My $80 AI Coding Stack: How I Ship 6 Apps in Parallel While Others Pay $200 for Cursor Pro

You're paying $200 monthly for Cursor's pro plan. I'm paying $80 and shipping faster.

The difference isn't the budget. It's knowing which AI models solve which problems.

The foundation: Cursor with strategic model switching

Cursor's $20 monthly plan gives you enough. I spend an extra $60 because I'm running six simultaneous projects, but you don't need that overhead for one or two apps.

The critical insight: Haiku on Cursor specifically outperforms its performance elsewhere. Something about Cursor's implementation makes Haiku fast and accurate for code generation.

When Anthropic models slow down, I don't wait. I switch.

Kimi K2: My secondary workhorse

Kimi K2 became my secondary workhorse. Cheap, fast, handles most refactoring and feature additions. Not as sophisticated as Claude for architecture decisions, but perfect for the 80% of coding that's straightforward implementation.

GLM 4.6: The budget powerhouse

GLM 4.6 costs $6 monthly for me in API costs and punches above its weight. Slightly underpowered compared to Anthropic's models, but a fraction of the cost.

The workflow: Strategic model deployment

The workflow: Primary model for new features and complex logic. Secondary models for refinements, bug fixes, and straightforward additions.

Conductor.Build: AI doing my SEO work

While AI generates code for my main project, I switch monitors and work on Conductor. It's handling SEO for my 100indiebets landing page.

Fifteen pull requests in one day. All SEO-related tasks I would've procrastinated on for weeks.

The key: I don't merge until I review. Conductor creates the PRs. I verify the changes make sense. Then merge.

This parallel workflow doubles my effective output. Main project compiles or AI thinks meanwhile I'm productive elsewhere.

Zenmux for provider flexibility

Zenmux lets me add any LLM provider to Claude Code. When one provider is slow or expensive, I switch without changing my entire setup.

I've tested it with Kimi K2 specifically. The ability to use powerful models through a unified interface matters more than most developers realize.

Lock-in kills productivity. Tool flexibility maintains it.

Music creation: Minimax Music 2

Not a development tool, but relevant for anyone building apps with audio features.

I created a custom lo-fi emo rap track with my own lyrics. Tried it with Claude 4.5 and Kimi K2 for lyrics generation. Finalized the text, pasted it into Minimax Music 2, specified the style, and got a usable track in one shot.

Now building an AI Cover Song app around this workflow. The technology is accessible enough that non-musicians can create production-quality audio.

The Mac-specific advantage

I'm a Mac user. Not for the aesthetic, for the ecosystem integration.

Tools like Hazel (though I didn't love the UI) exist because Mac's file system and automation layer make certain tasks trivial. Windows and Linux can do the same things, but the friction is higher.

If you're building iOS apps specifically, the Mac advantage compounds. Xcode, TestFlight, App Store workflows—everything's smoother on native hardware.

The decision framework: when to use which tool

The common mistake: using the most powerful (expensive) model for everything. The correct approach: match model capability to task complexity.

Cost breakdown: AI Cost | October 2024

That's six apps in parallel development. Most developers spend more on coffee.

The efficiency comes from strategic model selection and parallel workflows, not bigger budgets.

What this stack enables

I'm shipping apps while others are still planning sprints. Not because I'm faster at typing—because I'm faster at deciding which tool solves which problem.

You don't need my exact stack. You need the framework: identify your workflow bottlenecks, find AI tools that eliminate them, and verify outputs instead of generating them yourself.

The tools will change. The framework won't.