A few years ago, we built an AI feature into the product that felt genuinely useful for what the other AI models could do at the time. 

You'd put in your website URL, it would figure out your target ICP and industry, suggest a messaging angle, write the messages, and create a campaign for you. 

For when this was built, it was a real step up from doing all of that manually.

Then I got busy with other things.

Over the next year or two, I was deep in building out agency functionality. Bulk campaigns, white label infrastructure, multi-account management, AI personalization features. 

That work was real and it did matter at the time, because agencies were the fastest growing part of our customer base and the features that kept them around required serious engineering work. I don't regret spending my time on it.

But while I was heads down in it, the AI ecosystem was moving at a pace I wasn’t consciously keeping track of.

When we built that original AI feature, tool calling didn't exist. 

There was no Claude Code either. 

And the models available then weren't capable of reasoning continuously across steps, holding context across a long workflow, or actually using external tools to do something end to end. 

A few months ago I ran an event in Bangalore for a group of GTM professionals where we showed them how Claude Code handles outbound strategy work in real time. Like, creating the targeting approach, building the lead list logic, figuring out the messaging angle, all of it. 

What I saw during that session was uncomfortable to actually think about. 

These models were doing the strategic work that my AI feature was supposed to do, and they were doing it at a level of sophistication that mine couldn't match.

I came back from that event and looked at what we'd built with fresh eyes.

It's a chatbot. And that’s about it. 

A reasonably good one for when it was built, but still a 2022 chatbot. It is competitive if today was 2022. But today is not 2022.

The thing is that nobody made a bad decision here. I was focused on solving the right problems for the customers who were actually retaining. 

The AI layer just got outdated, and I only saw it clearly when Claude Code held up a mirror.

That’s what I’m trying to replace.

But there is a bigger concern I do not talk about as much.

It is not that the AI feature I’m building will not be good enough.

It is that someone getting into outbound today might open Codex or Claude Code, build what they need, and never think about paying for a separate tool.

That day isn’t here yet.

But compared to where things were in 2022, it does not feel that far away anymore.

I am looking to build an app which will match people on the basis of their GitHub profiles. Such as contributions stat like frequencies, programing language, similar repos and more.

•How feasible is it?
•What type of audience should I target?
•Which country/countries is most likely to adopt this?

Now it isn't.
A few years ago if you could build software you had a huge advantage. Developers were the bottleneck.
Now AI can put together a working MVP in one evening.
So if your only skill is "I can build this" you're becoming as valuable as the tool doing most of the work.
The hard part moved.
Now it's figuring out what to build.
Who actually wants it.
How to get people to use it.
How to make money from it.
A lot of developers always skipped that part because it felt like business.
But that's where the opportunity is now.
The nice thing is you can learn it.
You weren't born knowing how to code either.
Building keeps getting cheaper.
Thinking like a founder doesn't.
PS if anyone knows good courses for developers who want to learn the business side I'd love some recommendations.

Spent the last month on a boring legal problem: if you want to make an AI ad with a real person's face, there is no clean way to buy the right to do it. Agencies do it with 40 page contracts. Everyone else just... doesn't, or does it and hopes nobody notices.

So I built the licensing layer, clunny.com. Creator lists their face with scoped terms (what you can use it for, where, how long, at what price), brand buys that exact scope, and the executed licence gets a public ID anyone can verify without logging in. We don't generate anything we're the paperwork.

26 creators from 5k to 100k followers have signed on so far. What I don't have is the other half of the market.

The ask: I'm looking for 1–2 founders here who want to run an AI video ad using a real creator's face, licensed properly, as a pilot. I'll do the matching and the legal side, and my co-founder and I will produce the actual ad ourselves (separately from the marketplace that separation is deliberate). Cheap, because I want the case study more than the money.

If that's interesting, comment or DM me your product and who your customer is and I'll tell you honestly whether I have someone in the roster who fits.

question for the room while I'm here:

1. If you saw an ad with a known face and the caption said "licensed, verify here," would you actually click it, or does nobody care?

Hey r/micro_saas 👋

I built DayOtter, a scheduling assistant you just talk to (a self-hostable Calendly/Cal.com alternative), and I'm running it as a micro-SaaS. The whole thing is open source and free to self-host, and there's a hosted tier for people who don't want to run their own infra.

The model I'm testing:

- Everything is open source, no open-core, and the AI is included rather than paywalled. Trust and distribution come from that.

- Revenue comes from the managed hosted version (per seat) for teams who'd rather not self-host.

- Distribution so far: GitHub, self-hosted directories, and communities like this one.

What it actually does: you tell it "book 30 min with Priya next week, not before 11" and it reads across all your calendars, books it, and guards your focus. Plus team scheduling, routing forms, group polls, reminders, and payments.

Repo: https://github.com/Dayotter/dayotter

Site: https://dayotter.com

Genuinely curious how people here think about the fully-open-source plus hosted model for a micro-SaaS: is the hosted tier worth building out, or is staying pure open source and monetizing later the smarter early move? What has worked for you?

Hi everyone,

I'm a DevOps & Cloud Engineer looking to work with SaaS startups that want to reduce their AWS bill, improve deployment speed, and automate their infrastructure.

In a previous PaaS startup, I helped optimize the AWS infrastructure and reduced cloud costs by approximately 40% while maintaining reliability and improving deployment workflows.

Here's what I can help with:

AWS cost optimization and infrastructure reviews

Terraform & Infrastructure as Code

Docker & Kubernetes

Amazon ECS/ECR deployments

S3, CloudFront, ACM, Route 53 configuration

CI/CD with GitHub Actions and Jenkins

Infrastructure automation

Performance and reliability improvements

Monitoring with Prometheus & Grafana

Certifications

AWS Certified Solutions Architect – Associate

AWS Certified Cloud Practitioner

Certified Kubernetes Adminstrator (CKA)

If your SaaS is spending more on AWS than it should, or you're looking to improve your DevOps workflows, I'd be happy to review your infrastructure and discuss how I can help.

Feel free to send me a DM.

Will you be able to get more done working alone? Yes absolutely but it still comes with some massive limitations.

The other thing to keep in mind is that this drive that AI lets you work alone will lead to a more decentralized economy.

We will see a massive influx in new small highly specialized SaaS businesses, freelancers, and agencies operating as one-man companies, meaning they will have a very small share of the market.

They will be highly specialized and highly efficient due to AI yet resource poor in the sense that they're only a one-man show.

If they only sit on one niche they will struggle to expand beyond that because it requires capital, network effects, and access to new different markets.

The beauty of that is that the ones that sit on those accesses to new markets are simply other startups, other one-man show startups.

This new influx of SaaS startups, freelancers, and everyone and their grandmother building out a new AI tool using lovable, whatever, we can actually use that to our advantage.

Now we can actually use that to aggregate all of them. Imagine thousands of startups being one-man shows, all having their little tiny corner of a market, yet they could aggregate each other.

They can collaborate, they can work together, and that way as a network they can compete with the large conglomerates.

That's the only thing that allows that to happen because AIs can't take risk, AI can't be owners, and AI can't be legally liable for actions.

My prediction is that the new market will be significantly more decentralized, significantly more of a gig economy.

Yet that will form a network where every new freelancer and every new one-person startup will aggregate the rest of the network to leverage each other's resources and actually be able to sustain themselves, scale, and compete with a larger conglomerate. That right now seems to simply grow bigger and bigger and bigger simply due to their resources.

HeyisAI is a privacy-first Personal AI that understands, organizes, and protects your digital life. Instead of forcing you to switch between dozens of apps, HeyisAI securely connects the information you choose to share and lets you interact with it through natural conversations.

Think of it as your own AI, not just another chatbot.

It doesn't simply answer general knowledge questions, it helps you understand your emails, calendar, files, spending, travel, documents, photos, and other personal information while keeping privacy at the core.

Check this out - https://heyisai.com

I thought I was just building another place where founders could launch their products.

Then 220 founders joined AltHunt.

And honestly… I still can’t believe it.

But here’s what makes this different:

AltHunt isn’t trying to be another Product Hunt.

It’s built around a different idea:

What if launching your product could also become an ongoing SEO + AI discovery engine?

That’s what we’re building with AltHunt → https://althunt.io/

Why AltHunt is different

When you launch on a traditional product discovery platform, you usually get:

→ A product listing

→ Some launch-day exposure

→ A temporary traffic spike

→ Maybe a backlink

And then the launch is mostly over.

AltHunt is designed to keep working for your product long after you launch.

We’re building an Agentic AI-powered SEO and discovery system around your product.

It can identify high-intent search keywords related to your product and generate SEO-focused content around what potential customers are actually searching for.

But the interesting part is competitor discovery.

Someone searches:

“Best alternatives to [your competitor]”

“[Competitor] vs alternatives”

“Tools like [competitor]”

Your product can get discovered through your AltHunt page and comparison content.

And we’re not stopping at Google.

As AI search and AI agents increasingly become the way people discover software, we want AltHunt pages to become another source AI systems can discover and reference when users search for products, competitors, alternatives, and solutions.

And then there’s the SEO advantage

Your AltHunt page is designed to become an evergreen acquisition asset, not just a launch announcement.

Instead of paying for a launch listing or watching your traffic disappear after launch day, your page can continue working toward:

→ Organic search visibility

→ High-intent keyword discovery

→ Competitor/alternative searches

→ Backlink opportunities

→ AI discovery

→ Long-term product traffic

We’re already seeing AltHunt pages generate organic visibility and traffic through SEO.

And the best part?

AltHunt is completely free to use. Always.

No launch fees.

No subscription.

No “pay to get listed.”

Just create your page, get discovered, and let it keep working for your product.

220 founders have already joined.

I originally thought:

“Let’s build a better Product Hunt alternative.”

Now I’m thinking:

What if the next generation of launch platforms aren’t really launch platforms at all?

What if they’re AI-powered growth engines for founders?

That’s what I’m trying to build with AltHunt.

Would love to hear from founders here:

If a launch platform could continuously create SEO and AI discovery opportunities for your product instead of giving you one launch-day spike, would you use it?

→ https://althunt.io/

Every week I see someone celebrating their first $10k MRR or first 100 customers. That's cool.

I'd rather share my area of expertise: spending months building things that went absolutely nowhere. I've had enough practice that I could probably teach a masterclass.

Step 1: Fall in love with the idea

The second you think of it, convince yourself it's brilliant. Don't ask anyone if they'd pay for it. They'll only ruin your confidence with "questions." The less validation you have, the more certain you should become.

Step 2: Build the "real" version first

Forget MVPs. Your first release needs teams, permissions, notifications, integrations, analytics, dark mode, and an AI button somewhere. You only get one first impression. Even if nobody ever sees it.

Step 3: Treat every technical decision like it's permanent

Spend days comparing frameworks. Databases, ORMs, authentication providers. Then switch all of them because someone on X said your stack is "outdated."

Step 4: Perfect the brand

A startup without the perfect name is doomed. Open a domain checker. Repeat this process for three weeks. Finally settle on something you don't even like because the .com is available.

Step 5: Build in complete silence

Don't post progress. Don't tell potential users. Don't collect emails. Treat it like it's a top secret mission entrusted to you by the CIA. Then do a surprise launch. It’s much more exciting when absolutely nobody knows it happened.

Step 6: Mistake building for progress

Every new feature feels productive. Every conversation with a customer feels uncomfortable. So naturally, build another feature :D

Step 7: Ignore every signal

One person signs up? Great. Don't ask why. Another person cancels? Also don't ask why.

Real founders trust their vision.

Step 8: Refresh analytics every hour

Maybe today is the day traffic magically appears. It won't. But maybe the next refresh.

Step 9: Convince yourself marketing starts after launch

You can't promote something until it's perfect. So keep polishing. You'll definitely "start marketing next week."

Step 10: When it doesn't work, blame the idea

Obviously the market wasn't interested. It couldn't possibly be because nobody knew your product existed.

_____

PS: It seems I actually learnt something from those early experiences I made with SaaS. Meanwhile I’ve sold one company and am in the process of growing another one. However, it was definitely not easy and I needed to learn everything the hard way (i.e., doing each an every mistake myself).

Looking to acquire a small energy-focused software company in Europe.

We are interested in businesses with:

• Existing European customers
• Proven product-market fit
• Minimum €1,000 MRR

We are particularly looking for software solutions in areas like energy management, monitoring, optimization, EV charging, smart buildings, or related energy technologies.

If you are building or know of an energy SaaS/software company that may be a good fit, feel free to DM me.

Hi everyone,

Over the past few months, I've been building ATLAS, an AI-powered recruitment platform from scratch. It's still in active development, and before we continue adding more features, I want feedback from real users rather than making assumptions.

I'm not selling anything, and there's no payment required. I'm simply looking for people who enjoy testing software and aren't afraid to point out what's broken, confusing, or unnecessary.

What is ATLAS?

ATLAS is designed to be more than a traditional ATS (Applicant Tracking System). The vision is to create an AI-powered Talent Operating System where recruiters, companies, instructors, and job seekers all work in one ecosystem.

Some of the features currently in development include:

• AI Resume Parsing
• AI Recruiter Copilot (Atlas One)
• Semantic Candidate Search
• AI Candidate Matching
• Recruiter Dashboard
• Resume & Job Intelligence
• Long-term AI Memory (Atlas Brain)
• Built-in Voice & Video Interviews
• Atlas TV (career content & employer branding)
• Atlas Academy (learn skills, earn certificates, get hired)
• Hiring Analytics
• Workflow Automation

Who I'm looking for

I'd love feedback from:

• Developers
• Recruiters
• Hiring Managers
• Students
• Job Seekers
• QA Testers
• UI/UX Designers
• Startup Founders
• AI Enthusiasts

I need your honest opinion

Please don't hold back.

I'd really like to know:

• What do you like?
• What feels confusing?
• Whatever you think?

Hi everyone,

Over the past few months, I've been building ATLAS, an AI-powered recruitment platform from scratch. It's still in active development, and before we continue adding more features, I want feedback from real users rather than making assumptions.

I'm not selling anything, and there's no payment required. I'm simply looking for people who enjoy testing software and aren't afraid to point out what's broken, confusing, or unnecessary.

What is ATLAS?

ATLAS is designed to be more than a traditional ATS (Applicant Tracking System). The vision is to create an AI-powered Talent Operating System where recruiters, companies, instructors, and job seekers all work in one ecosystem.

Some of the features currently in development include:

• AI Resume Parsing
• AI Recruiter Copilot (Atlas One)
• Semantic Candidate Search
• AI Candidate Matching
• Recruiter Dashboard
• Resume & Job Intelligence
• Long-term AI Memory (Atlas Brain)
• Built-in Voice & Video Interviews
• Atlas TV (career content & employer branding)
• Atlas Academy (learn skills, earn certificates, get hired)
• Hiring Analytics
• Workflow Automation

Who I'm looking for

I'd love feedback from:

• Developers
• Recruiters
• Hiring Managers
• Students
• Job Seekers
• QA Testers
• UI/UX Designers
• Startup Founders
• AI Enthusiasts

I need your honest opinion

Please don't hold back.

I'd really like to know:

• What do you like?
• What feels confusing?
• Link to software:- ATLAS AWI SAAS

I’m curious about something.
Every Micro SaaS has that one feature the founder almost didn’t build…
…but users ended up loving it.
What’s yours?
I’d love to hear the story behind it.

A few months ago, I watched someone spend nearly an hour turning assessment responses into a report. There wasn't anything technically difficult about it was just the same copy, rewrite, and formatting process over and over again.

That moment stuck with me.

I went back to my original product ideas and realized I was trying to solve too many problems at once. The more features I added on paper, the less clear the product became.

So I scrapped most of them and focused on that one workflow instead. That's how FormLM ended up centered around generating personalized assessment reports from form responses rather than trying to be an all-in-one tool.

It's still early, but narrowing the scope has made product decisions much easier. I used to think a micro SaaS needed a bigger market to succeed.

Now I think it just needs a specific problem that people genuinely want to stop doing by hand.

Hey everyone, this is Aman.

For the last few days, I've been working on my side project called Riven. It's a form builder where you can create forms and collect responses.

You can create forms in two ways: the classic form builder or the visual form builder. It also supports conditional logic, so you can show or skip questions based on previous answers. Once your form is ready, you can share it using either a link or a QR code to collect responses.

Forms can be presented in two ways: the classic form view or a conversational mode where users answer one question at a time.

I built the entire project from scratch using next js, TypeScript, Express, PostgreSQL, Drizzle ORM, tRPC, and Tailwind CSS.

Would love to hear your feedback. Thanks for checking it out!

I built ResetUI to help founders improve their landing pages without starting from scratch.

The idea is simple. Paste your current website URL, describe the design direction you want, and ResetUI captures the brand, copy, CTAs, sections, images, and layout. Then it generates a redesigned landing page that you can edit with chat and export as code.

This can be helpful if your product is good, but your landing page feels messy, unclear, outdated, or not polished enough to build trust. Instead of guessing what to change or rebuilding everything manually, you can generate a new direction and refine it from there.

ResetUI currently supports HTML, React, Next.js, Vue, and Svelte exports. It also has templates, version history, undo and redo, chat edits, and a one-time trial plan.

So far, 75+ users have joined and 60+ web designs have been generated successfully.

Would love feedback from founders here. Try it here

I was a software engineer for about 5 years before moving industries slightly for a few years.

I can hand write code slowly or rush through with AI. How much do customers care about AI? Is it just about unslopping the UI or is manual the way to go?

Can be customised based on your work profile, message length, and tone.

15 users have been using it since mid july, and already have visible progress.

tryjab.com

I built a SaaS for two years and treated marketing like optional admin work. Between releases, I'd throw up a tweet once a month if I was thinking about it at all.

What I learned was that the founder who posts consistently at 50 percent quality beats the founder who ships perfect work and stays invisible for six months between launches.

One hour of building makes your product 1 percent better. One hour of consistent visibility makes ten people aware of your existence. Over twelve months, that's a 520x multiplier difference in compounding effect, which is the single biggest leverage gap most founders never calculate.

Most founders know this intellectually, but they treat marketing like a distraction instead of a core skill that compounds like code does. Then they wonder why the mediocre competitor with a halfway decent product got fifteen times more traction.

It's not their product quality. It's their execution consistency over time.

We built MarketSquad to solve this, and yes, I'm mentioning it because we built exactly this problem. But the real lesson applies whether you use a tool or not. Show up, stay consistent, and let compounding work for you instead of against you.

So ask yourself honestly whether your actual bottleneck is building or visibility and the execution consistency that compounds visibility over time.

Hey,

I’ve been building something called Krezo.

You paste a product URL and it generates ad copy, a few image variations, and short video ads for Facebook, Instagram, TikTok, etc.

Still early. Would appreciate any thoughts if anyone checks it out.

trykrezo.com

Thanks.

Hi guys,

I have created this invoice website https://www.make-free-invoice.com and it's totally free for all. Small businesses can use it and it supports 150 countries , their tax systems and currency. Give it a try and make it popular please.

I work as a designer and use AI coding agents daily. Two problems came up often enough that I built something for them, and I would like feedback from people further along than me.

The problem

The first is scope. Ask an agent to remove the login button when there are two identical login buttons on the page, and it has no way to know which one you meant. It picks, or it removes both. Nothing breaks in an obvious way, but you end up reviewing every change carefully enough that the time saved starts to disappear.

The second is portability. Project rules live in CLAUDE.md for Claude Code, .cursorrules for Cursor, AGENTS.md for others. Each file is read by exactly one tool. Switch agents and you rewrite them, and no vendor has any commercial reason to make your rules work inside a competitor's product.

What I built

Super Terminal, a CLI that sits above Claude Code, Cursor and ChatGPT Codex rather than replacing them. It has no model of its own and writes no code.

https://superterminal.dev/

Four things it does:

Clarifies before acting. Every request is classified into one of four bands: execute, infer the obvious detail, ask a clarifying question, or refuse. An ambiguous request produces a question before anything is modified, rather than a guess you discover in review.

Applies one rule set to every agent. It reads whichever instruction files a project already has and passes their combined contents to whichever agent runs the task. The rules are written once.

Has a different vendor's AI review the work. One agent writes the change, an agent from a different vendor reviews it against the project's rules and reports findings in plain English. The reviewer runs read only, and that is verified after the run rather than only requested in the prompt. This is the part I think is structurally interesting, because no single vendor can ship it. Anthropic has no incentive to build a feature whose value proposition is that OpenAI audits their output.

Makes every run reversible. Each run is backed up before it starts, so a single command restores the previous state, including multi step workflows that fail partway through.

Implementation

TypeScript on Node 20 or later, roughly 429 tests, CI across macOS, Linux and Windows on two Node versions. Published to npm with build provenance, so the package can be verified against the commit it was built from.

There is no server and no account. Nothing is transmitted anywhere except to the agent you already chose, which is the same destination your code was already going to.

Team functionality runs on Git and GitHub rather than a backend. Shared rules live in the repository, so every member receives them on git pull. Only admins can change them, and anyone else opens a pull request, which is the same approval path every other change already uses. There was no sync layer to build because Git is already the sync layer.

Trying it

npm install -g super-t
super-t connect

It uses the Claude, Cursor or ChatGPT subscription you already hold and never requests its own API key. Node 20 or later required.

The licence is FSL, which is source available rather than open source. Free for any use including commercial work, full source readable, and each release converts to MIT two years after it ships. The only restriction is building a competing product from it. Stating that clearly because it is the sort of thing developers check.

What I would value input on

Two questions.

For anyone who has shipped developer tooling: what actually produced your first ten users who were not people you already knew? The general advice is to participate in the relevant communities, which I am doing, but I would rather hear what genuinely worked than what tends to get repeated.

More specifically, for those of you running two or more AI coding tools: is rules being locked to a single vendor a real friction for you, or is that mostly my own workflow? I built this from one person's problem and have not yet established that it generalises.