Connexion
Today, Anthropic rolled out Opus 5, the newest update for the model that has recently become a popular choice for coding and other software development tasks, among other things.
While this is a noteworthy bump for Opus, it doesn't seem to be an Opus 4.5-level breakthrough in agentic coding performance.
I wanted a clock that, annoyingly, didn't seem to exist.
Since childhood, my bedside clocks have been a series of red, seven-segment LED clock-radio specials from Walmart or Target. They are invariably cheap, simple, and long-lived—but they require manual intervention at the start and end of Daylight Saving Time and whenever the power flickers. After a recent power flicker, as I found myself standing by the sideboard holding down "TIME" and mashing the "HOUR" button, frustration boiled over, and I thought to myself, "We're a quarter of the way through the 21st century. There has to be a better way!"
My perfect clock would be self-setting. It would offer auto-DST adjustment (or not, depending on how this bill fares!). It would manage drift and always show the exact sub-second time. It would show that time on a red seven-segment display—not blue, not green, not yellow, and absolutely not white. And I shouldn't have to install any privacy-destroying garbage apps to make it work.
There are a lot of AI coding applications out there, and as impressive as large language models and the agents they enable have become, many of the most recent developments in AI-assisted development have been in the software that manages those models, not just the models themselves.
Earlier this summer, I spoke with the head of product for Claude Code, Anthropic's Cat Wu, about that company's approach to building that software.
Mozilla developer Peter Wilson has taken to the Mozilla.ai blog to announce cq, which he describes as "Stack Overflow for agents." The nascent project hints at something genuinely useful, but it will have to address security, data poisoning, and accuracy to achieve significant adoption.
It's meant to solve a couple of problems. First, coding agents often use outdated information when making decisions, like attempting deprecated API calls. This stems from training cutoffs and the lack of reliable, structured access to up-to-date runtime context. They sometimes use techniques like RAG (Retrieval Augmented Generation) to get updated knowledge, but they don't always do that when they need to—"unknown unknowns," as the saying goes—and it's never comprehensive when they do.
Second, multiple agents often have to find ways around the same barriers, but there's no knowledge sharing after said training cutoff point. That means hundreds or thousands of individual agents end up using expensive tokens and consuming energy to solve already-solved problems all the time. Ideally, one would solve an issue once, and the others would draw from that experience.
Amazon’s ecommerce business has summoned a large group of engineers to a meeting on Tuesday for a “deep dive” into a spate of outages, including incidents tied to the use of AI coding tools.
The online retail giant said there had been a “trend of incidents” in recent months, characterized by a “high blast radius” and “Gen-AI assisted changes” among other factors, according to a briefing note for the meeting seen by the FT.
Under “contributing factors” the note included “novel GenAI usage for which best practices and safeguards are not yet fully established.”
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