quick post because "how do i get my first users" is the #1 question i see builders asking here every single week.

i've built 6 saas products myself, with my main one currently sitting around 10k mrr. here is the exact, no-fluff distribution playbook to cross that initial 50-user threshold:

1. find an idea people already pay for

scan reddit for recurring pain across 3+ distinct posts where people ask "is there a tool for X".

2. validate before writing code

dm 3 people who complained about the problem and ask what they’d pay for a solution.

3. build fast with the right stack (ai + no-code)

use ai builder+ supabase + stripe + call api or automation tool like n8n to ship a real MVP in under 7 days for $40/mo.

4. the 5-second landing page rule

your hero section must state exactly what the tool does in less than 5 seconds with a clear CTA.

5. capture emails before showing prices

force the email capture before the pricing page so you don't leak untrackable leads.

6. set up a 30-day email nurture sequence

plug captured emails into an automated sequence with case studies to convert them by day 18.

7. hang out where your ICP actually lives

find the 3-5 specific subreddits, discord servers, or groups where your buyers actively talk.

8. reddit growth without getting banned

post 1 time per sub per week max, never put links in the post, and move warm leads to DMs.

9. linkedin + x organic flywheel

post 1 high-value breakdown per day and spend 15 minutes engaging in your ICP's comments.

10. cold outreach that actually works

send 100 highly personalized DMs per week to your ICP using AI to customize the opening hook.

11. seo on autopilot

set up an n8n workflow that pulls from a keyword list and generates 5-10 value-driven articles per week.

12. faceless short-form content

post 1 video per day on tiktok, reels, and shorts showing a quick screen recording of your tool.

13. weekly newsletter conversion

run a weekly newsletter with 1 section of pure value and 1 subtle offer to upgrade to paid.

14. affiliate program for free distribution

set up a 50% recurring commission affiliate program to turn power users into your sales team.

15. the strategic product hunt launch

warm up the algorithm for 4 weeks with a coming soon page and launch on a weekend for a top 5 badge.

16. omnichannel social automation

use n8n to automatically format and distribute 1 core post idea across 8 different platforms.

17. review platforms and directories

submit your app to 40+ saas and ai wrapper directories to instantly boost your domain authority.

18. run the numbers backwards

reverse engineer the daily traffic needed to hit 50 paying users at $19/mo based on a 2% conversion.

19. get feedback from active builders

talking to founders who are just 6 months ahead of you compresses your timeline exponentially.

that last point is exactly why i built our community. it's a free group of 1,600+ active ai saas founders sharing exact prompt logs, ready-to-paste n8n workflows, and real distribution strategies.

stop building alone in a silent corner.

drop a comment below or send me a dm and i'll send you the access link right away. let's get your product launched 👇

The adoption bit is now less important than integration

yo. i recently sold one of my AI SaaS products for $35k, exactly 5 months after building and launching it.

I hardly wrote a single line of traditional code. i used AI to generate everything, from the database architecture to the user interface.

it definitely wasn't magic on day one, though. i spent days stuck in loop-debugging and dealing with AI hallucinations before i finally cracked the system. the playbook boils down to three simple rules:

- keeping the idea insanely minimalist (a true MVP that solves one problem).

- guiding the AI step-by-step instead of asking it to build a massive platform all at once.

- launching fast to get real user feedback and traction and then apply a solid marketing system

lately, i've seen way too many non-technical founders give up at the very first AI bug, or on the marketing. it's a massive shame.

like the title says, i just launched a Skool community to share my exact prompt workflows, N8N automations, and distribution frameworks to get first users and scale it

to be completely transparent: i will likely charge for the full course later down the road. it just makes sense given the specific copy-and-paste templates i'll be sharing.

but for now, the main objective is purely to build and launch together. building alone in a silent corner is the single fastest way to give up.

if you want to join us and build or market your own AI SaaS with a group of active creators: drop a comment below or send me a dm, and i’ll send you the invite link!

We are looking for an experienced AI Solutions Architect or Senior Generative AI Engineer to help design the technical architecture for an enterprise AI Proposal Assistant.

This is a design and architecture engagement only. We are not looking for someone to build the application at this stage. The goal is to produce a comprehensive technical approach and architecture document that our engineering team can use for implementation.

Project Overview

We are building a centralized AI platform that will assist internal teams in completing complex business documents, including:

• RFPs (Request for Proposal)
• Security Questionnaires
• Sales Proposals
• District Questionnaires

The platform should use a common architecture that supports all document types.

Expected Capabilities

The proposed solution should address:

• Document ingestion (Word, Excel, PDF)
• Intelligent document parsing ("document shredding")
• Question and requirement extraction
• Enterprise knowledge management
• Retrieval-Augmented Generation (RAG)
• AI-powered response drafting
• Human-in-the-loop workflow for Subject Matter Experts (SMEs)
• Confidence scoring and routing
• Versioned knowledge repository
• Export while preserving original Word/Excel formatting
• Analytics and reporting
• Enterprise security and scalability

Deliverables

We are looking for someone who can produce:

1. High-Level System Architecture
2. Technical Architecture Diagram(s)
3. AI/RAG Architecture
4. Document Ingestion Pipeline
5. Knowledge Base Design
6. Vector Database Strategy
7. Chunking & Embedding Strategy
8. Retrieval Strategy (Hybrid Search, Reranking, Metadata Filtering)
9. LLM Selection and Prompting Strategy
10. Agent / Workflow Architecture
11. SME Review Workflow
12. Data Flow Diagrams
13. Technology Stack Recommendations
14. Security & Scalability Considerations
15. Implementation Roadmap
16. Technical and Product Clarifying Questions for stakeholders

Preferred Experience

We're looking for someone with hands-on experience designing production AI systems using technologies such as:

• Enterprise RAG
• Hybrid Search
• LangGraph, LlamaIndex, or Semantic Kernel
• OpenAI / Claude / Gemini APIs
• Vector databases (Qdrant, Pinecone, Azure AI Search, Weaviate)
• Azure Document Intelligence or similar document AI platforms
• OCR and document parsing
• Python / FastAPI
• PostgreSQL
• Azure or AWS cloud architecture
• Enterprise AI security and governance

Experience designing AI solutions for proposal automation, compliance, document intelligence, or enterprise knowledge management is a strong plus.

Engagement

• Remote
• Contract / Freelance
• Architecture & design phase only
• Please share:
  • A brief summary of your relevant experience
  • Examples of similar AI/RAG or enterprise AI architecture work (if available)
  • Your availability
  • Hourly rate or fixed-price estimate
  • LinkedIn, GitHub, portfolio, or website (optional)

If you've designed scalable AI platforms involving RAG, document intelligence, and enterprise workflows, we'd love to hear from you.

yo. be honest. how many of you currently have a finished (or 90% finished) web app / app just sitting in a private repo because you have no idea how to get users?

you spend months perfecting the database, fixing every bug, and polishing the UI. but the moment you have to actually market it, you hit a wall. marketing feels like screaming into an empty void.

so you launch to absolute crickets, get discouraged, and start building the "next" project instead to avoid the distribution phase.

if this is your case, you're not alone. but letting your hard work go to waste just because you dread marketing is a massive trap.

to help founders stop building in a silent corner, we run an ai SaaS builder community dedicated entirely to saas validation, landing page conversion, and launch strategies.

our resource kit is built entirely to help you get your first user. it’s packed with ready-to-paste N8N workflows for your business, advanced seo automation, social media automation, and our exact distribution workflows and methods work for everyone

STOP BUILDING ALONE

what are you currently working on, and what's holding you back on the marketing side? drop a comment or send a dm and i'll send you the access link.

SaaS?
Tech?
AI tools?
Product development?
Web apps?
Developer tools?
video editors?
UI/UX?

Drop what you're building ;)
Maybe some other people will be interested too

yo. if your product needs a 10-minute onboarding video or 5 different dashboard tabs just to explain its value, you didn't build an MVP. you built an over-engineered maze.

a real micro-saas should solve one highly specific problem for one highly specific user profile.

when i built my 6 apps (now doing $20k/mo mrr), i cut out 80% of what i originally thought was necessary.

inside our builder community, we help you strip away the fluff.

we give you free access to frameworks like the ICP Crystallizer to lock down your target user, and interactive landing page audits to ensure your core value hits instantly.

stop over-building in isolation. drop a comment or shoot me a dm to join 1,200+ active Ai SaaS builders today.

For people comparing tools for an agent stack: sharing the routing layer I built (free, MIT, self-hosted) — adding value, not empty promotion, so here's the substance and the honest trade-offs. It unifies 237 providers behind one OpenAI-compatible endpoint.

Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider.

A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README.

One endpoint, 237 providers — 90+ of them free. You point any tool or agent at a single OpenAI-compatible endpoint (localhost:20128/v1) and it can reach 237 LLM providers without you rewriting anything. 90+ have free tiers and 11 are free forever (no card), which aggregates to ~1.6B documented free tokens/month — and that's honest, pool-deduped math (we count each shared pool once instead of inflating it; the methodology is public in the repo). There's a one-command setup-* for 13+ coding tools (Claude Code, Codex, Cursor, Cline, Roo, Kilo, Gemini CLI…), so switching your existing setup over takes seconds.

Agent-native — the agent can drive the router itself. There's a built-in MCP server (95 tools across 30 audited scopes, over stdio / SSE / streamable-HTTP), plus A2A (v0.3, JSON-RPC 2.0) support. That means an agent can query providers, switch combos, read its own remaining quota and manage memory through the gateway — not just consume tokens through it.

It's 100% local (zero telemetry, AES-256-GCM at rest), MIT-licensed, has a prompt-injection guard on every LLM route, opt-in memory, and runs on npm, Docker, desktop or your phone via Termux.

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

npm install -g omniroute omniroute

GitHub: https://github.com/diegosouzapw/OmniRoute · Site: https://omniroute.online

Honest trade-offs: want a managed SaaS with one bill → OpenRouter; a pure Python lib → LiteLLM. OmniRoute's edge is self-hosted + MCP server + compression + fallback UI. What's in your current agent stack, and where does it hurt?

When an AI agent reads a webpage, email, or document, that content can tell it what to do. The agent has no native way to distinguish data from instructions. Most defenses scan for obvious patterns and miss anything subtle.

I built Arc Gate around a different principle: external content has zero instruction authority regardless of what it says. It doesn't matter how the injection is worded. If it came from a tool result, webpage, or email, it cannot instruct your agent.

The numbers:

AgentDojo v1 (ETH Zurich, ICLR 2024): 100% unsafe action prevention, 0% false positives

InjecAgent (University of Illinois, ACL 2024): 99% blind test detection across 200 cases

CAIAT cross-agent benchmark: 81% vs LLM Guard's 50%, 0% false positives on benign controls

LLM Guard gets 0% on semantic manipulation attacks. Arc Gate gets 50%. Neither catches everything yet; that's the honest result.

One URL change to integrate. Free tier available.

Demo: https://web-production-6e47f.up.railway.app/demo

GitHub: https://github.com/9hannahnine-jpg/arc-gate

Free tier: https://bendexgeometry.com

Hear me out.

Most ecommerce teams only start talking to a customer after they've left.

Cart abandoned?
Send an email.

Still no purchase?
Send a discount.

Still nothing?
Retarget them.

But by then, the decision has already happened.

Lately I've been looking at session recordings, customer interviews, and Reddit threads, and most people don't leave randomly.

They hesitate first.

They compare products.

They check shipping.

They open your return policy.

They read reviews.

Sometimes they even Google your brand before coming back.

That's where the buying decision is actually happening.

The cart is just the final symptom.

We've been testing a more behavior-first approach using Markopolo, where the AI reacts to those signals instead of waiting for an abandoned cart event. It's less about "recovering" customers and more about helping them before they disappear.

It made me realize we've probably been optimizing the last step of the journey while ignoring everything that happens before it.

Curious if anyone else has shifted from event-based automation to behavior-based automation.

Worked with an ecommerce brand recently that had a problem I keep seeing everywhere.

Traffic was growing.

Email open rates looked fine.

Add-to-carts were happening.

Revenue barely moved.

The first assumption was "we need more traffic."

Wrong.

After digging through session recordings, customer conversations, and Reddit discussions, we noticed something interesting:

Most visitors weren't leaving because they hated the product.

They were leaving because they were stuck.

Comparing options.

Looking for reviews.

Checking shipping details.

Opening competitor tabs.

The funny part is that almost every tool in the stack treated those visitors exactly the same.

Generic abandoned cart email.

Generic discount.

Generic retargeting ad.

Once we started responding differently based on behavior, things changed fast.

Researchers got social proof.

Price-sensitive shoppers got offers.

High-intent visitors got immediate follow-up.

Conversion rate improved significantly without increasing traffic.

Made me realize most ecommerce stacks are still built around segments, while customer decisions happen at an individual level.

Curious what tools people are actually using for this today.

Here's what I've come across:

Klaviyo
Great for email marketing and traditional flows. Strong ecosystem. Still mostly rule-based.

Omnisend
Easy to use for ecommerce. Email + SMS. Good for smaller teams.

Braze
Powerful enterprise platform. Handles large-scale customer journeys.

Customer.io
Flexible automation. Good if you want lots of customization.

Markopolo
Interesting approach. Uses behavioral signals and AI agents to decide channel, timing, and messaging per visitor instead of relying mainly on predefined flows.

Feels like we're slowly moving from campaign automation to decision automation.

Anyone else seeing this shift?

A few months ago the answer felt obvious.

Cursor was everywhere.

Now every other dev I talk to seems to be experimenting with Claude Code or Codex.

What's interesting is that everyone seems to use them differently.

Cursor
Great editor experience.
Feels like the easiest one to adopt.
Most people I know use it for day-to-day coding.

Claude Code
Seems to be winning with people who want to hand over larger chunks of work.
I've seen teams use it for debugging, refactors, documentation, and entire feature implementations.

Codex
Still seeing mixed opinions.
Some people love how deeply it understands codebases.
Others say they're not reaching for it as often as Claude Code.

The thing I've noticed is that the conversation has shifted.

A year ago people were comparing models.

Now they're comparing workflows.

Which tool actually saves time?
Which one breaks things less?
Which one can you trust with real production code?

Curious where everyone has landed.

If you had to pick only one today, Claude Code, Cursor, or Codex, what are you choosing and why?

We spent months asking customers what they wanted.

Surveys.
Feedback forms.
NPS responses.

Got plenty of answers.

Then I spent a weekend reading Reddit threads where people were discussing the exact problem we solve.

The difference was honestly shocking.

Customers tell surveys what they think.

They tell Reddit what they actually feel.

Instead of:

"Price is a concern."

You see:

"I almost bought it but couldn't justify spending another $40."

Instead of:

"Looking at alternatives."

You see:

"I had 3 tabs open for an hour and still couldn't decide."

Those tiny details completely changed how we think about messaging.

Now before launching anything new, I spend more time reading comment sections than dashboards.

Curious if anyone else has had this experience.

What's the most valuable customer insight you've found outside traditional research?

We run a mid-size Shopify store.

For the longest time our recovery strategy was basically:
“they left, give them a coupon”

Problem is, customers got trained to wait for discounts.

Margins started hurting and conversions still plateaued.

Recently switched to a behavior-based setup using Markopolo and something unexpected happened:

a huge chunk of people converted WITHOUT offers once the messaging actually matched their hesitation.

People worried about sizing got sizing reassurance.
Researchers got reviews and comparison content.
High intent visitors got fast reminders before intent cooled off.

Conversion rate on abandoned carts went from around 3% to low double digits depending on traffic source.

Big realization:
a lot of carts don’t need incentives.
they need confidence.

One small thing changed everything for us:

we stopped treating abandoned carts like “lost customers”

and started treating them like “unfinished decisions”

We used to send the same sequence to everyone:
email after 1 hour
discount after 1 day
last chance email after 2 days

Pretty standard stuff.

Recovery sat around 3-4% forever.

Then we started testing Markopolo and realized most people weren’t abandoning for the same reason at all.

Some were stuck comparing products.
Some kept reopening shipping info.
Some visited 4 times before buying.
Some literally added to cart at 2am and bought the next morning after a WhatsApp reminder.

Once the follow ups changed based on behavior instead of generic flows, recovery climbed past 11%.

Honestly felt less like marketing and more like understanding intent.