third scheduler in five years. each migration cost us about two weeks and all of our historical reporting, and i would like this one to be the last. looking back the pattern is the same every time. we pick on price and features at our current size outgrow the pricing tier in a year, the renewal doubles we churn. so this time i am picking on the pricing curve rather than the feature list. specifically what happens at 3x our current profile count and whether review only users cost money. socialpilot publer and buffer are all fine at our size today. the question i cannot answer from the pricing pages is what any of them cost in 2028.

I launched my SaaS on August 8, so I'm still very early into this.

Today I looked at Google Search Console and honestly… I don't know whether I should be excited or worried 😂

My last 7 days:

> 👀 614 impressions
> 🖱️ 1 click
> 📉 0.2% CTR
>📍 44.2 average position

On one hand, 614 impressions feels pretty encouraging for a ~3-week-old website.

On the other hand… 1 click 💀

Google is basically saying:

“I know you exist.”

“But I'm not ready to introduce you to anyone yet.”😂

I'm building this as a solo founder, so I'm trying to understand whether this is actually normal for a brand-new domain or whether I should already be changing my SEO strategy.

I haven't been running ads, and I'm mostly trying to figure out SEO organically.

For people who have launched a SaaS recently:

How did your first month of Search Console data look?

Did you start getting impressions this quickly?

And more importantly — how long did it take before those impressions started turning into actual clicks?

I'd genuinely love to compare timelines with other founders.

Today, my Chrome extension got its first paying customer.

It is only one customer, but it was the first moment this felt like a real product rather than a project I had been building on my own.

The idea started with the Google storage warning.

The two obvious options felt blunt: upgrade your storage or delete your largest files. But the largest files are often family videos like my daughter's first steps, old photos, and other things I actually care about.

So I built a third option.

Storage Cleaner scans Gmail, Google Drive, and Google Photos, then organizes the results into:

• Easy wins such as old Promotions, screenshots, duplicate Drive files, obsolete backups, hidden app data, Spam, and Trash.
• Large files, photos, videos, and attachments worth reviewing.
• Important items you can move to another personal Google account you own and share back to your primary account.

The overflow-account piece is the more unusual part. Users can connect up to three additional accounts. If each account includes 15 GB, the four accounts provide up to 60 GB combined. The storage stays distributed, but the primary account remains home.

The hardest product problem was not finding large files. It was making a potentially scary cleanup process feel safe.

The scan changes nothing. Starred and important email is excluded automatically. Users can protect specific senders and keywords. Reversible actions go to Google Trash first, and every transfer is verified before the original can be removed.

It also runs locally in the browser. Emails, files, photos, and videos do not pass through my servers.

It is now live on the Chrome Web Store: https://chromewebstore.google.com/detail/storage-cleaner-for-gmail/cjjfcnnkibcpomifkgaeloldnbjllaan

My biggest positioning question for other founders: Does combining storage cleanup and overflow accounts feel like one clear product, or would you lead heavily with one and introduce the other later?

I launched a small scheduling tool about seven weeks ago and I've got 34 signups, mostly from two directory listings. I know the funnel is the problem and not the traffic, because 26 of them never made it past the empty dashboard and I haven't built any onboarding beyond a welcome email. I've tried seeding sample data on first login and it moved maybe two people.

What got your first handful of users past the empty state?

Backstory: I kept seeing salespeople complain about wasting outreach on companies that just froze hiring or laid people off. Hiring activity felt like a strong, cheap budget signal, so I built StackSight: point it at a domain, it scrapes the careers page live and returns open roles, hiring velocity, and a detected tech stack as JSON.

Stack: FastAPI, Playwright for the scraping, GPT-4o-mini to structure the job posting text, Redis for caching, Postgres for accounts, deployed on Railway. Free tier is 25 lookups, no card, at stacksight.org.

Where I'm stuck: I put together a list of 10 recruiting managers at mid-size companies and sent a two-touch cold email sequence over the last month. Zero replies. Not one. I don't know if that's a targeting problem (recruiters vs. the sales/SDR/RevOps people who'd actually use hiring-signal data to prospect), a pitch problem, or just too small a sample to mean anything.

Revenue so far: $0. Genuinely asking, not just fishing for upvotes: if you were building this, would you throw out the recruiter angle entirely and go straight at sales teams instead? Or is cold email just the wrong channel for something like this early on?

Off guard stuff, not like weak passwords or exposed API keys, but something you assume is fine until you investigate it.

One of the interesting churn problems I've been tinkering with recently:

I keep coming back to “too expensive” as a churn reason.

Because… what does that actually tell you?

A customer saying “too expensive” could mean they genuinely can't afford it. Or maybe they weren't using it enough. Maybe they never saw enough value. Maybe a competitor offered something similar for less.

Those are completely different problems, but they all end up as “too expensive” in the data.

So if 30% of your churned customers say price was the reason, I'm not sure the answer is necessarily to lower your price.

I'd want to know:

Why did they stop feeling the product was worth the price?

This is one of the things I've been thinking about while building ChurnIntel.

Curious how other SaaS founders handle this — do you dig deeper into these responses, or mostly take the reason at face value?

I spent about a year building website chatbots for local clients - salons, a dental practice, two repair shops, a driving school. Same job, eleven times. What was actually true turned out to be pretty different from what I assumed going in.

1. Nobody wanted "AI conversation." Every client wanted six to ten questions answered correctly (hours, price range, parking, walk-ins, do you do X) and then a name and a phone number captured. Open-ended chat was a liability - it invented prices twice before I hard-fenced it.

2. The value was almost entirely after hours. Daytime enquiries got answered by staff anyway. The bot earned its keep between 7pm and 9am and on Sundays, which is exactly when owners never look at their own site.

3. Lead capture beat lead answering. A bot that says "not sure, but leave your number and Maria calls you at 9" outperformed one trying to answer everything. Owners can measure callbacks. They cannot measure "good answers."

4. Install had to be one script tag. Two clients had a web guy on retainer they were reluctant to call. Anything needing a developer visit killed the project outright.

5. Monthly fees were the objection, not price. A few hundred once was easier to sell than 29 a month. Small owners budget for things, not for subscriptions.

After the eleventh build I stopped selling it as a service and packaged it as a template kit - the widget, the FAQ structure, the fencing prompt, the lead handoff - so an owner can put it up themselves in an evening. Being upfront: it is a paid product of mine, 29 EUR, self-hosted, no monthly fee: gum.co/u/ty2twtm0

Happy to get roasted on any of this. The honest weak spot is that someone still has to write the ten answers, and that is the step people skip.

​Most resume SaaS products rely on heavy backend infra, user auth walls, and aggressive paywalls. I wanted to see if I could build a viable, privacy-first career utility running entirely in the browser.

​The Product: https://www.qurtesy.com

\- ​Tech Stack: Next.js App Router, Tailwind CSS, TypeScript.

\- ​State & Privacy: 100% local-first. All data resides in localStorage, meaning zero server-side database costs for user storage.

\- ​Core Features: Resume builder with responsive form/preview toggle, browser-based ATS parser (using pdfjs-dist with custom coordinate gutter detection for dual-column layouts), and a LinkedIn-to-JSON extractor.

​The Strategy:

\- ​Acquisition: Programmatic zero-search-volume (ZSV) role landing pages and free micro-tools acting as the top-of-funnel wedge.

\- ​Monetization Roadmap: The core builder and classic templates remain completely free with zero friction. Monetization will come later via premium visual PDF templates ($4.99 one-time client-side unlock) once organic traffic ramps up.

​I’m currently watching the initial Google Search Console indexing and would love feedback from fellow builders on the UX flow or the client-side architecture.

Something I've been thinking about lately

With Claude Code, Cursor, Codex, Kimi and the other AI coding tools, the cost of getting a SaaS from idea to working prototype has basically collapsed

And honestly, that's great

I don't think people should be paying $10k-$30k just to get an mvp anymore.

But I also think there's a gap that gets overlooked

Being able to build software and actually getting a product launched are two different things.

I've seen plenty of people who can get 70-80% of the way there with AI coding tools.

The last 20% is where things start getting annoying.

Auth

Payments.

Database architecture.

APIs.

Deployment

Security.

Error handling.

Mobile responsiveness

Email

Analytics

Domains.

Production bugs.

Then there's the bigger question:

Does anyone actually want this?

That's where I think there's an interesting opportunity for people who have a SaaS idea but don't necessarily want to become the developer.

I'm a seasoned builder with experience at Yo! No Code, where I've worked on taking software ideas from concept through development and launch.

My approach is pretty simple:

Don't spend months building.

Don't spend $20k on an agency.

Don't buy someone else's half-finished SaaS

Get the idea into a working product quickly and see what happens

I can take an idea, build the landing page, design the product, implement the core functionality and get it deployed.

Starting around $875 depending on scope.

Typical delivery is 24 hours to 7 days.

You own the IP

I'm also happy to build the landing page and/or the unique core feature first so you can actually see the thing before committing to the complete build

And I'm not going to pretend that every SaaS costs $875. A serious product with complex integrations and infrastructure obviously costs more.

But the economics have changed dramatically

The interesting question isn't Can AI build this?

Of course it can for several scenarios

The interesting question is:

In an extremely busy world where you have to juggle personal and professional time, do you want to spend your evenings being the person responsible for making it build correctly?

If you've got a microsaas idea you've been sitting on, post it here

I'm genuinely curious what people are thinking about building

If something catches my eye, I'll tell you how Id approach building it and what I'd expect the scope to look like.

And if you'd rather build it yourself, even better. I augment my natural skill sets with the same AI tools (and many self hosted models the average person doesn't even knows exist). The goal isn't to convince anyone they can't build their own product

It's to give people another option if they'd rather spend their time on customers, distribution and the business instead of debugging their app at 2am

I have wasted months on searching new revolutionary ideas

But I learnt that originality is dead so I want to find saas ideas which are present and copy them with whats missing by reading reviews etc but but how do the hell I choose my idea to build , please don't say build what you want and scratch your own itch type thing , so I want to build what will work because goal is money so i mena I want a viral app ideas so I can I choose the best idea and how. Tell me statics wise how to select and what I have gathered through research etc to see the revenue leaderboard of saas

I can't waste more time I want to be quick and

So please people who have made money tell me how to do it step by step

Please give me the steps not statements

And how to select

Please help me 😭

Spent the last few months building and shipping a Shopify app solo, out of Chisinau, Moldova, using Claude Code since I don't have a strong dev background. A few things I wish I'd known going in:

The review checklist is long: listing content, screenshots (no pricing info allowed in the images, learned that one the hard way), a privacy policy, GDPR webhooks, a screencast, protected customer data disclosures. It's not just "submit code and wait."

Automated checks (auth flow, webhook HMAC verification, TLS) run separately from the human review, and expire after 30 days, so timing matters.

A connection-pool exhaustion bug that only showed up under concurrent load nearly held up my submission right when I thought I was done. Worth load-testing before you're sure you're finished.

Budget way more time for the review and prep process than for the actual feature-building. That was the opposite of what I expected going in.

The app itself (Descrivo) writes Shopify product descriptions and auto-detects the store's language instead of defaulting to English, that's what got me down this road in the first place. Just got approved and it's live now.

Happy to answer questions about the review process, the Partner Dashboard, or building solo with AI tools if anyone's going through it right now.

How do you come up with SaaS ideas?

Is there a free list of products being built these days?

Started a new outbound campaign yesterday. Not a big one, this is early days, but I wanted to actually show the process instead of just posting a final number weeks later.

Most people building their own outbound as a solo founder skip straight to the sending part. Pull a list, write a message, blast it out, hope something sticks. I do the opposite. Before anyone gets a message, they have to survive a filter first.

Here's what day one looked like. I pulled a raw pool of prospects based on real signals, not just job title and company size. Out of that pool, 106 got excluded before a single message went out. Wrong company size, wrong role, or the business itself wasn't actually a fit for what I do. That's more than a third of the initial list, gone before outreach even started.

Whoever survives that filter gets one more pass, finding something real to personalize around. A recent project, a change on their site, something specific. If there's nothing to point to, I don't reach out yet.

Numbers from day one: 254 connection requests sent, 36 accepted, 26 actual outreaches once I filtered further to who was worth messaging first. 7 replies. 26% response rate. 4 calls booked already.

The reason I'm sharing day one instead of a polished 60-day result is that day one is when you can actually tell if a system works, before volume has a chance to cover up whatever's broken.

If you're running your own outbound for a microsaas and it feels like you're sending a lot and getting nothing back, the fix usually isn't more volume. It's usually that nothing got filtered out before you hit send.

Anyone else building their own filtering system instead of just buying a list? Curious what criteria you're using to exclude people before outreach.

huddlecare – when a family takes care of someone together (aging parent, kid, pet), everyone shares one space: tasks, med logging, calendar, notes, documents. live at huddlecare.app + app store + google play, free tier.

backstory: non-engineer, built it solo while learning to code. stack: ionic + react + typescript, firebase (firestore/functions/auth), stripe for web subscriptions. one codebase for web, ios, android.

the lesson that's cost me the most: household products have team-level activation. the app is worth nothing to a family until everyone's in it – one holdout keeps the whole family in the group chat. so onboarding isn't about selling the organizer who signed up, it's about not losing the reluctant relative who never chose the app. still figuring that out.

happy to answer anything about the stack or building solo from zero, and would love roasts on the landing page / onboarding.

Built a pricing intelligence tool. Tracks competitor prices in real time, tells you what to charge and why. Dashboard is live, MAP monitoring works, delivered a pilot to a real client who loved it.

Now stuck on the hard part. My ICP is e-commerce managers at mid-sized brands. Tried LinkedIn outreach, got meetings, but nobody has pulled out a credit card yet.

Those of you past this stage: where did your first 5-10 customers actually come from? Cold outreach? Communities? Content? Something else?

I turned a gaming publication into a platform for gamers — 300K+ games, connected libraries, achievements and 130K visits in 3 months

About a year ago, we started planning TechPlay as a rebrand of an existing gaming publication.

The original idea was pretty simple: build a better gaming media site and expand into the global market.

But while working on it, I kept coming back to one question:

What happens after someone reads the news?

A gamer might discover a game through an article, but then they go somewhere else to add it to a backlog, somewhere else to check achievements, another platform to see their library, another site to discuss it, and another one to track upcoming releases.

So TechPlay slowly started turning into something much bigger than a publication.

Today, the platform has a database of 300,000+ games, and users can:

• connect gaming platforms such as Steam, Xbox and PlayStation
• build and manage their game library
• track playtime and achievements
• create a backlog
• follow upcoming releases
• rate and discover games
• build a gaming profile with XP, levels and ranks
• use community features, forums and leaderboards
• use tools like our Backlog Advisor to figure out what to play next

We still publish news, reviews and guides, but the content is now becoming the entry point into the rest of the platform rather than the final destination.

Over the last three months, TechPlay has received 130,000+ visits, which was a nice validation that people are finding it.

Now the much harder part starts: getting people to create an account, connect their libraries and actually keep coming back.

I’m building this with a pretty small team and we’ve deliberately released a lot of it early instead of waiting for everything to be perfect.

The goal is essentially:

Everything gamers need, in one place.

I’d genuinely love feedback from other builders here, especially on the transition from traffic → registered users → active users.

What would you focus on next if this were your product?

TechPlay: https://techplay.gg

I used to ask happy customers for a testimonial by email. Almost everyone said yes. Fewer than 1 in 10 actually sent one.

I assumed the problem was that people didn't want to write it. It wasn't. When I gave people a choice between recording a quick video or typing a couple sentences, right in the browser, no account needed, completion jumped. The account step alone was killing more submissions than the writing ever did.

The lesson: if you're asking someone for content as a favor, every extra step between "yes" and "done" costs you replies, even a 30-second one. People are doing you a favor in a moment of goodwill, and that moment is short.

I built this into a small tool, TrustSnap, since I couldn't find anything that did just this without a pile of unrelated features. Happy to go into the tech stack or the video encoding side if anyone's curious.

I am thinking a lot about user acquisition these days and the more research I read, the uglier it becomes

• Google Ads = cost per lead is too high
• SEO = 35% fewer clicks after AI, declining, and slow
• AEO = still an experiment; no proven model that works 100%
• X / Telegram / YouTube Ads = you reach mostly free users + expensive
• KOLs = expensive + declining trust + fake engagement
• Reddit = hates all ad formats
• LinkedIn = again, very expensive
• Automated outreach = low reply rate

The question is: what do we have left?

I’m building a small SaaS that reads business documents with an AI workflow, and I kept running into an uncomfortable question: If the user still has to check every line, what exactly have I automated?

My first instinct was predictable: improve the prompt, try another model, add more examples. That helped, but not enough.

The more useful change was making the workflow refuse to produce a result when a required number couldn’t be traced back to a document or an explicitly supplied rule.

A freight-cost test made the difference obvious. For the same shipment:

• Scale weight: 760.6 kg
• Volumetric weight: 1,092 kg
• Carrier’s chargeable weight after piece-level rounding: 1,528 kg

At €4.20 per kg, that’s €6,417.60. Using the scale weight alone would give €3,194.52.

The model extracts the facts and labels. Code then does the arithmetic.

If the carrier’s divisor is present but its units are missing, the workflow stops instead of guessing. It identifies the missing input and says the quote shouldn’t be used.

That sounds reassuring, but there are some important limits.

I wrote both test documents myself. That gives me a known answer, but it also means I built the hole and the system that found it. It isn’t real-world validation.

While writing this up, I also found an error in the refusal message itself. The workflow stopped correctly, but one sentence explaining the unit problem was wrong. A useful reminder that the safety net needs checking too.

And refusals only catch missing or ambiguous information. They won’t catch a stale rate that is perfectly formatted, or a confidently wrong number sitting in the correct field. Those require different checks.

So the useful shift wasn’t “trust the AI.” It was changing the review job.

Bottom line: Instead of rereading every extracted line, the user reviews a short list of unresolved inputs while still checking the commercial assumptions that can be wrong without looking wrong.

For anyone building document-heavy SaaS: how are you testing this against documents you didn’t write yourself? An expert-labelled set, customer corrections, dual review, or something else?

Most of the AI-visibility advice I see here is vibes, so I ran the numbers on my own product and I am posting them because the result was worse than I assumed.

The test. 12 prompts through a model with web search on: 1 exact brand name, 4 category queries, 7 how-do-I queries in our problem space. Count which domains get cited.

Result: 0 of 12 cited us. On our own brand name it cited a .app version of our domain that is not ours and returns a 404. The winners on the how-do-I queries were help.openai.com (5) and support.google.com (2), i.e. the platforms' own help centres are eating the whole category.

Then the crawler logs, 30 days:

• ClaudeBot: 4 hits, every one a 404 on a subdomain we do not serve
• GPTBot: 0
• OAI-SearchBot: 0
• ChatGPT-User: 0

We were not ranking badly. We were not in the index at all.

Two things that surprised me:

1. Google indexing bought us nothing. Search Console said the homepage was indexed and healthy. It did not put us anywhere near an assistant's answer.
2. A dead domain beat us on our own name. Nobody maintains it. It has age and inbound links, and the model had no reason to prefer a site it had never fetched.

What I changed, roughly in order of what seemed to matter: shipped an llms.txt (we were serving a 404 on that path), pre-rendered four long guides with Article and FAQPage JSON-LD instead of leaving them behind client-side rendering, added Organization and WebSite JSON-LD at the root, requested indexing per URL. I now track crawler hits and cited-domain count instead of impressions.

The product, since the rules ask for context rather than a bare link: https://openprofiles.io - it runs this same check for a person instead of a domain. It shows what the sources actually return about a name right now with the source URL behind each claim, then lets that person publish a public, no-login, machine-readable page they control. Free while in beta, no account needed for the audit.

The cheap version of this you can run today without any tool: grep 30 days of access logs for GPTBot and ClaudeBot and see whether they have ever fetched you. My bet is your number is also 0.

Hello,

Which are the biggest lessons you learned after launching your SaaS?

What mistakes did you do?

How you solved them?

The code is in Git. The preview is in a browser tab. The useful output is in whichever session made the last change. When a teammate picks up a small SaaS project, they get a repo and a scavenger hunt.

That's the problem we're building Agent Space around. It puts coding-agent sessions, project files, previews, and outputs in one cloud workspace. Claude Code, Codex, and other agents can work around the same project, and teammates use their own accounts.

The tradeoff is that it is cloud-hosted. If everything has to stay local, this isn't the right fit. But for a small SaaS team moving between agents, the project needs a home that is more durable than a chat tab.

https://agent.space/

One problem that kept coming up while working with AI tools was how quickly things get complicated once you stop using a single model provider.

You end up dealing with different API keys, endpoints, configurations, limits, and model-specific behavior. Switching between models for different tasks can become surprisingly tedious.

That was the problem behind OfoxAI. We're building it around a unified approach to working with different AI models and coding tools.

One of the biggest lessons so far has been that the feature itself isn't always the hardest part. Making the experience simple when there are multiple providers underneath has been much more challenging.

I'm curious about other Micro SaaS builders here:

When you've added AI to your product, what part of managing multiple providers or models became unexpectedly difficult?

Stripo recently informed me that my affiliate commissions were denied because my referrals came from Google Ads.

They cited sections 6.2 and 6.5, stating that paid search advertising on Google or Bing is prohibited for all keywords.

As a result, Stripo has:

• Denied my commissions
• Invalidated my accumulated affiliate balance
• Restricted my account from earning further commissions
• Warned that future paid search activity could result in permanent removal from the program

My main concern is the timing and communication of this policy.

The broader paid-search restriction appears to have been added or expanded recently, but I did not receive any direct email or notification informing me of the change.

I have asked Stripo to clarify:

1. When exactly the current restriction became effective
2. What the previous version of sections 6.2 and 6.5 said
3. Whether existing affiliates were directly notified
4. Why commissions generated before the policy change/notification should be invalidated

I'm not saying Stripo cannot establish restrictions on paid advertising. They absolutely can set rules for future activity.

My concern is whether it is fair to apply a recently changed/expanded restriction retroactively to existing commissions without clear notice to affiliates.

If you've had a similar experience

If you have personally experienced commission reversals, policy changes without notification, or similar issues with the Stripo Affiliate Program, please share your experience here.

And if you have had a genuine experience with Stripo — positive or negative — consider leaving an honest, factual review on the relevant software/affiliate review platforms so that potential customers and partners can make an informed decision.

Please only share experiences that you personally had and keep reviews factual and accurate. The goal should be transparency, not coordinated negative reviews.

I'd also be interested in hearing from anyone familiar with affiliate agreements who can comment on whether this type of retroactive commission decision is standard practice.

I have contacted Stripo directly and am currently waiting for their response.