Blog - MattBits

I am wanting to tackle an issue with best practice in AWS - IAM policies and resource policies. The immediate reaction would be to say: All IAM policies should be scoped to the actions and resources that they interact with All resource policies should be scoped to the principals that interact with them In a distributibuted micro-service architecture, where each application is deployed indepdently, this can become a bottleneck.

Before I get into GitLab functions and components, I wanted to cover how I manage build and deployment artifacts, as it’s a dependency for a lot of what comes later. I generally block internet access across my homelab. Aside from specific external APIs, everything is either pre-downloaded or accessed through a caching proxy. I use Nexus for PyPI, apt, go etc. package pull-through caches. For binaries, however, I’ve moved a couple of times from a basic machine (called Vault - unfortunately this 15 year old machine pre-dated me starting to use Hashicorp Vault), through various technologies and now use Nexus.

Homelab history I’ve been running some form of home infrastructure for a long time now. Looking back through old notes and photos, it’s interesting seeing how much it’s changed. Some of it was planned, a lot of it wasn’t, and there were more than a few dead ends along the way. Like most long-running homelabs, it grew one project at a time rather than from any grand plan. Packard Bell Club A Packard Bell Club.

Over the past couple of days, I’ve had a couple of things that have been annoyed me and have resulted in a learning and a success! Dependency OOMs I’ve been doing a bit more work on my virtual machine agent and had been trying to build it and began getting OOM errors. I followed some of the failures (it was a strong assumption that the library it was failing to build was the culprit, rather than the straw that broke the camel’s back).

Having gotten an AMD AI 395+-based machine this month, I’ve been using Lemonade to run models, which takes care of bootstrapping RocM, I now wanted to bring this into a workflow I had been tested a couple of months ago which was toying around with making a game. My goal was to create a point-and-click game, but given that I’m awful at graphics, I had been taking photos and using AI to generate the style of image and to make tweaks in preparation to build into a game.

This is a summary of how I built and tuned a local OpenCode-style coding setup on AMD ROCm using Lemonade + llama.cpp, moving through multiple models, context issues, and performance tuning before settling on Gamma 26B A4B Instruct (IT). 1. Base Setup Started with Lemonade running llama.cpp on ROCm via Docker: docker run -d \ --name lemonade-server \ -p 13305:13305 \ -v lemonade-cache:/root/.cache/huggingface \ -v lemonade-llama:/opt/lemonade/llama \ ghcr.

The beginning I’ve been running a Hashicorp stack for a while now - Vault, Consul and Nomad across a small cluster of machines. Over time, I’d built up a pretty reasonable setup, but it was always a bit… scattered. Terraform here, manual config there, some docker-compose files scattered about. The thing is, I’m running all of this on self-hosted hardware. There’s no AWS ECS or Google Cloud Run to fall back on.

This is a pledge to myself and for any one else interested in following the same journey. I don’t know jack about Kubernetes. I know about docker, integrated with it’s API, cgroups etc. I’ve used nomad and docker swarm heavily and rancher. History I have taken steps in the past to master technologies in various levels, for example Terraform, nomad Terraform Used Terraform for about a decade, managed and supporting hundreds of projects using it Working knowledge to create agnostic re-usable modules Created and fixed bugs in Terraform providers Created a Terraform module and provider registry Created a working PoC of an open source Terraform cloud alternative: implementing the Terraform cloud APIs, reverse engineered the official Terraform cloud agent APIs to a fully functional state implemented missing features (such as environments) working implementation of a custom state Vault state backend into OpenTofu (https://github.

My homelab and work life have followed very different trajectories, though often influencing one another. I like to try out interesting thought experiments at home, see how they work out to determine whether they’re worth investing in. Right now, my homelab consists of a load of VMs and my initial goal was to find a new way of monitoring (often these small tasks lead to a big spray of different tasks).

What is this? I work in close proximity to a lot of AI/ML/big data engineers and have friends who are of the same variety. I somewhat understand the basic concepts of a neural network and I have some knowledge of vectors etc. but I’m tired of being the one left confused when having conversations… So, my plan is to get stuck in a little.. at least to train something and to work my way into a problem enough that I get confused.