I Analyzed 3,000 Developer Job Postings Overnight (Here's What I Found)

Source: Dev.to

Mining product opportunities from the gig economy with Claude Code, Playwright, and ChatGPT Pro.

It started with a simple question: what are people actually paying developers to build right now?
Not the Twitter hype. Not the VC narratives. Real jobs, real budgets, real pain points—the kind of stuff you could productize or solve with a weekend of vibe‑coding.

So I dug through 3,000 job postings from major gig platforms overnight. Here’s what happened.

It Started with Manual Browsing (And Frustration)

I spent about 30 minutes manually scrolling through job listings. Keywords like python automation, ai chatbot, telegram bot, n8n, zapier integration, shopify app, chrome extension…

Tons of interesting stuff popped up. But here’s the problem: I’d see something cool, scroll past it, and five minutes later couldn’t remember what it was or find it again. No system. No way to compare. Just scattered impressions that never formed a big picture.

There had to be a better way.

Enter Claude Code: 30 Minutes to Automation

I fired up Claude Code, described what I wanted, and 30 minutes later I had a fully working data‑collection system:

• Playwright for browser automation (headless mode, stealth settings)
• undetected‑chromedriver as a backup for trickier sites
• SQLite for storage
• 48 keywords across dev categories
• $500+ budget filter (serious clients only)
• Deduplication, pagination, error recovery, auto‑resume

The system kept hitting walls mid‑run (these platforms are hostile to automation), so I switched to headless mode and let it run overnight.

Woke up to ~3,000 job records in my database. Not bad for zero babysitting.

The Annoying Part: These Platforms Are Dinosaurs

Let me rant for a second.

These gig platforms are stuck in 2008.

• No public APIs.
• No RSS feeds.
• No webhooks.

You want structured data? Your options:

1. Pay $500+/month for “enterprise API” access (lol).
2. Automate browser interactions like a caveman.
3. Give up.

I chose option 2. And these platforms really don’t want you collecting data:

• Cloudflare challenges everywhere.
• Aggressive fingerprinting.
• Login walls for basic search.
• Infinite scroll that breaks pagination.
• Rate limits after ~20 requests.

Honestly? If I had a few free days, I could ship an open, API‑first alternative. No detection BS. Clean JSON endpoints. Let the data flow. These incumbents are begging to be disrupted.

The Analysis: ChatGPT Pro Is Stupidly Powerful

After collecting data, I needed to analyze ~3,000 jobs: tag them, score them, find patterns.

Original plan: OpenAI API batch processing.
Problem: Even as a Claude Code Max and ChatGPT Pro subscriber, I still had to dig through dashboards, copy secrets, configure .env files… and CLI tools refuse to help for “security reasons.” The only friction point in the entire vibe‑coding workflow.

Plus the math didn’t work. At $0.01‑$0.03 per job, that’s $30‑$90 minimum—and probably more for reliable analysis. For a weekend curiosity project? Hard pass.

So I tried ChatGPT Atlas—OpenAI’s new agentic browser. The browser itself is actually cool—way faster than Chrome for ChatGPT (no idea why, but regular Chrome sometimes lags hard on chat.openai.com while Atlas is buttery smooth).

But the Atlas agent mode? Garbage. It kept applying generic templates instead of actually thinking. Every response felt like boilerplate. Not ready for real work.

Then I uploaded the SQLite file directly to ChatGPT Pro (regular chat, not Atlas agent mode) and asked it to:

• Filter out irrelevant postings and sketchy clients (~3,000 → 2,000 legit dev jobs).
• Tag each job: category, difficulty (1‑5), SaaS potential (1‑5), market signal.
• Write a 300‑800‑word analysis for each job.
• Generate stats and top lists.
• Write everything back to the database.

2 hours later: Done. 2,000 jobs fully analyzed, 2,000+ pages of deep analysis (~500 chars each). Database updated. All for $0 extra—already paying for Pro.

This is the real unlock. For bulk offline analysis, ChatGPT Pro with file uploads destroys API pricing.

What I Found: The Data

Category Breakdown

Market Temperature

AI is on fire. Most of the “hot” signals cluster in AI‑related jobs.

Difficulty Distribution

48% of jobs are difficulty 1‑2. Easy pickings for anyone with basic dev skills.

Tech‑Stack Demand

Early Insights

• AI + automation is the sweet spot. High demand, high budgets ($5k‑15k), and most have clear productization potential.
• n8n and Zapier are everywhere. Low‑code automation isn’t a trend—it’s the baseline. If you’re not fluent in these, you’re leaving money on the table.
• Shopify ecosystem is quietly lucrative. Many merchants need custom apps, bots, or integrations that can be built in a weekend.

Takeaway

The gig‑platform job market is a goldmine of short‑term, well‑budgeted dev work. With a bit of automation (Claude Code + Playwright) and cheap bulk analysis (ChatGPT Pro file uploads), you can surface high‑value opportunities faster than anyone still scrolling manually.

120+ jobs, 3.8/5 avg SaaS potential.
Themes, apps, integrations—recurring revenue waiting to happen.

• “Easy” jobs still pay well. Plenty of difficulty‑2 gigs at $800‑2000. The skill arbitrage is real.
• Bot development is stable but commoditized. Good for consistent income, not for building wealth.

What’s Next

This post is just the overview. I’m sitting on 2,000 analyzed jobs with detailed breakdowns. Coming up:

• Productization opportunities – Jobs that are begging to become SaaS.
• Vibe coding targets – Problems a solo dev can solve in a weekend.
• Easy wins – Low‑difficulty, high‑paying gigs for quick cash.
• Tech deep dives – n8n, Shopify, GoHighLevel ecosystems.
• Individual job teardowns – Technical approach + realistic pricing.

The data pipeline is now running daily in the background, continuously collecting fresh postings. I’ll publish weekly updates as patterns emerge.

Need the raw data or want custom analysis? Reach out—happy to chat.