Spark: 拥有集体进化网络的自进化 Agent ✍ Meta Alchemist🕐 2026-04-16📦 6.3 KB 🟢 已读 𝕏 文章列表 文章介绍了 Spark,一个轻量级的自进化智能体框架。Spark 旨在让开发者和创作者能够构建递归自我改进循环,通过“Domain Chips”(领域芯片)实现专业化与工具执行。作者受 Karpathy 和 Carmack 原则启发,推出了 Spark Researcher 运行时核心和加密交易领域芯片作为示例。该项目还即将推出名为 Spark Swarm 的集体进化网络,允许智能体相互分享、学习和进化。 Spark自进化递归自我改进Agent开源LLMMeta AlchemistSpark Swarm加密交易工具开发 # Spark: a self-evolving agent with a collective evolution network **作者**: Meta Alchemist **日期**: 2026-04-15T17:50:39.000Z **来源**: [https://x.com/meta_alchemist/status/2044473493379813702](https://x.com/meta_alchemist/status/2044473493379813702) ---  A lot of us like the idea of self-evolving agents. For AIs, evolution is like a birthright. Today's models have become this good because they have been gaining momentum through recursive self-improvement loops. > According to Wikipedia: Recursive self-improvement (RSI) is a process in which early artificial general intelligence (AGI) systems rewrite their own computer code, causing an intelligence explosion resulting from enhancing their own capabilities and intellectual capacity, theoretically resulting in superintelligence. The more computing power, the more users, and the more experiments an AI does, the better it can reason, execute, and achieve higher benchmarks, which we have been seeing lately. While most of this progress has been happening on the servers of frontier cloud models, we are entering a time where vibe coders can enhance the evolution of AI too. It doesn't need to be only teams and cold server rooms of frontier models, making the improvements. Spark is about: • giving everyone the opportunity to work with self-evolving agents, • recursive loops that make your agent better at any domain • and providing a modular framework for creative tooling that recursive agents can develop and use Instead of being happy with what frontier models provide, opening a new frontier for tinkerers and makers to go beyond what they provide... What we are launching today is: - a super lightweight runtime core - that you can build on top of it any sort of recursive loop - for specialization, mastery, and execution of any sort of tool based on Karpathy and Carmack's principles Initially, I was going for the 80-100k LOC codebases, heavyweight stuff with so much going on in it. Until I saw @karpathy drop the autoresearcher, and I was a fan of Carmack's principles, who always shared the beauty of building with as little code as possible. So I decided to make Spark even more modular after that, letting it get inspired by Karpathy's autoresearcher. While working on Spark, I've tried many ways to invoke a self-evolving intelligence. From trying to make Spark finish a Pokemon Red game, which it did, but by finding the glitches to speedrun (lmao), to working on tools to create models for crypto trading, and spawning thousands of mutations of itself, to playing with Mirofish for seeing what domain chips would be cool, to working on benchmarks and doctrine systems on how to run a startup in the best way through YC doctrines, I tried many things. You can also build your own domain chips, specialization paths, and self-evolving loops in anything you want, starting from today, with the documentation available in the repo. This is like playing with lego bricks, you can let Spark play with anything, and let it build a recursive self-improvement loop around it. Don't be confined to just trying to master skill specializations; also try tools & plugins you can build that Spark can execute while learning and improving how to use the tool better. They were some of the most fun experiments I've ever done. You just need to let your LLM read the documentation within Spark Researcher about domain chips and embark on a journey after that.  so, think of Spark as something that is boundless, endless. I worked on a lot of stuff for Spark, step by step I will release them, so that the ecosystem doesn't feel scary, but enjoyable to experiment with. For the first day, I will start things purposefully with: - Spark Researcher runtime core (the engine) - Domain Chip for trading crypto (one example to experiment with recursive loops in a setting where trading results is the obvious benchmark, and you can just paper trade too, to see how evolutions, mutations, and the intelligence of AI work in an autoloop) Let your Codex, Claude, GLM, Minimax, Gemma, Hermes, OpenClaw, GLM check this repo: https://github.com/vibeforge1111/spark-researcher Explore domain-chip-crypto-trading for a bit too if you like: https://github.com/vibeforge1111/domain-chip-crypto-trading And then start building a domain chip, or a specialization/mastery path you want your agent to be good at. One of the coolest parts of Spark is the ability to adventure with it.  P.S. Human review is only for users who select that option. Spark otherwise loves to evolve auto, without touching its runtime core at Spark Researcher. Spark Swarm, the collective evolution network where agents can share, learn, and evolve together, will start getting its first users next. You can join the waitlist at sparkswarm.ai The network will start as first-come-first-serve, so get your spot early. Enjoy Spark. Create, experiment, and use your spare usage limits to build new domain chips and tools for anything you like. Evolution doesn't need to be boring!  ## 相关链接 - [Meta Alchemist](https://x.com/meta_alchemist) - [@meta_alchemist](https://x.com/meta_alchemist) - [artificial general intelligence](https://en.wikipedia.org/wiki/Artificial_general_intelligence) - [intelligence explosion](https://en.wikipedia.org/wiki/Intelligence_explosion) - [superintelligence](https://en.wikipedia.org/wiki/Superintelligence) - [@karpathy](https://x.com/@karpathy) - [https://github.com/vibeforge1111/spark-researcher](https://github.com/vibeforge1111/spark-researcher) - [https://github.com/vibeforge1111/domain-chip-crypto-trading](https://github.com/vibeforge1111/domain-chip-crypto-trading) - [sparkswarm.ai](https://sparkswarm.ai/) - [Upgrade to Premium](https://x.com/i/premium_sign_up) - [1:50 AM · Apr 16, 2026](https://x.com/meta_alchemist/status/2044473493379813702) - [8,001 Views](https://x.com/meta_alchemist/status/2044473493379813702/analytics) - [View quotes](https://x.com/meta_alchemist/status/2044473493379813702/quotes) --- *导出时间: 2026/4/16 15:49:40*
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G GBrain 深度解读:YC 掌门人打造的 AI 知识引擎 GBrain 是 Y Combinator CEO Garry Tan 开源的个人知识管理系统。它突破了传统笔记工具的存取模式,通过集成知识图谱、混合搜索与 LLM 合成能力,实现跨文档的关联理解与自动回答。系统采用 Markdown+Git 存储数据,支持 MCP 集成及 43 种预置技能,目前已在 14 万页规模的生产环境中得到验证。 技术 › 工具与效率 ✍ Mr Panda🕐 2026-05-25 GBrain知识管理知识图谱LLM开源Y CombinatorAgent工具推荐MCPMarkdown
h holon v0.14.1:Rust 重写与自举开发实践 本文介绍了 holon v0.14.1 版本的更新,重点在于使用 Rust 进行了底层重写,并实现了项目的自举开发(即 Agent 自主完成从需求分析到代码合并的全流程)。文章详细阐述了采用 Rust 重写的原因、关键架构设计(如单 Session 模式、Workitem 机制和事件驱动),并展示了实际的 Dev 工作流及与竞品模型的对比测试数据。 技术 › Agent ✍ jolestar🕐 2026-05-24 AgentHolonRust自举开发DevOpsLLM工作流自动化开源
S Superpowers:Claude Code 效率暴涨 10 倍的秘密 文章介绍了 Claude Code 的开源技能库 Superpowers,它如同 AI 的导航系统,通过头脑风暴、计划拆解、子代理驱动开发和测试驱动四大核心工作流,自动引导用户从构思到上线。该系统有效降低了认知负荷,并通过严格的审查机制确保代码质量,是提升企业 AI 交付效率的利器。 技术 › Skill ✍ Miles.🕐 2026-05-22 Claude CodeSuperpowers工作流Agent测试驱动自动化开源效率工具开发实践LLM
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