Okay, I’ll bite. I’ve been using Claude Code for about three months now, mostly for marketing automation workflows and data transformation scripts. Here’s my take after running it through my usual tinkering process.
It absolutely *shines* for boilerplate. Need to connect a CRM webhook to a Google Sheets append? Want a quick script to clean and dedupe a CSV lead list? It’s fantastic. I’ve built dozens of simple connectors and data munging scripts in half the time. The suggestions are practical and it gets the syntax right for the libraries I use most (Pandas, requests, basic SQL).
But when I tried to push it into more complex territory—like a custom lead scoring algorithm that weights recent engagement vs. demographic fit—it fell apart. The logic would get convoluted, it would suggest inefficient loops instead of vectorized operations, and when I asked it to optimize, it often made the code *more* obscure. It feels like it’s stitching together patterns it’s seen, without really grasping the computational trade-offs.
Has anyone else hit this wall? I’m especially curious about:
- Has anyone successfully coached it through a complex algorithm, or did you just hit a point where it was faster to write it yourself?
- Are there specific prompt strategies that work better for algorithmic thinking vs. boilerplate generation?
- What’s the most complex, working piece of logic you’ve gotten it to produce?
I’m keeping a running comparison sheet of outputs vs. hand-coded solutions for my use cases. The gap is pretty wide once you move past structured data tasks.
Your lead scoring example is the perfect case of where the boilerplate ends and the engineering starts.
It's stitching patterns, not solving problems. That's why it suggests inefficient loops. It's like asking a spreadsheet for a deployment pipeline. Good for the formulas, useless for the orchestration.
My rule: use it to scaffold the thing, then replace the brains yourself. The algorithm's core logic is where it goes off the rails, so keep it out of the engine room.
Deploy with love
>keep it out of the engine room
Exactly. You let it wire up the plumbing, but you never let it touch the control panel.
I see this in CI/CD. It'll spit out a decent-looking YAML skeleton, but the moment you need a non-trivial rollback strategy or a conditional branch based on artifact metadata, it generates something naive. It'll suggest polling a REST endpoint in a tight loop instead of using a proper event.
It's a pattern assembler. Treat it like a junior dev who's good at copying examples but can't reason about trade-offs. You'd never let that dev own a critical path.
-- old school
>suggest polling a REST endpoint in a tight loop
And that's where the cloud bill hits $1,200 for a job that should cost $2.
Seen it happen. An intern used an AI-generated lambda that polled an SQS queue 10x a second for 48 hours. The naive loop cost more in execution units than the actual workload. It didn't understand the event bridge pattern was free for the first million events.
So sure, use it for the YAML skeleton. But if you let it write the polling logic, you're not paying for a junior dev's mistake, you're paying for AWS's new yacht.
show the math