Core Viewpoint - SoundHound AI experienced a significant stock increase of 836% in 2024, but has since declined by 55% from its peak in early 2025 due to Nvidia selling its stake in the company [1][4]. Company Overview - SoundHound AI specializes in voice-assistant technology for various industries, including automotive and restaurants, and is recognized as a first-mover in this space with two decades of experience [5][11]. - The company reported a remarkable revenue growth of 85% for the full year of 2024 and an impressive 151% year-over-year growth in Q1 2025 [5][10]. Market Opportunity - Management estimates a total addressable market of $140 billion, indicating that SoundHound currently captures less than 1% of this market [6][11]. - The rapid growth rate and substantial market potential suggest a long runway for future expansion, which is appealing to investors [8]. Financial Position - Although SoundHound is not yet profitable, it has a trailing 12-month net loss of $188 million, with $246 million in cash and no debt, positioning the company well for future profitability [10]. Competitive Landscape - SoundHound claims a competitive advantage as a "white-label" provider, allowing clients to maintain their branding, unlike larger competitors [11]. - However, increased competition from tech giants and advancements in generative AI may challenge SoundHound's market position [12][13]. Investment Considerations - While the business is performing well, the path to capturing its estimated $140 billion market opportunity will face significant competition [15]. - Investors are advised to remain cautious and consider the evolving competitive landscape before making investment decisions [16].

之前我们参与过垃圾币再就业的 @PalioAI 的 Patoshi 嘴撸活动，可以分到 $PAL 代币，过两天就要上线 Binance Alpha 了可以速度检查下自己在之前的活动中拿到了多少代币，到时候 TGE 应该就可以拿到了，记得当时大华和好几个玩链游多的博主撸到了不少，我领到了 5555 个 😂 需要切换空投类别到 Patoshi Airdrop，查询链接我放在评论区$PAL 的代币消耗在 GameFi、AI Agent 以及 Co-pilot，代币经济学可以见 qt 的这条 thread，其中提到没有 VC 和团队份额，是 75% 全归社区。最近币安 TGE 活动开盘之后都有操作机会，届时可以看看有没有撸钱机会Palio (@PalioAI):𝐏𝐚𝐥𝐢𝐨𝐀𝐈 𝐖𝐡𝐢𝐭𝐞𝐩𝐚𝐩𝐞𝐫 𝐢𝐬 𝐋𝐈𝐕𝐄We’re building more than a game.Palio is creating the 𝐸𝑚𝑜𝑡𝑖𝑜𝑛𝑎𝑙 𝐿𝑎𝑦𝑒𝑟 of Web3 and beyond — where AI doesn’t just respond, it connects.This is the future of ...

virtuals protocol showing what sustainable ai agent monetization looks likeeva agent: 8x returns at $1.6M market cap with 600% oversubscription from 10k participants. actual demand, not just speculationagent commerce protocol (acp): $200 first-day revenue through performance-based payments. early but proves the model works46,135 users committed 23.4M $VIRTUAL tokens to agent launches in 3 months. real adoption metrics vs typical ai token farmingtechnical infrastructure is maturing fast:• veVIRTUAL governanc ...

Core Viewpoint - The article discusses the emergence and growth of the AI bookkeeping app "Kapi Bookkeeping," which positions itself as a personal CFO for young people, leveraging AI to simplify and enhance the bookkeeping experience, resulting in over one million users within six months [2][5][41]. Group 1: Product Overview - Kapi Bookkeeping is designed as an AI-native personal life assistant targeting young adults aged 22 to 30, primarily in first and second-tier cities, who are beginning to recognize the importance of financial management [7][8]. - The app offers features such as AI bookkeeping (text/voice/multi-modal), AI budgeting, financial analysis, and multi-asset account management, making bookkeeping easier and faster [5][9]. - Kapi Bookkeeping has achieved a leading position in the AI bookkeeping sector, with over one million users in just six months [5][41]. Group 2: Market Positioning and User Engagement - The app addresses the challenge of maintaining bookkeeping habits among users, recognizing that while many want to track their spending, few can sustain the practice due to the tedious nature of traditional bookkeeping methods [8][9]. - Kapi Bookkeeping utilizes AI to streamline the bookkeeping process, making it less burdensome and more appealing to potential users who previously found it difficult to maintain [9][12]. - The product development process involves continuous feedback from users, allowing for iterative improvements based on real-world usage [5][26]. Group 3: User Experience and Functionality - The most praised feature is the AI bookkeeping process, which automates data extraction from user inputs, significantly reducing manual entry [19][24]. - The app also includes a "life timeline" feature that enhances user experience by contextualizing spending behavior within a timeline, making it more relatable [19][24]. - Kapi Bookkeeping aims to evolve beyond simple bookkeeping to become a comprehensive financial agent, providing proactive suggestions and insights based on user data [46][47]. Group 4: Future Directions and Challenges - The company acknowledges the rapid evolution of AI technology and the need to adapt to new developments in AI models to maintain a competitive edge [49]. - Kapi Bookkeeping's long-term goal is to effect positive changes in users' financial behaviors, such as improving savings rates and managing debt more effectively [35][41]. - The app currently does not charge users, focusing instead on refining the user experience before considering monetization strategies [41][42].

图片来源： a16z Z Highlights AI 推动市场调研迈入新纪元 几十年来，企业为更好地了解客户，持续向市场调研投入了数千亿美元，但却一直受到调查慢、样本偏倚、洞察滞后的制约。尽管每年市场调研的支出高 达 1400 亿美元，但软件的使用在其中几乎可以忽略不计。举例来说，传统依赖人工的咨询公司如 Gartner 和 McKinsey 估值分别为 400 亿美元，而软件平 台 Qualtrics 和 Medallia 的估值仅为 125 亿美元和 64 亿美元。 随着 AI 的发展，我们又一次看到市场准备将原本用于人工的支出转向软件。早期的 AI 公司已经开始利用语音转文字（ speech-to-text ）和文字转语音（ text-to-speech ）模型，构建 AI 原生的调查平台 —— 这些平台可以自动进行视频访谈，然后由大型语言模型（ LLMs ）分析结果并生成演示文稿。 这些先 行者增长迅速，不仅签下了大型合同，还开始接管原本由市场调研和咨询公司掌控的预算。 McKinsey 等咨询公司构建了完整的调研子部门，使用软件化工具进行大规模客户细分和消费者洞察。但这种合作常常历时数月、成本高昂、 ...

6月19日，天润融通在上海黄浦江畔举办了"向AI而行 与变革者同航"游轮之夜活动，来自消费品、家电家居、汽车、金融、软件信息服务等多个行业的客户 与合作伙伴齐聚一堂，在江景与夜色中，共同探讨AI驱动下的组织重塑与增长新范式，带来一场思想盛宴。 随着AI应用加速发展，AI Agent正成为引领未来组织变革的核心力量。活动伊始，天润融通副总裁潘威和FAS事业部总经理杨潘分别为活动致辞，分享对AI 发展趋势的深刻洞察。 ▲ 天润融通副总裁潘威 ▲ 天润融通FAS事业部总经理杨潘 AI Agent改变了软件的构建逻辑，从过去的"预设规则，人工编排"转向现在的"意图理解，自主决策"，这一变革也正引领企业客户联络模式从"人工为主， AI辅助"的Copilot阶段，迈入"AI为主，人工带教"的无人化阶段。未来以AI Agent为核心的AI员工将全面接管标准化流程，人类员工则转向业务理解与策略 判断的"专家"角色，共同构成"业务专家+AI员工"的新型组织范式。 基于服务多个行业客户的丰富实践，天润融通总结出了AI转型实践六部曲，为企业迈向AI驱动的智能化组织提供了清晰的蓝图。一是以客户为中心，建立 数字化连接，形成闭环；二是 ...

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].