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The article's primary subject is AI, specifically focusing on the evolution of Large Language Models (LLMs) and their practical application in coding agents. It details incremental improvements in model capabilities that have shifted these tools from experimental to reliable for daily development workflows.
Sources coveringSimon Willison, Quantum Frontiers, LessWrong, Latent Space
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

2026 in LLMs (so far)

Simon Willison23 min read

On Friday I gave the closing keynote at the WeAreDevelopers World Congress North America in San Jose. I tied together the key trends from the past year into a chronological exploration of everything that happened in 2026. The video is on YouTube; here are my annotated slides and notes to accompany the talk. I’m going to give a lightning tour of everything that has happened so far in 2026. The year isn’t over yet! For me, 2026 started a couple of months earlier in November 2025. November saw the release of two important models: Claude Opus 4.5 and GPT-5.1. As is usually the case with new models, these were incremental improvements on the models that came before them. But every now and then when a model improves, it crosses an invisible line where something that didn’t really work starts working. In this case, the thing that started working was their coding agents. Claude Code had been around since February 2025, Codex was a little younger. These two new models, when paired with their respective coding agent harnesses, improved from “often make mistakes” to “reliable enough to use on a day-to-day basis”. For a couple of years now I’ve been evaluating…

Excerpt shown under the publisher's RSS terms · 200 words · full text and figures are on the publisher's site

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