[verdict][curated prompt][source: llmpulse.ai]
Can I vibecode LLM Pulse?
// buildable in a weekend, but real gaps stay open
The core loop is a realistic weekend build: run a fixed prompt set against a model API, detect brand and competitor mentions, collect citations, and chart the results. The gap appears when you need dependable runs across many models, long-term evidence, team access, exports, alerts, and the broader visibility and reputation workflow.
confidence: medium
what it is: Track brand mentions, citations, sentiment, competitors, and AI referral traffic across major AI platforms
Buildability index · an editorial game
- Price 57 $/month weight: plus 3
- Time multi-day weight: plus 1
- Category seo and marketing no weight
- Moat infrastructure scale · integrations · execution quality weight: minus 4
- Confidence medium weight: zero
- What you lose 5 items weight: minus 2
- Site llmpulse.ai (si apre in una nuova scheda) no weight
The moat outweighs the price: rebuilding this is a project, not an evening.
Gioco editoriale: il verdetto dice se un agente può, l’indice se conviene.
What you build
Run a fixed prompt set against one model API, store the answers, detect brand and competitor mentions and citations, and show weekly trends for one project.
what you need
- one compatible model API key
- Node.js 22
- SQLite
- a scheduled local process
- a small API budget
The personal tracking loop is approachable. The managed multi-model product and its operational depth are not a one-shot replacement.
The prompt
A weekend with a coding agent. The gaps that stay are right below, under “what you lose”.
Build a local, single-user AI visibility tracker for one brand.
Use Node.js 22, TypeScript, Express, better-sqlite3, server-rendered HTML, and vanilla JavaScript.
Bind the app to localhost:4173 and provide one documented command for the first run.
Keep MODEL_BASE_URL, MODEL_API_KEY, and MODEL_NAME in .env and ship a safe .env.example.
Support one JSON chat endpoint configured entirely through those environment variables.
Document the endpoint contract and isolate it behind one small adapter so it can be replaced later.
Let the user configure one brand, aliases, three competitors, and up to 25 prompts.
Run prompts manually and on a weekly local schedule with a clear API budget limit.
Limit concurrency, retry transient failures, and keep failed prompts visible instead of dropping them.
Store every prompt, raw answer, model name, timestamp, latency, and error in SQLite.
Detect case-insensitive brand and competitor mentions using editable aliases.
Extract and normalize URLs from answers, then preserve the source answer for every citation.
Use one structured model pass to label brand sentiment as positive, neutral, negative, or absent.
Calculate mention rate, citation rate, competitor share of voice, and net sentiment with documented formulas.
Show current results, weekly trends, and a prompt-level evidence table on a compact dashboard.
Make every aggregate metric link back to the raw answers used to calculate it.
Export prompts, answers, mentions, citations, and weekly metrics as CSV files.
Add backup and restore commands for the SQLite database.
Do not add accounts, billing, teams, telemetry, web crawling, or an integration catalog.
Do not claim parity with managed multi-model collection, reputation workflows, traffic analytics, or production monitoring.
Write tests for alias matching, URL normalization, retry handling, and metric calculations.
Include a README with setup, API cost controls, data location, backup steps, and limitations.
Run the tests and production build before finishing, then list the exact commands used.Curated prompt: written and reviewed by hand for this app. In English on purpose — it is the language coding agents work best in.
What you lose
- managed execution across the full model set
- long-term historical comparisons and evidence
- reputation, source, traffic, and competitor workflows
- team permissions, exports, alerts, and integrations
- production monitoring and support
Why people still pay
Teams pay to keep large prompt sets running on schedule, preserve evidence over time, and analyze mentions, citations, sentiment, competitors, and traffic in one dependable workflow without maintaining the execution pipeline themselves.
moat: Infrastructure scale Integrations Execution quality what a moat is
managed multi-model runs, historical evidence, and workflow depth
Free alternatives
Not in the mood to build it? These already exist, they are free or open source, and we checked them one by one.
Rejected (3) — and why
- GEO/AEO Tracker (si apre in una nuova scheda) — It has mentions, citations, sentiment and competitors; 221 stars and a compulsory commercial scraper keep it below the bar.
- OneGlanse (si apre in una nuova scheda) — Broad engine coverage, tiny proof: 141 stars, no releases and an operations diagram that reads like a procurement list.
- OpenSEO (si apre in una nuova scheda) — Healthy project, wrong breadth: two AI surfaces, no sentiment and no AI-referral analytics.
Do you agree?
The vote balance
Ancora nessun voto: il tuo è il primo.
Nessun voto ancora — il primo pesa.
