[verdict][curated prompt][source: gojiberry.ai]
Can I vibecode Gojiberry AI?
// one session, and a personal version you can actually use
Every stage of this loop is already a bought part. Signals come from ready-made Apify actors, or from a Hermes agent running the watch on a schedule. Finding and enriching the people behind them is a single call to MoltSets or Prospeo. The LinkedIn send is Unipile, which will hold several connected accounts at once, so the DIY version is not capped at the two senders Pro gives you. What you actually write is the glue, the ICP scoring, and the send queue, and that is one sitting. The asterisk: you are renting four services instead of writing them, and the outreach still runs on real LinkedIn accounts with real limits.
confidence: medium
what it is: Watches public buying signals, scores the people behind them, and sends the first LinkedIn message
Buildability index · an editorial game
- Price 99 $/month weight: plus 3
- Time one sitting weight: plus 3
- Category sales outreach no weight
- Moat execution quality · integrations weight: minus 2
- Confidence medium weight: zero
- What you lose 5 items weight: minus 2
- Site gojiberry.ai (si apre in una nuova scheda) no weight
The maths works, but not by much: look at what you lose before you start.
Gioco editoriale: il verdetto dice se un agente può, l’indice se conviene.
What you build
Poll watched competitor pages and creators for likers, commenters, followers, and job changes, score each person against an ICP, enrich the ones worth contacting, draft a connection note from the exact signal that fired, and send and follow up across several of your own LinkedIn accounts until someone replies.
what you need
- a signal source: Apify actors, or a Hermes agent running the watch on a schedule
- an enrichment provider: MoltSets or Prospeo
- Unipile account with one or more LinkedIn accounts connected
- OpenAI/Anthropic API key
- Node with SQLite (better-sqlite3)
Every stage is a bought part: Apify or a Hermes agent for the signals, MoltSets or Prospeo for the enrichment, Unipile for the send. The one-shot build is the ICP scoring and the queue between them, and Unipile holding several accounts means the DIY version outsends the $99 tier.
The prompt
One session with a coding agent and your version runs.
Build me a signal-triggered LinkedIn outreach agent to replace Gojiberry AI. Requirements:
- Local Node + TypeScript service: Express dashboard on localhost:3000, better-sqlite3
for storage, node-cron for the loop. No frontend framework.
- I define my ICP once in icp.yaml: titles, company sizes, geos, and the competitor
LinkedIn pages and creator profiles to watch.
- Every 6 hours, pull signals with apify-client (token in .env): likers and commenters
on watched posts, new followers, and job changes. Upsert each person into a prospects
table with the signal, its URL, and the date it fired.
- Score each prospect 0-100 against the ICP in one LLM call (key in .env) with a
two-line reason. Under 70 is never contacted.
- Enrich everyone above 70 through one provider, MoltSets or Prospeo · pick whichever
ships a Node client, and cache by profile URL so I never pay twice for the same person.
- Draft a connection note under 300 characters plus two follow-ups, written from the
profile and the exact signal that fired.
- Send through unipile-node-sdk. Connect several LinkedIn accounts and round-robin
across them at 20 invites and 40 messages per account per day, randomized gaps in
business hours, invite first and follow-ups only after acceptance. Poll replies every
15 minutes and stop the sequence the moment one lands.
- Drafts wait in an approval queue until I click Send · a --auto flag skips it. The
dashboard lists prospect, signal, score, sender account, and thread. No accounts, no
telemetry, everything on my machine except the Apify, enrichment, Unipile, and LLM
calls.
- Out of scope: email sequences and a hosted control plane. Do not scrape LinkedIn
directly, every LinkedIn action goes through Unipile.
- README: the Apify actors used, how to connect each LinkedIn account in Unipile, the
.env keys, and a warning that per-account limits are real, so keep the caps low for
the first two weeks.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
- one enrichment provider instead of a 15+ provider waterfall, so coverage on the hard contacts is thinner
- cross-customer benchmarking and the weekly self-tuning
- the ten-minute setup: your version does not exist until you build it
- someone else absorbing the breakage when an actor or a LinkedIn endpoint changes
- a support line when a sending account gets restricted
Why people still pay
They pay to skip the assembly and the maintenance. Gojiberry turns a website URL into a running agent in ten minutes, keeps the scrapers working when a page layout changes, and puts the signal source, the enrichment waterfall, and both channels on one bill. Rent the parts yourself and the monthly cost drops, but you own every break, you are reconciling four dashboards, and the signals only stay useful if you keep feeding the watchlist new pages and creators.
moat: Execution quality Integrations what a moat is
the agent framing and the 10-minute setup, not the data
Who has already built it
Starting from here is still vibecoding: the prompt is for when you want it exactly your way.
- Hermes Agent (opens in a new tab) — self-hosted agent that can run the signal watch on a schedule instead of a cron service you write
- n8n (opens in a new tab) — self-hostable workflow automation, the usual no-code way to wire signals to outreach
- Mautic (opens in a new tab) — open-source marketing automation with contacts, campaigns, sequences, and suppression
Do you agree?
The vote balance
Ancora nessun voto: il tuo è il primo.
Nessun voto ancora — il primo pesa.
