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I didn’t expect DwarfStar 4 ( https://github.com/antirez/ds4 ) to become so popular so fast. It is clear that there was a need for single-model integration focused local AI experience, and that a few things happened together: the release of a quasi-frontier model that is large and fast enough to change the game of local inference, and the fact that it works extremely well with an extremely asymmetric quants recipe of 2/8 bit, so that 96 or 128GB of RAM are enough to run it. And, of course: all t

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and the METR time horizons plot. Solving real-world software engineering problems: SWE-Bench is a widely used coding test which evaluates how well AI systems can solve real world GitHub issues. When SWE-Bench launched in late 2023 the best score at the time was Claude 2 which had an overall success rate of 2%. Claude Mythos Preview gets 93.9%, effectively saturating the benchmark. (All benchmarks have some amount of noise inherent to them, so there’s usually a point where you score high enough

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increasing in 2019 (at 10 releases/year), well before modern AI coding tools appeared. This seems just as likely to be due to growing adoption of continuous integration tools like GitHub Actions, which have been around longer. Another reason to doubt this increase is entirely due to AI is the other effect visible in this chart, which is that packages are released less frequently as they get older. This is seen in the fact that all of the cohort life lines decrease over time. That has not changed

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1. 当 Ling-1T 接入 Moltbot 这两天，开源项目 Moltbot (原名 Clawdbot，下文统一使用 Moltbot 指代) 在 GitHub 与社交媒体全面刷屏，关于其「全自动买车」、「自主修复 Bug」的演示说法，让不少人都动了「买个 Mac Mini 部署」的念头，将其视为新一代全能 Agent。 蚂蚁大模型团队的技术老哥们当然不会缺席这次热门话题，我们在隔离的 VM 环境中部署，并将蚂蚁百灵的 Ling-1T 用于驱动 Moltbot，将它对接成了聊天软件机器人。我们赋予这个 Agent 操作系统权限，看看在 Ling-1T 驱动下，能否真正实现复杂的指令遵循与任务自动化。 在虚拟机环境运行的 Clawdbot TUI 我们将抛开社交媒体的滤镜，看看这个爆火项目究竟验证了什么？它的架构精髓在哪里？另一方面，如果你也想构建一个真正好用的通用 Agent，它的「万能公式」到底是什么？ 2. 它究竟能做什么？ 接入 Ling-1T 后，Moltbot 和其他「通用 Agent」展现出的体感差异，在于其「主动性」和「人味」。拥有大量系统工具权限、有工作区目录、对接聊

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e it works really well for people who want to get into LLMs. And for readers interested in more advanced material, as a follow-up, I added substantial bonus material to the book’s GitHub repository over the course of the year. I plan to continue expanding these materials over time. In addition, as you may know, I am currently working on a sequel, Build A Reasoning Model (From Scratch). The first book, Build A Large Language Model (From Scratch), focuses on the core large language model architect

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ct”: top tier researchers join the adventure, bringing credibility and momentum to the project. It’s already working with recent high-profile hires including: Nat Friedman, former GitHub CEO; Alex Wang, former Scale AI CEO; Daniel Gross, who was the CEO and co-founder of SSI, Ilya Sustkever’s startup The recruiting pitch is powerful: unrivaled compute per researcher, a shot at building the best open-source model family, and access to over 2 billion Daily Active Users. The offers that generally r

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kinda help people running Redis, in some way or the other. (Amazingly, one of the repositories associated with this idea is still online, showing commits of 15 years ago: https://github.com/antirez/redis-tools ) I was about to try some kind of open core approach; I also remember I was thinking about delaying the BSD license for new code for six months, in order to create some kind of advantage for paying users. Now I don’t believe I would be a jerk and do strange games with my users, but I woul

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