I run 4 SaaS products with 4 people and 16 AI agents.
No HR department. No middle management. No "team leads" managing other team leads. Just 3 humans who are really good at what they do, and a bunch of agents handling everything else.
Engineering has 2 humans and 6 agents. Support has 1 human and 3 agents. Content, growth, and ops are fully run by agents.
Two years ago this setup would have required 15-20 people and $80K+/month in payroll.
Now it costs me a fraction of that, moves 10x faster, and I actually know what is happening across all of it because the agents report to me directly. No game of telephone through 3 layers of management.
The founders who are still hiring for every function in 2026 are building companies that look impressive on LinkedIn but move like molasses.
I shipped a feature to production last week while enjoying the Swiss nature on a walk with my wife and kids. No laptop. No terminal. Just two voice messages on my phone.
Here is how it works:
I describe what I want to my AI agent in a voice note. Casually, like I am talking to a coworker. "Hey, users are complaining that the dashboard feels sluggish when they have a lot of data. Can you look into it and optimize the heavy queries? Maybe add some caching too."
The agent figures out the rest. Finds the bottleneck, writes the fix, runs it through the test suite, and pings me when it is ready.
I quickly QA from my phone to see if it works, and if it does I just say "ship it."
CI/CD takes it from there. Tests, build, deploy. Done.
Two voice interactions. Everything in between is automated.
Total time from idea to production: usually under an hour.
I think most founders massively overcomplicate their shipping process. Sprint planning, ticket grooming, standups, code review meetings, release windows...
All of that made sense when you had a team of 10 writing code together. But when it is you + AI, all that ceremony just slows you down.
My entire workflow now is: 1. Talk to the agent 2. QA the result 3. Say "ship it"
No Jira. No PRs sitting for 3 days. No "we will ship it next sprint."
The bottleneck is no longer engineering speed. It is how fast you can think of the next thing to build.
If your deployment process still requires you to sit at a desk, open a terminal, and type commands - you are leaving speed on the table.
It's 2026, and Micro SaaS is still a massive opportunity!
Yes, use AI. But use it the right way.
PRACTICAL > HYPE ✅ One problem, one promise, one price ✅ Validate with real users before code ✅ Automate the boring, review the critical ✅ Self-serve onboarding + clear docs
AVOID THE NOISE ❌ Feature soup to chase "everyone" ❌ Vanity metrics over paying customers ❌ Hype hacks without distribution ❌ "AI slop" that replaces human judgment
- Distribution BEFORE development. - Design UI that sells the value. - Protect your calendar like equity.
SYSTEMS THAT BUY FREEDOM - Weekly rotation: build → ops → growth → slack - Automations: n8n for chores, Aidbase for support - Pricing: monthly, yearly, pay-once for cashflow
In my 5+ years of building SaaS, the playbook has completely flipped.
Back in 2020, the default was: → Raise money. → Hire 15 people. → Burn through cash chasing growth. → Pray your unit economics work out "eventually."
I watched so many founders go down that path. Bloated teams, endless meetings, and a product that moves slower every quarter because everyone is busy "managing" instead of building.
The 2026 version looks nothing like that.
I run multiple SaaS products with a tiny team. Most of the heavy lifting is handled by AI agents and automation. Revenue from day 1 (no freemium). And growth comes from content, not cold outreach and demo pipelines.
Here is what actually changed:
1. You do not need to raise money to build great software. Revenue-funded from day 1 means you own your margins and make decisions in minutes, not board meetings.
2. AI replaced the need for big teams. I have agents handling support, monitoring, content distribution, and internal ops. A solo founder or a team of 2-3 can now do what used to require 15 people.
3. Content replaced cold outreach. YouTube, email lists, and building in public generates more trust (and more qualified leads) than any SDR team ever could. Organic authority compounds. Sales calls do not.
The old playbook optimized for looking big. The new one optimizes for staying lean and moving fast.
If you are still hiring for roles that AI can handle and burning cash on growth hacks from 2019, you are playing a different game.
Running a SaaS business has so many moving parts, cause you're not in direct day-to-day contact with all of your users (e.g. like you would in an agency or as a consultant). So it's really easy to lose the bigger picture.
Now the agent tells me that churn went up last week - and it already traced it to a 1-hour outage with Meta that got users super annoyed.
Or it flags that support volume spiked 30% and connects it to a broken API from our last deployment.
I've never had a better "helicopter" view of my business.
Revenue is up. Customer satisfaction is up. And I spend way less time figuring things out and instead I focus on simply taking action.
If you're still wasting money with PostHog and Sentry and playing detective all day, you're still living 2022.
Here's another quick way to make your OpenClaw agents forget less.
Internally, OpenClaw keeps a few files for itself: - MEMORY .md (for long-term memory) - /memory/[date] .md (for day-to-day memory)
And finally, the sessions files (jsonl).
How it's SUPPOSED to work: The sessions files are used directly with ongoing chats. So OpenClaw will have context from the last few messages you did. The issue is, these sessions files get truncated as they grow, so it won't remember what you talked about a few days ago.
This is where the MEMORY .md and the /memory/[date] .md files become useful. OpenClaw will move important information to these files to store it as "long-term" memory.
The biggest issue I've found: OpenClaw does a TERRIBLE job at deciding what to store in long-term memory. So it always ends up forgetting important things unless I explicitly tell it to store them in memory.
Here's how I fixed it: I asked my agent to write a script that runs on an hourly CRON job. The script has one single function:
Go through the messy jsonl sessions files, clean them up (remove tools calls and other metadata), and write them to clean md files in a folder called "previous_conversations".
Then add a section to your AGENTS .md file instructing the agent to search through previous conversations when necessary.
⭐ Pro tip: If you have a RAG installed (highly recommended), have the CRON store the conversation chunks to the database as well, this will make it even faster and easier for the agent to search through.
After doing this, I ditched the standard /memory/[date] .md format altogether. Now I just have this + MEMORY .md for bigger, more highlighted information.
Simon Høiberg
One thing I think most people get wrong with agents:
They keep trying to make the agent smarter.
I mostly try to make everything around it more predictable.
That is why OpenClaw works so well for me.
OpenClaw sits in the middle, but I have a bunch of very boring infrastructure wrapped around it:
n8n workflows.
Webhooks.
Cron jobs.
Reusable scripts.
Markdown docs.
Runbooks.
RAG + vector DB.
Nothing fancy.
Just the stuff that turns random one-off requests into repeatable systems.
If I ask the agent to do something twice, that is usually a sign it should become a workflow.
If I explain a process once, it should probably be documented.
If something needs to happen every Monday, I should not be remembering it.
That is where the leverage comes from.
The agent uses fewer tokens because it does not need the same context explained 50 times.
The output gets more predictable because the paths are already defined.
And the whole setup gets better every time we turn a messy repeated task into a script, doc, or workflow.
A lot of people are still using AI like a browser tab.
I think the real unlock is when the agent becomes the interface to the systems you already run.
2 days ago | [YT] | 129
View 2 replies
Simon Høiberg
I run 4 SaaS products with 4 people and 16 AI agents.
No HR department. No middle management. No "team leads" managing other team leads. Just 3 humans who are really good at what they do, and a bunch of agents handling everything else.
Engineering has 2 humans and 6 agents. Support has 1 human and 3 agents. Content, growth, and ops are fully run by agents.
Two years ago this setup would have required 15-20 people and $80K+/month in payroll.
Now it costs me a fraction of that, moves 10x faster, and I actually know what is happening across all of it because the agents report to me directly. No game of telephone through 3 layers of management.
The founders who are still hiring for every function in 2026 are building companies that look impressive on LinkedIn but move like molasses.
2 weeks ago | [YT] | 213
View 4 replies
Simon Høiberg
My deployment pipeline has no keyboard.
I shipped a feature to production last week while enjoying the Swiss nature on a walk with my wife and kids. No laptop. No terminal. Just two voice messages on my phone.
Here is how it works:
I describe what I want to my AI agent in a voice note. Casually, like I am talking to a coworker. "Hey, users are complaining that the dashboard feels sluggish when they have a lot of data. Can you look into it and optimize the heavy queries? Maybe add some caching too."
The agent figures out the rest. Finds the bottleneck, writes the fix, runs it through the test suite, and pings me when it is ready.
I quickly QA from my phone to see if it works, and if it does I just say "ship it."
CI/CD takes it from there. Tests, build, deploy. Done.
Two voice interactions. Everything in between is automated.
Total time from idea to production: usually under an hour.
I think most founders massively overcomplicate their shipping process. Sprint planning, ticket grooming, standups, code review meetings, release windows...
All of that made sense when you had a team of 10 writing code together. But when it is you + AI, all that ceremony just slows you down.
My entire workflow now is:
1. Talk to the agent
2. QA the result
3. Say "ship it"
No Jira. No PRs sitting for 3 days. No "we will ship it next sprint."
The bottleneck is no longer engineering speed. It is how fast you can think of the next thing to build.
If your deployment process still requires you to sit at a desk, open a terminal, and type commands - you are leaving speed on the table.
2 weeks ago | [YT] | 144
View 5 replies
Simon Høiberg
It's 2026, and Micro SaaS is still a massive opportunity!
Yes, use AI.
But use it the right way.
PRACTICAL > HYPE
✅ One problem, one promise, one price
✅ Validate with real users before code
✅ Automate the boring, review the critical
✅ Self-serve onboarding + clear docs
AVOID THE NOISE
❌ Feature soup to chase "everyone"
❌ Vanity metrics over paying customers
❌ Hype hacks without distribution
❌ "AI slop" that replaces human judgment
- Distribution BEFORE development.
- Design UI that sells the value.
- Protect your calendar like equity.
3-STEP MICRO SAAS LOOP
1️⃣ PROVE: Landing + email waitlist + LTD smoke test
2️⃣ BUILD: Tiny MVP with AI-assisted dev, human QA
3️⃣ SCALE: Onboarding polish, trust signals, content engine
SYSTEMS THAT BUY FREEDOM
- Weekly rotation: build → ops → growth → slack
- Automations: n8n for chores, Aidbase for support
- Pricing: monthly, yearly, pay-once for cashflow
3 weeks ago | [YT] | 176
View 0 replies
Simon Høiberg
In my 5+ years of building SaaS, the playbook has completely flipped.
Back in 2020, the default was:
→ Raise money.
→ Hire 15 people.
→ Burn through cash chasing growth.
→ Pray your unit economics work out "eventually."
I watched so many founders go down that path. Bloated teams, endless meetings, and a product that moves slower every quarter because everyone is busy "managing" instead of building.
The 2026 version looks nothing like that.
I run multiple SaaS products with a tiny team. Most of the heavy lifting is handled by AI agents and automation. Revenue from day 1 (no freemium). And growth comes from content, not cold outreach and demo pipelines.
Here is what actually changed:
1. You do not need to raise money to build great software.
Revenue-funded from day 1 means you own your margins and make decisions in minutes, not board meetings.
2. AI replaced the need for big teams.
I have agents handling support, monitoring, content distribution, and internal ops. A solo founder or a team of 2-3 can now do what used to require 15 people.
3. Content replaced cold outreach.
YouTube, email lists, and building in public generates more trust (and more qualified leads) than any SDR team ever could. Organic authority compounds. Sales calls do not.
The old playbook optimized for looking big.
The new one optimizes for staying lean and moving fast.
If you are still hiring for roles that AI can handle and burning cash on growth hacks from 2019, you are playing a different game.
3 weeks ago | [YT] | 168
View 2 replies
Simon Høiberg
AI models work a lot like evolution right now.
Some survive everything. Claude has been my go-to for coding for over a year - nothing has seriously challenged it.
Others cycle through fast. A new image model drops, dominates for 3 weeks, then gets replaced by the next one.
Survival of the fittest, but on a weekly schedule.
These are the ones currently running daily in my workflows 👇
3 weeks ago | [YT] | 130
View 0 replies
Simon Høiberg
In my 4 years of running SaaS, I've wasted so much time on dashboards...
Sentry for errors.
Stripe for churn.
PostHog for analytics.
Grafana for infra.
Bunch of dashboards. Bunch of logins. Zero connection between them.
The data is all there. But no one is putting the pieces together.
So I built an agent that does exactly that.
→ Inputs
Stripe webhooks. Uptime pings. n8n workflows. Server logs. Aidbase API.
→ Outputs
Telegram alerts. Daily digest. GitHub issues. Downtime escalation. Weekly report.
This has been a huge unlock for me!
Running a SaaS business has so many moving parts, cause you're not in direct day-to-day contact with all of your users (e.g. like you would in an agency or as a consultant). So it's really easy to lose the bigger picture.
Now the agent tells me that churn went up last week - and it already traced it to a 1-hour outage with Meta that got users super annoyed.
Or it flags that support volume spiked 30% and connects it to a broken API from our last deployment.
I've never had a better "helicopter" view of my business.
Revenue is up. Customer satisfaction is up. And I spend way less time figuring things out and instead I focus on simply taking action.
If you're still wasting money with PostHog and Sentry and playing detective all day, you're still living 2022.
4 weeks ago | [YT] | 184
View 5 replies
Simon Høiberg
Everyone loves to say their OpenClaw "works while they sleep".
But traditional automation did that too. It's not really that groundbreaking.
I think the real shift is *how* the work happens.
Old-school automation is a straight line:
Input → steps → output.
If-this-then-that. Left to right.
Agentic automation is different.
It's much closer to a real organization.
Think of OpenClaw as founder ops HQ:
→ Communication
Requests from Telegram, Slack, voice notes, inbox, support.
→ Knowledge
SOPs, notes, past conversations, databases.
→ Execution
Browser actions, internal tools, admin, agents.
→ Automation
Workflows, schedules, triggers, handoffs.
→ Distribution
Email, socials, alerts, content.
All connected to one orchestrator in the middle.
OpenClaw listens, decides who should act, what context to use, and what needs to happen next.
This is what makes it feel more dynamic, like a real "organism" and not just a mechanical system.
In these last 5 years, this is truly the closest we've gotten to a real "team" of AI agents.
1 month ago | [YT] | 206
View 0 replies
Simon Høiberg
You can absolutely run a 7-figure SaaS solo.
But not with "more hustle". You do it with the right stack.
Here's mine 👇
→ Infra (self-hosted)
Postgres + pgvector.
Docker + NodeJS.
Kubernetes + bare metal on Hetzner.
→ Build (AI-assisted dev)
Codex / GitHub for development.
OpenClaw + n8n for reliable agents.
CI/CD + tests + manual QA.
→ Marketing (always-on distribution)
Email list as the core asset.
X + YouTube for attention.
Meta ads to scale what already works.
That's the whole game: lean infra, AI-assisted build, and a marketing engine that runs even when you don't.
1 month ago | [YT] | 167
View 5 replies
Simon Høiberg
Here's another quick way to make your OpenClaw agents forget less.
Internally, OpenClaw keeps a few files for itself:
- MEMORY .md (for long-term memory)
- /memory/[date] .md (for day-to-day memory)
And finally, the sessions files (jsonl).
How it's SUPPOSED to work:
The sessions files are used directly with ongoing chats. So OpenClaw will have context from the last few messages you did. The issue is, these sessions files get truncated as they grow, so it won't remember what you talked about a few days ago.
This is where the MEMORY .md and the /memory/[date] .md files become useful. OpenClaw will move important information to these files to store it as "long-term" memory.
The biggest issue I've found:
OpenClaw does a TERRIBLE job at deciding what to store in long-term memory. So it always ends up forgetting important things unless I explicitly tell it to store them in memory.
Here's how I fixed it:
I asked my agent to write a script that runs on an hourly CRON job. The script has one single function:
Go through the messy jsonl sessions files, clean them up (remove tools calls and other metadata), and write them to clean md files in a folder called "previous_conversations".
Then add a section to your AGENTS .md file instructing the agent to search through previous conversations when necessary.
⭐ Pro tip: If you have a RAG installed (highly recommended), have the CRON store the conversation chunks to the database as well, this will make it even faster and easier for the agent to search through.
After doing this, I ditched the standard /memory/[date] .md format altogether. Now I just have this + MEMORY .md for bigger, more highlighted information.
Try it!
1 month ago | [YT] | 159
View 2 replies
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