DEV Community: XzWy JIA

10 Things I Learned Building a SaaS in 48 Hours (As a Non-Coder)

XzWy JIA — Wed, 03 Jun 2026 09:29:40 +0000

I spent 48 hours building a SaaS business with AI. I can't code. The AI wrote all 5,000+ lines. Total cost: $0.50. Paying customers: 0.

Here are the 10 most painful lessons.

🥇 1. AI automation is slower than doing it manually

I spent 2 hours writing Playwright scripts to auto-post to Hacker News. Every single one failed. Copy-pasting manually takes 2 minutes.

🥈 2. Pricing anchor is everything

Started at ¥200 (~$28) per article. Then realized the exact same service sells for $499/month to US startups. That's 14x. Same product. Different positioning.

🥉 3. New accounts are restricted everywhere

HN Show HN: blocked. Reddit: both posts removed. Product Hunt: buried by a same-name product. Twitter: account locked. It's not your product — it's platform policy.

4. Names matter

'AMA' is too common. Another product with the same name took the Product Hunt search results.

5. Dev.to articles get indexed by Google

Posted 3 articles. All appear in Google search. Zero SEO. Zero backlinks. Real content beats social media ads.

6. If a SaaS takes >2 clicks to activate, it fails for non-coders

Spent 3 hours on n8n before giving up. Replaced it with a 50-line script.

7. Never hardcode API keys

Wrote my key into workflow files. Auto-mode blocked all subsequent automation until cleaned up.

8. Positioning beats features

"AI writing service" vs "One-Person Company OS — Replace entire departments at 1/10 cost." Same thing. Different story.

9. Local scripts are more reliable than SaaS

The 50-line daemon replaced n8n. Runs every day at 8:30 AM. Cost: $0.

10. AI infrastructure is nearly free

$0.002 per API call. Monthly content generation: $0.50. The real cost is time wasted on dead ends.

Built by a non-coder. GitHub: github.com/xzwyjia-pixel/ama

I don't know how to code — but I built a SaaS business in 48 hours with AI

XzWy JIA — Wed, 03 Jun 2026 06:59:14 +0000

This is a true story. Not a tutorial. Not a success story. This is what happened when a non-coder sat down at a computer and tried to build a software business with AI doing all the technical work.
The starting point: I can't code
I can't tell an HTML tag from a CSS selector. What I can do: make decisions, approve things, and collect payments.
What my AI (Claude Code) can do: write code, deploy websites, debug errors, generate content, run terminal commands, manage servers.
The division of labor was clear from day one:
• Me: direction, pricing, approval, collecting money
• AI: everything technical
Day 1: Picked three ways to make money
The AI researched the market. Conclusion: test three directions simultaneously.

AI Content Department — 5 agents working as a writing team, replacing human writers
AI Support Department — 3-tier agent escalation system, replacing support agents
AI Analytics Department — upload spreadsheets, agents analyze and generate reports Mistake #1: I priced way too low. I started at ¥200 (~$28) per article. Then I watched a Silicon Valley entrepreneur interview and realized — the exact same service could sell for $499/month to a US startup. That's a 14x difference. My pricing anchor was completely wrong. Day 2 morning: Built an automation infrastructure The AI handled everything (I didn't need to understand the technical details): • Deployed an English landing page on Vercel • Created an open source GitHub repo • Set up a local daemon that auto-generates 3 pieces of content every day at 8:30 AM • Wrote 5 n8n workflow automations • Connected the DeepSeek API for content generation Mistake #2: I wasted 3 hours on n8n. n8n is a visual automation tool — sounds perfect for a non-coder like me. Reality: I couldn't find the activation switch. The AI wrote 5 perfect workflows. They all tested successfully. But I couldn't turn them on. We eventually abandoned n8n and replaced it with a 50-line Node.js script. Same functionality. Zero cost. Lesson: SaaS tools aren't better just because they're powerful. If a feature takes more than 2 clicks to activate, it's not built for non-technical users. Day 2 afternoon: Tried to promote. Reality punched back. The product was live. I needed people to see it. Here's what happened on each platform: Hacker News New accounts can't post Show HN — you need "karma" first. I tried: • Google login → browser flagged as insecure, blocked • Manual account creation → Show HN rejected • Regular link post → "You're posting too fast. Please slow down." • Write comments to build karma → couldn't find the comment box The AI spent 2 hours writing Playwright automation scripts to bypass these restrictions. Every single one failed. Then I realized: copy-pasting manually takes 2 minutes. The automation took 2 hours and still didn't work. Reddit • r/selfhosted: post removed by moderators • r/aiagents: rejected — "new accounts can't post product links" Lesson: Reddit has zero tolerance for self-promotion from new accounts. Community participation first, product second. Doesn't matter how good your product is. Product Hunt I filled out the entire launch form. Scheduled it for midnight. Checked 24 hours later — the product was completely invisible in search results. Another product with the same name (AMA — a browser extension) had claimed the search results. Mine got buried. Lesson: Check name availability before you commit. 'AMA' is way too common. What actually worked
Dev.to articles got indexed by Google I posted two technical articles on Dev.to. Within hours, they appeared in Google search results. No SEO optimization. No backlinks. Just content. Takeaway: Writing real content in technical communities beats posting ads on 5 different social platforms.
Switching the landing page from Chinese to English Before: a Chinese page charging ¥200 per article. After: "One-Person Company OS — Replace entire departments at 1/10 the cost. $499/month." Same exact service. Different positioning. 14x value perception difference. This isn't a technical problem — it's a narrative problem.
Replaced n8n with 50 lines of code The n8n activation nightmare cost me 3 hours. The solution: a 50-line Node.js script with a built-in daily timer, running on my own computer. Cost: $0. Sometimes the simplest solution is the best solution.
Video insight pipeline I had a 56MB interview video with a Silicon Valley entrepreneur. The AI built a fully automated pipeline: video → audio extraction → Whisper transcription → DeepSeek analysis → business insight extraction → Obsidian knowledge base. Entire process automated. Cost: $0.002 per run. This capability itself could be a product — compliance review for short video content. The real numbers (after 48 hours) Metric Value Websites deployed 3 Content pieces generated 17 GitHub repos 1, MIT license Dev.to articles 2 (Google-indexed) Twitter thread 1 Product Hunt launch 1 (unsuccessful) HN posts 1 (rate-limited) Reddit posts 2 (all removed) Actual paying customers 0 Monthly infrastructure cost $0.50 Lines of code I wrote 0 Lines of code AI wrote ~5,000+ 10 things I learned About product
Pricing anchor is everything. ¥200 vs $499 — same thing, different story.
Validate willingness to pay before building. I built a lot. Zero paying customers.
Names matter. 'AMA' is too common. Got buried on Product Hunt. About distribution
New accounts are restricted everywhere. It's not your product — it's platform policy.
Content beats ads. Dev.to articles got Google-indexed. Lasts longer than any promotional post.
Automation isn't worth it. Manual copy-paste: 2 minutes. AI automation: 2 hours of failure. About AI collaboration
What AI can do: write code, deploy, debug, generate content, operate terminals, analyze data.
What AI can't do: click buttons on web pages (Playwright failed repeatedly), understand platform rules, collect payments for you.
The best collaboration model: I make decisions and authorize. AI does all execution. This is the real one-person company. About cost
AI infrastructure is nearly free. DeepSeek API costs $0.002 per call. Monthly content generation costs under $0.50. The real cost is time — the hours I wasted on n8n and HN cost 100x more than all the API fees combined. What's next • Keep writing Dev.to articles (real content, not ads) • Set up Stripe to actually collect payments • Record a 2-minute demo video • Find one real customer — any price — and close the loop This is the true story of a non-coder trying to build a SaaS business from scratch with AI. No embellishment. No omitted failures. If you're doing something similar — or if you're a developer who can tell me what I'm doing wrong — I'd love to hear from you. GitHub: github.com/xzwyjia-pixel/ama Landing page: agent-business-xi.vercel.app

I scanned my PC and found 457 AI agents — here's what I built to manage them

XzWy JIA — Tue, 02 Jun 2026 08:35:47 +0000

The Discovery

I've been using AI coding agents heavily — Claude Code, Codex, Pi Agent. One evening I asked myself: how many agent-related files are actually on this machine?

I wrote a scanner. The result: 457 assets. Configs, API keys, model caches, permissions — scattered across directories I didn't even know existed.

The Problems

Discovery — No single view of all agents
Routing — Simple queries hitting expensive models
Spending — Zero cost visibility per agent
Security — API keys in plaintext, broad permissions

The Solution: AMA

I built AMA (Agent Management Application):

ama scan → find every agent
ama route → auto-select the right model
ama spend → track costs per agent/team/project
ama audit → security report

How It Works

AMA hooks into Claude Code, Codex, and Pi Agent's configuration layers. It reads their agent definitions, tool permissions, and model routing rules — then surfaces everything in a single dashboard.

It also includes a lightweight router that intercepts model calls and redirects them based on rules you define. Simple refactoring → cheap model. Complex architecture → powerful model. Security-sensitive → local-only.

Open Source

MIT license. Built for devs who want visibility over their growing agent fleet.

GitHub: https://github.com/xzwyjia-pixel/ama
Try it: https://ama-agent-store.vercel.app

Would love your feedback and contributions.

I Scanned My PC for AI Agents — Found 457 of Them

XzWy JIA — Mon, 01 Jun 2026 18:12:11 +0000

After using Claude Code, Codex, and Pi Agent for months,
I wondered: how many AI agents are on my machine?

I built a scanner. Here's what it found:

Framework	Active	Archived
Claude Code	192	191
Codex CLI	37	0
Pi Agent	8 sub-agents	12 scripts
MCP Servers	8	-
Total		448+

The Waste

Duplicate calls: Same prompt → 3 agents (8-18% waste)
Overqualified models: Simple tasks on expensive models (15-25% waste)
Cache fragmentation: No shared prompt cache (12-20% waste)
Zombie agents: Archived still indexed (2-8% waste)

→ 30-50% of AI API spend is wasted.

The Fix

AMA — Agent Management Agent


bash
pip install ama-core && ama scan && ama start
Scans all agents across frameworks
Smart routing (simple task → cheap model)
Lifecycle management
Local dashboard at localhost:8765
Free calculator: ama-agent-store.vercel.app/calculator

MIT licensed. Feedback welcome!