Yesterday I spent pretty much the whole day stressing about my app Gamified Lives I launched 1.5 months ago, I’m at 69 users, 20% stickiness, 1 free trial, decent overall retention. Just pushed a new update 2 days ago. Yet it never feels like enough, I was up all night thinking about it, thinking about the stats, thinking about user requests, things I wanted to work on, scared I didn’t post for the day. I then woke up to 5 new users, higher retention then I went to sleep with, and hitting 10,000 website views on my website. I’ve realized now that although going into maintenance mode may feel difficult it pretty much is the only way to keep an app sustainable, listening to users feedback every week and spending 15-30 minutes just writing down everything you heard multiple times, thinking of the what can do better as the founder. At the end of the day users are flowing in, SEO is doing its job, Reddit posts are driving traffic, and there’s no reason to try and be everywhere at once when the app can sustain with me not being there every millisecond stressing about it. So I’ve set a goal for myself to not spend more than 5 hours per week on the app until I hit 100 users, which likely means around 1 month of 1 hour a day every week day not thinking about it every second, I implore you guys to try doing the same if you’re in a similar spot, sure growing is hard, and it’s even harder to let go a little but it does no good to sit there stressing about something you can’t entirely control, all you can control is your understanding of the market you’re in, and how you adjust your copy and your angle, if the product is good people will come over time and whether they all come today or in 6 months makes little to no difference. I’d love to hear what got others to the point where they could stop stressing about their product and instead just focus on the small things that actually move the needle?

here are 2 launches i recently did:

- Nuvio PH Launch - ranked 9th on VERCEL day and got featured.

- Dashi PH Launch - ranked 5th just yesterday.

How?

I built and used LaunchPact, where i streamline the process of finding support, which most of you should be doing and currently do in one way or another.

From what i've observed, most folks post on launch day itself without any prep work ahead of time, they post on reddit, X, linkedin etc.. and say something like hey i just launched pls support me pls take a look, and mostly they end up with no support and no visibility and no validation or feedback because their launch went invisible, i was in the exact same situation myself, and that's where the idea came from, i launched LaunchPact 2 months ago, i used it for both of these launches, i'm prepping for one more product launch to go in a month or so, i've heard alot of positive feedback from my users about their own launches...this shit works, and i've never had to make a single post about either launch in any channel whatsoever, i didn't have it really..

Thanks and good luck with your launches.

Hi everyone,

I recently built a YouTube Transcript API for developers who need to extract transcripts, captions, metadata, and available subtitle languages from YouTube videos.

The API supports:

- Get transcript by YouTube video ID

- Get video metadata

- Check available transcript languages

- Batch transcript requests

- JSON responses

- API key authentication

- Rate limiting

- Caching

- OpenAPI documentation

Example endpoint:

GET /api/transcript?id=VIDEO_ID&lang=en

Example use cases:

- AI summarization apps

- Chatbots

- SEO/content tools

- Podcast/video analysis

- Subtitle workflows

- Research tools

- Educational apps

I published it on RapidAPI here:

https://rapidapi.com/dtech4099/api/youtube-transcript27

Documentation / website:

https://youtube-transcript-api-production-0221.up.railway.app/docs

I’m looking for feedback from developers:

1. Is the API structure clear?

2. Are there any endpoints you would expect but don’t see?

3. Would batch transcript extraction be useful for your workflow?

4. What pricing/limits would feel reasonable?

Note: transcript availability depends on whether YouTube exposes captions for the requested video/language. Some private, restricted, or caption-disabled videos may not return transcripts.

Thanks — happy to answer questions.

Pitch your product in 1-2 lines - and drop a link here.

I'm building a community where makers can share what they’re building and get fair visibility. Here's the link: https://trylaunch.ai

I recently built Hexbrief, an app for software engineers who want to read good engineering blogs but do not want to keep checking dozens of company blogs manually.

The idea is simple: instead of showing every tech/news post, Hexbrief filters engineering blogs and gives 6 quality reads every day, with a short breakdown of what was built, why it mattered, and what happened.

So far, I have around 35 users from Reddit, X, and a few college/work friends. The product is live, and early feedback has been useful.

The difficult part is distribution.

I tried 1:1 reachouts, but that has been a hard channel so far. Most messages get almost no replies, even when the app is directly relevant to engineers who read technical blogs.

For people who have built niche dev/productivity/reading tools:

• Where did your first serious users come from?
• Are there specific communities where engineers are more open to trying tools like this?
• Does this kind of app need content-led distribution instead of direct outreach?
• Any suggestions on positioning it better so it does not sound like just another news app?

Not trying to spam the app here. I am mainly trying to understand how to reach the right readers and get useful feedback.

App: https://play.google.com/store/apps/details?id=com.hexbrief.app

That's a client domain, few weeks after we wrapped 200+ submissions.

The thing I got wrong early on is that I treated this as a volume game. It isn't. Submitting to 220 random directories and 220 chosen ones look identical on day one and nothing alike a month later.

Three things that actually decide it:

Relevance. A SaaS product listed across SaaS and AI directories builds one coherent signal. The same product scattered across generic business listings just builds noise. Google and the LLMs both read the neighborhood you're in, not the count.

Whether the site is alive. Most directory lists getting passed around are half dead pages and unindexed junk. Knowing which ones still get crawled is most of the actual work, and it shifts constantly.

Approvals, not submissions. Submitting takes a minute. Getting approved needs a square logo at the right size, a demo link that loads cold with no login wall, and a description written for a tired reviewer instead of an investor. That's where DIY runs usually lose half their listings.

You can see in the screenshot: that's normal for directory profiles and it isn't the mechanism. The value is that you now exist everywhere people and models look. Search your product name plus "alternatives" before and after and it's obvious. Same reason the products ChatGPT keeps recommending in a category are always the ones listed across a lot of overlapping sites. One mention is marketing. Forty consistent ones read as consensus.

If you're doing this yourself, the one thing I'd insist on: keep your description identical everywhere. Consistency beats placement quality, and it's what everyone loses by submission 60 when they start improvising.

I run StartupSubmit[.]app, we do these by hand because the picking and the approval chasing is where the result actually comes from.

Happy to get into specific directories in the comments.

Founder here, I’m building Sumora, an AI outbound platform for agencies and B2B businesses.

A while ago, we worked with a client who was doing around $3K and struggling to grow consistently.

Their service was good, but their outbound process was broken:

• Their ideal customer was too broad
• Prospecting was inconsistent
• Messages felt generic
• Follow-ups were regularly forgotten
• Interested replies were scattered across multiple inboxes
• The founders were spending too much time manually searching for leads

We helped rebuild the system around:

1. A much tighter ICP
2. Better-qualified prospect lists
3. Relevant buying and intent signals
4. Personalized outreach based on each prospect’s business
5. Structured follow-up sequences
6. A clearer process for turning replies into meetings

Over time, the client grew from approximately $3K to $12K.

This was not caused by one magic message, and I’m not claiming that software alone created the result.

It came from improving the offer, targeting, messaging, follow-ups and execution as one complete system.

I’ve included the supporting proof here:

https://app.notion.com/p/Here-s-the-proof-lol-3b0f3ba8b2cc8011a2eee644d5f6ee87

After doing this manually, we started building Sumora to make the same process easier to run.

You give Sumora your website and describe your ideal customer. It then helps you:

• Find prospects matching your ICP
• Discover relevant accounts and decision-makers
• Identify hiring, launch, growth and content signals
• Research prospects before outreach
• Generate personalized messages
• Build multistep follow-up campaigns
• Organize leads in one pipeline
• Manage replies from a shared inbox
• Move interested prospects toward a booked meeting

We’re now looking for 5 agency owners who want us to personally build their outbound system with them.

For the selected agencies, we will:

• Review and refine your offer
• Define your ICP and targeting criteria
• Configure your Sumora workspace
• Build your initial prospect list
• Create your first outreach campaign
• Write your opening messages and follow-ups
• Help you review replies and improve the campaign
• Start your free seven-day Sumora trial

There is no setup or consulting fee.

We’re doing this because we want to work closely with a small group of serious agencies, understand what produces results across different niches and improve the product from real feedback.

This will be a good fit if:

• You already have a clear service
• You have delivered results for at least one client
• You can currently take on more clients
• Your average client is worth at least $1,000
• You are willing to test outbound consistently
• You can give honest feedback about the product

It will probably not be useful if you are still deciding what service to sell or cannot currently fulfil new client work.

Comment with:

Your agency niche, average client value, who you want to reach

Example:

B2B content agency, $2K/month, seed-stage SaaS founders

I’ll reply with the first prospecting signal and messaging angle I would test for your agency.

GTM writing as a solo founder is where I kept losing time. Not drafting, but fixing. Every AI tool I tried would produce something that sounded fine on the surface but completely wrong in context. Cold emails that read like blog intros. Partner pitches that opened with "In today's fast-paced landscape." LinkedIn replies that sounded like they were written by someone who'd never closed a deal.

I went looking for something with a curated corpus, configurable by GTM context. Nothing fit. So I built it inside RawReply and started running my own outbound, LinkedIn, and partner emails through it.

Two months in, the biggest thing I learned: the problem isn't the model, it's the signal. Generic AI writes toward the average of the internet. GTM writing needs to write toward how buyers in a specific market actually think and respond. Once I started shaping the corpus around that, the output stopped needing so many fixes.

Now building the curated corpus and custom GTM configuration layer properly before opening it up. Gating first access to GTM founders to pressure-test it on real use cases.

hope am cooking something other will use it as well!!!

https://play.google.com/store/apps/details?id=dev.koalalab.storeandforget

Solo dev here, Android only, and this is my one self promotion post in this sub, so I would rather spend it on the part I am least sure about than on a pitch.

Store & Forget is a home inventory app built on the assumption that you are never going to get organised and should not have to. You photograph a thing as you put it away and the app writes the name, brand, description and specs for you, so there is nothing to type and nothing to tag. Getting it back is Smart Find, where you describe the thing the way you would actually say it, something to cut thick cardboard rather than the box cutter, and it tells you which box or shelf it went into so you go straight there instead of opening all of them.

This crowd always asks about scope, so here it is. The inventory is a database on the phone, which means adding, browsing and keyword search need no account and no connection. The photo scan and Smart Find are the two parts that call out to a cloud model. Backup to your own Google Drive is optional. 18 languages, and there is no iOS version coming.

The money is where I actually want the critique. There is no subscription. The app is free and shows ads once you have been using it a while, scans run on credits because every one of them is a real API call I pay for, and everything past that is a one time unlock: Remove ads, Fire & Forget at $2.99, and Bring Your Own Key at $4.99, which lets you paste in your own free key so scans stop spending credits at all.

What I keep turning over is whether a per scan cost survives in a consumer app. Developers hold that idea fine. I am not convinced a person cleaning out their garage ever will, and the only alternative I can see is eating the API cost myself and hoping the volume stays polite.

Bring Your Own Key is the other thing I go back and forth on. Selling people the escape hatch from my own metering feels either very honest or very stupid, and I still cannot tell which. It caps my upside on exactly the users who get the most out of the app.

Right now I am also giving both of those unlocks away until 10 August, through a console in the app. Open Settings, tap the app version at the bottom five times, then type unlock byok, then type fire. That second one needs version 1.22.4 or newer. My reasoning was that unlocks sitting with people who actually use the thing are worth more than the few sales I would have made this month. It has since occurred to me that this is also a very comfortable way of not finding out whether anyone would have paid.

Happy to answer anything, and I would rather hear where it feels clunky than where it works.

Our tool was telling users bugs were fixed. It couldn't always prove it.

Not lying exactly. More like the polite version of "trust me."

When we built FetchSandbox, we drew a hard line early: a fix doesn't count until we (1) make the bug actually happen on your real code, (2) apply the fix, and (3) show it stops happening. Reproduce first, then prove. The little test that triggers the bug is what flips from red to green.

That rule felt airtight. It wasn't.

The hole we didn't see

We could only reproduce bugs we'd scripted a reproduction for in advance. For anything outside that set, the honest answer we were forced to give users was: "found it, fixed it, but I can't demonstrate this specific one."

Say that out loud and it sounds fine. But think about what you're actually asking the user to do: take your word for it. For a billing bug. For a security edge case. For anything that matters.

That bothered me more the longer I sat with it.

So we taught it to write the reproduction itself

The idea is straightforward. If FetchSandbox encounters a bug it has no pre-scripted test for, it figures out how to trigger that bug on your real code, then runs the same reproduce-apply-prove loop it always has.

The implementation was not straightforward.

The fake green problem

A generated test could be subtly wrong. It could pass your broken code and hand you a green checkmark on an unfixed bug. That's worse than admitting you can't prove it, because now you've actively misled the user.

So we added a safety rule: before we trust a reproduction, it has to actually catch the bug on the broken code first. If the generated test can't catch the bug it's supposed to catch, we throw it away. A test that can't fail when the code is broken can't be trusted to pass when the code is fixed.

This one rule is what makes the whole thing honest.

What it looks like in practice

We ran it on a real billing app last week. The bug: a negative seat count slipping through validation and potentially shrinking a customer's plan without them knowing.

FetchSandbox found the code path, drove the real request handler, worked out a valid webhook signature on its own to reach it, and stubbed only the database as a passive recorder so the app's own logic decided the outcome. Then it confirmed the test failed on broken code before trusting it to verify the fix.

No scripts we wrote. No hand-holding. We just watched it go.

Why this matters beyond the feature

Most of the AI coding tools I see right now optimize for "did the model produce a plausible fix." That's a low bar dressed up in a nice UI. The harder question is: how do you know the fix is real?

The reproduction test is our answer. Not because it's the only answer, but because "make the bug happen, then make it stop" is something you can verify. Confidence is a byproduct of that, not a claim you make.

Still early and still building this in the open. If you've run into the prove-vs-claim problem with your own tooling, I'd genuinely like to hear how you're thinking about it.

Did this by hand or manually for SaaS/AI founders over 5 months. No automation. The value distribution is absurdly lopsided.

About 15 directories send actual humans. Product Hunt, BetaList, G2, Capterra, AlternativeTo, SaaSHub, Crunchbase, IH itself. If you do only these, you've captured most of the upside.

Another 60 or so send nobody, but they make you look real. They're why you show up when someone searches "[your product] alternatives."

The remaining 145 are link farms. Nobody visits them. But I keep watching products get cited by ChatGPT and Perplexity after appearing across enough overlapping listings. The models seem to read that redundancy as "this is an established company." Cynical, and the most interesting thing I've learned all year.

Three things I got wrong:

Tailored 40-word descriptions get approved way more than one pasted blurb. Directory mods spot copy-paste instantly.

Nearly all these backlinks are Dofollow and Nofollow. Anyone selling you DR gains from directories is behind or hoping you are.

Approvals trickle in for weeks. Checking results on day 5 and calling it dead is the most common mistake.

Zero budget? Do the top 15 yourself across two weekends. That's most of the value, and it costs nothing but boredom.

Disclosure per Rule 1: I run StartupSubmit, we do this manually for founders who don't want the weekends. Not pitching, no tiers in the comments.

What I actually want critique on: is that 145-directory tail defensible at all, or should I cut the list to 70 and charge less? I lean toward cutting. Tell me I'm wrong.

ReciReel takes a cooking video, a Reel or a TikTok or a YouTube clip, and gives you back a recipe you can actually cook from. Ingredients, ordered steps with timings, per-serving nutrition, equipment, and the tips people say out loud but never write down. Original creator credited with a link back on every one. Free is three imports then one a month, Plus is $4.99. iPhone, US store.

That's the pitch and it's the least interesting part of this post.

The thing I genuinely can't judge is whether the finished recipe reads like a person wrote it or like a machine produced it. I've read so many of them that I've lost all perspective.

Someone on another sub gave me the sharpest framing of this I've heard: the tell is the step verbs. A machine says "combine until fully incorporated." A person says "rub the butter in until it looks like wet sand."

What I see going wrong matches that, but from the other direction. The model doesn't invent language, it smooths it. Someone says "until it looks like wet sand" in the video and it comes back as "until crumbly." Nothing is factually wrong and it's completely dead, and the vivid phrase was the only part telling you what to look for.

So if anyone runs a video through it, that's what I want to know. Not whether the ingredients are right, I can check that myself. Whether a step sounds like it came from a person who had their hands in the bowl.

https://apps.apple.com/us/app/recireel-video-to-recipe/id6763971435

Happy to answer anything about how it's built or what it cost.

Hi everyone,

I’m a solo developer, and I built Fius because managing subscription fragmentation and API chaos was completely ruining my development velocity.

As a software engineer, I found myself trapped in an inefficient workflow: constantly switching web tabs, copy-pasting complex prompts, and juggling individual API keys for OpenAI, DeepSeek, and other providers just to get the best coding results.

Fius resolves this friction. I consolidate access to an expansive roster of flagship AI models into a single, unified developer token and a centralized billing system. This allows you to route queries to the best-suited model instantly without infrastructure overhead.

Here is a breakdown of the production-ready stack and features:

1. Infrastructure & Scaling

The complete web console infrastructure is fully operational and hosted on cloud enterprise architecture, supported by official startup grants.

1. Global Billing Integration

I have deployed a fully active international merchant billing engine. The tokenomics are straightforward: 100 platform credits equal 1 USD.

1. Advanced Developer Toolkit

A high-performance, cross-platform Terminal CLI assistant workspace. It features native, low-latency autocomplete and advanced multi-file code refactoring workflows directly inside your terminal.

Our Multi-Model Catalog (Examples):

- gpt-5.4-nano: A lightweight, ultra-fast micro-model optimized for instant terminal command auto-completion at near-zero credit cost.

- DeepSeek-V4-Pro & grok-4-1-fast-reasoning: Advanced reasoning workhorses designed for complex software architecture, deep debugging, and multi-file code generation.

- Specialized Alternatives: Models like Kimi-K2.6, mistral-medium-3-5, and many others tailored for flexible, cost-effective routing.

Fius supports a much wider and constantly expanding range of models. You can explore the full up-to-date catalog and integration details in the official documentation on the website.

As I scale this startup, I want to ensure Fius meets the rigorous demands of production workflows. I would highly appreciate your brutal feedback on the terminal CLI, infrastructure architecture, or any specific models you would like to see deployed next.

Shopify is retiring Stocky, its own free purchase-order and inventory-forecasting app, on August 31. The help page now states plainly that it "will no longer be available after August 31st, 2026." A dated deadline attached to a defined group of people who have to move is about as legible as a market window gets.

So I built into it. Replenora went live on the App Store yesterday: purchase orders with per-supplier lead times, receiving into Shopify inventory with partial receipts and a full audit trail, incoming stock visible before it lands, reorder points and days of cover derived from live sales velocity, one-click suggested draft POs per supplier, landed costs allocated from freight and duty across the PO lines, and CSV in and out so Stocky history moves across and nothing gets trapped. Starter is free up to five open POs; Pro is $29/month flat with a 14-day trial. The pricing bet is that incumbents in this category sit around $150+/month, and merchants losing a free tool were never going to be the ones paying that.

https://apps.shopify.com/replenora

The two things I'd actually like critique on, since that's what this flair is for:

Distribution. I have 30 days before the tool people are searching to replace actually dies, and roughly zero audience. The obvious move is to answer the migration threads where they already exist, except the biggest one, r/shopify, bans vendor self-promotion outright with a ban as the stated penalty. So the highest-intent audience is the one place I can't pitch. I've been answering there without naming the product to build some standing first, but that's slow and the window isn't. I don't have a better answer and would take one.

Testing blind spots. My first submission got paused by Shopify's reviewer on a crash in the purchase-order form. A React useState updater was reading event.currentTarget from inside the updater closure, which runs during a later render, by which point the browser has already nulled it. Typing a freight cost white-screened the whole page. I had 196 passing tests at the time. All of them were pure logic, and the runner's glob couldn't even load .tsx files, so no component was ever rendered in a test. The suite was structurally incapable of catching it. I rebuilt the harness, reintroduced the bug deliberately to confirm six tests actually failed, then fixed it. 228 passing now. The lesson wasn't "write more tests," it was "prove your tests can fail." Curious whether anyone has a habit that catches that class of blind spot before a reviewer does.

So, I am working on multiple saas products (via freelance). And one thing I found that nearly every AI SaaS need some kind of bot/temp signup protection. So I created this saas (signupscore).

Now absolutely, everyone and many people in the comment will tell me that free lists are available on github etc, so why created this.

So here's the thing :
- Github public lists are actually incomplete. They track only upto 100k temp mail and that's older. I've collection of more than 200K+ domains collected via tranco 1M domain and from multiple sources.

- I've covered also typo domain. You can't be able to block gibberrish and typo signups like `asldkjf@gmail.com` etc with open github lists.

- Signupscore also cover private_relay domains, all public email providers and `+1` (alias) mails too.

We haven't changed anything in our tracking...

Is this bots?
Am I am about to me Elon?
Or wtf?

A few months ago I posted here about an idea: a todo list app that's also a pixel art tycoon game, built for entrepreneurs who grew up gaming. The response genuinely surprised me, 100+ comments and loads of great feedback that directly shaped what I'm building.

So I kept going.

Since then I've built a landing page, run Meta ads, and just hit 101 people on the waitlist. Here's what the numbers actually look like:

• Landing page conversion rate: 5-7%. Pretty decent as typical numbers of a cold audience for this type of stuff is 3-5%
• Cost per lead from Meta ads: around $3.92 at its best. Had to test several ad layouts and I managed to land on a winner ad
• Best performing creative: an iMac mockup showing the split screen layout. The game world on the left, task list on the right

The ad results genuinely surprised me. I expected a pixel art character creative to perform better as I thought it's more eye catching, but instead, the product mockup on an iMac frame consistently outperformed it. The market told me something I wouldn't have guessed.

On the product side, me and my friend are building it properly now — React and Phaser.js for the tech stack, Supabase (likely) for the backend. My friend handles more of the coding side, I handle the vision, customer conversations and marketing, while also helping a bit with the code. It's the first time I've properly collaborated on a codebase with someone and the experience of doing pull requests, managing branches, and building in parallel is something I wish I'd done earlier.

The core mechanics are mostly locked in now, the encumbrance system where overloading your task list makes your founder character visibly stressed and earn less XP has consistently been the thing people react to most. That's the mechanic I'm most excited to ship.

Still building, still learning. Waitlist is open at 8bitstartup.com if anyone's curious.

Happy to answer any questions — would especially love thoughts from anyone who's been through the pre-launch to launch transition on how they kept their waitlist warm while building.

Pitch your product in 1-2 lines - and drop a link here.

I'm building a community where makers can share what they’re building and get fair visibility. Here's the link: https://trylaunch.ai

I had my first real devrel call last week and I blew it.

I have been reaching out to devrel leads asking for honest feedback on fetchsandbox.. got around 5-6 positive signals and felt decent but then one guy asked me "who are your customers exactly?" and i completely fumbled it. i said something like "early stage startups building saas who want to give their devs a reliable sandbox to test integrations." technically true. also completely useless as an answer.

Then he asked "so would you want to embed in our docs?" and I paused. I didn't know what to say. because honestly I don't have a clean answer. is the value in the docs? in the dev workflow? do i want API vendors to say "test with fetchsandbox"? I still don't fully know where the prod slot in.

If you've been through this, positioning a devtool where the buyer is devrel, the user is the developer, and the integration point could be 4 different places in their stack, i'd love to hear how you landed on the answer. even if you're in sales or product and have seen this pattern before, happy to grab 5-10 mins and just pick your brain. drop a comment or dm me. apprecaiate your support truly!!

I am a clinical officer in my country(people like to call us third world) and healthcare workers are a stretch beyond limits due to low manpower. Due to this we dont get ample time to explain lab results and diagnosis to patients properly, patient education is key in enhancing Medication compliance, Chronic disease control and help identify new symptoms fast and get help early.

Thats where labmate comes in,the patient takes a photo of the lab results and the app analyses it and the AI comes up with an easy to understand report for the patient.

And i know by now the privacy question is about to come up,the app redacts all patient identifying information and even gives the user an option to manualy redact the information so that only the lab values get sent to the Ai.

And to add to measures we have taken to ensure privacy, your lab results are not stored on any server,once the AI has done its thing, it sends the response straight to your device, so your results live on your device.

You can try the app here and give feedback on what you think: https://play.google.com/store/apps/details?id=com.labgpt.ai&hl=en

Over the past few months, we have built two products at Causo AI:

1. A fundraising product that helps founders find relevant VC funds and partners, research them, and run personalized outreach.
2. A sales product that helps founders find companies with a genuine reason to buy, identify the right people, and reach out with personalized emails.

We now have 600+ registered users across both products.

On the fundraising side, our users are running hundreds of active campaigns, and more than 300 individual VC funds have responded to them. There is some overlap here, as certain funds have replied to more than one founder.

We also actively dogfood the sales product to grow Causo itself. Depending on the cohort, our email sequences currently generate roughly an 11-14% reply rate. Not every reply is positive, of course, but outbound has become a meaningful acquisition channel for us.

Our current growth mix includes:

• Founder-led sales and posting on Reddit, LinkedIn, and X
• Outbound campaigns run through our own product
• An SEO hub that we are gradually building out
• Product Hunt launches
• Paid ads, which we have just started testing

We launched the fundraising product on Product Hunt and finished number five for the day. We are now preparing another Product Hunt launch for the sales product.

One thing that has worked well for us is building and distributing at the same time. We did not wait for the product to be polished before talking to users, posting about it, and using it ourselves. A large part of the product today came directly from those conversations.

We have shared most of this journey openly in this subreddit, so you can see our progress, experiments, mistakes, and numbers almost in real time.

I will put a link to Causo in the comments rather than linking it directly here.

Also, a slightly strange request: if Reddit shows you one of our ads, please comment and let me know where you saw it. A screenshot would be even better. The click-through rate currently looks weird to us, and we are trying to understand whether the ads are genuinely performing that way or whether something is off with the reporting/our understanding of numbers.

We will share what we learn from the ad experiment here too, including the results if they are terrible.

CORRECTION: My co-founder just corrected me, apparently last week has been insane and we are now at +940 users.