与「Loops」相关的搜索结果

Gemini 4 Carbon Developer Guide 🧑‍💻 Wrote down tips to get started with Carbon: 🤔 - When to use Carbon vs. Argon? 💭 - Set thinking levels based on the task 🎯 - Set clear goals 🔄 - Use build and verify loops Read the full guide 👇

for the first time ever, I'm sharing everything I do across all my SaaS to rank on Google and get mentioned by AI every SEO and AEO lesson I've learned is here: 1. we show up in Reddit threads. AI pulls a lot of its answers from Reddit, so Bazzly finds the threads that are fresh, and even the ones already ranking on Google, and gets our products into the top reply 2. we get our products listed #1# in listicles that already rank on Google, with Outrank. when someone asks ChatGPT for the best tool, those are the pages it reads 3. we turn Reddit questions into blog articles. Bazzly shows us what people actually ask, and we feed those questions to Outrank 4. I regularly sit down and find new niches where our products could be useful. I research each one and feed it to Outrank, and Outrank finds new ones on its own too, so our keyword plans keep expanding into new markets 5. we publish an SEO article on our own blogs every day, also with Outrank 6. Outrank helps us refresh old articles regularly. AI and Google both prefer recent pages, so every page has to stay current 7. we build new backlinks every week with Outrank, and every placement gets verified. we also pitch guest posts using Outrank's free guest post database of 278 checked sites, all DR 20+ 8. we regularly ship extremely useful free tools for potential users, and each one ranks on Google and brings people into our products 9. every month we check which AI answers name our products and which don't Outrank's AI Visibility Checker asks ChatGPT, Claude, Gemini and Perplexity real buyer questions, then shows whether we got named, which competitors did, and which sources got cited we feed those sources back into Bazzly, Outrank and our internal social content all of this brings in new users, and then we run loops that turn them into champions who spread the word 10. we incentivize our users to share honest reviews of our products on Instagram, TikTok, X and LinkedIn, and on public review sites 11. we let our users become affiliates, and then we help them make better content about our products. in the last 30 days, ~$8K has been generated by Revid affiliates alone basically every SEO and AEO lesson I learn building Outrank and Bazzly goes straight into every product I run and the idea behind all of it is simple AI recommends the most mentioned product so we make sure every one of our products gets mentioned everywhere Google and AI look: all sorts of blogs, listicles, Reddit, review sites and social and this setup does the job extremely well, for every product we run

谷歌刚刚放出一门 70 分钟的官方课，手把手教你打造一个7 * 24小时完全自动化的生产级 Agent 。 很多人学 Agent 看了几十个小时零散教程依然搭不出可用的东西，核心是没有搞清玩具 Demo 与工业级架构的边界。 谷歌这门 1 小时课程没有任何水分，只聚焦 5 个真正的工程核心： 1. 从 0 跑通首个 Agent：拆解模型调用、工具绑定与执行环境的基础闭环。 2. 状态持久化（Persistent Memory）：告别单次对话即失忆，构建跨会话的记忆索引与上下文回填。 3. 长时自主循环（Agentic Loops）：摆脱线性问答，让 Agent 具备自我反思、容错重试与多轮状态流转能力。 4. 吃透 MCP 核心架构：彻底理清 Model Context Protocol 与传统 API 的本质区别，学会构建标准化的上下文协议。 5. 多智能体协同系统（Multi-Agent）：把分散的专职 Agent 串联成高协作度的自动化系统。 完全免费。 比市面上价值700刀的课还要干货。 建议收藏，今晚就观看 。🔖

Anthropic 专家工程师刚刚公开了一门 1 小时的硬核课程： 如何用循环（Loops）与图（Graphs）搭建 Agent 团队。 整门课的工程进阶脉络极其清晰： 1. 从最底层的 CLAUDE.md 上下文约束与 Plan 模式的任务规划切入，到封装可复用的 Skills 并挂载 Hooks 自动化钩子； 2. 再从单个主 Agent 的任务调度，跃迁至 Subagents 子代理集群的分工协作； 3. 最终由单任务自主闭环的 Loops，一步步演进为支撑整个复杂系统自我迭代的拓扑 Graphs。 在Anthropic 内部，每天都在实打实运行这套架构。 Prompts → Agents → Loops → Graphs 一个循环（Loop），无需人工介入，自主把单项任务跑到底； 一个图（Graph），决定有哪些任务需要被执行，并把每次被验证合格的成果带入下一次运行。 收藏它，你会回来看至少10次以上的。

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What Menlo has done over the last 24 months is exceptional. Yes, they did Anthropic’s round at $4BN that will make them $50BN+ as a firm. NICE! But they then took that and went f****** hard. They do Lovable, Higgsfield, Legora. They compounded the brand win of Anthropic to cement their position from respectfully a slightly ageing firm to being a dominant venture leader at the forefront with Thrive, a16z, Benchmark etc. It is incredible to see and I sat down with @venkyganesan to shoot the s*** on this. 1. Every Seed Investment Is an Option Bet Seed-stage deals function as financial options to discover true outlier potential before committing major capital. VCs must construct a broad enough portfolio to secure sufficient at-bats, only sizing up their position when quantifiable, revenue-based metrics prove an outlier has emerged. 2. What Is the First Sign of the AI Wave Cracking? Market cycles rarely collapse due to equity write-downs. They fracture when levered debt defaults occur. When market participants take on heavy leverage during an AI boom, precise timing becomes as critical as being directionally correct, making debt obligations the primary failure vector when cycles turn. 3. Why Every VC Must Now Prioritize IRR Every startup in the current landscape writes a structural tax to the Mag 7 and hyperscalers for compute and foundation models. Because LPs can buy index exposure to those same public tech giants without fees or carry, private VCs must generate outsized IRR performance, beating public markets by at least 1,000 basis points, to justify private capital allocation. 4. Why LPs Wanting Smaller Funds and Slower Deployment Is BS LPs cannot demand both smaller fund sizes and multi-year deployment paces in the middle of a generational platform shift. Because AI startups face immense capital requirements to scale compute, VC firms that refuse to deploy capital rapidly will watch competitors step in and capture the category winners. 5. The Theory of Reflexivity and How It's Causing Markups Reflexivity creates self-reinforcing loops where fast revenue growth triggers rapid valuation markups, attracting elite human and financial capital that further accelerates growth. However, when copycat investors chase markups for momentum rather than underlying fundamentals, reflexive valuation bubbles inflate until market realities force an inevitable correction. 6. Sometimes You Pay Up Because You Have Unique Insight Premium entry prices are not always the result of irrational bidding. They often stem from an investor's ability to recognize a fundamentally larger total addressable market than the consensus. When a VC possesses unique conviction around an expanding market opportunity, paying above current rates reflects calculated foresight rather than price-taking behavior. 7. There's No Limit to What a Person Can Do as Long as They Don't Care Who Gets the Credit Personal ego remains one of the most expensive liabilities in venture capital and company building. True long-term success requires setting aside individual notoriety, focusing ruthlessly on generating LP returns, and empowering teammates and founders without worrying about who receives the public credit. (links in comments)

Anthropic Engineering: "At Anthropic, we don't write prompts anymore. we build loops" in 42 minutes, she shows how the Claude team creates loops that prompt themselves if this cost $400, people would call it the best agents course of the year it's free watch it, and Bookmark.

Learn Harness Engineering from scratch! This repo is a full learning path around harness engineering, with 14 lectures, 8 projects, reusable templates, and hands-on exercises. The course focuses on the environment around coding agents and how that environment affects whether an agent can reliably work inside a real repository across longer tasks and multiple sessions. It starts with the fundamentals of harness engineering and gradually gets into things like repository structure, persistent state, task boundaries, verification, observability, session handoffs, automated loops, and graph engineering. A harness in this context has five core parts: - Instructions that tell the agent how to work inside the repo - State that persists progress across sessions - Verification through tests, linting, type checks, and full-pipeline runs - Scope controls that keep the agent focused on one task at a time - Session lifecycle so every run starts and ends in a usable state The projects are also connected rather than being isolated examples. You keep working on the same Electron-based knowledge app while gradually improving the harness around it. You start by comparing prompt-only vs. rules-first agents, then build multi-session continuity, runtime feedback, verification, a complete harness, automated maker-checker loops, and eventually represent the workflow as a graph with parallel execution, rollback, and human approval. What I like is that you can actually take parts of the repo and use them directly in your own projects. There are templates for things like "AGENTS. md", "init. sh", feature lists, progress tracking, loop state, maker-checker prompts, and a "harness-creator" skill that can scaffold the basic harness structure for an existing project. The repo also covers the progression from a single agent loop to graph engineering, including when you actually need nodes, shared state, routing, parallelism, and recovery instead of forcing everything into one loop. A solid resource if you're working with coding agents and want to understand the engineering around making them reliable over longer tasks. 100% open source

this is unreal… the SpaceXAI engineer who leads Grok Bot, Lauren Tan - shipped 2,500 PRs last month - and reviews them after they land, in the morning I took SpaceXAI's Grok Bot docs + her 1-hour livestream and turned them into a 3-page prompting blueprint: the 10-field prompt structure, the 4-rung trust ladder, the two loops, and the guardrails that let agents ship while you sleep. send this PDF to your Grok Bot, save it as a skill, and turn it into an always-on team that actually finishes work