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?

Building an AI-Powered 3D Interview Avatar: A Smarter Way to Practice Job Interviews

I recently built an AI-driven mock interview platform designed to help candidates prepare for real-world recruiter conversations through interactive, personalized interview simulations.

The core idea was simple: instead of practicing with static question lists, users upload their CV/resume and engage in a live conversation with an intelligent 3D avatar that acts like a recruiter.

What makes this system powerful is its ability to understand the candidate’s professional background and generate context-aware interview questions based on their actual experience, skills, projects, and career history—similar to how recruiters conduct interviews in real meetings.

How it works:

• Users upload their CV/resume • The system processes and extracts key professional information • A Retrieval-Augmented Generation (RAG) pipeline organizes the data into vector embeddings • The AI agent builds personalized interview flows based on resume context • A real-time 3D avatar conducts the interview through voice interaction • The avatar responds dynamically with lip-sync, facial expressions, and body animations for realistic conversation flow

This project combines conversational AI with immersive 3D interaction to create a much more natural and engaging interview preparation experience.

Tech Stack:

Backend & AI

Python

LangChain

OpenAI

Pinecone (Vector Database)

Retrieval-Augmented Generation (RAG)

Frontend & Avatar System

React

Three.js

Real-time avatar animation system

Lip-sync engine

Facial expression mapping

Body motion integration

Key Challenges Solved:

✔ Resume parsing and contextual knowledge extraction ✔ Dynamic interview question generation ✔ Real-time voice conversation pipeline ✔ Avatar lip-sync synchronization ✔ Emotion-driven facial expressions ✔ Memory-aware conversational flow

This project reflects how AI agents can move beyond text-based chatbots into immersive, human-like digital experiences.

AI agents + 3D avatars + contextual intelligence are opening new possibilities for training, education, customer support, and simulation systems.

This is one example of how I build AI-powered interactive systems that combine intelligence, design, and real-time user experience.

#AI #AIAgents #LangChain #OpenAI #ThreeJS #Python #React #MachineLearning #3DDevelopment #InterviewPreparation #RAG #Pinecone