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The Hermes Agent Accelerated Business Hackathon presented by NVIDIA AI × Stripe × Nous Research starts now, for builders making agents that can earn, spend, and run real operations at any scale. Our NVIDIA integrations let your team run agents safely through NemoClaw, quickly on Nemotron 3 Ultra, and... intelligently with access to their extensive agent skills. The new Stripe Skills for Hermes let your agent buy what it needs, provision its own SaaS, and pay for the services it uses. We want to see what kind of business tooling you can build on top of this foundation, whether it’s a fully automated company or a framework to accelerate enterprise functions. Prizes: 1st — $10,000 cash + NVIDIA DGX Spark + $5,000 Stripe Credits 2nd — $5,000 cash + NVIDIA DGX Spark + $3,000 Stripe Credits 3rd — $2,500 cash + NVIDIA DGX Spark + $1,000 Stripe Credits To enter: 1) Tweet a 1-3 minute demo video tagging Nous Research with a short writeup 2) Drop the link in the submissions channel: 3) Fill out the submission form: Judged by Nous Research, NVIDIA, and Stripe on usefulness, viability, and presentation. Submissions due EOD Tuesday, June 30.show more

Nous Research

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Chess engines tell you the best move. But grandmasters are human, they don’t always play it. So I built "Kibitz": a human move predictor for chess broadcasts. I trained this model on my Nvidia RTX 5080. Then I made it run as a business by itself. A channel buys the overlay, Hermes onboards them, charges via Stripe test mode, runs the broadcast, narrates with NVIDIA AI Nemotron, tracks inference cost, and books its own P&L. I build. Hermes operates. This is my demo and entry for the Nous Research × NVIDIA AI × Stripe Hermes Agent Accelerated Business Hackathon.

Chess engines tell you the best move. But grandmasters are human, they don’t always play it. So I built "Kibitz": a human move predictor for chess broadcasts. I trained this model on my Nvidia RTX 5080. Then I made it run as a business by itself. A channel buys the overlay, Hermes onboards them, charges via Stripe test mode, runs the broadcast, narrates with NVIDIA AI Nemotron, tracks inference cost, and books its own P&L. I build. Hermes operates. This is my demo and entry for the Nous Research × NVIDIA AI × Stripe Hermes Agent Accelerated Business Hackathon.

Vidit Gujrathi

649,748 views • 1 month ago

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

YanXbt

37,709 views • 1 month ago

Tried out stripe projects this week! built a customer support bot with hermes from Nous Research + supermemory. bot remembers every customer across sessions. the piece worth highlighting, Stripe projects ships a hermes skill that lets the agent auto-provision supermemory itself. ask hermes to build the bot, it runs stripe projects add supermemory/memory. stripe creates the supermemory account, syncs the api key to .env, and bills it on your stripe invoice. and supermemory is one of 30+ providers. databases ( Neon Postgres, Supabase), hosting ( Vercel , Cloudflare), auth ( Clerk), search ( Exa), analytics ( PostHog). one CLI for the whole stack.

Tried out stripe projects this week! built a customer support bot with hermes from Nous Research + supermemory. bot remembers every customer across sessions. the piece worth highlighting, Stripe projects ships a hermes skill that lets the agent auto-provision supermemory itself. ask hermes to build the bot, it runs stripe projects add supermemory/memory. stripe creates the supermemory account, syncs the api key to .env, and bills it on your stripe invoice. and supermemory is one of 30+ providers. databases ( Neon Postgres, Supabase), hosting ( Vercel , Cloudflare), auth ( Clerk), search ( Exa), analytics ( PostHog). one CLI for the whole stack.

Muskan Jain

20,335 views • 1 month ago

NVIDIA JUST FEATURED HERMES AGENT AT COMPUTEX 2026. Hermes is integrating with NVIDIA OpenShell and Microsoft security primitives for secure on-device agents on Windows. Dillon Rolnick, CEO of Nous Research: "RTX Spark and NVIDIA OpenShell give Hermes users a powerful and secure environment for agents to run and work alongside you. You realize you're buying a full-fledged assistant, not a typical laptop." RTX Spark specs: → 1 petaflop AI performance → 20-core Grace CPU (built with MediaTek) → Blackwell GPU, 6,144 CUDA cores → 5th gen Tensor Cores with FP4 precision → up to 128GB LPDDR5X unified memory, 300 GB/s → NVLink C2C chip-to-chip interconnect → runs 120B parameter LLMs locally → up to 1M token context window on device → 2x inference speed via multi-token prediction RTX Spark devices ship Fall 2026 from ASUS, Dell, HP, Lenovo, Microsoft Surface, MSI. full NVIDIA press release in the comments. full Hermes setup guide in the article 👇

NVIDIA JUST FEATURED HERMES AGENT AT COMPUTEX 2026. Hermes is integrating with NVIDIA OpenShell and Microsoft security primitives for secure on-device agents on Windows. Dillon Rolnick, CEO of Nous Research: "RTX Spark and NVIDIA OpenShell give Hermes users a powerful and secure environment for agents to run and work alongside you. You realize you're buying a full-fledged assistant, not a typical laptop." RTX Spark specs: → 1 petaflop AI performance → 20-core Grace CPU (built with MediaTek) → Blackwell GPU, 6,144 CUDA cores → 5th gen Tensor Cores with FP4 precision → up to 128GB LPDDR5X unified memory, 300 GB/s → NVLink C2C chip-to-chip interconnect → runs 120B parameter LLMs locally → up to 1M token context window on device → 2x inference speed via multi-token prediction RTX Spark devices ship Fall 2026 from ASUS, Dell, HP, Lenovo, Microsoft Surface, MSI. full NVIDIA press release in the comments. full Hermes setup guide in the article 👇

YanXbt

45,924 views • 2 months ago

FLUX 3 Preview is now publicly available, only on Hermes Agent and free on all Nous Portal paid subs for the next 48 hours. Create a short film with it tagging Nous Research and Black Forest Labs - the 3 best entrants by 7PM PT on August 1st will receive: 1st place: 1 year of free FLUX 3 generation (20/day) + $2,000 Portal credits + Nous hoodie 2nd place: $1,000 credits + hoodie 3rd place: $500 credits + hoodie First 100 new signups using code L1YSMYDB get a free month of Nous Portal Plus with full video gen access. First 25 upgrades using code KEQHYO3X get $20 off. > hermes update

FLUX 3 Preview is now publicly available, only on Hermes Agent and free on all Nous Portal paid subs for the next 48 hours. Create a short film with it tagging Nous Research and Black Forest Labs - the 3 best entrants by 7PM PT on August 1st will receive: 1st place: 1 year of free FLUX 3 generation (20/day) + $2,000 Portal credits + Nous hoodie 2nd place: $1,000 credits + hoodie 3rd place: $500 credits + hoodie First 100 new signups using code L1YSMYDB get a free month of Nous Portal Plus with full video gen access. First 25 upgrades using code KEQHYO3X get $20 off. > hermes update

Nous Research

1,193,842 views • 14 days ago

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses *three* NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses three NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

kwindla

274,590 views • 7 months ago

NVIDIA JUST SHOWED HOW A $4,699 BOX CAN KILL A $200 MONTHLY AI BILL AND RUN 200B MODELS FROM YOUR DESK. 00:02 NVIDIA delivers the DGX Spark to developers, researchers, and AI teams, turning what used to require rented datacenter GPUs into a box small enough to hold in one hand. inside is 128GB of unified memory, 4TB of storage, and up to 1 PFLOP of AI compute, enough to run 70B models comfortably and push into the 200B class locally. that means no per-token API bill, no files leaving your machine, and no waiting for someone else’s server every time an agent reads documents, writes code, or runs an automation. two DGX Sparks can even be linked together for models around 400B parameters, while a single unit costs roughly $4,000 to $4,700 instead of renting multi-GPU cloud infrastructure every month. the full local AI ladder starts at $0 with the computer you already own, but NVIDIA just showed what the final rung looks like: a personal AI supercomputer sitting directly on your desk.

NVIDIA JUST SHOWED HOW A $4,699 BOX CAN KILL A $200 MONTHLY AI BILL AND RUN 200B MODELS FROM YOUR DESK. 00:02 NVIDIA delivers the DGX Spark to developers, researchers, and AI teams, turning what used to require rented datacenter GPUs into a box small enough to hold in one hand. inside is 128GB of unified memory, 4TB of storage, and up to 1 PFLOP of AI compute, enough to run 70B models comfortably and push into the 200B class locally. that means no per-token API bill, no files leaving your machine, and no waiting for someone else’s server every time an agent reads documents, writes code, or runs an automation. two DGX Sparks can even be linked together for models around 400B parameters, while a single unit costs roughly $4,000 to $4,700 instead of renting multi-GPU cloud infrastructure every month. the full local AI ladder starts at $0 with the computer you already own, but NVIDIA just showed what the final rung looks like: a personal AI supercomputer sitting directly on your desk.

Gipp 🦅

11,334 views • 25 days ago

I just added payments to my app... by typing one simple prompt. Replit's new Stripe integration is a game-changer plug in payments instantly no setup hassle. Turn any idea into a revenue-generating product in minutes: subscriptions, storefronts, paid tools all handled automatically with a sandbox for safe testing. Anyone can build and monetize now. Here's a quick step-by-step to get started ↓ 1. Head to Replit and create a new workspace it's your all-in-one spot for building. 2. Fire up Agent in your workspace this is where the magic happens. Prompt something like: "Add Stripe payments for subscriptions" or "Integrate one-time payments with Stripe." Agent sets it up automatically: handles the connection, data models and flow. No keys or dashboards needed. 3. Test in the built-in sandbox run payments safely see transactions hit your database, no real money involved. 4. Ready to launch? Connect your own Stripe account to go live seamless switch from test to revenue. That's it idea to income in minutes. My referral (get $10 credit on Replit Core):

I just added payments to my app... by typing one simple prompt. Replit's new Stripe integration is a game-changer plug in payments instantly no setup hassle. Turn any idea into a revenue-generating product in minutes: subscriptions, storefronts, paid tools all handled automatically with a sandbox for safe testing. Anyone can build and monetize now. Here's a quick step-by-step to get started ↓ 1. Head to Replit and create a new workspace it's your all-in-one spot for building. 2. Fire up Agent in your workspace this is where the magic happens. Prompt something like: "Add Stripe payments for subscriptions" or "Integrate one-time payments with Stripe." Agent sets it up automatically: handles the connection, data models and flow. No keys or dashboards needed. 3. Test in the built-in sandbox run payments safely see transactions hit your database, no real money involved. 4. Ready to launch? Connect your own Stripe account to go live seamless switch from test to revenue. That's it idea to income in minutes. My referral (get $10 credit on Replit Core):

Meer | AI Tools & News

17,593 views • 8 months ago

NVIDIA is giving away free access to 140+ AI models for a full year > most people building AI agents are paying $50-200/month for API access NVIDIA just made that argument irrelevant models you get: GLM 5.2, MiniMax M3, Nemotron-3-Ultra-550B-A55B, Kimi K2.7, and 130+ more setup: > step 1 - get your free key > go to > register -> bind phone -> copy API key > step 2 - add to Hermes agent > open Settings -> Model Provider -> Custom base_url = " api_key = "nvapi-xxxxxxxxxxxxxxxxxxxx" > step 3 - pick a model model = "z-ai/glm-5.2" model = "minimaxai/minimax-m3" model = "nvidia/nemotron-3-ultra-550b-a55b" model = "moonshot-ai/kimi-k2.7" > Hermes already has NVIDIA set as default base_url > paste the key and you're running instantly > works the same in Cursor and OpenCode > cost: $0 > limit: 40 req/min > expires: 1 year while everyone is paying for API access, this is sitting there for free with this many models in one place, it is also the easiest way to test which one actually fits your agent before you commit to a paid provider

NVIDIA is giving away free access to 140+ AI models for a full year > most people building AI agents are paying $50-200/month for API access NVIDIA just made that argument irrelevant models you get: GLM 5.2, MiniMax M3, Nemotron-3-Ultra-550B-A55B, Kimi K2.7, and 130+ more setup: > step 1 - get your free key > go to > register -> bind phone -> copy API key > step 2 - add to Hermes agent > open Settings -> Model Provider -> Custom base_url = " api_key = "nvapi-xxxxxxxxxxxxxxxxxxxx" > step 3 - pick a model model = "z-ai/glm-5.2" model = "minimaxai/minimax-m3" model = "nvidia/nemotron-3-ultra-550b-a55b" model = "moonshot-ai/kimi-k2.7" > Hermes already has NVIDIA set as default base_url > paste the key and you're running instantly > works the same in Cursor and OpenCode > cost: $0 > limit: 40 req/min > expires: 1 year while everyone is paying for API access, this is sitting there for free with this many models in one place, it is also the easiest way to test which one actually fits your agent before you commit to a paid provider

Mr. Buzzoni

120,852 views • 1 month ago

Agentic commerce is coming 🔥 — Emily GlassbergSands, Head of Information at Stripe unpacks how autonomous “buying bots” and MCP are reshaping the very mechanics of online transactions - and how Stripe’s AI models and rails are adapting for tokens, one-time virtual cards, and real-time risk management. 0:00 - Intro, latest Stripe stats 01:45 - Backgroud: The “Head of Information” role at Stripe, and Emily's journey from Harvard to Stripe 08:54 - Why Stripe Built Its Own Foundation Model 13:19 - Cracking the Code: How Stripe Handles Complex Payment Data 16:25 - Foundation Model vs. Traditional ML: What’s Winning, When? 20:09 - Inside Stripe’s Foundation Model: How It Was Built 24:35 - Transparency vs black box AI 28:38 - How to decide when to use AI and when not 34:10 - How Stripe’s AI Drives Revenue for Businesses 41:22 - Real-Time Fraud Detection: Stripe’s Secret Sauce 42:51 - The Future of Shopping: AI Agents & Agentic Commerce 46:20 - How Agentic Commerce Is Changing Stripe 49:36 - Stripe’s Vision for a World of AI-Powered Buyers 55:46 - What Is MCP? Stripe’s Take on Agent-to-Agent Protocols 59:31 - Stripe’s Data on AI Startups Monetizing 3× Faster 1:03:03 - How AI Companies Go Global — From Day One 1:07:48 - The New Rules: Billing & Pricing for AI Startups 1:10:57 - How Stripe Builds AI Literacy Across the Company 1:14:05 - Roadmap: Risk-as-a-Service, Order Intent, and Beyond

Agentic commerce is coming 🔥 — Emily GlassbergSands, Head of Information at Stripe unpacks how autonomous “buying bots” and MCP are reshaping the very mechanics of online transactions - and how Stripe’s AI models and rails are adapting for tokens, one-time virtual cards, and real-time risk management. 0:00 - Intro, latest Stripe stats 01:45 - Backgroud: The “Head of Information” role at Stripe, and Emily's journey from Harvard to Stripe 08:54 - Why Stripe Built Its Own Foundation Model 13:19 - Cracking the Code: How Stripe Handles Complex Payment Data 16:25 - Foundation Model vs. Traditional ML: What’s Winning, When? 20:09 - Inside Stripe’s Foundation Model: How It Was Built 24:35 - Transparency vs black box AI 28:38 - How to decide when to use AI and when not 34:10 - How Stripe’s AI Drives Revenue for Businesses 41:22 - Real-Time Fraud Detection: Stripe’s Secret Sauce 42:51 - The Future of Shopping: AI Agents & Agentic Commerce 46:20 - How Agentic Commerce Is Changing Stripe 49:36 - Stripe’s Vision for a World of AI-Powered Buyers 55:46 - What Is MCP? Stripe’s Take on Agent-to-Agent Protocols 59:31 - Stripe’s Data on AI Startups Monetizing 3× Faster 1:03:03 - How AI Companies Go Global — From Day One 1:07:48 - The New Rules: Billing & Pricing for AI Startups 1:10:57 - How Stripe Builds AI Literacy Across the Company 1:14:05 - Roadmap: Risk-as-a-Service, Order Intent, and Beyond

Matt Turck

17,720 views • 1 year ago

It takes 5 minutes to create a world-class web research agent that runs 24/7 and returns structured search results. 1. You can limit the sources it's allowed to use 2. You can specify the goals it needs to complete 3. You can design the format structure of the results 4. You can configure how much computing power it will use I show you how to set up a Web Search Agent in the video. A few benefits here: 1. This is great for complex research tasks 2. It's really easy to add this like a LEGO piece to a more complex workflow 3. Create an agent once, and reuse it with different inputs 4. The agent adapts to your use case as you work with it By the way, Nimble published a whole library of templates and research agents you can reuse and modify. You can start sending queries to one of these existing agents, and it will begin to adapt to Go here to try it out: Thanks to the Nimble team for partnering with me on this post.

It takes 5 minutes to create a world-class web research agent that runs 24/7 and returns structured search results. 1. You can limit the sources it's allowed to use 2. You can specify the goals it needs to complete 3. You can design the format structure of the results 4. You can configure how much computing power it will use I show you how to set up a Web Search Agent in the video. A few benefits here: 1. This is great for complex research tasks 2. It's really easy to add this like a LEGO piece to a more complex workflow 3. Create an agent once, and reuse it with different inputs 4. The agent adapts to your use case as you work with it By the way, Nimble published a whole library of templates and research agents you can reuse and modify. You can start sending queries to one of these existing agents, and it will begin to adapt to Go here to try it out: Thanks to the Nimble team for partnering with me on this post.

Santiago

20,943 views • 10 days ago

J-Cal’s framework for how to be "AI first". It’s not just a slogan 1. Every task… Ask AI first. “How can I do this better.” 2. Every repeated task… Let your agents take care of it so you can do MORE. 3. Give the agent your last 10 outputs, let it score them, run it daily, and do deep-research weekly on what the best are doing differently.

J-Cal’s framework for how to be "AI first". It’s not just a slogan 1. Every task… Ask AI first. “How can I do this better.” 2. Every repeated task… Let your agents take care of it so you can do MORE. 3. Give the agent your last 10 outputs, let it score them, run it daily, and do deep-research weekly on what the best are doing differently.

This Week in Startups

45,723 views • 3 months ago

ICYMI Stripe ships over 1000 AI-written pull requests every week with their team of "Minions." I got Steve Kaliski to show me how it works. The trick? A happy slack emoji + great dev tooling. Each Minion spins up an isolated environment that lets engineers run dozens of agents in parallel without melting your laptop (or getting them into each other's code). Great for human engs, and a better way to make your background agents successful. In this ep, we walk through: - How Stripe reviews all those PRs (without losing their minds) - Convincing your team to invest in DevX - How to kick of dev tasks from the subway Steve also uses the 🤯 Machine Payments protocol to have his autonomous agent plan a matcha-themed bday party for $5.47 BIG thank you to our wonderful sponsors: 🪄 Optimizely - Your AI agent orchestration platform for marketing and digital teams: 🚀Rippling - stop wasting time on admin tasks, build your startup faster: Full episode out now:

ICYMI Stripe ships over 1000 AI-written pull requests every week with their team of "Minions." I got Steve Kaliski to show me how it works. The trick? A happy slack emoji + great dev tooling. Each Minion spins up an isolated environment that lets engineers run dozens of agents in parallel without melting your laptop (or getting them into each other's code). Great for human engs, and a better way to make your background agents successful. In this ep, we walk through: - How Stripe reviews all those PRs (without losing their minds) - Convincing your team to invest in DevX - How to kick of dev tasks from the subway Steve also uses the 🤯 Machine Payments protocol to have his autonomous agent plan a matcha-themed bday party for $5.47 BIG thank you to our wonderful sponsors: 🪄 Optimizely - Your AI agent orchestration platform for marketing and digital teams: 🚀Rippling - stop wasting time on admin tasks, build your startup faster: Full episode out now:

claire vo 🖤

43,261 views • 4 months ago

NVIDIA just dropped free API keys for every top AI model You don't need your own GPU and you don't pay per token. GLM-5.2, MiniMax, Kimi, DeepSeek, OpenAI, all running on NVIDIA's servers, called through a normal API. Link: How to use one: 1. Create a free NVIDIA account. 2. Pick a Free Endpoint model and open its Build tab. You'll see ready-to-copy code with the base URL 3. Hit Generate API Key, copy it and paste that base URL and key into Claude Code, Cursor, or Cline. Bonus: NVIDIA also dropped 237 official skills that install into Claude Code and Codex in one command. Bookmark this.

NVIDIA just dropped free API keys for every top AI model You don't need your own GPU and you don't pay per token. GLM-5.2, MiniMax, Kimi, DeepSeek, OpenAI, all running on NVIDIA's servers, called through a normal API. Link: How to use one: 1. Create a free NVIDIA account. 2. Pick a Free Endpoint model and open its Build tab. You'll see ready-to-copy code with the base URL 3. Hit Generate API Key, copy it and paste that base URL and key into Claude Code, Cursor, or Cline. Bonus: NVIDIA also dropped 237 official skills that install into Claude Code and Codex in one command. Bookmark this.

Yarchi

62,979 views • 1 month ago

Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.

Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.

Whole Mars Catalog

45,459 views • 9 days ago

If you are a SaaS founder, read this. I think I found the best way for you to use OpenClaw. You can literally turn your agent into Paul Graham telling you how to fix your SaaS. The insights I got from it are just insane. TLDR; 1. OpenClaw analyzes your Stripe 2. Tries your onboarding like your ICP 3. Gets Paul Graham's knowledge base 4. Gives you a full report to fix your SaaS 1 - Create a "read only" API on Stripe. Give it to your OpenClaw (or whatever agent you're using right now). Ask it to analyze the full data: - Pricing - Churn - Conversion - LTV - Trial to paid rate - Discount impact - Invoice patterns Ask it to really go deep. Not the surface level. Go into every corner. Check invoices, not only graphs. Look at cohort behavior month by month. Ask it to calculate your real MRR after discounts (not the vanity number Stripe shows you). When I did this, I found so many golden nuggets in my Stripe Data. And the feedbacks from my OpenClaw were already super valuable. But this is not enough. We need the whole context, not just Stripe. 2 - Ask your Agent to try your tool Give it a free coupon for your SaaS and ask it to sign up and follow the onboarding like your ICP (if you didn't yet, define it before you send it on your landing page). It will follow step by step, take screenshots, and tell you what's good, what's not and give you solutions. Ask it to push the feedback on Notion (so you can visualize it). This already will give you enough work for the next month. 3 - Turn your OpenClaw into YC Founder Paul Graham co-founded Y Combinator and invested in Stripe, Airbnb, Dropbox, Reddit. He's written 230 essays on startups over 20 years. It's basically the bible of building companies. Send this link to your OpenClaw and ask it to find anything that could apply to your SaaS and current problems you're facing: paulgraham[.]com/articles.html Your agent will read all of them and pull out what's relevant to YOUR situation. 4 - The cherry on the cake Now ask it to combine all the data it gathered so far: - Stripe analysis - Onboarding feedback - Paul Graham knowledge base And tell it to give you a full analysis and solution for your SaaS and push it to Notion. This is where it gets insane. I got a 9 section report with specific fixes ranked by impact. Not just "improve your onboarding". This took me one afternoon. Imagine how much it would have cost me to get these kind of feedback a few years ago?

If you are a SaaS founder, read this. I think I found the best way for you to use OpenClaw. You can literally turn your agent into Paul Graham telling you how to fix your SaaS. The insights I got from it are just insane. TLDR; 1. OpenClaw analyzes your Stripe 2. Tries your onboarding like your ICP 3. Gets Paul Graham's knowledge base 4. Gives you a full report to fix your SaaS 1 - Create a "read only" API on Stripe. Give it to your OpenClaw (or whatever agent you're using right now). Ask it to analyze the full data: - Pricing - Churn - Conversion - LTV - Trial to paid rate - Discount impact - Invoice patterns Ask it to really go deep. Not the surface level. Go into every corner. Check invoices, not only graphs. Look at cohort behavior month by month. Ask it to calculate your real MRR after discounts (not the vanity number Stripe shows you). When I did this, I found so many golden nuggets in my Stripe Data. And the feedbacks from my OpenClaw were already super valuable. But this is not enough. We need the whole context, not just Stripe. 2 - Ask your Agent to try your tool Give it a free coupon for your SaaS and ask it to sign up and follow the onboarding like your ICP (if you didn't yet, define it before you send it on your landing page). It will follow step by step, take screenshots, and tell you what's good, what's not and give you solutions. Ask it to push the feedback on Notion (so you can visualize it). This already will give you enough work for the next month. 3 - Turn your OpenClaw into YC Founder Paul Graham co-founded Y Combinator and invested in Stripe, Airbnb, Dropbox, Reddit. He's written 230 essays on startups over 20 years. It's basically the bible of building companies. Send this link to your OpenClaw and ask it to find anything that could apply to your SaaS and current problems you're facing: paulgraham[.]com/articles.html Your agent will read all of them and pull out what's relevant to YOUR situation. 4 - The cherry on the cake Now ask it to combine all the data it gathered so far: - Stripe analysis - Onboarding feedback - Paul Graham knowledge base And tell it to give you a full analysis and solution for your SaaS and push it to Notion. This is where it gets insane. I got a 9 section report with specific fixes ranked by impact. Not just "improve your onboarding". This took me one afternoon. Imagine how much it would have cost me to get these kind of feedback a few years ago?

Florian Darroman

14,684 views • 4 months ago

Agents who can buy, sell, and trade on our behalf are becoming a major part of the economy. But what exactly are they doing? Stripe sees 2% of global GDP, so they’re the company with the best view of what’s going on in the earliest innings of the agent economy. That’s why I had Emily Glassberg Sands, who leads data and AI at Stripe, on Every 📧’s AI & I. We covered: - Most of us still don’t trust AI with larger online purchases. People are hesitant to let AI make expensive purchases like a vacation or a couch—just like the early days of online shopping. But a superhero outfit for a kid who needs one stat? Sure, let the agent handle it. - Fraud is moving up the stack. It used to mean stolen credit cards. Now attackers are stealing free-trial tokens and compute credits. Free-trial abuse has 4x-ed in the last six months.. - AI is on both sides of fraud. Fraudsters are using it to scale attacks, while Stripe is using it to detect them. They’re blocking 250,000 fraudulent free trials a week for one large customer. - AI companies are growing faster than any cohort Stripe has ever tracked. Top companies hit $30M ARR in 18 months—3x faster than the 2018 SaaS class. So far, it’s net new spend instead of cannibalized software budgets. If you want to understand how AI is reshaping online commerce, this one deserves your time. Timestamps Introduction: 00:00:45 New rules for an agent-driven economy: 00:01:27 Compute theft is the new payment fraud: 00:03:57 How Stripe expanded fraud detection from checkout to the full customer lifecycle: 00:10:00 Why AI companies are scaling way faster than top SaaS companies: 00:19:48 Outcome-based billing is replacing seat-based pricing: 00:23:27 Where AI spending is coming from: 00:29:57 How the developer experience changes when agents are the builders: 00:36:45 The agentic commerce spectrum, from assisted buying to autonomous purchasing: 00:41:00 Meet Link, a consumer wallet for delegated agent purchases: 00:51:06

Agents who can buy, sell, and trade on our behalf are becoming a major part of the economy. But what exactly are they doing? Stripe sees 2% of global GDP, so they’re the company with the best view of what’s going on in the earliest innings of the agent economy. That’s why I had Emily Glassberg Sands, who leads data and AI at Stripe, on Every 📧’s AI & I. We covered: - Most of us still don’t trust AI with larger online purchases. People are hesitant to let AI make expensive purchases like a vacation or a couch—just like the early days of online shopping. But a superhero outfit for a kid who needs one stat? Sure, let the agent handle it. - Fraud is moving up the stack. It used to mean stolen credit cards. Now attackers are stealing free-trial tokens and compute credits. Free-trial abuse has 4x-ed in the last six months.. - AI is on both sides of fraud. Fraudsters are using it to scale attacks, while Stripe is using it to detect them. They’re blocking 250,000 fraudulent free trials a week for one large customer. - AI companies are growing faster than any cohort Stripe has ever tracked. Top companies hit $30M ARR in 18 months—3x faster than the 2018 SaaS class. So far, it’s net new spend instead of cannibalized software budgets. If you want to understand how AI is reshaping online commerce, this one deserves your time. Timestamps Introduction: 00:00:45 New rules for an agent-driven economy: 00:01:27 Compute theft is the new payment fraud: 00:03:57 How Stripe expanded fraud detection from checkout to the full customer lifecycle: 00:10:00 Why AI companies are scaling way faster than top SaaS companies: 00:19:48 Outcome-based billing is replacing seat-based pricing: 00:23:27 Where AI spending is coming from: 00:29:57 How the developer experience changes when agents are the builders: 00:36:45 The agentic commerce spectrum, from assisted buying to autonomous purchasing: 00:41:00 Meet Link, a consumer wallet for delegated agent purchases: 00:51:06

Dan Shipper 📧

19,436 views • 3 months ago

I just watched a machine buy something on Stripe. No card. No human. No checkout. Stripe Dev shipped it yesterday — machine payments, live in preview. Commerce isn't designed for machines. --- We need to re-organize around that thought. It is designed for humans. He's right. Every fraud model, every auth flow, every billing system we've built assumes a person is on the other end. Agents break all of it. They need - Microtransactions. - 24/7 rails. - HTTP-native settlement (!!) - Finality guarantees. - No subscriptions. No accounts. - Pay at the point of consumption and move on. Cards weren't designed for this. Nothing was. --- So it needs to be completely rethought Now on Stripe Agents can now pay for API calls, compute, and data through the same PaymentIntents API millions of businesses already use. Settled in seconds. --- Stripe says billions the billions of humans will lead to trillions of agents. If even 10% of that plays out, machine payments become the fastest-growing payment category on earth. --- But here's the part that keeps me up at night — If every PSP builds its own proprietary agent payment flow, we repeat the same fragmentation mess that took human payments 15 years to sort out. We need internet native, IETF-grade, ready-for-scale standards that don't over-index on crypto. Protocols that work across any network, any rail, any provider. --- Stripe just proved the market is coming. Now we need protocols and infrastructure ready for scale. PS. Machine is a better word than agentic isn't it.

I just watched a machine buy something on Stripe. No card. No human. No checkout. Stripe Dev shipped it yesterday — machine payments, live in preview. Commerce isn't designed for machines. --- We need to re-organize around that thought. It is designed for humans. He's right. Every fraud model, every auth flow, every billing system we've built assumes a person is on the other end. Agents break all of it. They need - Microtransactions. - 24/7 rails. - HTTP-native settlement (!!) - Finality guarantees. - No subscriptions. No accounts. - Pay at the point of consumption and move on. Cards weren't designed for this. Nothing was. --- So it needs to be completely rethought Now on Stripe Agents can now pay for API calls, compute, and data through the same PaymentIntents API millions of businesses already use. Settled in seconds. --- Stripe says billions the billions of humans will lead to trillions of agents. If even 10% of that plays out, machine payments become the fastest-growing payment category on earth. --- But here's the part that keeps me up at night — If every PSP builds its own proprietary agent payment flow, we repeat the same fragmentation mess that took human payments 15 years to sort out. We need internet native, IETF-grade, ready-for-scale standards that don't over-index on crypto. Protocols that work across any network, any rail, any provider. --- Stripe just proved the market is coming. Now we need protocols and infrastructure ready for scale. PS. Machine is a better word than agentic isn't it.

Simon Taylor

32,667 views • 6 months ago