Posts

Heads up: this post mentions Claude. If you want to try it, I've got a referral link — it gives us both a bit of extra credit, no pressure: claude.ai via my referral. TL;DR Most AI tooling still treats an LLM like a search bar — you prompt, it answers, the loop ends. Useful, but not what I wanted. For my homelab’s ops + trading intelligence platform (OpenClaw), I needed agents that could run for hours, do real work against a real cluster, and then tap me on the shoulder when they found something I should see. Claude turned out to be the model I kept coming back to for the “thinking” layer — it’s both comfortable with long tool-use chains and happy to write structured output a human won’t need to decode. This is a tour of how I’ve actually wired that up: k3s CronJobs doing the heavy lifting, LiteLLM as the routing layer, Slack as the interrupt bus, and named cat-bot personas so I can tell at a glance who’s knocking. ...

TL;DR I run 3-5 parallel Claude Code sessions against the same homelab. One tab mid-refactor, one tab doing docs, one tab chasing a bug. They don’t know about each other, so every so often one tab “tidies up” a file another tab is actively editing — and Claude, being a dutiful little overwriter, just clobbers the work. I built a small Go binary that hooks into SessionStart / SessionEnd / PreToolUse, tracks file claims on disk, and injects a warning straight into the LLM’s context window when it’s about to step on another session’s toes. Optional Slack mirror so I can watch the timeline from my phone. MIT, single binary, no runtime deps. Repo: github.com/zolty-mat/claude-session-guard. ...

TL;DR AI agents fail when they don’t know what you know. I built a Slack bot that conducts structured 5-layer interviews to extract tacit knowledge — operating rhythms, decision criteria, dependencies, friction points, leverage opportunities — and generates soul.md, user.md, and heartbeat.md config files for provisioning agents. The interview surfaces ~30% more actionable context than documentation alone. Full source code below. The Problem Nobody’s Talking About Nate B. Jones has a video that nails the core issue with AI agents: they fail because they lack tacit knowledge. Not the stuff in your docs — the stuff in your head. The 20-year veteran who just knows that the staging deploy takes longer on Thursdays because the batch job runs. The designer who can feel when a color palette is wrong without being able to articulate why. ...

TL;DR I spent a full day trying to automatically generate professional architecture diagrams for a 7-node k3s homelab. Figma’s MCP integration was perfect but requires a paid subscription. I tried Excalidraw (JSON generation + Kroki rendering), Mermaid, and finally landed on raw SVG generation in Python. The result is 27 diagrams with tech icons, drop shadows, and curved arrows — but the process is more manual than I’d like. I’m curious if anyone else has found a truly automated, free solution. ...

TL;DR You have a 24GB VRAM GPU. You want a private, self-hosted AI assistant that rivals ChatGPT – no subscriptions, no data leaving your machine. This guide walks you through setting up Ollama (local model runtime) and OpenClaw (AI gateway with a web UI) on Windows using Docker Desktop. But the real value here is the model recommendations. I ran 5,475 evaluations across 21 prompt variants and 6 models on real trading data. The results contradicted almost everything the community recommends. Finance-tuned models performed worse than a coin flip. Chain-of-thought reasoning models were anti-patterns. The winners were general-purpose MoE (Mixture-of-Experts) models that nobody talks about for specialized tasks. ...

TL;DR I loaded Z.ai’s GLM-5.1 — a 744B parameter MoE model with 40B active parameters — onto a Mac Studio M3 Ultra with 256GB unified memory using a 2-bit quantized GGUF via llama.cpp. It runs at 5.8 tok/s with a 120-second time to first token. The financial analysis quality is genuinely impressive, but it eats 222GB of the 256GB available, leaving room for literally nothing else. It’s a “clear the schedule” model, not an always-on one. ...

TL;DR I deployed ComfyUI natively on my Mac Studio M3 Ultra using Apple’s MPS GPU backend, proxied it through k3s Traefik ingress with Authentik SSO, wired it into Open WebUI as the image generation backend (replacing $0.04/image Bedrock calls), and built an MCP server so AI agents can generate images programmatically. The whole pipeline is Ansible-managed and generates images for free on local hardware. Why native instead of containerized ComfyUI needs GPU access. On Linux, that’s straightforward — pass through the GPU via device plugins. On macOS, there’s no container runtime that exposes MPS (Metal Performance Shaders) to containers. Docker Desktop on Mac runs a Linux VM — no Metal, no MPS. ...

TL;DR My Mac Studio M3 Ultra runs Ollama with 70B+ models but isn’t a k3s node. I needed it to show up in Grafana next to the cluster workloads. The solution: node_exporter for system metrics, a Go reverse proxy for per-model inference metrics, a custom Python exporter for model inventory and VRAM tracking, and Grafana Alloy for shipping logs to Loki. All four services managed by Ansible, all metrics scraped by the cluster’s Prometheus. ...

TL;DR OpenClaw, my self-hosted AI trading agent, was running in a fat container with 46 Critical CVEs, no network restrictions, and no automated vulnerability scanning. I fixed all three: multi-stage Dockerfile dropped the CVE count to single digits, default-deny NetworkPolicies locked down traffic, and a daily CronJob triages Trivy scan results via local LLM and posts a digest to Slack. Total cost of the automated triage: $0/day. The problem with AI agent containers AI agent containers are uniquely bad from a security perspective. They need: ...

TL;DR I built an “Ops Dream Worker” — a Kubernetes CronJob that runs at 3 AM, inspects the cluster, identifies improvements, and files GitHub issues with specific fixes. It runs entirely on local models (Mac Studio M3 Ultra), costs $0 per run, and went through 240 A/B test iterations to optimize the prompts. The anti-hallucination patterns were harder to get right than the analysis itself. The idea I have a k3s cluster with ~40 deployed services. I maintain it solo. There’s always something that could be better — a deployment missing resource limits, a CronJob that’s been failing silently, an ingress without SSO protection, a container image with known CVEs. These improvements pile up because I’m usually focused on building features, not auditing infrastructure. ...