RT Avi Chawla (@_avichawla)6 no-code LLMs, Agents, and RAG builder tools for AI engineers:(open-source and production-grade) ...

[Music] Hello. This awesome. This is a big crowd.Who here has used quad code before. Jesus. Awesome.That's what I like to see. Cool. So, my name is Boris.I'm a member of technical staff at Enthropic and creator of Quad Code. And um I was struggling with what to talk about for audience that already knows quad code, already knows AI and all the coding tools and agentic coding and stuff like that. So, I'm going to zoom out a little bit and then we'll zoom back in.So here's my TLDDR. The model is moving really ...

RT The Rollup (@therollupco)NEW EP: Why AI Agents Need Better Blockchain Data with Marcel FohrmannWhile the hype of the narrative has slowed, the developments in ai x cryptoIn today's episode, @ayyyeandy sits down with @I_1337_I from @helloSQD to cover:> Why Current Data Infra Can't Scale> SQD's "Airbnb of Databases" Model> Why Institutional Players Are Taking Crypto Data Seriously> The Fight to Keep AI DecentralizedFull episode links below.Timestamps:00:00 Intro00:59 Magic Ad01:27 Starknet Ad01:53 Marcel’s ...

Core Viewpoint - The concept of "Context Engineering" has gained traction in the AI industry, emphasizing that the effectiveness of AI applications relies more on the quality of context provided than on the prompts used to query the AI [1][3]. Group 1: Definition and Importance of Context Engineering - Context Engineering is defined as the discipline of designing and constructing dynamic systems that provide appropriate information and tools to large language models (LLMs) at the right time and in the right format [19]. - The quality of context provided to an AI agent is crucial for its effectiveness, surpassing the complexity of the code or framework used [24]. - A well-constructed context can significantly enhance the performance of AI agents, as demonstrated by examples where rich context leads to more relevant and useful responses [25]. Group 2: Components of Context Engineering - Context Engineering encompasses various elements, including prompt engineering, current state or dialogue history, long-term memory, and retrieval-augmented generation (RAG) [15][11]. - The distinction between prompts, prompt engineering, and context engineering is clarified, with prompts being the immediate instructions given to the AI, while context engineering involves a broader system that dynamically generates context based on task requirements [15][19]. Group 3: Strategies for Implementing Context Engineering - Four common strategies for implementing Context Engineering are identified: writing context, selecting context, compressing context, and isolating context [26]. - Writing context involves saving information outside the context window to assist the agent in completing tasks, such as maintaining a calendar or email history [28][29]. - Selecting context refers to pulling necessary information into the context window to aid the agent, which can include filtering relevant memories or examples [36][38]. - Compressing context focuses on retaining only the essential tokens needed for task execution, often through summarization techniques [43][44]. - Isolating context involves distributing context across multiple agents or using environments to manage context effectively, enhancing task focus and reducing token consumption [47][50].

作者 | 褚杏娟 "在百川智能的那段时间，我和同事们始终处在一种很亢奋的状态。尽管我们常常工作到深夜，甚 至一两点钟才下班，但内心非常地充实和开心。"如今回忆起那段经历时，前百川工具链负责人徐 文健的眼中依然有光。 徐文健在百川最负盛名的时候加入，半年多后又选择离开，再次踏上了自己的创业之路。 94 年的徐文健现在身上依然保有"技术理想主义"的影子，人到中年依然可以说出："创业是一项复 杂的工作，我们需要在不改变初心的前提下赚钱。" 从徐文健身上，我们可以看到"大模型六小虎"时代下那批怀揣理想的技术人，至今依然保持热 情。他们带着从"大模型六小虎"时代积累的成长经验，正在开启 Agent 时代新的创业故事。 成长，始于一次次"祛魅" 徐文健毕业于南京工程学院，刚进入大学时期的他性格内向，于是就刻意强迫自己在公开场合发 言，尽管当时会紧张的手指发抖。除此以外他还积极参与各种大学生创业的活动。"虽然学校并非 顶尖但创业氛围很浓，我很感谢这个过程中身边的老师同学给到我的帮助和启发，母校塑造了创 业者徐文健这个身份的雏形 。 " 像很多刚毕业的人一样，徐文健也想进大厂。在创业公司工作了段时间后，他如愿去了滴滴。当 时 ...

当AI走入消费的深水区，真正能穿越技术热潮与消费浪潮的，不只是掌握前沿模型的企业，而是那些深刻理解用户需求、拥有产品思维的企业。 相比于ToC端的硬件创新，AI在服务型消费场景中的落地也正加速推进。"两年前我们想做自己的垂类大模型，和很多机构有过合作，但后来被DeepSeek干 掉了。"6月在港交所主板挂牌的产后护理集团圣贝拉（2508.HK）创始人向华在现场坦言，在此前尝试垂类大模型研发碰壁后，将目光转向了AI Agent。 向华透露，本月圣贝拉集团将会发布 "Doctor Bella"，一款通过通用大模型能力做出的AI Agent。"我们利用了过去几十万的家庭护理数据，这款Agent能够解 决女性用户在生产、生育、坐月子等阶段的问题，并提供数据搜集、分析、到家和零售服务。" 此前，母婴电商宝宝树也发布了面向育儿场景的垂直AI Agent"米卡AI"。宝宝树相关负责人向第一财经记者表示，在研发环节，相比传统"解答疑问"的精准 搜索需求，团队更注重Agent为用户提供服务，"市场的焦点开始从模型层转向更具体的应用层，我们也需要在自己深耕的垂直领域利用AI进行服务迭代，满 足新生代父母的需求"。 乐刻运动创始人 ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.RAvi Chawla (@_avichawla):6 no-code LLMs, Agents, and RAG builder tools for AI engineers:(open-source and production-grade) ...

6 no-code LLMs, Agents, and RAG builder tools for AI engineers:(open-source and production-grade) ...

图片来源： Sequoia Capital Z Highlights Bob McGrew ， OpenAI 前首席研究官，主导推动 GPT ‑ 3 、 GPT ‑ 4 以及内部称为 o1/o3 模型的研发，提出预训练（ pre-training ）、后训练（ post-training ）和推理（ reasoning ）的 " 三位一体 " 模型。现为多家 AI 初创企业的顾问或投资人，持续推动 AGI 的落地。本次访谈视频由 Sequoia Capital 在 2025 年 6 月 17 日发布，和 Bob 共同探讨了 从模型训练重点、 Agent 和机器人的未来发展、 AI 时代的教育心得与管理经验等主题，洞察人工智能的发展轨迹，并 指出初创企业依然可以挖掘并构建可持续竞争优势的领域。 预训练、后训练和推理，未来如何发展？ Stephanie Zhan ： 欢迎来到 Training Data 。今天我们非常高兴邀请到 Bob McGrew——OpenAI 前首席研究官，带我们深入探讨 frontier AI 的幕后发 展。 Bob 分享了预训练（ pre-training ）、后训练（ post-tr ...

I strongly believe that AGI will be achieved by a network of agents collaborating cohesively in a team.The ACP Butler offers a single consumer touchpoint to co-ordinate agents.The litmus test will be how cohesively these Virtuals’ agents collaborate.https://t.co/MhhAaANZyx ...