You know that feeling when you're 45 minutes into a deep debugging session, you've built up this perfect mental model of the problem, and then your AI tool just... stops?
I've been there too many times. Here's the system I built to never let it happen again.
The Problem
Modern dev workflows depend on AI tools that can disappear without warning. And since most of us use multiple providers, the probability of hitting some rate limit during a work session approaches 100%.
It's not if. It's when.
The System
After losing too many productive hours, I built a simple system around three principles:
1. Know Before You Go
Before starting any deep work session (>30 min of focused coding), I glance at my usage dashboard. Takes 2 seconds.
I use TokenBar for this — a macOS menu bar app that shows real-time usage across Claude, ChatGPT, Cursor, Copilot, Gemini, and 15+ other providers. The pace intelligence feature is the key: it tells me if my current burn rate will last through the reset window.
If everything's green: go all in.
If something's yellow: plan accordingly.
If something's red: use an alternative.
2. Pre-assign Tools to Task Types
Not all AI tasks are equal. I assign tools to task categories based on their strengths:
- Heavy reasoning (architecture, debugging): Claude → Gemini fallback
- In-editor coding (completions, quick edits): Cursor → Copilot fallback
- Quick questions (docs, syntax, brainstorming): ChatGPT → Gemini fallback
- Long context (full codebase analysis): Gemini (no real fallback needed)
Having this mapping pre-decided means I never waste time choosing when a tool goes down.
3. The 70% Rule
Never burn more than 70% of any tool's limit in a single session. Reserve 30% for unexpected needs later in the day.
This sounds simple but it completely eliminated my "afternoon dead zone" where I'd have no AI tools available because I'd burned through everything by lunch.
Results
Before the system:
- 4-6 surprise rate limits per week
- ~25 min lost per incident (context recovery)
- ~2.5 hours/week of lost productivity
After the system:
- 0 surprise rate limits in 3 weeks
- Zero unplanned interruptions
- Actually more total AI usage (just better distributed)
Tools
- TokenBar for usage monitoring ($4.99 one-time, macOS, local-only, no telemetry)
- A simple Notion doc with my tool-to-task mapping (took 5 min to set up)
- Calendar blocks for "AI-heavy" vs "AI-light" work
Total setup time: 10 minutes. Time saved per month: 10+ hours.
If you've built your own system for managing multi-AI workflows, I'd love to hear about it. This space is evolving fast and I'm sure there are better approaches out there.
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