Lovable, Claude, Codex, Miro or Amplitude? Give Every Tool a Job

The fastest way to make an AI workflow confusing is to ask every tool to do everything. The second fastest is to choose the stack before defining the problem.

I use a simpler rule: give each tool a primary job, make the hand-offs explicit and keep a human owner for the decision. This is not a permanent league table. The products change quickly. It is an operating map for choosing what happens where.

Miro: frame the shared problem

Miro is strongest at the moment when a team needs to see the same situation together: actors, constraints, evidence, assumptions, workflow and unresolved questions. A board can hold text, cards, links, images and diagrams on a shared visual surface.

That matches Miro’s own description of a board as a workspace for exchanging information through visual items.

Primary job: turn a messy conversation into a visible brief that a team can challenge. Do not let the board become an archive nobody can act on. End with an objective, owners, decisions and the smallest build.

Lovable: make the product surface real

Lovable is useful when the team needs a working web product quickly: interface, flows, database-backed behaviour, authentication and integrations. It is particularly effective when non-engineers and engineers need to react to the same live thing.

The Lovable platform overview describes a full-stack workflow with editable code and GitHub integration.

Primary job: move from an agreed product brief to a testable application. Ask for states, behaviour, constraints and instrumentation—not “make me a beautiful app”.

Claude: reason through ambiguity and complex material

Claude is useful for working through long documents, exploring options, critiquing a brief and, through Claude Code, operating against a repository. It can help investigate a system before a change is made.

Anthropic’s current Claude Code documentation covers its repository-oriented command-line workflow.

Primary job: analyse, structure and implement where substantial context must stay coherent. The team still needs to verify sources, decisions and code.

Codex: execute and verify repository work

Codex fits when the task lives in a codebase and requires inspection, editing, testing and a clear hand-off. It is well suited to bounded engineering work, debugging and implementation that should be checked rather than merely suggested.

Use the current OpenAI developer documentation for capabilities and setup; this area changes too quickly for a static feature checklist.

Primary job: carry a defined code change through to evidence—tests, a diff, a working result and remaining risks.

Amplitude: turn behaviour into evidence

Amplitude belongs in the loop when the team needs to understand what users did, where a flow broke and whether a product change improved a meaningful behaviour.

Amplitude explains that events and their properties form the basis of product analysis.

Primary job: connect the build to observable user behaviour. Define the questions and events before launch; validate the data before trusting the chart.

The hand-off that works

• Frame in Miro: situation, evidence, objective, constraints and owner.

• Build the product surface in Lovable.

• Use Claude or Codex when the work needs deeper reasoning or repository-level implementation.

• Instrument meaningful behaviour and inspect it in Amplitude.

• Return the finding to the brief and decide what changes next.

Where the tools overlap

There is overlap. Lovable can plan; Claude and Codex can create interfaces; Miro now includes AI capabilities; Amplitude can surface recommendations. Overlap is not a problem until ownership becomes unclear.

Choose the tool that preserves the context and evidence required for the current job. If a hand-off repeatedly loses information, reduce the number of tools or automate that boundary.

The stack is not the transformation

A team has changed when it can select better problems, build and validate faster, work safely and repeat the method without a facilitator. Buying five tools does not create that capability. A disciplined loop might.

Capabilities referenced here were checked against official documentation in September 2026. Recheck access, security and product details before standardising a company workflow.

See how the loop works in practice in Lovable + Amplitude: From Prototype to Evidence.