Vibecode Ideabrowser
track this build5 steps, step by step0%Scraping Reddit/search trends and having an LLM draft a business-opportunity writeup per idea is a weekend build. What's genuinely hard to replicate is the editorial curation, the accumulated database of 1,000+ pre-researched ideas, and the trend-signal pipeline built up over time. A DIY version can generate plausible-sounding ideas on demand but starts from zero data and zero curation.
You are building a lean indie version of Ideabrowser. 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 ===== # Ideabrowser indie build ## Goal Build the smallest trustworthy replacement for the core Ideabrowser workflow for one developer or a tiny team. ## Scope A script that pulls trending Reddit/Google-Trends signals, feeds them to an LLM with a fixed prompt template, and outputs a formatted market-opportunity brief. ## 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: - 1,000+ already-curated and human-vetted ideas - historical trend database and idea archive - AI Research Agent for arbitrary custom ideas - community, coaching and Q&A strategist sessions - data exports and idea-suggest personalization If those capabilities are essential, use praw 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 me a startup-idea trend scanner as a Next.js app. Stack: Next.js + TypeScript, SQLite via better-sqlite3, Tailwind. No auth, single user, localhost only. Core loop: 1. A cron-style script (runnable manually via a button) that fetches the top posts from a fixed list of subreddits (via Reddit's public JSON endpoints, no API key needed) mentioning pain points or requests. 2. Pass each candidate post to the Anthropic API (key from .env) with a fixed prompt asking it to draft a one-page opportunity brief: problem, target user, rough market size guess, an MVP scope, and a one-line pricing idea. 3. Save each brief to SQLite and show them in a simple feed, newest first, with a save/star toggle. 4. A search box to filter saved briefs by keyword. Out of scope: payments, email delivery, multi-user accounts, historical trend charts, editorial curation. Include a README covering setup, the .env variable, and a note that brief quality depends entirely on the source posts fetched. ## Required capabilities - OpenAI/Anthropic API key - Reddit or Google Trends API access ## 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 Ideabrowser. 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 ===== # Ideabrowser indie build ## Goal Build the smallest trustworthy replacement for the core Ideabrowser workflow for one developer or a tiny team. ## Scope A script that pulls trending Reddit/Google-Trends signals, feeds them to an LLM with a fixed prompt template, and outputs a formatted market-opportunity brief. ## 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: - 1,000+ already-curated and human-vetted ideas - historical trend database and idea archive - AI Research Agent for arbitrary custom ideas - community, coaching and Q&A strategist sessions - data exports and idea-suggest personalization If those capabilities are essential, use praw 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 me a startup-idea trend scanner as a Next.js app. Stack: Next.js + TypeScript, SQLite via better-sqlite3, Tailwind. No auth, single user, localhost only. Core loop: 1. A cron-style script (runnable manually via a button) that fetches the top posts from a fixed list of subreddits (via Reddit's public JSON endpoints, no API key needed) mentioning pain points or requests. 2. Pass each candidate post to the Anthropic API (key from .env) with a fixed prompt asking it to draft a one-page opportunity brief: problem, target user, rough market size guess, an MVP scope, and a one-line pricing idea. 3. Save each brief to SQLite and show them in a simple feed, newest first, with a save/star toggle. 4. A search box to filter saved briefs by keyword. Out of scope: payments, email delivery, multi-user accounts, historical trend charts, editorial curation. Include a README covering setup, the .env variable, and a note that brief quality depends entirely on the source posts fetched. ## Required capabilities - OpenAI/Anthropic API key - Reddit or Google Trends API access ## 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 Ideabrowser. 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 ===== # Ideabrowser product brief ## Problem Scraping Reddit/search trends and having an LLM draft a business-opportunity writeup per idea is a weekend build. What's genuinely hard to replicate is the editorial curation, the accumulated database of 1,000+ pre-researched ideas, and the trend-signal pipeline built up over time. A DIY version can generate plausible-sounding ideas on demand but starts from zero data and zero curation. ## Product outcome A script that pulls trending Reddit/Google-Trends signals, feeds them to an LLM with a fixed prompt template, and outputs a formatted market-opportunity brief. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - OpenAI/Anthropic API key - Reddit or Google Trends API access ## Explicit non-goals for v1 - 1,000+ already-curated and human-vetted ideas - historical trend database and idea archive - AI Research Agent for arbitrary custom ideas - community, coaching and Q&A strategist sessions - data exports and idea-suggest personalization ## 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 me a startup-idea trend scanner as a Next.js app. Stack: Next.js + TypeScript, SQLite via better-sqlite3, Tailwind. No auth, single user, localhost only. Core loop: 1. A cron-style script (runnable manually via a button) that fetches the top posts from a fixed list of subreddits (via Reddit's public JSON endpoints, no API key needed) mentioning pain points or requests. 2. Pass each candidate post to the Anthropic API (key from .env) with a fixed prompt asking it to draft a one-page opportunity brief: problem, target user, rough market size guess, an MVP scope, and a one-line pricing idea. 3. Save each brief to SQLite and show them in a simple feed, newest first, with a save/star toggle. 4. A search box to filter saved briefs by keyword. Out of scope: payments, email delivery, multi-user accounts, historical trend charts, editorial curation. Include a README covering setup, the .env variable, and a note that brief quality depends entirely on the source posts fetched. ## 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 Ideabrowser capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
# Ideabrowser indie build ## Goal Build the smallest trustworthy replacement for the core Ideabrowser workflow for one developer or a tiny team. ## Scope A script that pulls trending Reddit/Google-Trends signals, feeds them to an LLM with a fixed prompt template, and outputs a formatted market-opportunity brief. ## 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: - 1,000+ already-curated and human-vetted ideas - historical trend database and idea archive - AI Research Agent for arbitrary custom ideas - community, coaching and Q&A strategist sessions - data exports and idea-suggest personalization If those capabilities are essential, use praw instead of pretending the gap is solved.
# 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 ## Original build brief Build me a startup-idea trend scanner as a Next.js app. Stack: Next.js + TypeScript, SQLite via better-sqlite3, Tailwind. No auth, single user, localhost only. Core loop: 1. A cron-style script (runnable manually via a button) that fetches the top posts from a fixed list of subreddits (via Reddit's public JSON endpoints, no API key needed) mentioning pain points or requests. 2. Pass each candidate post to the Anthropic API (key from .env) with a fixed prompt asking it to draft a one-page opportunity brief: problem, target user, rough market size guess, an MVP scope, and a one-line pricing idea. 3. Save each brief to SQLite and show them in a simple feed, newest first, with a save/star toggle. 4. A search box to filter saved briefs by keyword. Out of scope: payments, email delivery, multi-user accounts, historical trend charts, editorial curation. Include a README covering setup, the .env variable, and a note that brief quality depends entirely on the source posts fetched. ## Required capabilities - OpenAI/Anthropic API key - Reddit or Google Trends API access ## 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.
# 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.
# Ideabrowser product brief ## Problem Scraping Reddit/search trends and having an LLM draft a business-opportunity writeup per idea is a weekend build. What's genuinely hard to replicate is the editorial curation, the accumulated database of 1,000+ pre-researched ideas, and the trend-signal pipeline built up over time. A DIY version can generate plausible-sounding ideas on demand but starts from zero data and zero curation. ## Product outcome A script that pulls trending Reddit/Google-Trends signals, feeds them to an LLM with a fixed prompt template, and outputs a formatted market-opportunity brief. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - OpenAI/Anthropic API key - Reddit or Google Trends API access ## Explicit non-goals for v1 - 1,000+ already-curated and human-vetted ideas - historical trend database and idea archive - AI Research Agent for arbitrary custom ideas - community, coaching and Q&A strategist sessions - data exports and idea-suggest personalization ## 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 ## Starting brief Build me a startup-idea trend scanner as a Next.js app. Stack: Next.js + TypeScript, SQLite via better-sqlite3, Tailwind. No auth, single user, localhost only. Core loop: 1. A cron-style script (runnable manually via a button) that fetches the top posts from a fixed list of subreddits (via Reddit's public JSON endpoints, no API key needed) mentioning pain points or requests. 2. Pass each candidate post to the Anthropic API (key from .env) with a fixed prompt asking it to draft a one-page opportunity brief: problem, target user, rough market size guess, an MVP scope, and a one-line pricing idea. 3. Save each brief to SQLite and show them in a simple feed, newest first, with a save/star toggle. 4. A search box to filter saved briefs by keyword. Out of scope: payments, email delivery, multi-user accounts, historical trend charts, editorial curation. Include a README covering setup, the .env variable, and a note that brief quality depends entirely on the source posts fetched. ## 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.
# 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.
# 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 ## 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 Ideabrowser 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
Builders pay to skip the research grind: a constantly refreshed, pre-vetted idea backlog plus market sizing is worth more to them than the marginal cost of prompting an LLM themselves, especially since the curation quality is hard to bootstrap solo.
x1,000+ already-curated and human-vetted ideas
xhistorical trend database and idea archive
xAI Research Agent for arbitrary custom ideas
xcommunity, coaching and Q&A strategist sessions
xdata exports and idea-suggest personalization
Don't feel like building it? These folks already made it free.
no votes, no pay-to-list · just what's real
Ideabrowser pricing
starter$41.58/mo · annual effective per month · $498.96/yr
free tierFree plan gives one fully-researched Idea of the Day with full market analysis, but no access to the searchable database or AI tools.
verified 2026-08-10 · source ↗
Is Ideabrowser free?
Free plan gives one fully-researched Idea of the Day with full market analysis, but no access to the searchable database or AI tools. Paid is Starter at $41.58/mo (checked 2026-08-10).
Vibecode Ideabrowser
Kinda. The core of Ideabrowser is buildable in a weekend with the prompt on this page, but there are real gaps: 1,000+ already-curated and human-vetted ideas, historical trend database and idea archive. Read the honest list above before committing.
How much does Ideabrowser cost?
Ideabrowser costs about $41.58/month (Starter, checked 2026-08-10), which is $498.96 per year.
What do I lose by replacing Ideabrowser?
Honestly: 1,000+ already-curated and human-vetted ideas; historical trend database and idea archive; AI Research Agent for arbitrary custom ideas; community, coaching and Q&A strategist sessions; data exports and idea-suggest personalization. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Ideabrowser?
Yes: IdeaProof (Free searchable database of startup ideas with market size and viability scores, no subscription.) The prompt is for when you want it exactly your way.