I just fired up a local instance of Gitea for stuff I don’t want Micro$oft and GitHub training CoPilot on, or want anyone else scraping. However, one challenge with such a setup is running your trusty CI/CD workflows without GitHub’s infrastructure. Docker Compose to the rescue! After some wrong turns and some rubber ducking with Claude, I managed to construct a relatively simple Docker Compose file to keep a minimum number of ephemeral runner replicas up and registered with Gitea without requiring a complex Kubernetes setup. Now I can have my self-hosted Gitea instance run the same CI/CD jobs that...
Continue reading...Self-hosting GenAI code assistants
I’ve been playing with Claude Code for a few months now, and have been very impressed – but sometimes hit the limits of even the Pro Max plan when I’m multitasking. Which got me to thinking… I have a DGX Spark that is idle while not training or fine-tuning LLM models for a side hustle, which can handle pretty large models (albeit not at thousands of token/s generation like commercial offerings). Maybe it would be fun to see how far I can get with self-hosted OSS solutions so I could experiment with things like Ralph and larger projects with BMAD...
Continue reading...Metadata Matters
Current data collection systems are literally drowning in data. Data scientists are trying to figure out how to leverage it. Metadata can help.
Continue reading...Autonomous Baby Steps
It seems like every AI/ML feed I follow, there is yet another company claiming to go from fully-manual to fully-autonomous in a single, giant, disruptive leap. This kind of overhype is doing way more harm than good.
Continue reading...Working with Microsoft’s ONNX Runtime
We’ll explore leveraging the Microsoft ONNX Runtime as a deployment tool in the Java ecosystem for pre-trained ML models.
Continue reading...Natural Language Processing vs. Understanding
By now, almost anyone reading this blog has experienced a conversational agent, or “chatbot” in some form or another. The funny thing is, as impressive as these modern chatbots are, they still have no real concept of understanding.
Continue reading...The Conceptual Limits of Deep Learning
There are many tasks machine learning is inherently not well suited for – these are tasks that require conceptual comprehension; curve fitting or pattern matching just won’t get you there.
Continue reading...Knowledge as Graphs; Graphs as Knowledge
In this article, we’ll briefly explore knowledge-oriented graph types and common uses in artificial intelligence as data structures and reasoning systems.
Continue reading...The Web API Trap
[X]aaS is great for proof-of-concept, rapid prototyping, and other quick turnaround development efforts; especially when they provide easy to use API endpoints. However, becoming over-dependent on these third-party vendors for you production systems can be a hidden liability to your product and business.
Continue reading...A quick look at the GDELT Mentions schema
The GDELT Project introduced in a previous post discussed the origins and high-level information included in the project’s datasets. This post will specifically explore the version 2.0 features of the Mentions dataset, which records where GDELT Events are initially discovered or later referenced, and statistics surrounding the mentions of the event in question.
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