AI coding & development
Write code. Bring your ideas to life.
Start with a task
Follow relevant guides from selection through cost, implementation and operation.
Edit and review code with AI
Compare development tools around scope, review and verification.
Solve operational problems
Explore a focused topic
Building an app with AI: prototype, data, release and handoff
Plan an AI-built app from the first interface through data, release and ongoing ownership.
Read the guideAI coding-agent workflows: investigate, change and review
A starting point for repository investigation, editing, verification, permissions and budgets.
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Guides for this category
Using AI for refactoring without changing behavior
Plan AI-assisted refactoring with behavior records, small patches and independent checks.
Read the guideTriage AI code review findings by reproduction, impact and decision
Triage AI review findings by reproducibility, requirement uncertainty, maintenance value and incorrect assumptions.
Read the guideChoosing AI coding tools by the work and the patch you can accept
Choose AI coding tools by the actual work: completion, investigation, implementation, requirements and maintainable delivery.
Read the guideReviewing AI-generated database migrations before release
Review proposed PostgreSQL changes for existing data, compatibility, verification and recovery.
Read the guideReview AI-generated SQL for correct results, scope and execution plans
Review generated SQL for output meaning, join cardinality, boundaries, authorized scope and the distinction between plans and execution.
Read the guideHand off an AI-assisted pull request with reproducible evidence
Prepare an AI-assisted pull request with a reproducible problem, bounded diff, verification evidence and recovery limits.
Read the guideBuild synthetic test fixtures for AI-assisted debugging
Build small synthetic debugging fixtures with independent expected results, isolated state and bounded cleanup.
Read the guideGenerating unit tests with AI: expected behavior and boundary cases
Review AI-generated tests for independent expectations, meaningful boundaries and reproducible failures.
Read the guideAider vs Claude Code: file scope and delegation
Compare Aider and Claude Code through scoping, investigation, Git review and spending.
Read the guideAider vs Cline: Git-centered editing and supervised operations
Compare Aider and Cline through edit scope, provider use and recovery.
Read the guideAntigravity free and paid access: allowances and credit overages
Choose Antigravity access using actual allowance needs and optional overage behavior.
Read the guideAntigravity vs GitHub Copilot: working surfaces and team process
Compare Antigravity and GitHub Copilot through interfaces, review and organizational fit.
Read the guideAI-assisted API clients: contracts, authentication and errors
Build and review API clients against an explicit contract, including types, missing values, authorization and retries.
Read the guideChoosing a first AI app builder you can publish and maintain
Evaluate beginner app builders through a small task, persistence, correction, ongoing costs and a maintainable handoff.
Read the guideBolt pricing: monthly Pro, tokens and publication capacity
Read Bolt monthly Pro and Teams pricing, Free limits, token consumption and cancellation conditions.
Read the guideClaude Code alternatives for controlled repository changes
Consider Aider, Cline and Continue as alternatives for specific repository workflows.
Read the guideEstimating Claude Code costs: allowance, API usage and accepted changes
Build a Claude Code budget around the access route, overages and completed development tasks.
Read the guideClaude Code vs Cline: supervision and model access
Compare Claude Code and Cline through provider choices, action review and recovery.
Read the guideClaude Code vs GitHub Copilot: fit with existing engineering practice
Compare Claude Code and GitHub Copilot using investigation, review and administration needs.
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