l!ᴧǝ◖sǝ!ɹ∀ @hash_tree
if i rest i rust Joined January 2015-
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@suwakopro @mylifcc 其实没啥大用,代码量大了无解,跟go这种比编译速度是没法比的,再怎么优化都不行
A14Team 宣布 LabClaw 开源——这是斯坦福-普林斯顿团队为 LabOS 开发的技能操作系统层。 一条命令即可将任何 OpenClaw 智能体变成一名完整的 AI 合作科学家。 演示: labclaw-ai.github.io 龙虾军团报到🦞
We’re thrilled to open-source LabClaw — the Skill Operating Layer for LabOS by Stanford-Princeton Team One command turns any OpenClaw agent into a full AI Co-Scientist. Demo: labclaw-ai.github.io Dragon Shrimp Army reporting for duty 🦞🔬 #AIforScience #OpenClaw
Introduction to non-lexical lifetimes (by Niko Matsakis): smallcultfollowing.com/babysteps/blog…
I came across two excellent articles by Russ Cox on memory models - one from the hardware perspective, the other from the programming language. Explains how concurrency works under the hood and why it matters for real-world code research.swtch.com/hwmm research.swtch.com/plmm
We just released a @duckdb community extension that automatically privatizes complex queries. It is based on MIT’s new PAC framework (news.mit.edu/2025/new-metho…), and in collaboration with them (🙏 🙏). We are 👀 and eager for your feedback! paper: arxiv.org/pdf/2603.15023…
PAC is now available as a DuckDB community extension! github.com/cwida/pac
YC 的这个CEO 有几把刷子呀,github.com/garrytan/gstack 最好用的是/plan-ceo-review,在讨论一个新的 idea,以及针对这个 idea 做 10x 的商业模式设计时候,还是震撼到了我。 在这个上面比 Superpowers 专业。
The Little Book of Semaphores by Allen B. Downey greenteapress.com/wp/semaphores/
One of the best coverage on concurrency primitives ostep.org
Obsidian Dashboard GitHub:github.com/InlitX/Obsidia…
IDA Pro plugin for LLM-powered reverse engineering github.com/symgraph/IDAss…
NEW POST Conversations with AI are ephemeral, decisions made early lose attention as the conversation continues, and disappear entirely with a new session. @techygarg explains how to externalize the decision context into a living document. martinfowler.com/articles/reduc…
Alibaba just open-sourced OpenSandbox ( a general-purpose execution environment ) to give AI agents an isolated environment to run code safely. 8k+ Github stars ⭐️ This stops your AI Agent based applications from accessing your actual host infrastructure. By removing the hardest security roadblock, this release will massively accelerate how fast developers can build autonomous Agent based tools. OpenSandbox puts the agent inside isolated runtimes like gVisor or Firecracker. You can run it locally using Docker or scale it up using Kubernetes. The system includes a code interpreter and a file system that the agent uses to complete tasks. It also manages network traffic so you control exactly what the agent accesses online. I think this will become the standard infrastructure for autonomous systems because building custom sandboxes is too dangerous for most teams.
This tool turns JSON into visual diagrams. Perfect for debugging APIs and large datasets. Project: github.com/AykutSarac/jso…
发现一个宝藏:Github 上 196K 星的 OSSU 项目,集齐了哈佛、MIT、普林斯顿等名校的 CS 公开课,从编程到算法到系统安全,该有的都有。 项目方说:每周学 20 小时,2 年拿下完整本科 CS 知识体系。Discord 社区随时答疑,课程全免费,实在要钱也能申请助学金。 我寻思这不就是「用爱发电」的终极版本吗? 每周 20 小时是什么概念?相当于你得有一份朝九晚五的工作,然后下班回家继续上第二份「班」。周末?不存在的,继续学。 所以能完成的都是什么人?要么财务自由提前退休,要么还在上学有大把时间挥霍,要么就是传说中那种「我可以」的卷王。 2.4 万人 fork 了这个项目,但我猜真正学完的比例...大概和健身卡的使用率差不多😂 github.com/ossu/computer-…
Go struct terminal output dumper github.com/goforj/godump
meet VMkatz, github.com/nikaiw/VMkatz
cool
Just open-sourced mem7 — Long-term memory for LLM agents powered by Rust. •Dual-path retrieval: vector + graph in parallel •Ebbinghaus forgetting curve: decay + reinforcement •First OpenClaw memory plugin Inspired by mem0, with time dimension. github.com/mem7ai/mem7
1/4 LLMs solve research grade math problems but struggle with basic calculations. We bridge this gap by turning them to computers. We built a computer INSIDE a transformer that can run programs for millions of steps in seconds solving even the hardest Sudokus with 100% accuracy
这个哥们牛逼plus啊。 把一个 WebAssembly(WASM)解释器 hard-coded的方式嵌入到了 Transformer 模型的权重里,而且是无损的(losslessly)。 这相当于在 LLM 内部运行着一台真正的计算机。 这台计算机可以 实际执行计算(run computations),而不是像现在大多数模型那样,只是通过infer来给出计算结果。 这个思路有点类似于TI的dsp芯片, arm负责逻辑思维,然后dsp专门负责高速数据计算。各取所长。 那么类比一下,LLM 老算不清3.11 与3.8哪个大的问题可以通过构建一种 混合架构(hybrid architectures): 1. 神经网络 负责 推理和理解(reasoning) 2. 嵌入式解释器 / 计算引擎 负责 高精度计算 这样就能同时兼顾 智能推理能力 和 确定性计算精度。 这对,数值计算,物理模拟,金融建模,密码学运算有很大好处。
1/4 LLMs solve research grade math problems but struggle with basic calculations. We bridge this gap by turning them to computers. We built a computer INSIDE a transformer that can run programs for millions of steps in seconds solving even the hardest Sudokus with 100% accuracy
Jemalloc is back. Maybe. engineering.fb.com/2026/03/02/dat…
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