Ai

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 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. ...

TL;DR I deployed Whisper (speech-to-text), Piper (text-to-speech), and openWakeWord (wake word detection) as Kubernetes workloads on my k3s cluster. Home Assistant connects to them over the Wyoming protocol for fully local voice pipelines. Total resource cost: ~1 CPU core and 1.75GB RAM. Total cloud cost: $0. Why run voice services in Kubernetes Home Assistant’s voice pipeline needs three things: something to listen for a wake word, something to transcribe speech, and something to speak back. The usual approach is running these on the same box as HA, or on a dedicated Pi. Both work fine until you want the services to survive node failures, be independently upgradeable, or share resources with other workloads. ...

TL;DR Someone leaked the Claude Code source on GitHub. OpenClaw, the open-source AI coding agent with 346k stars, solves the same problem with a completely different architecture. I compared both codebases at the structural level. The verdict: these are independent implementations that converge on the same tool-use patterns because that is what the problem demands — not because one copied the other. Background In late March 2026, a repository appeared on GitHub containing what appears to be the full source code for Anthropic’s Claude Code — the terminal-based AI coding agent I wrote about switching to last month. The repo has two commits (“init” and “add readme”), 1,932 files, and weighs 43MB. ...

TL;DR Multi-user AI chat with privacy guarantees, dual model providers (Anthropic direct API + AWS Bedrock via LiteLLM), and per-user cost tracking via Prometheus and Grafana. The admin cannot read other users’ conversations. Three family members authenticate via Google OAuth, each getting isolated chat sessions. Anthropic serves as the primary model provider with lower latency, and Bedrock via LiteLLM acts as a fallback. Per-user spend is tracked through LiteLLM’s Prometheus metrics without any surveillance of conversation content. This is a follow-up to the OpenClaw on k3s setup post. ...

TL;DR I replaced Open WebUI with OpenClaw – a lighter, WebSocket-based AI assistant gateway that installs from npm, supports multiple chat channels (web, Telegram, Discord, WhatsApp), and deploys on k3s as a single Deployment with a custom Docker image. The primary model provider is Anthropic’s direct API (Claude Sonnet 4.5), with LiteLLM/Bedrock as a fallback. The biggest deployment lesson: OpenClaw binds to loopback by default, which makes it invisible to Kubernetes Services and health probes. The fix is --bind lan, which requires a gateway token for authentication. ...

TL;DR The Linux kernel is 36 million lines of C. Rust has been slowly entering the kernel since Linux 6.1, but progress is measured in individual drivers and abstractions – a few thousand lines per release cycle. What if you skipped the incremental approach and asked Claude to rewrite major subsystems wholesale? I spent a weekend scoping this as a serious project plan: breaking the kernel into translatable units, estimating token costs, identifying the hard walls, and testing Claude’s ability to produce correct Rust translations of real kernel C. The conclusion: Claude can translate isolated, well-bounded kernel modules with surprising accuracy. It cannot translate the kernel. The difference between those two statements is the entire lesson. ...