Core Insights - AT&T and T-Mobile are leading players in the North American telecommunications industry, providing a wide range of services including wireless, broadband, and cloud-based solutions [1][3] - The industry is experiencing growth due to increased data traffic from high data-intensive applications, federal initiatives for digital inclusivity, and the adoption of AI technologies [2] AT&T Analysis - AT&T reported 479,000 post-paid net additions in Q2, with a postpaid churn rate of 1.02% and an increase in ARPU to $57.04, driven by improved international roaming and higher-priced plans [4] - The company is expanding its fiber broadband business, achieving 243,000 net fiber additions and 203,000 Internet Air subscribers in Q2, with a goal to reach 50 million customer locations by 2030 [5] - AT&T is acquiring wireless spectrum licenses from EchoStar to enhance its 5G capabilities across 400 markets, although this comes with increased capex burden [6] T-Mobile Analysis - T-Mobile leads the 5G market with coverage for 98% of Americans, utilizing the mid-band 2.5 GHz spectrum for superior speed and coverage [7] - The company added 1.7 million postpaid net customers in Q2, with a postpaid churn rate of 0.9% and an increase in average revenue per account to $149.87 [8][9] - T-Mobile's acquisition of US Cellular's wireless operations has strengthened its home broadband offerings and fixed wireless products [9] Competitive Landscape - Both companies face intense competition in a saturated market, with T-Mobile launching low-priced plans to attract customers, which is impacting margins [11] - T-Mobile's stock is trading at a premium valuation compared to the industry, raising concerns for investors [11] - AT&T's focus on operational efficiency and fiber expansion, along with its recent performance, positions it favorably compared to T-Mobile [19] Financial Estimates - The Zacks Consensus Estimate projects T-Mobile's 2025 sales growth at 6.48% and EPS growth at 9.83%, while AT&T's sales growth is estimated at 2.16% with a decline in EPS by 9.29% [12][14] - Over the past year, T-Mobile's stock has gained 17.4%, while AT&T has outperformed with a gain of 32.8% [15] Valuation Metrics - T-Mobile's shares trade at a forward P/E ratio of 20.50, higher than the industry average of 13.59, while AT&T trades at 13.47 [15]

Challenges in Building AI Applications at BlackRock - BlackRock faces challenges in prompt engineering, requiring significant time investment from domain experts to iterate, version, and compare prompts effectively [10] - BlackRock encounters difficulties in selecting appropriate LLM strategies (e.g., RAG, chain-of-thought) due to instrument complexity and document size variations, impacting data extraction [11] - BlackRock experiences deployment challenges, including determining suitable cluster types (GPU-based inference vs burstable) and managing cost controls for AI applications [12][14] BlackRock's Solution: Sandbox and App Factory - BlackRock developed a framework with a "sandbox" for domain experts to build and refine extraction templates, accelerating the app development process [15][17] - BlackRock's "sandbox" provides greater configuration capabilities beyond prompt engineering, including QC checks, validations, constraints, and interfield dependencies [19][20] - BlackRock's "app factory" is a cloud-native operator that takes a definition from the sandbox and spins out an app, streamlining deployment [15] Key Takeaways for Building AI Apps at Scale - BlackRock emphasizes investing heavily in prompt engineering skills for domain experts, particularly in the financial space, due to the complexity of financial documents [26] - BlackRock highlights the importance of educating the firm on LLM strategies and how to choose the right approach for specific use cases [27] - BlackRock stresses the need to evaluate the ROI of AI app development versus off-the-shelf products, considering the potential cost [27] - BlackRock underscores the importance of human-in-the-loop design, especially in regulated environments, to ensure compliance and accuracy [28]

AI Development Strategy - Building successful AI applications requires a sophisticated engineering approach beyond just writing good prompts [1] - The industry emphasizes the importance of evaluations (evals) as a core component of the development process [1] - Evaluations should be intentionally engineered to reflect real-world user feedback and drive product improvements [1] Technical Focus - "Context engineering" is emerging as a new frontier, focusing on optimizing the entire context provided to the model [1] - Context engineering includes tool definitions and their outputs [1] - The industry advocates for a flexible, model-agnostic architecture [1] Adaptability - The architecture should quickly adapt to the rapidly evolving landscape of AI models [1] - Optimize the entire evaluation system, not just the prompts [1]

DDN is bringing to bear some really powerful experiences, products and engineering to bear with deep deep expertise. We are able to bring the best of high performance computing and workloads that we need to process in the cloud as well as onrem. I believe that DDN is at a juncture now where we can add huge value in processing any kind of AI workload.Why. Because we are AI data platform ready. We're ready to go and connect the two whether you're onrem, hybrid or purely in the cloud.We're working with hypersc ...

Core Problem & Solution - Traditional software development lifecycle is insufficient for AI applications due to non-deterministic models, requiring a data science approach and continuous experimentation [3] - The key is to reverse engineer metrics from real-world scenarios, focusing on product experience and business outcomes rather than abstract data science metrics [6] - Build evaluations (evals) at the beginning of the process, not at the end, to identify failures and areas for improvement early on [14] - Continuous improvement of evals and solutions is necessary to reach a baseline benchmark for optimization [19] Evaluation Methodology - Evaluations should mimic specific user questions and criteria relevant to the solution's end goal [7] - Use Large Language Models (LLMs) to generate evaluations, considering different user personas and expected answers [9][11] - Focus on the details of each evaluation failure to understand the root cause, whether it's the test definition or the solution's performance [15] - Experimentation involves changing models, logic, prompts, or data, and continuously running evaluations to catch regressions [16][18] Industry Specific Examples - For customer support bots, measure the rate of escalation to human support as a key metric [5] - For text-to-SQL or text-to-graph database applications, create a mock database with known data to validate expected results [22] - For call center conversation classifiers, use simple matching to determine if the correct rubric is applied [23] Key Takeaways - Evaluate AI applications the way users actually use them, avoiding abstract metrics [24] - Frequent evaluations enable rapid progress and reduce regressions [25] - Well-defined evaluations lead to explainable AI, providing insights into how the solution works and its limitations [26]

Knowledge Graph & LLM Application - Knowledge graphs combined with large language models (LLMs) can be used to build AI applications, particularly with graph retrieval augmented generation (RAG) architecture [2] - Graph RAG replaces vector databases with knowledge graphs built on graph databases, enhancing retrieval strategies [3] - Using a knowledge graph provides richer retrieval strategies beyond vector semantic search, including contextualization and structured queries [4] - Property graph model implements nodes and relationships, nodes represent entities and relationships connect them [4][5] Ontology & Schema - Ontologies provide an implementation-agnostic approach to representing schemas, facilitating knowledge graph creation for both structured and unstructured data pipelines [14][17] - Ontologies describe a domain with definitions of classes and relationships, matching well with graph models [15] - Financial Industry Business Ontology (FIBO) is a public financial industry ontology example [15] - Storing ontologies in the graph can drive dynamic behavior in retrievers, allowing for on-the-fly adjustments by modifying the ontology [29][30] Retrieval Strategies - Graph captures text chunks with embeddings, creating a new search space for vector search [20] - Vector search finds vectors in proximity, which can be dereferenced back to the graph for contextualization, navigation, and enrichment [20] - Dynamic queries, driven by ontologies, can be used to create dynamic retrievers, enabling data-driven behavior [26][29]

AI Training Gap - 55% of IT decision makers surveyed in AI application companies report a lack of AI training initiatives [1] - The industry needs to train individuals to become prompt engineers to effectively leverage AI [1] AI Integration Challenges - Individuals need training and systems to integrate AI tools effectively [2] - Individuals are still getting used to integrating AI tools into their normal day [2]

Langsmith Platform Overview - Langsmith is an observability and evaluation platform for AI applications, focusing on tracing application behavior [1] - The platform uses tracing projects to collect logs associated with applications, with each project corresponding to an application [2] - Langsmith is framework agnostic, designed to monitor AI applications regardless of the underlying build [5] Tracing and Monitoring AI Applications - Tracing is enabled by importing environment variables, including Langmouth tracing, Langmith endpoint, and API key [6] - The traceable decorator is added to functions to enable tracing within the application [8] - Langsmith provides a detailed breakdown of each step within the application, known as the run tree, showing inputs, outputs, and telemetry [12][14] - Telemetry includes token cost and latency of each step, visualized through a waterfall view to identify latency sources [14][15] Integration with Langchain and Langraph - Langchain and Langraph, Langchain's open-source libraries, work out of the box with Langsmith, simplifying tracing setup [17] - When using Langraph or Langchain, the traceable decorator is not required, streamlining the tracing process [17]

Financial Data and Key Metrics Changes - Baozun Group's total net revenues for Q1 2025 increased by 4.3% year over year to RMB 2.1 billion [15] - E-commerce revenue grew slightly by 1.4% to RMB 1.7 billion, while brand management revenue rose by 23% to RMB 387 million [15] - Blended gross margin for product sales at the group level was 32.4%, with gross profit increasing by 18.9% year over year [17] - Adjusted loss from operations was RMB 46 million for the e-commerce segment, a decline of RMB 58 million from the same period last year [18] Business Line Data and Key Metrics Changes - E-commerce product sales revenue increased by 7.3% year over year to RMB 423 million, driven by strong performance in new categories [16] - Gross margin for e-commerce product sales expanded to 15%, a 130 basis point improvement compared to 13.7% a year ago [17] - Brand management (BBM) achieved 23% year on year sales growth, with same store sales growth improving to 5% for the quarter [28] Market Data and Key Metrics Changes - The home appliance and consumer electronics categories are ramping up well due to government subsidies, while luxury and fashion categories are also catching up [42] - E-commerce platforms like JD and Tmall are heavily investing in apparel and sportswear categories, leading to increased competition [66] Company Strategy and Development Direction - The company is focused on a strategic transformation towards an innovation-led platform, emphasizing long-term value creation [13] - Plans to open three new stores in Beijing, Shanghai, and Guangzhou to capitalize on current momentum [12] - The company aims to balance scale expansion with profitability, targeting to open 50 new stores this year [50] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in the ongoing transformation and operational excellence, with a focus on technology and AI applications to enhance efficiency [11] - The company is cautiously optimistic about onboarding new brands due to the current macroeconomic situation, while still seeing synergies between existing brands [61] Other Important Information - The company published its 2024 sustainability report, achieving a 36% reduction in Scope 1-2 carbon emissions compared to the base year 2021 [19] - The company received multiple awards for its content creation efforts at the Alibaba Ecosystem Conference [26] Q&A Session Summary Question: Expectations for the 6.18 campaign and platform performance - Management noted that the 6.18 campaign is the longest yet, with positive early results and strong performance in home appliance and luxury categories [39][42] Question: Updates on BBM strategy and potential acquisitions - Management clarified that the strategy for Gap includes enhancing product marketing and balancing local assortments with global products, while being cautious about acquiring new brands due to the macroeconomic situation [48][61] Question: Key drivers behind the strong performance in apparel and sports categories - Management highlighted that significant investments in marketing and product differentiation are driving growth in these categories, with expectations for continued momentum [66]

Financial Data and Key Metrics Changes - Revenue for fiscal Q3 was $164.6 million, representing a 9.7% year-over-year increase but a 4.9% sequential decline [6][21] - Non-GAAP gross margin was 22.5%, down from 24.2% in the previous quarter and 25.2% a year ago [21] - Non-GAAP EPS was a loss of $0.10, compared to a loss of $0.09 in the prior quarter and a loss of $0.04 a year ago [22] Business Segment Data and Key Metrics Changes - Computing segment revenue increased nearly 15% year-over-year and 3.6% sequentially, accounting for 47.9% of total revenue [10][12] - Consumer segment revenue decreased 9% year-over-year and 4.9% sequentially, representing 13% of total revenue [13] - Communications segment revenue was up 5.8% year-over-year but down 14.4% sequentially, making up 17.2% of total revenue [15] - Power Supply and Industrial segment revenue increased 32.4% year-over-year but declined 6.2% sequentially, accounting for 19.9% of total revenue [16] Market Data and Key Metrics Changes - The company noted robust demand for graphics and AI accelerated cards, particularly driven by a key customer scaling their next-generation platform [11] - The U.S. and Korea are expected to see growth in smartphone customers, while sales from China are anticipated to slow [15] Company Strategy and Development Direction - The company aims to transform from a component supplier to a total solutions provider, leveraging customer relationships to expand market share and increase bond content [9][19] - The focus remains on executing the strategy and delivering sustained value for stakeholders despite near-term uncertainties [19] Management's Comments on Operating Environment and Future Outlook - Management highlighted a dynamic landscape with macroeconomic, geopolitical, and trade-related uncertainties impacting visibility for the second half of 2025 [9] - The company expects low to mid-single-digit sequential revenue growth for June, driven by strength in Computing and Consumer segments [19] Other Important Information - Operating cash flow for the quarter was $7.4 million, down from $14.1 million in the prior quarter [23] - The company completed the quarter with a cash balance of $169.4 million, down from $182.6 million at the end of the last quarter [24] Q&A Session Summary Question: Can you quantify the magnitude of the pull-ins on the PC side and discuss graphics card success? - Management noted increased demand due to customers taking advantage of tariff situations, with an estimated $6 million of revenue attributed to notebook increases [29] Question: What is the tariff impact on the company? - Direct exposure to tariffs is minimal due to limited U.S. shipments, but indirect impacts on overall demand remain uncertain [32] Question: How is the margin guidance for June despite the fall-off in licensing revenue? - Margin guidance is based on a better product mix and higher factory utilization, contributing to a sequential margin rebound [34][36] Question: Can you provide an update on cash flow dynamics and CapEx for the year? - Cash flow is expected to remain stable, targeting $40 million to $50 million for the year, with CapEx for June projected at $12 million to $14 million [45] Question: What is the pricing environment and competitive landscape? - ASP erosion is tracking historical trends, with increased competition prompting the company to roll out new products to reset ASP [49]