Context window collapse: Even 200K context windows degrade significantly after ~50K tokens of code — agents start losing track of relationships between files, variable scoping breaks, and refactors become inconsistent across the codebase.
Requirement ambiguity is fatal: Agents excel at implementing specs, but 80% of real development is clarifying what the spec should be. Ask an agent to 'add user authentication' and you'll get OAuth boilerplate that doesn't match your data model, your security posture, or your existing session handling.
Mac Mini reality: Apple Silicon has ~128GB unified memory. A 7B model uses ~14GB for kv cache, leaving headroom for only small codebases. 70B models are essentially unusable locally. Cloud API dependency means latency, cost, and rate limits for any serious project.
No architectural thinking: Agents pattern-match solutions to similar problems. When your codebase has accumulated 10 years of decisions that make sense in aggregate but look wrong in isolation, agents refactor them into locally-correct but globally-broken code.
Debugging is where they die: Agents can read stack traces, but connecting a symptom to its cause across 50 files of framework code, async callbacks, and subtle race conditions is where human expertise is irreplaceable today.
Claude Code, Cursor, and Copilot Workspace are not the same as 2023-era Copilot. They now handle multi-file edits, run terminal commands, use git, and can complete features end-to-end — not just autocomplete single lines.
The right architecture makes them reliable: agentic systems with proper task decomposition, verification loops, and bounded context windows can reliably handle standard CRUD apps, API integrations, test generation, and DevOps automation.
Mac Mini + Claude Code is viable: Claude Code uses cloud API, so local compute is irrelevant for the AI — you're just paying per-token. A Mac Mini M4 Pro handles the agent's own operations (file I/O, git, terminal) at native speed. The bottleneck is the cloud model, which you control via API calls.
Swe-bench verified: Frontier models now solve 40%+ of real GitHub issues from the SWE-bench benchmark — issues that require multi-file understanding and complex reasoning. This was <5% two years ago.
The leverage point: A senior developer + AI agents is 3-5x more productive than either working alone. The constraint isn't whether AI can code — it's whether you can design the right agentic scaffolding to direct it.