Steven M. Muskal, Ph.D.

AI-Steve: Personal Research & Coaching Assistant

My daily companion for research recall, journaling, and family knowledge capture. AI-Steve pulls in emails and attachments, calendar events, to-do items, iMessages, Mac Photos plus curated imports, chat sessions, Q&A, and Socratic pairs extracted from mail, embedding them into PostgreSQL so Claude can respond with grounded context. Search Content, sentiment, and health intelligence fuel daily reflections (including 1, 3, 5, 7, and 10-year lookbacks) plus end-of-week recaps that project the week ahead. Built entirely through natural-language coding (speaking into Wispr Flow driving agentic CLIs like Droid, Claude Code, Codex, Gemini CLI) and able to handle small coding or automation projects on demand, similar to how I built Toast apps and AI-Dad.

Features at a glance

PostgreSQL + embeddings Claude reasoning Multi-source ingest Chat history memory

Chat UI grounded in embeddings and transcripts with instant access to images and stats.

Multi-year reflections surfaced daily (1, 3, 5, 7, 10 years ago).

Live mix of emails, Q&A, photos, attachments, calendar, to-dos, iMessages, and curated imports.

User-modifiable RAG weight control.

AI-Steve Architecture →

AI-Dad: Preserving Legacy Through Conversational AI

An innovative application of RAG (Retrieval-Augmented Generation) technology to create an interactive AI assistant embodying 60+ years of intellectual property legal expertise and family wisdom. Built using natural language programming and Claude Code, this deeply personal project preserves my father's extensive knowledge in IP law alongside decades of family history and personal interactions, making his guidance on both legal and life matters accessible for future generations.

Legal Expertise

Family Wisdom

AI-Dad: Always here for you - Combining decades of legal expertise with heartfelt family wisdom

Explore AI-Dad Project →

Coming Soon: AI-IP-Lawyer →

Intellectual Property Attorney for over 60 years

Image Explorer: Visual Memory Engine

A CLIP (Contrastive Language-Image Pre-training) + pgvector-powered explorer for my photo archives. Nightly clustering keeps similar sets together so I can browse clusters, select many images at once, and annotate entire groups without touching each file. I can also annotate straight from similarity search, including sub-images and video frames, accelerating how visuals become structured context for AI-Steve’s RAG. Built by speaking English into Wispr Flow to orchestrate Droid, Claude Code, Codex, Gemini CLI, similar to Toast apps, AI-Dad, and AI-Steve.

What it does

• 🔍 Similarity search with thresholds
• 🗂️ Nightly clustering; annotate whole sets
• 📝 Inline annotations saved to DB + JSON
• 🖼️ Finder deep links for fast triage

Bulk similarity hits with instant “Annotate Selected” to speed group labeling.

Annotation modal on similarity results to label entire groups in one pass.

Face extraction and clustering from Mac Photos and video frames, enabling rapid tagging and propagation.

Robust matching across sunglasses, hats, and lighting to keep face IDs consistent.

AI-Steve Code Directives: Domain-Specific Auto-Coding

Added into my AI-Steve infrastructure is the ability to auto-code projects in a domain-specific way using simple natural language project descriptions on top of Droid, Claude, and/or Codex. What brought it to the next level is using AI-Steve’s RAG system to wrap a project direction in my voice - imagine enabling all your coders to code given their own past projects and insights. It is akin to saying: build this new OS in the voice of Linus Torvalds.

Domain-specific guidance + RAG voice overlay drive code generation, review loops, and polished reporting.

Key elements: domain prompts, RAG context, peer review, self-healing retries, and packaged reports.

Designed to make project requests feel like they were built by the same voice that created the original system.

Food Health AI

Food Health AI turns meals into structured, reviewable nutrition data with photo capture, note capture, AI analysis, and fast edit flows. It is the practical product expression of the March 8, 2026 Renaissance Circle piece, Food Is Medicine. But First It Has to Become Data.

Current Demo

The existing walkthrough shows the current meal-to-data capture flow on the live app.

New V4 Demo

The newer V4 video adds another quick look at the updated Food Health AI experience.

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Share Health: App Store Export Companion

Share Health is the companion iOS app that makes consistent, time‑aligned Apple Health exports simple. It packages daily signals for longitudinal analysis with clean CSV outputs, optional face imagery, and on‑device Face → Health modeling.

Face → Health modeling + export workflows (3×2 layout).

App Store → More Details → GitHub Source →

Apple Health Integration: Quantified Sleep + Recovery

Daily Apple Health exports power a dedicated analytics pipeline inside AI-Steve. Correlation engines, lag analysis, and a sleep-concentration model surface the behaviors most tied to deep sleep and REM recovery. The resulting plots are stored as visual artifacts so they’re searchable and reviewable alongside photos and other memory assets.

Full correlation heatmap across Apple Health metrics.

Sleep-focused correlation matrix highlighting top 30 predictors.

Top 20 features driving sleep concentration prediction.

Top correlates with deep sleep for recovery drivers.

Top correlates with REM sleep for cognitive recovery signals.

Face → Health: Vision as a Longitudinal Signal

Face → Health treats the face as a sensor, not a narrative. Daily images support both retrospective (last night) and predictive (tonight) sleep modeling with strict temporal alignment. Health-specific vision analysis, dual embeddings, and a materialized ML view make this a defensible, longitudinal wellness signal.

Temporal self‑awareness: face today → sleep tonight (recorded tomorrow).

CT Viewer Story: DICOM → Interactive 3D

A real-world build story: I received a USB drive full of DICOM CT slices after a root canal, and instead of using a PC-only viewer, I described the problem to an AI coding assistant and walked away. Minutes later, I had a working browser-based viewer with 3D rotation, cross-sectional MPR views, and contrast controls.

Interactive 3D jaw reconstruction with axial, sagittal, and coronal slice navigation.

AI-Driven Drug Discovery

Developing machine learning models for predicting drug-target interactions and optimizing lead compounds to accelerate the path from discovery to clinical trials.

40+ years of neural network and AI research journey

Polypharmacophore / PharmPrint

Multi-conformer 3-point and 4-point pharmacophore fingerprinting technology for AI-driven virtual screening, food-based drug discovery, and cross-species conservation analysis. Originally published in J. Chem. Inf. Comput. Sci. (1999, 2000) and reborn for AI-era natural-product matching.

Triplet-based pharmacophore matching across drug compounds, natural products, and target binding sites.

🔬 Drug to Table Project (Finding Drugs Hidden in Your Food)

🥂 Toast Our Friend - A New Way to Celebrate

While the world focuses on self-promotion, we're building a place to honor the people around you. Share stories, create tributes, and celebrate the lives that touch yours.

Each app focuses on different communities and ways to celebrate friendships through meaningful connections.

Scott's Circle

A tribute to Scott Miller - celebrating friendship, music, and memories through shared connections.

toastscott.com

Mike's Circle

Honoring Mike Szwajkowski - connecting people through music, friendship, and shared experiences.

toastmike.com

Tom's Circle

Honoring Tom Shearer - celebrating a gentle soul who brought light, laughter, and love to all who knew him.

toasttom.com

Coop's Circle

Celebrating Steve Cooper - building community through shared memories and connections.

toastourfriend.com/coop