Artificial Intelligence

Hardened AI for Enterprise Linux.

Overview

Having a background in hard-core Python, AI infrastructure and applications is second-nature to me. I ship a vast amount of Python - from OpenStack, to Airflow, to my Plone/ECM. My BastionLinux/Desktop is focused upon both security and curated AI tools for developers and users. I ship LiteLLM for configuration/API keys/authorisation, instrumentation and Prometheus metrics/analytics. I use MLFlow and Phoenix, and even proprietary LangSmith and a lot of open telemetry and open inference for observability, and LangFlow to LiteLLM for UI/prompt engineering assistance. I run vLLM and OpenVino as dedicated inference engines.

Drivers and GPU’s

I have great expertise and resources building kernel modules, drivers, and firmware, even for proprietary NVIDIA, AMD’s ROCm and build high-performance physical and virtual machines to drive GPU-intensive workloads.

Prompt Engineering

I have quite some expertise writing prompt engines. These use JSONSchema to force structured data from the providers, and then do loads of interesting things with Pydantic to guarantee validity of results. My prompts live in relational and object databases and are accessible via MCP.

I ship Grafana, PostgreSQL, shell. I have written MCP and LangChain services around my own BastionLinux build/deploy and Yum/DNF services aimed at validating, building, healing, and administering RHEL stacks - based on my complete Distro/Vendor backends. I use and coordinate all my MCP services with the Argus and IBM/Chuk CLIs clients and Agent/Managers of MCP backends. This is all a highly evolving field, and I am of course not proscriptive about any of this - I am simply explaining the ecosystem I deploy.

Vector Databases

I have built and tried a bunch of Vector databases. In my main BastionLinux build stack, which is built upon an Enterprise Content Management stack - so well aware of document indexing, stemming, and other features, I have built a vector index for Zope and this supports my own local BastionLinux/LLM services.

Cloud

Implementing production-grade agent architectures and multi-agent workflows across AWS utilizing Amazon Bedrock foundation models and Amazon Bedrock AgentCore managed runtimes. Technical execution involves executing spec-driven physical and software development using Kiro, configuring event-driven Agent Hooks, and anchoring environmental context through persistent Steering Files. Agent reasoning built with Strands Agents SDK, leveraging Strands Agent SOPs to enforce guardrails during execution.