How Lovable, Cursor, Claude and Codex fit into one development process

Most AI coding comparisons ask which tool is best. In my actual workflow that question matters less than deciding which cognitive job each tool should own.

The value of an AI tool stack comes from non-overlapping responsibilities, not the number of tools.

These roles are intentionally temporary

The products and models change quickly. My role split is not a permanent capability ranking. It is the distribution that currently reduces friction in my work.

I think in work modes, not AI brands

1type WorkMode =2  | "explore"3  | "shape"4  | "implement"5  | "audit"6  | "verify";

Lovable: make product direction visible quickly

I find Lovable especially useful when a layout, landing page or product shell needs to become visible before the decision can be evaluated. Its risk is letting a fast prototype silently become canonical architecture.

Cursor: everyday repository implementation

Cursor fits naturally when I am already inside an existing repository and want small or medium changes that follow local patterns. Repository context still does not replace product context, so preserve/forbid constraints remain important.

1const cursorTask = {2  goal: "fix the current behavior",3  preserve: ["existing design", "data contracts", "route semantics"],4  avoid: ["new dependency", "parallel architecture", "unrequested redesign"],5};

Claude: reframing and difficult root-cause work

When several patches have failed, I value a tool that can step away from the current implementation and challenge the model behind it. That often matters more than generating another patch immediately.

Codex: scoped engineering, audits and verification

I often use Codex for engineering-heavy repository tasks, structured audits and implementation where preflight, change and verification can be kept explicit.

1const codexFlow = [2  "verify repo and branch",3  "inspect existing architecture",4  "make scoped change",5  "run available checks",6  "report unverified layers explicitly",7];

GitHub sits above all of them

Different agents can create different worktrees, previews and local states. GitHub remains my canonical code truth. Agents may change; the source of truth should not.

I rarely ask four agents to implement the same feature

Parallelism works better for independent cognition: one audit, one review, one alternative UI direction. Competing implementations of the same files usually create merge work and architectural inconsistency.

Independent review should actually be independent

Instead of telling the second agent that the first solution is probably correct, I give it requirements and the diff, then ask for mismatches before fixes.

1Do not assume the previous implementation is correct.2Compare the requested behavior, current code and actual diff.3Report mismatches first.

Tool switching has a context cost

Changing tools every time an answer disappoints me means rebuilding context repeatedly. I try to switch when the task mode changes, not when I become impatient.

Verification belongs to the task, not the tool

A visual prototype may need human review. A web fix may require typecheck, build and browser verification. A native bug may require a real device. The completion standard comes from the risk surface.

My current allocation

Conclusion

The durable system is not the brand mix. Product authority stays with the human, GitHub stays canonical, each task is scoped, and “done” is attached to a verification level rather than an agent's confidence.

1const aiWorkflow = {2  productAuthority: "human",3  codeTruth: "GitHub",4  toolChoice: "task-dependent",5  implementation: "scoped",6  done: "verified-not-declared",7};