After many years of coding small projects and experimenting with GitHub, mainly around development and pentesting, I’ve found myself increasingly drawn into the AI world.
The speed at which AI is evolving is honestly fascinating. I’m now looking beyond simply using AI tools and want to properly understand how to build with them from better prompting techniques, to running models locally, to understanding how these systems actually work.
One of my biggest interests is creating a truly offline AI setup. I’ve been exploring local models, GGUF and abiliterated formats, running models on desktop hardware or even from portable USB/SSD setups, and finding practical alternatives to constantly relying on API keys (especially when experimenting with larger or more capable models can quickly become expensive). So looking for new models to run, or potentially learn to build.
There is so much happening right now across:
local LLMs
coding models
image/video generation
text-to-image and text-to-video workflows multimodal models
fine tuning and custom models
RAG systems and private knowledge bases
The amount of choice is both exciting and overwhelming.
A little background:
I’ve been developing for 25+ years across a range of technologies, including:
Python
PHP / Laravel
Vue.js / jQuery
LAMP stack
MySQL
NoSQL databases
Elasticsearch / Lucene
legacy web systems and application development
I’m now looking to add AI engineering to that skill-set not just as a user, but by learning how to build, evaluate, run, and optimise models.
My current goals are:
understanding the best ways to instruct models
learning when to use prompting vs RAG vs fine tuning
building local AI assistants
experimenting with coding models
creating private/offline AI workflows
understanding the practical side of deploying AI systems
I’d love to hear from the community