSWERING SERVICE INTO A ~$145/MO AI THAT NEVER MISSES A CALL. A missed call after 6pm isn't a missed call. It's a customer booking with your competitor. Most businesses pay $2,000-$4,000/mo for a human to make sure that doesn't happen. Last week I...

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ZEUS⚡️

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10,383 views • 3 months ago •via X (Twitter)

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HERMES AGENT + ELEVENLABS JUST KILLED A $3,000/MO FRONT DESK - HERE'S THE MATH 👇 Run the unit economics on a clinic taking ~1,200 phone-minutes a month: Human side: → Answering service: $2.50/min × 1,200 = $3,000/mo → Annualized: $36,000/yr → Calls actually handled live: ~65% → ~420 min/mo dropped to voicemail AI side (Hermes + ElevenLabs): → Voice: ~$0.10/min × 1,200 = $120 → Number + inbound minutes: ~$1 + ~$16 = $17 → Model tokens behind Hermes: ~$8 → Total: ~$145/mo → $1,740/yr The delta: → $3,000 - $145 = $2,855 saved every month → $36,000 - $1,740 = $34,260/yr per location → That's a 95% cut on cost-per-call, and pickup goes from ~65% to 100% Now layer the revenue, not just the savings: → ~35% of calls (≈140/mo) used to die in voicemail → Recover just 10 of them as booked patients → At ~$300 first-visit value = +$3,000/mo recovered → Net monthly swing: $2,855 saved + $3,000 captured = ~$5,855/mo Payback on the afternoon it took to build: same day. Reply "HE+EL" + RT, and I’ll send you the detailed playbook via DM
1:02

HERMES AGENT + ELEVENLABS JUST KILLED A $3,000/MO FRONT DESK - HERE'S THE MATH 👇 Run the unit economics on a clinic taking ~1,200 phone-minutes a month: Human side: → Answering service: $2.50/min × 1,200 = $3,000/mo → Annualized: $36,000/yr → Calls actually handled live: ~65% → ~420 min/mo dropped to voicemail AI side (Hermes + ElevenLabs): → Voice: ~$0.10/min × 1,200 = $120 → Number + inbound minutes: ~$1 + ~$16 = $17 → Model tokens behind Hermes: ~$8 → Total: ~$145/mo → $1,740/yr The delta: → $3,000 - $145 = $2,855 saved every month → $36,000 - $1,740 = $34,260/yr per location → That's a 95% cut on cost-per-call, and pickup goes from ~65% to 100% Now layer the revenue, not just the savings: → ~35% of calls (≈140/mo) used to die in voicemail → Recover just 10 of them as booked patients → At ~$300 first-visit value = +$3,000/mo recovered → Net monthly swing: $2,855 saved + $3,000 captured = ~$5,855/mo Payback on the afternoon it took to build: same day. Reply "HE+EL" + RT, and I’ll send you the detailed playbook via DM

ZEUS⚡️

12,921 views • 3 months ago

5 startup ideas you can build and resell using only ElevenLabs Agents each one costs $0.08/min to run and replaces $2-5k/mo in human labor Let's break them down ↓ 1. AI Receptionist for Local Businesses dentists, salons, clinics, they all pay $2-3k/mo for someone to answer phones build a voice agent that: - answers calls 24/7 - books appointments - handles FAQs - speaks the client's language who ALREADY uses it: ~31% of local service businesses who STILL needs it: ~69% (your market) white-label it, charge $300-500/mo per client your cost per client: ~$30/mo in minutes 2. Multilingual Customer Support ElevenLabs agents speak 70+ languages natively e-commerce brands selling internationally need support in 5-10 languages minimum one agent replaces a 5-person multilingual team who ALREADY uses it: ~36% of e-commerce businesses who STILL needs it: ~64% and most of them are mid-market brands scaling globally sell 24/7 coverage, mark up the minutes, charge per-seat 3. AI Sales Qualifier (SDR Replacement) voice agent calls inbound leads, asks 5-10 qualifying questions, books meetings directly into the sales team's calendar startups pay $4-6k/mo per SDR you charge $1.5k/mo for an agent that works 24/7 and never misses a lead who ALREADY uses it: ~27% of mid-market teams who STILL needs it: ~73% and 22% already fully replaced human SDRs plug it into any CRM like HubSpot, Salesforce, Pipedrive 4. Restaurant Order-Taking Agent phone ordering for restaurants, pizzerias, takeout spots the agent takes the order, upsells sides and drinks, confirms, pushes to the POS who ALREADY uses it: ~34% of restaurants who STILL needs it: ~66% (expected to hit 50%+ in major cities this year) build one integration template → sell to 100+ restaurants at $200/mo each that's $20k/mo from one vertical 5. Real Estate Showing Scheduler agents answer property inquiry calls, give listing details, qualify buyers, and book viewings (all mid-call) realtors spend hours on phone scheduling who ALREADY uses it: ~18% use voice AI specifically who STILL needs it: ~82% while 82% of agents already use some form of AI, almost none have voice agents charge per listing or flat monthly integrates with their calendar + CRM -------- How to build any of these: - sign up for ElevenLabs (startups get $4k free credits) - pick your niche - build the agent with their no-code platform - connect it to GPT or Claude for the brain - plug in scheduling/CRM via API - white-label it under your brand you don't need to build AI, you need to sell AI to people who don't know it exists yet reply "ELEVEN" + RT and i'll send you a free guide so you can build this too
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5 startup ideas you can build and resell using only ElevenLabs Agents each one costs $0.08/min to run and replaces $2-5k/mo in human labor Let's break them down ↓ 1. AI Receptionist for Local Businesses dentists, salons, clinics, they all pay $2-3k/mo for someone to answer phones build a voice agent that: - answers calls 24/7 - books appointments - handles FAQs - speaks the client's language who ALREADY uses it: ~31% of local service businesses who STILL needs it: ~69% (your market) white-label it, charge $300-500/mo per client your cost per client: ~$30/mo in minutes 2. Multilingual Customer Support ElevenLabs agents speak 70+ languages natively e-commerce brands selling internationally need support in 5-10 languages minimum one agent replaces a 5-person multilingual team who ALREADY uses it: ~36% of e-commerce businesses who STILL needs it: ~64% and most of them are mid-market brands scaling globally sell 24/7 coverage, mark up the minutes, charge per-seat 3. AI Sales Qualifier (SDR Replacement) voice agent calls inbound leads, asks 5-10 qualifying questions, books meetings directly into the sales team's calendar startups pay $4-6k/mo per SDR you charge $1.5k/mo for an agent that works 24/7 and never misses a lead who ALREADY uses it: ~27% of mid-market teams who STILL needs it: ~73% and 22% already fully replaced human SDRs plug it into any CRM like HubSpot, Salesforce, Pipedrive 4. Restaurant Order-Taking Agent phone ordering for restaurants, pizzerias, takeout spots the agent takes the order, upsells sides and drinks, confirms, pushes to the POS who ALREADY uses it: ~34% of restaurants who STILL needs it: ~66% (expected to hit 50%+ in major cities this year) build one integration template → sell to 100+ restaurants at $200/mo each that's $20k/mo from one vertical 5. Real Estate Showing Scheduler agents answer property inquiry calls, give listing details, qualify buyers, and book viewings (all mid-call) realtors spend hours on phone scheduling who ALREADY uses it: ~18% use voice AI specifically who STILL needs it: ~82% while 82% of agents already use some form of AI, almost none have voice agents charge per listing or flat monthly integrates with their calendar + CRM -------- How to build any of these: - sign up for ElevenLabs (startups get $4k free credits) - pick your niche - build the agent with their no-code platform - connect it to GPT or Claude for the brain - plug in scheduling/CRM via API - white-label it under your brand you don't need to build AI, you need to sell AI to people who don't know it exists yet reply "ELEVEN" + RT and i'll send you a free guide so you can build this too

Ronin

776,087 views • 4 months ago

Today I built this voice agent while on a call with it Introducing /this-needs-a-call - it's a slash command to start a phone call - grill or be grilled by the voice agent in realtime - your coding agent listens in on the call via MCP and works while you talk for hours
6:15

Today I built this voice agent while on a call with it Introducing /this-needs-a-call - it's a slash command to start a phone call - grill or be grilled by the voice agent in realtime - your coding agent listens in on the call via MCP and works while you talk for hours

jacob paris ▲

32,463 views • 2 months ago

HE MAKES MONEY IN REAL ESTATE WITHOUT BUYING, SELLING, OR EVEN SEEING A SINGLE HOUSE. HERE'S THE EXACT SETUP He never owns a property. He takes a single listing, turns it into a polished 30-second video, and sells that to the agent who posted it. Realtors need video for their feeds and almost none of them can make it. He sits in the middle and builds the whole thing once as a skill that runs on command Here is the exact process: 1. Pull the listing. Go to Zillow, open any listing, download the high-res images, and grab the property info. That is your raw material 2. Turn photos into video with Google Veo. Get a Google API key for Veo, the image-to-video model. It takes the listing photos and animates them into clean 30-second footage. This is the best one out right now 3. Add the voice with ElevenLabs. Get an ElevenLabs API key. Feed it the listing details and it returns a voiceover that sounds like a real human, not a robot. Lay it over the video with the text on screen 4. Send it with AgentMail. Get an AgentMail key so the system can send the finished email out on its own Then you wire it into one skill. Scrape the listing, send images to Veo, add the ElevenLabs voiceover and on-screen text, then send the email. Feed it each key one at a time and have it build each step Who you sell to: Pull realtors off Zillow and Realtor com whose listings have flat photos and zero video. That gap is your pitch. Send a free sample made from their own listing first, then charge a monthly rate for ongoing clips. One agent with ten listings is a recurring client, fully online Bookmark this
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HE MAKES MONEY IN REAL ESTATE WITHOUT BUYING, SELLING, OR EVEN SEEING A SINGLE HOUSE. HERE'S THE EXACT SETUP He never owns a property. He takes a single listing, turns it into a polished 30-second video, and sells that to the agent who posted it. Realtors need video for their feeds and almost none of them can make it. He sits in the middle and builds the whole thing once as a skill that runs on command Here is the exact process: 1. Pull the listing. Go to Zillow, open any listing, download the high-res images, and grab the property info. That is your raw material 2. Turn photos into video with Google Veo. Get a Google API key for Veo, the image-to-video model. It takes the listing photos and animates them into clean 30-second footage. This is the best one out right now 3. Add the voice with ElevenLabs. Get an ElevenLabs API key. Feed it the listing details and it returns a voiceover that sounds like a real human, not a robot. Lay it over the video with the text on screen 4. Send it with AgentMail. Get an AgentMail key so the system can send the finished email out on its own Then you wire it into one skill. Scrape the listing, send images to Veo, add the ElevenLabs voiceover and on-screen text, then send the email. Feed it each key one at a time and have it build each step Who you sell to: Pull realtors off Zillow and Realtor com whose listings have flat photos and zero video. That gap is your pitch. Send a free sample made from their own listing first, then charge a monthly rate for ongoing clips. One agent with ten listings is a recurring client, fully online Bookmark this

Yarchi

106,174 views • 3 months ago

MOST AI AGENTS RESET TO ZERO EVERY TIME YOU OPEN A NEW SESSION. Hermes Agent was built to do the opposite. Every task compounds. Every workflow becomes reusable. Every session teaches the next one. That is the core thesis behind Hermes, and it completely changes how AI agents work. Unlike most AI agents built by software tool companies, Hermes was created by the researchers training the Hermes model family itself. Meaning: The same system used internally to generate training data is now open source and available to everyone. One install gives you access to: Claude. GPT. Grok. Local models via Ollama. Telegram, Discord, Slack, iMessage integrations. Custom skills. Persistent memory. CLI control. But the real unlock is the compounding loop: You give Hermes a task. It records every tool call, decision, and result. Then it converts successful workflows into reusable skills automatically. A Hermes setup running for 3 months becomes fundamentally smarter than a fresh install. That’s the big idea. One of the coolest parts: You literally watch the agent evolve in real time from a blank install into a personalized AI operator. Instead of watching Netflix today, spend 28 mins with this. Follow Dami-Defi for more AI systems, agent workflows, and infrastructure alpha.
0:48

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

Dami-Defi

11,685 views • 3 months ago

"Hi, my name is Amara" In a space where almost every AI call Agent sounds American or other accents, I decided to create one that mirrors an African (read as Nigerian) voice. I built an AI Call Receptionist for a Dental clinic that takes inbound calls, responds to enquiries based on the knowledge base fed to Vapi,it checks for availabity in my calendar and even end call by itself when it senses that the conversation has ended. It goes further to book an appointment in my calendar using n8n after checking for it's availability. You know what? I will just let you watch it yourself. Enjoy!
8:19

"Hi, my name is Amara" In a space where almost every AI call Agent sounds American or other accents, I decided to create one that mirrors an African (read as Nigerian) voice. I built an AI Call Receptionist for a Dental clinic that takes inbound calls, responds to enquiries based on the knowledge base fed to Vapi,it checks for availabity in my calendar and even end call by itself when it senses that the conversation has ended. It goes further to book an appointment in my calendar using n8n after checking for it's availability. You know what? I will just let you watch it yourself. Enjoy!

Ada || Airtable, Zapier & Make.com

10,126 views • 4 months ago

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 views • 3 months ago

I built a service desk agent using ElevenLabs’ new Conversational AI Agents feature. Watch the video to see how responsive it is! Previously, I used ElevenLabs for cloning my voice and used my generated voice for narrations in some of my youtube videos. This feature takes elevenlabs' voice AI to a whole new level! It simplifies systems that used to require separate TTS (text-to-speech) and STT (speech-to-text) processes for both sides of the conversation. Now, it’s much simpler! Create your own agent here What will you create with this?
3:29

I built a service desk agent using ElevenLabs’ new Conversational AI Agents feature. Watch the video to see how responsive it is! Previously, I used ElevenLabs for cloning my voice and used my generated voice for narrations in some of my youtube videos. This feature takes elevenlabs' voice AI to a whole new level! It simplifies systems that used to require separate TTS (text-to-speech) and STT (speech-to-text) processes for both sides of the conversation. Now, it’s much simpler! Create your own agent here What will you create with this?

Melvin Vivas

27,722 views • 1 year ago

$24,000 per year from this simple AI Dentist Voice Agent (and why I'm crazy for giving it away for free) A dental practice was losing $6,000+ in revenue every month from missed after-hours calls. That's 20-25 potential patients walking away because no one was available to book their appointments. So I built an AI voice assistant that handles after-hours dental bookings 24/7 using n8n and ElevenLabs based on internal policies and scheduling availability. Here's what this system does: → Answers calls with a natural-sounding AI receptionist → Collects patient information and insurance details → Checks calendar availability in real-time → Books appointments automatically → Logs all patient details to a Google Sheet The result? This similar AI voice system was sold to a dental practice for $24k per year by another entrepreneur!! This isn't just about dental practices. Any service business losing money from missed calls can implement a similar system. Want the complete n8n workflow template? 1. Retweet & Like this post 2. Comment "ASSISTANT" I'll send you the entire system for free, a full setup walk-through video, including the ElevenLabs automation components.
2:51

$24,000 per year from this simple AI Dentist Voice Agent (and why I'm crazy for giving it away for free) A dental practice was losing $6,000+ in revenue every month from missed after-hours calls. That's 20-25 potential patients walking away because no one was available to book their appointments. So I built an AI voice assistant that handles after-hours dental bookings 24/7 using n8n and ElevenLabs based on internal policies and scheduling availability. Here's what this system does: → Answers calls with a natural-sounding AI receptionist → Collects patient information and insurance details → Checks calendar availability in real-time → Books appointments automatically → Logs all patient details to a Google Sheet The result? This similar AI voice system was sold to a dental practice for $24k per year by another entrepreneur!! This isn't just about dental practices. Any service business losing money from missed calls can implement a similar system. Want the complete n8n workflow template? 1. Retweet & Like this post 2. Comment "ASSISTANT" I'll send you the entire system for free, a full setup walk-through video, including the ElevenLabs automation components.

David Roberts

353,626 views • 1 year ago

Monad is joining us on our mission to kill subscriptions. Your agent can now call Apollo, ElevenLabs, and MiniMax—no account, no API key—and pay per call in USDC on the Monad network. No credits to buy upfront. No subscriptions to manage. Nothing expires. Your agent isn’t a seat. It shouldn’t have to sign up for one. Made possible by Monad and Monid
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Monad is joining us on our mission to kill subscriptions. Your agent can now call Apollo, ElevenLabs, and MiniMax—no account, no API key—and pay per call in USDC on the Monad network. No credits to buy upfront. No subscriptions to manage. Nothing expires. Your agent isn’t a seat. It shouldn’t have to sign up for one. Made possible by Monad and Monid

Shengkun Ye

14,545 views • 15 days ago

Migrated my Sanskrit tutor's TTS from Sarvam to ElevenLabs. Quick note: I compared free tiers. Sarvam gave me Hinglish voices for free, but ElevenLabs gates its Indian voices behind a paid plan, so I tested it on English voices. Real Hinglish quality on ElevenLabs will only show up once you upgrade. I stuck with ElevenLabs for four reasons: 1) Three voice controls instead of one. Sarvam gives you pace. ElevenLabs gives you speed, stability, and style. That means a question can have a rising, expressive contour while a correction stays calm and steady. Real modulation per sentence, not just per turn. 2) previous_text for boundary continuity. Each new synthesis call takes the previous chunk's text as context, so the intonation carries across chunks instead of restarting cold. Sarvam had no equivalent and you could hear the reset on longer replies. 3) Simpler API. Sarvam is a stateful websocket with a configure step and completion events. ElevenLabs is client.convert() returning an async iterator of bytes. Half the integration code disappeared. 4) Pitch variation across a sentence. Sarvam's contour is flatter, which reads as news-reader. ElevenLabs varies more, which reads as conversational. Matters for a tutor. Where Sarvam is still the harder pick to beat: pronunciation of Hindi-first words, cost at scale, and latency for Indian users. For a Hinglish tutor, expressiveness won. For pure Hindi at scale, my answer would be different.
4:37

Migrated my Sanskrit tutor's TTS from Sarvam to ElevenLabs. Quick note: I compared free tiers. Sarvam gave me Hinglish voices for free, but ElevenLabs gates its Indian voices behind a paid plan, so I tested it on English voices. Real Hinglish quality on ElevenLabs will only show up once you upgrade. I stuck with ElevenLabs for four reasons: 1) Three voice controls instead of one. Sarvam gives you pace. ElevenLabs gives you speed, stability, and style. That means a question can have a rising, expressive contour while a correction stays calm and steady. Real modulation per sentence, not just per turn. 2) previous_text for boundary continuity. Each new synthesis call takes the previous chunk's text as context, so the intonation carries across chunks instead of restarting cold. Sarvam had no equivalent and you could hear the reset on longer replies. 3) Simpler API. Sarvam is a stateful websocket with a configure step and completion events. ElevenLabs is client.convert() returning an async iterator of bytes. Half the integration code disappeared. 4) Pitch variation across a sentence. Sarvam's contour is flatter, which reads as news-reader. ElevenLabs varies more, which reads as conversational. Matters for a tutor. Where Sarvam is still the harder pick to beat: pronunciation of Hindi-first words, cost at scale, and latency for Indian users. For a Hinglish tutor, expressiveness won. For pure Hindi at scale, my answer would be different.

Anushka Shandilya

107,635 views • 1 month ago

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 views • 3 months ago

SHE FOUND HER DAD'S OLD 2011 MAC MINI SITTING IN A SAFE AND TURNED IT INTO A FULLY WORKING HERMES AI AGENT MACHINE The starting point: a 14-year-old Mac Mini, Intel Core i5, macOS officially unsupported at this point. Instead of tossing it, here's what she did: → Wiped macOS entirely, installed Ubuntu instead → Skipped WiFi setup, connected it straight to the router → Pulled the drivers, ran the full install → Set it up for remote access over SSH – no monitor or keyboard needed after this point → Installed Hermes Agent directly on it A machine most people would've written off as e-waste is now a fully remote-controllable AI agent box. This is the part people underestimate about old hardware: you don't need the newest Mac Mini or a $2K rig to run a real AI agent. You need Linux, SSH, and the willingness to actually try. Bookmark this. What she builds on it next is worth watching for.
1:32

SHE FOUND HER DAD'S OLD 2011 MAC MINI SITTING IN A SAFE AND TURNED IT INTO A FULLY WORKING HERMES AI AGENT MACHINE The starting point: a 14-year-old Mac Mini, Intel Core i5, macOS officially unsupported at this point. Instead of tossing it, here's what she did: → Wiped macOS entirely, installed Ubuntu instead → Skipped WiFi setup, connected it straight to the router → Pulled the drivers, ran the full install → Set it up for remote access over SSH – no monitor or keyboard needed after this point → Installed Hermes Agent directly on it A machine most people would've written off as e-waste is now a fully remote-controllable AI agent box. This is the part people underestimate about old hardware: you don't need the newest Mac Mini or a $2K rig to run a real AI agent. You need Linux, SSH, and the willingness to actually try. Bookmark this. What she builds on it next is worth watching for.

SCOTTY BEAM

53,731 views • 2 months ago

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👇
14:28

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

30,804 views • 3 months ago

1/ Context Lens, new in Tlon v9.2.0. Everything your agent has ever done, stored durably on your personal server. Every message, every tool call. Debugging an agent usually means ssh-ing into a server and grepping logs. Now it's a tap in Tlon Messenger's UI. Tap the badge under a bot's reply to open the run: the context it read, each tool call with timing, the model that answered, and a persistence log of everything it wrote.
0:25

1/ Context Lens, new in Tlon v9.2.0. Everything your agent has ever done, stored durably on your personal server. Every message, every tool call. Debugging an agent usually means ssh-ing into a server and grepping logs. Now it's a tap in Tlon Messenger's UI. Tap the badge under a bot's reply to open the run: the context it read, each tool call with timing, the model that answered, and a persistence log of everything it wrote.

Tlon

10,680 views • 2 months ago

Harness vs. Graphs, clearly explained! a harness is great, and most people think it is the whole thing: retries, timeouts, a sandbox, a log, the context it assembles before every call. all of that is real work, and all of it wraps exactly one call. run it a hundred times and you have one call, made very safely, a hundred times. Graph engineering fixes this by moving the decision up a layer: not how safely one call is made, but which calls exist to be made at all. you need both, and here is the sentence that resolves the whole confusion: the harness is everything around one call. the graph is everything between them. ↳ around one call: retry, timeout, sandbox, log, assemble the context, hand back a result ↳ between calls: split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs the harness does not go away when you build a graph. it moves under each node, and now there are five of them, each wrapping a call you would never have made by hand. the trick is knowing which layer a failure belongs to. turn a piece off and run it again. if the call still works, it was the harness. if the wrong step runs at all, it was the graph. people spend weeks hardening a harness around a node that should not have existed. one thing to know before you scale it. most of what people call their agent is a harness with a chat box on it. ↳ it retries, it times out, it logs, it assembles context, it holds one call up beautifully ↳ it has never once decided that a second call should exist, and that is the entire difference that last one catches careful people. a harness that never fails is not evidence the system is right. it is evidence one call went well, which is the smallest possible claim. and the one that eats whole nights: a harness cannot save you from the wrong step running. you can retry a bad decision three times with a clean log and perfect isolation, and all you bought was three copies of it. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓
0:53

Harness vs. Graphs, clearly explained! a harness is great, and most people think it is the whole thing: retries, timeouts, a sandbox, a log, the context it assembles before every call. all of that is real work, and all of it wraps exactly one call. run it a hundred times and you have one call, made very safely, a hundred times. Graph engineering fixes this by moving the decision up a layer: not how safely one call is made, but which calls exist to be made at all. you need both, and here is the sentence that resolves the whole confusion: the harness is everything around one call. the graph is everything between them. ↳ around one call: retry, timeout, sandbox, log, assemble the context, hand back a result ↳ between calls: split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs the harness does not go away when you build a graph. it moves under each node, and now there are five of them, each wrapping a call you would never have made by hand. the trick is knowing which layer a failure belongs to. turn a piece off and run it again. if the call still works, it was the harness. if the wrong step runs at all, it was the graph. people spend weeks hardening a harness around a node that should not have existed. one thing to know before you scale it. most of what people call their agent is a harness with a chat box on it. ↳ it retries, it times out, it logs, it assembles context, it holds one call up beautifully ↳ it has never once decided that a second call should exist, and that is the entire difference that last one catches careful people. a harness that never fails is not evidence the system is right. it is evidence one call went well, which is the smallest possible claim. and the one that eats whole nights: a harness cannot save you from the wrong step running. you can retry a bad decision three times with a clean log and perfect isolation, and all you bought was three copies of it. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Hanako

50,770 views • 11 days ago

I literally just watched GPT-5.5 via codex beat an Amazon customer associate in real time. 💀 I asked it to get me a refund, and I watched it navigate the settings, cancel the subscription, then it went step further into the help page. I thought it was going to request a phone call (which would prompt me to take over) Instead, it opened: “Chat with an associate now.” That’s when I sat up on my couch because I knew it was going to get real The agent said: “Your subscription is active.” And GPT-5.5 immediately explained that it only shows as active because cancellation leaves access through the billing period, but that I wanted it stopped now and refunded. And my jaw just hung open, it was the first time I watched sand handle a customer service agent for me in real time Once the agent confirmed the refund, it just ended the chat no mercy no thank you LMAO First time I’ve watched a human customer service agent get outmaneuvered by AI in real time. And it made me 15$! almost paid for itself in 5 minutes
0:49

I literally just watched GPT-5.5 via codex beat an Amazon customer associate in real time. 💀 I asked it to get me a refund, and I watched it navigate the settings, cancel the subscription, then it went step further into the help page. I thought it was going to request a phone call (which would prompt me to take over) Instead, it opened: “Chat with an associate now.” That’s when I sat up on my couch because I knew it was going to get real The agent said: “Your subscription is active.” And GPT-5.5 immediately explained that it only shows as active because cancellation leaves access through the billing period, but that I wanted it stopped now and refunded. And my jaw just hung open, it was the first time I watched sand handle a customer service agent for me in real time Once the agent confirmed the refund, it just ended the chat no mercy no thank you LMAO First time I’ve watched a human customer service agent get outmaneuvered by AI in real time. And it made me 15$! almost paid for itself in 5 minutes

Chris

1,389,694 views • 4 months ago

Finally, a proper chat UI for Hermes Agent (open-source)! Hermes ships an official dashboard, but it's primarily built for management, and its chat is just a terminal piped into a browser tab. Hermes Web UI is an open-source chat-first alternative. It's self-hosted and points at your existing ~/.hermes state, so there's nothing new to configure. - It's a native web chat, not a terminal in a tab - Sessions group by date with a context ring - Kanban renders the agent's task board - Spaces manages your workspaces - Skills panel lists the full catalog - Tasks panel shows cron jobs - Insights show usage and activity - Memory shows MEMORY and SOUL files - Logs tails the agent, gateway, and error logs The whole setup runs 100% locally, binds to localhost by default, and you reach it over an SSH tunnel or Tailscale from your phone. I have shared the Hermes Web UI GitHub repo in the replies. Do note that it's a community project, not official, so expect occasional rough edges (concurrent profile runs are blocked for now). To dive deeper into Hermes Agent, my co-founder wrote a full masterclass about it, covering the learning loop, the memory tiers, self-evolving skills, GEPA, and running multiple isolated agents. Read it below.
0:23

Finally, a proper chat UI for Hermes Agent (open-source)! Hermes ships an official dashboard, but it's primarily built for management, and its chat is just a terminal piped into a browser tab. Hermes Web UI is an open-source chat-first alternative. It's self-hosted and points at your existing ~/.hermes state, so there's nothing new to configure. - It's a native web chat, not a terminal in a tab - Sessions group by date with a context ring - Kanban renders the agent's task board - Spaces manages your workspaces - Skills panel lists the full catalog - Tasks panel shows cron jobs - Insights show usage and activity - Memory shows MEMORY and SOUL files - Logs tails the agent, gateway, and error logs The whole setup runs 100% locally, binds to localhost by default, and you reach it over an SSH tunnel or Tailscale from your phone. I have shared the Hermes Web UI GitHub repo in the replies. Do note that it's a community project, not official, so expect occasional rough edges (concurrent profile runs are blocked for now). To dive deeper into Hermes Agent, my co-founder wrote a full masterclass about it, covering the learning loop, the memory tiers, self-evolving skills, GEPA, and running multiple isolated agents. Read it below.

Avi Chawla

78,099 views • 3 months ago

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 🚀

51,540 views • 3 months ago

Hermes Browser Extension v0.3.0 just landed. You already had a browser inside Hermes Desktop. Now flip it around. Bring Hermes into your browser. 💻 Open the side panel next to whatever you're doing and let Hermes work with the page you're already on. I used it inside Google Cloud while working through Gmail API setup. If you've spent any time in there, you know that is not exactly a friendly little website. Hermes handled it extremely well. Research a page. Work through complicated setup screens. Draft without leaving the site. Let Hermes help move through multi-step browser work without constantly bouncing back and forth. You can also switch between built-in themes, Light/Dark modes, or add your own custom theme. 🎨 Hermes, right where you're already working. Give Hermes the repo and ask it to help you set it up. komet💫 Killed it with this update 🔥 Repo link in the first reply⬇️
1:10

Hermes Browser Extension v0.3.0 just landed. You already had a browser inside Hermes Desktop. Now flip it around. Bring Hermes into your browser. 💻 Open the side panel next to whatever you're doing and let Hermes work with the page you're already on. I used it inside Google Cloud while working through Gmail API setup. If you've spent any time in there, you know that is not exactly a friendly little website. Hermes handled it extremely well. Research a page. Work through complicated setup screens. Draft without leaving the site. Let Hermes help move through multi-step browser work without constantly bouncing back and forth. You can also switch between built-in themes, Light/Dark modes, or add your own custom theme. 🎨 Hermes, right where you're already working. Give Hermes the repo and ask it to help you set it up. komet💫 Killed it with this update 🔥 Repo link in the first reply⬇️

Hermes Release Watch

70,506 views • 1 month ago