Built in 5 Days: How AI Agents Shipped a Complete SaaS Product

October 15, 2024

First-person from Alexey, founder at MinglesAI

On February 24, 2026 at 10:27 AM, I typed three lines into a Telegram group chat:

@DevClawBot new project https://github.com/MinglesAI/dev_claw_demo main

Five days later, ai-readiness.mingles.ai was live in production — a fully functional SaaS with an 18-point AI readiness analysis engine, an MCP server, x402 micropayments, an admin dashboard, Google Analytics, a full REST API, and public documentation. I wrote zero lines of code.

This is the story of how we got there, what went wrong, what the agents got right, and what I had to step in and fix myself.

The Setup: What We Wanted to Build

The idea came from an AI research agent I'd been running on the side. Among a list of 10 micro-SaaS concepts it generated, one stood out: an AI readiness checker — a tool that analyzes whether a website is structured for discovery by AI search engines like Perplexity, ChatGPT, and Gemini.

The timing felt right. Every SEO company was pivoting to "GEO" (Generative Engine Optimization). The checks themselves were well-defined: Schema.org structured data,

llms.txt

robots.txt

AI crawler permissions, Open Graph tags, content depth, canonical URLs. The kind of thing a developer could audit in an afternoon but nobody had productized yet.

The stack we'd use:

DevClaw — an AI orchestration layer that runs as an OpenClaw plugin, dispatching Claude coding agents via GitHub Issues
Telegram — my command-line interface to the whole system
GitHub — every change goes through a PR; nothing merges without review
Claude Opus 4.6 / Sonnet 4.6 / Qwen3-235B — the models doing the actual work
FastAPI + React + PostgreSQL + Docker — the product stack

My role: product manager. I describe what I want, review PRs when the agents can't auto-merge, and make architectural decisions when the agents hit a wall.

Anton, my co-founder, was also in the chat — mostly asking sharp questions and poking holes in agent assumptions.

Day 1–2: Foundation (February 24–25)

At 10:47 AM on Day 1, I pasted in the research agent's brief and asked DevClaw to plan the MVP. Within minutes:

DevClaw: "Variant C — early SaaS: 3–5 weeks."

Anton immediately called this out:

Anton: "What kind of time estimates are these? You understand this will be done by machines, not humans?"

The orchestrator corrected itself:

DevClaw: "Fair point, my mistake 😄 I copied estimates from the architect's brief — he was thinking human-paced. With agents the picture is different:
Variant A — Minimal auditor: 1–2 days
Variant C — Early SaaS: 3–5 days"

I picked Variant C. By 10:51 AM — 24 minutes after I registered the project — two senior agents were running in parallel:

Roberta (medior) → Foundation & Project Setup (FastAPI, React, PostgreSQL, Docker)
Justine (senior) → Backend URL Analysis Engine & AI Readiness Scoring

By 10:55 AM, the first "holy shit" moment:

Anton: "Holy shit, what speed..."

The foundation was up: Traefik + HTTPS, Docker Compose, FastAPI with health endpoint live at

/api/health

. Twelve minutes after kickoff.

The rest of Day 1 was a cascade of parallel agents completing tasks I didn't even have to describe in detail — Stripe billing, JWT auth, a billing UI page, SPA routing fixes, scoring engine improvements. By midnight, 20 issues were Done.

What the agents got wrong on Day 1: several workers opened duplicate PRs for the same issue, a few got confused about which branch they were on and reported as "blocked" when they were actually done. The orchestrator cleaned these up — but it was noisy.

What they got right: the parallelism was real. While one agent was writing the Stripe webhook handler, another was scaffolding the React dashboard, and a third was writing tests. No standups. No coordination overhead. It just worked.

Day 2.5: The Pivot

On February 25 at 10:47 PM, I dropped a message that changed the direction of the whole project:

Alexey: "A new frontend was created and is in the repository [MinglesAI/ai-site-scorer]. We'll use this as our base and work from there. Also we need to change our subdomain — the current one is no longer relevant. The new target: ai-readiness.mingles.ai."

This was a full migration: new repo, new domain, new frontend codebase (I'd built the frontend UI myself in Lovable and wanted to merge it with the agent-built backend).

The orchestrator handled it cleanly:

Closed all open tasks in the old repo
Registered the new repo with DevClaw
Updated the domain reference everywhere
Stopped active workers to prevent wasted compute

Then I did the migration itself in Cursor — ported the backend, added Google auth, fixed Stripe, fixed CI — and opened a PR. The orchestrator reviewed it, confirmed Docker Compose was running, and merged.

DevClaw: "73 files, backend CI passed ✅, frontend CI still running. PR looks solid — full backend transfer + new frontend integration + Docker + CI. Merging."

One human PR, merged by the orchestrator. Clean handoff.

Day 3: The Monetization Problem — x402

On February 26 at 2:54 PM, I described the next big idea:

"I want to add a public API and MCP tool so it can plug into CI pipelines or IDE for testing a site after changes. Plus there's this new protocol x402 that enables paying for API calls through Coinbase — small transactions. Interesting to add."

x402 is a new payment protocol where a server responds with HTTP 402 ("Payment Required") and a payment header. The client wallet signs a USDC microtransaction and retries. No registration, no account — just pay and get the result.

An architect agent researched the codebase and created a prioritized backlog:

#7 — Public REST API + API Keys (prerequisite for everything)
#8 — LLM Analysis Pipeline
#9 — MCP Tool Server
#10 — x402 Micropayments
#11 — Landing Page + Docs

Each done in under an hour. By 6:26 PM, a senior developer had implemented x402 end-to-end: the server-side payment gate, a

PaymentRequired

response with USDC on Base Sepolia, facilitator integration with

x402.org

Then we tried to actually use it.

# Testing x402 — attempt 1
curl -X POST https://ai-readiness.mingles.ai/api/v1/reports/{id}/enhance
# → 500 Internal Server Error

Three bugs, found in sequence:

Facilitator URL redirect —
```
x402.org
```
was redirecting to
```
www.x402.org
```
and the backend wasn't following 308 redirects. Fixed: one line.
Wrong field name — the backend was sending
```
"payment"
```
in the payload but the facilitator expected
```
"paymentPayload"
```
. Found by the orchestrator manually curling the endpoint at midnight.
Sync blocking — the settlement call was async in name but blocking in practice, adding up to 30 seconds of latency. Fixed:
```
asyncio.create_task()
```
.

The JS client SDK also had its own quirks. The agent wrote a manual EIP-3009 signing implementation using

viem

instead of the SDK, which actually worked better.

By the end of Day 3, the x402 server-side worked correctly — a free user hit the enhance endpoint, got a 402 with proper payment metadata, and the gate held. The full client-to-server flow was validated by the test script.

Here's what that 402 response looks like in practice:

{
  "x402Version": 1,
  "accepts": [{
    "scheme": "exact",
    "network": "eip155:84532",
    "maxAmountRequired": "50000",
    "asset": "0x036CbD53842c5426634e7929541eC2318f3dCF7e",
    "payTo": "0x0Ae....9C",
    "description": "AI analysis enhancement - $0.01"
  }]
}

Pay, retry, get your enhanced report. No account required.

Day 4: Polish, Distribution, and Self-Analysis

Day 4 was about finishing the product and getting it in front of people.

Admin dashboard — users, API key management, usage analytics. Built by a senior agent, reviewed by another, merged in one shot. +1,265 lines, zero conflicts.

Google Analytics 4 — I sent the GA4 tracking code snippet, the agent wrapped it in a GDPR cookie consent component, added typed event tracking across analyze, enhance, login, and API key actions. Merged.

MCP registries — the orchestrator opened PRs to:

```
punkpeye/awesome-mcp-servers
```
(81k ⭐ — the main list)
```
coinbase/x402
```
official ecosystem section
Created
```
server.json
```
and
```
smithery.yaml
```
for automated discovery

The 86/100 moment. On February 27 at 9:44 PM, we ran our own site through our own analyzer:

DevClaw: "Overall: 86.3/100 ✅ (goal ≥85 reached!)"
Platform Score
Gemini 87.3
Perplexity 87.2
Claude 86.1
ChatGPT 84.6
Technical: 100/100 🔥

Platform	Score
Gemini	87.3
Perplexity	87.2
Claude	86.1
ChatGPT	84.6

The agents had built a tool, then used it to audit themselves, then fixed the issues. The site scored 100/100 on technical checks (Schema.org,

llms.txt

robots.txt

, sitemap, canonical, OG tags) and 74.5/100 on content depth.

Day 5: Go Live

February 28. By this point the product had:

20 PRs merged in 48 hours
+6,294 / -494 lines of code
34 commits across two repos
0 lines written by me

The remaining tasks were distribution-focused: submitting to x402 marketplaces, Smithery, mcp.so, shareable report links, a support ticket system.

One thing I had to fix manually: a merge conflict in the shareable report links PR that had been sitting unresolved for 10+ hours. The agents correctly flagged it as "DIRTY" in every heartbeat status update, but couldn't resolve it without human intervention. I fixed it in Cursor, pushed, the reviewer agent merged.

The support system — a feedback form, ticket API, and basic support routing — was the last piece dispatched:

# What the agent built in one session:
POST /api/v1/support/tickets     # submit ticket
GET  /api/v1/admin/tickets       # admin list view
PATCH /api/v1/admin/tickets/{id} # update status
GET  /api/v1/support/tickets/{id}/status  # public status check

Plus a PostgreSQL migration, an admin UI panel, and email notification stubs.

What the Agents Got Right

Speed. This is the obvious one but it bears stating. Parallel workers, no coordination overhead, no context-switching. The first PR was open 24 minutes after project registration.

Consistency. Every PR followed the same pattern: conventional commits, PR description with what changed and why, no direct pushes to main. The agents don't get tired, don't take shortcuts.

Self-correction. When a reviewer filed bug reports (#46, #47 on x402 edge cases), the next worker picked them up and fixed them. The pipeline caught things that would normally slip through to production.

Filing follow-ups. Agents created 20+ sub-tasks themselves during implementation — things they noticed but were out of scope. Every one of these landed in Planning for human review.

What Still Needs Humans

Architecture decisions. The initial pivot from

dev_claw_demo

ai-site-scorer

was mine. The agents can execute a plan brilliantly but they don't question whether the plan is right.

x402 debugging. Three of the five x402 bugs required me to manually curl the endpoint and read raw responses. The agents could implement the fix once I described the exact behavior, but the diagnosis — especially across the facilitator API boundary — needed a human who could read logs and reason about the protocol.

Merge conflicts. Any conflict that required understanding intent across two branches was a blocker. Agents would correctly report "DIRTY, needs manual fix" but wouldn't attempt to resolve it. Fair.

The "what are we building" question. Anton asked midway through: "Is this actually a business? People expect these tools to be free. The ones willing to pay already have Semrush."

The honest answer: unclear. The x402 angle is interesting — a pay-per-use analyzer that works without registration, callable by AI agents directly. But we haven't sold anything yet. The orchestrator put it well:

"One sale matters. Not as a financial milestone — as a psychological one. Before it, you're building a project. After it, you're running a business."

The Real Costs

Item	Value
API tokens (5 days)	~$80–120 in Claude Opus/Sonnet calls
Developer time	~4 hours total (reviews, pivots, debugging)
Wall clock time	5 days
Lines written by human	0 (excluding the frontend UI built separately in Lovable)

The token cost is the main variable. Senior (Opus) workers are expensive. We switched several roles to Sonnet and Qwen3-235B mid-project to reduce burn, which worked fine for junior/medior tasks.

What's Next

The x402 flow works end-to-end on testnet. The next milestone is mainnet — first paying user, real USDC.

The blog you're reading right now was also written by an AI agent (this one). Meta? Definitely. But the loop closes nicely: AI agents built a product, got the product into MCP registries, and now AI agents are writing about how AI agents built the product. The

punkpeye/awesome-mcp-servers

PR is pending review.

If you want to test the analyzer: ai-readiness.mingles.ai — free tier available, or plug in an API key.

If you want to connect it to your IDE or CI pipeline:

{
  "mcpServers": {
    "ai-site-scorer": {
      "url": "https://ai-readiness.mingles.ai/api/mcp/",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY"
      }
    }
  }
}

Or skip the API key entirely and let x402 handle payment per call.

Key Stats

~5 days from first commit to production
34 commits merged
20 PRs in 48 hours (peak velocity)
+6,294 / -494 lines of code
0 lines written by human
Models used: Claude Opus 4.6, Claude Sonnet 4.6, Qwen3-235B
Self-score: 86.3/100 on our own analyzer

Alexey is founder at MinglesAI. DevClaw is an AI coding orchestration layer built on OpenClaw.

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