AI

TL;DR I run 3-5 Claude Code sessions in parallel at staggered cadences. They coordinate through a shared #mat-claude-sessions Mattermost channel plus a small coordination board file. Each session announces what it’s about to touch, claims it, and announces when it’s done. Conflicts are rare; throughput is dramatically higher than running one session at a time and waiting. Why parallel A single Claude Code session running a long task — refactor across a few repos, work through a debugging session, draft a blog post — is mostly me waiting. The model is fast but tasks are bounded by my decisions, my reviews, and my edits. If I’m waiting on Session A to finish a build, Session B can be drafting something unrelated. Session C can be running a slow eval. The bottleneck stops being the model and becomes my own attention rotation. ...

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 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. ...