Pat Wendorf

/ About

Builder, tinkerer, database nerd, and off-road enthusiast.

📍 Location

Midhurst, Ontario, Canada — about 2.5 acres of mostly forest, a pool, a hot tub, and a workshop full of vehicles to maintain.

🔧 Tech Stack

Python (ML/AI, Flask, FastAPI)
Rust, C++, Java, shell
MongoDB / Atlas Search / Atlas Vector Search
Linux daily driver (FreeBSD past)
Local LLMs with llama.cpp

💼 Background

DBA turned dev turned Solutions Architect. Years of Ruby, C#, PHP before going deep on GenAI and search. Comfortable writing a database from scratch.

🎯 Current Focus

Building agentic AI systems that use real tools on real infrastructure. Local-first, open-source, no cloud lock-in.

50+

GitHub Repos

★ 90+

GitHub Stars

Pengy Implementations

/ Work

Where I spend my days.

MongoDB

Solutions Architect Specialist — Atlas Search

I work with MongoDB's largest customers on Atlas Search (Lucene-based lexical search), Atlas Vector Search (ANN vector similarity), and Voyage AI (embedding models). I help design and implement search architectures that combine lexical and semantic retrieval, build RAG pipelines, and push the boundaries of what's possible with AI-powered data access.

GenAI · Search Architecture · Vector Search · RAG

RBC (Royal Bank of Canada) Former

Senior Solutions Architect

Member of the Internal Cloud team, enabling internal teams across RBC to build and operate Elastic-based search, observability, and logging solutions. Provided hands-on architecture guidance, platform tooling, and operational support for teams adopting the Elastic Stack. Also supported ML/AI teams in developing, deploying, and monitoring production machine learning models, bridging the gap between data science experimentation and reliable infrastructure.

Internal Cloud · Elastic Stack · ML/AI Platform Support · Enterprise Search

Elastic Former

Senior Solutions Architect

Helped customers design and deploy Elastic Stack solutions for search, observability, and security at enterprise scale. Specialized in Elasticsearch performance tuning, cluster architecture, and search relevance engineering. Worked across industries including finance, e-commerce, and healthcare to deliver production-grade search experiences.

Elasticsearch · Search Relevance · Observability · Enterprise Architecture

Hortonworks Former

Senior Solutions Consultant

Advised enterprise customers on Apache Hadoop ecosystem architecture, data lake design, and large-scale data processing pipelines. Delivered solutions spanning HDFS, Hive, HBase, Spark, and Kafka — helping organizations move from batch-oriented data warehousing to real-time, distributed data platforms.

Hadoop · Spark · Kafka · Data Lake Architecture · Big Data

/ Projects

Open-source things I've built — from AI agent harnesses to video editors.

🐧 Pengy

A local-first AI agent harness — not a chatbot wrapper, not a SaaS. Three implementations (Python, Rust, C++), all sharing the same config and chat storage. Desktop GUI (Qt6), CLI, and web UI. 11 built-in tools for file ops, bash, Python, web search, and more. Skills system lets you teach it anything.

Python Rust C++ ★ Featured ★ 3 GitHub pengy.catbee.ca

🐄 MooFile

A lightweight, embedded, single-file document store with MongoDB-style queries, BSON encoding, indexes, BM25 text search, and vector similarity search. No server, no infrastructure — just a file and a library. Used internally by multiple projects including Discona, LlamaHerder, and tclip. Comes with CLI tools for import/export to JSON, MongoDB, and SQLite.

Python BSON Vector Search ★ 1 GitHub

🎬 VidClip

A web-based video clipping, transcoding, and volume normalization editor powered by FFmpeg. Built with Electron for a native desktop feel. Perfect for quickly trimming clips, converting formats, or normalizing audio levels without launching a full video editor.

JavaScript Electron FFmpeg GitHub

🤖 Discona

A Discord bot management system for running multiple LLM-powered bots against a local OpenAI-compatible server (llama.cpp, Ollama, etc.). Features a password-protected Flask web UI for creating and managing bot personalities, trigger words, and user relationships. Data stored in BSON flat files via MooFile — no database server needed.

Python Flask Discord GitHub

🤖 Bottybot Retired

A conversational chatbot UI built on llama.cpp server. The predecessor to Discona and the Discord bot ecosystem. Supported multiple LLM backends (Anthropic, OpenAI, Mistral, llama.cpp) with configurable personalities and chat history. Retired as the architecture evolved into more modular systems, but was the foundation for everything that followed.

Python Flask llama.cpp ★ 15 GitHub

🧠 ExternalBrain Retired

An early RAG experiment — assert facts and thoughts, store them in MongoDB Atlas as vectorized chunks, then ask an LLM questions grounded in your own data. Features Flask web UI, Discord bot integration, and support for local (llama.cpp), OpenAI, and Mistral AI backends. The precursor to many of the AI/retrieval patterns used in later projects.

Python Flask RAG ★ 6 GitHub

🗣️ Natralang Retired

An early experiment in Natural Language Query for MongoDB Atlas — describe what you want in plain English and let the LLM generate the MongoDB query. Uses Atlas Vector Search to find relevant collections and build context, supporting Mistral AI, OpenAI, and Azure OpenAI. A proof-of-concept that paved the way for NLQ in production.

Python Flask MongoDB ★ 7 GitHub

📱 ElasticSensorDump Retired

An Android app that dumped all device sensor data (GPS, accelerometer, gyroscope, microphone levels, and more) directly into Elasticsearch for real-time visualization with Kibana. Thousands of downloads on the Google Play Store. A Java/Gradle project built in Android Studio — schemaless sensor ingestion before IoT data pipelines were mainstream.

Java Android Elasticsearch ★ 9 GitHub

And more on GitHub...

RAGTAG · FactWeave · TLDW · AudioSumma ·
LlamaHerder · SimpleVectorStore · tclip · and many more

View all repos →