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Title:AI-Powered WhatsApp Automation with n8n
Description:
I built an end-to-end automation workflow using n8n that connects WhatsApp with an intelligent AI Agent.
Key features:
WhatsApp Trigger: Automatically listens for incoming customer queries.
AI Agent Integration: Uses a Large Language Model (LLM) with memory to understand context and provide human-like responses.
Data Retrieval: The agent can pull product data from a Google Sheet or database to answer specific questions.
Confirmation Email: Automatically sends a follow-up email via Gmail once a query is resolved.
This workflow significantly reduces response time and automates lead nurturing for businesses.
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A Telegram bot built as a parallel layer to a productivity system, designed to intercept moments of negative impulse and convert them into structured reflection conversations.
How it works:
Every user message triggers an AI intervention conversation grounded in the user's Chief Definite AIM, recent insights, current goal pacing, and reflection history
Voice messages auto-transcribed via OpenAI Whisper, treated as text turns in the conversation
/done command ends the session, prompts trigger word + outcome (avoided / indulged / unclear), persists everything to SQLite for downstream daily summary integration
/restart wipes session state for fresh context
System prompt is brother-tone, AIM-grounded, 80–200 words per response, deliberately not therapy-speak
Runs in the same Node process as a separate productivity bot — two bots, two tokens, one codebase.
Tech: Node.js, Telegram Bot API, Claude API, OpenAI Whisper, SQLite.
The idea of intercepting negative impulse moments in real time is genuinely clever. most productivity tools wait for you to come to them — this flips it. voice transcription via Whisper is a smart touch too, removes friction completely. how's the response latency holding up in practice?
A goal-tracking sub-system that grounds an AI assistant in the user's full life-state — target amount, hard deadline, days remaining, current balance, pace status (ahead / on-pace / behind), and reflection streak.
Core features:
Multi-entry timestamped reflection log — plain Enter submits, Shift+Enter newline, optimistic UI, all entries inside one unified container with centered timestamp dividers
Ephemeral Summarize chat modal — loads today's braindumps as live AI context, brother-tone system prompt, conversation NOT persisted (privacy by design)
Nightly auto-summary cron at 23:55 — pulls reflections, runs through Claude, creates a standalone daily summary that downstream AI engines pick up automatically
Pace analysis — surfaces required-vs-actual daily rate, projected gap at deadline, status flag
Each AI engine pulling this data adapts its suggestions based on whether the user is ahead or behind on their goal — recursive context that compounds over time.
Tech: Claude API, SQLite, Node.js cron, custom retrieval logic.
A weekly and monthly AI suggestion engine that ingests the full user state and returns three ranked, goal-aligned recommendations per call.
What goes into the LLM context:
Chief Definite AIM (text, target, deadline, days remaining, current balance, pace status)
Perpetual goals (with AIM-link flags so aligned goals weight heavier)
Non-negotiable habits and their streaks
Recent insights with integration status (active / implemented / integrated:date)
Last 7–14 daily summaries
Past 28-day (weekly) or 90-day (monthly) suggestion history — for recursive learning
What it returns: three suggestion cards per call, each with priority + difficulty + AIM-alignment + tags + rationale.
Includes:
Intent gate — default AIM-aligned mode vs. custom prompt mode
Auto-detected response shape (ranked / analysis / hybrid)
Unified rate limiting across four endpoints (weekly default, monthly default, weekly custom, monthly custom)
Persistence to suggestion_history table for the next round's recursive context
Tech: Claude API, OpenAI API, custom prompt engineering, SQLite, Node.js, in-memory rate limiting.
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