Company Overview - Innodata (INOD) shares closed at $50.10, approximately 29.4% below the 52-week high of $71 reached on February 21, 2025, with a year-to-date appreciation of 26.8% [1][9] - The company has outperformed competitors such as Cognizant, Infosys, and ExlService, whose shares have declined by 1.8%, 15.8%, and 4.3% respectively [2] Investment and Growth Strategy - Innodata is set to benefit from significant investments from major tech companies, including Microsoft's $80 billion and Meta Platforms' $64-$72 billion, focusing on AI technology [3] - The company plans to invest $2 million in the second quarter of 2025 to support its largest customer [3] - Expected revenues for 2025 are projected to rise by 40% year-over-year to $238.6 million, driven by growing enterprise demand and contracts with eight Big Tech firms for LLM data work [9][14] Market Position and Client Expansion - Innodata is expanding its Generative AI capabilities, targeting a market expected to be worth $200 billion by 2029 [12] - The company is enhancing its relationships with key clients, securing approximately $8 million in new engagements from four Big Tech customers [13] - New customer acquisitions are anticipated to provide significant upside to both revenue and earnings [14] Financial Performance and Valuation - The Zacks Consensus Estimate for second-quarter 2025 earnings is 11 cents per share, unchanged over the past 60 days, compared to break-even earnings in the year-ago quarter [16] - The forward 12-month Price/Sales ratio for Innodata is 5.84X, significantly higher than the Zacks Computer Services industry's 1.76X, indicating a premium valuation [19] Conclusion - Current shareholders may find justification in holding the stock due to Innodata's strong positioning in the generative AI safety domain and impressive revenue growth prospects [22] - New investors might consider waiting for a more favorable entry point as the stock remains overvalued [22]

That's a wrap!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.Avi Chawla (@_avichawla):A simple technique makes RAG ~32x memory efficient!- Perplexity uses it in its search index- Azure uses it in its search pipeline- HubSpot uses it in its AI assistantLet's understand how to use it in RAG systems (with code): ...

Overview - The author, Avi Chawla (@_avichawla), shares tutorials and insights on Data Science (DS), Machine Learning (ML), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAGs) daily [1] - The author has been using Jupyter Notebooks for over 9 years [1] Jupyter Notebook Features - The author highlights 5 hidden features of Jupyter Notebooks that most users are unaware of [1]

Financial Data and Key Metrics Changes - Revenue for Q2 2025 reached $58.4 million, representing a year-over-year increase of 79% [6][16] - Adjusted EBITDA grew 375% to $13.2 million, reflecting an adjusted EBITDA margin of 23% compared to 9% in the same quarter last year [6][16] - Net income was $7.2 million, a significant improvement from a loss of $14,000 in the same period last year [16] - Cash increased from $56.6 million at the end of Q1 to $59.8 million at the end of Q2, with an additional $8 million collected shortly after the quarter close [7][17] Business Line Data and Key Metrics Changes - The company reported strong performance from its largest customer, generating approximately $33.9 million in revenue from this account in Q2 [33][34] - New projects with the largest customer are expected to significantly increase revenue, with forecasts indicating $10 million from another Big Tech customer in the second half of the year [10][33] Market Data and Key Metrics Changes - The company is experiencing strong demand across a diverse range of existing and new customers, positioning itself well for future growth [8][11] - The competitive landscape is shifting due to the acquisition of Scale AI by Meta, which may create new opportunities for the company [22][23] Company Strategy and Development Direction - The company is raising its full-year 2025 revenue growth guidance to 45% or more, up from 40%, based on a robust pipeline of new deals [8][57] - Investments will focus on custom annotation pipelines, agent development, and expanding global delivery capabilities, particularly for LLM testing and deployment [14][18] - The company aims to align with the growing demand for high-quality complex training data and agentic AI, which is expected to drive future growth [12][13] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in the business momentum, describing it as "nothing short of amazing" and indicating a strong outlook for the second half of the year [17][18] - The company is committed to investing in capabilities that will compound value over the next decade, despite incurring approximately $1.4 million in operating expenses viewed as investments in Q2 [14][18] Other Important Information - The company has not drawn on its $30 million credit facility, providing additional financial flexibility [7][17] - Management emphasized the importance of organic growth, distinguishing it from inorganic growth strategies employed by other companies [44] Q&A Session Summary Question: Impact of Scale AI's acquisition by Meta - Management congratulated Scale AI and noted that their success highlights the importance of data in model performance, suggesting that the shift in focus could accelerate market opportunities for the company [22][23] Question: Timing of business shifts post-acquisition - Management indicated that they are actively engaging with market participants and have stepped up outreach efforts, anticipating exciting opportunities in the coming months [24] Question: Variance in revenue expectations - Management clarified that previous guidance was cautious due to dependencies on engineering teams, but they are optimistic about the current pipeline and opportunities [31][34] Question: Go-to-market strategy for enterprises - Management stated that they are already targeting enterprises and finding receptivity across various markets, with a focus on integrating new technologies into workflows [36][38] Question: Future investment scale - Management plans to increase investments in sales and delivery capabilities by approximately $1.5 million in Q3, capitalizing on significant market opportunities [39][40] Question: Organic growth and competitive pressures - Management highlighted that while there is competitive pressure, the quality of their data and services is the primary focus for customers, making them less price-sensitive [46][47] Question: Revenue opportunities and customer relationships - Management confirmed that there is a larger pipeline of opportunities compared to the previous quarter, with many projects progressing well [48][49]

招股书显示，Ambiq的平台号称能将能耗降低至传统芯片的五分之一。这家由Arm(ARM.US)支持的公 司的芯片技术正在推动AI算力从数据中心向智能手表、健身追踪器等可穿戴设备迁移，这些设备的电 力限制此前曾成为AI功能发展的障碍。 Ambiq首席技术官Scott Hanson表示："公司发展的第一阶段是帮助客户延长设备电池寿命，但我们真正 的价值主张在于，在保持相同电池续航的同时，新增此前无法实现的AI功能。"他补充称，Ambiq技术 的其他潜在应用包括将大型语言模型(LLMs)嵌入到增强现实(AR)和虚拟现实(VR)眼镜等设备中。 Ambiq客户群相对集中。根据招股书，今年第一季度，该公司最大客户贡献了38%的收入，但未披露该 客 户 身 份 。 第 一 季 度 中 占 销 售 额 10% 以 上 的 最 终 客 户 包 括 Garmin(GRMN.US) 、 谷 歌 母 公 司 Alphabet(GOOGL.US)以及另一位未公开的客户。根据招股书，在截至3月31日的三个月内，Ambiq的净 亏损为830万美元，营收为1570万美元;而2024年同期的净亏损为980万美元，营收为1520万美元。 智通财 ...

Financial Performance - Consolidated GMS was $2.8 billion, a decrease of 4.8% year-over-year, or 5.8% on a currency-neutral basis[7] - Consolidated revenue reached $673 million, representing a 3.8% year-over-year increase[7] - Adjusted EBITDA amounted to $169 million, resulting in a 25.1% adjusted EBITDA margin[7] - Excluding Reverb, Q2 2025 Consolidated GMS was $2.7 billion, down 2.6% Y/Y on the same basis[7] Marketplace Dynamics - Etsy marketplace GMS decreased by 5.4% year-over-year, or 6.3% on a currency-neutral basis, totaling $2.4 billion[11] - Depop marketplace GMS increased by 35.3% year-over-year, or 34.7% on a currency-neutral basis, reaching $250 million[11] - Etsy app GMS grew year-over-year and accounted for approximately 45% of total GMS[12, 59] Buyer & Seller Metrics - Etsy marketplace had 87.3 million active buyers, a decrease of 4.6% year-over-year[55] - New buyers totaled 4.8 million, a decrease of 14.5% year-over-year[82] - GMS per active buyer was $120, a decrease of 2.9% year-over-year[57, 82] Strategic Initiatives & Investments - Approximately 40% of marketing messages are now personalized, an increase from approximately 27% in Q4 2024[16] - Share repurchases during Q2 2025 amounted to approximately $335 million[75] - The company is aiming for near-total personalization of marketing messages by year-end[16]

I'm so excited to be here. Well, actually, I'm not. I mean, I'm excited, but I'm mostly nervous and I'm scared to mess up.I mean, maybe I just did. But how come it's so easy for me to lie to you. And how come we're often incentivized to lie to each other.I think one problem is that talk is cheap. Incidentally, this also a type of lies and cheap data that we train our current artificial intelligence on, namely large language models. This is also why we say that actions speak louder than words.But if that's t ...

Core Problem & Solution - Vertical AI applications face a "last mile problem" in understanding industry-specific context and workflows, which is more critical than model sophistication [4][6] - Anterior proposes an "adaptive domain intelligence engine" to convert customer-specific domain insights into performance improvements [17] - The engine consists of measurement (performance evaluation) and improvement (iterative refinement) components [17] Measurement & Metrics - Defining key performance metrics that users care about is crucial, such as minimizing false approvals in healthcare or preventing dollar loss from fraud [18][19][20] - Developing a failure mode ontology helps categorize and analyze different ways the AI can fail, enabling targeted improvements [21][22] - Combining metric tracking with failure mode analysis allows prioritization of development efforts based on the impact on key metrics [26][27] Iteration & Improvement - Failure mode labeling creates ready-made datasets for iterative model improvement, using production data to ensure relevance [29] - Domain experts can suggest changes to the application pipeline and provide new domain knowledge to enhance performance [32][33] - This process enables rapid iteration, potentially fixing issues the same day by adding relevant domain knowledge and validating with evals [37] Domain Expertise - The level of domain expertise required depends on the specific workflow and optimization goals, with clinical reasoning requiring experienced doctors [38][39] - Bespoke tooling is recommended for integrating domain expert feedback into the platform and workflows [41] - Domain expert reviews provide performance metrics, failure modes, and suggested improvements, all in one [38] Results & Performance - Anterior achieved a 95% accuracy baseline in approving care requests, which was further improved to 99% through iterative refinement using the described system [14][15]

AI发展与应用 - DeepMind 的使命是负责任地构建 AI，以造福人类，深度学习已成为解决图像分类、语音识别和机器翻译等问题的最佳方法 [5][6] - Transformer 架构促成了大型语言模型的构建，这些模型在大量公开数据上进行训练，能够解决广泛的问题 [8] - 现代基础模型（LLM）已超越文本，成为多模态模型，能够处理文本、手写文本和图像，为个性化辅导等学习方式带来可能性 [11][12] - Gemini 1.5 Pro 能够处理高达 1 million 多模态 tokens 的上下文窗口，可以处理大量信息作为输入 [15] - AI Agents 不仅限于简单的聊天机器人，还可以进行语音交互，甚至在 3D 世界中进行实时交互 [16] AI的包容性与可及性 - 行业致力于弥合英语和其他语言（特别是印度语言）之间 AI 能力的差距，目标是开发能够理解 125 种以上印度语言的模型 [19][20][21][22] - Vani 项目与印度科学研究所合作，旨在收集印度各个角落的语音数据，目标是从印度每个地区收集数据，以覆盖更多零语料库语言 [24][25] AI在特定领域的应用 - 行业正在构建数字农业堆栈的基础层，利用卫星图像识别农田边界、作物类型和水源，为农民提供个性化服务，如作物保险 [26][27][28] - AlphaFold 通过预测蛋白质结构，将原本需要 5 年的研究缩短到几秒钟，并在不到一年的时间内完成了 200 million 个蛋白质结构的预测，并免费提供数据，极大地加速了科学发现 [29][30][31][32] 未来展望 - 行业期望 AI 能够帮助更多人，使他们能够做出诺贝尔奖级别的贡献 [35]

Overview - Taipy is an open-source Python AI & data web application builder [1] - Taipy can build prototypes and robust production-ready data apps [1] Technology & Features - Taipy eliminates the need to learn JavaScript, CSS, or HTML [1] - Taipy's VS Code extension provides no-code functionalities to build data apps [2] - Taipy is presented as a more robust version of Streamlit [1] - Taipy has a noticeable latency difference compared to other apps [1] Community & Adoption - Taipy is fully open-source with over 18 thousand stars [2]