RAG Challenges & HyDE Solution - Traditional RAG faces challenges due to semantic dissimilarity between questions and answers, leading to irrelevant context retrieval [1] - HyDE addresses this by generating a hypothetical answer to the query and embedding it to retrieve relevant context [2] - HyDE leverages contriever models trained with contrastive learning to filter out hallucinated details in the hypothetical answer [3] HyDE Performance & Trade-offs - Studies indicate HyDE improves retrieval performance compared to traditional embedding models [4] - The improvement in retrieval performance comes at the cost of increased latency and higher LLM usage [4] HyDE Implementation - HyDE involves using an LLM to generate a hypothetical answer, embedding the answer using a contriever model, querying the vector database, and passing the hypothetical answer, retrieved context, and query to the LLM for the final answer [2]

So we've always set the bar very very high. Uh and that combined with the fact that we started out at DDN solving massive scale data set challenges gave us the foundation to pivot our technology into AI and DDN is the only company today which is solving the right at scale challenge which applies to LLM and Gen AI. power consumption, the movement of data, the ability to support hundreds and hundreds of software suites of AI frameworks and make sure that each and every one of them operates at the highest leve ...

Hey, this is Lance from Langchain. I want to talk of a few general context engineering principles and how they show up in various popular agents like manis like cloud code and also in our recently released deep agents package and CLI. So first agent can be simply thought of as an LLM calling tools in a loop. An LLM kind of makes a tool call.Tool is executed. observation from a tool goes back to the LM and this continues until some termination condition. Now the length of tasks that AI agents can perform is ...

Financial Data and Key Metrics Changes - Revenue increased by 11% year over year to $72 million, aligning with expectations [5][11] - Customer base grew 15% year over year to over 6,000 ARR customers [5] - Reported eighth consecutive quarter of positive free cash flow, generating $43 million over the past eight quarters [9][12] - Normalized free cash flow for the quarter was $3 million, with a 4% free cash flow margin [12] - Remaining performance obligations (RPO) totaled $268 million at the end of Q3, up 26% year over year [12] Business Line Data and Key Metrics Changes - ARR from GenAI Intelligence products grew rapidly to over $1 million since the launch in April [6] - App Intelligence ARR increased to above $10 million, with over 580 customers using the product [8] - 58% of ARR is contracted under multi-year contracts, up from 45% last year, indicating revenue durability [11] Market Data and Key Metrics Changes - Overall net revenue retention (NRR) was 98% across all customers and 105% for customers with over $100,000 of ARR [12] - Gross revenue retention (GRR) showed improving trends, reaching the highest level in two years [12] Company Strategy and Development Direction - Focus on three high-impact opportunities: GenAI intelligence, data selling for LLM, and AI agents [5][6] - Launched Web Intelligence 4.0, integrating GenAI capabilities into the web intelligence solution [6][8] - Investment in go-to-market strategies is ramping, with 30% more sellers than the previous year [9] Management's Comments on Operating Environment and Future Outlook - Management reiterated guidance for 2025 revenues, expecting total revenue in the range of $285 million to $288 million, representing 15% year-over-year growth at the midpoint [13] - Raised non-GAAP operating profit guidance to between $8.5 million and $9.5 million, reflecting disciplined execution [13] - Confidence in converting large data contracts for LLMs into ARR deals in the future [19][20] Other Important Information - New CFO Ron Verrett will join in December, bringing over 20 years of finance experience [10] - The company is focused on delivering profitable growth and achieving long-term profit and free cash flow targets [9] Q&A Session Summary Question: Can you talk about your gross revenue retention? - Management noted that NRR reflects expansion primarily from large contracts booked in previous quarters, which may not yet show in NRR [16][17] Question: How is the LLM data selling evolving? - Management confirmed that the process of converting one-time data sales into ARR deals is lengthy but expressed confidence in future conversions [18][19] Question: Where are App Intelligence customers coming from? - Majority of App Intelligence customers are cross-sell from existing clients, leveraging their existing digital data needs [23][25] Question: Why did RPU decline slightly? - RPU was impacted by the addition of new customers, particularly larger ones, which may not yet reflect in revenue [32][33] Question: Any impact on demand for web intelligence due to SEO traffic decline? - Management observed increased demand for solutions as companies seek to fill gaps from declining SEO traffic [35][36] Question: Can you highlight any customer conversations around GenAI products? - There is significant interest in GenAI optimization products, with customers keen to understand brand perceptions and sentiment [50][52] Question: Any changes in the mix between enterprise and mid-market customers? - No significant changes in the customer mix between SMB and enterprise were observed [56] Question: How should we think about next year? - Guidance for next year will be provided in the next quarter, with a focus on ongoing engagement with large data contracts [72][73]

Financial Data and Key Metrics Changes - Revenue increased by 11% year over year to $72 million, in line with expectations [5][11] - Customer base grew 15% year over year to over 6,000 ARR customers at quarter end [5] - Reported eighth consecutive quarter of positive free cash flow, generating $43 million over the past eight quarters [9] - Normalized free cash flow for the quarter was $3 million, with a 4% free cash flow margin [12] - Remaining performance obligations (RPO) totaled $268 million at the end of Q3, up 26% year over year [12] - Overall net revenue retention (NRR) was 98% across all customers and 105% for customers with over $100,000 of ARR [12] Business Line Data and Key Metrics Changes - ARR from GenAI Intelligence products grew rapidly to over $1 million since the launch in April [6] - App Intelligence ARR increased rapidly to above $10 million, with over 580 customers using the product [8] - 58% of ARR is contracted under multi-year contracts, up from 45% last year, indicating revenue durability [11] Market Data and Key Metrics Changes - The company launched Web Intelligence 4.0, integrating GenAI capabilities into its web intelligence solution [6] - Digital app data now covers over 4 million iOS and Android apps across 58 countries, providing comprehensive data coverage [9] Company Strategy and Development Direction - Focus on three high-impact opportunities: GenAI Intelligence, data selling for LLM, and AI agents [5][6] - Investment in go-to-market strategies is ramping up, with a 30% increase in sales personnel compared to the previous year [9] - The company aims to deliver profitable growth over time while achieving long-term profit and free cash flow targets [9] Management's Comments on Operating Environment and Future Outlook - Management reiterated guidance for 2025 revenues, expecting total revenue in the range of $285 million to $288 million, representing 15% year-over-year growth [13] - The transition to a new CFO is expected to drive growth, efficiency, and strategic transformation [10] - Management expressed confidence in converting current data engagements into ARR deals, particularly in the LLM space [19][20] Other Important Information - The company is seeing strong customer interest in GenAI data and solutions, which are among the fastest-growing revenue streams [5] - The launch of Similarweb MCP Server is expected to enhance the integration of digital market intelligence data into AI agents and workflows [8] Q&A Session Summary Question: Can you talk about your gross revenue retention? - Management noted that NRR reflects expansion primarily from large contracts booked in previous quarters, which will convert into ARR deals over time [16][17] Question: Can you discuss the evolution of LLM training data partnerships? - Management confirmed that the process of converting upfront data purchases into long-term relationships is lengthy but expressed confidence in future conversions [18][19] Question: Where are App Intelligence customers coming from? - The majority of App Intelligence customers are cross-sell from existing clients who trust the company’s digital data offerings [23][25] Question: Why did RPU decline slightly despite focusing on up-market customers? - RPU was impacted by the addition of new customers, particularly larger ones, and management expects fluctuations over time [32][33] Question: Is there an uptick in competition for GenAI products? - Management acknowledged increased interest in GenAI but expressed confidence in maintaining a dominant position due to unique datasets and strong client relationships [57][58]

Financial Data and Key Metrics Changes - Revenue increased by 11% year over year to $72 million, in line with expectations [4][10] - Customer base grew 15% year over year to over 6,000 ARR customers at quarter end [4] - Reported an eighth consecutive quarter of positive free cash flow, generating $43 million over the past eight quarters [8] - Normalized free cash flow for the quarter was $3 million, with a 4% free cash flow margin [11] - Remaining performance obligations (RPO) totaled $268 million at the end of Q3, up 26% year over year [11] - Net revenue retention (NRR) was 98% across all customers and 105% for customers with over $100,000 of ARR [11] Business Line Data and Key Metrics Changes - ARR from GenAI Intelligence products grew rapidly to over $1 million since the launch in April [5] - App Intelligence ARR increased rapidly to above $10 million, with over 580 customers using the product [6] - 58% of ARR is contracted under multi-year contracts, up from 45% last year, indicating revenue durability [10] Market Data and Key Metrics Changes - The company launched Web Intelligence 4.0, integrating GenAI capabilities into its web intelligence solution [5] - Digital app data now covers over 4 million iOS and Android apps across 58 countries, providing comprehensive data coverage [6] Company Strategy and Development Direction - The company is focused on three high-impact opportunities: GenAI intelligence, data selling for LLM, and AI agents [4][5] - The investment in go-to-market strategies is ramping as planned, with a 30% increase in sellers compared to the previous year [8] - The company aims to deliver profitable growth over time while achieving long-term profit and free cash flow targets [8] Management's Comments on Operating Environment and Future Outlook - Management reiterated guidance for 2025 revenues and raised profit guidance for the year due to disciplined execution [4][12] - There is strong customer interest in GenAI data and solutions, which are among the fastest-growing revenue streams [4] - Management expressed confidence in converting current engagements into ARR deals, particularly in the LLM space [15][16] Other Important Information - The company is preparing for the arrival of a new CFO with over 20 years of finance experience [9] - The company is seeing initial good signs of monetization from the new pricing schema for Web Intelligence [29] Q&A Session Summary Question: Can you talk about your gross revenue retention? - Management noted that NRR reflects expansion activity primarily from large contracts booked in previous quarters, which will convert into ARR deals over time [14][15] Question: Can you discuss the evolution of LLM training data partnerships? - Management confirmed that the process of converting data purchases into long-term relationships is lengthy but expressed confidence in future conversions [17][18] Question: Where are App Intelligence customers coming from? - The majority of App Intelligence customers are cross-sell from existing clients who trust the company’s digital data offerings [20][21] Question: Why did RPU decline slightly despite focusing on up-market customers? - RPU was impacted by the addition of new customers, particularly larger ones, which may not have contributed significantly to ARR yet [27][28] Question: What is the impact of declining SEO traffic on demand for web intelligence? - Management observed an increase in demand for solutions as companies seek to fill gaps from declining SEO traffic [29][30] Question: How is the ramp-up of sales representatives progressing? - The company is seeing improvements in go-to-market execution and higher participation from salespeople in generating revenues [31][32] Question: What is driving the lower costs in sales and marketing? - Cost savings are attributed to optimizing sales rep productivity and letting go of underperforming hires while retaining successful ones [36][57] Question: Can you characterize the big deals in the pipeline? - The company is seeing success in selling data for LLM companies, which are critical for building and training models [60][61]

Core Insights - Yann LeCun, a prominent figure in AI research at Meta, has announced his departure from the company to pursue entrepreneurial ventures, marking a significant shift in Meta's AI strategy [1][5][11]. Group 1: Departure and Market Reaction - LeCun's resignation led to a 1.5% drop in Meta's market value during pre-market trading, equating to a loss of over $20 billion [2]. - The ongoing adjustments in Meta's AI strategy, particularly under the leadership of Alexandr Wang, have contributed to LeCun's decision to leave [5][11]. Group 2: Internal Changes and Discontent - LeCun's dissatisfaction with Meta has been building due to frequent restructuring within the AI department, with four reorganizations occurring in just six months, hindering research progress [6][10]. - The recent layoffs of over 600 employees from LeCun's FAIR lab, including key personnel, intensified his frustrations and ultimately influenced his decision to resign [5][10]. Group 3: Strategic Divergence - LeCun's vision for AI, focusing on long-term foundational research and the "world model" architecture, contrasts sharply with Meta's current push towards rapid product development and large language models (LLMs) [11][12]. - The leadership changes at Meta, including the appointment of Wang, have marginalized LeCun's role, leading to a significant shift in the company's AI research direction [8][11]. Group 4: Historical Context - LeCun joined Meta in 2013 and established the FAIR lab, which was known for its academic freedom and focus on foundational AI research, earning him the Turing Award in 2018 [13][14]. - His departure signifies the end of an era for Meta's "academic" approach to AI research, as the company pivots towards a more aggressive, product-driven strategy [16].

Core Viewpoint - Yann LeCun's departure from Meta signifies a critical shift in the company's AI strategy, moving away from long-term foundational research towards a more aggressive, product-driven approach [2][19][27]. Group 1: LeCun's Departure - LeCun announced his departure from Meta, intending to pursue entrepreneurial ventures [2]. - Following the news of his exit, Meta's market value dropped by approximately 1.5%, equating to over $20 billion [4]. - LeCun's dissatisfaction had been building due to frequent restructuring within Meta's AI division, which hindered research progress [8][12]. Group 2: Changes in AI Strategy - Meta's AI strategy has undergone multiple shifts, with four reorganizations in just six months, leading to instability in research [8]. - The appointment of a new chief scientist, who is significantly younger and less experienced, further marginalized LeCun's role within the organization [12]. - Meta's new direction under CEO Mark Zuckerberg emphasizes rapid product development and resource allocation towards competitive AI technologies, particularly large language models (LLMs) [22][27]. Group 3: Conflict of Vision - LeCun advocates for a "world model" approach, which he believes is a long-term vision requiring a decade to develop, contrasting sharply with Meta's immediate focus on LLMs [19][20]. - The internal conflict is exacerbated by a shift towards closed-source models, which opposes LeCun's open-source philosophy [21]. - LeCun's departure marks the end of an era for Meta's AI research, which had been characterized by a commitment to foundational science and open collaboration [27].

Welcome to this demonstration. Today we'll be showing how DDN enables a one-click high-performance rag pipeline for enterprise use. Our rag pipeline solution is enterprise class and easy to deploy and use in any cloud environment whether AWS, GCP, Azure, any NCP cloud and of course on prem.Let's take a closer look at the architecture. This rag pipeline solution is made of several NVIDIA Nemo NIMS or NVIDIA inference microservices which host embedding reranking LLM models a milild vector database a front-end ...

Core Viewpoint - Yann LeCun's departure from Meta signifies a critical shift in the company's AI strategy, moving away from long-term foundational research towards a more aggressive, product-driven approach [1][5][36]. Group 1: Departure and Immediate Impact - LeCun announced his departure plans to colleagues, intending to pursue entrepreneurship [2]. - Following the news of his departure, Meta's market value dropped by 1.5%, equating to over $20 billion [4]. - The decision to leave was influenced by ongoing dissatisfaction with Meta's AI strategy and organizational changes, including significant layoffs within his team [6][10][22]. Group 2: Strategic Shifts at Meta - Meta's AI strategy has undergone multiple reorganizations, with four internal restructurings in just six months, hindering research progress [10][11]. - The appointment of a new chief scientist for the MSL lab effectively sidelined LeCun, altering his influence within the organization [13][14]. - Meta's shift towards a more aggressive AI strategy under CEO Mark Zuckerberg contrasts sharply with LeCun's long-term vision for foundational AI research [27][36]. Group 3: Philosophical and Ideological Differences - LeCun advocates for a "world model" approach, which he believes is essential for true AI understanding, while Meta is focusing on large language models (LLMs) for immediate product development [24][25]. - The ideological clash is further emphasized by the internal discussions at Meta regarding the potential closure of Llama's future versions, which LeCun opposes [26]. - LeCun's commitment to open-source principles stands in stark contrast to the direction taken by Meta's new leadership [26]. Group 4: Historical Context and Legacy - LeCun joined Meta in 2013 and established the FAIR lab, which was known for its academic freedom and focus on foundational research [31][32]. - His contributions to AI were recognized with the Turing Award in 2018, marking a peak in Meta's reputation in AI research [33]. - The end of LeCun's tenure at Meta represents the conclusion of a decade-long era of academic-style research within the company [37].