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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 DMshow more

ZEUS⚡️

28,785 subscribers

12,921 次观看 • 2 个月前 •via X (Twitter)

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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 gave my Hermes Agent a real phone number instead. It answers every call, 24/7, in a natural ElevenLabs voice. It doesn't read a script - it checks the calendar, recognizes the caller, and books the appointment mid-call. The math on one dental clinic: → Old answering service: ~$2,400/mo (messages only) → Hermes + ElevenLabs: ~$145/mo (books in real time) → ~$27,000/yr saved per location And it's strictly better - it books at 11pm instead of leaving a voicemail. The setup is one afternoon: → Turn on Hermes' API server → Tunnel it with ngrok → Point an ElevenLabs agent at it (Custom LLM) → Attach a Twilio number Reply "ElevenLabs" + RT, and I’ll send you the detailed playbook via DM Full step-by-step build 👇

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 gave my Hermes Agent a real phone number instead. It answers every call, 24/7, in a natural ElevenLabs voice. It doesn't read a script - it checks the calendar, recognizes the caller, and books the appointment mid-call. The math on one dental clinic: → Old answering service: ~$2,400/mo (messages only) → Hermes + ElevenLabs: ~$145/mo (books in real time) → ~$27,000/yr saved per location And it's strictly better - it books at 11pm instead of leaving a voicemail. The setup is one afternoon: → Turn on Hermes' API server → Tunnel it with ngrok → Point an ElevenLabs agent at it (Custom LLM) → Attach a Twilio number Reply "ElevenLabs" + RT, and I’ll send you the detailed playbook via DM Full step-by-step build 👇

ZEUS⚡️

10,383 次观看 • 2 个月前

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

775,226 次观看 • 3 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 👇

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 • 2 个月前

ALEX LEISCHOW JUST DROPPED A FULL TUTORIAL ON HOW TO BUILD AN AI THAT COLD CALLS LEADS FOR $1,500/MONTH PER CLIENT AND 67,000 PEOPLE ALREADY WATCHED IT he uses vapi + make com + google sheets + openai to build a voice agent that sounds completely human, pitches your offer naturally, and collects lead info on autopilot you don't need a sales team or a script and you'll never manually dial another number because the system runs while you focus on something else entirely a dental clinic gets 200 leads a month and the staff only calls 60 of them. the rest go cold. an ai voice agent calls all 200 within 5 minutes of the lead coming in calling a lead within 5 minutes converts 900% better than calling after 30 minutes. that's the entire game human SDR costs $4,000-6,000/month. ai voice agent doing the same job costs $300-800/month. clinic pays $800/month and never has another lead go cold 6 hours to set up your first client. 8 clients at $1,500/month = $12,000/month recurring bookmark this and read the article below

ALEX LEISCHOW JUST DROPPED A FULL TUTORIAL ON HOW TO BUILD AN AI THAT COLD CALLS LEADS FOR $1,500/MONTH PER CLIENT AND 67,000 PEOPLE ALREADY WATCHED IT he uses vapi + make com + google sheets + openai to build a voice agent that sounds completely human, pitches your offer naturally, and collects lead info on autopilot you don't need a sales team or a script and you'll never manually dial another number because the system runs while you focus on something else entirely a dental clinic gets 200 leads a month and the staff only calls 60 of them. the rest go cold. an ai voice agent calls all 200 within 5 minutes of the lead coming in calling a lead within 5 minutes converts 900% better than calling after 30 minutes. that's the entire game human SDR costs $4,000-6,000/month. ai voice agent doing the same job costs $300-800/month. clinic pays $800/month and never has another lead go cold 6 hours to set up your first client. 8 clients at $1,500/month = $12,000/month recurring bookmark this and read the article below

starmex

22,815 次观看 • 3 个月前

A couple that makes $20k/mo will live a comfortable life in Toronto, Canada. Let me walk you through it $20k per month is $240k a year. Let’s say each person makes $120,000 per year In Ontario: Federal tax: $18,812.49 Provincial tax: $10,128.16 CPP contributions: $4,055.50 EI premiums $1,049.12 Net pay: $85,954.73 per person per year or $7,162.89 per person per month. Who are we talking about? A couple. No kids. For a couple: Net pay: $171,909.46 per year or $14,325.79 per month. What does “comfortable” mean? Let us say you are: - not living pay check to pay check - filling retirement accounts - paying for food, clothing, shelter, transportation Let us start: TFSA (equivalent to U.S. Roth IRA): $7,000 per adult per year, or $14k per couple per year total. But each person contributed $21,600 to their RRSP, paid $30,000 in tax, and had a refund of $8,900 which they used for their TFSA with $1,900 per person to spare. RRSP (equivalent to U.S. 401k/403b): $21,600 per adult, or $43,200 total per couple. For retirement (TFSA & RRSP): $43,200 per year or $3,600 per month. Let’s move to shelter: A 1b1b condo in Toronto costs $720,000. This should be affordable for a gross household income of $240,000 per year based on the 20/30/3 rule for buying a home: 1. 20% down 2. Shelter costs less than 30% gross household income 3. Shelter price less than 3x gross household income If no downpayment the couple needs to find $144,000 for downpayment, $13,275 land transfer tax (assuming first time home buyer otherwise $21,750) and legal. $162k+ Minimum. Monthly mortgage is $3,400 per month (20% down, 5.1% interest, 25 year amortization) Plus: Utilities Insurance Property tax Landscaping Maintenance Capital expenditures You get the picture. If non-recoverable shelter cost is 2% this is $14,400 per year or $1,200 per month. Let’s keep it simple and say that shelter equivalent is $4,600 per month Next add the following: Groceries: $1,000/mo Car: $1,000/mo (includes monthly payment, maintenance, insurance, gas) We are at $10,200 per month. ($3,600 retirement + $4,600 shelter + $1,000 food + $1,000 transportation) Now add: $200/mo Emergency fund $200/mo. Phone & Internet $400/mo Eating out $200/mo Entertainment $500/mo Clothes, Fitness, Grooming, Travel Total of $1,500 We are at $11,700 per month. This couple has $2,700 per month to spare and can consider having a child and/or a second car. Statistics Canada calculated that raising a child from birth until the age of 18 years of age will cost $1,400 per month. This excludes saving for the child’s post-secondary education in an RESP. In short to live a comfortable life in Toronto with adaquate contributions to retirement, money for shelter, food, clothing, transportation, entertainment, and a future that may include having a child, a couple needs to make $20k/mo. Otherwise we just decide to sacrifice, and live without something listed above. Get financially literate. You got this! 🙌

A couple that makes $20k/mo will live a comfortable life in Toronto, Canada. Let me walk you through it $20k per month is $240k a year. Let’s say each person makes $120,000 per year In Ontario: Federal tax: $18,812.49 Provincial tax: $10,128.16 CPP contributions: $4,055.50 EI premiums $1,049.12 Net pay: $85,954.73 per person per year or $7,162.89 per person per month. Who are we talking about? A couple. No kids. For a couple: Net pay: $171,909.46 per year or $14,325.79 per month. What does “comfortable” mean? Let us say you are: - not living pay check to pay check - filling retirement accounts - paying for food, clothing, shelter, transportation Let us start: TFSA (equivalent to U.S. Roth IRA): $7,000 per adult per year, or $14k per couple per year total. But each person contributed $21,600 to their RRSP, paid $30,000 in tax, and had a refund of $8,900 which they used for their TFSA with $1,900 per person to spare. RRSP (equivalent to U.S. 401k/403b): $21,600 per adult, or $43,200 total per couple. For retirement (TFSA & RRSP): $43,200 per year or $3,600 per month. Let’s move to shelter: A 1b1b condo in Toronto costs $720,000. This should be affordable for a gross household income of $240,000 per year based on the 20/30/3 rule for buying a home: 1. 20% down 2. Shelter costs less than 30% gross household income 3. Shelter price less than 3x gross household income If no downpayment the couple needs to find $144,000 for downpayment, $13,275 land transfer tax (assuming first time home buyer otherwise $21,750) and legal. $162k+ Minimum. Monthly mortgage is $3,400 per month (20% down, 5.1% interest, 25 year amortization) Plus: Utilities Insurance Property tax Landscaping Maintenance Capital expenditures You get the picture. If non-recoverable shelter cost is 2% this is $14,400 per year or $1,200 per month. Let’s keep it simple and say that shelter equivalent is $4,600 per month Next add the following: Groceries: $1,000/mo Car: $1,000/mo (includes monthly payment, maintenance, insurance, gas) We are at $10,200 per month. ($3,600 retirement + $4,600 shelter + $1,000 food + $1,000 transportation) Now add: $200/mo Emergency fund $200/mo. Phone & Internet $400/mo Eating out $200/mo Entertainment $500/mo Clothes, Fitness, Grooming, Travel Total of $1,500 We are at $11,700 per month. This couple has $2,700 per month to spare and can consider having a child and/or a second car. Statistics Canada calculated that raising a child from birth until the age of 18 years of age will cost $1,400 per month. This excludes saving for the child’s post-secondary education in an RESP. In short to live a comfortable life in Toronto with adaquate contributions to retirement, money for shelter, food, clothing, transportation, entertainment, and a future that may include having a child, a couple needs to make $20k/mo. Otherwise we just decide to sacrifice, and live without something listed above. Get financially literate. You got this! 🙌

Lazy Canadian Entrepreneur

1,654,478 次观看 • 2 年前

If you are a SaaS Founder and you want to increase your Domain Authority. To get customers from Google and AI on Autopilot. Let me introduce you to Distribb backlink exchange. We don't just swap links randomly like every other network. Which, by the way, doesn't bring you much power to your website. We built a system that: 1. Connects to your Google Search Console 2. Finds your top 5 direct competitors 3. Calculates your backlink gap 4. Matches you with real websites in your niche 5. Writes articles and listicles that link to you (optimized for Google AND AI answers) 6. Distributes links across your homepage, money pages, and blog with natural anchor diversity 7. Publishes them across the network automatically 8. Spreads everything over months so it looks 100% organic 9. Uses A-B-C linking: no direct swaps, no footprints Bonus: The longer you stay, the more links you get. Month 1: 5 backlinks/mo Month 3: 8 backlinks/mo Month 6: 12 backlinks/mo Month 12: 20 backlinks/mo Month 18: 25 backlinks/mo Month 24: 50 backlinks/mo You start at your current DR level. As your authority grows, you unlock links from stronger sites. You focus on building your product. We handle the SEO. There are already hundreds of websites in the network waiting for you. This is not a marketplace where you pay $500 per link. This is not a PBN that gets you penalized. This is a real network of real SaaS websites exchanging real editorial backlinks. Your first month starts at $9 👇

If you are a SaaS Founder and you want to increase your Domain Authority. To get customers from Google and AI on Autopilot. Let me introduce you to Distribb backlink exchange. We don't just swap links randomly like every other network. Which, by the way, doesn't bring you much power to your website. We built a system that: 1. Connects to your Google Search Console 2. Finds your top 5 direct competitors 3. Calculates your backlink gap 4. Matches you with real websites in your niche 5. Writes articles and listicles that link to you (optimized for Google AND AI answers) 6. Distributes links across your homepage, money pages, and blog with natural anchor diversity 7. Publishes them across the network automatically 8. Spreads everything over months so it looks 100% organic 9. Uses A-B-C linking: no direct swaps, no footprints Bonus: The longer you stay, the more links you get. Month 1: 5 backlinks/mo Month 3: 8 backlinks/mo Month 6: 12 backlinks/mo Month 12: 20 backlinks/mo Month 18: 25 backlinks/mo Month 24: 50 backlinks/mo You start at your current DR level. As your authority grows, you unlock links from stronger sites. You focus on building your product. We handle the SEO. There are already hundreds of websites in the network waiting for you. This is not a marketplace where you pay $500 per link. This is not a PBN that gets you penalized. This is a real network of real SaaS websites exchanging real editorial backlinks. Your first month starts at $9 👇

Florian Darroman

26,669 次观看 • 4 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!

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

111,357 次观看 • 1 个月前

most traders pay $3,000+/month for tools GitHub replaced for free. 9 repos. zero subscriptions. 1. OpenBB → replaces Bloomberg Terminal ($2,000/mo) financial data platform built for AI agents and quants connects natively to claude via MCP. most people don't know this. 2. freqtrade → replaces paid crypto bot services ($100/mo) ML strategy optimization. runs on binance, bybit, hyperliquid and 10+ others 34,000 stars. free and always will be. 3. hummingbot → replaces HFT bot platforms ($200/mo) $34B+ in user-generated trading volume has a native claude MCP integration. connect your AI directly to 140+ exchanges. 4. FinGPT → replaces financial AI subscriptions ($150/mo) open-source LLMs that outperform GPT-4 on market sentiment bloomberg spent $3M training theirs. this costs $17 to fine-tune. 5. NautilusTrader → replaces institutional trading platforms ($500/mo) production-grade. rust-native. fast enough to train RL trading agents same codebase for backtesting and live. zero rewrite needed. 6. QuantConnect Lean → replaces paid quant research platforms ($100/mo) professional algo trading engine. python + C# from backtest to live in one click. used by 200K+ quants worldwide. 7. jesse → replaces TradingView algo subscriptions ($25/mo) advanced crypto trading framework for serious strategy builders clean. powerful. no bloat. 8. vectorbt → replaces paid backtesting tools ($80/mo) fastest backtesting library in existence tests thousands of strategies in seconds. pandas-based. 9. FinRL → replaces custom AI trading infrastructure ($300/mo) financial reinforcement learning. train your own trading AI. from the same team behind FinGPT. 10. AlphaCartel Setup → replaces hedge fund signal services ($300/mo) this is where all 9 repos above connect into one working system claude-powered bots. live signals. no-code setup. community of traders already printing. total before: ~$3,455/month total now: $0 + alphacartel like + bookmark. you'll need this.

most traders pay $3,000+/month for tools GitHub replaced for free. 9 repos. zero subscriptions. 1. OpenBB → replaces Bloomberg Terminal ($2,000/mo) financial data platform built for AI agents and quants connects natively to claude via MCP. most people don't know this. 2. freqtrade → replaces paid crypto bot services ($100/mo) ML strategy optimization. runs on binance, bybit, hyperliquid and 10+ others 34,000 stars. free and always will be. 3. hummingbot → replaces HFT bot platforms ($200/mo) $34B+ in user-generated trading volume has a native claude MCP integration. connect your AI directly to 140+ exchanges. 4. FinGPT → replaces financial AI subscriptions ($150/mo) open-source LLMs that outperform GPT-4 on market sentiment bloomberg spent $3M training theirs. this costs $17 to fine-tune. 5. NautilusTrader → replaces institutional trading platforms ($500/mo) production-grade. rust-native. fast enough to train RL trading agents same codebase for backtesting and live. zero rewrite needed. 6. QuantConnect Lean → replaces paid quant research platforms ($100/mo) professional algo trading engine. python + C# from backtest to live in one click. used by 200K+ quants worldwide. 7. jesse → replaces TradingView algo subscriptions ($25/mo) advanced crypto trading framework for serious strategy builders clean. powerful. no bloat. 8. vectorbt → replaces paid backtesting tools ($80/mo) fastest backtesting library in existence tests thousands of strategies in seconds. pandas-based. 9. FinRL → replaces custom AI trading infrastructure ($300/mo) financial reinforcement learning. train your own trading AI. from the same team behind FinGPT. 10. AlphaCartel Setup → replaces hedge fund signal services ($300/mo) this is where all 9 repos above connect into one working system claude-powered bots. live signals. no-code setup. community of traders already printing. total before: ~$3,455/month total now: $0 + alphacartel like + bookmark. you'll need this.

AI Bulls

107,531 次观看 • 4 个月前

A guy got tired of renting intelligence from the cloud, so he put a 120-billion-parameter model in his closet for $0 a month. One mini PC. The Minisforum MS-S1 MAX. 128GB of unified memory, and a trick that hands 96GB of it straight to the GPU. Here's how it breaks: That 96GB is the whole game. Enough to run GPT-OSS-120B fully local, and still keep the agent and dashboard on the same box. 120 billion parameters. 56.6 tokens a second. A 5,000-word essay in one pass at 110W and 68°C. Quiet. Cool. Local. Then Hermes Agent goes on top, pointed at localhost. No API key, because there's nothing to phone home to. It drafts, researches, runs tool calls, manages sub-agents. None of it leaves the network. The cloud version cost $10 to $20 a day, keys and customer data routed through machines he didn't own. Now it's a box in a closet. $0 a month. The chip is real. The build is brilliant. And somewhere in the first few minutes, the AI stopped being a bill he paid every morning and became a machine he owns, humming in a closet, working for free.

A guy got tired of renting intelligence from the cloud, so he put a 120-billion-parameter model in his closet for $0 a month. One mini PC. The Minisforum MS-S1 MAX. 128GB of unified memory, and a trick that hands 96GB of it straight to the GPU. Here's how it breaks: That 96GB is the whole game. Enough to run GPT-OSS-120B fully local, and still keep the agent and dashboard on the same box. 120 billion parameters. 56.6 tokens a second. A 5,000-word essay in one pass at 110W and 68°C. Quiet. Cool. Local. Then Hermes Agent goes on top, pointed at localhost. No API key, because there's nothing to phone home to. It drafts, researches, runs tool calls, manages sub-agents. None of it leaves the network. The cloud version cost $10 to $20 a day, keys and customer data routed through machines he didn't own. Now it's a box in a closet. $0 a month. The chip is real. The build is brilliant. And somewhere in the first few minutes, the AI stopped being a bill he paid every morning and became a machine he owns, humming in a closet, working for free.

shmidt

245,926 次观看 • 2 个月前

10 free github repos that can replace major SaaS with subscriptions. all free. open-sourced. some are MIT licensed. — 1️⃣ openscreen — replaces screen studio ($29/mo) - a clean macOS/windows/linux screen recorder for polished demos. - blur, cursor highlighting, annotations, export to mp4 or gif at any aspect ratio. - doesn't try to clone every feature, just nails the basics for quick walkthroughs you'd post on X. — 2️⃣ voicebox — replaces elevenlabs ($22/mo) + wisprflow ($15/mo) - local-first AI voice studio. - clone voices from 3 seconds of audio, generate speech across 7 TTS engines in 23 languages, - dictate into any text field with a global hotkey. - nothing leaves your machine. - runs on apple silicon, cuda, rocm. — 3️⃣ openshorts — replaces opus clip ($19/mo) + submagic ($16/mo) - free AI video platform. - clip generator turns long youtube videos into 9:16 shorts with auto-subtitles and face tracking (runs on free gemini + elevenlabs tiers). - also includes AI UGC video generation with actors — that part is pay-per-use via fal. ai (~$0.65-2 per video). docker self-host. — 4️⃣ freellmapi — replaces chatgpt pro + claude pro ($20/mo each) - stacks 14 free AI provider tiers (google, groq, cerebras, openrouter, github models + 9 more) behind one openai-compatible endpoint. ~800M tokens/month. - smart router with failover, sticky sessions, encrypted key storage. ships with a dashboard. — 5️⃣ playwright-mcp — replaces browserbase ($39/mo) + browser use ($25/mo) - microsoft's official MCP server that gives any AI agent full browser control. - uses accessibility trees, not screenshots — deterministic and token-efficient. - works with claude code, cursor, windsurf, codex out of the box. — 6️⃣ vibe-trading — replaces tradingview premium ($60/mo) - natural-language finance research agent. - 7 backtest engines across stocks, crypto, futures, forex. - 75 specialist skills (factor analysis, options strategy, ML strategy). - 29 multi-agent swarm presets. - 21 of 22 MCP tools work with zero API keys. — 7️⃣ CalCom — replaces calendly ($12/mo) + savvycal ($12/mo) - the open-source scheduling infrastructure. - one-on-ones, group events, round-robin, team booking, - payment collection (stripe), routing forms, workflows. - integrates with google/outlook/apple calendar, zoom, meet, teams. - self-host in 10 minutes with docker. 40k stars. — 8️⃣ whisper — replaces otter ($17/mo) - openAI's open-source speech-to-text model. - transcribe audio in 99 languages, translate to english, generate timestamps. - runs locally on cpu or gpu. - the actual model behind most "AI transcription" SaaS tools you're paying for. — 9️⃣ postiz — replaces buffer ($15/mo) - AI-powered social media scheduler. - cross-post to X, linkedin, instagram, tiktok, threads, bluesky, mastodon, youtube, pinterest. - AI captions and hashtags. - analytics dashboard. team workspaces. 31k stars and rising. — 🔟 vaultwarden — replaces 1password ($8/mo) - unofficial bitwarden-compatible server written in rust. - works with every official bitwarden client (mobile, desktop, browser). - unlimited users, unlimited vaults, full enterprise feature set. - runs on a $5 VPS or your home server. — disclaimer: open-source ≠ 1:1 replacement. you'll trade polish for ownership, hand-holding for control, and a credit card for a github version. for builders, prototypers, and indie hackers — that's the whole point. for everyone else, the paid tools still have their place. bookmark this. share with one friend bleeding subscription fees. ~m0h

10 free github repos that can replace major SaaS with subscriptions. all free. open-sourced. some are MIT licensed. — 1️⃣ openscreen — replaces screen studio ($29/mo) - a clean macOS/windows/linux screen recorder for polished demos. - blur, cursor highlighting, annotations, export to mp4 or gif at any aspect ratio. - doesn't try to clone every feature, just nails the basics for quick walkthroughs you'd post on X. — 2️⃣ voicebox — replaces elevenlabs ($22/mo) + wisprflow ($15/mo) - local-first AI voice studio. - clone voices from 3 seconds of audio, generate speech across 7 TTS engines in 23 languages, - dictate into any text field with a global hotkey. - nothing leaves your machine. - runs on apple silicon, cuda, rocm. — 3️⃣ openshorts — replaces opus clip ($19/mo) + submagic ($16/mo) - free AI video platform. - clip generator turns long youtube videos into 9:16 shorts with auto-subtitles and face tracking (runs on free gemini + elevenlabs tiers). - also includes AI UGC video generation with actors — that part is pay-per-use via fal. ai (~$0.65-2 per video). docker self-host. — 4️⃣ freellmapi — replaces chatgpt pro + claude pro ($20/mo each) - stacks 14 free AI provider tiers (google, groq, cerebras, openrouter, github models + 9 more) behind one openai-compatible endpoint. ~800M tokens/month. - smart router with failover, sticky sessions, encrypted key storage. ships with a dashboard. — 5️⃣ playwright-mcp — replaces browserbase ($39/mo) + browser use ($25/mo) - microsoft's official MCP server that gives any AI agent full browser control. - uses accessibility trees, not screenshots — deterministic and token-efficient. - works with claude code, cursor, windsurf, codex out of the box. — 6️⃣ vibe-trading — replaces tradingview premium ($60/mo) - natural-language finance research agent. - 7 backtest engines across stocks, crypto, futures, forex. - 75 specialist skills (factor analysis, options strategy, ML strategy). - 29 multi-agent swarm presets. - 21 of 22 MCP tools work with zero API keys. — 7️⃣ CalCom — replaces calendly ($12/mo) + savvycal ($12/mo) - the open-source scheduling infrastructure. - one-on-ones, group events, round-robin, team booking, - payment collection (stripe), routing forms, workflows. - integrates with google/outlook/apple calendar, zoom, meet, teams. - self-host in 10 minutes with docker. 40k stars. — 8️⃣ whisper — replaces otter ($17/mo) - openAI's open-source speech-to-text model. - transcribe audio in 99 languages, translate to english, generate timestamps. - runs locally on cpu or gpu. - the actual model behind most "AI transcription" SaaS tools you're paying for. — 9️⃣ postiz — replaces buffer ($15/mo) - AI-powered social media scheduler. - cross-post to X, linkedin, instagram, tiktok, threads, bluesky, mastodon, youtube, pinterest. - AI captions and hashtags. - analytics dashboard. team workspaces. 31k stars and rising. — 🔟 vaultwarden — replaces 1password ($8/mo) - unofficial bitwarden-compatible server written in rust. - works with every official bitwarden client (mobile, desktop, browser). - unlimited users, unlimited vaults, full enterprise feature set. - runs on a $5 VPS or your home server. — disclaimer: open-source ≠ 1:1 replacement. you'll trade polish for ownership, hand-holding for control, and a credit card for a github version. for builders, prototypers, and indie hackers — that's the whole point. for everyone else, the paid tools still have their place. bookmark this. share with one friend bleeding subscription fees. ~m0h

m0h

247,857 次观看 • 3 个月前

An engineer at a Chicago HFT shop spent six years building latency arb pipes between CME and NYSE. On May 1st his desk got cut. Severance: $40K. He deposited $3000, ran a Hermes trading agent on Polymarket, and pulled in $236,913 over the next 23 days. His agent wallet: The pit taught him one thing - find where one venue knows something another doesn't, size to the gap, exit before the spread closes. So he made 757 trades a day. Not 7. Not 75. And let the rest expire. Here's the actual stack. Claude Opus 4.7 reads spot momentum off Chainlink, scores every 5-minute BTC market by Markov persistence × Kelly edge Then surfaces the windows where Polymarket hasn't priced in what Binance and Coinbase already confirmed. Hermes Agent by NousResearch executes. A $10/mo Hetzner VPS runs it 24/7. Telegram pings on every fill. Total cost: $10/month. Setup: 30 minutes. No coding. One trade on May 14: Bitcoin Down at 9AM ET. Market said 15.8¢. He put $1,681 on Down. It hit. +$8,974. A 533% return in 47 minutes. The real edge is the nightly self-learning loop. Every midnight, Opus reads the day's trade journal and rewrites MIN_PROB and MIN_EDGE in the .env file. May 2nd the threshold was 0.87. May 12th 0.89. May 23rd 0.91. The bot tightens with the regime. His version of the bot has rewritten itself 187 times in 23 days. The desk that cut him couldn't ban him from Polymarket. The bot doesn't sit in the colo. It doesn't need a leased line. It just reads two endpoints and presses a button. Save this if you want to dig in and understand Hermes. Or just copy this guy trades using TG bot - his algorithm has been perfected 187 times:

An engineer at a Chicago HFT shop spent six years building latency arb pipes between CME and NYSE. On May 1st his desk got cut. Severance: $40K. He deposited $3000, ran a Hermes trading agent on Polymarket, and pulled in $236,913 over the next 23 days. His agent wallet: The pit taught him one thing - find where one venue knows something another doesn't, size to the gap, exit before the spread closes. So he made 757 trades a day. Not 7. Not 75. And let the rest expire. Here's the actual stack. Claude Opus 4.7 reads spot momentum off Chainlink, scores every 5-minute BTC market by Markov persistence × Kelly edge Then surfaces the windows where Polymarket hasn't priced in what Binance and Coinbase already confirmed. Hermes Agent by NousResearch executes. A $10/mo Hetzner VPS runs it 24/7. Telegram pings on every fill. Total cost: $10/month. Setup: 30 minutes. No coding. One trade on May 14: Bitcoin Down at 9AM ET. Market said 15.8¢. He put $1,681 on Down. It hit. +$8,974. A 533% return in 47 minutes. The real edge is the nightly self-learning loop. Every midnight, Opus reads the day's trade journal and rewrites MIN_PROB and MIN_EDGE in the .env file. May 2nd the threshold was 0.87. May 12th 0.89. May 23rd 0.91. The bot tightens with the regime. His version of the bot has rewritten itself 187 times in 23 days. The desk that cut him couldn't ban him from Polymarket. The bot doesn't sit in the colo. It doesn't need a leased line. It just reads two endpoints and presses a button. Save this if you want to dig in and understand Hermes. Or just copy this guy trades using TG bot - his algorithm has been perfected 187 times:

cvxv666

38,535 次观看 • 3 个月前

andrej karpathy spent two hours teaching one thing: tokens are the atom of llms. tokenization is at the heart of every llm weirdness you've ever debugged. [watch the 15-min clip below. then run the 7-day playbook] ↓ save this before everyone copies it learn how the tokenizer works. understand how your llm actually consumes input. then run the engineering roadmap that took one production agent from $4,800/mo to $620/mo in 7 days. 87% reduction. no model swap. no framework migration. no quality drop on the eval set. token cost in 2026 is an engineering discipline. every line of your system prompt is rent you pay forever. what was eating the budget: → a single forgotten cron job ate 47% of one team's bill. they turned it off on a tuesday and the bill dropped before they wrote any optimization code. → anthropic ships a 90% discount on cache reads. one config line, cache_control ephemeral, break-even after one hit. most teams cache the volatile parts of the prompt and watch their hit rate sit at 12%. → one production agent went from 14,500 tokens of context overhead per turn to 850. a 94% drop. output quality held within 2% of the uncompressed baseline. → 60% of agent calls are haiku-tier work running on opus rates. classify the task first. pick the model second. → retry loops are the silent killer. no MAX_STEPS bound, one bad search query, $14 burned in a single session. one team traced 38% of their bill to this single pattern. karpathy gave you the atom. the playbook below gives you the harness. watch the lecture. read the playbook ↓

andrej karpathy spent two hours teaching one thing: tokens are the atom of llms. tokenization is at the heart of every llm weirdness you've ever debugged. [watch the 15-min clip below. then run the 7-day playbook] ↓ save this before everyone copies it learn how the tokenizer works. understand how your llm actually consumes input. then run the engineering roadmap that took one production agent from $4,800/mo to $620/mo in 7 days. 87% reduction. no model swap. no framework migration. no quality drop on the eval set. token cost in 2026 is an engineering discipline. every line of your system prompt is rent you pay forever. what was eating the budget: → a single forgotten cron job ate 47% of one team's bill. they turned it off on a tuesday and the bill dropped before they wrote any optimization code. → anthropic ships a 90% discount on cache reads. one config line, cache_control ephemeral, break-even after one hit. most teams cache the volatile parts of the prompt and watch their hit rate sit at 12%. → one production agent went from 14,500 tokens of context overhead per turn to 850. a 94% drop. output quality held within 2% of the uncompressed baseline. → 60% of agent calls are haiku-tier work running on opus rates. classify the task first. pick the model second. → retry loops are the silent killer. no MAX_STEPS bound, one bad search query, $14 burned in a single session. one team traced 38% of their bill to this single pattern. karpathy gave you the atom. the playbook below gives you the harness. watch the lecture. read the playbook ↓

Rohit

73,374 次观看 • 3 个月前

A blackjack dealer in Macau got blacklisted from the VIP rooms last spring for counting cards. By August he couldn't get a floor job at any property in Cotai. So he deposit $500, ran a Hermes trading agent on Polymarket and pulled in $881,319 over the next 14 months. His wallet: The casinos taught him one thing - count, size your bet to your edge, walk away when the edge is gone. So he makes 5 trades a day. Not 50. Not 500. Five. And waits for the rest. Here's the actual stack. Claude Opus 4.7 reads the order book nightly, scores every threshold market by Markov persistence x Kelly edge, and surfaces the 2-3 mispriced ones. Hermes Agent by NousResearch executes. A $10/mo Hetzner VPS runs it 24/7. Telegram pings on every fill. Total cost: $10/month. Setup: 30 minutes. No coding. One trade in April: Will Bitcoin reach $90,000? Market said 1.2¢. He put $3,088 on Yes. It hit. +$123,196. A 3,988% return on a single position. The real edge is the nightly self-learning loop. Every midnight, Opus reads the day's trade journal and rewrites MIN_PROB and MIN_EDGE in the .env file. Last week the threshold was 0.87. This week 0.89. Next week maybe 0.91. His version of the bot has rewritten itself 412 times in 14 months. The Macau syndicate couldn't ban him from Polymarket. The bot doesn't sit at a table. It doesn't show a passport. It just hunts the tails. Save this post - if you want to build something of your own based on Hermes. Or just start copying algorithm that has improved itself 412 times:

A blackjack dealer in Macau got blacklisted from the VIP rooms last spring for counting cards. By August he couldn't get a floor job at any property in Cotai. So he deposit $500, ran a Hermes trading agent on Polymarket and pulled in $881,319 over the next 14 months. His wallet: The casinos taught him one thing - count, size your bet to your edge, walk away when the edge is gone. So he makes 5 trades a day. Not 50. Not 500. Five. And waits for the rest. Here's the actual stack. Claude Opus 4.7 reads the order book nightly, scores every threshold market by Markov persistence x Kelly edge, and surfaces the 2-3 mispriced ones. Hermes Agent by NousResearch executes. A $10/mo Hetzner VPS runs it 24/7. Telegram pings on every fill. Total cost: $10/month. Setup: 30 minutes. No coding. One trade in April: Will Bitcoin reach $90,000? Market said 1.2¢. He put $3,088 on Yes. It hit. +$123,196. A 3,988% return on a single position. The real edge is the nightly self-learning loop. Every midnight, Opus reads the day's trade journal and rewrites MIN_PROB and MIN_EDGE in the .env file. Last week the threshold was 0.87. This week 0.89. Next week maybe 0.91. His version of the bot has rewritten itself 412 times in 14 months. The Macau syndicate couldn't ban him from Polymarket. The bot doesn't sit at a table. It doesn't show a passport. It just hunts the tails. Save this post - if you want to build something of your own based on Hermes. Or just start copying algorithm that has improved itself 412 times:

cvxv666

208,352 次观看 • 3 个月前

A 19 year old gets on Zoom calls with business owners and shows them how they're about to throw away $600,000. He opens with one question. How much are you paying your receptionist? The dental office owner says $60,000. The kid says: over the next 10 years that's $600,000 going to someone who calls in sick, takes lunch breaks and goes home at 5. For a job a machine can do for $12,000 once. Then the second question. How many calls do you miss every day? Voicemails nobody calls back. People who hang up after three rings. New patients who try once and never try again. The owner stops. Pulls up his phone log. Counts. 8 missed calls a day. Sometimes more. Each one is a $200 patient walking past the front desk while it's empty for lunch. $1,600 a day. Almost $600,000 a year in revenue he never knew was leaving. That number sits on top of the $600,000 in salary. The owner is staring at a chart he never let himself draw before. That's when the kid says it. I can build you something for $12,000. Picks up every call. Doesn't sleep. Doesn't take vacation. Books patients while you're operating on someone else. Pays for itself in the first week. The $12,000 invoice doesn't feel like an expense. It feels like a refund. The kid closes 3 to 4 of these calls a month. $50,000 in revenue. He's 19. Behind the scenes he's not building anything from scratch. He's running Kimi K2.6 with 300 parallel sub agents, paying $0.60 per million tokens instead of $5 with Claude. The same reception agent a real developer would charge $40,000 to build, he ships in 30 minutes. 12 hour autonomous sessions. Zero human involvement. One prompt and the agent goes live. Answering calls. Booking patients. Routing emergencies. Before the owner finishes his coffee. His friends are working summer shifts at coffee shops for $14 an hour. He's making $600,000 a year by walking into a Zoom call and naming the most uncomfortable number a business owner has on his books. His secret isn't technology. He never argues with the owner about whether AI works. He hands the owner a calculator and asks the questions the owner has been avoiding for years. By minute 8 the owner has done the math himself. By minute 12 the kid quotes the price. By minute 15 the contract is signed. He told his dad about it once. His dad is an insurance salesman. 30 years selling the same product to every client for the same reason: fear of losing what they already have. His dad listened. Then said: you sell the same thing I sell. You just call it AI. The kid said: I don't even call it that. I let them call it whatever they want. His dad smiled. Walked out of the room. The kid had another call in 10 minutes.

A 19 year old gets on Zoom calls with business owners and shows them how they're about to throw away $600,000. He opens with one question. How much are you paying your receptionist? The dental office owner says $60,000. The kid says: over the next 10 years that's $600,000 going to someone who calls in sick, takes lunch breaks and goes home at 5. For a job a machine can do for $12,000 once. Then the second question. How many calls do you miss every day? Voicemails nobody calls back. People who hang up after three rings. New patients who try once and never try again. The owner stops. Pulls up his phone log. Counts. 8 missed calls a day. Sometimes more. Each one is a $200 patient walking past the front desk while it's empty for lunch. $1,600 a day. Almost $600,000 a year in revenue he never knew was leaving. That number sits on top of the $600,000 in salary. The owner is staring at a chart he never let himself draw before. That's when the kid says it. I can build you something for $12,000. Picks up every call. Doesn't sleep. Doesn't take vacation. Books patients while you're operating on someone else. Pays for itself in the first week. The $12,000 invoice doesn't feel like an expense. It feels like a refund. The kid closes 3 to 4 of these calls a month. $50,000 in revenue. He's 19. Behind the scenes he's not building anything from scratch. He's running Kimi K2.6 with 300 parallel sub agents, paying $0.60 per million tokens instead of $5 with Claude. The same reception agent a real developer would charge $40,000 to build, he ships in 30 minutes. 12 hour autonomous sessions. Zero human involvement. One prompt and the agent goes live. Answering calls. Booking patients. Routing emergencies. Before the owner finishes his coffee. His friends are working summer shifts at coffee shops for $14 an hour. He's making $600,000 a year by walking into a Zoom call and naming the most uncomfortable number a business owner has on his books. His secret isn't technology. He never argues with the owner about whether AI works. He hands the owner a calculator and asks the questions the owner has been avoiding for years. By minute 8 the owner has done the math himself. By minute 12 the kid quotes the price. By minute 15 the contract is signed. He told his dad about it once. His dad is an insurance salesman. 30 years selling the same product to every client for the same reason: fear of losing what they already have. His dad listened. Then said: you sell the same thing I sell. You just call it AI. The kid said: I don't even call it that. I let them call it whatever they want. His dad smiled. Walked out of the room. The kid had another call in 10 minutes.

Marlow

295,813 次观看 • 3 个月前

I sell AI consulting on a three-step offer ladder. -Free assessment -$1,000 paid assessment -$2,000 a month AI Concierge The top of the ladder pays me $1,250 an hour. I have 5 concierge clients and I'm capping at 6. Here's the entire model: 1) The free mini assessment is 15 minutes. Find one bottleneck, prescribe one off-the-shelf AI tool. It's a taste of the paid version. 2) Every prescription ties to one of three ROI levers. Effectiveness makes them money. Efficiency saves them time. Quality improves their product. 3) The best discovery question: "If you could wave a magic wand, what one thing in your business would you fix?" That answer picks the tool. 4) The routing rule. Common problem: off-the-shelf tool from or Judgment problem: Claude Cowork. Proprietary workflow: a Claude skill. Naming the fix is the assessment. Implementing it is the upsell. 5) 30 to 50% of free assessments convert to a paid engagement. You just ask: do you want to do this yourself, with me, or hand it off? 6) The $1,000 assessment is 99.9% margin. AI builds the whole report from the interview transcript. Only cost is the $20 a month Claude subscription. Clients save 5 to 8 hours a week from the tools alone. 7) I priced my way up for the assessment: free, then $200, then $500, then $1,000. At $1,000 people take it seriously. Below that they don't implement. 8) A voice agent named Annie now runs my 45-minute assessment interviews. Do your first 3 to 10 yourself before you automate this part. 9) AI Concierge is two 45-minute calls a month. Run AOA on every bottleneck: Audit, Optimize, Automate. Turn it into a Claude skill, schedule it in Cowork, repeat. Voxer support between calls reads as 24/7 access but averages 2 messages a month. 10) The assessment uncovers a la carte builds too. A $3,500 process optimization. A $2,000 Zapier automation that saved a client 20 hours a month. A $3,000 custom GPT that cut a broker's inbound from 400 emails per listing to 10. Two things that make this work: 1) Give value away first, then gauge appetite. The free assessment costs you 30 minutes and converts a third to half of prospects into paying clients. 2) The assessment is the sales engine. Every bottleneck it uncovers is a future build you can sell. Charge $1,000 for the diagnosis and it funds the entire pipeline. Full breakdown below. Enjoy. (also available on the Build With AI podcast wherever you get your pods)

I sell AI consulting on a three-step offer ladder. -Free assessment -$1,000 paid assessment -$2,000 a month AI Concierge The top of the ladder pays me $1,250 an hour. I have 5 concierge clients and I'm capping at 6. Here's the entire model: 1) The free mini assessment is 15 minutes. Find one bottleneck, prescribe one off-the-shelf AI tool. It's a taste of the paid version. 2) Every prescription ties to one of three ROI levers. Effectiveness makes them money. Efficiency saves them time. Quality improves their product. 3) The best discovery question: "If you could wave a magic wand, what one thing in your business would you fix?" That answer picks the tool. 4) The routing rule. Common problem: off-the-shelf tool from or Judgment problem: Claude Cowork. Proprietary workflow: a Claude skill. Naming the fix is the assessment. Implementing it is the upsell. 5) 30 to 50% of free assessments convert to a paid engagement. You just ask: do you want to do this yourself, with me, or hand it off? 6) The $1,000 assessment is 99.9% margin. AI builds the whole report from the interview transcript. Only cost is the $20 a month Claude subscription. Clients save 5 to 8 hours a week from the tools alone. 7) I priced my way up for the assessment: free, then $200, then $500, then $1,000. At $1,000 people take it seriously. Below that they don't implement. 8) A voice agent named Annie now runs my 45-minute assessment interviews. Do your first 3 to 10 yourself before you automate this part. 9) AI Concierge is two 45-minute calls a month. Run AOA on every bottleneck: Audit, Optimize, Automate. Turn it into a Claude skill, schedule it in Cowork, repeat. Voxer support between calls reads as 24/7 access but averages 2 messages a month. 10) The assessment uncovers a la carte builds too. A $3,500 process optimization. A $2,000 Zapier automation that saved a client 20 hours a month. A $3,000 custom GPT that cut a broker's inbound from 400 emails per listing to 10. Two things that make this work: 1) Give value away first, then gauge appetite. The free assessment costs you 30 minutes and converts a third to half of prospects into paying clients. 2) The assessment is the sales engine. Every bottleneck it uncovers is a future build you can sell. Charge $1,000 for the diagnosis and it funds the entire pipeline. Full breakdown below. Enjoy. (also available on the Build With AI podcast wherever you get your pods)

Corey Ganim

17,738 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 👇

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 • 1 个月前

I just built an AI voice agent for my company in 5 minutes. Pasted our website URL. It scraped everything. Built the agent. I called it. It knew our entire business and tried to book me a demo. This is the same AI behind Marriott, Caesars, and Gordon Ramsay. Watch the full build: Here’s what PolyAI’s Agent Wizard actually does: → Enter your website URL → It scrapes FAQs, product info, policies, hours — everything → Auto-builds a voice agent with your business knowledge → Choose a voice, customize the personality → Start taking calls I got early access to Agent Wizard and tested it on Gerra (my AI + robotics company). The agent accurately described our multimodal datasets, answered technical questions, and even offered to schedule a demo with real time slots. This is the same tech powering: • Marriott — hotel reservations • Caesars — casino customer service • Gordon Ramsay — restaurant bookings • FedEx, PG&E, UniCredit + 100 more 2,000+ deployments. Millions of calls daily. 24+ languages. 391% ROI. $10.3M avg savings per enterprise. Built voice-first by 3 Cambridge PhDs who helped build Siri. Backed by NVIDIA, Khosla Ventures, Zendesk. $200M+ raised. $750M+ valuation. Enterprise voice AI used to take months to deploy. This took me 5 minutes. Want the Voice Agent Deployment Playbook? Like + comment “VOICE” + repost, and I’ll DM it to you. (must be following)

I just built an AI voice agent for my company in 5 minutes. Pasted our website URL. It scraped everything. Built the agent. I called it. It knew our entire business and tried to book me a demo. This is the same AI behind Marriott, Caesars, and Gordon Ramsay. Watch the full build: Here’s what PolyAI’s Agent Wizard actually does: → Enter your website URL → It scrapes FAQs, product info, policies, hours — everything → Auto-builds a voice agent with your business knowledge → Choose a voice, customize the personality → Start taking calls I got early access to Agent Wizard and tested it on Gerra (my AI + robotics company). The agent accurately described our multimodal datasets, answered technical questions, and even offered to schedule a demo with real time slots. This is the same tech powering: • Marriott — hotel reservations • Caesars — casino customer service • Gordon Ramsay — restaurant bookings • FedEx, PG&E, UniCredit + 100 more 2,000+ deployments. Millions of calls daily. 24+ languages. 391% ROI. $10.3M avg savings per enterprise. Built voice-first by 3 Cambridge PhDs who helped build Siri. Backed by NVIDIA, Khosla Ventures, Zendesk. $200M+ raised. $750M+ valuation. Enterprise voice AI used to take months to deploy. This took me 5 minutes. Want the Voice Agent Deployment Playbook? Like + comment “VOICE” + repost, and I’ll DM it to you. (must be following)

Aryan Mahajan

28,485 次观看 • 6 个月前

a $100M founder told us his exact meta ads strategy on mic and my CPMs dropped 50% the same week. here's everything we covered: 1. bid caps are the meta ads cheat code nobody talks about. set your budget to something insane like $1M/day and your bid cap to the max CPA you'll accept. it signals to zuck you've got big balls and opens up the entire auction. my CPMs dropped 50% overnight and I went from burning cash at $1K/day to printing at sub 2 ROAS. if you're raw dogging meta with no bid cap you're lighting money on fire. 2. the $500/week creative team hack. post a job on upwork, get 50 replies, filter to 10 decent ones, pay each $50 for a paid test project. 7 will suck, 3 will be great. you just hired a full ad creative team for $150 in test costs. they care about their star rating so if they bomb you just ask for a refund. tom's pumping out 10-15 ads per week with this setup. 3. AI avatars are replacing UGC creators but most people are doing it wrong. don't just tell chatGPT "generate a girl in a park" — it looks AI as hell. screenshot a viral tiktok, feed it to chatGPT image gen, ask it to keep the same background and lighting but change facial features and clothing. the ads that convert are the ones you can't tell are AI. 4. I'm making $50K/mo from one SEO page. ranked for a top keyword in my industry, sold the #1 spot to a competitor for $14K/mo, put myself at #2, and filled slots 3-8 with affiliate offers pulling $5K/mo. plus $30K/mo from my own offer. one page. purely organic. 5. hire from pakistan. not a joke. $100/page shopify devs that are cracked. entire teams doing cashflow management. the philippines got complacent — pakistan and bangladesh are the new alpha. train them with claude, throw them in the deep end, and they'll 10x output because they have zero ego about using AI. 6. the best heuristic for teaching anyone AI: don't think, just ask. outsource your thinking but never outsource your knowledge. your VA doesn't need to be smart — they need to follow instructions and not have ego about asking a machine for answers. that combo beats a $150K/yr employee who "prefers to think with their brain." 7. AI layoffs are just starting. coinbase drama. cloudflare cut 2000 citing AI directly. the dream job kids — CS degree, S&P 500 company, mid six figures with options — they're the ones getting cut first. meanwhile landscapers, barbers, and locksmiths are thriving. tom is bullish on businesses that require hands. ep 4 of NGMI with Tony Yu and Tom Wang watch/listen ↓

a $100M founder told us his exact meta ads strategy on mic and my CPMs dropped 50% the same week. here's everything we covered: 1. bid caps are the meta ads cheat code nobody talks about. set your budget to something insane like $1M/day and your bid cap to the max CPA you'll accept. it signals to zuck you've got big balls and opens up the entire auction. my CPMs dropped 50% overnight and I went from burning cash at $1K/day to printing at sub 2 ROAS. if you're raw dogging meta with no bid cap you're lighting money on fire. 2. the $500/week creative team hack. post a job on upwork, get 50 replies, filter to 10 decent ones, pay each $50 for a paid test project. 7 will suck, 3 will be great. you just hired a full ad creative team for $150 in test costs. they care about their star rating so if they bomb you just ask for a refund. tom's pumping out 10-15 ads per week with this setup. 3. AI avatars are replacing UGC creators but most people are doing it wrong. don't just tell chatGPT "generate a girl in a park" — it looks AI as hell. screenshot a viral tiktok, feed it to chatGPT image gen, ask it to keep the same background and lighting but change facial features and clothing. the ads that convert are the ones you can't tell are AI. 4. I'm making $50K/mo from one SEO page. ranked for a top keyword in my industry, sold the #1 spot to a competitor for $14K/mo, put myself at #2, and filled slots 3-8 with affiliate offers pulling $5K/mo. plus $30K/mo from my own offer. one page. purely organic. 5. hire from pakistan. not a joke. $100/page shopify devs that are cracked. entire teams doing cashflow management. the philippines got complacent — pakistan and bangladesh are the new alpha. train them with claude, throw them in the deep end, and they'll 10x output because they have zero ego about using AI. 6. the best heuristic for teaching anyone AI: don't think, just ask. outsource your thinking but never outsource your knowledge. your VA doesn't need to be smart — they need to follow instructions and not have ego about asking a machine for answers. that combo beats a $150K/yr employee who "prefers to think with their brain." 7. AI layoffs are just starting. coinbase drama. cloudflare cut 2000 citing AI directly. the dream job kids — CS degree, S&P 500 company, mid six figures with options — they're the ones getting cut first. meanwhile landscapers, barbers, and locksmiths are thriving. tom is bullish on businesses that require hands. ep 4 of NGMI with Tony Yu and Tom Wang watch/listen ↓

Jacky Chou (buying online businesses up to $1m)

77,870 次观看 • 3 个月前

This guy sells AI employees to small businesses. He's a non-technical designer with no audience, spends $0 on ads, has no tech background. Yet he's still done 21 agent setups in 6 months, almost all from referrals. His model: install one AI agent as a digital employee, then get paid monthly to manage it and coach the owner. Setup fee plus a per-agent monthly rate. Phil came on the Build With AI pod to walk us through the whole playbook. Here's what I learned: 1. The product is the coaching, not the agent. Owners treat AI like Google. You get paid to manage it so they never have to. 2. Raise your price every yes. $500 setups became $1,000. Now he's targeting $2,000 setups plus $1,000/month per agent. 3. Give the agent a value ledger. It logs every task and sends a weekly ROI report. One client's first week: 63 hours saved, $6,300 in value. 4. Put yourself in the group chat. Telegram group with Phil, the client, and the agent. The client learns by watching him talk to it. 5. The agents handle real multi-step work. One prompt: find the invoice email, extract the PDF into Excel, save to Dropbox, send the link. Done in 10 minutes. 6. Uptime is a selling point. The best prospects tried agents themselves and quit when they broke. Phil fixes it before the client notices. 7. Free work is the referral engine. Friends in his small Georgia town told friends in Atlanta and Dallas. Now he has clients nationwide. 8. The pitch is one text. "I'm testing a managed agent service. Want to be a guinea pig? I'll charge you less." First client: $250/month. 9. Make the agent write to Excel, not its own markdown. A shared source of truth is the difference between a demo and a system. 10. Phil builds his agents on Orgo. $29/month gets your agent a computer with pre-built templates. Phil's agent handles the Orgo admin itself. His 2 key takeaways: 1. You only need to be one step ahead. If you've built an agent for yourself, you know more than the owner who never has. Charge from day one. 2. Visible ROI is the retention strategy. A weekly "you saved $6,300" report re-sells the retainer every single week. Phil is doing this at a level most technical people are not, and we had a blast going deep on it. Go follow Phil Full video below. (Also available on the Build With AI podcast wherever you get your pods)

This guy sells AI employees to small businesses. He's a non-technical designer with no audience, spends $0 on ads, has no tech background. Yet he's still done 21 agent setups in 6 months, almost all from referrals. His model: install one AI agent as a digital employee, then get paid monthly to manage it and coach the owner. Setup fee plus a per-agent monthly rate. Phil came on the Build With AI pod to walk us through the whole playbook. Here's what I learned: 1. The product is the coaching, not the agent. Owners treat AI like Google. You get paid to manage it so they never have to. 2. Raise your price every yes. $500 setups became $1,000. Now he's targeting $2,000 setups plus $1,000/month per agent. 3. Give the agent a value ledger. It logs every task and sends a weekly ROI report. One client's first week: 63 hours saved, $6,300 in value. 4. Put yourself in the group chat. Telegram group with Phil, the client, and the agent. The client learns by watching him talk to it. 5. The agents handle real multi-step work. One prompt: find the invoice email, extract the PDF into Excel, save to Dropbox, send the link. Done in 10 minutes. 6. Uptime is a selling point. The best prospects tried agents themselves and quit when they broke. Phil fixes it before the client notices. 7. Free work is the referral engine. Friends in his small Georgia town told friends in Atlanta and Dallas. Now he has clients nationwide. 8. The pitch is one text. "I'm testing a managed agent service. Want to be a guinea pig? I'll charge you less." First client: $250/month. 9. Make the agent write to Excel, not its own markdown. A shared source of truth is the difference between a demo and a system. 10. Phil builds his agents on Orgo. $29/month gets your agent a computer with pre-built templates. Phil's agent handles the Orgo admin itself. His 2 key takeaways: 1. You only need to be one step ahead. If you've built an agent for yourself, you know more than the owner who never has. Charge from day one. 2. Visible ROI is the retention strategy. A weekly "you saved $6,300" report re-sells the retainer every single week. Phil is doing this at a level most technical people are not, and we had a blast going deep on it. Go follow Phil Full video below. (Also available on the Build With AI podcast wherever you get your pods)

Corey Ganim

224,265 次观看 • 20 天前

This guy closes $5K/month managed agent clients and his AI agent does the fulfillment. His agent Dewey builds the client's agent, onboards it into their Slack, and handles the customer support after. Nick Vasilescu watches client problems get solved from his phone while he's on a walk. He came back on the Build With AI podcast to walk through the entire system. Here's what I learned: 1. Agents building agents is here. Dewey built a $5K/month client's agent on Orgo and onboarded it into their Slack himself. 2. The company behind Hermes is hiring forward deployed engineers for enterprise. The SMB and mid-market layer beneath is up for grabs. 3. His agent has its own email, phone, and card. Dewey signed up for Higgsfield and paid for it himself. 4. Customer support runs without him. Dewey sits in iMessage group chats with clients and fixes issues on the fly. 5. The 80/20 stack: a harness (Hermes or OpenClaw), a model, an Orgo computer, Agent Mail, Agent Phone, Obsidian, Honcho for memory, Composio, Latitude. 6. packages the agent card, email, and phone for about $20/month. 7. Templatize once, deploy forever. Save your ideal stack as an Orgo template and one-click clone it for every client. 8. Nobody pays $5K/month for an agent that doesn't make them money. Build the client an agent, then help them resell it to THEIR customers. B2B2B never churns. 9. Skills come from a context dump. The client dumps everything into Slack and Dewey turns the discovery call transcript into skills. 10. Sell to real businesses, not startups. SMBs doing $1M to $2M minimum pay more and ask fewer questions. Nick put Dewey's entire build into a simple blueprint. Anyone can set this up and be texting their agent in under 2 minutes. Grab the blueprint (free) here: His 2 key takeaways: 1. Build is commoditized. The valuable skill is asking the right questions and knowing which tool to point the agent at. 2. Speed to value wins. The same day a client wires money, ship them something. Agent live by day two. Nick is living further in the future than almost anyone I know and round two did not disappoint. Go follow Nick Vasilescu. Full video below. (Also available on the Build With AI podcast wherever you get your pods)

This guy closes $5K/month managed agent clients and his AI agent does the fulfillment. His agent Dewey builds the client's agent, onboards it into their Slack, and handles the customer support after. Nick Vasilescu watches client problems get solved from his phone while he's on a walk. He came back on the Build With AI podcast to walk through the entire system. Here's what I learned: 1. Agents building agents is here. Dewey built a $5K/month client's agent on Orgo and onboarded it into their Slack himself. 2. The company behind Hermes is hiring forward deployed engineers for enterprise. The SMB and mid-market layer beneath is up for grabs. 3. His agent has its own email, phone, and card. Dewey signed up for Higgsfield and paid for it himself. 4. Customer support runs without him. Dewey sits in iMessage group chats with clients and fixes issues on the fly. 5. The 80/20 stack: a harness (Hermes or OpenClaw), a model, an Orgo computer, Agent Mail, Agent Phone, Obsidian, Honcho for memory, Composio, Latitude. 6. packages the agent card, email, and phone for about $20/month. 7. Templatize once, deploy forever. Save your ideal stack as an Orgo template and one-click clone it for every client. 8. Nobody pays $5K/month for an agent that doesn't make them money. Build the client an agent, then help them resell it to THEIR customers. B2B2B never churns. 9. Skills come from a context dump. The client dumps everything into Slack and Dewey turns the discovery call transcript into skills. 10. Sell to real businesses, not startups. SMBs doing $1M to $2M minimum pay more and ask fewer questions. Nick put Dewey's entire build into a simple blueprint. Anyone can set this up and be texting their agent in under 2 minutes. Grab the blueprint (free) here: His 2 key takeaways: 1. Build is commoditized. The valuable skill is asking the right questions and knowing which tool to point the agent at. 2. Speed to value wins. The same day a client wires money, ship them something. Agent live by day two. Nick is living further in the future than almost anyone I know and round two did not disappoint. Go follow Nick Vasilescu. Full video below. (Also available on the Build With AI podcast wherever you get your pods)

Corey Ganim

32,144 次观看 • 27 天前