Customer: AI-natives and CSP/NCPs deploying LLM inference at scale on Kubernetes Decision: Which orchestration layer to build on for production serving? Date: February 2026 | Dynamo 0.8.1 vs llm-d 0.5.0
Me: "Would analyzing my actual repo help evaluate if SpecKit is genuinely useful or just a security blanket?"
Claude: "Yes, let me look at the evidence."
Some context: I've been using GitHub's SpecKit for spec-driven development - the idea that you write elaborate specifications, plans, and task breakdowns before coding, and the AI follows them to prevent mistakes. Sounds great, right?
But I had a nagging feeling it was just making me feel productive without actually preventing problems. So I asked Claude to evaluate it using concrete evidence from a real project we built together: a SpecKit updater tool for Claude Code.
| // ==UserScript== | |
| // @name youtube popup killer | |
| // @namespace http://tampermonkey.net/ | |
| // @version 0.3 | |
| // @description try to take over the world! | |
| // @author Selbereth | |
| // @match https://*.youtube.com/* | |
| // @icon https://www.google.com/s2/favicons?sz=64&domain=youtube.com | |
| // @grant none | |
| // ==/UserScript== |
| prep: | |
| helm repo add nvidia https://helm.ngc.nvidia.com/nvidia \ | |
| && helm repo update | |
| install: | |
| helm install --wait nvidiagpu \ | |
| -n gpu-operator --create-namespace \ | |
| --set toolkit.env[0].name=CONTAINERD_CONFIG \ | |
| --set toolkit.env[0].value=/var/lib/rancher/k3s/agent/etc/containerd/config.toml \ | |
| --set toolkit.env[1].name=CONTAINERD_SOCKET \ |
Docker Desktop for Windows uses WSL to manage all your images and container files and keeps them in a private virtual hard drive (VHDX) called ext4.vhdx.
It's usually in C:\Users\YOURNAME\AppData\Local\Docker\wsl\data and you can often reclaim some of the space if you've cleaned up (pruned your images, etc) with Optimize-Vhd under an administrator PowerShell shell/prompt.
You'll need to stop Docker Desktop by right clicking on its tray icon and choosing Quit Docker Desktop. Once it's stopped, you'll want to stop all running WSL2 instances with wsl --shutdown
Mine was 47gigs as I use Docker A LOT so when I optimize it from admin PowerShell from the wsl\data folder
| # app/jobs/geocode_job.rb | |
| class GeocodeJob < ApplicationJob | |
| def perform(model) | |
| model.geocode | |
| end | |
| end |
I liked the way Grokking the coding interview organized problems into learnable patterns. However, the course is expensive and the majority of the time the problems are copy-pasted from leetcode. As the explanations on leetcode are usually just as good, the course really boils down to being a glorified curated list of leetcode problems.
So below I made a list of leetcode problems that are as close to grokking problems as possible.
| Recording Rule Example 1 | |
| ================================ | |
| # Aggregating up requests per second that has a path label: | |
| - record: instance_path:requests:rate5m | |
| expr: rate(requests_total{job="myjob"}[5m]) | |
| - record: path:requests:rate5m | |
| expr: sum without (instance)(instance_path:requests:rate5m{job="myjob"}) | |
| Recording Rule Example 2 |