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Yeah, this one is a bit out there, but it was super fun. I built an AI that actually understands memes. It looks at the image and the text together using PyTorch, TensorFlow, and GPT 4. I used early and late fusion techniques to combine vision and language, plus Tesseract OCR to grab text from the image itself. The model hits over 95% accuracy on meme classification. It’s not your typical corporate project, but it shows how multimodal AI can understand humor, culture, and context. Definitely one of my favorite experiments.
For more details, please visit: https://arham-nexus.vercel.app/work/memechecker
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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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