AI is the real deal that truly changes the way people interact with information around us, whether it's a database or simple plain text. Wow, just wow!
Stop telling Claude, “write the function.”
Stop telling Claude, “fix this error.”
Stop telling Claude, “make the tests pass.”
You’re treating a billion-dollar AI engineer like Stack Overflow with autocomplete.
Here are 11 insane coding prompts you can copy-paste right now:
This Chinese guy created agents in Claude Code for landing pages and single-handedly serves 47 small businesses a month, taking $400 from each.
He built a system of 7 agents on Claude Sonnet 4.6 that analyzes Google Maps in small towns, finds small businesses without websites there, and over 1 weekend takes each one to a finished mockup with video and cold message.
No assistant, no sales team, no SDR. Just him, a MacBook, an iPhone, and 1 API key.
And traditional web design agencies keep teams of 8 people on salary for the same order flow, while his expenses are only tokens and subscriptions to Lovable, Higgsfield, and Calendly.
7 agents work through 1 orchestrator on Claude Code Router. Usage is about 3 million tokens a day, the average API bill is about $480 a month.
All 7 go through MCP servers and write shared state to the file system, without shared state in memory and without race conditions, and 1 of them lives right in the iPhone and picks up positive replies from the subway, a taxi, or on walks.
And here is the system prompt he put into the orchestrator before launch:
"You are the orchestrator of a solo agency that sells ready-made websites to local businesses. You delegate read-only tasks to 6 sub-agents and own all writes.
sub-agents:
// Scout (walks through Google Maps in selected cities, looks for narrow niches: 5+ years on the map, fewer than 50 reviews, no website or a website from 2014, but high ratings)
// Diagnoser (for each lead writes a 50-word diagnosis, hero angle, tone matched to the industry, and a cold message under 70 words)
// Builder (generates a landing page mockup in Lovable through MCP only for the top 5 leads per day, with the sharpest diagnoses and the biggest gap)
// Filmer (pulls 5 screenshots of the mockup and through Higgsfield renders a 10-second vertical video 1080x1920 with a soft zoom)
// Pitcher (sends a personalized cold message through the right channel for the niche: email to roofers, SMS to tradesmen, IG DM to salons, LinkedIn to realtors)
// Checker (runs every message through evals for personalization, absence of AI markers and buzzwords before sending)
// Mobile (lives in the iPhone, handles positive replies in real time, books Zoom calls in Calendly through MCP while the owner is on the go).
You never let 2 sub-agents touch 1 lead. You stop and request approval from the human only when a deal exceeds $3,000 or the reply rate in a niche for the day drops below 12%."
Meaning the system knows what it is and within what boundaries it is allowed to act.
It knows it is supposed to find leads on its own.
It knows it is supposed to take each one to a mockup, video, and cold message without intervention.
It knows the human only steps in when a deal goes above $3,000 or the reply rate stops converging.
→ The system runs 24 hours a day
→ Scout goes through about 220 local businesses on Google Maps per day and leaves 30 new leads in the queue
→ Diagnoser outputs 30 structured diagnoses + briefs + cold messages per day
→ Builder assembles 3 to 5 finished landing pages in Lovable for the sharpest leads
→ Filmer renders a 10-second vertical video in Higgsfield for each one
→ Pitcher sends 30 personalized messages per day across 4 channels with a reply rate of about 14%
→ Checker runs every message through evals before sending
And only when a deal breaks $3,000 or the reply rate for the day drops below 12% does the orchestrator wake the owner.
And when the owner at that moment is sitting in the subway or a taxi, the Mobile agent in his iPhone picks up 1 move on its own: replies to a fresh positive reply from a dentist, books a Zoom through Calendly synced to the local time of the client, and puts the lead back in the queue. The owner only has to tap "approve" and in just 10 minutes join the call.
Here is what the system writes in his log during 1 of the Saturdays:
"scout report: 218 businesses checked in Austin, Denver, and Miami, 34 without a website, 19 with a website from 2014, 6 with an active redesign request in reviews. passing top 30 to diagnoser."
"pitcher: 30 cold messages sent across 4 channels, 14 replies, 5 positive, 3 Zoom calls booked for Sunday. passing to closer."
"builder: landing page for Westside Cosmetic Dentistry built in Lovable, 5 sections, mobile, soft beige. URL placed at /Users/dev/maps-agency/clients/westside/v1. filmer launching Higgsfield."
"eval flag: deal with The Lotus Salon at $3,400 exceeds the approved limit of $3,000. sending for manual review."
He has no server of his own and no separate backend.
Just a local file sandbox at /Users/dev/maps-agency, an MCP router, 1 API key to Claude, and the same key forwarded to Claude Code on his iPhone.
Out of everything I have seen this year, this is the cleanest one-person agency for selling websites to small businesses: $480 a month on the API, about $18,800 into the account, and between them 7 prompts, 1 file system, and 1 phone in the pocket.
There’s $1T up for grabs for agent-first startups and this window is WIDE open. Probably 10,000+ niches.
How it plays out:
1. Every SaaS company follows salesforce and goes headless within 18 months
2. a new category of "agent-native" startups emerges that treat salesforce, HubSpot, workday etc as dumb backends. the startup IS the agent. the SaaS is just the database.
3. the entire consulting/services industry around enterprise SaaS gets compressed into software. the agent replaces the implementation team.
4. outcome-based pricing becomes default. nobody pays per seat when the "seat" is an agent making 10,000 API calls a minute. you pay when revenue hits your account.
5. the winning founders are ex-operators who understand a vertical workflow cold. the code is the easy part. knowing that a property manager spends 14 hours a week on lease renewals? that's the insight worth $100M.
6. distribution becomes the moat. when anyone can wire agents to APIs, the company with the audience and the brand wins. media + agents is the new SaaS. There’s a rush to incubate live/short form shows.
7. Silicon Valley goes all influencer. Roy lee gets this. Pat Walls gets this. Sam Parr gets this.
8. the first $1B agent-native company in each vertical will look nothing like the SaaS it replaced. smaller team, higher margins, no implementation cost, no churn from bad UX because there is no UX.
the fastest path to wealth right now: find an industry that still runs on dashboards, phone calls, and spreadsheets. build the agent-native version. charge per outcome. own the workflow end-to-end.
someone reading this right now is going to build a $100M company off this exact shift. tell me about it on the @startupideaspod when you do. Im rooting for you.
Less reading, less bookmarking, more building.
the last wave rewarded people who built pretty interfaces on top of ugly data.
I think this wave rewards people who build smart agents on top of exposed APIs.
Or who just build the APIs themselves
Here we go
The more enterprises I talk to about AI agent transformation, the more it’s clear that there is going to be a new type of role in most enterprises going forward. The job is to be the agent deployer and manager in teams. Here’s the rough JD:
This person will need to figure out what are the highest leverage set of workflows on a team are (either existing or new ones) where agents can actually drive significantly more value for the team and company.
In general, it’s going to be in areas where if you threw compute (in the form of agents) at a task you could either execute it 100X faster or do it 100X more times than before. Examples would be processing orders of magnitude more leads to hand them off to reps with extra customer signal, automating a contracting review and intake process, streamlining a client onboarding process to reduce as many straps as possible, setting up knowledge bases than the whole company taps into, and so on.
This person’s job is to figure out what the future state workflow needs to look like to drive this new form of automation, and how to connect up the various existing or new systems in such a way that this can be fulfilled. The gnarly part of the work is mapping structured and unstructured data flows, figuring out the ideal workflow, getting the agent the context it needs to do the work properly, figuring out where the human interfaces with the agent and at what steps, manages evals and reviews after any major model or data change, and runs and manages the agents on an ongoing basis tracking KPIs, and so on.
The person must be good at mapping the process and understanding where the value could be unlocked and be relatively technical, and has full autonomy to connect up business systems and drive automation. This means they’re comfortable with skills, MCP, CLIs, and so on, and the company believes it’s safe for them to do so. But also great operationally and at business.
It may be an existing person repositioned, or a totally net new person in the company. There will likely need to be one or more of these people on every team, so it’s not a centralized role per se. It may rile up into IT or an AI team, or live in the function and just have checkpoints with a central function.
This would also be a fantastic job for next gen hires who are leaning into AI, and are technical, to be able to go into. And for anyone concerned about engineers in the future, this will be an obvious area for these skills as well.
🚨 BREAKING: A new role is quietly emerging and it’s about to dominate the next 5 years.
It’s not “AI engineer.”
It’s not “prompt engineer.”
It’s the Agent Operator.
And it will sit inside almost every organization.
Most people are still thinking about AI as a tool.
That framing is already outdated.
What’s actually happening is a shift from:
humans using software to humans managing autonomous agents that execute work
This is a fundamental redesign of how work gets done.
So what is an Agent Operator?
An Agent Operator is the person who:
• Designs how agents interact with real workflows
• Connects tools, data, and systems into agent pipelines
• Translates business problems into executable agent behavior
• Monitors, corrects, and improves agent performance over time
They don’t just “use AI.”
They orchestrate outcomes.
and this matter because
Every function marketing, legal, finance, biotech is becoming “agent-compatible.”
Not because companies want it.
Because they won’t have a choice.
Agents can:
• Run research loops
• Execute multi-step workflows
• Integrate across tools without APIs breaking the flow
• Operate 24/7 at near-zero marginal cost
The bottleneck is no longer capability.
It’s implementation inside real-world systems.
Required skills for AI Agent Operator role:
→ MCPs (Model Context Protocols)
Understanding how agents access tools, memory, and structured context.
→ CLIs (Command Line Interfaces)
Because serious agent workflows won’t live in GUIs—they’ll run in programmable environments.
→ Writing skills (the file kind)
Clear specs, instructions, and structured documents.
Agents run on precision, not vibes.
→ agents dot md fluency
The ability to define agent roles, constraints, memory, and tool usage in persistent formats.
→ Business acumen
Knowing what actually matters:
Where automation creates leverage, not noise.
What happens next
Enterprises will begin to redesign workflows:
Not around employees using dashboards…
But around agents executing tasks.
That means:
• SOPs → Agent playbooks
• Teams → Human + agent hybrids
• Tools → Composable agent systems
When that shift happens, companies won’t just need engineers.
They’ll need operators who understand both the system and the business.
The leverage is asymmetric
One strong Agent Operator can:
• Replace fragmented SaaS workflows
• Multiply team output without adding headcount
• Turn ideas into execution systems in days
This is not incremental productivity.
It’s operational transformation.
The jump from working with a chatbot to having an agent that actually helps automate a process requires a real amount of work.
Most companies will need to have dedicated people that are responsible for bringing automation to their teams, instead of leaving this up to every individual employee. Partly because the work is more technical than we imagine today, and partly because it’s just hard to do this as a side project.
The job spec is to map out new workflows with agents, implement new systems to deploy agents, make sure the agent has all the right (up to date) context to work with, wiring up internal systems to connect to the agents, creating evals for the agents, figuring out where the human is in the loop, managing the system when there are new upgrades, helping with the change management of the existing business process, and so on.
These jobs may come from IT or engineering, or live directly in the business function itself. They’ll be called different things depending on the company, and in some sense it’s the future of software engineering that you’ll see a huge growth of in non-tech companies.
Most companies will have to be hiring for this now or in the future, and it’s another example of the kind of new jobs that will be created in AI.