与「Loops」相关的搜索结果

Raven 0.2.0 — The Harness of Harnesses, built for RSI. 🐦‍⬛ One harness can't be best at everything. Raven combines its own specialist harnesses (Research, Code, Design, Oncall) with the agents you already use (Claude Code, Codex and more) into one team. And it's built for RSI, and not just at the skill level. The whole harness can be rewritten by AI: prompts, policies, strategy code, playbooks. Every sub-harness, including the orchestration layer itself, is its own instance that can be improved. With Raven you can: 1. Orchestrate many agents as one team. Raven's sub-harnesses and external agents work in one task graph with shared memory across sub-agents, powered by leading orchestration (0.963 Node F1 on the Multi-Agent Orchestration Benchmark). 2. Run long, complex tasks. Oncall and proactive execution keep work going for days, from scientific research loops to shipping a full Godot game. 3. Build vertical agents with RSI. Use Raven's RSI to develop and refine an agent for your domain, and we'll optimize it with you. Experimental for now; reach out to the Raven team(Discord: More in the video and slides below. Open source, Apache-2.0. (lots of work made with Raven lives there, and much of this launch's material was made with Raven too)

Introducing ScienceBuddy — a free workspace for scientific agents that improve through researcher collaboration. Use GPT-6 in ScienceBuddy at no cost. GPU-accelerated, and fused with the JEV framework. 🧵 Two loops: 🔹 Inner loop — refines the agent harness 🔹 Outer loop — trains the model with rubric-guided RL Together: Recursive-in-Recursive Self-Improvement. ScienceBuddy explores how scientific agents can improve through sustained collaboration with researchers. 🔬Try it free: #ScienceBuddy# #PhAILabs# #AI4Science#

Shopify 这篇文章写的很好啊：《Native is now the future of mobile at Shopify》 它们要从 React Native 迁回到 Swift，原因就是现在大模型把不同平台写好几遍的成本压下去了，可以用 iOS 实现当参考写 Android，反过来也行。 现在像 RN 这种跨平台的优势已经不在了， 后面它们的实践非常值得好好看看：采取完全重写的方式，RN 版本做参考，Shop 这个应用 在 AI 辅助下 团队从概念验证做到完整原生版上架，只用了 12 周 最大的 Shopify App也在迁，计划今年晚些时候上线。 还有怎样避免 AI Slop，怎样加快反馈 loops 等等，干货很多，值得收藏看看。

2026年想赚到钱，需要掌握这些： - 用Astra 6 High做规划，Low负责执行 - 用Grok Bot管理日常事务 - Coding Loops - 用好Codex的电脑操作能力 - 用Herd管理大量Agent - /goal - 运行本地模型 - Karpathy的Autoresearch - 在X上建立受众 - 制作视频 - 理解机器学习 - 理解数据库，最近在用Convex - 使用API - 训练自己的LoRA - 停止无休止地刷信息流 - 最重要的是：专注

Everything you need to master in 2026 to get rich: • Using Astra 6 high for planning, low for execution • Grok Bot to manage day to day • Coding loops • Taking advantage of Codex's computer use • Herdr to manage tons of agents • /goal • Running local models • Karpathy's Autoresearch • Building an X audience • Creating videos • How machine learning works • How databases work (been using Convex) • Using API's • Training your own LoRAs • Elimination of doom scrolling • Most importantly: focus

ex-Apple engineer gave Grok 4.6 two jobs inside Cursor, went to sleep, and opened the results live the next morning: no babysitting, no checking every generation, just a model left running on real work for hours. • 00:42 - reveal the website Grok redesigned overnight • 23:21 - go from a voice prompt to a full software stack • 45:43 - inspect the generated code + architecture • 01:21:26 - Grok 4.6 vs Opus 5 • 01:46:31 - live PR + Cloud Agent workflow Most coding demos test an AI for 5 minutes. This tests the thing that matters for agents: can the model keep working when you stop watching it? SpaceXAI built Grok 4.6 specifically around longer-running agent tasks, coding and more ambitious visual work. The endgame isn’t prompting faster. It’s giving an agent a job at night and reviewing finished work in the morning. Worth watching before you choose which model runs your overnight agent loops.

Gemini 3.7 Flash is live! - Low latency and lower token costs by 50% while advancing reasoning for coding and agentic loops. - Software Engineering Performance (DeepSWE v1.1): 37.0% ➔ 65.3% - Enterprise Automation (AutomationBench): 13.4% ➔ 30.4% Available now via our APIs, @GoogleAIStudio and @antigravity!

🧩 DeepSeek Harness v0.1 is now available in Developer Preview! 🔹 We’re opening it up to developers building agent harnesses worldwide and open-sourcing the codebase in MIT license. 🔹 Powered by the Cordis meta-framework, DeepSeek Harness is an agent harness built around one core idea: Everything is a plugin. Models, tools, skills, sessions, sandboxes, filesystems, loops, orchestration, and UI are ALL implemented as plugins, and can be mixed, matched, replaced, and extended. Try it now!

Google just released a 2-hour course on building AI systems that write themselves. Free. From the same team that ships Gemini agents in production You are somewhere on this chain right now: Prompt → Agent → Graph → Loop → Self-Building System Almost everyone stops after step 1. A few made it to step 2. Google is standing at step 5 and teaching what it takes to get there. 10:32 ship your first working agent 41:19 the prompts Google's team actually uses 55:02 wire agents into a graph that talks to itself 1:20:28 loops inside graphs and when to close them 1:43:51 the graph that rewrites its own structure The final section is the one that changes the game. A self-building graph doesn't just execute tasks. It spawns new agents when it needs them. Rewrites its own edges. Retires nodes that stopped performing. The whole thing improves without a human touching it. Every $2,000 "AI agent" course sold this quarter teaches material Google just gave away in the first hour. The other hour is what nobody selling a course has actually shipped. Watch it before your next quarter's OKRs get set.