Web Club

Applying technology, systems and networking to grow a global community of like minded individuals, entrepreneurs and freelancers.

[Corey Ganim @coreyganim]

I'm making over $1,000 an hour with one AI offer.

The entire thing runs on Claude Opus 4.8.

I call it the AI Concierge. Clients pay me $1,500+ a month for two 45-minute calls where we build their AI systems live, on their screen.

I have 4 clients. I'm capping at 6.

Here's the entire model:

1) The intake form is the audit. A 10-minute JotForm (built by Claude) surfaces their time sinks and hands me 1-3 AI opportunities before call one.

2) Done-with-you, not done-for-you. They share their screen. We build skills, set up Cowork, and write context files together. They learn to drive. (Done-for-you is the upsell.)

3) Every session runs through AOA: Audit, Optimize, Automate. Fix the process first, then turn it into a skill. Automating chaos just gives you faster chaos.

4) Day one has to move the needle. We ship at least one skill or automation on call one. No first-call win, dead engagement.

5) Unlimited Voxer between calls. They send a voice message, I reply in under 12 business hours. A 24/7 partner, not a guy they see twice a month.

6) The Notion hub is the renewal mechanism. Every call logs a quantified list of what we built. "Call one: 2 skills, 3 context files, Cowork live" makes $1.5K a month a no-brainer.

7) I never fill Notion out by hand. Two Claude skills log the call, pull the action items, and draft the recap email. 30 seconds.

8) Pricing ladder: $1,000/month, then $1,500 at 2 clients, then $1,800. At $1,500 you're already at $1,000/hour. If everyone says yes then you're priced too low.

Two things that make this work:

1) Build the fulfillment infrastructure once. An afternoon. Then it runs itself outside the calls.

2) The value must be visible. People renew what they can measure.

Full breakdown below.

https://t.co/K1vLz2Wn2f

Go watch.

[Alex Olim @alexolim_]

TLDR:

- Build the product with marketing in mind - product and distribution are one

- Positioning > features; get the frame right before you build more

- The market will tell you what to build if you listen; let viral videos rewrite your product

- Rebuild your screens until users can "get it" instantly

- Nurture your creators and make them feel like part of the team

[AI Edge @aiedge_]

https://t.co/b3mb01yTQ4

[Web Club @WebClubCo]

@HedgieMarkets The biggest bubble is our lifetime. Can't wait for the pop. Billions & trillions valuations yeah right!!

[Rahul @sairahul1]

https://t.co/4U0aNsoAmo

[Matt @mattgittleson]

https://t.co/ledaiBQRaO

[starmex @starmexxx]

GREG ISENBERG JUST INTERVIEWED A SOLO FOUNDER WHO MAKES $1,000,000 A YEAR BUILDING AI AGENTS FOR REAL ESTATE AGENTS

the agents cost $5,000 to $10,000 a month per client. boring industries pay the most. real estate, insurance, manufacturing, legal. they have money and they hate using ai

he uses obsidian as his second brain because notion can't run agents that actually do work for him

his entire stack: granola, trello, loom, superhuman, asana, codex, hermes, orgo, composio, agent mail. gpt 5.5 by default. claude opus for long coding sessions

99% of the market doesn't know what an ai agent is yet. that's the moat. you don't need to be the smartest. you need to be the one who calls them first

the full breakdown is in the 45 minute video and it's worth every minute

bookmark this and read the article below

[Web Club @WebClubCo]

@gregisenberg All speculative but viable. Like any start-up ideas are nothing without reach and luck.

[GREG ISENBERG @gregisenberg]

The 36 BIGGEST startup opportunities right now

1. biggest b2c: solving loneliness. third spaces, community apps, IRL

2. biggest b2b: managed AI employees for businesses

3. biggest overlooked: elder tech. 70 million boomers who want products that make them happier & healthier

4. biggest mobile: action apps that do things, not apps you stare at

5. biggest trades: matching platforms for electricians, plumbers, HVAC. supply shrinking

6. biggest consumer social: small social. group chats as products, no feeds, no ai slop

7. biggest ecommerce: agents that recommend products you'll like, shop, buy for you

8. biggest creator: live shows and unscripted content

9. biggest edtech: AI tutors that adapt through conversation

10. biggest SaaS: pay-per-outcome pricing

11. biggest auto: AI service advisor for dealerships. answers the same 15 questions 24/7

12. biggest talent: training non-technical people to operate agents

13. biggest boredom: curated offline experiences delivered to your door. kits, games, challenges. anti-screen products

14. biggest spiritual: the need for belonging is exploding, new formats of spiritual get togethers

15. biggest wellness: longevity biomarkers you actively manage

16. biggest mobile: action apps that do things, not apps you stare at

17. biggest one to solve ai slop: digital verification that you're a real human. every platform will need this within 2 years

18. biggest infrastructure: agent permissions, security, audit trails

19. biggest media: AI native media companies. build distribution, sell products later.

20. biggest parenting: family ops automation. forms, scheduling, logistics

21. biggest accounting: bookkeeping agents that charge per transaction

22. biggest fashion: brand-owned resale. every brand wants to control their secondary market

23.biggest hobbies: adult learning for joy. pottery, woodworking, drawing.

24. biggest skincare: at-home diagnostics. scan, get a protocol, track progress

25. biggest agriculture: precision farming tools for small farms. enterprise version exists, family farm doesn't

26. biggest pest control: subscription pest prevention instead of reactive treatment. the model flip that lawn care already made

27. biggest regulated: on-device AI. healthcare, legal, finance open up when data stays local

28. biggest gaming: AI characters with real memory and relationships

29. biggest dating: agent-mediated matchmaking

30. biggest fitness: adaptive coaching that rewrites your program daily

31. biggest travel: autonomous trip planning and rebooking

32. biggest food: personalized nutrition based on blood work and gut biome

33. biggest pet: health monitoring. $140B industry, almost no tech

34. biggest defense: AI-native security and compliance tools

35. biggest robotics: physical AI. $30 brains on existing hardware

36. biggest nostalgia: products that feel analog. vinyl, paper, handmade. counter-positioning against AI everything

[Anatoli Kopadze @AnatoliKopadze]

https://t.co/xHKvUDKhG2

[Chamath Palihapitiya @chamath]

A framework to understand how value accrues across the AI stack.

This is a blueprint for understanding what builds AI into its pragmatic parts: what each layer is, where it ends, and where value is accrued. So here’s how you can think about it:

1. Layer 1 - Infrastructure

Before any AI model trains or any robot moves, an industrial foundation must exist. Land, energy grids, cooling systems, critical minerals, and fabrication facilities. Infrastructure is the constraint that all the other layers depend on.

2. Layer 2 - Chips

Transistors that are etched onto silicon wafers using extreme ultraviolet light. This is what allows both physical and digital AI to take an input, process it, and return a predictive output. The more transistors that fit on a chip, the more computation it can perform.

3. Layer 3 - Data

Both digital and physical models train on data. Digital models train on text, code, and images; physical models train on gravity, friction, depth, and sensor streams. The more accurate the data, the more accurate the output.

4. Layer 4 - Models

A model is a system that learns from examples. Feed it enough examples of inputs paired with correct outputs, and it adjusts its internal structure until it can predict correct outputs on inputs it has never seen before.

LLMs represent a specific class trained on text. They learn by processing billions of examples of human language, developing the ability to write, reason, summarize, and generate code.

5. Layer 5 - Execution

This is what lets models take actions on behalf of users. The execution layer lets models pursue objectives through sequential action: observing the environment, reasoning about the next step, acting, and looping until the goal is reached.

6. Layer 6 - Application

All of the AI Stack’s revenue originates at the application layer, then goes to the layers below.

Every dollar paid for AI is paid for an outcome, a task completed, and an answer delivered. Nobody wants H100s for their own sake. They want H100s because someone, somewhere, wants to run an application.

These are the different layers that make up the entire ecosystem of AI.

We did a full study on the AI stack. If you want to read about it, head over to my Substack (https://t.co/uaxeJk63aO)

[GREG ISENBERG @gregisenberg]

I'm giving away a FULL course on how to build a managed AI agent business solo using Hermes Agent, Orgo, Obsidian, Codex, Claude Code etc.

Here's everything (47 minutes):

1. The offer: unlimited agents, unlimited usage, all infrastructure and security included. The customer gets a digital employee. They never think about tokens or models. You handle everything.

2. Don't niche down too fast. Try marketing agencies, law firms, insurance, manufacturing, real estate. See where the market pulls you. Then go vertical. Diverge first, converge later.

3. Every executive has the same problems regardless of industry. Too many emails, too many meetings, too many follow-ups, too many open loops. Solve those first. Then layer in vertical-specific skills.

4. The stack: Hermes Agent for the agent harness. Codex or Claude Code desktop to build and configure. Orgo for cloud computers so every agent lives in its own sandbox. Composio for one-click authentication across thousands of apps. Agent Mail to give every agent its own email. Obsidian for the knowledge base.

5. Use agents to build agents. Don't stress about setup. Use Claude Code or Codex to install and configure Hermes inside a VM. Use Perplexity MCP, Context7, and Exa for up-to-date docs. Your agent sets up your customer's agents.

6. GPT 5.5 is the best model right now. Efficient with tool calls. Doesn't eat tokens like Opus 4.7. For cheaper tasks, GLM 5.1 from ZAI is the best open source option.

7. Set up watchdogs for gateway crashes so they auto-restore. Have agents email you when cron jobs break or skills fail. Your customer should never have to tell you something is broken.

8. Get customers through content. If someone jumps on a call and already knows who you are and what you sell, that's the position you want. Content is the most leveraged thing you can do in 2026.

9. Keep scope tight. One to two requests at a time, delivered in under 48 hours. Use Trello for customer-facing project management. Send Loom updates at random hours to show you're always working on their agents.

10. If you can set up Claude Code, Hermes, or OpenClaw, you have a skill that 99% of business owners don't have and would pay $5k/month for. You're probably not giving yourself enough credit.

shoutout to @nickvasiles from @orgodotai for coming back on @startupideaspod and sharing the full playbook. tools, stack, fulfillment, everything.

this type of episode isn't shared anywhere on the internet. this is the alpha people keep for themselves.

i will keep sharing if you keep watching.

you could watch netflix or you can watch this (link below)

https://t.co/Z4PM5I7d0S

watch

[Will @athcanft]

https://t.co/HF8u46eRWR

[Cursor @cursor_ai]

We’re introducing the Cursor SDK so you can build agents with the same runtime, harness, and models that power Cursor.

Run agents from CI/CD pipelines, create automations for end-to-end workflows, or embed agents directly inside your products.

[a16z @a16z]

AI is going mobile incredibly quickly

Charts of the Week: https://t.co/jdqNhO0y8Y

[Okara @askOkara]

how to get the first 100 users for your startup

1. launch everywhere. product hunt, devhunt, betalist, peerlist, indie hackers, etc. if a platform lets you list your product, list it

2. leverage your network and cold outreach. email friends, former colleagues, and people in your extended network. reach out directly to potential users on linkedin or via cold email with a short, personalized one-sentence pitch

3. find reddit posts where people complain about problems you've solved. use our ai cmo to find relevant threads and generate replies (link in comments)

4. dm 10 to 15 ugc creators on tiktok or instagram. ask them to create content about your product, ideally from a fresh account. pay them a fixed fee ($15–$30 per video) plus performance incentives ($1k for 1 million views, etc).

5. figure out where your icp hangs out. are they active in specific groups/communities? do they read certain newsletters? listen to podcasts? follow specific creators? reach out to those people for paid promos.

6. stalk your competitors. find their top backlinks and submit your product to the same places. do it manually or use a tool, but just get it done.

7. show, don’t tell. most people won’t get it if you just explain the product. record a short demo using screen studio and post it on x and linkedin

8. every week there's a new trend going on x. hop on trends and plug your product

9. build in public. share early wins, bugs, things you’re learning. one post/ day won't move a needle. aim for 3-4x/ day if you can. okara ai cmo helps you stay consistent with content

10. find tweets going viral in your niche. reply with value and mention your product if it’s relevant

[AI Edge @aiedge_]

https://t.co/uugJmcg347

[deno @denohawari]

https://t.co/41YiUeH9Gp

[Web Club @WebClubCo]

@tibo_maker SEO

[ᴅᴀɴɪᴇʟ ᴍɪᴇssʟᴇʀ 🛡️ @DanielMiessler]

https://t.co/pZqPX8mKvV