# Build brief — a focused alternative to Gojiberry AI > **Verdict:** Yes, for personal use · **Buildability:** 81/100 · **Category:** Sales Outreach > **Source:** https://www.canitbevibecoded.com/gojiberry > Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from Gojiberry AI. Verify current pricing and capabilities before acting. ## Context **Gojiberry AI** — Watches public buying signals, scores the people behind them, and sends the first LinkedIn message. It currently costs $99/mo. 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 a focused implementation. The asterisk: you are renting four services instead of writing them, and the outreach still runs on real LinkedIn accounts with real limits. This brief describes a focused, single-operator replacement for the part of Gojiberry AI 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 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. - Track contacts, sequence outreach, and log replies for a pipeline you own. - A responsive interface with real empty, loading, success, and error states. ## Requirements ### Functional - An enrichment provider: MoltSets or Prospeo. - Unipile account with one or more LinkedIn accounts connected. - Node with SQLite (better-sqlite3). ### Data and integrations - A signal source: Apify actors, or a Hermes agent running the watch on a schedule. - OpenAI/Anthropic 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 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. ## 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: - 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. ## 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. ## Prior art Working open-source software you can read, fork, or borrow from before starting: - [Hermes Agent](https://github.com/NousResearch/hermes-agent) — self-hosted agent that can run the signal watch on a schedule instead of a cron service you write - [n8n](https://github.com/n8n-io/n8n) — self-hostable workflow automation, the usual no-code way to wire signals to outreach - [Mautic](https://github.com/mautic/mautic) — open-source marketing automation with contacts, campaigns, sequences, and suppression --- Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/gojiberry