Vibecode Gojiberry AI
track this build5 steps, step by step0%YES · one-shottable
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.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Gojiberry AI. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Gojiberry AI indie build ## Goal Build the smallest trustworthy replacement for the core Gojiberry AI workflow for one developer or a tiny team. ## Scope 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. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - 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 If those capabilities are essential, use Hermes Agent instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build 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. ## Required capabilities - 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) ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of Gojiberry AI. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Gojiberry AI indie build ## Goal Build the smallest trustworthy replacement for the core Gojiberry AI workflow for one developer or a tiny team. ## Scope 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. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - 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 If those capabilities are essential, use Hermes Agent instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build 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. ## Required capabilities - 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) ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of Gojiberry AI. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # Gojiberry AI product brief ## Problem 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. ## Product outcome 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. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - 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) ## Explicit non-goals for v1 - 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 ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting 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. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Gojiberry AI capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# Gojiberry AI indie build ## Goal Build the smallest trustworthy replacement for the core Gojiberry AI workflow for one developer or a tiny team. ## Scope 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. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - 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 If those capabilities are essential, use Hermes Agent instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build 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. ## Required capabilities - 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) ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# Gojiberry AI product brief ## Problem 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. ## Product outcome 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. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - 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) ## Explicit non-goals for v1 - 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 ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting 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. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Gojiberry AI capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent
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.
xone enrichment provider instead of a 15+ provider waterfall, so coverage on the hard contacts is thinner
xcross-customer benchmarking and the weekly self-tuning
xthe ten-minute setup: your version does not exist until you build it
xsomeone else absorbing the breakage when an actor or a LinkedIn endpoint changes
xa support line when a sending account gets restricted
share on X ↗"I just replaced Gojiberry AI ($99/mo) with one prompt"
Gojiberry AI pricing
| plan | monthly | annual (per mo) | what you get |
|---|---|---|---|
| pro | $99/workspace | — | Email + socials; live in 5 minutes; numeric lead/message caps not published. |
free tierno free tier; free trial mentioned but no numeric caps published.
billingmonthly only; no annual plan verified
hidden costsNo numeric usage caps, seat limits or overages were published on the crawled page.
verified 2026-08-10 · source ↗
Vibecode Gojiberry AI
Yes. A competent AI coding agent (Claude Code, Codex, Cursor) can build a usable personal Gojiberry AI replacement in one session with the prompt on this page. It runs on your own machine or server with no subscription.
How much does Gojiberry AI cost?
Gojiberry AI costs about $99/month (Pro, checked 2026-08-03), which is $1188 per year. That's what you save by replacing it with one prompt.
What do I lose by replacing Gojiberry AI?
Honestly: 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. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Gojiberry AI?
Yes: Hermes Agent (self-hosted agent that can run the signal watch on a schedule instead of a cron service you write), n8n (self-hostable workflow automation, the usual no-code way to wire signals to outreach), Mautic (open-source marketing automation with contacts, campaigns, sequences, and suppression). Using prior art is also vibecoding; the prompt is for when you want it exactly your way.