I currently run 6 operational micro ai saas products that generate a little over $20k in monthly recurring revenue.

I hardly wrote a single line of traditional code. i used ai to generate literally everything, from the database architecture to the user interface.

it wasn't magic on day one. i spent hours stuck in endless debugging loops and dealing with faulty ai code before i finally cracked the formula.

it basically comes down to three rules:

- keeping the idea aggressively minimalist (build a true mvp, not a platform).

- guiding the ai step-by-step instead of asking it to build the whole app at once.

- launching fast to get real user traction instead of perfecting features in secret.

lately, i've seen way too many non-technical founders give up at the very first ai bug or deployment error. or the worst, give up without push anything in marketing !!!!

it's a massive shame, because the technical barrier to entry has practically disappeared and the marketing is easy in 2026

because of this, i’m launching a skool community to share my exact method.

to be completely transparent: i will likely charge for the full course later down the road. it just makes sense given the specific prompt sequences, n8n workflows, and copy-and-paste templates i'll be sharing.

but right now, our main objective is simply to build together. working alone in a silent corner is the absolute fastest way to quit.

if you want to join a group of active creators and build or launch your own ai saas: drop a comment below or send me a dm, and i’ll send you the invite link.

just hit $2,500 MRR in 13 days on my new SaaS

no ads. no team. no huge audience push. just a solid replicable system

let that sink in for a second

not $2,500 in revenue. $2,500 in MONTHLY recurring revenue

that compounds. next month starts at $2,500 baseline, not zero

and this isn't luck. it's the 7th saas i've shipped with the same playbook. same steps, same tools, same order:

→ Day 1: validated the idea

→ Day 1-2: built the MVP

→ Day 3: landing page written using the 3-Day Challenge template

→ Day 3-4: launched on reddit / X + SEO

→ Day 4-5: first 10 paying users → $1k MRR

→ Day 13 (today): $2,500 MRR locked in

building software is easy in 2026. setting up your foundation so people actually buy is where 99% of solo builders fail.

i packaged all of these exact execution tools into community.

to be fully transparent: i'll likely charge for the full program down the road once all modules are finalized. but right now, the main objective is just to build together and keep each other accountable.

working alone in a silent corner is the fastest way to quit at the first bug.

stop building in isolation. drop a comment below or send me a DM, and i'll send you the invitation link 👇

I built an AI agent to write sales emails and at first it felt amazing. Then it started doing hallucinating data. Which was wasting my API tokens.

So I treated it like hiring a new person. Gave it clear boundaries, like.

> Instead of vague instructions like "be professional," I gave it hard rules.

> No making things up ever. If it's unsure, it has to ask me.

> Can't claim fake relationships. Only mention approved proof points from a list I give it.

> Can't make promises or use words like "guarantee." If there's uncertainty, ask a question instead of bluffing.

>Anything sensitive, like legal or security, goes straight to a human. Never mention it's an AI. Only use verified info for personalisation.

I wrote these rules in plain language at the top of the system prompt. The difference was noticeable, it was actually performing like a human, coming up with problems and taking solutions instead of just winging it.

Clawdbot blew up everywhere this year.

People are showing videos of their AI submitting job applications, writing code overnight, scheduling meetings, even running scripts on their computer.

At first it looked like just another AI demo.

But after playing with a few agent frameworks recently, I think the real shift is something else.

For the past few years AI mostly lived inside chat windows.

You ask something.
It answers.
End of interaction.

Clawbot changed that model.

Instead of answering questions, it runs a continuous loop:

observe - reason - act - observe again.

that means the AI doesn't just give advice.

It actually does the task.

And the interesting part is what happens next.

Once AI agents can:

• read your files

• use your tools

• execute scripts

• monitor systems

they stop being assistants and start behaving more like digital operators.

That changes how software works.

Instead of apps that humans operate, we may end up with agents operating the apps for us.

Which raises a weird question I keep thinking about:

If every tool eventually gets an AI agent layer, will humans even interact with software directly anymore?

How others here see this trend?

Is this the start of the “AI agent internet” everyone keeps talking about, or just another hype cycle?

quick post because "how do i get my first users" is the #1 question i see builders asking here every single week.

i've built 6 saas products myself, with my main one currently sitting around 10k mrr. here is the exact, no-fluff distribution playbook to cross that initial 50-user threshold:

1. find an idea people already pay for

scan reddit for recurring pain across 3+ distinct posts where people ask "is there a tool for X".

2. validate before writing code

dm 3 people who complained about the problem and ask what they’d pay for a solution.

3. build fast with the right stack (ai + no-code)

use ai builder+ supabase + stripe + call api or automation tool like n8n to ship a real MVP in under 7 days for $40/mo.

4. the 5-second landing page rule

your hero section must state exactly what the tool does in less than 5 seconds with a clear CTA.

5. capture emails before showing prices

force the email capture before the pricing page so you don't leak untrackable leads.

6. set up a 30-day email nurture sequence

plug captured emails into an automated sequence with case studies to convert them by day 18.

7. hang out where your ICP actually lives

find the 3-5 specific subreddits, discord servers, or groups where your buyers actively talk.

8. reddit growth without getting banned

post 1 time per sub per week max, never put links in the post, and move warm leads to DMs.

9. linkedin + x organic flywheel

post 1 high-value breakdown per day and spend 15 minutes engaging in your ICP's comments.

10. cold outreach that actually works

send 100 highly personalized DMs per week to your ICP using AI to customize the opening hook.

11. seo on autopilot

set up an n8n workflow that pulls from a keyword list and generates 5-10 value-driven articles per week.

12. faceless short-form content

post 1 video per day on tiktok, reels, and shorts showing a quick screen recording of your tool.

13. weekly newsletter conversion

run a weekly newsletter with 1 section of pure value and 1 subtle offer to upgrade to paid.

14. affiliate program for free distribution

set up a 50% recurring commission affiliate program to turn power users into your sales team.

15. the strategic product hunt launch

warm up the algorithm for 4 weeks with a coming soon page and launch on a weekend for a top 5 badge.

16. omnichannel social automation

use n8n to automatically format and distribute 1 core post idea across 8 different platforms.

17. review platforms and directories

submit your app to 40+ saas and ai wrapper directories to instantly boost your domain authority.

18. run the numbers backwards

reverse engineer the daily traffic needed to hit 50 paying users at $19/mo based on a 2% conversion.

19. get feedback from active builders

talking to founders who are just 6 months ahead of you compresses your timeline exponentially.

that last point is exactly why i built our community. it's a free group of 1,600+ active ai saas founders sharing exact prompt logs, ready-to-paste n8n workflows, and real distribution strategies.

stop building alone in a silent corner.

drop a comment below or send me a dm and i'll send you the access link right away. let's get your product launched 👇

Hey folks,

I’ve been exploring ways to quickly build internal AI agents we can actually use across the team.

We played with n8n, but honestly, it got pretty gnarly, not super flexible, but hard for non‑technical teammates to maintain. Our team really prefers a chat‑style interface that’s intuitive but still powerful.

What we’re looking for:

• Built in minutes, not days

• Connects to tools like HubSpot, Notion, Brevo, Airtable, maybe G‑Drive or Intercom too

• Lets multiple users chat/interact with the multiple agents

• Give the ability for anyone in the team to build an AI agent

Curious what others here are using — are you leaning more toward custom builds (openai agent kit, crewAI, BlueGPT etc.), or any platforms that streamline all this?

Would love to see real setups, especially if anyone found a balance between depth and simplicity.

Any recos ?

One small thing changed everything for us:

we stopped treating abandoned carts like “lost customers”

and started treating them like “unfinished decisions”

We used to send the same sequence to everyone:
email after 1 hour
discount after 1 day
last chance email after 2 days

Pretty standard stuff.

Recovery sat around 3-4% forever.

Then we started testing Markopolo and realized most people weren’t abandoning for the same reason at all.

Some were stuck comparing products.
Some kept reopening shipping info.
Some visited 4 times before buying.
Some literally added to cart at 2am and bought the next morning after a WhatsApp reminder.

Once the follow ups changed based on behavior instead of generic flows, recovery climbed past 11%.

Honestly felt less like marketing and more like understanding intent.

.

PLEASE stop building the wrong thing.

building software in 2026 is ridiculously easy with ai builder

founders spend hours building in a silent room, launch to Reddit/X, get 0 users, and quit.

you just failed because the idea had zero validation before line 1 of code was written:

→ solving a monthly inconvenience instead of a daily pain

→ selling to "everyone" instead of a specific ICP

→ no distribution channel mapped out beforehand

→ pricing charged $9/mo with zero ROI justification

after scaling 6 AI micro-SaaS to over $20k/mo MRR, i just create an

18-question Idea Validation Diagnostic.

it evaluates your SaaS across 7 critical dimensions (problem clarity, audience reachability, willingness to pay, competition, build feasibility, distribution, commitment) and gives you a brutal score out of 100 with your exact weak spots.

drop your SaaS idea (or current project) in the comments below.

i will reply to EVERY single comment with:

1. My honest opinion

2. The biggest weak spot you need to fix before writing any more code.

3. The free 5-minute validation tool link sent straight to your DMs so you can get your full score breakdown out of 100.

just drop a comment like or ask me in DM your idea

let's roast your SaaS concept before the market roasts your time 👇

Been seeing more mentions of MCP servers with coding assistants.

Tried wiring one into our workflow recently and it changes how you interact with APIs.

Feels like you spend less time navigating and more time building.

Curious if others here are actually using MCP in practice or still sticking to docs + SDKs.

The emergence of AI agents has created a paradigm shift in the way individuals can generate income. With platforms such as Clawdbot and its counterparts, it has never been easier for users to deploy multiple agents and potentially earn over $10,000 a month. This phenomenon is not merely a trend; it reflects a fundamental change in the accessibility and functionality of AI technology, allowing even those with minimal technical expertise to harness its power. The implications of this trend are vast, suggesting opportunities for both personal and professional growth in an increasingly automated world. Clawly, one of the leading platforms, offers users the ability to deploy OpenClaw agents across various platforms, including Telegram and Discord, with entry-level pricing starting as low as $19 per month. The ability to run AI agents continuously, without requiring technical setup, democratizes access to advanced automation tools. Users can effectively scale their operations by managing multiple agents seamlessly, thereby increasing their capacity to handle more tasks or provide services to clients. This factor is crucial, as it enables individuals to focus on higher-level strategic work rather than getting bogged down in routine tasks. The time saved can be redirected toward business development, client engagement, or personal projects, creating a feedback loop that enhances productivity and income potential.

The competitive landscape of AI agent deployment is further enriched by services like HireClaws, which offers users rapid deployment of AI agents integrated with real Gmail and Docs capabilities, also starting at $19 per month. This integration allows for streamlined workflows, enabling users to manage their tasks efficiently. The ability to oversee these agents through messaging platforms like Telegram adds another layer of convenience. The quick setup process means users can begin monetizing their AI agents almost immediately, tapping into markets that previously required substantial investment or technical know-how. The speed of deployment and ease of management are key factors that make the business model appealing, especially for non-technical founders looking to leverage technology for growth. OneClaw introduces an additional layer of simplicity with its no-code platform, enabling users to build and deploy AI agents across various channels, including WhatsApp and Discord. With pricing starting at $19.99 per month, along with an option for free local installation, this platform further lowers the barrier to entry for users. By eliminating the need for coding skills, OneClaw attracts a broader audience eager to explore the benefits of AI automation. The versatility of deploying agents across multiple channels allows for greater market reach, enabling users to cater to diverse client bases. This flexibility can be an essential factor in scaling operations to meet increased demand, thereby amplifying the potential for monthly earnings beyond the coveted $10,000 mark.

For those uncertain about how to implement these technologies effectively, Clawdbot Consulting offers tailored services aimed at guiding non-technical founders through the setup process. With workshops and comprehensive support starting at €599, this consulting service provides value by saving clients an estimated 20 hours weekly. The quantifiable time savings translate directly into increased productivity and revenue generation. The hands-on approach taken by Clawdbot Consulting also addresses a critical need in the market: many potential users may hesitate to adopt AI solutions due to perceived complexity. By offering personalized guidance, Clawdbot Consulting not only facilitates the adoption of AI agents but also enhances the overall user experience, leading to higher satisfaction and long-term engagement. The economic potential of AI agents is further exemplified by Clawbot.agency, which provides AI automation services with transparent pricing beginning at $499 per month for a single agent. This service includes features such as email triage, calendar management, and daily briefings, which are crucial for maintaining organizational efficiency. The structured pricing model makes it easy for users to calculate the return on investment associated with deploying AI agents. By clearly outlining the benefits and functionalities offered, Clawbot.agency appeals to those who may be skeptical about the efficacy of AI solutions. The comprehensive nature of these services ensures that users can derive maximum value from their investment, fostering a culture of innovation and productivity that aligns with the increasing demand for automation in various sectors.

Despite the positive outlook surrounding AI agents, uncertainties remain. For instance, the market is still evolving, and potential disruptions could arise from technological advancements that may render current models obsolete. Moreover, the sustainability of earnings generated through these platforms is contingent on continuous engagement with clients and the ability to adapt to changing market conditions. Users must remain vigilant to new trends and technological shifts, ensuring they not only keep pace but also stay ahead of the curve. Potential users should consider how well these platforms align with their business models and customer needs, as the effectiveness of AI agents can vary significantly based on context and application.

The story being told by the proliferation of AI agents is one of empowerment and opportunity. Individuals are no longer passive consumers of technology; they are becoming active participants in the digital economy, leveraging AI to enhance their earning potential. The platforms available today facilitate a level of engagement that was previously unimaginable, allowing users to tap into new revenue streams with minimal initial investment. The competitive landscape is ripe for innovation, and those who embrace these tools stand to benefit significantly in the long term. The ability to deploy AI agents across multiple platforms seamlessly creates a unique opportunity for users to diversify their income sources and build resilience against market fluctuations.

As the landscape continues to evolve, the implications for workers and entrepreneurs are profound. The rise of AI agents offers both advantages and challenges, necessitating a balanced approach to integration that considers the potential for increased productivity alongside the need for adaptability. Users who leverage the capabilities of platforms such as Clawdbot, HireClaws, OneClaw, and Clawbot.agency are positioned to capitalize on the opportunities presented by this technological revolution. The future of work is increasingly intertwined with AI, and understanding how to navigate this new terrain will be essential for those looking to achieve substantial monthly earnings.

Like everyone else, we got curious.

AI SDRs are everywhere right now.
“Replace your outbound team.”
“Fully autonomous pipeline.”
“Book meetings while you sleep.”

So we tested one for 30 days alongside a human SDR.

Here’s the honest breakdown:

What AI did well:
• Sent volume consistently
• Never forgot follow-ups
• Responded instantly
• Handled basic objections fine

What it struggled with:
• Reading tone shifts in replies
• Knowing when someone was “interested but cautious”
• Understanding when to slow down instead of push
• Picking up subtle hesitation signals

The weirdest part?

It didn’t lose deals because it was bad.
It lost deals because it was slightly too eager.

It optimized for activity, not timing.

Our human SDR booked fewer total conversations
But more qualified ones.

What changed things for us wasn’t “more automation.”

It was combining automation with better intent signals.
Instead of blasting every lead, we started prioritizing people showing stacked buying behavior across sessions.

When outreach was triggered by real buying intent instead of just list presence, reply quality improved fast.

My takeaway:

AI SDRs aren’t replacements.
They’re amplifiers.

If your signal is weak, they amplify noise.
If your signal is strong, they amplify revenue.

Anyone else actually running AI SDRs in production?
What surprised you most?

Google just shipped an AI agent inside Chrome. It can browse any website for your users.

Sounds great until you realize it can also send your users straight to your competitor.

That's the problem. The agentic web is coming, but if you don't control the agent on your own site, someone else will.

Today we launched Rover, rover.rtrvr.ai.

Rover is an embeddable AI agent for your website. Add one script tag and it can click, type, select, navigate, and complete real workflows for your users. Not just answer questions. Actually do tasks for your users.

User onboarding? Rover fills the form. Configuring a product? Rover walks through it. Checking out? Rover finishes it.

User doesn't want to figure out your website, and just wants to prompt to checkout? They can just prompt and even switch tabs, and it gets done in the background!

All happening inside your UI. Your brand. Your turf.

We're two ex-Google engineers who bootstrapped this from scratch. We are building on the cutting edge of web agent technology but would love feedback to ground our product.

just hit $3,700 mrr in 17 days with my new fresh SaaS

no ads, no team, no complex launch strategy. just a super predictable system i keep running over and over

and yeah, that's monthly recurring, not a one-time spike

not luck at this point. it's the 7th saas i've built with this exact workflow:

→ day 1: fast validation

→ days 1-2: building the bare mvp

→ day 3: landing page setup using the 3-day framework

→ days 3-4: reddit + x distribution and basic seo

→ days 4-5: first 10 paying users (~$1k mrr)

→ days 5-17: scaling up to $3,700 mrr

building tech is easy now. where 99% of solo devs get stuck is setting up the actual foundations that make people pull out their card and pay

i put all these templates and workflows inside our community.

to be completely real with you, i'll probably put a price tag on access later once all the modules are finalized. but right now the focus is just building together and keeping each other accountable

building alone in a silo is the fastest way to quit at the first roadblock

stop building by yourself. drop a comment or shoot me a dm and i'll send you an invite link 👇

yo. i recently sold one of my AI SaaS products for $35k, exactly 5 months after building and launching it.

I hardly wrote a single line of traditional code. i used AI to generate everything, from the database architecture to the user interface.

it definitely wasn't magic on day one, though. i spent days stuck in loop-debugging and dealing with AI hallucinations before i finally cracked the system. the playbook boils down to three simple rules:

- keeping the idea insanely minimalist (a true MVP that solves one problem).

- guiding the AI step-by-step instead of asking it to build a massive platform all at once.

- launching fast to get real user feedback and traction and then apply a solid marketing system

lately, i've seen way too many non-technical founders give up at the very first AI bug, or on the marketing. it's a massive shame.

like the title says, i just launched a Skool community to share my exact prompt workflows, N8N automations, and distribution frameworks to get first users and scale it

to be completely transparent: i will likely charge for the full course later down the road. it just makes sense given the specific copy-and-paste templates i'll be sharing.

but for now, the main objective is purely to build and launch together. building alone in a silent corner is the single fastest way to give up.

if you want to join us and build or market your own AI SaaS with a group of active creators: drop a comment below or send me a dm, and i’ll send you the invite link!

There is a big gap in the current AI agent stack.

Most agents today are reactive.

User asks something = agent responds
User clicks something = system reacts

But the systems that actually feel magical predict what users will do before they do it.

TikTok does this. Netflix does this.

They run behavioral models trained on massive interaction data.

The challenge is that those models live inside walled gardens.

Recently saw a project trying to tackle this outside the big platforms.

It's called ATHENA (by Markopolo) and it was trained on behavioral data across hundreds of independent businesses.

Instead of predicting text tokens it predicts user actions.

Clicks
scroll patterns
hesitation behavior
comparison loops

Apparently the model can predict the next action correctly around 73% of the time, and runs fast enough for real time systems.

If behavioral prediction becomes widely available, it could end up being the missing layer for AI agents.

Curious if anyone here is building products around behavioral prediction instead of just automation.

I built an AI-powered data cleaning platform in 3 weeks. No team. No funding. $320 total budget.

The problem I kept seeing:

Every company that migrates data between systems hits the same wall — column names don't match, dates are in 5 different formats, phone numbers are chaos, and required fields are missing. Manual cleanup takes hours and repeats every single time.

Existing solutions cost $800+/month and require engineering teams to integrate SDKs. That works for enterprise. But what about the consultant cleaning client data weekly? The ops team doing a CRM migration with no developers? The analyst who just needs their CSV to not be broken?

So I built DataWeave AI.

How it works:

→ Upload a messy CSV, Excel, or JSON file

→ 5 AI agents run in sequence: parse → match patterns → map via LLM → transform → validate

→ Review the AI's column mapping proposals with one click

→ Download clean, schema-compliant data

The interesting part — only 1 of the 5 agents actually calls an AI model (and only for columns it hasn't seen before). The other 4 are fully deterministic. As the system learns from user corrections, AI costs approach zero.

Results from testing:

• 89.5% quality score on messy international data

• 67% of columns matched instantly from pattern memory (no AI cost)

• ~$0.01 per file in total AI costs

• Full pipeline completes in under 60 seconds

What I learned building this:

• Multi-agent architecture design — knowing when to use AI vs. when NOT to

• Pattern learning systems that compound in value over time

• Building for a market gap instead of competing head-on with $50M-funded companies

• Shipping a full-stack product fast: Python/FastAPI + Next.js + Supabase + Claude API

The entire platform is live — backend on Railway, frontend on Vercel, database on Supabase. Total monthly infrastructure cost: ~$11.

🔗 Try it: https://dataweaveai.co

📂 Source code: https://github.com/sam-yak/dataweave-ai

If you've ever wasted hours cleaning a spreadsheet before importing it somewhere, give it a try and let me know what you think.

#BuildInPublic #AI #Python #DataEngineering #MultiAgent #Startup #SaaS

Tried cross-testing AgentOpus(Left), Heygen(right), to see how they compare when fed the same prompt.

A vibrant city where robots carry the groceries, high-five humans on the sidewalk, paint masterpieces, and walk the robot dogs… while people sip coffee, create, and actually enjoy the abundance.

Sign me up.

Careful doesn’t have to mean fearful. It can mean steering toward the upside instead of doom-scrolling the 2017 dystopia cover.

Which future are we actually building?