The signal in the feed.

Observability Design for the AI Era — Application / Infrastructure / CI / LLM, Each in Its Own Shape (Part 1)

The previous code-graph series was about reshaping a static analysis graph so AI could query it. The same kind of reshaping is needed on the observability side. This post walks through four axes — application / infrastructure / CI / LLM — and the deliberately different shapes each one ends up in. The design judgments worth calling out: computing Gemini cost client-side instead of from billing API, sending Claude Code OTel straight to BigQuery instead of Loki, and shipping CI logs via post-hoc pull instead of webhook push.

Someone Was Testing My Live App While I Was Asleep

I want to start with the comment, because the comment set the tone for everything that followed. It arrived on a GitHub issue in a repo that wasn't even the right project, filed by a dev.to reader who had apparently been quietly poking around my live deployment. It opened with "Heyho kid, isn't this like summer break or something?" and proceeded to describe, in the cadence of a friendly riddle, several things that were wrong with my application. It mentioned logs. It mentioned auth. It mentioned

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NSA and IETF: Fairness

How I Slashed My AI API Spend 95%: Real Numbers Inside

How I Slashed My AI API Spend 95%: Real Numbers Inside Six months ago I opened my team's LLM bill and nearly spit out my coffee. We were pushing north of $3,800 a month on inference for what was, frankly, a glorified FAQ bot. I'd been treating "AI API cost" as an ops problem. That was my first mistake. It's a statistics problem. What follows is my notebook — the actual numbers, the dead ends, and the seven moves that took us from burning cash to running lean. I leaned hard on the data because in

One ARIA bug, a million apps: meet aria-reach

Most accessibility tools audit your application. But in a modern JavaScript app, the accessibility of what ships is mostly decided one layer down — in the shared component libraries you import: the UI kit, the rich-text editor, the media player, the form framework. That layer is where accessibility bugs scale. A single misconfigured aria-* attribute in a popular library can propagate into downstream apps that import it. A wrong aria-live value in a media player can affect sites that embed it. Th

I made a Premium React E-Commerce Template with an Admin Dashboard, Dark Mode & Responsive Design

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US investors will soon get access to SK Hynix, another memory maker riding the AI boom

SK Hynix is experiencing a boom credited to AI. It will ride that to a multibillion-dollar U.S. IPO, expected to take place on Friday.

Ternlight – 7 MB embedding model that runs in browser (WASM)

Fable turned remarkable into Tom Riddle's diary from Harry Potter

Building Scalable Node.js APIs with Express: Navigating the Matrix of Backend Growth

The Quest Begins (The "Why") Honestly, I still remember the first time I tried to ship a Node.js API that actually felt production‑ready. I had a simple Express server that handled a handful of routes, and I was thrilled when it returned JSON without crashing. Then the real world hit: traffic spiked, my endpoints started to lag, and I found myself debugging memory leaks at 2 a.m. while my cat judged me from the keyboard. It felt like I was stuck in a loop—adding more app.get handlers, copying th

Cutting Vercel Fluid CPU with Next.js Server-Side Caching

Cutting Vercel Fluid CPU with Next.js Server-Side Caching Got an email from Vercel about reaching limits and checked to see Fluid Active CPU spiking for worldcuppicks.co. Obviously I don't want to go to premium, so I implemented certain measures to bring it down. We are in the knockout rounds now, so I wanted to get it sorted before things got worse. Going to the Vercel Observability tab, I was able to see which page was causing the heavy load. The home page function was clocking nearly 3 minute