The Great Irony: 5,000 GitHub Issues and a 'Solved' Problem
Let's start with the elephant in the room—or rather, the 5,000+ elephants in the GitHub repository. When Boris Cherny, creator of Claude Code, made the now-infamous statement that "coding is solved," the programming community did what programmers do best: they pointed to the evidence. Specifically, they pointed to the Claude Code GitHub repository with its staggering collection of open issues. The Reddit thread that sparked this discussion wasn't just snarky commentary—it was a genuine, collective head-scratch from developers who live in the messy reality of software creation.
I've been testing AI coding assistants since GitHub Copilot first dropped, and here's what I've learned: every time someone declares a problem "solved" in software, we're usually just discovering the next layer of complexity. The real question isn't why Claude Code has issues—all software has issues. The real question is why we keep expecting AI to magically transcend the fundamental challenges of software engineering that have persisted for decades.
What "Coding is Solved" Actually Means (And What It Doesn't)
When you dig into what Cherny and others mean by "coding is solved," you're usually talking about a very specific subset of programming tasks. We're talking about generating boilerplate code, implementing well-documented algorithms, or creating simple CRUD operations from clear specifications. And honestly? AI is pretty good at those things in 2026. I've watched Claude Code generate entire React components that would have taken me 30 minutes to write from scratch.
But here's where the community pushback makes perfect sense. Software development isn't just about writing code—it's about understanding requirements that are often contradictory, navigating legacy systems with undocumented behaviors, making trade-offs between performance and maintainability, and debugging issues that emerge from complex system interactions. These are the problems filling up that GitHub repository, and they're exactly the problems AI struggles with most.
Think about it this way: if coding were truly solved, why would we need 5,000 GitHub issues? Why wouldn't Claude Code just... fix itself? The Reddit commenters weren't being facetious—they were pointing out the logical inconsistency at the heart of the claim.
The Real Work Happens Between the Lines
Let me share something from my own experience. Last month, I was integrating Claude Code into a legacy banking system. The AI could generate beautiful, modern TypeScript interfaces. But it couldn't tell me why the 15-year-old COBOL backend would reject certain date formats, or how to handle the weird edge cases that only appear during full moons (yes, really—timezone handling with historical date changes).
This is what separates actual software engineering from just writing code. It's the domain knowledge, the institutional memory, the understanding of business constraints and user behaviors. The issues in Claude Code's repository aren't about syntax errors—they're about integration problems, performance quirks, unexpected interactions with other tools, and "it works on my machine" scenarios multiplied across thousands of different developer environments.
One commenter on the Reddit thread put it perfectly: "AI can write the code I tell it to write. It can't tell me what code I should be writing." That distinction matters more than ever in 2026.
Why GitHub Issues Are Actually a Good Sign
Here's a perspective shift that might surprise you: those 5,000+ issues aren't a failure—they're evidence of something working. Seriously. Think about it. Claude Code is being used enough, in enough different contexts, by enough developers, that they're encountering edge cases and reporting them. An unused tool has zero issues. A dead project has stale issues. A vibrant, actively-used tool? That's where you get thousands of issues from real users trying to do real work.
The community discussion actually revealed something important about developer expectations in 2026. We don't expect perfection. We expect transparency, responsiveness, and continuous improvement. The issue count itself isn't the problem—it's the gap between the "coding is solved" rhetoric and the reality of those issue reports.
What developers really want—and what the Reddit thread was essentially asking for—is acknowledgment of the complexity. We want tool creators who say, "Yes, AI can help with X, but you still need human judgment for Y and Z." That honesty builds trust far more than overpromising.
The Integration Problem: Where AI Tools Fall Short
This is where we get to the heart of why API and integration work remains stubbornly human-intensive in 2026. Let me give you a concrete example from just last week. I was using Claude Code to help build an integration between a modern GraphQL API and a SOAP service from 2008. The AI could generate perfect GraphQL resolvers. It could even create SOAP client code. But it couldn't understand the semantic mismatches between the two systems—the way "customer status" meant different things in each system, or how date ranges were interpreted differently.
Integration work is about translation between contexts, and AI still struggles with context. It's why those GitHub issues include things like:
- "Claude Code doesn't understand our custom authentication flow"
- "Generated code conflicts with our existing middleware"
- "API rate limiting handling doesn't match our retry policies"
These aren't coding problems—they're understanding problems. And they're exactly where human developers spend most of their mental energy.
Practical Tips for Using AI Coding Tools in 2026
So if AI hasn't "solved" coding, how should you actually use these tools? Based on my experience with Claude Code and similar tools, here's what works:
First, use AI for the repetitive stuff. Generating boilerplate, creating test data, writing documentation templates—these are where AI shines. I've saved countless hours by having Claude Code generate the skeleton of API clients or data models.
Second, treat AI as a pair programmer, not a replacement. Ask it to explain its code. Challenge its assumptions. Use it to explore alternative implementations. One technique I've found particularly effective: have the AI generate three different solutions to a problem, then evaluate the trade-offs yourself.
Third, be specific about context. The more you can tell the AI about your system constraints, business rules, and existing patterns, the better it performs. This is where many developers go wrong—they assume the AI "knows" their context when it really doesn't.
Finally, always review and test. This should be obvious, but I've seen teams get burned by blindly accepting AI-generated code. The issues in Claude Code's repository are a perfect reminder: if the tool creators themselves haven't automated away their own bug fixing, you shouldn't assume the generated code is perfect either.
Common Misconceptions About AI Programming Tools
Let's clear up some confusion that came through in the Reddit discussion:
"AI will replace developers" – This gets it backwards. What's actually happening is that AI is changing what developers do. We're spending less time on syntax and more time on architecture. Less time on boilerplate and more time on system design. The job isn't disappearing—it's evolving.
"More issues means worse software" – Not necessarily. Active projects with many users naturally accumulate more issues. The better metric is how those issues are handled, how quickly they're resolved, and whether the same problems keep recurring.
"AI understands requirements" – This is the big one. AI can process requirements you give it, but it can't discover missing requirements or challenge ambiguous ones. That critical thinking remains firmly in the human domain.
"Generated code is production-ready" – Rarely. AI-generated code often needs optimization, security review, integration with existing systems, and customization for specific use cases. It's a starting point, not a finished product.
The Future: Augmentation, Not Automation
Looking ahead to the rest of 2026 and beyond, here's what I think we'll see. The most successful development teams won't be the ones trying to replace developers with AI. They'll be the ones figuring out how to augment human intelligence with artificial intelligence.
We'll see tools that are better at understanding context—not just code context, but business context. Tools that can learn from your codebase and suggest improvements that align with your team's patterns. Tools that can explain not just what they're doing, but why they're doing it that way.
The GitHub issues for Claude Code and similar tools will start to change too. Instead of "generated code doesn't work," we'll see more issues like "suggested optimization conflicts with our performance benchmarks" or "recommended architecture doesn't match our scaling requirements." The conversation will shift from basic functionality to higher-level concerns.
What This Means for Your Development Workflow
If you're integrating AI tools into your workflow in 2026 (and you probably are), here's my advice: embrace the complexity. Don't expect magic. Do expect to spend time teaching the tool about your specific context. And most importantly, maintain your critical thinking skills.
The developers who thrive will be the ones who can ask better questions, not just write better prompts. They'll be the ones who can evaluate AI suggestions against business requirements, not just technical correctness. They'll understand that sometimes the right answer isn't the most elegant code—it's the code that solves the actual problem for actual users.
And about those 5,000 GitHub issues? They're not a bug—they're a feature. They're evidence of real people doing real work with real tools. They're the sound of progress being made, one messy, complicated, human problem at a time.
The truth is, coding was never just about writing code. It's about solving problems. And that? That's nowhere near solved. But with the right combination of human intelligence and AI assistance, we're getting better at it every day. Just don't expect the GitHub issues to disappear anytime soon.
