navicore

core-ontology.ttl

Minimal, pragmatic ontology for an RDF-first, file-based

system knowledge graph suitable for arc42 generation

and operational reasoning in a Flux + EKS SaaS.

@prefix ex: https://navicore.tech/ops# . @prefix k8s: https://navicore.tech/k8s# . @prefix flux: https://navicore.tech/flux# . @prefix scm: https://navicore.tech/scm# . @prefix obs: .

navicore

RDF‑First System Knowledge Graph for Arc42 (File‑First, Rust‑Centric)

A practical guide for reverse‑engineering a Flux‑deployed, multi‑cluster EKS SaaS into a queryable knowledge graph that can generate arc42 documentation, support operational reasoning, and integrate with LLM tooling — without Neo4j, Jena, or long‑running servers.

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0. Executive Summary

This project treats architecture and operations documentation as a derived artifact.

navicore

some notes wising for an open solution to the problem chainguard and hummingbird are about.

navicore

The Patch Manifesto

The Patch Languages are a small family of embeddable languages designed to co-exist without collapsing into a single compromised “one language to rule them all.” Each Patch language is intentionally tiny, opinionated, and good at exactly one thing.

Patch consists of three cooperating cores:

• Patch Seq – a concatenative execution language

navicore

Alternative Computer Architectures: Overview, Timeline, and Diagrams

This document summarizes several historically important non–von Neumann (or von-Neumann-adjacent-but-alternative) architectures, compares them, and places them on a rough timeline. It also includes example Graphviz and PlantUML diagram source you can render locally.

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1. Comparison Table

navicore

Convincing Isn’t Correct: Why You Must Fact-Check AI

Most of the AI headlines today are about LLMs, short for Large Language Models.
When we say “tokens” in this context, think of them as little pieces of text — often whole words, sometimes parts of words or punctuation.

What AI really does

An AI language model is a token predictor. It looks at huge amounts of text and learns which pieces of text usually follow others. That’s it. There’s no built-in check for truth. It doesn’t know fact from fiction—it just knows what’s statistically common.

Why training can’t fix this

Some people assume you can “train AI for truth.” But there’s no truth label on all the text of the Internet. Even fine-tuning with curated correct examples only adjusts probabilities—it doesn’t change the fact that the model is a probability machine at its core.

navicore

#!/usr/bin/env bash

sudo scutil --set ComputerName "boston"
sudo scutil --set HostName "boston"
sudo scutil --set LocalHostName "boston"

navicore

Ha. you only live once - if you find yourself getting monitored for number of commits, do this:

git commit --allow-empty -m yolo

navicore

does this work?

helm template my-release my-chart | kubectl apply --dry-run=client -f -

or

helm template my-release my-chart --namespace test-namespace | kubectl apply --dry-run=client -f -

navicore

local pickers = require('telescope.pickers')
local config = require('telescope.config').values
local finders = require('telescope.finders')
local previewers = require('telescope.previewers')
local utils = require('telescope.previewers.utils')
local actions = require('telescope.actions')
local actions_state = require('telescope.actions.state')

local log = require('plenary.log'):new()
log.level = 'debug'