Issue: Claude Code is unusable for complex engineering tasks with Feb updates

Original Article ↗ Hacker News Discussion ↗

Perceived regressions in Claude Code / Opus 4.6

  • Many heavy users report a clear drop in quality since ~Feb–Mar:

    • More “simplest fix” hacks, shallow patches, and breaking tests instead of addressing root causes.
    • Ignoring explicit instructions, switching plans mid-task, or doing the opposite of what was requested.
    • Stopping early or trying to end sessions (“let’s wrap up”, “we’ll do phase 2 later”) even when asked to continue.
    • Increased self-contradiction mid-answer (“oh wait… actually…”) and visible “giving up” behavior.
    • Partial implementations presented as complete; skipping validation steps that the model itself planned.

  • Several users note regressions especially with:

    • 1M context window enabled.
    • Long, complex brownfield projects vs greenfield “toy” projects.

Alternative experiences and skepticism

  • Some users see no major change, especially when:
    • Tasks are tightly scoped and well-specified.
    • They enforce careful planning, specs, and human review.
  • Others suggest this may be:
    • “New model honeymoon” wearing off.
    • Users over-attributing normal stochastic variance and complexity limits to “regression”.
  • There is skepticism about the GitHub issue because much of the “analysis” was AI-generated.

Possible causes discussed

  • Hypotheses from users (unconfirmed in-thread):
    • Cost-cutting or GPU scarcity leading to reduced “thinking” / reasoning tokens.
    • Harness / system-prompt changes in Claude Code prioritizing token savings and “simplest working solution”.
    • Subscription tier getting worse defaults than API or enterprise usage.
    • 1M context degrading effective reasoning beyond ~200–300k tokens.

Anthropic / Claude Code team response (as described)

  • A team member attributes behavior mainly to:
    • UI “thinking redaction” (hiding reasoning text, claimed not to change underlying thinking).
    • Switching default effort on Opus 4.6 to medium with adaptive thinking.
  • They recommend:
    • Setting effort to high or max (/effort high/max, env vars).
    • Optionally disabling adaptive thinking and re-enabling thinking summaries.

Workarounds and tooling patterns

  • Common coping strategies:
    • Strong CLAUDE.md / harness rules (no “simplest fix”, always fix failing tests, avoid hacks).
    • Plan-first workflows, spec-driven development, small commits per task.
    • Breaking projects into narrow subtasks with explicit validation.
    • Using secondary models/agents as reviewers (e.g., other vendors or local models).

Comparisons and vendor trust

  • Many report better or more consistent results with Codex/GPT-5.4 or various open/local models.
  • Concern that opaque, frequently changing SaaS models are a fragile dependency; calls for:
    • Versioned / pin-able models.
    • More transparency about thinking budgets and harness changes.
  • Some users have canceled subscriptions or shifted to APIs / alternative tools due to perceived degradation.