Vibecode LeetCode
track this build5 steps, step by step0%NOT REALLY · don't bother
An agent can produce a Monaco editor, a problem list, a submissions table and a Judge0 sandbox in a session, and none of that is the product. LeetCode is a content and coordination asset: thousands of curated problems with hidden test cases that actually catch off-by-one and TLE, editorials, company frequency tags, and the social fact that interviewers pull questions from it, which you cannot self-host. Clone the harness and you sit staring at an empty problems directory; authoring good problems with adversarial test cases is the actual job. Build the trainer if you want a private drill rig over problems you have already seen, but do not pretend it replaces the bank.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of LeetCode.
Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.
===== README.md =====
# LeetCode indie build
## Goal
Build the smallest trustworthy replacement for the core LeetCode workflow for one developer or a tiny team.
## Scope
A local judge that runs your submitted Python or JavaScript against test cases in Docker, stores every attempt, and resurfaces solved problems on a spaced repetition schedule.
## Quick start
1. Install the documented dependencies.
2. Copy `.env.example` to `.env`.
3. Run the development command chosen during implementation.
4. Complete the acceptance checks in `BUILD_PLAN.md`.
## Honest limits
This build deliberately does not replace:
- The problem bank itself, thousands of problems with hidden tests tuned to catch the specific ways people get it wrong
- Company tags and frequency data, which is the main reason people pay for Premium
- Editorials and the discussion threads where the actual learning happens
- Contests, ratings, and the mild public humiliation that makes you keep showing up
- Any signal that you are practicing the same questions your interviewer will ask
If those capabilities are essential, use LeetCode instead of pretending the gap is solved.
===== AGENTS.md =====
# Agent instructions
- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".
===== BUILD_PLAN.md =====
# Build plan
## Original build brief
Build a local coding interview trainer. One repo, single user, no accounts, no cloud, no telemetry. It runs on my machine.
Stack, not negotiable: Python 3.11 with FastAPI on the backend, SQLite through SQLAlchemy, a Vite + React + TypeScript frontend using the Monaco editor, Docker for code execution. Do not add auth. Do not add a hosted judge API.
Problems live on disk, not in the database. Each problem is a folder under ./problems/<slug>/ containing problem.md (the statement), meta.yaml (title, difficulty, topic tags, language stubs) and tests.json (an array of {input, expected} objects, including at least one large case for timing). Write 12 seed problems yourself covering arrays, two pointers, a stack, binary search, BFS on a grid, interval merging, and one small DP. Do not scrape or reproduce LeetCode content.
Execution: POST /api/run accepts {slug, language, code} and runs it in a throwaway Docker container. Only python:3.11-slim and node:20-slim images. Flags: --network none, --memory 256m, --cpus 0.5, --pids-limit 64, read-only root filesystem, non-root user, 5 second wall clock kill. Wrap the submitted function with a harness that feeds each test case and compares output. Return per-test pass or fail, stderr, and runtime in milliseconds. Never execute submitted code outside Docker, not even in dev mode, not even as a fallback.
Store every submission in SQLite: slug, language, code, verdict, runtime, timestamp. I want to diff my third attempt against my first.
Review queue: SM-2 style spaced repetition over solved problems. Correct solve pushes the next due date out, a failure resets it. GET /api/due feeds a Today view that tells me what to redo.
UI, three routes: problem list with difficulty, last verdict and next due date · problem view with statement left, Monaco right, Ctrl+Enter to run, results panel below · stats page with a solve calendar and per-topic pass rate.
Explicitly out of scope: contests, leaderboards, other people's solutions, discussion threads, company tags, languages beyond Python and JavaScript, mobile layout, deployment.
Ship a README with a single make dev command and a .env holding only PORT and DOCKER_HOST. Before you claim it works, prove the sandbox survives an infinite loop, a fork bomb, a 2 GB allocation, and an outbound HTTP request, and paste the output.
## Required capabilities
- Docker installed and a willingness to trust your own container flags with untrusted code
- Your own problem statements and test cases, written by hand or from openly licensed sets
- Python 3.11 and Node for the app itself
- Time budget for the boring part: authoring adversarial test cases, not the UI
## Delivery order
1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.
## Done when
- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden.
===== .env.example =====
# Copy to .env and document every variable when it is introduced.
# Never put real credentials in this file.
APP_ENV=development
# Add only values required by the selected implementation.You are building a lean indie version of LeetCode.
Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.
===== README.md =====
# LeetCode indie build
## Goal
Build the smallest trustworthy replacement for the core LeetCode workflow for one developer or a tiny team.
## Scope
A local judge that runs your submitted Python or JavaScript against test cases in Docker, stores every attempt, and resurfaces solved problems on a spaced repetition schedule.
## Quick start
1. Install the documented dependencies.
2. Copy `.env.example` to `.env`.
3. Run the development command chosen during implementation.
4. Complete the acceptance checks in `BUILD_PLAN.md`.
## Honest limits
This build deliberately does not replace:
- The problem bank itself, thousands of problems with hidden tests tuned to catch the specific ways people get it wrong
- Company tags and frequency data, which is the main reason people pay for Premium
- Editorials and the discussion threads where the actual learning happens
- Contests, ratings, and the mild public humiliation that makes you keep showing up
- Any signal that you are practicing the same questions your interviewer will ask
If those capabilities are essential, use LeetCode instead of pretending the gap is solved.
===== AGENTS.md =====
# Agent instructions
- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".
===== BUILD_PLAN.md =====
# Build plan
## Original build brief
Build a local coding interview trainer. One repo, single user, no accounts, no cloud, no telemetry. It runs on my machine.
Stack, not negotiable: Python 3.11 with FastAPI on the backend, SQLite through SQLAlchemy, a Vite + React + TypeScript frontend using the Monaco editor, Docker for code execution. Do not add auth. Do not add a hosted judge API.
Problems live on disk, not in the database. Each problem is a folder under ./problems/<slug>/ containing problem.md (the statement), meta.yaml (title, difficulty, topic tags, language stubs) and tests.json (an array of {input, expected} objects, including at least one large case for timing). Write 12 seed problems yourself covering arrays, two pointers, a stack, binary search, BFS on a grid, interval merging, and one small DP. Do not scrape or reproduce LeetCode content.
Execution: POST /api/run accepts {slug, language, code} and runs it in a throwaway Docker container. Only python:3.11-slim and node:20-slim images. Flags: --network none, --memory 256m, --cpus 0.5, --pids-limit 64, read-only root filesystem, non-root user, 5 second wall clock kill. Wrap the submitted function with a harness that feeds each test case and compares output. Return per-test pass or fail, stderr, and runtime in milliseconds. Never execute submitted code outside Docker, not even in dev mode, not even as a fallback.
Store every submission in SQLite: slug, language, code, verdict, runtime, timestamp. I want to diff my third attempt against my first.
Review queue: SM-2 style spaced repetition over solved problems. Correct solve pushes the next due date out, a failure resets it. GET /api/due feeds a Today view that tells me what to redo.
UI, three routes: problem list with difficulty, last verdict and next due date · problem view with statement left, Monaco right, Ctrl+Enter to run, results panel below · stats page with a solve calendar and per-topic pass rate.
Explicitly out of scope: contests, leaderboards, other people's solutions, discussion threads, company tags, languages beyond Python and JavaScript, mobile layout, deployment.
Ship a README with a single make dev command and a .env holding only PORT and DOCKER_HOST. Before you claim it works, prove the sandbox survives an infinite loop, a fork bomb, a 2 GB allocation, and an outbound HTTP request, and paste the output.
## Required capabilities
- Docker installed and a willingness to trust your own container flags with untrusted code
- Your own problem statements and test cases, written by hand or from openly licensed sets
- Python 3.11 and Node for the app itself
- Time budget for the boring part: authoring adversarial test cases, not the UI
## Delivery order
1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.
## Done when
- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden.
===== .env.example =====
# Copy to .env and document every variable when it is introduced.
# Never put real credentials in this file.
APP_ENV=development
# Add only values required by the selected implementation.You are building a production product version of LeetCode.
Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.
===== PRODUCT.md =====
# LeetCode product brief
## Problem
An agent can produce a Monaco editor, a problem list, a submissions table and a Judge0 sandbox in a session, and none of that is the product. LeetCode is a content and coordination asset: thousands of curated problems with hidden test cases that actually catch off-by-one and TLE, editorials, company frequency tags, and the social fact that interviewers pull questions from it, which you cannot self-host. Clone the harness and you sit staring at an empty problems directory; authoring good problems with adversarial test cases is the actual job. Build the trainer if you want a private drill rig over problems you have already seen, but do not pretend it replaces the bank.
## Product outcome
A local judge that runs your submitted Python or JavaScript against test cases in Docker, stores every attempt, and resurfaces solved problems on a spaced repetition schedule.
## Target user
A serious builder who needs a maintainable product foundation rather than a one-off demo.
## Required capabilities
- Docker installed and a willingness to trust your own container flags with untrusted code
- Your own problem statements and test cases, written by hand or from openly licensed sets
- Python 3.11 and Node for the app itself
- Time budget for the boring part: authoring adversarial test cases, not the UI
## Explicit non-goals for v1
- The problem bank itself, thousands of problems with hidden tests tuned to catch the specific ways people get it wrong
- Company tags and frequency data, which is the main reason people pay for Premium
- Editorials and the discussion threads where the actual learning happens
- Contests, ratings, and the mild public humiliation that makes you keep showing up
- Any signal that you are practicing the same questions your interviewer will ask
## Success criteria
- The primary workflow is measurable end to end.
- Setup is reproducible in a clean environment.
- Failure, recovery, and support paths are documented.
- Product claims match what the implementation actually guarantees.
===== ARCHITECTURE.md =====
# Architecture
## Starting brief
Build a local coding interview trainer. One repo, single user, no accounts, no cloud, no telemetry. It runs on my machine.
Stack, not negotiable: Python 3.11 with FastAPI on the backend, SQLite through SQLAlchemy, a Vite + React + TypeScript frontend using the Monaco editor, Docker for code execution. Do not add auth. Do not add a hosted judge API.
Problems live on disk, not in the database. Each problem is a folder under ./problems/<slug>/ containing problem.md (the statement), meta.yaml (title, difficulty, topic tags, language stubs) and tests.json (an array of {input, expected} objects, including at least one large case for timing). Write 12 seed problems yourself covering arrays, two pointers, a stack, binary search, BFS on a grid, interval merging, and one small DP. Do not scrape or reproduce LeetCode content.
Execution: POST /api/run accepts {slug, language, code} and runs it in a throwaway Docker container. Only python:3.11-slim and node:20-slim images. Flags: --network none, --memory 256m, --cpus 0.5, --pids-limit 64, read-only root filesystem, non-root user, 5 second wall clock kill. Wrap the submitted function with a harness that feeds each test case and compares output. Return per-test pass or fail, stderr, and runtime in milliseconds. Never execute submitted code outside Docker, not even in dev mode, not even as a fallback.
Store every submission in SQLite: slug, language, code, verdict, runtime, timestamp. I want to diff my third attempt against my first.
Review queue: SM-2 style spaced repetition over solved problems. Correct solve pushes the next due date out, a failure resets it. GET /api/due feeds a Today view that tells me what to redo.
UI, three routes: problem list with difficulty, last verdict and next due date · problem view with statement left, Monaco right, Ctrl+Enter to run, results panel below · stats page with a solve calendar and per-topic pass rate.
Explicitly out of scope: contests, leaderboards, other people's solutions, discussion threads, company tags, languages beyond Python and JavaScript, mobile layout, deployment.
Ship a README with a single make dev command and a .env holding only PORT and DOCKER_HOST. Before you claim it works, prove the sandbox survives an infinite loop, a fork bomb, a 2 GB allocation, and an outbound HTTP request, and paste the output.
## Boundaries
Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors.
## Production baseline
- Configuration: validated at startup with safe local defaults where possible.
- Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives.
- Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions.
- Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts.
- Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists.
- Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test.
## Decision records
For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
===== AGENTS.md =====
# Agent instructions
- Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code.
- Implement milestone by milestone; keep each change reviewable and leave the application runnable.
- Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present.
- Never invent cryptography or silently weaken a requirement to make a test pass.
- Use provider interfaces for external services and deterministic fakes in tests.
- Add migrations and rollback or recovery notes for persistent data changes.
- Log useful operational context without credentials, tokens, passwords, or personal data.
- Update documentation and run all checks before completing a milestone.
===== MILESTONES.md =====
# Delivery milestones
## M0 — Decisions and scaffold
- Confirm the runtime, persistence model, threat boundaries, and deployment target.
- Create a reproducible local environment and continuous checks.
## M1 — Core workflow
- Implement the smallest end-to-end product path with validation and tests.
- Keep integrations behind interfaces.
## M2 — Trust layer
- Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards.
- Test abuse cases and destructive operations.
## M3 — Operability
- Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration.
## M4 — Release gate
- Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise.
- Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
===== OPERATIONS.md =====
# Operations
## Before release
- Validate configuration and secrets at startup.
- Define backup, restore, and rollback procedures and test them.
- Document logs, error tracking, health signals, and alert ownership.
- Set dependency update and vulnerability review expectations.
## Incident checklist
1. Contain the issue without destroying evidence or user data.
2. Record the timeline and affected scope.
3. Rotate exposed secrets and revoke compromised sessions or credentials.
4. Restore from a verified source when needed.
5. Document the root cause, remediation, and regression test.
## Launch constraint
Do not market omitted LeetCode capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.README.md
# LeetCode indie build ## Goal Build the smallest trustworthy replacement for the core LeetCode workflow for one developer or a tiny team. ## Scope A local judge that runs your submitted Python or JavaScript against test cases in Docker, stores every attempt, and resurfaces solved problems on a spaced repetition schedule. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - The problem bank itself, thousands of problems with hidden tests tuned to catch the specific ways people get it wrong - Company tags and frequency data, which is the main reason people pay for Premium - Editorials and the discussion threads where the actual learning happens - Contests, ratings, and the mild public humiliation that makes you keep showing up - Any signal that you are practicing the same questions your interviewer will ask If those capabilities are essential, use LeetCode instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan
## Original build brief
Build a local coding interview trainer. One repo, single user, no accounts, no cloud, no telemetry. It runs on my machine.
Stack, not negotiable: Python 3.11 with FastAPI on the backend, SQLite through SQLAlchemy, a Vite + React + TypeScript frontend using the Monaco editor, Docker for code execution. Do not add auth. Do not add a hosted judge API.
Problems live on disk, not in the database. Each problem is a folder under ./problems/<slug>/ containing problem.md (the statement), meta.yaml (title, difficulty, topic tags, language stubs) and tests.json (an array of {input, expected} objects, including at least one large case for timing). Write 12 seed problems yourself covering arrays, two pointers, a stack, binary search, BFS on a grid, interval merging, and one small DP. Do not scrape or reproduce LeetCode content.
Execution: POST /api/run accepts {slug, language, code} and runs it in a throwaway Docker container. Only python:3.11-slim and node:20-slim images. Flags: --network none, --memory 256m, --cpus 0.5, --pids-limit 64, read-only root filesystem, non-root user, 5 second wall clock kill. Wrap the submitted function with a harness that feeds each test case and compares output. Return per-test pass or fail, stderr, and runtime in milliseconds. Never execute submitted code outside Docker, not even in dev mode, not even as a fallback.
Store every submission in SQLite: slug, language, code, verdict, runtime, timestamp. I want to diff my third attempt against my first.
Review queue: SM-2 style spaced repetition over solved problems. Correct solve pushes the next due date out, a failure resets it. GET /api/due feeds a Today view that tells me what to redo.
UI, three routes: problem list with difficulty, last verdict and next due date · problem view with statement left, Monaco right, Ctrl+Enter to run, results panel below · stats page with a solve calendar and per-topic pass rate.
Explicitly out of scope: contests, leaderboards, other people's solutions, discussion threads, company tags, languages beyond Python and JavaScript, mobile layout, deployment.
Ship a README with a single make dev command and a .env holding only PORT and DOCKER_HOST. Before you claim it works, prove the sandbox survives an infinite loop, a fork bomb, a 2 GB allocation, and an outbound HTTP request, and paste the output.
## Required capabilities
- Docker installed and a willingness to trust your own container flags with untrusted code
- Your own problem statements and test cases, written by hand or from openly licensed sets
- Python 3.11 and Node for the app itself
- Time budget for the boring part: authoring adversarial test cases, not the UI
## Delivery order
1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.
## Done when
- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden..env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# LeetCode product brief ## Problem An agent can produce a Monaco editor, a problem list, a submissions table and a Judge0 sandbox in a session, and none of that is the product. LeetCode is a content and coordination asset: thousands of curated problems with hidden test cases that actually catch off-by-one and TLE, editorials, company frequency tags, and the social fact that interviewers pull questions from it, which you cannot self-host. Clone the harness and you sit staring at an empty problems directory; authoring good problems with adversarial test cases is the actual job. Build the trainer if you want a private drill rig over problems you have already seen, but do not pretend it replaces the bank. ## Product outcome A local judge that runs your submitted Python or JavaScript against test cases in Docker, stores every attempt, and resurfaces solved problems on a spaced repetition schedule. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - Docker installed and a willingness to trust your own container flags with untrusted code - Your own problem statements and test cases, written by hand or from openly licensed sets - Python 3.11 and Node for the app itself - Time budget for the boring part: authoring adversarial test cases, not the UI ## Explicit non-goals for v1 - The problem bank itself, thousands of problems with hidden tests tuned to catch the specific ways people get it wrong - Company tags and frequency data, which is the main reason people pay for Premium - Editorials and the discussion threads where the actual learning happens - Contests, ratings, and the mild public humiliation that makes you keep showing up - Any signal that you are practicing the same questions your interviewer will ask ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture
## Starting brief
Build a local coding interview trainer. One repo, single user, no accounts, no cloud, no telemetry. It runs on my machine.
Stack, not negotiable: Python 3.11 with FastAPI on the backend, SQLite through SQLAlchemy, a Vite + React + TypeScript frontend using the Monaco editor, Docker for code execution. Do not add auth. Do not add a hosted judge API.
Problems live on disk, not in the database. Each problem is a folder under ./problems/<slug>/ containing problem.md (the statement), meta.yaml (title, difficulty, topic tags, language stubs) and tests.json (an array of {input, expected} objects, including at least one large case for timing). Write 12 seed problems yourself covering arrays, two pointers, a stack, binary search, BFS on a grid, interval merging, and one small DP. Do not scrape or reproduce LeetCode content.
Execution: POST /api/run accepts {slug, language, code} and runs it in a throwaway Docker container. Only python:3.11-slim and node:20-slim images. Flags: --network none, --memory 256m, --cpus 0.5, --pids-limit 64, read-only root filesystem, non-root user, 5 second wall clock kill. Wrap the submitted function with a harness that feeds each test case and compares output. Return per-test pass or fail, stderr, and runtime in milliseconds. Never execute submitted code outside Docker, not even in dev mode, not even as a fallback.
Store every submission in SQLite: slug, language, code, verdict, runtime, timestamp. I want to diff my third attempt against my first.
Review queue: SM-2 style spaced repetition over solved problems. Correct solve pushes the next due date out, a failure resets it. GET /api/due feeds a Today view that tells me what to redo.
UI, three routes: problem list with difficulty, last verdict and next due date · problem view with statement left, Monaco right, Ctrl+Enter to run, results panel below · stats page with a solve calendar and per-topic pass rate.
Explicitly out of scope: contests, leaderboards, other people's solutions, discussion threads, company tags, languages beyond Python and JavaScript, mobile layout, deployment.
Ship a README with a single make dev command and a .env holding only PORT and DOCKER_HOST. Before you claim it works, prove the sandbox survives an infinite loop, a fork bomb, a 2 GB allocation, and an outbound HTTP request, and paste the output.
## Boundaries
Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors.
## Production baseline
- Configuration: validated at startup with safe local defaults where possible.
- Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives.
- Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions.
- Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts.
- Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists.
- Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test.
## Decision records
For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted LeetCode capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent · this prompt is generated from the build plan · improve it via PR
Because thirty-five dollars for the month before an onsite is trivially cheap against the salary delta, and because nobody wants to author their own curriculum while also preparing for interviews. Premium buys company-filtered lists and editorials, which is a shortcut through the one resource candidates are actually short on: time. A self-hosted drill app competes on none of that. It competes on being a nicer place to redo problems you already understand, which is a real but much smaller need.
xThe problem bank itself, thousands of problems with hidden tests tuned to catch the specific ways people get it wrong
xCompany tags and frequency data, which is the main reason people pay for Premium
xEditorials and the discussion threads where the actual learning happens
xContests, ratings, and the mild public humiliation that makes you keep showing up
xAny signal that you are practicing the same questions your interviewer will ask
Nothing worth pointing at. That's why the prompt exists.
share on X ↗"I just replaced LeetCode ($35/mo) with one prompt"
Vibecode LeetCode
Not really. LeetCode's value is not the code: the moat is fifteen years of curated problems, editorials and company tags, plus everyone else practicing on the same set. See the honest breakdown above.
How much does LeetCode cost?
LeetCode costs about $35/month (Premium, checked 2026-08-16), which is $420 per year.
What do I lose by replacing LeetCode?
Honestly: The problem bank itself, thousands of problems with hidden tests tuned to catch the specific ways people get it wrong; Company tags and frequency data, which is the main reason people pay for Premium; Editorials and the discussion threads where the actual learning happens; Contests, ratings, and the mild public humiliation that makes you keep showing up; Any signal that you are practicing the same questions your interviewer will ask. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to LeetCode?
No mature open-source alternative worth pointing at, which is exactly why the one-shot prompt on this page exists.