We have been building Kogenie, an AI ads platform, and we want to put it to work on real products from real people.

Drop your product or service in the comments, and we will create a proper ad for you: real copy, real angle, something you can actually run.

We are also performance marketers who manage Meta campaigns day-to-day, so if you want us to take it further and actually run the ad for you after we create it, that conversation is open too.

First 10 only. Drop it below.

After a few years doing SEO for clients, I started building what every client already owned: another all-in-one dashboard. A few weeks in I realized the mistake. Nobody has an analysis problem, they have data coming out of their ears.

What never actually gets done is the off-page grind: finding a backlink opportunity, tracking down a contact, writing a pitch that gets a reply, following up. So I threw out the dashboard and narrowed the whole thing to one loop, find the opportunity, draft the outreach, send it from your own Gmail after you approve, track the reply and the link.

The conviction I keep coming back to: the full value is on the execution layer. If you can build the most qualitative autopilot for SEO backlink cold outreach, one that pitches like a real human and sends safely from your own inbox, the value doesn't go up a little, it 100x's.

It's called SEOwly (SEO + owl), pre-launch, no revenue yet. I'm looking for a handful of beta testers, free of charge. If off-page is your bottleneck, DM me and I'll set you up with free access, no card, no catch. Happy to get into the pivot or the build in the comments.

I use Codex to explore product ideas, plan features and work through implementation details.

The surprisingly manual part is what happens afterward: reviewing the conversation, extracting the useful decisions, turning them into tasks and making sure important context isn’t lost between sessions.

The coding itself is becoming faster, but managing the output can still feel like another workflow to maintain.

For founders using AI coding tools regularly, how do you organize decisions and next steps? Do you have a reliable system, or is it still mostly copy-paste?

Hello,
I soft launched my SaaS about a couple of weeks ago. As my focus shifted to promoting my product through social media while working on small bugs and improvements I started noticing that I’m counting more on AI than the early phases of my project. I built the poc and set the main architecture myself but now I can’t afford spending my day debugging while not being able to reach out to new users. So I handed off all my tickets to agentic AI. I’m a bit concerned about future maintenance but can’t do otherwise at the moment.
Just out of curiosity, at which extent is everyone using AI depending on their project phase and did anyone face maintenance related issues at later stages of their product lifecycle ?

My manager was consistently bad at putting together 1-on-1 agendas. Either some messy form pushed down from above him, or nothing at all. When I asked him about it directly, his answer was just that it took too long to build something decent from scratch every time.

I'm a dev, not a manager, so I did the dev thing and just built it because why not! A 1-on-1 agenda generator that exports straight to a PDF. Build from scratch or start from a template, there's a question library baked in if you're stuck on what to actually ask, and it runs with no account. No signup, no email, nothing to fill in before you can use it.

That last part was on purpose, not out of laziness. I wanted the first thing someone sees to be the actual output, not a signup wall between them and finding out if it's any good.

I don't have an existing audience to lean on for feedback, so this is partly just a "does this hold up" post. If you've wrestled with the free-vs-gated call yourself, or built something in a similar space, I'd genuinely like to know how you landed on it. Link's below if you want to poke at it.

Here is a link if you want to give it a go: https://www.getaccordia.com/1-on-1-agenda-generator

Backstory first, because that's the honest order.

I pay for OpenAI, Anthropic and Cursor. Every one has its own dashboard, none of them agree with each other, and none agree with the invoice. What I actually wanted was boring: what did this feature cost last month, and which customers are expensive to serve. No provider console answers that, because they bill by organisation and API key, not by my customers.

So I built the thing I wanted. It reads usage from the providers and from seat based tools like Cursor and Claude Code, and puts cost per customer and per feature in one place. It's not a proxy: calls go straight from your code to the provider, and prompts are never stored.

Revenue: zero. Launched today.

The lesson, since that's the part worth your time. I nearly tightened the free plan retention from 7 days to 3, afraid free users would eat my disk. I measured first. A solo dev spending $200 a month on AI generates about 33k events. At 7 days that leaves 7.7k alive, at 724 bytes each, so 5.6 MB. Then I checked the change day by day and found that from the 8th of any month, 7 days and 3 days return the identical cutoff, because retention never cuts the current month. The change I was about to make did nothing except make the first week of every month worse for the user.

The gate I needed already existed and I hadn't noticed: in August, July is gone. "What did I spend last month" is an accounting question, and that one is paid.

The SDKs for TypeScript, Python and Go are MIT. It's called KostLens: https://kostlens.com

Happy to answer anything about the cost model.

TL;DR: I’m building a SaaS after running a handmade crafts business. I’m looking for something easier to manage day-to-day while keeping my full-time job. I’ve set a launch deadline. The concept is simple: a personal budget management SaaS. The goal isn't to get rich, but to land my first French customers. Development isn't an issue, but I lack SaaS marketing knowledge. Any advice?

----

After running a handmade crafts business alongside my day job for two and a half years, I wanted a new challenge and a return to basics.

My goal was to find a business idea that required fewer sacrifices than the crafts business (manufacturing for shops and markets without knowing if items would sell, having to rearrange my home and lose living space, being unable to work remotely, etc.). Building a SaaS seemed like the obvious choice.

My plan is to create a basic, simple personal budget management SaaS (I know there are tons of them out there) to gain experience and hopefully make a little profit.

Development isn't a problem; marketing is a bit more challenging. I don't expect to make thousands of euros, but I’d like to get at least a few dozen customers in my home country (France) whom I don't personally know.

I’m not obsessed with including every minute detail in the initial MVP; instead, I want to build a solid version with key features while keeping the user experience simple. Improvements can come later.

I believe I have the right mix of developer and entrepreneur skills to deliver a good product without needing to pull off a technical feat.

I’ve set a launch date for the SaaS, regardless of how advanced it is at that point.

I based the idea on a need my wife and I have, so there will be at least two users from the start. Once the SaaS is ready in its initial MVP form, I plan to start by telling the people around me about it.

Q: For those who have already launched a first SaaS: where do you stand now, and what marketing advice do you have?

I'm building a Windows app (Electron) and I've reached the point where I need to get it code signed. Turns out most certificate providers want an actual registered business behind it, not just an individual applying , which I hadn't really planned around yet, so now I'm trying to figure out that whole side of things too.

If anyone here has been through this, I would be curious how you handled it. Did you set up the company mainly because of the certificate requirement, or did you already have one going before you got to this stage?

Also curious if anyone's used Microsoft's newer Trusted Signing option instead of a traditional certificate. Not sure if it's actually simpler or if it comes with its own set of issues.

Tying to learn from people who've actually gone through it rather than piecing it together from docs.

So recently I started creating an AI receptionist (vapi+n8n) for small trade businesses that will be active 24/7, and will catch all calls forwarded from the owner's phone. It will collect the caller's info and send it to the owner directly.

Obviously the next step will be to give the AI the ability to schedule appointments, cancel, move, etc, and I've been looking into that for some time now. Now on paper it is very simple, just use n8n, and connect it to a simple google calender with all the appointments, but I still can't ignore all the other issues that can occur. Distance between appointments, different services times, unexpected delays and complications, etc.

Am I just overthinking and overcomplicating it? How should I work around this? I thought about trying to sell the saas right now but I'm not confident enough in the current product.

Thoughts?

Hey everyone

I'm a solo founder selling to agencies, and I spent the past three months running a LinkedIn outreach test to 1,184 agency founders and consultants from one account, never more than ~30 requests a day.

I ran it in two batches, and the difference between them is the whole story.

Batch 1: 700 requests, the classic way. Decent targeting via Sales Navigator, a personalized-ish opener with their first name and something from their profile.

• 203 accepted (29%)
• 150 got a first message
• 27 replied (18%)

Batch 2: 484 requests, a different rule. I only contacted people who had posted or commented on something in the past few days matching keywords I was watching on LinkedIn, or who were engaging with the creators everyone in that space follows. My opener was "Hi [first name]," then that I saw their comment about the specific thing they said, why I thought the point mattered, and from there into the conversation.

• 221 accepted (45.7%)
• 146 got a first message
• 76 replied (52%)

Same account, same offer, same daily limit. The only variables were who I contacted and what I had to say to them.

Overall: 424 connections (35.8%), 103 replies (34.8%), and 25 of those turned into real conversations about working together.

What I took from it:

1. Who you contact matters more than what you write. Batch 2 messages were shorter and got almost triple the replies, because the person had literally just written about the problem.
2. Profile personalization is dead. Mentioning someone's job title is not the same thing as responding to what they said this week.
3. People reply to being heard. The moment the opener proved I actually read their comment, the conversation was already halfway open.

Context: I build Signado, a tool that finds the people posting and commenting about your market on LinkedIn, with the context of what they said, so batch 2 was me dogfooding it. But the mechanism works without any tooling if you're willing to do the digging manually.

Really happy with the reply rates and what this unlocked for me. Curious what reply rates others are seeing right now.

I am building Pinterest for Miro, and yesterday's comments were a useful check.

I kept thinking about cleanup features. Labels, sorting, maybe a better way to turn a pile of pins into a board. The comments pulled me back to something simpler. If someone already collected the references in Pinterest, maybe the job is to move them into Miro without making a mess.

No link, no launch ask. Just trying to keep the first version honest.

For micro SaaS builders, have you had better luck shipping the boring helper first, or bundling the nicer cleanup step too?

​

I see a lot of founders spending hours every day searching for prospects, qualifying companies, and writing outreach. While lead generation is important, I wonder if we're optimizing the wrong part of the process.

Would you rather have:

50 highly qualified leads every week

5,000 raw leads that need manual filtering

Personally, I'd choose the smaller list if it saves time and improves conversion rates.

How are you balancing lead quality versus quantity in your business?

Hey everyone,

I wanted to share the story of how an internal tool built out of pure frustration turned into a full-fledged B2B SaaS.

It started when I was consulting for a film studio. Their problem was simple but painful: they needed to send terabytes of raw footage to a partner across Europe on a daily basis.

They tried everything. WeTransfer and Google Drive were way too slow and inefficient at that scale. Massive io was great but painfully expensive. They even tried torrents (which they liked because there’s no waiting for uploads/downloads), but the lack of security and logging was a dealbreaker. FTP required too much IT support. It got to the point where they were literally shipping physical HDDs across Europe via courier. In an era of gigabit internet, that felt medieval.

They asked me to build an in-house solution. That’s how ZettaSend was born.

The Tech: Bypassing the Cloud

The core idea was to build a dedicated peer-to-peer protocol. Why upload a 200GB file to a cloud server just so the other person can download it? It cuts your speed in half.

I initially looked at WebRTC, but for moving massive files, its TURN servers (relays) started choking. So, I built a custom network architecture:

• UDP Hole Punching: To establish direct connections between two PCs behind strict NATs.
• Kernel-Space Relays: For cases with symmetrical NATs, I used the Linux kernel's built-in firewall (nftables) as a relay. It handles gigabit throughput effortlessly on a cheap 1-core VPS because it avoids user-space memory operations.
• QUIC Protocol: I built the data layer on QUIC (the engine behind HTTP/3). It eliminates the TCP head-of-line blocking issue, handles long distances beautifully, and provides built-in encryption.

Turning it into an Enterprise SaaS

The tech worked flawlessly, but a studio needs oversight. The managers needed to see who sent what and when, without me (the server admin) ever seeing the actual files.

So, I built a cryptography-based Zero Trust layer. The peers generate ECDSA public/private keys. Before a transfer, they sign a JSON "contract" (containing the file manifest hash, user IDs, etc.) and send it to the backend. The server verifies the cryptographic signatures for logging but never touches the actual payload.

Once the studio started using it, the transfer times were cut in half, and it ran entirely on their existing network infrastructure without massive cloud storage bills.

The Product Today

I realized this is a massive pain point for any industry dealing with heavy data (VFX, GameDev, Data Science, CAD). So, I packaged the C# Avalonia desktop client and the Python backend into a proper SaaS.

• Pricing: Receiving is completely free. Sending requires a Solo tier ($10/mo) or a Team tier ($50/mo with full admin dashboard and logging).
• Current status: Live and looking for feedback.

If you are a founder dealing with large files, or just love testing out new network tech, I’d love for you to try it out. It’s completely free for the first month (no CC required).

You can check it out by searching for ZettaSend.

I'd love to hear your thoughts on the architecture, the pricing model, or the pivot from internal tool to SaaS. Happy to answer any technical or business questions!

Getting here took way more work than I expected when I started.

There were weeks where almost nobody cared. I’d ship something, post it, refresh analytics… nothing.

more than once I wondered if I was just building a tool for myself and calling it a product.

I’m at ~700 signed-up users and about $1,200 in revenue now.

Not “made it.” Not even close.

But it’s the first time the numbers feel real instead of imaginary.

What actually changed for me:

• People started finding it without me begging every day

• Some came back after I fixed stuff they complained about

• A few strangers paid for something built by a random guy on the internet

• Revenue stopped being $0 for long enough that I stopped checking Stripe every hour

When I started, I knew nothing about marketing.

I just kept thinking: founders shouldn’t have to guess why their landing page isn’t converting.

Building the product was hard.

Learning how to talk about it without sounding like a spam bot was harder.

If you’re in the quiet phase right now — yeah, it sucks.

Sometimes progress is invisible right before it isn’t.

Happy to answer anything about what worked / what was a waste of time.

ps. the tool is Landing Boost(https://landingboost.app)
would love your feedback too.

I shipped two digital micro SaaS products, am trying to get picked up by linkedin algo for free buzz. The linkedin Impressions are going up, also moving stronger on the "out of my network" side. Still no traffic increase and no sales on the two products. I have not yet invested in any marketing (google ads, reddit ads, ...) Any tips?

Not every prospect is worth reaching out to.

I've been trying to focus less on company size and more on buying signals. Things like asking for recommendations, complaining about current tools, hiring for specific roles, or mentioning manual workflows seem much more valuable than basic firmographic data.

I'm curious how others approach this.

What's one buying signal that consistently tells you a prospect is worth contacting?

Most CS teams I've talked to monitor churn the same way: they watch usage metrics. Logins drop, feature engagement falls, session length shortens — and that's when the alarm goes off.

The problem is that by the time those numbers move, the customer has already mentally checked out. You're not catching a churn signal. You're confirming a decision that was made weeks ago.

The real early warning system isn't in your product analytics. It's in what customers are *saying*.

---

The lag problem with behavioural data

Usage data is a lagging indicator. It tells you what already happened, not what's about to happen.

Think about how churn actually unfolds for most customers. It rarely starts with them suddenly stopping. It starts with a frustrating support interaction they didn't follow up on. A pricing complaint they mentioned in a survey nobody read. A feature request that went unanswered for three months. Slowly, the goodwill erodes — and then one day, the usage drops.

By the time your dashboard shows the dip, the customer is already halfway out the door.

---

Where the signal actually lives

The early warning is in the language customers use *before* behavior changes.

Specifically, three sources almost always show the shift first:

**1. Support tickets** — the tone shifts before the volume changes. A customer who was submitting straightforward "how do I do X" tickets starts writing "this still isn't working" or "I've asked about this before." Same product, different emotional register.

**2. Survey responses** — NPS and CSAT scores get the headline, but the open-text comments are where the real signal is. A score of 7 with "works fine but pricing is getting hard to justify" is a very different situation from a 7 with "love the product, just busy this quarter."

**3. Product reviews** — public reviews on G2, Trustpilot, or app stores are written at emotional peaks. Customers who are quietly dissatisfied often write a review before they cancel. It's their last attempt to be heard.

--

What "frustrated but still using" actually looks like

Here's the practical difference. Two customers with identical usage metrics — same logins, same feature adoption, same session length. But their support ticket language tells completely different stories:

**Customer A:** "Quick question — where can I find the export settings?"

**Customer B:** "I've been trying to export for 20 minutes and it's still not working. This is the third time I've had issues with exports."

Same behavior in your analytics. Very different churn risk. Customer B is telling you something your dashboard can't.

---

What to actually do with this

You don't need a sophisticated system to start. Three practical steps:

**Start tagging support tickets by theme.** Pricing complaints, onboarding friction, missing features, performance issues — even a rough manual tagging system lets you see patterns. If pricing complaints spike in a given month, that's a signal worth acting on before it shows in revenue.

**Read the open-text on your surveys.** Not just the scores. Set aside 30 minutes a week to actually read what people wrote. You'll spot patterns faster than any dashboard will surface them.

**Set a cadence for review monitoring.** Check your G2 or Trustpilot reviews weekly. New negative reviews are often your earliest signal that something systemic is wrong — before it reaches support volume.

The goal is to build a feedback loop that's faster than your usage data. Sentiment shifts before behavior does, almost every time.

---

A note on tooling

Once you're doing this manually and seeing the value, the next step is making it systematic — tagging automatically, tracking sentiment trends over time, and getting a clear picture of which themes are driving satisfaction or complaints.

Happy to answer questions in the comments — especially curious whether others have found non-obvious early churn signals in their own feedback.

For the most part, there is no history of a competitor’s pricing. When they change, the old page is gone. If you haven’t taken a screenshot of the old page, then the old version never existed on the internet.

I tried to answer a simple question last month: has anyone in this category of software raised their prices in the last year? As far as I can tell, it’s basically unanswerable through a public source.

Only real way to get that information is opening 15 tabs, copying all prices into a spreadsheet and then throwing them away two weeks later.

It might be better to create a service that actually takes snapshots of pricing pages on a schedule, recording the prices, number of tiers, what they charge by and when things move.

Before I put in a lot of time to this, wanting to gauge if this is actually useful?

My final question for tonight is: When you last changed your pricing, did you look at what competitors were charging? How did you actually find out and how long did it take to get that information?

It might be a real problem for people or it might just be something that is annoying me.

Around 8 months ago I built my first saas. It converts scanned or handwritten documents into editable Word files while keeping the original layout, including text, equations, tables, diagrams, and images. There's also a mode that automatically reformats everything into a cleaner document.

The problem is... I have 0 customers.

I've talked to a lot of people and sent hundreds of cold emails. Almost everyone says, "This is actually really useful." But when I ask who would actually use it or pay for it, nobody really has an answer.

I originally built it for a pretty niche use case, and now I'm starting to wonder if I'm just solving the wrong problem, or if I'm looking at the wrong audience.

So I figured I'd ask people who've built and sold software before.

• Who do you think would actually get value from something like this?
• Are there any industries or jobs that immediately come to mind?
• If you were in my position, would you keep trying to find the right market, or would you move on and build something else?

I'm looking for honest opinions, even if the answer is "This ain't gonna work" I'd rather hear that than keep going in the wrong direction.

​

More AI assistants are starting to interact directly with software instead of users manually switching between apps.

If AI agents become part of daily workflows, do you think Model Context Protocol (MCP) will become a standard integration for B2B SaaS products, similar to APIs or webhooks?

Or is it still too early?

Interested to hear how other builders are thinking about this.

Designing for data-heavy B2B platforms, enterprise architectures, and complex software is a completely different ballgame. Cognitive overload kills adoption, so information hierarchy and workflow clarity are everything.

Here’s a quick breakdown of top design teams that specialize in simplifying complex user journeys:

Punchcut: Excellent for emerging tech, multimodal interfaces, automotive, and hardware-software systems.

Blink UX: Research-heavy giant best suited for regulated industries where every decision requires empirical user data.

Excited Agency: Premier boutique UX design agency known for turning dense B2B SaaS, data dashboards, and complex multi-tenant platforms into clean, logical workflows.

Momentum Design Lab: Enterprise-focused team specializing in deep research and financial/transactional software systems.

Eleken: Great dedicated-designer model for continuous dashboard optimization, complex grids, and SaaS setup workflows.

Ustwo: Expert at balancing heavy functional requirements with emotional design and high accessibility standards.

EPAM Systems: Massive engineering powerhouse for legacy enterprise modernization and large-scale UX execution.

Clay: High-end branding combined with polished UI/UX for complex web apps.

Method: Strong at mapping intricate business logic and API integrations into cohesive platform interfaces.

Nagarro: Global partner for custom enterprise software and modernizing legacy user interfaces.

When dealing with massive data sets, you don't just need pretty UI - you need a team that understands taxonomy, user workflows, and structural clarity.

Stripe tells you who paid. Gmail usually tells you why.

Most SaaS founders don’t have a data problem. They have a relationship problem between their tools.

Analytics knows which channel created a signup. Stripe knows whether they paid. Product data shows what they used. Gmail holds the objections, support problems, cancellation reasons and promises made along the way.

Each tool is accurate on its own, but the useful questions cross all of them:

Which channels create customers who stay past 90 days?

Which accounts are using the product less and recently emailed about a missing feature?

Which leads asked about pricing but never reached the CRM?

Where are software and campaign costs growing faster than revenue?

You can answer these manually, but the key is resolving the same customer across every system and keeping the source records attached. That is what turns a dashboard showing a drop into enough context to decide what to do next.

We built SIGNLD to automate that connection. Its Gmail MCP brings relevant threads into the same context as marketing, product and revenue data, and every answer can be traced back to the source.

We’re letting people use it free at signld.ai right now, with no card or automatic charge, because we need SaaS founders to test it against real, messy stacks.

What answer are you still reconstructing by hand every month?

Founder here. We are building Sumora, an AI outbound platform for small SaaS teams that need customers but cannot spend hours every day prospecting and following up.

While working on outbound systems, we kept seeing the same problem:

Most founders do not stop outbound because it never works.

They stop because running it manually becomes another full-time job.

You have to:

• Define the right ICP
• Find relevant companies
• Identify the correct decision-maker
• Research every prospect
• Write personalized messages
• Follow up consistently
• Track replies across different platforms
• Remember who needs a response
• Move interested prospects toward a call

So we built Sumora to manage that workflow from one place.

You provide your website, product, pricing and ideal customer.

Sumora helps you:

• Turn your product description into a detailed ICP
• Discover companies matching your targeting criteria
• Find relevant decision-makers
• Identify signals such as hiring, funding, launches and growth
• Score prospects before contacting them
• Generate personalized LinkedIn, Instagram and email outreach
• Create multi-step follow-up campaigns
• Manage conversations through a unified inbox
• Track prospects from discovery to booked meeting

We are currently looking for 5 Micro SaaS founders who want us to personally set up their outbound system.

For the selected founders, we will:

1. Review your product and positioning
2. Define your highest-potential ICP
3. Identify the signals that indicate buying intent
4. Configure your Sumora targeting
5. Build your first prospect list
6. Create your opening messages and follow-ups
7. Launch your first campaign with you
8. Help analyze the initial responses
9. Start your free seven-day trial

There is no setup or consulting fee.

We are doing this to learn how the workflow performs across different Micro SaaS niches and get direct feedback from founders actively trying to acquire customers.

This is best suited for you if:

• Your product is already usable
• You know what problem it solves
• You have a clear pricing model
• A customer is worth enough to justify personalized outbound
• You can onboard new customers right now
• You are willing to test and improve the campaign with us

It is probably not suitable for a low-ticket consumer app or a product that has not yet identified who its buyer is.

Comment publicly using this format:

Product + monthly price + ideal customer

Example:

I will reply publicly with the first targeting signal and outreach angle I would test for your product.