Ship production-ready code faster with an AI pair programmer that generates functions, refactors complex methods, writes unit tests, and reviews your PRs with actionable comments. Works across JavaScript/TypeScript, Python, Java, Go, C#, SQL, and more—so you can move from idea to implementation in minutes.

Start instantly in your browser with our free ai chat. No signup needed on the Free plan.

What is Ai Software Developer?

Ai Software Developer is an AI pair programmer that turns your specs into clean, idiomatic code. It scaffolds endpoints and services, refactors legacy files, writes unit/integration tests, and leaves PR-style review notes—so you can ship faster with fewer regressions.

Who is it for?

• Indie hackers & startups: Spin up MVP features (auth, CRUD, payments) in hours, not days. Plan sprints alongside the Free AI Task Organizer.
• Professional developers: Generate functions/files, refactor safely, and get actionable PR review feedback. For quick snippets, try the Free AI Coding Assistant.
• QA & SDET teams: Auto-generate unit & integration tests (Jest, PyTest, JUnit) with edge cases and fixtures.
• DevOps & platform engineers: Draft Dockerfiles, docker-compose, and CI/CD pipelines (GitHub Actions, GitLab CI) from a short brief.
• PMs & tech writers: Create READMEs, API docs, and release notes; polish copy with the Free Ai Content Writer.
• Students & new devs: Learn by example with step-by-step explanations and side-by-side refactors.
• Global teams: Generate code comments and docs in multiple languages with the Free AI Translator.

Key Features & Capabilities

Core coding power

• Multi-language code generation: JS/TS (Node/React/Next), Python (FastAPI/Django), Java (Spring), Go, C#, PHP/Laravel, SQL.
• Refactoring & cleanup: split big functions, reduce complexity, enforce idiomatic patterns and lint rules.
• Bug diagnosis: reason about stack traces, reproduce scenarios, and propose minimal, testable fixes.
• Framework scaffolds: endpoints, services, middleware, and config files from a short spec.

Tests & quality

• Unit/integration tests: Jest, Vitest, PyTest, JUnit, NUnit, Go test, PHPUnit—covers happy paths + edge cases.
• Contract & API tests: example payloads, schema validation, and error handling.
• Coverage-oriented suggestions: flags untested branches and generates fixtures/mocks.

DevOps & delivery

• Dockerfiles & docker-compose tailored to your runtime and caching strategy.
• CI/CD pipelines: GitHub Actions/GitLab CI with build, test, lint, and deploy steps.
• Env templates: .env.example, secrets placeholders, and config matrices (dev/stage/prod).

Data & SQL helpers

• Schema design & migrations: tables, indices, and relations with rationale.
• Query tuning: rewrite slow queries, add proper indexes, and estimate complexity.
• ORM mappings: Prisma/TypeORM/SQLAlchemy models with validation hints.

Reviews, docs & communication

• PR-style review comments: readability, performance, and security callouts with code examples.
• Docs & READMEs: setup, scripts, usage examples, and troubleshooting.
• Changelogs & release notes: human-friendly summaries of commits and breaking changes.

Architecture & patterns

• Design proposals: REST vs GraphQL, hexagonal architecture, caching tiers, and queue choices.
• Trade-off analysis: scalability, cost, simplicity—clear pros/cons to support decisions.
• Service boundaries: outline modules, interfaces, and data contracts.

Controls & customization

• Style targets: choose language level (ES2022/Node 20, Python 3.12…), lint presets, and testing frameworks.
• Output modes: single function, full file, patch diff, or PR review only.
• Prompt presets: “bugfix,” “refactor,” “add tests,” or “scaffold service.”
• Sprint helpers: break tasks into subtasks with acceptance criteria.
• Prompt crafting aid: formulate precise, reproducible requests with the Free AI Prompt Generator.

Guardrails (built-in suggestions)

• Reminders to avoid secrets and to use placeholders.
• Notes on dependency licenses, security headers, and common misconfigurations.
• Suggestions to run lint/test/scan before merging.

How It Works

Steps

1. Open the Ai Software Developer page and start the chat—no signup needed on Free.
2. Describe your goal in one sentence. Add stack details (language, framework, versions).
3. Paste code or errors (stack traces, failing tests, logs) for context.
4. Request a specific output: a function, refactor, unit tests, Dockerfile, or CI workflow.
5. Review and run locally. If something breaks, paste the result and ask for a fix.
6. Iterate: ask for alternatives, performance tweaks, or security notes.
7. Save or copy the result. Need larger sessions? Upgrade a plan.

Pro tip: Ask for tests + docs with every feature to keep quality high.

Input Checklist (for better answers)

• Language & version (e.g., Python 3.12, Node 20).
• Framework & libraries (Django, FastAPI, React, Spring, .NET).
• Target runtime (Docker, serverless, Linux, Windows).
• Output format (single file, module structure, patch diff).
• Constraints (style guide, linter rules, performance limits).
• Security considerations (auth model, secrets handling).

Plans & Limits

• Free — 20,000 tokens/day, no signup. Great for small features, bug fixes, and quick prototypes.
• Pro — 200,000 tokens/day for $19.99/month. Suitable for multi-file outputs, thorough refactors, and full test suites.
• Premium — 300,000 tokens/day for $179/year. Best value for sustained weekly building.
• See details or upgrade anytime at /subscribe/.
• Note: Token usage depends on prompt length and code size. Always review generated code and run your own tests.

Practical Use Cases & Short Examples

Backend & APIs

• Prompt → “Generate a FastAPI CRUD for Task (SQLite) + JWT auth.”
  Output → router.py, models.py, schemas.py, token utilities, and happy-path tests.
• Prompt → “Express.js route for /tasks with Zod validation + Prisma.”
  Output → tasks.route.ts, schema guards, typed controller, and Prisma queries.
• Prompt → “Spring Boot controller with pagination & filtering.”
  Output → TaskController.java, TaskService.java, request DTOs, and repository spec.

Frontend & UI

• Prompt → “React (TypeScript) paginated table with search (Tailwind).”
  Output → TasksTable.tsx with debounced search, empty state, and aria labels.
• Prompt → “Convert this JS component to strict TypeScript with props types.”
  Output → Component.tsx + interface definitions and unit tests.
• Prompt → “Next.js page with server actions and form validation.”
  Output → /app/tasks/page.tsx, server action handler, and Zod schema.

Data & Databases

• Prompt → “Design a PostgreSQL schema for e-commerce (products, orders, payments).”
  Output → DDL with FKs, composite indexes, and example SELECT queries.
• Prompt → “Write safe, parameterized SQL for product search with pagination.”
  Output → SQL with LIMIT/OFFSET, ILIKE, and bind parameters.
• Prompt → “Mongoose schema with validation and timestamps.”
  Output → TaskModel.ts with validators, indexes, and lean queries.

DevOps & CI/CD

• Prompt → “Dockerfile for Django + Gunicorn (multi-stage, slim image).”
  Output → production Dockerfile with build stage, .dockerignore, healthcheck.
• Prompt → “docker-compose for app + Postgres + Redis.”
  Output → compose.yml with volumes, networks, env vars, and readiness probes.
• Prompt → “GitHub Actions: Node 20, pnpm cache, ESLint, tests, build artifact.”
  Output → .github/workflows/ci.yml with matrix and caching.

Debugging & Quality

• Prompt → “Explain and fix this stack trace (NullReferenceException).”
  Output → root-cause analysis, safe null guards, and refactor suggestion.
• Prompt → “Write Jest tests for formatDate.ts with edge cases.”
  Output → tests for timezones, invalid inputs, and snapshot for ISO output.
• Prompt → “PyTest fixture for FastAPI client + example test.”
  Output → conftest.py + sample auth test.

Security & Performance

• Prompt → “Add basic rate limiting and Helmet to Express API.”
  Output → middleware setup, secure headers, and abuse-case notes.
• Prompt → “Reduce N+1 queries in Django ORM.”
  Output → select_related, prefetch_related, and query count check.

Scripting & Automation

• Prompt → “Python CLI to convert CSV → JSON with schema validation.”
  Output → cli.py using argparse, streaming IO, and error codes.
• Prompt → “Bash backup script with timestamped filenames and retention.”
  Output → POSIX-friendly script with trap and set -euo pipefail.

Reusable Prompt Pattern

Use this 4-part structure for consistent results:

1. Context: stack, versions, constraints.
2. Task: what to build or fix.
3. Output format: files, function names, or diff.
4. Acceptance criteria: tests, performance limit, lint style.

Need help crafting precise prompts? Try our Free AI Prompt Generator for fast, high-quality requests.

Tips & Best Practices

Set clear context

• State language, framework, and versions up front (e.g., “Node 20 + Express 5 + Prisma 5”).
• Mention runtime/hosting (Docker, serverless, Linux) and any constraints (lint rules, code style, performance targets).

Structure your prompt

• Use a 4-part pattern: Context → Task → Output format → Acceptance criteria.
• Prefer one focused goal per request (e.g., “generate user.service.ts with unit tests”).
• Provide examples of inputs/outputs or a small snippet to guide style.

Control the output

• Ask for file-by-file outputs or a diff/patch to reduce noise.
• Specify naming conventions (folders, class names) and coding standards (ESLint, Prettier, Black).
• For long features, request an outline first, then “expand step 1”.

Work in small loops

• Keep snippets small and self-contained—avoid pasting entire repos.
• After each reply, run locally, then share errors or failing tests to iterate quickly.
• Ask for alternatives (two approaches with trade-offs) before committing.

Bake in quality

• Always request unit/integration tests (Jest, Vitest, PyTest, JUnit) with edge cases.
• Include logging and error handling patterns from the start.
• Ask for docstrings/README updates and example usage.

Security & privacy hygiene

• Share placeholder values for secrets and redact tokens/keys.
• Request input validation, authZ/authN notes, and dependency checks.
• For APIs, ask for OpenAPI/Swagger plus simple rate limiting guidance.

Performance mindfulness

• Mention expected scale and SLAs; ask for profiling tips.
• Watch for N+1 queries, blocking I/O, unnecessary re-renders; request fixes explicitly.
• Prefer streaming/async patterns where appropriate.

Collaboration & maintainability

• Ask for refactors that reduce complexity and improve readability.
• Request commit message suggestions or PR descriptions to speed up reviews.
• Encourage feature flags and config-driven design for safer rollouts.

Limitations, Accuracy & Disclaimers

Not a replacement for engineers

Ai Software Developer suggests code and patterns, but you own architectural decisions, reviews, and releases.

Potential inaccuracies

• May reference outdated APIs, wrong versions, or unsupported options.
• Can misread incomplete context and produce incorrect logic or types.
• Always run tests and validate behavior before merging.

Security & compliance

• Outputs may omit hardening steps (validation, authZ, rate limiting, secrets rotation).
• Perform your own security reviews (e.g., OWASP-style checks) and dependency scans.
• Verify licenses of any suggested packages and third-party snippets. For policy or licensing questions, consider the Free AI Legal Assistant.

Execution & environment limits

• The assistant does not run code, access your repo, or connect to infrastructure.
• Provide stack details and error logs; test locally or in CI to confirm fixes.

Dependencies & versions

• Pin versions and review changelogs; suggested packages may have breaking changes or CVEs.
• Use your ecosystem’s tools (npm audit, pip-audit, mvn versions, etc.) and lockfiles.

Performance & scalability caveats

• Generated solutions are starting points; profile and load-test for your workload and SLAs.
• Ask for alternatives and trade-offs before committing to a design.

Intellectual property & originality

• Code is synthesized from patterns and public knowledge. Ensure originality where required and follow your organization’s contribution policies.

No warranties

• All outputs are as-is for educational and development purposes.
• Do not deploy to production without human review, tests, and security validation.

Privacy & Data Handling

You control what you share

• Don’t paste production credentials, API keys, personal data, or confidential client information.
• Replace secrets with placeholders (e.g., DB_PASSWORD=***) and anonymize logs before sharing.

No automatic access to your systems

• The assistant cannot read your repositories, issue commands, or connect to databases, servers, or cloud accounts.
• Everything you share is limited to the chat content you provide.

Redaction & safe sharing

• Share minimal reproducible snippets instead of full projects.
• Remove customer identifiers, tokens, and internal URLs from code, configs, and stack traces.

Storage & improvement notes

• Conversation content may be processed to generate responses and improve quality. Avoid sensitive or proprietary material.
• Review your organization’s data policies before sharing any work code.

Ownership & licensing

• You remain responsible for ensuring you have the right to share code and that outputs fit your project’s license.
• Verify third-party dependencies and license compatibility before shipping.

Compliance reminders

• Follow your company’s security, privacy, and open-source policies.
• For regulated data (e.g., healthcare, finance), consult a qualified professional and your compliance team before sharing details.

Plans & Pricing (Details)

Free

• 20,000 tokens/day — no signup required.
• Great for quick prototypes, bug fixes, and small refactors.
• Access the on-page chat and copy results instantly.
• Upgrade anytime if you hit daily limits.

Pro — $19.99/month

• 200,000 tokens/day for bigger features and multi-file outputs.
• Ideal for steady development, comprehensive refactors, and full test suites.
• Best pick if you frequently request code + tests + docs in one go.

Premium — $179/year

• 300,000 tokens/day billed annually (≈ $14.9/mo).
• Designed for teams/creators building weekly with higher volume.
• Most cost-effective for long-term projects.

Compare at a glance

• Daily capacity: 20k (Free) · 200k (Pro) · 300k (Premium)
• Use cases: small tasks (Free) → medium/large features (Pro) → sustained building (Premium)
• Flexibility: upgrade or switch plans as needs grow.

How tokens work

• Tokens measure request + response size. Longer prompts and large code blocks consume more.
• To stay efficient, share minimal, focused snippets and ask for file-by-file outputs.

FAQs

Call to Action

Ship production-ready features faster with the Ai Software Developer—generate code, fix bugs, write tests, and document changes in one focused chat.

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