Kimi K3, Moonshot AI’s latest open-weight model, ranks 1st on our single-shot Frontend Arena with an Elo of 1392. This is 10 positions higher than Kimi K2.6 and 16 positions higher than Kimi K2.7 Code, marking the largest jump we’ve seen in the Moonshot model line.
However, we found that Kimi K3 uses an extreme amount of thinking tokens, using over 12x more reasoning than Claude Opus 4.8 and over double that of Kimi K2.6. This is a lot of reasoning, and we decided to dig deeper to find out exactly what Kimi K3 is thinking about.
We find that Kimi K3’s performance can be primarily ascribed to its unique chain-of-thought approach where it appears to iterate upon designs much like how a full AI agent would, but inside its chain of thought. This strategy produces intricate, intentional websites with creative component design, as well as improves Kimi K3’s ability to integrate with outside dependencies. — Read More
Daily Archives: July 24, 2026
Inside the Model Factory
Poolside’s co-CEO on how his small team of top researchers built a model factory capable of training Laguna S – a 118B MOE beating Thinky’s ~1T open weights model… and this is just the beginning.
… Poolside’s recent tech report got a lot of praise due to their level of detail. … From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. — Read More
Comparing Open-Source AI Code Security Harnesses
There’s a huge new wave of open-source AI security projects that point a large language model at a codebase to find vulnerabilities. We looked at some popular (or less popular, but from interesting companies) OSS tools and compared how they work, and when you’d reach for each.
We find three complementary categories in the new world of harnesses: LLM-led exploitgen, LLM-skill-boosting, and SAST+LLM hybrids. — Read More
Are AI labs pelicanmaxxing?
For the past few years, Simon Willison has tested every major LLM release with the same prompt: “Generate an SVG of a pelican riding a bicycle”.
… The benchmark is now famous enough that there’s plenty of discussion about its usefulness and about whether AI labs might be benchmaxxing on it.
I wanted to find out, so I put together a small experiment. I generated 1,008 SVGs across seven frontier models, scored them with an LLM judge, and used Claude Fable 5 for the analysis. — Read More
Why Software Factories Fail
We’re all racing to put AI coding into production. A lot has been said about loop engineering, and the prevailing wisdom is that we should probably write more loops.
… Our friend Mario got up at AI Engineer Europe and begged us to slow down — because companies that have no business having outages due to coding-agent mishaps, are, well… having outages due to coding-agent mishaps.Our friend Mario got up at AI Engineer Europe and begged us to slow down — because companies that have no business having outages due to coding-agent mishaps, are, well… having outages due to coding-agent mishaps.
…[T]he whole point of this post is to be wary of slop data, but it feels directionally valid based on what I’ve seen. — Read More
Why AI Needs a “Genie Coefficient”
Major benchmarks measure what AI can do. None measure whether it does what you mean: the distance between what you ask an AI to do and the unspoken assumptions about how you want the AI to do it. We propose a new metric: the Genie coefficient.
There’s often a gap between one person’s request and another’s understanding. Most of the time, we bridge it using general knowledge. For example, if you ask a friend to get you coffee, they’ll pour a cup from the pot or buy one from a coffee shop. They won’t bring you a bag of raw beans or snatch a cup from a stranger and hand it to you. You never specified any of this. You never had to.
One might think the fix is just to specify tasks, questions, and intent better. … This situation has major implications for AI agents that are increasingly being given requests by humans and expected to fulfill them. They have enormous latitude to get it wrong. — Read More
InfoQ Live: The Infrastructure Challenge behind Production AI
The panelists explain the realities of running AI systems reliably at scale. While building models is solved, maintaining production databases under constant pressure is not. They discuss the emerging architectural decisions separating teams that scale gracefully from those facing catastrophic outages, and what engineering leaders must rethink today. — Read More
Forum: Xi Jinping Headlines World AI Conference
China’s ninth annual World AI Conference (WAIC) was held from July 17–20 in Shanghai. Escalating points of tension in the U.S.-China AI competition, including tit-for-tat cybersecurity measures aimed at frontier models and U.S. labs’ reiterated emphasis of the risks posed by China’s AI ascendancy, meant this year’s gathering would be a crucial venue for Beijing to pitch an alternate model for global AI development. Top Chinese leader Xi Jinping’s decision to give the WAIC’s keynote address further emphasized the Chinese government’s focus on the issue. DigiChina invited a group of specialists to weigh in on the implications of Xi’s speech, a new initiative released by the Cyberspace Administration of China and the National Development and Reform Commission, the establishment of the Shanghai-based World AI Cooperation Organization, and the seemingly coordinated release of Moonshot AI’s Kimi K3 model. — Read More
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