I’ve just started building and shipping products, but I’m stuck on the part nobody really explains: distribution.

I have basically no audience, no followers, and no existing community to launch to.

For people who started from zero:

• How did you get your first 100 users?
• Where did you actually find people to talk to?
• What worked better: Reddit, X, Product Hunt, communities, cold outreach, etc.?
• How much should I be posting vs. directly reaching out to people?
• What are the biggest mistakes beginners make with distribution?

Also, which subreddits/communities are actually worth participating in for indie hackers, SaaS builders, and AI products?

I’m not looking for generic “build in public” advice. I’d love to hear what actually worked for you when nobody knew who you were.

I have my first two agency calls this week. I'd rather not spend an hour nodding through a slide deck and realize afterward that I learned nothing. What should I ask that will tell me whether a team is good at delivery, not just sales? I'm trying to avoid the "$25K later with nothing to show for it" story that keeps coming up in my research.

What should I ask that will tell me whether a team is good at delivery, not just sales? I'm trying to avoid the "$25K later with nothing to show for it" story that keeps coming up in my research.

I saw someone else do this with SaaS feedback so thought I'd try it out with content!

Most content feedback is very generic but I will go deep in order to help you identify literally exactly what's wrong and how you can do better.

Drop your account and I'll audit in these areas:

• Scroll Test: I'll mimic someone landing on your page and their first impression of your account
• Profile: Anything from pfp to bio to username and tags
• Social proof: Anything you have and what you can do to improve this

To submit, leave a comment with:

1. URL: (Public social media page)
2. Target Audience: Who is your target audience?

I'll respond to as many people as I can!!

A few months ago, our business hit a major bottleneck: we needed to send regular broadcast updates to customers, but traditional communication channels weren't cutting it.

Like many business owners, I started with Chrome extensions and unofficial bulk-send tools. The result was predictable: WhatsApp permanently banned multiple numbers, wiping out weeks of customer momentum.

I looked into Meta’s official WhatsApp Cloud API to do things legitimately, but quickly ran into another roadblock: official Business Solution Providers (BSPs) charge huge monthly subscription fees on top of standard Meta conversation rates, packed with features a lean business doesn't need.

Since I have a CS background, I decided to build my own lightweight tool that talks directly to Meta's Cloud API:

• Direct Meta Cloud API integration (no risk of unofficial bans)
• Bulk broadcast campaigns with clean contact tagging
• Zero markups on Meta's base message rates
• Lean, fast UI focused solely on message delivery

It's currently in early beta and running smoothly for our daily operations.

I’d love to get feedback from other builders here on the UI, and if anyone running a business wants early access to test it out and save on messaging costs, feel free to drop a comment or DM!

I originally built a workflow for myself when I was working as an SDR in tech selling investor management software. I was spending a ridiculous amount of time researching prospects, finding something relevant to mention, and trying to make emails feel personalized without spending 20 minutes on every lead.

Over a few months, that workflow eventually turned into SimpleSend. I originally built it just for myself, but after talking with various SaaS sales managers and reps that were either running outbound or looking to build an outbound motion, it gradually developed into an actual product.

I do have a few reps and teams using it now, and the early results and feedback have honestly been better than I expected.

Now the problem.

Recently, I've also been trying to figure out the right GTM messaging/approach for the product. So I started using SimpleSend for my own outbound to get people to the website.

I've sent emails to around 600-700 sales reps so far, and according to my analytics, about 60% of those people have clicked through my website and ~90% of traffic have clicked into the account creation page.

But here's the part I'm trying to figure out: almost nobody actually signed up or used the product.

At the time, I didn't have a free trial, which I've recently changed, but I'm wondering if there are other things that immediately stand out as blockers.

The main idea behind the tool is helping personalize outbound at scale by researching each lead and finding actual signals you can use in an email through a CSV.

For example, one use case I've tested is having it identify technology or tools a company is using and working that naturally into an email:

"Hey John, noticed your team is using XYZ for investor onboarding..."

It also does lead enrichment and helps generate cold call talk tracks, but the personalization/research side is really what I originally built it for.

I'm genuinely curious from people who still do cold outbound:

Would something like this actually be useful to you, or does it just feel like another AI tool?

And if you checked out the site, is there anything that would immediately stop you from signing up or trying it?

I'm happy to send the site over in a DM or link it here if that's allowed. I'm not looking to hard sell anyone, I mostly just want to figure out where the disconnect is between people showing interest and actually trying the product.

I know Reddit gets flooded with "I built an AI SaaS" posts, so feel free to be brutally honest.

McKinsey put out data showing that roughly 32% of companies (and over 40% in tech) decided against buying software recently because their internal teams could build simple versions themselves using coding agents.

If you sell B2B, you've probably felt the downstream effect of this already. Cold outreach that pitches features or workflow automation gets brushed off much faster now cause engineering leads feel confident they can spin up an internal tool in a few days.

The reps I see still getting traction have changed a lot when and why they reach out

Instead of scraping a static list of titles to value proposition, they're using models like Claude as a qualification filter on live triggers (we're hosting a signal-based outbound with AI).

The basic pipeline looks like this: ingesting raw company events (a specific tool deprecation, a mid-level leadership change, sudden open roles in a bottleneck department) and prompting the model strictly to analyze what immediate operational friction that event introduces inside the company and finally reaching out with a single observation tied to that friction, rather than pitching a product.

When the problem is acute and tied to an event that happened this week, the "we'll just build it" excuse rarely comes up because the pain is already slowing them down.

Version 1 (live now):
A system design + coding interview practice platform. Solve problems two ways, write code against real test cases, or design a system (architecture diagram + API contract + database schema) and get it auto-graded by 32 deterministic rule-checks, not a vague AI opinion. Windows 95-themed UI throughout, user profiles with follow/streak tracking, a Discuss forum, threaded comments with voting, and an admin review pipeline for community-submitted problems.

Coming very soon:
More design problems that actually exercise the new API/database grading ,right now most of the catalog only grades the diagram, with a handful of gaps (API-only or schema-only problems don't exist yet). That's the next update. A contest mode comes after that.

I'm curious how common this is, if there's a name for it, etc. Like you create a landing page, see if there's interest, and then if so, create the product?

I'm curious what this is called and also what's the conversion rate, like if 1000 people say they're interested in something, how many would in turn become paying customers?

Most SaaS feedback posts only give vague one-liners like "looks clean."

Drop your link below, and I’ll run a comprehensive teardown across ~13 core dimensions:

• Positioning & Messaging: Value proposition clarity, target audience alignment, decision triggers, and copy quality.
• Product & UX: Design execution, usability flow, accessibility, and docs/help center readiness.
• Trust & Polish: Social proof integrity, editorial QA, and perceived reliability.
• Technical Health: Page performance, baseline technical SEO, and visible stability risks.

To submit, leave a comment with:

1. URL: (Public landing page or demo link)
2. One-line pitch: Who is this for, and what problem does it solve?

I'll post all detailed reviews directly as public replies to keep it transparent.

Reviewing the first 5 today.

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’ve been building MarketPeel, a small SaaS for people who want to track US insider and congressional trades without opening every filing.

My first assumption was that the hard part was separating real open market buys from grants, option exercises and shares withheld for tax. Early testers found the more awkward problem: a Form 4 can say P for purchase and still be misleading because the footnote says it came from a financing round or was taken instead of salary.

That changed what I’m building. Showing the filing is not enough if the useful context only appears after someone clicks through and decodes it.

The stack is Next.js, TypeScript and Supabase. The data comes from SEC filings and public congressional disclosures.

I’m testing a simpler loop now:

open the insider screener choose a company you follow add it to a free watchlist switch on the daily recap tell me whether the alert gives enough context to decide if the filing is worth opening

It’s here: https://marketpeel.com/screener

The free account has a five company watchlist, no card and no expiry. I built it, so obvious bias disclosed.

The question I’m stuck on is what belongs in the alert itself. transaction type, dollar value, a plain English footnote summary, recent price move, or something else?

Blunt feedback is more useful than a polite “looks good”

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

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.

I’ve been building a chess product called BoardSignal.

It started much more manually: looking across a player’s Chess.com games, finding recurring patterns in their play, and turning that into an improvement Review.

Once I began turning that process into a real product, one question started driving a lot of the development:

What would make somebody actually want to come back?

I didn’t want BoardSignal to become another tool someone opens once, gets an interesting result from, and forgets.

So a lot of the development has been deliberately geared around return behaviour and engagement.

I’ve spent time looking at product psychology, current engagement patterns and what other successful products do to create continuity without just adding meaningless gamification.

That influenced a lot of what BoardSignal became.

Instead of treating every visit like a reset, the product began remembering the player’s history. Reviews became something that could build over time.

Current guidance was added so there was something useful between completed Reviews. Coaching explanations could change when somebody returned instead of showing the exact same thing forever.

Players could ask for another explanation, react to whether guidance was useful, create notes, ask questions, and see their progress continue rather than disappear after one session.

A lot of the work was also less glamorous: debugging onboarding, fixing historical Review recovery, improving reliability, tightening account behaviour, making sure returning players landed in the right state, and removing little points of friction that could give someone a reason not to come back.

For a while, one encouraging signal was that some players were beginning to reach R2, R3 and R4.

That looked like progress because BoardSignal is intentionally longitudinal. It’s supposed to become more useful as your chess develops.

But continued development exposed an uncomfortable measurement problem:

a later Review existing is not necessarily the same thing as a player choosing to return and use the product.

Historical onboarding can legitimately create completed Reviews. A player can keep playing chess and the system can produce another Review.

Those are real outputs, but they aren’t automatically evidence of engagement.

So the measurement model had to evolve alongside the product.

BoardSignal currently has:

101 canonical active players

83 Reviews produced

53 players with at least one completed Review

Those 83 Reviews include 14 from the original manual process, 31 organic live Reviews and 38 historical onboarding Reviews.

I still consider all of that legitimate product history. I just no longer pretend it all means the same thing.

A newer engagement layer now tracks explicit behaviour separately: authenticated Player Room entries, first → second visit transitions, foreground engaged time, Notes, Ask usage, coaching reactions, requests for another explanation, and meaningful-return presentation changes.

That newer instrumentation currently has 42 authenticated Player Room entries and 8 confirmed first → second visit transitions.

And I’m deliberately not turning that into some neat percentage against all 101 players because the instrumentation was introduced later. The populations aren’t equivalent.

That has probably been one of the bigger lessons from building this.

I went from asking:

Can I automate this?

to:

Can I make it useful enough that someone wants to come back?

to:

What should I actually change in the product to encourage that?

and now:

How do I prove that those changes are producing real return behaviour rather than flattering myself with output metrics?

I’m still developing around engagement and retention. The difference now is that I have a stricter standard for what I’m willing to call evidence.

Disclosure: I built [BoardSignal](https://www.adminhub-global.com/)

Self explainatory home page and instructions...

For anyone building something designed for weekly or periodic use: what behaviour eventually convinced you that people weren’t just curious, but were actually forming a reason to return?

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?

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 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.

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 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

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?

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

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?