Automation

The Rabbit Hole Generator gave me NEEDLE this week, and it lands right on a question I keep running into: once you let a coding agent run unattended, who decides whether it actually did the job? Most of the excitement around coding agents is about making them smarter. NEEDLE is interested in something much less glamorous. It assumes the agent is unpredictable, wraps it in a loop where every step around the agent is fixed and logged, and then refuses to take the agent’s word that it finished. I didn’t want to point a real AI model with its permission prompts switched off at anything, so I ran the whole thing against a fake agent: a 20-line shell script that succeeds, fails, hangs, and lies on cue. The lying one turned out to be the most interesting part. ...

Every post in this series starts the same way: I open Notion on a Friday and there’s a tool waiting for me that I didn’t pick. That’s on purpose, and it took me building an actual automation to get there. For a long time before this series existed, I was stuck in what I’d call zero learning mode. I like learning new things, genuinely, but I’d sit down with time set aside and just… not know what to point it at. Too many options, no filter, no momentum. I wasn’t short on curiosity, I was short on a starting point. So I built one: an n8n workflow that picks a tool for me every week, does the research, and hands me a scoped 30-minute exercise, no decision required on my end except whether to actually do it. ...

Most “agent frameworks” are libraries that push the hard parts of production (orchestration, secrets, versioning, and audit logging) into ad-hoc glue code. As teams move from “chatting with a PDF” to shipping internal copilots, the need for a governed, observable runtime becomes undeniable. This is where Dify enters the stack. It’s a self-hostable “LLM app runtime” designed to turn agentic prototypes into real business services. What is Dify used for in LLM application development? Dify is an open-source LLM application development platform that provides a unified workflow engine, model gateway, and RAG subsystem. It allows developers to model AI applications as explicit directed acyclic graphs (DAGs), enabling governed orchestration of LLM calls, external tools, and retrieval policies with built-in operational telemetry and multi-tenant isolation. ...

Most security monitoring tools rely on server-side logs or cloud-native telemetry. But what happens when the client is opaque? When a mobile app, a single-page application (SPA), or a proprietary service is behaving in ways the server doesn’t fully document, you need a way to look at the wire directly. This post also marks the beginning of a new experiment: The Rabbit Hole Series. Every Friday, a “Rabbit Hole Generator” I built (which I later took apart in its own post) spits out a new technical topic for me to explore for 30 minutes. This week, the generator gave me mitmproxy. ...

The best automation is the script you wrote once and forgot about. Until you realize you’d be miserable without it. That’s the bar I hold every workflow script to: will this save me more time than it took to write? Most “productivity automation” content you find online fails that test. The scripts look clever in a conference talk, end up in your dotfiles, and collect dust. They solve hypothetical problems, not real ones. ...