[verdict][prompt curatoriat][sursă: llmpulse.ai][traducere: la coadă]
Pot să vibecodez LLM Pulse?
// se construiește într-un weekend, dar rămân goluri reale
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.
încredere: medie
ce e: Track brand mentions, citations, sentiment, competitors, and AI referral traffic across major AI platforms
Indice de constructibilitate · joc editorial
- Preț 57 $/lună greutate: plus 3
- Timp câteva zile greutate: plus 1
- Categorie seo și marketing nu cântărește
- Moat scara infrastructurii · integrări · calitatea execuției greutate: minus 4
- Încredere medie greutate: zero
- Ce pierzi 5 elemente greutate: minus 2
- Site llmpulse.ai (si apre in una nuova scheda) nu cântărește
Moat-ul cântărește mai mult decât prețul: refăcut, e un proiect, nu o seară.
Gioco editoriale: il verdetto dice se un agente può, l’indice se conviene.
Ce construiești
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.
ce îți trebuie
- 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.
Promptul
Un weekend cu un agent de coding. Golurile care rămân sunt mai jos, la „ce pierzi”.
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. Prompt curatoriat: scris și revizuit manual pentru această aplicație. În engleză intenționat — e limba în care agenții de coding se descurcă cel mai bine.
Ce pierzi
- 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
De ce se plătește în continuare
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: Scara infrastructurii Integrări Calitatea execuției ce e un moat
managed multi-model runs, historical evidence, and workflow depth
Alternative gratuite
N-ai chef să ți-o construiești? Astea există deja, sunt gratuite sau open source, și le-am verificat una câte una.
Respinse (3) — și de ce
- 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.
Ești de acord?
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