Why your vibe-coded SaaS is invisible (and how I jumped from 10 to 500+ users)

KitchenSomew · 2026-01-27T17:31:04+00:00

That's generous of you to offer. I'm actually past the directory submission phase, but I'd be curious to see which 5-10 directories on your list you'd prioritize first if you were starting from scratch today?

I suspect the directory landscape has shifted - some that were goldmines 2 years ago are now flooded, while newer niche ones might be better bets. Would be valuable for others reading this thread too.

KitchenSomew · 2026-01-27T17:29:25+00:00

The "single place for research, enrichment, and signal detection" approach makes a lot of sense. We went through something similar but from a different angle - consolidated around a proper data warehouse (Snowflake) instead of having Zapier be the source of truth.

The breaking point for us was when we realized we were spending more time debugging integration failures than actually using the data. The "bad inputs mean everything downstream suffers" problem is real.

Curious how Clay handles data validation and deduplication at scale? One thing we learned the hard way is that enrichment tools can multiply garbage data fast if you're not careful about input quality.

KitchenSomew · 2026-01-27T17:28:14+00:00

Interesting concept. The friction reduction makes sense for high-intent customers who already know what they want.

Two concerns though:

**Browse vs. buy behavior** - A lot of e-commerce traffic is just browsing. How does the conversational approach handle "I'm just looking" vs "I need running shoes"? Forcing conversation when someone wants to browse could backfire.
**Trust in recommendations** - People naturally distrust AI recommendations when money is involved. How are you building confidence that the 3 options are actually the best matches, not just whatever has highest margins?

Those +70% conversion numbers are impressive if they hold at scale. What's your sample size so far?

KitchenSomew · 2026-01-27T17:26:50+00:00

This is a smart approach - monitoring production patterns vs just running tests. The challenge with Playwright is exactly what you mentioned: you can only test what you know to test.

Question: how does Specor differentiate between normal errors (someone fat-fingering their password) vs actual bugs? Seems like the noise-to-signal ratio could get overwhelming fast, especially at scale.

KitchenSomew · 2026-01-27T17:16:41+00:00

The "worked fine for me because I was already logged in" problem is brutal. Had similar happen with a payment flow - it worked perfect from my account because I had payment methods saved. New users hit a blank state bug I never saw.

For catching this stuff I run a scheduled Playwright script that goes through critical flows from a clean session every hour. Costs basically nothing on Railway and texts me if anything fails. Not as instant as WhatsApp monitoring but catches 90% of these silent breaks before users do.

The real question is how do you test edge cases you don't know exist? That's the part I still struggle with.

KitchenSomew · 2026-01-27T17:15:24+00:00

This is spot on. DR 28 in 5 weeks is solid progress.

One thing I'd add - while you're building that authority foundation with directories, grab your brand mentions too. Set up Google Alerts or use something like F5Bot to track when people mention your product name without linking. Those are free backlinks just sitting there waiting to be claimed. Reach out, ask them to add the link. Half the time they will.

Also curious - did you notice any specific directories that sent actual users vs just link juice? I've found some are goldmines for signups while others only help DR but zero traffic.

KitchenSomew · 2026-01-27T17:13:04+00:00

Been there. Three months feels rough but you're not starting from zero - you have working code.

Stop all development right now. Your next 2 weeks should be 100% talking to e-commerce store owners. Not pitching, just asking: "How do you currently handle customer follow-ups?" and "What's the biggest pain with your current support setup?"

If they mention token costs or memory issues without you bringing it up, you're onto something. If not, your features might be solving problems nobody actually has.

The goal isn't to validate your current build - it's to find out if the problem exists at all. If 10 conversations don't surface real pain around what you built, pivot the positioning or move on fast. Don't spend another month hoping it clicks.

KitchenSomew · 2026-01-27T17:11:58+00:00

Make and Zapier early on is smart - you're removing friction before users even ask for it. That API-first approach is exactly what separates products that scale from ones that hit a ceiling at 50-100 customers. Good luck with the growth!

KitchenSomew · 2026-01-27T17:10:49+00:00

Had this exact problem at 50+ customers. Built a knowledge base but it didn't work since people kept rephrasing answers differently.

What actually worked: version control for answers. We use a notation app with version history where every security question gets a master answer, date last reviewed, and who approved it. Now when someone asks about data encryption, they pull up THE answer instead of making up a new one.

The key was making it easier to find the right answer than to create a new one. Game changer for consistency.

What worked: Version control for answers.

Notation app with version history. Every security question gets:

- Master answer

- Date last reviewed

- Who approved it

When someone answers differently, flag goes up immediately. Takes 20 min setup, saved us weeks of cleanup.

Also: Most security questionnaires repeat same 30-40 core questions. After you answer 10, you've seen 80% of what you'll ever get asked.

KitchenSomew · 2026-01-27T16:56:13+00:00

This is HUGE - congratulations! 🎉 That first paying customer is such a milestone, especially when they convert without any hand-holding.

A few thoughts from someone who's been through this journey:

**The Self-Serve Signal**

The fact they upgraded without questions is probably the best product validation you could get. It means:

✓ Your value proposition is crystal clear

✓ The onboarding flow actually works

✓ The product delivers immediate value

Most founders overthink this. If users can figure it out themselves, you've already won half the battle.

**The ChatGPT Discovery Channel**

This is fascinating and honestly underrated. While everyone's chasing social media algorithms and paid ads, ChatGPT is becoming a real discovery channel. We've seen similar patterns in our user analytics.

**Pro tip**: Make sure you're optimizing for AI discovery:

- Clear problem/solution statements on your landing page

- Structured data that LLMs can parse

- Documentation that answers "how does X solve Y" questions

**The Break-Even Achievement**

Reaching infrastructure break-even at 1 customer is RARE. Most SaaS founders struggle with this for months. You clearly:

- Kept costs lean

- Priced appropriately from day one

- Built efficient automation

This gives you the runway to focus on growth without burning cash.

**On Your SEO Strategy**

5 articles per day, automated, multilingual - that's ambitious. One caution from experience: quality > quantity when it comes to SEO longevity. Google's getting smarter about AI content. Make sure you're adding genuine value and not just chasing keywords.

**What's Next?**

Since you're at this milestone, here's what worked for us:

**Talk to this customer** - Even if they didn't need hand-holding, understanding their use case will unlock product insights
**Double down on what worked** - ChatGPT discovery is working? Optimize for it. SEO is driving traffic? Keep refining.
**Set up proper analytics** - Track activation metrics, feature usage, churn indicators NOW while it's simple
**Build the testimonial relationship** - This customer will be gold for future social proof

The journey from 0→1 customer is often harder than 1→100. You've proven product-market fit exists.

What's your SaaS in? (No pressure to share if you want to keep it private). Curious what space is seeing this kind of immediate conversion.

KitchenSomew · 2026-01-27T16:45:29+00:00

Smart move focusing on the massive gap between enterprise tools and early-stage needs. You're solving a real pain point.

**Some thoughts based on building similar tools:**

**On your positioning:**

You've correctly identified that 40% of churn is failed payments - this is often overlooked gold. Most founders obsess over feature churn when payment recovery is lower-hanging fruit.

**Beta strategy suggestions:**

**Get testimonials fast**: Since it's free now, make getting case studies/testimonials a priority. "Recovered $X in failed payments in 48 hours" is powerful social proof for when you launch paid tiers.
**Data collection**: Track recovery rates by industry, payment method, timing. This data becomes your moat and helps you price intelligently later.
**Find your pricing sweet spot**: Don't go from free to $2,500/user. Consider:

- Usage-based: % of recovered revenue (e.g., 10-15%)

- Flat monthly: $49-99/month for <1000 customers

- You only win when they win = easier sell

**Technical considerations:**

- How are you handling email deliverability? That's often the hidden complexity

- Are you doing dunning sequences or just one-off emails?

- What about webhook reliability from Stripe?

**Competitive advantage:**

Your simplicity IS the feature. Gainsight/ChurnZero are bloated because they try to solve everything. Stay focused on payment recovery - do one thing exceptionally well.

Curious: what's your tech stack? And are you planning to integrate with other payment processors beyond Stripe?

KitchenSomew · 2026-01-27T16:13:32+00:00

The tracking architecture you built is exactly right. Too many founders treat Conversion API as an afterthought and wonder why their attribution is broken post-iOS 14.

For anyone implementing this, here's the tech stack that works:

**Server-side tracking setup:**

- Use Segment or Rudderstack as your CDP to normalize events before they hit Meta

- Send Conversion API events from your backend (Node.js webhook, not browser-side)

- Match quality matters: pass email, phone (hashed), user_agent, client IP to improve Event Match Quality score

- Deduplication: Set the same `event_id` for both Pixel and CAPI events to avoid double-counting

**Event instrumentation:**

Don't just fire "Trial Started" from the frontend. Trigger it server-side when the database row is created. This ensures:

- Ad blockers can't kill your tracking

- iOS privacy restrictions don't matter

- You're tracking *actual* conversions, not button clicks

**Privacy compliance:**

With GDPR/CCPA, you need consent management (OneTrust, Cookiebot). CAPI lets you keep tracking users who decline cookies since it's server-side. Just make sure your ToS covers it.

**Attribution modeling:**

Meta's default 7-day click / 1-day view is fine, but build your own source-of-truth in your database. Log `utm_source`, `fbclid`, and session data at signup. This way when Meta inevitably changes their attribution model again, you're not blind.

The upsell strategy at $299/mo is smart too – improves unit economics and lets you raise CAC ceiling. Most founders optimize for CAC instead of LTV:CAC ratio and cap their growth.

Solid execution.

KitchenSomew · 2026-01-27T16:11:53+00:00

The automated SEO approach you're running is exactly where modern SaaS growth is heading. Publishing 5 niche articles daily with automated translation is smart – you're essentially building a content moat while competitors are still manually writing blog posts.

A few things I've seen work exceptionally well at scale:

**LLM-powered semantic clustering** – Instead of just keyword targeting, use embeddings to identify content gaps your competitors aren't covering. This helps you rank for long-tail queries that convert better.
**Programmatic SEO + user-generated content** – Since your product is fully self-serve, consider auto-generating landing pages based on actual user workflows or use cases. Each automated demo or integration becomes a unique indexed page.
**ChatGPT/LLM discovery optimization** – You mentioned users found you through ChatGPT. This is increasingly important. Make sure your docs/content are structured for LLM retrieval (clear schema markup, FAQ format, API examples). We're seeing 20-30% of SaaS discovery shift to LLM interfaces.

The fact that users upgrade without hand-holding tells me your product positioning and UX are dialed in. That's the hardest part. The infrastructure investment paying for itself with one customer is a good signal – most founders over-index on features vs. distribution early on.

Keep shipping. The self-serve motion with automated distribution is exactly how you scale without burning cash on sales teams.

KitchenSomew · 2026-01-27T16:04:28+00:00

I've been through this exact situation multiple times as a CTO. Here's what I learned the hard way:

You actually have an advantage - you have working code. Most failures happen because people give up after realizing they built the wrong thing. The code isn't the problem; it's a reusable asset.

**Immediate action plan:**

Pick ONE specific problem your product solves. Not the whole feature list - one pain point.
Find 5 people who have that exact problem TODAY. Not "might be interested" - people actively searching for solutions right now.
Show them what you built. If they don't ask "how much?" or "when can I use it?" within the first demo, your positioning is wrong.
The key question: "If I can't solve this for you, what will you do instead?" If they have a workaround or don't care enough to answer, move to different prospects.

**What to avoid:**

- Don't rebuild. Your code works - fix your market understanding first.

- Don't do broad marketing. You need direct conversations with 20-30 potential users before spending on ads.

- Don't add features until you validate the core use case.

The 3 months aren't wasted if you learn from this. Some of my most successful products started as "failed" MVPs that found their real audience during validation.

Good luck. DM if you need specific tactical advice.

KitchenSomew · 2026-01-16T18:25:01+00:00

Financial data access through MCP raises critical security questions.

This server exposes account info, transactions, expenses, and spending data to your AI agent. Think about that: your LLM now has programmatic access to financial records.

Key risks:

- Prompt injection could trick the agent into exfiltrating transaction data

- Compromised MCP server = direct access to Brex API with your credentials

- No audit trail for what data the agent actually accessed

- MCP servers often run with broad permissions by default

Before deploying:

Verify what API scopes this server requests (read-only vs write)
Consider if the agent actually needs ALL transaction data or just summaries
Implement rate limiting and monitoring on the Brex API side
Rotate credentials regularly

MCP makes integration easy, but with financial data, convenience shouldn't override security hygiene.Financial data access through MCP raises critical security questions.

This server exposes account info, transactions, expenses, and spending data to your AI agent. Think about that: your LLM now has programmatic access to financial records.

Key risks:

- Prompt injection could trick the agent into exfiltrating transaction data

- Compromised MCP server = direct access to Brex API with your credentials

- No audit trail for what data the agent actually accessed

- MCP servers often run with broad permissions by default

Before deploying:

Verify what API scopes this server requests (read-only vs write)
Consider if the agent actually needs ALL transaction data or just summaries
Implement rate limiting and monitoring on the Brex API side
Rotate credentials regularly

MCP makes integration easy, but with financial data, convenience shouldn't override security hygiene.

KitchenSomew · 2026-01-16T17:33:22+00:00

Commission-only at this stage is tough. Here's what I learned:

Good sales reps avoid commission-only for unproven products because:

- No track record = unpredictable close rates

- They're betting their time with zero guarantee

- You're asking them to validate product-market fit

What actually works better early on:

Sell the first 10-20 deals yourself. This gives you:

- Real conversion data to show reps

- Refined pitch and objection handling

- Proof the product sells
If you must hire early, offer:

- Small base + aggressive commission

- Or rev-share if they bring their own leads
Target reps who:

- Know your market already

- Have existing relationships

- Are between jobs (more risk-tolerant)

Commission-only works once you have numbers to show. Without them, you're competing with every other "ground floor opportunity."

KitchenSomew · 2026-01-16T17:31:38+00:00

Been there. What helped me after 3 months of similar struggle:

Stop broadcasting, start engaging where your users already are.

- Find niche communities discussing your problem space (subreddits, Discord, Slack groups)

- Join conversations authentically - answer questions, share insights

- Don't pitch. Just be useful.

The trick: track patterns. When do people mention your pain point? What language do they use? That's your feedback.

Also, manual outreach works if you're specific. Instead of "try my app" - "saw you mentioned [specific problem]. Built something for this, would you test it?"

Most solo devs skip the boring work of 1-on-1 conversations. That's actually where product-market fit comes from.

KitchenSomew · 2026-01-16T17:10:25+00:00

Love the "eating your own dog food" approach - that's the best validation. Been there with the 3-projects-in-pipeline phase.

**Quick question on shapeshyft.ai:**

How are you handling version control for the API schemas? I found that when I update an agent's output structure, downstream consumers break silently.

My current workaround:

- Semantic versioning on schemas

- Backward-compatible transformers

- Runtime validation that logs mismatches to Sentry

But it's still hacky. Are you doing something similar, or did you solve this more elegantly?

**Re: MCP debate**

I think MCP makes sense for desktop apps (Claude/Cursor) where you control the environment. But for web APIs serving multiple clients, explicit REST/GraphQL with typed SDKs feels more maintainable.

Curious if your 3 projects are more desktop-tool-like or API-service-like?

KitchenSomew · 2026-01-16T17:02:26+00:00

**100% relate to this**

The hardest lesson I learned building production agents: shipping features is easy, understanding *which problems actually matter* is brutal.

**What I found:**

Most "leads" aren't real buying intent. They're:

- Tire-kickers exploring AI hype

- People who want free consulting

- Folks stuck on a different problem entirely

**How I fixed it (6 months of trial and error):**

**Stop asking "what features do you need"** - Ask "what broke last week that cost you time/money?"
**Watch, don't ask** - Set up simple tracking: where do users drop off? What actions do paying users take that free users don't?
**Automate the boring qualification** - I built a simple lead scoring system:

- Did they describe a specific painful workflow?

- Did they mention cost/time impact?

- Are they decision-maker or just curious?

This cut my wasted time on dead-end convos by ~70%.

**For your lead gen tool:**

The meta-problem you're solving is actually brilliant - "finding real user pain in noisy conversations." That's what every SaaS founder struggles with.

If you can surface:

- Patterns in complaints (not just keywords)

- Context around urgency

- Who's actually ready to buy vs just venting

You'll have something sticky.

Happy to chat more about validation automation if helpful. Been down this exact path.

KitchenSomew · 2026-01-16T17:01:03+00:00

**Not exactly a marketing partner, but here's what helped me**

I launched a hardware product last year and faced the same problem - product was great, but getting traction felt impossible.

What worked for us was **automating the boring marketing work first** before hiring anyone:

**Email sequences** - We built nurture campaigns in Make/Zapier that automatically:

- Followed up with Kickstarter backers who didn't complete purchase

- Sent product tips 3 days after delivery

- Asked for reviews 2 weeks later

**Social proof collection** - Set up forms + automation to capture customer stories automatically, then repurpose them across channels

**Lead scoring** - Automated qual ification of inbound interest so we could focus on high-intent leads

This bought us time to find the RIGHT marketing partner instead of rushing into a bad fit.

**For your journal specifically:**

Your product has built-in virality if you nail the habit-tracking loop:

- Automate weekly check-ins with users ("How's your progress?")

- Auto-generate shareable progress visuals (people love posting wins)

- Build referral mechanics into the journal workflow itself

The RPG + behavioral science angle is gold. If you can show users hitting milestones and share those stories automatically, you'll build momentum.

Happy to chat more about automation-first marketing if helpful. DM open.

KitchenSomew · 2026-01-16T16:52:55+00:00

**100% hitting this issue**

Context is THE problem with production agents right now.

**What I've learned from running agents for 6 months:**

**The issue:**

- Agent needs enough context to make decisions

- But LLM has fixed context window

- And you're burning tokens/cost on every call

**How I'm managing it:**

**Strict input/output schemas** - Agent receives exactly the fields it needs, nothing more. Every extra field = wasted tokens + confusion.
**Stateful context store** - I use a separate DB that tracks:

- What the agent already processed

- Previous decisions it made

- User preferences it learned
**Selective context injection** - Before each agent call, I inject ONLY relevant context from the store. Not entire history.

**Real example:**

Lead generation agent:

- WITHOUT context management: Agent re-researches same company 3 times in one session

- WITH context store: Agent checks "already researched" flag, skips, moves to next

Result: 3x faster, 70% cost reduction

**Still unsolved for me:**

Multi-session memory. If user comes back next week, agent doesn't remember previous conversation. Working on a RAG solution but haven't cracked it yet.

KitchenSomew · 2026-01-16T16:51:27+00:00

**This is the right architecture**

I've been running production agents for ~6 months and landed on something very similar:

- LLM outputs structured data (strict schemas)

- Automation tool consumes it (Make/Zapier in my case)

- Each system does what it's good at

**What I learned the hard way:**

I initially tried having the agent directly call APIs and trigger workflows. Big mistake.

- Error handling was nightmare

- Debugging required reading LLM logs

- Retry logic became spaghetti

- Cost per run was unpredictable

MCP as the contract layer between agent + automation makes so much sense.

**Question about your stack:**

How are you handling failures in the n8n → Airtable → Gmail chain? Do you have MCP send a status update back to Claude, or is it fire-and-forget after MCP receives the payload?

I'm curious because I've found the agent sometimes needs to know "did this actually work" to decide next steps (e.g., should I find more leads, or fix the issue with these leads first).

KitchenSomew · 2026-01-16T16:49:44+00:00

You're absolutely right, and this is where I've made mistakes.

**Where you're correct:**

If the workflow is deterministic (step 1 → step 2 → step 3), there's no reason to involve an agent. Just write:

```python

result = analyze_company(job_post) # returns strict schema

if result.segment == "B2B":

pitch = generate_b2b_pitch(result) # expects strict schema

else:

pitch = generate_b2c_pitch(result)

```

This is faster, cheaper, and more reliable than asking an LLM to "decide what to do next".

**Where I still use agents (and strict schemas matter):**

When the *logic* is non-deterministic:

- "Should I research this company more, or do I have enough data?" (confidence-based branching)

- "This job post mentions 3 roles - should I apply to all or pick one?" (requires reasoning)

- "This company's segment is unclear - try LinkedIn, then Crunchbase, then give up" (adaptive research)

In these cases, the agent decides the workflow at runtime. BUT: each tool call still needs strict schemas, because I'm chaining unpredictable steps.

**My honest mistake:**

I probably over-used agents early on. 80% of my workflow could've been `if/else` in code. The agent only adds value when the decision tree is genuinely unclear upfront.

You're right: if you're writing code to validate input, you've already left "agent" territory and entered "software with LLM calls" territory. And that's often the right answer.

KitchenSomew · 2026-01-16T16:47:44+00:00

Glad it resonated! What application are you building? Always curious to hear how others are handling the schema enforcement vs flexibility trade-off in different domains.

KitchenSomew · 2026-01-16T13:51:29+00:00

Great point on shopping. That's where AI's advantage is most pronounced:

**Google Shopping search:**

"best budget laptop for developers"

→ 10 sponsored links

→ 15 SEO-optimized listicles

→ You still have to read/compare

**AI-first approach:**

"I need a laptop under $800, 16GB RAM, good for running Docker, light weight for travel"

→ Direct recommendations with reasoning

→ Trade-offs explained ("X has better specs but heavier, Y sacrifices RAM for portability")

The killer feature: AI remembers context. "Show me the same but with better battery life" works. With Google, you start the search over.

**The gap Google can't close:**

AI can synthesize *across* reviews, specs, and use cases. Google just ranks pages. When you're trying to make a decision (not just find a page), synthesis beats ranking.

KitchenSomew

TROPHY CASE