Can Rankhog be vibe coded?
Finds Reddit threads that rank in Google and AI answers, drafts the reply, and posts it through your own browser
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.
Jump to the build brief ↓Checked Jul 2026
What you pay today, before any DIY hosting
high editorial confidence
Buildability by layer
Screens, forms, and focused interactions
The repeatable job the product performs
Availability and legality of required data
Uptime, queues, support, and maintenance
Security, compliance, and user confidence
The achievable core
- Watch subreddits for my keywords, keep the threads already ranking in Google, and draft a rules-aware reply I post myself.
- Automate a bounded research or reporting workflow using permitted data sources.
- A responsive interface with real empty, loading, success, and error states.
The parts a prompt cannot buy
- 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
- Trust, audits, and counterparties matter more than feature parity.
- The last 20 percent is sync, migration fidelity, speed, and edge cases.
Why people still pay
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.
Trust, audits, and counterparties matter more than feature parity.
The last 20 percent is sync, migration fidelity, speed, and edge cases.
The brief
Context, requirements, acceptance criteria, non-goals, and the full production standard — as Markdown, ready for any coding agent.
Build brief — a focused alternative to Rankhog
Context
**Rankhog** — Finds Reddit threads that rank in Google and AI answers, drafts the reply, and posts it through your own browser. It currently costs $99/mo.
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.
This brief describes a focused, single-operator replacement for the part of Rankhog that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest.
What you are building
Watch subreddits for my keywords, keep the threads already ranking in Google, and draft a rules-aware reply I post myself.
Automate a bounded research or reporting workflow using permitted data sources.
A responsive interface with real empty, loading, success, and error states.
Requirements
Functional
A Reddit account you post from yourself.
Weeks of ordinary participation before promoting.
Data and integrations
LLM API key.
SERP API key (Serper.dev).
Each of these needs a real account, credential, or quota. Set them up before writing feature code.
Non-functional
Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support.
Security: server-side secrets, validated input, and no credentials in the client bundle.
Reliability: retries with backoff on external calls, and a clear failure state when a provider is down.
Portability: the operator can export their data and leave without losing it.
Implementation 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.
Delivery standard
Inspect the repository first, then write a short implementation plan before writing code.
Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data.
Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations.
Include responsive layouts plus genuine empty, loading, success, validation, and failure states.
Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data.
Add structured logs around every external call and return actionable errors without leaking sensitive details.
Write unit tests for the core logic and one automated test of the main user journey.
Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations.
Acceptance criteria
A clean install starts the app using only the README and .env.example.
The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable.
Invalid input, missing configuration, provider failure, and an empty database each have a usable state.
The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control.
Tests, type checking, linting, and a production build all pass with no ignored failures.
No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified.
Non-goals
Do not build these, and do not claim to have replaced them:
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.
Trust, audits, and counterparties matter more than feature parity.
The last 20 percent is sync, migration fidelity, speed, and edge cases.
What you still own after launch
Secure credentials, rotate secrets, and handle provider rate limits.
Run migrations, backups, restores, and dependency updates.
Test the critical journey after every model, API, or hosting change.
Monitor failures and fix the edge cases a first prompt will miss.
Risk
**Manageable.** A personal version is realistic if you test the critical journey and keep reliable backups.
Editorial confidence in this assessment: high. No independent one-shot implementation is linked yet.
Prior art
Working open-source software you can read, fork, or borrow from before starting:
[redsignal](https://github.com/ivucicev/redsignal) — Watches subreddits for keyword matches, filters the noise with an LLM, and drafts replies. Covers the finder half, no license declared
Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/rankhog
You still own the product
- Secure credentials, rotate secrets, and handle provider rate limits.
- Run migrations, backups, restores, and dependency updates.
- Test the critical journey after every model, API, or hosting change.
- Monitor failures and fix the edge cases a first prompt will miss.
Open-source prior art
Before you start
Can Rankhog be vibe coded?
Partly, if you narrow it. 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.
What can an AI coding agent reproduce from Rankhog?
Watch subreddits for my keywords, keep the threads already ranking in Google, and draft a rules-aware reply I post myself. Automate a bounded research or reporting workflow using permitted data sources. A responsive interface with real empty, loading, success, and error states.
What will a DIY Rankhog replacement still be missing?
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; Trust, audits, and counterparties matter more than feature parity.; The last 20 percent is sync, migration fidelity, speed, and edge cases.
What do I still own after building a Rankhog alternative?
Secure credentials, rotate secrets, and handle provider rate limits. Run migrations, backups, restores, and dependency updates. Test the critical journey after every model, API, or hosting change. Monitor failures and fix the edge cases a first prompt will miss.