34 posts tagged with "ai"

The Chrome team shipped Modern Web Guidance 1.0 on November 10, 2026, and a day later published the top 10 things AI gets wrong in web development. We first mentioned the project in our Google I/O recap when it was an early preview. With the stable release, it is worth a closer look.

This post covers what Modern Web Guidance (MWG) is, how it works, what it fixes, and how it fits our recommended tech stack. Short version: the guidance is good and mostly matches what we want. But two parts need care: our component libraries already handle some of the things MWG wants to rewrite, and the default install command breaks our supply-chain rules.

"Rewrite it in X, agents are better at X" has become a common argument in frontend teams, and it almost never comes with numbers. Nolan Lawson's The AI cost of JavaScript frameworks (October 28, 2026) is one of the first attempts to measure it. He had the same coding agent build the same app in 12 frameworks, five times each, and tracked what it cost, how long it took, how much code it produced, and how fast the result loaded. This post summarizes the findings, the caveats that matter, and what they mean for the Aliz tech stack.

This site was built with heavy AI assistance. That raises an uncomfortable question for the person who built it: if an AI can produce a frontend, what exactly is a frontend developer for? The internet has two loud answers, and both come with evidence. This post lays out the strongest version of each side, what they actually agree on, and what a frontend developer should be bringing to a team in 2026. It is written for engineers and for the people who decide whether to hire them.

TanStack AI 1.0 landed on npm on October 22, 2026, and the announcement post followed a day later. When we covered the beta in June, the pitch was the "Switzerland of AI tooling": any provider, any UI framework, the open AG-UI protocol on the wire, and no hosted platform attached. That pitch has not changed. What changed is the commitment behind it. The core APIs are now stable, 76 packages moved to 1.0 together, every API that 0.x had deprecated is gone, and a new agent harness ships beside the core as an explicitly experimental 0.x package. This post covers what "stable" includes, what landed between the August release candidate and 1.0, what an upgrade from 0.x costs, how the harness works, and what all of it means for Aliz projects.

AI makes submitting a plausible patch cheap. Verifying it still takes human time. Recent restrictions from Sindre Sorhus and SWC expose that imbalance, but their responses and stated reasons differ. The question is not simply whether AI belongs in open source. It is who pays for verification.

Three releases landed in one week: Opus 5.5 and GPT-6 Sol/Luna arrived in GitHub Copilot on September 22, and Sonnet 5.5 followed on September 28. I don't think the top end is the story. The story is cost: the cheap tiers now compete with last month's premium models. Every benchmark number below is vendor-reported, so read it as a claim, not a measurement.

The EU AI Act is now an execution issue, not just a legal headline. If your product has AI features used in the EU, teams need a clear operating model for risk classification, UX transparency, release gates, and incident readiness.

OJ — nicknamed "Orange Juice" — is a single Rust binary from Lovable that runs a React app the way Vite's dev server does. To be clear up front, because this is where most of the coverage goes wrong: it is not a fork of Vite's source code, and it is not a full rewrite of Vite. It reimplements one layer — the dev-server layer — in Rust, while leaning on the same Rust foundations Vite itself already uses. That correction matters, but it isn't the real story. The real story is the debate OJ set off: a Socket.dev article and a widely-shared Evan You post arguing that AI is collapsing the cost of reimplementing open-source dependencies, and what that does to the ecosystem. If you want the background on why Rust keeps showing up under node_modules, that's a separate post — see The Rust Wave Under Your node_modules. This one is about OJ specifically, and the argument it started.

This is my token telemetry for July 2026 — one developer's month, pulled from the Aliz AI usage dashboard. Earlier this month I wrote You Don't Deserve Fable (Yet), which argued that routing discipline comes before reaching for the biggest model on the menu. This post is a month of receipts for that argument, including the parts where I plainly haven't applied my own advice. It's also a mixture of before and after: the routing discipline was still forming while the month ran, so this isn't a steady state. Some of the chart describes habits I'd already changed, and some of it describes habits I was in the middle of changing.

Claude Fable is the most capable model Anthropic has shipped — above Opus class, frontier reasoning, genuinely impressive on hard problems. It's also the fastest way to burn through your token budget if you haven't built the discipline to use it correctly. Most developers will reach for it because it's the best, use it for tasks Haiku could handle, and wonder why their costs exploded. This post is about earning the right to use Fable by mastering the cheaper models first.