Vibecode Rankhog
track this build5 steps, step by step0%KINDA · weekend project
price $99/moyou'd save $1,188/yrbuild time one sittingcategory 📈 seo & marketingreplaced by 0 people
The code here is the easy half, and it is worth building. A script that watches subreddits, keeps the threads already ranking in Google, and drafts a reply against the sub's rules is a one-sitting build that will genuinely find you the conversations worth joining. The gap is what happens next. No agent writes you a Reddit account with a year of comment history in the subs you care about, and Reddit's spam enforcement is aimed exactly at accounts that show up new and start mentioning a product. A shadowban is silent: your comments look live to you and are invisible to everyone else, so you learn about it weeks later. Build the finder, then spend the weeks yourself, or pay someone to have already spent them.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Rankhog. 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 ===== # Rankhog indie build ## Goal Build the smallest trustworthy replacement for the core Rankhog workflow for one developer or a tiny team. ## Scope Watch subreddits for my keywords, keep the threads already ranking in Google, and draft a rules-aware reply I post myself. ## 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: - account age, karma, and comment history in the subs that matter - the warm-up: weeks of ordinary participation before you can mention a product - posting through a real browser session rather than the API, which is what keeps accounts unflagged - per-subreddit rule knowledge and pacing judgment - shared accounts across a team, and the managed service If those capabilities are essential, use redsignal 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 Reddit opportunity finder and reply drafter to replace Rankhog, personal scale. Requirements: - A Node CLI plus a local page: node find.js runs the sweep, then a page on 127.0.0.1:3000 lists the hits with a draft beside each thread. - My keywords and target subs live in keywords.json. Sweeps hit Reddit's public JSON endpoints (/r/<sub>/search.json, no OAuth) with a real User-Agent, one request every two seconds. - Keep only the threads already ranking in Google for the keyword, checked via Serper.dev (key in .env), those are the ones the models read back. - Store hits in SQLite (better-sqlite3) with age, upvotes, comment count, and whether my keyword appears in the top comments. Never resurface one I dismissed. - Draft each reply with an LLM (key in .env), fed the thread text and the sub's rules from /r/<sub>/about/rules.json, told to answer first and name my product only where it fits. Store the draft, do not send it. - The page shows title, score, the rule summary, and the draft in an editable box with a copy button and a link out. Everything stays on my machine, localhost only, no accounts, no telemetry. I post by hand, from my own account. - Out of scope: anything that posts, comments, upvotes, or logs into Reddit for me. Automated posting is what gets accounts shadowbanned, say so in the README. - README: the Serper and LLM keys, cost per sweep, and a note to spend a few weeks commenting in my target subs before I mention the product. ## Required capabilities - LLM API key - SERP API key (Serper.dev) - a Reddit account you post from yourself - weeks of ordinary participation before promoting ## 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 Rankhog. 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 ===== # Rankhog indie build ## Goal Build the smallest trustworthy replacement for the core Rankhog workflow for one developer or a tiny team. ## Scope Watch subreddits for my keywords, keep the threads already ranking in Google, and draft a rules-aware reply I post myself. ## 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: - account age, karma, and comment history in the subs that matter - the warm-up: weeks of ordinary participation before you can mention a product - posting through a real browser session rather than the API, which is what keeps accounts unflagged - per-subreddit rule knowledge and pacing judgment - shared accounts across a team, and the managed service If those capabilities are essential, use redsignal 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 Reddit opportunity finder and reply drafter to replace Rankhog, personal scale. Requirements: - A Node CLI plus a local page: node find.js runs the sweep, then a page on 127.0.0.1:3000 lists the hits with a draft beside each thread. - My keywords and target subs live in keywords.json. Sweeps hit Reddit's public JSON endpoints (/r/<sub>/search.json, no OAuth) with a real User-Agent, one request every two seconds. - Keep only the threads already ranking in Google for the keyword, checked via Serper.dev (key in .env), those are the ones the models read back. - Store hits in SQLite (better-sqlite3) with age, upvotes, comment count, and whether my keyword appears in the top comments. Never resurface one I dismissed. - Draft each reply with an LLM (key in .env), fed the thread text and the sub's rules from /r/<sub>/about/rules.json, told to answer first and name my product only where it fits. Store the draft, do not send it. - The page shows title, score, the rule summary, and the draft in an editable box with a copy button and a link out. Everything stays on my machine, localhost only, no accounts, no telemetry. I post by hand, from my own account. - Out of scope: anything that posts, comments, upvotes, or logs into Reddit for me. Automated posting is what gets accounts shadowbanned, say so in the README. - README: the Serper and LLM keys, cost per sweep, and a note to spend a few weeks commenting in my target subs before I mention the product. ## Required capabilities - LLM API key - SERP API key (Serper.dev) - a Reddit account you post from yourself - weeks of ordinary participation before promoting ## 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 Rankhog. 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 ===== # Rankhog product brief ## Problem The code here is the easy half, and it is worth building. A script that watches subreddits, keeps the threads already ranking in Google, and drafts a reply against the sub's rules is a one-sitting build that will genuinely find you the conversations worth joining. The gap is what happens next. No agent writes you a Reddit account with a year of comment history in the subs you care about, and Reddit's spam enforcement is aimed exactly at accounts that show up new and start mentioning a product. A shadowban is silent: your comments look live to you and are invisible to everyone else, so you learn about it weeks later. Build the finder, then spend the weeks yourself, or pay someone to have already spent them. ## Product outcome Watch subreddits for my keywords, keep the threads already ranking in Google, and draft a rules-aware reply I post myself. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - LLM API key - SERP API key (Serper.dev) - a Reddit account you post from yourself - weeks of ordinary participation before promoting ## Explicit non-goals for v1 - account age, karma, and comment history in the subs that matter - the warm-up: weeks of ordinary participation before you can mention a product - posting through a real browser session rather than the API, which is what keeps accounts unflagged - per-subreddit rule knowledge and pacing judgment - shared accounts across a team, and the managed service ## 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 Reddit opportunity finder and reply drafter to replace Rankhog, personal scale. Requirements: - A Node CLI plus a local page: node find.js runs the sweep, then a page on 127.0.0.1:3000 lists the hits with a draft beside each thread. - My keywords and target subs live in keywords.json. Sweeps hit Reddit's public JSON endpoints (/r/<sub>/search.json, no OAuth) with a real User-Agent, one request every two seconds. - Keep only the threads already ranking in Google for the keyword, checked via Serper.dev (key in .env), those are the ones the models read back. - Store hits in SQLite (better-sqlite3) with age, upvotes, comment count, and whether my keyword appears in the top comments. Never resurface one I dismissed. - Draft each reply with an LLM (key in .env), fed the thread text and the sub's rules from /r/<sub>/about/rules.json, told to answer first and name my product only where it fits. Store the draft, do not send it. - The page shows title, score, the rule summary, and the draft in an editable box with a copy button and a link out. Everything stays on my machine, localhost only, no accounts, no telemetry. I post by hand, from my own account. - Out of scope: anything that posts, comments, upvotes, or logs into Reddit for me. Automated posting is what gets accounts shadowbanned, say so in the README. - README: the Serper and LLM keys, cost per sweep, and a note to spend a few weeks commenting in my target subs before I mention the product. ## 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 Rankhog capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# Rankhog indie build ## Goal Build the smallest trustworthy replacement for the core Rankhog workflow for one developer or a tiny team. ## Scope Watch subreddits for my keywords, keep the threads already ranking in Google, and draft a rules-aware reply I post myself. ## 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: - account age, karma, and comment history in the subs that matter - the warm-up: weeks of ordinary participation before you can mention a product - posting through a real browser session rather than the API, which is what keeps accounts unflagged - per-subreddit rule knowledge and pacing judgment - shared accounts across a team, and the managed service If those capabilities are essential, use redsignal 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 Reddit opportunity finder and reply drafter to replace Rankhog, personal scale. Requirements: - A Node CLI plus a local page: node find.js runs the sweep, then a page on 127.0.0.1:3000 lists the hits with a draft beside each thread. - My keywords and target subs live in keywords.json. Sweeps hit Reddit's public JSON endpoints (/r/<sub>/search.json, no OAuth) with a real User-Agent, one request every two seconds. - Keep only the threads already ranking in Google for the keyword, checked via Serper.dev (key in .env), those are the ones the models read back. - Store hits in SQLite (better-sqlite3) with age, upvotes, comment count, and whether my keyword appears in the top comments. Never resurface one I dismissed. - Draft each reply with an LLM (key in .env), fed the thread text and the sub's rules from /r/<sub>/about/rules.json, told to answer first and name my product only where it fits. Store the draft, do not send it. - The page shows title, score, the rule summary, and the draft in an editable box with a copy button and a link out. Everything stays on my machine, localhost only, no accounts, no telemetry. I post by hand, from my own account. - Out of scope: anything that posts, comments, upvotes, or logs into Reddit for me. Automated posting is what gets accounts shadowbanned, say so in the README. - README: the Serper and LLM keys, cost per sweep, and a note to spend a few weeks commenting in my target subs before I mention the product. ## Required capabilities - LLM API key - SERP API key (Serper.dev) - a Reddit account you post from yourself - weeks of ordinary participation before promoting ## 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
# Rankhog product brief ## Problem The code here is the easy half, and it is worth building. A script that watches subreddits, keeps the threads already ranking in Google, and drafts a reply against the sub's rules is a one-sitting build that will genuinely find you the conversations worth joining. The gap is what happens next. No agent writes you a Reddit account with a year of comment history in the subs you care about, and Reddit's spam enforcement is aimed exactly at accounts that show up new and start mentioning a product. A shadowban is silent: your comments look live to you and are invisible to everyone else, so you learn about it weeks later. Build the finder, then spend the weeks yourself, or pay someone to have already spent them. ## Product outcome Watch subreddits for my keywords, keep the threads already ranking in Google, and draft a rules-aware reply I post myself. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - LLM API key - SERP API key (Serper.dev) - a Reddit account you post from yourself - weeks of ordinary participation before promoting ## Explicit non-goals for v1 - account age, karma, and comment history in the subs that matter - the warm-up: weeks of ordinary participation before you can mention a product - posting through a real browser session rather than the API, which is what keeps accounts unflagged - per-subreddit rule knowledge and pacing judgment - shared accounts across a team, and the managed service ## 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 Reddit opportunity finder and reply drafter to replace Rankhog, personal scale. Requirements: - A Node CLI plus a local page: node find.js runs the sweep, then a page on 127.0.0.1:3000 lists the hits with a draft beside each thread. - My keywords and target subs live in keywords.json. Sweeps hit Reddit's public JSON endpoints (/r/<sub>/search.json, no OAuth) with a real User-Agent, one request every two seconds. - Keep only the threads already ranking in Google for the keyword, checked via Serper.dev (key in .env), those are the ones the models read back. - Store hits in SQLite (better-sqlite3) with age, upvotes, comment count, and whether my keyword appears in the top comments. Never resurface one I dismissed. - Draft each reply with an LLM (key in .env), fed the thread text and the sub's rules from /r/<sub>/about/rules.json, told to answer first and name my product only where it fits. Store the draft, do not send it. - The page shows title, score, the rule summary, and the draft in an editable box with a copy button and a link out. Everything stays on my machine, localhost only, no accounts, no telemetry. I post by hand, from my own account. - Out of scope: anything that posts, comments, upvotes, or logs into Reddit for me. Automated posting is what gets accounts shadowbanned, say so in the README. - README: the Serper and LLM keys, cost per sweep, and a note to spend a few weeks commenting in my target subs before I mention the product. ## 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 Rankhog 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
Because the failure mode is invisible and expensive. Getting a product mentioned on Reddit needs an account people and moderators already trust, and building one is weeks of participation before the first mention. People pay to skip the warm-up, to have posting happen through a real browser session instead of an API that gets flagged, and to have someone else carry the ban risk.
xaccount age, karma, and comment history in the subs that matter
xthe warm-up: weeks of ordinary participation before you can mention a product
xposting through a real browser session rather than the API, which is what keeps accounts unflagged
xper-subreddit rule knowledge and pacing judgment
xshared accounts across a team, and the managed service
share on X ↗"I just replaced Rankhog ($99/mo) with one prompt"
Rankhog pricing
| plan | monthly | annual (per mo) | what you get |
|---|---|---|---|
| standard | $99/workspace | — | 1 product; monitoring, strategy, and warm-up; 1 Reddit account warm-up; unlimited teammates. |
| managed reddit growth | $1000/workspace | — | Managed service starting at $1,000/month; exact posting, outreach, and reporting deliverables are custom. |
free tierno free tier (3-day card-required trial only)
billingmonthly only; no public annual plan
hidden costsEach additional product requires another $99/month Standard subscription.
verified 2026-08-14 · source ↗
Vibecode Rankhog
Kinda. The core of Rankhog is buildable in a weekend with the prompt on this page, but there are real gaps: account age, karma, and comment history in the subs that matter, the warm-up: weeks of ordinary participation before you can mention a product. Read the honest list above before committing.
How much does Rankhog cost?
Rankhog costs about $99/month (Standard, checked 2026-07-30), which is $1188 per year.
What do I lose by replacing Rankhog?
Honestly: account age, karma, and comment history in the subs that matter; the warm-up: weeks of ordinary participation before you can mention a product; posting through a real browser session rather than the API, which is what keeps accounts unflagged; per-subreddit rule knowledge and pacing judgment; shared accounts across a team, and the managed service. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Rankhog?
Yes: redsignal (Watches subreddits for keyword matches, filters the noise with an LLM, and drafts replies. Covers the finder half, no license declared.). Using prior art is also vibecoding; the prompt is for when you want it exactly your way.