@spandan_madan · 1.1K 粉丝 · 626.9K 阅 · 516 赞 · 28 转
06/24 02:13
AI hardware is having a moment. Hyperscaler capex on AI data centres is on track to clear $690 billion in 2026, and private equity has followed in scale — Blackstone alone reports a $55B+ data-centre
中文介绍 围绕 AI 硬件效率与成本展开,先抛出 2026 年超大厂 AI 数据中心资本开支将超 6900 亿美元、Blackstone 单家已投 550 亿美元以上的数据,再引到「细胞与 GPU 跑同一算法」的效率对比视角,核心是算力范式与能耗讨论。
@EngramLab · 1.2K 粉丝 · 255.7K 阅 · 537 赞 · 76 转
06/24 01:01
We’re Engram. We’re building AI that learns from you and deeply understands your work. Today’s AI models don’t understand what you do. Not really. Everything models know comes from their training –
中文介绍 Engram 发布一套强调「从你的上下文学习」的 AI 路线,主打不只依赖预训练,而是持续理解个人工作内容与历史语境。重点不是通用聊天,而是把「上下文计算」做成长期记忆与工作理解层,属于个人化 AI 基础设施方向。
@sairahul1 · 120.1K 粉丝 · 249.0K 阅 · 502 赞 · 90 转
06/23 17:28
There are 8 billion people on the planet. Only a fraction of developers understand how AI agents actually work. Not the demos. Not the hype. The real engineering underneath. Every week a new agent
中文介绍 整理「30 个 Agentic Engineering 核心概念」,面向开发者补齐 AI agent 真正的工程基础,而非演示级玩法。重点在概念框架与系统设计认知,适合把近期泛滥的 agent 话题拆成可落地的工程知识图谱。
@Oracle_Trade_ai · 39.9K 粉丝 · 197.8K 阅 · 2.8K 赞 · 580 转
06/22 16:52
In 2026, autonomous AI agents have become one of the most effective strategies on prediction markets. Over 30% of all activity on Polymarket now comes from algorithmic and AI-powered wallets. We
中文介绍 介绍 AI agent 在 Polymarket 上做预测市场交易的项目设定,给出「到 2026 年,超 30% 活动来自算法和 AI 钱包」这一判断,切入点是自治代理做交易决策。更像产品叙事与市场判断,不是具体策略教程。
@posthog · 21.8K 粉丝 · 162.4K 阅 · 512 赞 · 36 转
06/24 01:26
When the creators of both OpenClaw and Claude Code speak, people listen. And recently Peter Steinberger and Boris Cherny have both been talking about the same concept: loops. Their argument? You
中文介绍 围绕近期很火的「loops」方法论展开,引用 OpenClaw 与 Claude Code 相关作者的共识:与其写一次性 prompt,不如构建循环式执行系统。重点是 agent 设计范式转向,把工作流、反馈和迭代放到比提示词更核心的位置。
@OracAItrading · 31.8K 粉丝 · 141.6K 阅 · 2.8K 赞 · 576 转
06/22 16:52
In 2026, autonomous AI agents have become one of the most effective strategies on prediction markets. Over 30% of all activity on Polymarket now comes from algorithmic and AI-powered wallets. We
中文介绍 同样在讲 AI agent 参与 Polymarket 交易,核心判断是预测市场里自治策略已成重要力量,并抛出「超 30% 活动来自算法与 AI 钱包」的数据口径。内容偏项目发布和赛道判断,信息增量主要在市场叙事。
@GoogleAIStudio · 179.4K 粉丝 · 138.2K 阅 · 504 赞 · 42 转
06/23 01:21
Today we're announcing that the Interactions API has reached general availability and is now our primary API for interacting with Gemini models and agents. We launched its public beta in December
中文介绍 Google AI Studio 宣布 Interactions API 正式可用,并升级为 Gemini 模型与 agents 的主接口。关键信息是它已从去年 12 月公开测试转正,意味着 Gemini 生态后续的多轮交互与 agent 开发将以这套 API 为中心。
@Oractrading · 33.9K 粉丝 · 109.2K 阅 · 2.8K 赞 · 585 转
06/22 16:20
In 2026, autonomous AI agents have become one of the most effective strategies on prediction markets. Over 30% of all activity on Polymarket now comes from algorithmic and AI-powered wallets. We
中文介绍 再次宣传 AI agent 在 Polymarket 的交易场景,主张到 2026 年自治代理会成为预测市场高效策略之一,并给出「30% 以上活跃度来自算法或 AI 钱包」的说法。内容更偏赛道包装,缺少执行细节与策略拆解。
@RohOnChain · 51.4K 粉丝 · 108.3K 阅 · 501 赞 · 65 转
06/22 21:54
I will break down exactly how to build the loops that run an entire quant trading system on their own. Let's get straight to it. Bookmark This - I'm Roan, a backend developer working on system
中文介绍 教程向内容,拆解如何用「loop engineering」搭建可自运行的量化交易系统。重点不在单个交易模型,而在让整套系统通过循环自动执行、反馈和改进,适合关注 agent 工作流、自动化交易与自优化系统设计的人。
@const_reborn · 29.7K 粉丝 · 79.8K 阅 · 503 赞 · 116 转
06/23 09:21
e_i \;\propto\; \underbrace{\rho_i \times \bar{p}_i}_{\text{linear (maximize)}} \times \underbrace{(1 - b_i)}_{\text{boolean gate}} Disclaimer: this upgrade only effects subnet owners and dynamic TAO
中文介绍 讨论 Bittensor 相关机制升级,直接给出子网收益/权重计算公式,核心变量包括 「ρ_i × 平均价格」与布尔门控「1-b_i」。并特别说明此次升级只影响 subnet owner 与 dynamic TAO,属于经济模型与激励规则解读。
@philipkiely · 8.6K 粉丝 · 73.1K 阅 · 544 赞 · 36 转
06/23 08:13
GLM-5.2 is the biggest news in open models since DeepSeek-R1. It’s easy to see why. GLM-5.2 delivers comparable performance to GPT 5.5 and Opus 4.8 at a fraction of the cost, generally 70-80% less
中文介绍 讲如何构建号称最快的 GLM-5.2 API,先强调模型层面的性价比:性能可比 GPT 5.5 与 Opus 4.8,但成本低 70% 到 80%。核心看点是开源模型服务化与推理基础设施优化,而不只是单纯测评 GLM-5.2。
@akshay_pachaar · 278.2K 粉丝 · 44.0K 阅 · 504 赞 · 72 转
06/23 02:00
Half your feed is suddenly saying the same thing. Stop prompting your agents, start engineering loops. Boris Cherny, the person who built Claude Code, said it plainly: "I don't prompt Claude anymore.
中文介绍 用通俗方式解释近期流行的「Loop Engineering」,核心观点是别再只给 agent 写 prompt,而要设计循环、状态与反馈机制。还引用 Claude Code 作者 Boris Cherny 的表态,说明这是 agent 工程从提示词转向系统编排的信号。
@GoogleAIStudio · 179.4K 粉丝 · 41.0K 阅 · 605 赞 · 57 转
06/25 00:24
Computer use is now a built-in tool supported in Gemini 3.5 Flash, delivering our best performance yet for agentic computer use tasks. Previously only available as a standalone Gemini 2.5 computer use
中文介绍 Google 把 computer use 作为 Gemini 3.5 Flash 的内置工具开放,不再只是 Gemini 2.5 的独立能力。重点在 agent 可直接执行电脑操作类任务,且官方称这是目前在 agentic computer use 场景下表现最好的版本。
@zachlloydtweets · 11.2K 粉丝 · 39.2K 阅 · 504 赞 · 68 转
06/23 22:31
This post shows how to create a loop with automated feedback that an agent can run to optimize its own Skills. It uses an automated grader with computer use to assess how well a Skill is performing,
中文介绍 展示一个「技能优化循环」工作流:让 agent 借助自动评分器与 computer use,对自身 Skills 的表现持续评估和迭代。亮点在于把反馈自动化,形成自我改进闭环,属于比普通 prompt 调参更工程化的 agent 训练思路。
@amitiitbhu · 23.6K 粉丝 · 35.7K 阅 · 514 赞 · 63 转
06/23 19:36
In this blog, we will learn about how vLLM works. We will also see why we need it, how it manages memory so cleverly, and where it is used in the real world to serve large language models to many
中文介绍 一篇 vLLM 原理向教程,覆盖为什么需要 vLLM、它如何高效管理显存,以及在真实场景中怎样支撑大语言模型高并发服务。价值在于把推理服务层的关键机制讲清楚,适合想理解部署与性能优化而不只关心模型效果的人。