Can CueScout be vibe coded?
Tracks AI visibility on Perplexity and ChatGPT, mines Reddit and Hacker News for buyer questions, and turns the gaps into a dated writing plan
The visibility-check loop is the straightforward to build half: generate buyer questions, run them through Perplexity and ChatGPT, detect brand and competitor mentions, normalize citations, and score a GEO number. That much is close to what a $49/mo plan actually ships, since Basic only covers one engine. What does not fit in a prompt is the continuous Reddit and Hacker News scan that mines buyer questions from real threads instead of guessing them, the writing-plan-to-draft loop that turns a score into dated work, and weeks of trend history without which one run tells you almost nothing.
Jump to the build brief ↓Legacy-calibrated assessment
Checked Aug 2026
What you pay today, before any DIY hosting
medium editorial confidence
Tracked separately from the pricing check
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
- Generate buyer questions for a product, run them through Perplexity and ChatGPT, detect brand and competitor mentions, collect citations, and score a basic GEO number.
- 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
- the continuous Reddit and Hacker News scan and buyer-question clustering, which finds where buyers are already asking instead of guessing questions to feed a model
- Google rank badges on matched threads, sourced from a paid search API
- the writing-plan-to-draft loop that turns a gap into a dated 30-day plan and AI-ready page drafts, not just a dashboard number
- weeks of trend history and a competitor 'what moved' digest; a single run is mostly noise
- The last 20 percent is sync, migration fidelity, speed, and edge cases.
- Connectors, OAuth flows, and vendor API changes require constant upkeep.
Build, switch, or keep paying
Narrower, with trade-offs
Generate buyer questions for a product, run them through Perplexity and ChatGPT, detect brand and competitor mentions, collect citations, and score a basic GEO number.
Use the build brief ↓1 checked option
- Elmo ↗Covers the visibility-check half well; it has no Reddit/HN buyer-thread mining and no writing-plan or draft generation on top of the scores.
$49/mo
They're paying for the parts that don't fit in a prompt: a script that runs every day for months without babysitting, buyer questions mined from real Reddit and Hacker News threads instead of guessed ones, a writing plan and AI-ready drafts wired to the same gaps the scores found, and a link they can hand a client. None of that is a moat a bigger company can't cross; it's the upkeep most people quit paying attention to by week three.
Visit CueScout ↗Why people still pay
They're paying for the parts that don't fit in a prompt: a script that runs every day for months without babysitting, buyer questions mined from real Reddit and Hacker News threads instead of guessed ones, a writing plan and AI-ready drafts wired to the same gaps the scores found, and a link they can hand a client. None of that is a moat a bigger company can't cross; it's the upkeep most people quit paying attention to by week three.
The last 20 percent is sync, migration fidelity, speed, and edge cases.
Connectors, OAuth flows, and vendor API changes require constant upkeep.
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 CueScout
Context
CueScout — Tracks AI visibility on Perplexity and ChatGPT, mines Reddit and Hacker News for buyer questions, and turns the gaps into a dated writing plan. It currently costs $49/mo.
The visibility-check loop is the straightforward to build half: generate buyer questions, run them through Perplexity and ChatGPT, detect brand and competitor mentions, normalize citations, and score a GEO number. That much is close to what a $49/mo plan actually ships, since Basic only covers one engine. What does not fit in a prompt is the continuous Reddit and Hacker News scan that mines buyer questions from real threads instead of guessing them, the writing-plan-to-draft loop that turns a score into dated work, and weeks of trend history without which one run tells you almost nothing.
This brief describes a focused, single-operator replacement for the part of CueScout 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
Generate buyer questions for a product, run them through Perplexity and ChatGPT, detect brand and competitor mentions, collect citations, and score a basic GEO number.
Automate a bounded research or reporting workflow using permitted data sources.
A responsive interface with real empty, loading, success, and error states.
Requirements
Functional
Durable per-run storage.
A scheduler for repeat runs.
Data and integrations
Perplexity API key.
OpenAI API key.
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 local AI buyer-question visibility tracker for one product. Requirements:
Node 22, TypeScript, SQLite via better-sqlite3, a CLI, and a plain server-rendered dashboard. Local only, no accounts, no telemetry.
product.json holds my product name, aliases, domain, and up to 5 competitor names.
track questions generates 20 buyer-intent questions from the product name and a one-paragraph description, or accepts a JSON list I supply.
track run sends every question through Perplexity's API and OpenAI's Chat Completions API with web search enabled where supported. Keys live in .env.
Store one immutable row per run, question, and provider: raw answer, cited URLs, model id, and error text. Never overwrite a prior run.
Retry transient failures twice with backoff; keep failed cells visible in the report instead of dropping them.
Detect brand and competitor mentions case-insensitively against the alias list.
Normalize citation URLs to hostname plus canonical path, strip tracking parameters, then compute a top-sources table and an owned-domain citation rate.
Compute one GEO score per run: mention rate times citation rate, shown with the raw counts behind it, never just the number.
track serve renders visibility over time by provider, share of voice against each competitor, and the sources table.
track export writes questions, answers, mentions, and citations to CSV.
Fixture tests for mention detection, URL normalization, and the GEO score calculation.
Out of scope: Reddit/Hacker News monitoring, Google rank tracking, writing-plan or content generation, hosted sharing, teams, and billing.
README: setup, per-run API cost estimate, and a cron line for a daily run.
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:
The continuous Reddit and Hacker News scan and buyer-question clustering, which finds where buyers are already asking instead of guessing questions to feed a model.
Google rank badges on matched threads, sourced from a paid search API.
The writing-plan-to-draft loop that turns a gap into a dated 30-day plan and AI-ready page drafts, not just a dashboard number.
Weeks of trend history and a competitor 'what moved' digest; a single run is mostly noise.
A hosted, shareable report link you can hand a client without exposing your own infra.
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.
Maintain every third-party integration as APIs and OAuth rules change.
Risk
Operational risk. The code is achievable; dependable data, integrations, and ongoing operations are the real cost.
Editorial confidence in this assessment: medium. No reviewed project implementation is linked yet.
Existing alternatives
Before building, compare these checked options:
Elmo — Covers the visibility-check half well; it has no Reddit/HN buyer-thread mining and no writing-plan or draft generation on top of the scores
Prior art
Working open-source software you can read, fork, or borrow from before starting:
Elmo — MIT-licensed self-hosted AEO/GEO tracker: runs prompts across the major answer engines and records mentions, competitors, and cited sources
Generated by Can It Be Vibe Coded? · Full report: https://www.canitbevibecoded.com/cuescout
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.
- Maintain every third-party integration as APIs and OAuth rules change.
Projects built from this idea
No reviewed implementation has been linked for CueScout yet. A submission is evidence for review, not automatic proof that the whole product was replaced.
Built a version of CueScout?Submit the project as evidence for this report.
Open-source prior art
Before you start
Can CueScout be vibe coded?
Partly, if you narrow it. The visibility-check loop is the straightforward to build half: generate buyer questions, run them through Perplexity and ChatGPT, detect brand and competitor mentions, normalize citations, and score a GEO number. That much is close to what a $49/mo plan actually ships, since Basic only covers one engine. What does not fit in a prompt is the continuous Reddit and Hacker News scan that mines buyer questions from real threads instead of guessing them, the writing-plan-to-draft loop that turns a score into dated work, and weeks of trend history without which one run tells you almost nothing.
What can an AI coding agent reproduce from CueScout?
Generate buyer questions for a product, run them through Perplexity and ChatGPT, detect brand and competitor mentions, collect citations, and score a basic GEO number. 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 CueScout replacement still be missing?
the continuous Reddit and Hacker News scan and buyer-question clustering, which finds where buyers are already asking instead of guessing questions to feed a model; Google rank badges on matched threads, sourced from a paid search API; the writing-plan-to-draft loop that turns a gap into a dated 30-day plan and AI-ready page drafts, not just a dashboard number; weeks of trend history and a competitor 'what moved' digest; a single run is mostly noise; The last 20 percent is sync, migration fidelity, speed, and edge cases.; Connectors, OAuth flows, and vendor API changes require constant upkeep.
What do I still own after building a CueScout 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. Maintain every third-party integration as APIs and OAuth rules change.