My AI-Assisted Workflow

Meta‑discussion: “AI workflow” articles

• Several see these posts as repetitive “influencer-style” content, akin to people sharing .vimrc files, but with fewer concrete takeaways and more vibes.
• Others defend them as useful for newcomers, friends, or small audiences; even if workflows are similar, one good article may be the only one a reader sees.
• Some criticize posting such pieces to HN/Reddit as implicit self‑promotion or clout seeking.

Spec‑driven / “waterfall-ish” workflows

• Many describe workflows that start with problem discussion, then detailed specs/PRDs, then task breakdown, then implementation and review.
• Some observe this strongly resembles a scaled‑down waterfall model; others stress that frequent feedback and changeability distinguish it from classic waterfall.
• There is agreement that “thinking first, coding second” is core software engineering, regardless of AI.

Where AI helps vs. fails

• Supporters: AI is helpful as a “rubber duck” to clarify problems, draft specs, generate boilerplate, mutate code for test coverage, and act as a verbose search engine.
• Critics: AI often produces wrong, overcomplicated, or unsafe code, especially for concurrency or non‑trivial logic; manual review is mandatory.
• Consensus: AI is better for one‑off, iterative tasks than for fully automated, repeatable, high‑stakes workflows.

Prompting, “skills,” and meta‑evaluation

• Some describe elaborate agent “skills” and orchestrators (e.g., PRD‑to‑issues pipelines, status workflows, sub‑agents).
• One camp argues you should feed skills back into an LLM for critique; they see this as a kind of “lint” or introspection.
• Another camp calls this unreliable “vibes scoring,” noting LLMs optimize for plausible text, not truth, and that repeated runs would yield inconsistent scores.

Productivity, hype, and skepticism

• Several worry current AI workflows are “productivity theater”: more process and overhead, not clearly better outcomes.
• Strong skeptics say LLMs underdeliver compared to their marketing (“nation of PhDs,” mass unemployment predictions) and that reliance on them may be harmful.
• Others argue that, despite overhype, LLMs are still useful tools if used cautiously and combined with solid testing and human judgment.