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Data Cloud Vector Database and Hyperforce
07Jun

Data Cloud Vector Database and Hyperforce

Salesforce World Tour Highlights: Data Cloud Vector Database and Hyperforce At the Salesforce World Tour on June 6, 2024, at the Excel Centre in east London, the focus was on advancements in the Data Cloud and Slack platforms. The event, sponsored by AWS, Cognizant, Deloitte, and PWC, showcased significant innovations, particularly for GenAI enthusiasts. Data Cloud Vector Database and Hyperforce. Vector Database in Data Cloud A key highlight was the announcement of the general availability of a Vector Database capability within the Data Cloud, integrated into the Einstein 1 Platform. This capability enhances Salesforce’s CRM platform, Customer 360, by combining structured and unstructured data about end-users. The Vector Database collects, ingests, and unifies data, allowing enterprises to deploy GenAI across all applications without needing to fine-tune an off-the-shelf large language model (LLM). Addressing Data Fragmentation Salesforce reports that approximately 80% of customer data is dispersed across various corporate departments in an unstructured format, trapped in PDFs, emails, chat conversations, and transcripts. The Vector Database unifies this fragmented data, creating a comprehensive profile of the customer journey. This unified approach not only improves customer engagement but also enhances organizational agility. By consolidating data from all corporate silos, companies can quickly and efficiently address issues such as product recalls and returns. Hyperforce: Enhancing Data Residency and Compliance During the keynote, Salesforce emphasized the importance of personalization in customer engagement and the benefits of deploying GenAI in customer-facing sectors. The event highlighted the need to overcome the fear and mistrust of GenAI and showcased how enterprises can enhance employee productivity through upskilling in GenAI technologies. One notable announcement was the general availability of Hyperforce, a solution designed to address data residency issues by integrating all Salesforce applications under the same compliance, security, privacy, and scalability standards. Built for the public cloud and composed of code rather than hardware, Hyperforce ensures safe delivery of applications worldwide, offering a common layer for deploying all application stacks and handling data compliance in a fragmented technology landscape. Salesforce AI Center The Salesforce AI Center was also introduced at the event. The first of its kind, located in the Blue Fin Building near Blackfriars, London, this center will support AI experts, Salesforce partners, and customers, facilitating training and upskilling programs. Set to open on June 18, 2024, the center aims to upskill 100,000 developers worldwide and is part of Salesforce’s $4 billion investment in the UK and Ireland. Industry Reactions and Future Prospects GlobalData senior analyst Beatriz Valle commented on Salesforce’s continued integration of GenAI across its portfolio, including platforms like Tableau, Einstein for analytics, and Slack for collaboration. According to Salesforce, the Data Cloud tool leverages all metadata in the Einstein 1 Platform, connecting unstructured and structured data, reducing the need for fine-tuning LLMs, and enhancing the accuracy of results delivered by Einstein Copilot, Salesforce’s conversational AI assistant. Vector databases, while not new, have gained prominence due to the GenAI revolution. They power the retrieval-augmented generation (RAG) technique, linking proprietary data with large language models like OpenAI’s GPT-4, enabling enterprises to generate more accurate results. Competitors such as Oracle, Amazon, Microsoft, and Google also offer vector databases, but Salesforce’s early investments in GenAI are proving fruitful with the launch of the Data Cloud Vector Database. Data Cloud Vector Database and Hyperforce Salesforce’s AI-powered integration solutions, highlighted during the World Tour, underscore the company’s commitment to advancing digital transformation. By leveraging GenAI and innovative tools like the Vector Database and Hyperforce, Salesforce is enabling enterprises to overcome the challenges of data fragmentation and compliance, paving the way for a more agile and competitive digital future. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 7, 2024in Einstein 1 Platform, Hyperforce, Salesforce, Salesforce Data Cloud
Salesforce Legacy Chat Service Retirement
07Jun

Salesforce Summer 24 Customization Release

Customization Salesforce Summer 24 Customization Release Manage users more easily with the user access, public group, permission set, and permission set group summaries. Give record page users more of what they need where and when they need it with Lightning record page enhancements such as blank space support and visibility rules on individual tabs. Salesforce Summer 24 Customization Release Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 7, 2024in Salesforce
Consumer Chatbot Technology
07Jun

Consumer Chatbot Technology

The Reality Behind AI Chatbots and the Path to Autonomous AI In the rush to adopt the latest Consumer Chatbot Technology, it’s easy to overlook a fundamental reality: consumer chatbot technology isn’t ready for enterprise use—and it likely never will be. The reason is simple: AI assistants are only as effective as the data that powers them. Most large language models (LLMs) are trained on data from public websites, which lack the specific business and customer data that enterprises need. This means consumer bots can’t adequately assist employees in selling products, marketing merchandise, or improving productivity, as they lack the necessary personalization and business context. To achieve the vision of AI that goes beyond simple chatbots performing basic tasks—like drafting emails, essays, blogs, or graphics—to a more advanced role where AI acts autonomously and addresses business-critical needs, a different approach is needed. This vision involves AI taking action with minimal human intervention, using digital agents to identify and respond to these needs. At Salesforce, we are pursuing a clear path to AI that not only takes action but also automates routine tasks, all while adhering to established business rules, permissions, and context. Instead of relying solely on LLMs, which primarily focus on generating human-like text, future AI assistants will depend on large action models (LAMs) that integrate decision-making and action-taking capabilities. The Journey Toward AI Autonomy Our journey towards this vision began with the Salesforce Data Cloud, a robust data engine built on the Einstein 1 Platform. This platform integrates data from across the enterprise and third-party repositories, enabling companies to activate their data, automate workflows, personalize customer interactions, and develop smarter AI solutions. Recognizing the shift from generative AI to autonomous AI, Salesforce introduced Einstein Copilot, the industry’s first conversational, enterprise-class AI assistant. Integrated across the Salesforce ecosystem, Einstein Copilot utilizes an organization’s data, whether it’s behind a firewall or in an external data lake, to act as a reasoning engine. It interprets user intents, interacts with the most suitable AI model, solves problems, generates relevant content, and provides decision-making support. Expanding the Role of AI in Business Since its launch in February 2024, Salesforce has been expanding Einstein Copilot’s library of actions to meet specific business needs in sales, service, marketing, data analysis, and industries like ecommerce, financial services, healthcare, and education. These “actions” are akin to LEGO blocks—discrete tasks that can be assembled to achieve desired project outcomes. For example, a sales representative might use Einstein Copilot to generate a personalized close plan, gain insights into why a deal may not close, or review whether pricing was discussed in a recent call. Einstein Copilot then orchestrates these tasks, provides recommendations, and compiles everything into a detailed report. The ultimate goal is for AI not only to gather and organize information but also to take proactive action. Imagine a sales representative instructing their digital agent to set up meetings with top prospects in a specific territory. The AI could not only identify suitable contacts but also suggest meeting times, plan travel schedules, draft emails, and even create talking points—all of which it could execute autonomously with the representative’s approval. Tectonic dreams of the day AI is smart enough to interpret our search engine typos and produce the results for what we were actually looking for! The Future of AI Autonomy The possibilities for semi-autonomous or fully autonomous AI are vast. As we continue to develop and refine these technologies, the potential for AI to transform business processes and decision-making becomes increasingly tangible. At Salesforce, they are committed to leading this charge, ensuring that our AI solutions not only meet but exceed the expectations of enterprises worldwide. Salesforce is in a strong position to deliver on all of them because of the volume and breadth of data housed in Data Cloud, the heavy workflow traffic in our Customer 360 CRM, and the fact we’ve delivered an enterprise-class copilot that is rapidly expanding its library of actions. It will not happen overnight. The technology needs to advance, organizations and people have to be able to trust AI and be trained to use it in the right ways, and more work will need to be done to ensure the right balance between human involvement and AI autonomy. But with our continued investment in CRM, data, and trusted AI, we will achieve that vision before too long. Salesforce is in a strong position to deliver on all of them because of the volume and breadth of data housed in Data Cloud, the heavy workflow traffic in our Customer 360 CRM, and the fact we’ve delivered an enterprise-class copilot that is rapidly expanding its library of actions. Jayesh Govindarajan, Senior Vice President, Salesforce AI Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 7, 2024in Einstein 1 Platform, Generative AI, Salesforce Data Cloud
Field Types to Custom Report Type
07Jun

Field Types to Custom Report Type

How to Add Fields to a Custom Report Type If a report based on a Custom Report Type is missing some fields, you’ll need to edit the layout of the report type to make those fields available. For example, if an ‘Activities with Accounts and Contacts’ report type lacks certain fields in the ‘Select Columns’ step of the Report Builder, follow the steps below to resolve this. Steps to Add Missing Fields to Your Custom Report: Note: When previewing the layout, you’ll see all fields and objects, even those you don’t have permission to access. You can only access data stored in fields or objects that you have the appropriate permissions for. Once you’ve completed these steps, the missing fields will be available in the report. Additional Resources For more tips, visit the Salesforce Help page: Design the Field Layout for Reports Created From Your Custom Report. You may also want to explore more about creating a Custom Report Type and Standard Report Types for further customization. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 7, 2024in Salesforce
ChatGPT 5.0 is Coming
06Jun

ChatGPT 5.0 is Coming

Sam Altman Teases ChatGPT-5: Here’s What We Know GPT-5: A Major Leap in AI Following the release of GPT-4, anticipation for its successor, GPT-5, has been growing. According to reports from Business Insider, GPT-5 is expected to debut in mid-2024, potentially marking a significant advancement in AI capabilities. Insiders describe GPT-5 as “materially better,” with enhancements that could transform AI-driven communication and composition. ChatGPT 5.0 is Coming. The Journey to GPT-5 After GPT-4’s launch, speculation about GPT-5’s arrival intensified. OpenAI CEO Sam Altman has hinted at the upcoming release, assuring groundbreaking advancements. However, concrete details were scarce until recent reports provided a clearer timeline for GPT-5’s debut. What to Expect from GPT-5 Early demonstrations of GPT-5 have impressed insiders, with one CEO describing it as “really good.” The model promises significant improvements, showcasing its versatility in real-world applications. From unique use cases for individual enterprises to autonomous AI agents, GPT-5 is poised to expand the boundaries of AI capabilities. Evolution of Language Models Understanding GPT-5’s significance involves tracing the evolution of OpenAI’s language models. From the groundbreaking GPT-3 in 2020 to the iterative improvements leading to GPT-4 Turbo, each iteration has advanced the sophistication of AI-driven communication tools. ChatGPT 5.0 is Coming: A Multimodal Approach Building on its predecessors, GPT-5 is expected to offer a multimodal experience, integrating text and encoded visual input. This capability opens up numerous applications, from content generation to image captioning, further embedding AI in various domains. Next-Token Prediction and Conversational AI At its core, GPT-5 remains a next-token prediction model, generating contextually relevant responses based on input prompts. This functionality underpins conversational AI applications like ChatGPT, enabling seamless user-AI interactions. Challenges and Opportunities Ahead As OpenAI prepares for GPT-5’s launch, the focus shifts to the challenges and opportunities it presents. Addressing concerns about model performance and reliability, and exploring novel use cases, the journey towards realizing the full potential of AI-driven language models is filled with possibilities. Ensuring Safety and Reliability Ensuring the success of GPT-5 involves rigorous testing and validation to guarantee its safety and reliability. As AI continues to advance, maintaining transparency and accountability in its development is crucial. Unlocking New Frontiers Beyond immediate applications, GPT-5 represents a significant step towards unlocking new frontiers in AI innovation. From enhancing natural language understanding to facilitating human-machine collaboration, the implications of GPT-5 extend far beyond its initial release. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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June 6, 2024in Salesforce Einstein
Salesforce Announces Slack Lists for Project Management
06Jun

Salesforce Announces Slack Lists for Project Management

Salesforce Transforms Slack into Project Management Platform with New Slack Lists Feature Salesforce Inc. has introduced Slack Lists, a new feature designed to transform Slack from a simple collaboration tool into a comprehensive project management platform. Initially rolling out in select regions, Slack Lists will soon be available to all paid Slack users, integrating project and task management directly into the Slack interface. Salesforce Announces Slack Lists for Project Management. And this answers the age old question, can Salesforce be used as a project management tool? Key Benefits and Features Collaborative Tools Slack Lists excels in fostering collaboration by allowing team members to bring relevant coworkers into conversations and create message threads for specific list items. These threads consolidate discussions about particular items, requests, or issues into single, organized conversations. This ensures tasks are not forgotten and keeps all team members informed about project progress. Executive Insights Denise Dresser, CEO of Slack, stated that Slack Lists transforms conversations into actionable tasks that drive work forward. “Now those loose steps shared in a project channel can be tracked across teams,” she explained. “With project management in Slack, teams across organizations will have everything they need to complete projects faster and increase productivity.” Practical Applications Salesforce emphasized that Slack Lists can benefit any organization within an enterprise. For example: Holger Mueller of Constellation Research Inc. noted that Salesforce has long aimed to extend Slack’s capabilities beyond core messaging and collaboration. “Salesforce has opted to go generic with Slack Lists, allowing users to create list entries for projects, service requests, and more,” he said. “This ideally makes collaboration on those projects easier, making Slack more goal-oriented and changing the future of work.” Early Adoption Marriott International Inc. participated in the pilot phase of Slack Lists before its general release. Lori Drake, Director of System Strategy at Marriott Digital Services, expressed enthusiasm about the feature, saying, “We’re excited to use lists to manage projects and track requests, and think it can unlock significant time savings for our teams.” Availability Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 6, 2024in Generative AI, Salesforce, Salesforce Slack
GPT-4o GPT4 and Gemini 1.5
06Jun

GPT-4o GPT4 and Gemini 1.5

An Independent Analysis of GPT-4o’s Classification Abilities Article by Lars Wilk OpenAI’s recent unveiling of GPT-4o marks a significant advancement in AI language models, transforming how we interact with them. The most impressive feature is the live interaction capability with ChatGPT, allowing for seamless conversational interruptions. GPT-4o GPT4 and Gemini 1.5 Despite a few hiccups during the live demo, the achievements of the OpenAI team are undeniably impressive. Best of all, immediately after the demo, OpenAI granted access to the GPT-4o API. In this article, I will present my independent analysis, comparing the classification abilities of GPT-4o with GPT-4, Google’s Gemini, and Unicorn models using an English dataset I created. Which of these models is the strongest in understanding English? What’s New with GPT-4o? GPT-4o introduces the concept of an Omni model, designed to seamlessly process text, audio, and video. OpenAI aims to democratize GPT-4 level intelligence, making it accessible even to free users. Enhanced quality and speed across more than 50 languages, combined with a lower price point, promise a more inclusive and globally accessible AI experience. Additionally, paid subscribers will benefit from five times the capacity compared to non-paid users. OpenAI also announced a desktop version of ChatGPT to facilitate real-time reasoning across audio, vision, and text interfaces. How to Use the GPT-4o API The new GPT-4o model follows the existing chat-completion API, ensuring backward compatibility and ease of use: pythonCopy codefrom openai import AsyncOpenAI OPENAI_API_KEY = “<your-api-key>” def openai_chat_resolve(response: dict, strip_tokens=None) -> str: if strip_tokens is None: strip_tokens = [] if response and response.choices and len(response.choices) > 0: content = response.choices[0].message.content.strip() if content: for token in strip_tokens: content = content.replace(token, ”) return content raise Exception(f’Cannot resolve response: {response}’) async def openai_chat_request(prompt: str, model_name: str, temperature=0.0): message = {‘role’: ‘user’, ‘content’: prompt} client = AsyncOpenAI(api_key=OPENAI_API_KEY) return await client.chat.completions.create( model=model_name, messages=[message], temperature=temperature, ) openai_chat_request(prompt=”Hello!”, model_name=”gpt-4o-2024-05-13″) GPT-4o is also accessible via the ChatGPT interface. Official Evaluation GPT-4o GPT4 and Gemini 1.5 OpenAI’s blog post includes evaluation scores on known datasets such as MMLU and HumanEval, showcasing GPT-4o’s state-of-the-art performance. However, many models claim superior performance on open datasets, often due to overfitting. Independent analyses using lesser-known datasets are crucial for a realistic assessment. My Evaluation Dataset I created a dataset of 200 sentences categorized under 50 topics, designed to challenge classification tasks. The dataset is manually labeled in English. For this evaluation, I used only the English version to avoid potential biases from using the same language model for dataset creation and topic prediction. You can check out the dataset here. Performance Results I evaluated the following models: The task was to match each sentence with the correct topic, calculating an accuracy score and error rate for each model. A lower error rate indicates better performance. Conclusion This analysis using a uniquely crafted English dataset reveals insights into the state-of-the-art capabilities of these advanced language models. GPT-4o stands out with the lowest error rate, affirming OpenAI’s performance claims. Independent evaluations with diverse datasets are essential for a clearer picture of a model’s practical effectiveness beyond standardized benchmarks. Note that the dataset is fairly small, and results may vary with different datasets. This evaluation was conducted using the English dataset only; a multilingual comparison will be conducted at a later time. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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June 6, 2024in Salesforce Einstein, Salesforce Tableau
Five9 Salesforce AI Integration
05Jun

Five9 Salesforce AI Integration

Five9 and Salesforce Enhance AI-Powered Solutions for Superior Customer Experiences Five9 (NASDAQ: FIVN), a provider of the Intelligent CX Platform, today announced the next step in its collaboration with Salesforce. Five9 Salesforce AI Integration. This partnership aims to deliver AI-powered solutions to enhance customer experiences (CX) in contact centers. The latest release, Five9 for Service Cloud Voice with Partner Telephony, integrates Salesforce Einstein with Five9’s suite of AI solutions. This empowers agents to better service customer requests, improves management’s understanding of contact center operations, and delivers customer resolutions that exceed expectations. Using Five9’s open APIs and Five9 TranscriptStream, the Einstein AI engine identifies opportunities to provide real-time solutions for agents, prompting ‘Next Best Action’ guidance. The solution also offers real-time transcription of customer conversations, ensures call recordings’ accuracy and relevance, and integrates with Salesforce Einstein Conversation Insights to enhance conversation intelligence. “Five9 understands the power of elevating the customer experience through innovative technology and seamless integrations,” said Dan Burkland, President of Five9. “Our collaboration with Salesforce pushes the boundaries of what is possible. Infusing Einstein’s AI insights into the contact center and CRM eliminates repetitive tasks while guiding agents with the next best actions to help them be more effective.” A Long-Standing Partnership The Salesforce-Five9 collaboration, now over 15 years strong, recently introduced Five9 call dispositions for agents within the Salesforce Omni-Channel widget or Voice Call page. This allows organizations to automatically update call dispositions in the Five9 call database, ensuring accurate reporting across the integration. Both companies are meeting the growing demand for AI solutions to enhance customer engagement throughout the customer journey. “Five9’s deeper integration with Salesforce Einstein offers a new level of choice for customers seeking AI capabilities that best match their contact center needs and existing technology investments,” said Sheila McGee-Smith, President & Principal Analyst at McGee-Smith Analytics. “Coupled with features like Five9 TranscriptStream, organizations can significantly reduce an agent’s workload while enhancing the customer’s overall experience. This next step in the Salesforce-Five9 relationship demonstrates each company’s commitment to their joint customer base, enabling them to leverage the latest AI innovations easily.” “Service Cloud Voice with Five9 uses AI to deliver a better customer experience,” said Ryan Nichols, Chief Product Officer of Service Cloud, Salesforce. “Our collaboration focuses on more than just a ‘single pane of glass’– we’re bringing together customer data, knowledge, and real-time conversation transcripts to help make agents more productive and delight customers.” Availability and Further Information These new enhancements to Five9 for Service Cloud Voice with Partner Telephony will be available starting June 30. For a deeper look into the Five9 integration with Service Cloud Voice and to explore common use cases, register for the webinar “Unlock Efficiency with the Power of AI: Five9 and Salesforce Service Cloud Voice” on Tuesday, July 23. An on-demand playback of the December 2023 Five9 and Salesforce joint webinar is also available, covering topics such as using data for personalization, best practices for leveraging engagement data to improve experiences, and how companies can become more customer-centric. Salesforce, Einstein, and other related marks are trademarks of Salesforce, Inc. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 5, 2024in Salesforce, Salesforce Einstein, Salesforce Service Cloud
Unified Knowledge in Salesforce
05Jun

Unified Knowledge in Salesforce

A year following Salesforce’s introduction of the Einstein Trust Layer, aimed at safeguarding against the potential pitfalls of implementing Generative AI (GenAI) in enterprise settings, the discourse surrounding GenAI has remained both intriguing and cautionary. Business leaders are navigating its optimal applications to enrich customer and employee experiences. Enter Unified Knowledge in Salesforce. Unlocking the Power of Unified Knowledge The Einstein Trust Layer addressed critical concerns about GenAI, focusing on mitigating unwanted behaviors and preserving customer and corporate privacy. However, the current hurdle facing GenAI adoption pertains to data management. The efficacy of GenAI hinges on access to comprehensive and pertinent knowledge. This underscores the challenges in aggregating and accessing the right information, prompting Salesforce’s recent unveiling of Unified Knowledge in collaboration with Zoomin. This initiative aims to streamline data utilization across platforms, facilitating seamless integration of corporate data. Challenges in Data Aggregation and Preparation Enterprises typically grapple with fragmented data across various systems. Integrating disparate data formats and siloed systems poses a formidable challenge. Historically, the absence of automated systems to extract insights from unstructured data hindered effective data preparation. However, the advent of GenAI has underscored the need for advanced solutions to access extensive data repositories effortlessly. Salesforce’s partnership with Zoomin addresses this need, offering sophisticated tools to simplify data aggregation and preparation. Zoomin’s Role in Enhancing Salesforce Capabilities Zoomin’s technology facilitates integration with diverse third-party data sources, including Google Drive, AWS S3, Zendesk, and other Salesforce orgs. Beyond integration, Zoomin streamlines data preparation and integration processes, fostering a structured approach to managing unstructured data. Standardization through Taxonomy: Zoomin categorizes data into a hierarchical structure, enabling organizations to standardize content classification. This taxonomy is instrumental in aiding GenAI’s comprehension and retrieval of relevant information. Enhanced Search and Filtering: Tags and facets defined in the taxonomy facilitate refined searches, enhancing accessibility to specific content based on various parameters. Automated Categorization and Syncing: Zoomin’s auto-categorization features automate document classification according to the defined taxonomy. This ensures data remains current and organized within Salesforce’s ecosystem. Zoomin’s technology alleviates manual data preparation efforts through features like content tagging, auto-categorization, and seamless syncing with Salesforce Knowledge. For instance, technical manuals stored in Google Drive are automatically categorized, tagged, and synced with relevant sections in Salesforce Knowledge, ensuring quick access to accurate information. Unlocking the Power of Unified Knowledge Salesforce and Zoomin’s collaboration exemplifies efforts to harness distributed knowledge resources effectively. Unified Knowledge, currently in open Beta, is set to enhance GenAI capabilities and streamline data management. However, knowledgeable employees are essential for initial tagging to ensure accuracy. This approach ensures precise information delivery, enhancing the intelligence and responsiveness of GenAI-driven service platforms. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 5, 2024in Salesforce, Unified Knowledge
Snowflake With AWS Salesforce and Microsoft
04Jun

Snowflake With AWS Salesforce and Microsoft

In anticipation of its sixth annual user conference, Snowflake Summit 2024, Snowflake has unveiled the Polaris Catalog, a vendor-neutral, open catalog implementation for Apache Iceberg. This open standard is widely used for implementing data lakehouses, data lakes, and other data architectures. Snowflake With AWS Salesforce and Microsoft. The Polaris Catalog will be open-sourced for the next 90 days, offering enterprises like Goldman Sachs and the Iceberg community increased choice, flexibility, and control over their data. It also promises comprehensive enterprise security and compatibility with Apache Iceberg, enabling interoperability with AWS, Confluent, Dremio, Google Cloud, Microsoft Azure, Salesforce, and more. “We are collaborating with numerous industry partners to provide our mutual customers the ability to mix and match various query engines and coordinate read and write operations without vendor lock-in, and most importantly, to do so in an open manner.” Christian Kleinerman, Snowflake’s EVP of Product Kleinerman further highlighted that this initiative can “simplify how organizations access their data across diverse systems, enhancing flexibility and control.” Apache Iceberg, which became a top-level Apache Software Foundation project in May 2020 after emerging from incubation, has quickly become a leading open-source data table format. Building on this success, Polaris Catalog offers users a centralized location for any engine to discover and access an organization’s Iceberg tables with open interoperability. To ensure Polaris Catalog meets the evolving needs of the community, Snowflake is collaborating with the Iceberg ecosystem to advance the project. Chris Grusz, MD of technology partnerships at AWS, noted AWS’s commitment to working with partners on open-source solutions that enhance customer choice: “We’re pleased to work with Snowflake to continue to make Apache Iceberg interoperable across our engines.” Similarly, Raveendrnathan Loganathan, EVP of software engineering at Salesforce, mentioned that Apache Iceberg’s popularity has established an open storage standard simplifying zero-copy data access for organizations. “We’re thrilled to have Snowflake as a member of our Zero Copy Partner Network, and we’re excited about how this new open catalog standard will further zero-copy access in the enterprise,” he said. This development follows the recent expansion of the partnership between Snowflake and Microsoft, supporting leading open standards for storage formats, including Apache Iceberg and Apache Parquet. With Polaris Catalog, they aim to continue their mission of enabling users to leverage their enterprise data, regardless of its location, to develop AI-powered applications at scale. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 4, 2024in Salesforce, Snowflake
Salesforce with SharpLaunch
04Jun

Salesforce with SharpLaunch

Supercharge Your Salesforce with SharpLaunch Streamline Your Sales ProcessIntegrate Salesforce with SharpLaunch to simplify and automate your commercial real estate (CRE) operations. Key Features Push Leads Directly to SalesforceSay goodbye to manual data entry. Automatically transfer valuable leads from SharpLaunch to Salesforce and focus on closing deals. Continuously Sync DataKeep your CRM updated effortlessly. Lead data captured in SharpLaunch syncs automatically to your Salesforce fields, ensuring accuracy and up-to-date information. Boost ProductivityBy connecting SharpLaunch with Salesforce, you can streamline workflows, reduce administrative tasks, and accelerate your sales cycle. Why SharpLaunch Stands Out Fully Customizable Designs Tailor every digital asset to match your brand.From property websites to interactive maps, SharpLaunch delivers personalized, client-facing solutions that elevate your brokerage’s presence. Enterprise-Level Integrations Unify your tech stack seamlessly.SharpLaunch integrates with any tools you’re already using, fitting into your workflows to maximize efficiency with minimal effort. World-Class Service Enjoy dedicated, personalized support.Skip the chatbots and ticket queues. Work directly with your Customer Success Manager from setup to success. Complete Data Ownership Keep control of your information.With SharpLaunch, you retain full ownership of your data, ensuring sensitive client and property information stays secure and private. Ready to Transform Your Salesforce Experience? Connect SharpLaunch to Salesforce today and empower your sales team to close deals faster while maintaining full control over your data and brand identity. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 4, 2024in Data, Enterprise, Salesforce

AI Agents

Lessons Learned in the First Year of Developing AI Agents In the first year of working on AI agents, valuable insights emerged from direct collaboration with engineers and UX designers, as they iterated on the overall product experience. The objective was to create a platform for customers to use standard data analysis agents and build custom agents tailored to specific tasks and data structures relevant to their business. This platform integrates connectors to databases like Snowflake and BigQuery with built-in security, supports RAG over a metadata layer describing database contents, and facilitates data analysis through SQL, Python, and data visualization tools. Feedback on the effectiveness of these developments came from both internal evaluations and customer insights. Users from Fortune 500 companies utilize these agents daily to analyze their internal data. Key Insights on AI Agents Additional Insights Further insights on code and infrastructure include: These lessons underscore the importance of focusing on reasoning, iterative improvements to the agent-computer interface, understanding model limitations, and building robust supporting infrastructure to enhance AI agent performance and user satisfaction. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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June 4, 2024in Salesforce Einstein
Trust Einstein Copilot for Tableau
04Jun

Trust Einstein Copilot for Tableau

Are you prepared to utilize the capabilities of Einstein Copilot to expand your organization’s analytical advantages? This robust tool facilitates data exploration, insights generation, and visualization development at an unprecedented pace. However, before immersing yourself in its capabilities, it’s crucial to grasp how Einstein Copilot upholds Tableau and Salesforce’s core value: Trust. Let’s discover how the Einstein Trust Layer safeguards your data, ensures result accuracy, and facilitates auditing, addressing common questions and concerns raised by our customers.Trust Einstein Copilot for Tableau. What is Einstein Copilot for Tableau? Using generative AI and statistical analysis, Einstein Copilot for Tableau is able to understand the context of your data to create and suggest relevant business questions to help kickstart your analysis. A smart, conversational assistant for Tableau users, Einstein Copilot for Tableau automates data curation—the organization and integration of data collected from various sources—by generating calculations and metadata descriptions. Einstein Copilot for Tableau can fill data gaps and enhance analysis by creating synthetic datasets where real data is limited. Einstein Copilot helps you anticipate outcomes with predictive analytics that simulate diverse scenarios and uncover hidden correlations. Additionally, generative models can increase data privacy by producing non-traceable data for analysis.  Fulfilling the promise of generative AI, Einstein Copilot for Tableau presents an efficient, insightful, and ethical approach to data analytics. Think of it as an intelligent assistant integrated into the Tableau suite of products to make everyone successful in their analysis workflow—whether they’re an experienced data analyst or a data explorer. As your intelligent analytics AI assistant, Einstein Copilot for Tableau guides you through the process of creating data visualizations in Tableau by assisting you with recommended questions, conversational data exploration, guided calculation creation, and more. Understanding the Einstein Trust Layer The Einstein Trust Layer constitutes a secure AI architecture embedded within the Salesforce platform. Comprising agreements, security technology, and data privacy controls, it ensures the safety of your data while exploring generative AI solutions. Built upon the Einstein Trust Layer, Einstein Copilot for Tableau and other Tableau AI features inherit its security, governance, and Trust capabilities. The Einstein Trust Layer is a secure AI architecture, built into the Salesforce platform. It is a set of agreements, security technology, and data and privacy controls used to keep your company safe while you explore generative AI solutions. Tableau has been on the journey to help people see and understand their data for over two decades. Thanks to data analysts, this mission has been a success and will continue to be a success. Data analysts are the backbone of organizations that champion data culture, capture business requirements, prep data, and create data content for end users. Data Access and Privacy Who Accesses Your Data? A primary concern among our customers revolves around data access. Rest assured, the Einstein Trust Layer enforces strict policies to safeguard your organization’s data. Third-party LLM providers, including Open AI and Azure Open AI, adhere to a zero data retention policy. This means that data sent to LLMs isn’t stored; once processed, both the prompt and response are promptly forgotten. Additionally, each Einstein Copilot for Tableau customer receives their own Data Cloud instance, securely storing prompts and responses for auditing purposes. Data Residency and Access Control Einstein Copilot for Tableau respects permissions, row-level security, and data policies within Tableau Cloud, ensuring that only authorized personnel within your organization access specific data. Whether using Einstein Copilot or not, data access is restricted based on organizational roles and permissions. Data Handling and Processing Data Sent Outside of Tableau Cloud Site Einstein Copilot for Tableau operates within the confines of your Tableau site, scanning connected data sources to create a summary context. This summarized data is sent to third-party LLM providers for vectorization, enabling accurate interpretation of user queries. Importantly, the zero data retention policy ensures that summarized data is forgotten post-vectorization. Personally Identifiable Information (PII) Data To enhance data privacy, Einstein Copilot for Tableau employs data masking for PII data. This technique replaces sensitive information with placeholder text, ensuring privacy without sacrificing context. While our detection models strive for accuracy, continuous evaluation and refinement are paramount to maintain trust. Result Trustworthiness Ensuring Safe and Accurate Results Einstein Copilot for Tableau employs Toxicity Confidence Scoring to identify harmful inputs and responses. By combining rule-based filters and AI models, potentially harmful content is filtered and flagged for review. Furthermore, accuracy benchmarks ensure that generated results align closely with human-authored ones, bolstering trust in the platform. Future Trust Enhancements Trust remains an ongoing focus for our teams. Initiatives such as a BYO LLM solution and improved disambiguation capabilities are underway to further enhance trustworthiness. Continuous feedback, testing, and iteration drive our efforts to maintain your trust in Einstein Copilot for Tableau and the Einstein Trust Layer. Data analysis and data-driven decision-making have been part of the vocabulary in organizations over the years. And, while data analysis is one of the most in-demand tech skills sought by employers today, not everyone in an organization has “analyst” in their job title—myself included. Yet, so many of us use data daily to make informed decisions. The rise of generative AI presents a significant opportunity for us to bring transformative benefits to analytics. Businesses are eager to embrace generative AI because it can help save time, provide faster insights, and empower analysts to be even more productive with an AI assistant—freeing analysts to focus on delivering high-quality, data-driven insights. Is Tableau replacing Einstein analytics? Einstein Analytics has a new name. Say hello to Tableau CRM. Everything about how it works stays the same, just with that snazzy new name. When Tableau joined the Salesforce family, we brought together analytics capabilities of incredible depth and power. What is the difference between Einstein analytics and Tableau? If you’re only planning on analyzing Salesforce data, Einstein Analytics would probably make the most sense for you. However, if you need to analyze information that is coming from all over the place, Tableau will give your users more options. Tableau GPT infuses automation in every part of analytics – from preparation to communicating

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June 4, 2024in Einstein 1 Platform, Salesforce Tableau
Slack for Manufacturing and Automotive
03Jun

Slack for Manufacturing and Automotive

Slack for Manufacturing and Automotive Enhance productivity, reduce costs, and provide exceptional experiences with a consolidated perspective of your customer data. Streamline diverse systems, teams, and processes effortlessly through automation. Forge connections with external partners to integrate your entire ecosystem seamlessly. As manufacturing, automotive, and energy organizations transition to developing new powertrains and digital products for innovative service and revenue models, Slack offers an efficient platform for innovation. Pioneering Enterprise Security Setting the standard in enterprise security, Slack ensures data encryption in transit and at rest. It boasts comprehensive compliance and assurance programs, along with features such as audit logs, data loss prevention, and single sign-on. As the productivity platform for manufacturing, automotive, and energy, Slack ensures a secure environment. Utilizing Slack AI for Smarter Work Engage with a Slack sales representative or join the waitlist to experience the empowering capabilities of Slack AI throughout your organization. Leverage AI-powered search for swift answers, summarize conversations effortlessly, and rest assured with secure data handling by Slack AI. Explore Slack’s pivotal role in accelerating innovation across manufacturing, automotive, and energy sectors. Empowering Software Developers Discover how Slack empowers teams to introduce novel digital products and services, driving revenue and transforming customer experiences. For software developers, Slack accelerates the delivery of high-quality code, making it a preferred choice for the world’s leading producers of software, hardware, and services. Explore Slack’s webinar to uncover its potential for your team. Revolutionizing Fleet Management with Automile Challenges abound for businesses managing fleets, particularly in integrating solutions seamlessly with existing toolsets for increased productivity. Automile aims to disrupt the billion fleet management market by introducing a mobile-first API-centric solution. With REST-based JSON APIs and SDKs for PHP, Java, and C# .NET, Automile simplifies fleet management, offering web and mobile apps. Slack Integration with Automile Automile is set to release new features in March, including integrations such as Slack. By submitting the app to Slack’s App Directory, Automile aims to provide businesses with a streamlined fleet management experience within Slack. The upcoming Slack App supports Slash Commands, Interactive Messages, and Incoming Webhooks. Security First Approach Automile prioritizes security with the new Slack App, ensuring that authorized Slack team members have access. The app supports Slash Commands, enabling users to achieve specific tasks, such as checking out drivers and locating vehicles. The admin can control user access to these commands for added security. Fleet Management Commands Automile’s Slack App introduces Slash Commands for drivers and vehicles. The Driver command allows fleet managers to search for drivers, check their status, and interact with them directly from Slack. Similarly, the Vehicle command provides information on vehicle location, status, and enables task assignment. Driving Field Service Efficiency with Slack and Salesforce Service Cloud Witness how manufacturers harness the combined capabilities of Slack and Salesforce Service Cloud to empower field employees and enhance customer satisfaction. Slack’s Continued Impact Slack continues to thrive globally, supporting businesses of all sizes in achieving growth and skyrocketing productivity. Acquired by Salesforce in 2021, Slack remains an influential force in the business communication and collaboration landscape. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 3, 2024in Salesforce Implementation Services, Salesforce Managed Services, Salesforce Manufacturing Automotive Energy, Salesforce Service Cloud, Salesforce Slack
03Jun

An Eye on AI

Humans often cast uneasy glances over their shoulders as artificial intelligence (AI) rapidly advances, achieving feats once exclusive to human intellect. An Eye on AI should ease their troubled minds. AI-driven chatbots can now pass rigorous exams like the bar and medical licensing tests, generate tailored images and summaries from complex texts, and simulate human-like interactions. Yet, amidst these advancements, concerns loom large — fears of widespread job loss, existential threats to humanity, and the specter of machines surpassing human control to safeguard their own existence. Skeptics of these doomsday scenarios argue that today’s AI lacks true cognition. They assert that AI, including sophisticated chatbots, operates on predictive algorithms that generate responses based on patterns in data inputs rather than genuine understanding. Even as AI capabilities evolve, it remains tethered to processing inputs into outputs without cognitive reasoning akin to human thought processes. So, are we venturing into perilous territory or merely witnessing incremental advancements in technology? Perhaps both. While the prospect of creating a malevolent AI akin to HAL 9000 from “2001: A Space Odyssey” seems far-fetched, there is a prudent assumption that human ingenuity, prioritizing survival, would prevent engineering our own demise through AI. Yet, the existential question remains — are we sufficiently safeguarded against ourselves? Doubts about AI’s true cognitive abilities persist despite its impressive functionalities. While AI models like large language models (LLMs) operate on vast amounts of data to simulate human reasoning and context awareness, they fundamentally lack consciousness. AI’s creativity, exemplified by its ability to invent new ideas or solve complex problems, remains a simulated mimicry rather than authentic intelligence. Moreover, AI’s domain-specific capabilities are constrained by its training data and programming limitations, unlike human cognition which adapts dynamically to diverse and novel situations. AI excels in pattern recognition tasks, from diagnosing diseases to classifying images, yet it does so without comprehending the underlying concepts or contexts. For instance, in medical diagnostics or art authentication, AI can achieve remarkable accuracy in identifying patterns but lacks the interpretative skills and contextual understanding that humans possess. This limitation underscores the necessity for human oversight and critical judgment in areas where AI’s decisions impact significant outcomes. The evolution of AI, rooted in neural network technologies and deep learning paradigms, marks a profound shift in how we approach complex tasks traditionally performed by human experts. However, AI’s reliance on data patterns and algorithms highlights its inherent limitations in achieving genuine cognitive understanding or autonomous decision-making. In conclusion, while AI continues to transform industries and enhance productivity, its capabilities are rooted in computational algorithms rather than conscious reasoning. As we navigate the future of AI integration, maintaining a balance between leveraging its efficiencies and preserving human expertise and oversight remains paramount. Ultimately, the intersection of AI and human intelligence will define the boundaries of technological advancement and ethical responsibility in the years to come. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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June 3, 2024in Generative AI

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