I've been spending 6-7 hours a day building with Claude Code. It's been one of the more unusual learning experiences of my life — I'm not a developer, but Claude Code has let me build things that would have been completely inaccessible to me a year ago.
Most of the time, when I hit a problem, Claude Code can fix it. Or I describe the issue to o3, and it points me in the right direction. The combination works well enough that I'd started to think of these two as my main tools.
Then I ran into a bug that neither of them could solve.
The Bug
I was building an AI executive assistant. Connects to Slack, Telegram, Gmail, and Google Calendar. When I send it a natural language message, it figures out what I want and handles it.
Somewhere in the routing logic, something was breaking. The intent detection was misfiring on certain message types. The behavior was inconsistent in a way that was hard to pin down.
I tried Claude Code. It identified what it thought was the issue and made a fix. The bug persisted.
I tried o3. Different analysis, different suggested fix. Still broken.
I tried Gemini 2.5 Pro. Same result.
At this point I'd been working on this for two hours. I tried Grok 3 — same model I'd heard was good at code. Nothing.
I was genuinely out of options. Four different AI systems, each with a different take, none of them solving it.
What Happened With Grok 4
On instinct more than hope, I upgraded to Grok 4. Thirty dollars.
It read through the code and came back with something different from what the others had said. Instead of patching the symptom, it traced the logical flow back to where the problem actually originated — a mismatch in how the intent classification was being passed between two components. A root cause, not a surface fix.
It was done in minutes.
Why Different Models Miss Different Things
I've thought about this a lot since then.
The models I tried first are all capable. They're not bad at code. But they each have different strengths, different training emphases, and different ways of approaching a problem. Claude Code is excellent for general-purpose coding and tends to work well when you need it to hold a lot of context simultaneously. Grok 4 — at least in my experience — has a particular strength for tracing logical flow and finding root causes. Those are different skills.
The problem with tool loyalty is that when one model fails, you assume the problem is unsolvable or that you're asking the wrong question. But often the problem is just that the tool you're using isn't the best fit for that specific type of problem.
I've started to call this being multi-tool native. It's a concept I've been building into how I work: different tools for different jobs, and the skill is knowing which brain to bring in for which task. ChatGPT for general strategy and research. Claude for technical reasoning and coding. Gemini for visual tasks and Google-native workflows. Grok for real-time data and root-cause debugging. Perplexity when I need sourced, current information.
The shift: from tool loyalty to tool literacy.
The “Three Brains” Practice
Since that bug, I've changed how I approach hard technical problems.
When something stumps Claude Code, I don't just try harder with Claude Code. I describe the problem to multiple models simultaneously. I get three different reads on it. I look for where they agree, where they diverge, and why. Then I combine what seems most credible and feed it back to Claude Code to execute.
It takes a few extra minutes upfront. But it beats spending two more hours going in circles with a single model that isn't the right fit.
The broader principle: when you're stuck, switching tools isn't giving up. It's the smart move.
What This Costs
The Grok 4 upgrade was thirty dollars. That's less than an hour of most people's time. The bug fix it found had already cost me two hours.
The math on trying different tools is almost always favorable. The price of an upgrade or a new subscription is rarely the bottleneck. The bottleneck is the assumption that if one model couldn't do it, none of them can.
That assumption is wrong.
The Actual Lesson
I spent years thinking the skill with AI was finding the best tool and mastering it. One model. Deep expertise. Consistency.
That's the wrong mental model for where things are now.
Each model is a specialist with a different background. Asking one model to do everything is like hiring a copywriter and expecting them to also debug your database. They might try. They won't do it as well as someone for whom that's a core skill.
The skill now is routing — knowing which specialist to bring in for which job, and being willing to switch when the first choice isn't working.
Tool loyalty is a liability. Tool literacy is the skill.
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