Blog de Joe Freeman

LLM-supported AI is being stuffed into every computer-based and computer-assisted process. A lot is being said about the benefits of LLM, with some secondary discussions about security and behavioral risks. Many of the risk discussions have been unstructured bullet points or anecdotal stories of hacked systems. The OWASP Top 10 for LLM Applications 2025 provides a framework for understanding the top risks of an LLM-integrated system. The OWASP Top 10 for LLM Applications deserves the same attention the OWASP Top 10 for Web applications has held for over a decade. The Developer's Playbook for Large Language Model Security  by Steve Wilson provides a solid foundation for understanding risks existing in an LLM ecosystem. It also led me to the  OWASP Top 10 for LLM Applications 2025 .  This is a great resource for anyone looking to integrate LLMS into business processes or existing systems. I got this book as part of a set of O'Reilly books in a Humble Bundle. OWASP Top 10 fo...

You only understand something when you are forced to do it yourself. I've been looking for a way to force myself to build and test a multi-platform application.  A lousy game night scoring experience demanded an NIH solution. So, I built a multi-platform Flutter application using only CoPilot agent prompts. It was a learning experience where I had to cheat, relying on my knowledge to drive CoPilot where I wanted it to go. The end result is a multi-platform Flutter app,  fs_game_score that can be found on GitHub .  This is a generic game scoring app created almost  entirely using VSCode's Copilot  agent mode  with virtually no  hand coding. There were lots  of AI agent prompts with many undo/redo attempts. The application has been tested on Android, IOS, Chrome, macOS, and Windows 11.  At least a dozen lessons learned Coding It helps if you know what libraries you want to use. That lets you direct the copilot to the most up-to-date or best pra...

We used to "play" buzzword bingo in meetings and company events.  For everyday craziness, I've been using an internal MadLibs-style approach. I'll try and collect samples over time. Sample problems One (2025) Life is too short to ___________ in _____________ Two (2025) Real programmers can write ______________ in any __________ Sample Solutions One 2025 Life is too short to spend 5  hours per day in meetings Life is too short to spend more than 1/3  in work Life is too short to spend the whole day in doors   Two 2025 Real programmers can write Fortran  in any language Real programmers can  write Testable Code  in any schedule Revision History 2025 05 Created

General Impressions OpenVoice OS is an interesting project that runs on relatively lightweight hardware and lets you retain local voice processing. The most obvious use cases are Home Automation or local voice bot. AI has everyone's attention and all the voice agent work is now happening around AI integration for smarter responses. OpenVoice feels small as it currently exists.  It is mostly a curiosity on its own. OpenVoice LLM integration requires you to know something. IMO the best thing would be to have a zero-config Ollama or LocalLLM that is part of the default extended skill set.  The Raspberry Pi was great but it felt constrained. The most cost-effective and full-featured hardware setup for completely local voice recognition and processing is probably a Mac desktop.  It would be capable of running OpenVoice and local MLX-based LLMs. Revision History Created 2024 04

I had a pile of Raspberry Pi 3b and Raspberry Pi 400 hardware so I used that.  If I had been building a magic-mirror type unit then I would have had a GUI.  In my case, I wanted a headless agent.  The Rasberry Pi 3 was frustratingly slow.  The P400 was pretty usable with and without the GUI. In my case, I ended up running the Pi headless to create more headroom for any processing on the unit. The RaspOVOS image was dead simple to write to an SD card on my Windows machine using the Raspberry Pi OS installer program. The decision tree looks pretty much like Related Links https://community.openconversational.ai/latest https://github.com/OpenVoiceOS/ovos-releases https://github.com/OpenVoiceOS/raspOVOS Revision History Created 2024 04

I tried to plant something in an enterprise garden.  The garden was actually be a graveyard of bad decisions. First Version. I tried to plant something in an enterprise garden. It turned out that it was actually a graveyard of bad decisions. Revision History This page is subject to revision based on future input. Created 2025/04 Updated 2025/07  

The Azure Mongo DB version of Cosmos DB provides a sharable, scalable way of working with MongoDB across sites, different teams or different machines. I have a couple development machines and am always looking for ways to work with the same dataset independent of my current physical location and of the developer machine platform I'm using. Always Free Cosmos DB - Mongo DB Microsoft Azure has a Cosmos DB for MongoDB free tier offering. MongoDB (vCORE) has its own SKU (at this time). This means you may be able to use free tier Mongo DB and the free tier standard Cosmos DB You get a dedicated MongoDB cluster with 32 GB storage. The docs don't mention any compute or RU/s limit. From the docs Azure Cosmos DB for MongoDB (vCore) now introduces a new SKU, the "Free Tier," enabling users to explore the platform without any financial commitments. The free tier lasts for the lifetime of your account, boasting command and feature parity with a regular Azure Cosmos DB for MongoDB...

Cosmos DB has an Apache Tinkerpop /Gremlin, compatible persona that lets you run Gremlin Graph queries against a Cosmos DB data store.  This tool can be useful in a shared database in a personal project or startup situation. Cosmos DB Gremlin is targeted at teams migrating into Azure and off of some other COT product. Azure has an always-free tier Cosmos DB Gremlin instance offering reasonable limits and some compatibility constraints. Usage limits for the free tier are mentioned below. Caveat Emptor Microsoft is dropping Graph support in their Azure Databases VSCode extension. Microsoft's Cosmos DB Gremlin support document  that Cosmos DB is compatible with Gremlin 3.4 version drivers.  Later versions of Gremlin drivers don't work with Cosmos DB. At the time of this article, CAzure Cosmos DB supports the JSON format. It does not support the Gremlin bytecode format.  This means it doesn't work with some driver optimizations and libraries. What is gremlin Look at Kelv...

MLX is an ML framework targeted at Apple Silicon.  It provides noticeable ML performance gains when compared to the standard (GGUF) techniques running on Apple Silicon.  This MLX project describes MLX as:   MLX is an array framework for machine learning on Apple silicon, brought to you by Apple machine learning research. A notable difference from MLX and other frameworks is the   unified memory model . Arrays in MLX live in shared memory. Operations on MLX arrays can be performed on any of the supported device types without transferring data. LM Studio added support for Apple Silicon MLX models in 2024 . I totally ignored it until I saw a 2025/02 Reddit post in the /r/ocallama subreddit .  I wanted to execute their microbenchmark on my Mac to get a feel for the possible performance difference.  The performance improvement is exciting.  I am waiting on really jumping into the MLX until Ollama supports MLX something they are working on as of 2025/0...