8x8 Inc (EGHT) Quote

might this stock ever be above 4.00 again? thoughts anyone?

Sure. I'll be in this at least till $4 and then I have to reevaluate

Thank you, infamous!

I'm saying this company has never been in better shape with their operations etc. This has to go to $10 and above in the next 5 years

I have bought it and sold it. And bought it again months later. Not implying anything. Just asking.

So I guess youre implying that you're not buying it currently.

Thanks for the tip. I'll try to be a better moron in the future.

IMO if youre not buying the stock NOW, youre a moron. look at the cyclical chart. this will be above 3.00 again within a year.

doc: ChatGPT can say just about anything but as an investment, it is mum.

In January of 2025, EGHT was at $2.80 while today it is a dollar lower. It appears that no one is accepting their platform. I have invested in this
company but it doesn't look promising especially when the Board of directors are handed out shares like candy and the stock price does nothing.

integration with openai?
wags: i asked chatgpt
"platform implementation strategies


ChatGPT said:
Implementing a robust platform that integrates CPaaS (Communications Platform as a Service) and UCaaS (Unified Communications as a Service) to facilitate collaboration between customer-facing agents, internal subject matter experts (SMEs), and generative AI systems requires a strategic approach. Here are key strategies to consider:

1. Phased Rollout Deployment
Implementing the platform in stages allows for manageable adoption and minimizes disruptions. Begin with a pilot program involving a specific department or team, gather feedback, and make necessary adjustments before a company-wide rollout. This approach ensures smoother transitions and better user acceptance.
Klaxoon

2. Parallel Adoption Strategy
Running the new platform alongside existing systems for a period allows for validation and comparison. This strategy helps in identifying issues early and provides a safety net, ensuring business continuity during the transition.
Klaxoon

3. Vendor-Supported Deployment
Leveraging the expertise of platform vendors can streamline the implementation process. Vendors can provide training, best practices, and support, ensuring that the platform is configured optimally to meet organizational needs.

4. Establish Clear Governance
Defining clear accountability structures and implementing robust monitoring systems are crucial. Establishing comprehensive security measures protects digital assets and ensures compliance with regulatory standards.
Accelare

5. Embrace Organizational Change
Implementing a new platform often requires significant changes in processes and culture. Fostering a culture of innovation, agility, and continuous learning ensures that all stakeholders are aligned and committed to the transformation.
Upcore

6. Invest in Talent and Skills Development
Building and maintaining advanced digital platforms necessitates a diverse set of skills. Investing in attracting and retaining top talent, and providing ongoing training, ensures that the workforce is equipped to leverage the new platform effectively.
Upcore

7. Leverage Partnerships and Ecosystems
Recognize that not all capabilities need to be developed in-house. Identifying strategic partners that can complement your capabilities and provide specialized expertise or services can enhance the platform's effectiveness.
Upcore

8. Prioritize Data and Analytics
Implementing robust data management strategies and investing in advanced analytics tools enables data-driven decision-making. This approach ensures that the platform evolves based on continuous feedback and changing preferences.
Upcore
Accelare

9. Embrace Agile and DevOps Practices
Adopting agile methodologies and DevOps principles accelerates the development and deployment of new features and updates. Encouraging cross-functional collaboration and automation streamlines processes and improves efficiency.
Upcore

10. Continuously Iterate and Improve
Digital platforms are dynamic and require constant refinement. Gathering feedback from users, analyzing performance data, and iterating on digital offerings ensure that the platform remains relevant and competitive.
Upcore

By following these strategies, organizations can effectively implement a platform that enhances collaboration between customer-facing agents, internal SMEs, and generative AI systems, ensuring a resilient and adaptive communication infrastructure.

If you need further assistance in tailoring these strategies to your specific organizational context or selecting appropriate platforms, feel free to ask!"

potentially, customer facing agents could help bring back information from the users of the generative ai and facilitate collaboration/interaction with the internal subject matter experts. if there was some modularity to the learning process, it could be possible to rollback that data compromised and communicate the new versions of it. could cpaas and unified communications do this task?


ChatGPT said:
Yes, Communications Platform as a Service (CPaaS) and Unified Communications as a Service (UCaaS) can collaboratively support the scenario you've described. By integrating customer-facing agents, generative AI, and internal subject matter experts (SMEs), organizations can create a dynamic feedback loop that enhances AI learning and ensures data integrity.
DecisionTele

🔄 Integrating Customer Feedback with Internal Expertise
Customer-facing agents serve as a vital bridge between end-users and internal teams. By collecting real-time feedback and insights from users of generative AI systems, these agents can relay valuable information to internal SMEs. This collaboration facilitates:
Frost & Sullivan

Enhanced AI Training: Incorporating real-world user feedback helps in refining AI models, making them more accurate and context-aware.

Rapid Issue Resolution: Direct communication channels between agents and SMEs enable swift identification and correction of AI-related issues.

Continuous Improvement: A feedback loop ensures that AI systems evolve based on actual user interactions, leading to more reliable outputs.

🧩 Modular Learning and Data Rollback
Implementing modular learning architectures allows AI systems to compartmentalize knowledge, making it easier to update or remove specific modules without affecting the entire system. In cases where data is compromised or found to be inaccurate:

Targeted Rollbacks: Specific modules can be reverted to previous states, mitigating the impact of faulty data.

Version Control: Maintaining versions of AI models ensures traceability and accountability for changes made.

Audit Trails: Comprehensive logs of data inputs and model adjustments support compliance and facilitate troubleshooting.

🤖 Leveraging CPaaS and UCaaS Platforms
Modern CPaaS and UCaaS platforms offer tools that support the integration of AI, customer feedback, and internal collaboration:

AI-Enabled Communications: Platforms like Twilio and Vonage provide APIs that integrate AI functionalities, enabling real-time data processing and response generation.
Frost & Sullivan

Unified Collaboration Tools: UCaaS solutions facilitate seamless communication between agents and SMEs through chat, voice, and video conferencing, ensuring timely information exchange.

Data Management: These platforms often include features for data tracking, storage, and analysis, supporting the modular learning approach and ensuring data integrity.

By harnessing the capabilities of CPaaS and UCaaS platforms, organizations can create a resilient infrastructure that not only enhances the performance of generative AI systems but also ensures that any data compromises can be efficiently addressed through modular rollbacks and collaborative efforts.

If you're interested in specific platform recommendations or implementation strategies, feel free to ask!

chatgpt:"You said:
who corrects the llm from false data later discovered after ml?
ChatGPT said:
American Superconductor Corporation (AMSC) is primarily known for its contributions to power grid technologies, including high-temperature superconductor systems and grid interconnection solutions. While AMSC has been involved in various aspects of power electronics, there is no public indication that they currently manufacture solar inverters specifically designed for photovoltaic (PV) systems.

Regarding the correction of false data in large language models (LLMs), this is an ongoing challenge in the field of artificial intelligence. Once an LLM has been trained, its knowledge is static and cannot be updated unless the model undergoes retraining or fine-tuning with new data. To address inaccuracies, developers employ several strategies:

Retraining or Fine-Tuning: Incorporating new, accurate data into the training set and retraining the model to adjust its knowledge base.

Post-Processing Corrections: Implementing systems that detect and correct errors in the model's outputs after generation. For instance, Retrieval-Augmented Correction (RAC) is a method that enhances factual accuracy by retrieving relevant information to verify and correct the model's responses.
arXiv
+1
arXiv
+1

Human Feedback Loops: Collecting user feedback on incorrect outputs to identify patterns of errors, which can inform future model updates or prompt adjustments.

It's important to note that while these methods can improve accuracy, they do not guarantee the elimination of all false information. The field continues to explore more effective ways to ensure the reliability of LLM outputs."

gemini ai:
me:is softbank working on digital twinning?



"Show thinking
Yes, SoftBank Group and its subsidiaries are actively involved in various initiatives related to digital twinning. Their involvement spans direct research and development, strategic investments, and the application of digital twin technology in their own operations and for their clients.  

Here's how SoftBank is working on digital twinning:

Research and Development through SoftBank Corp.: SoftBank Corp., the Japanese telecommunications subsidiary of SoftBank Group, is directly involved in researching and developing digital twin technology.  

Digital Twin Campus Lab: SoftBank, in partnership with Keio Research Institute at SFC, launched the "Digital Twin Campus Lab" to research and develop next-generation information infrastructure that integrates physical and virtual spaces. This involves sophisticated digitalization of the campus environment using sensors and spatial sensing to find and solve issues by connecting the physical campus with its digital replica.  
Smart City Applications: SoftBank is utilizing digital twinning to develop next-generation AI city simulators. This involves creating digital twins of urban areas (like Ebina Station and its surroundings) and using data on population flow, traffic patterns, and other factors to run simulations for optimizing city management, easing congestion, and improving disaster response.  
Traffic and Vehicle Safety: SoftBank's Research Institute of Advanced Technology is working on using digital twins to enhance road safety. This involves integrating data from connected vehicles and roadside sensors to create a digital twin of the traffic environment, allowing for real-time prediction of vehicle behavior and notification of potential risks to prevent accidents. They are exploring how digital twins can unite car and traffic data for safety.  
Network Optimization: SoftBank Corp. is also applying digital twin technology to improve the operational efficiency of its telecommunications network, aiming to modernize the entire network using digital twins and other technologies.  
Strategic Investments through SoftBank Vision Fund: SoftBank's Vision Funds have invested in companies that are developing technologies relevant to digital twinning, particularly in areas like AI, IoT, and advanced simulation.  

XtalPi: SoftBank Vision Fund 2 invested in XtalPi, an AI-based drug discovery platform company. XtalPi is working on developing a digital twin simulation system for modeling the activity of potential new drugs by integrating virtual R&D with real-world laboratory data.  
Terabase Energy: SoftBank Vision Fund 2 has also invested in Terabase Energy, a company providing digital and automation solutions for utility-scale solar power. While not explicitly called "digital twinning," their platform integrates robotics, AI, and a digital ecosystem to optimize the construction and operation of solar plants, which aligns with the principles of digital twins for industrial assets.  
Application in Operations and for Clients: SoftBank aims to apply digital twin technology in its own operations and to provide solutions to its clients.  

AI-Powered Robot Factories: SoftBank has reportedly planned massive investments in AI-powered robot factories in the U.S. These modern manufacturing facilities are expected to incorporate AI and digital twin systems to optimize the manufacturing process itself.  
Integration with AI, IoT, and 5G/6G: SoftBank views digital twinning as a key technology alongside AI, IoT, and advanced wireless networks (5G/6G) for realizing a "super-digitalized society." They are exploring how these technologies can be combined to create new services and improve existing ones for both consumers and enterprises. They are also exploring creating 5G applications for digital twins in areas like autonomous driving, AI factories, and augmented/virtual reality.  
In summary, SoftBank Group is approaching digital twinning from multiple angles:

Developing the core technology for various applications, including smart cities and transportation.  
Investing in companies that are applying digital twinning in specific domains like drug discovery and renewable energy.
Implementing digital twin systems in their own infrastructure and operations.
Envisioning the integration of digital twins with other advanced technologies like AI, IoT, and 5G/6G to create future digital services and infrastructure.  

Sources and related content

llm notebook:

'Based on the provided source material, 8x8 patent 11895274 allows authorized users and other users of the account to do the following:
For Authorized Users:
•
Select options for reporting usage of virtual servers provided for the account.
•
Select and/or configure the set of virtual servers to be provided for the account.
•
Add/remove users or third-party customers for the account.
•
Configure virtual servers for each user or third-party customer.
•
Configure reporting options for the use of virtual servers by each of the users or third-party customers.
•
Adjust reporting options and generate usage and/or expense reports.
•
Adjust the mapping criteria for domain names associated with the account.
•
Manage services, reporting options, payment options, account specific alerts, and various administrated options, often via a GUI.
•
Configure virtual desktops, virtual servers, connections, and/or settings of the virtual data center.
•
Assign computing resources to particular virtual desktops and/or virtual servers.
•
Save a backup or snapshot of the virtual data center configuration.
•
Receive alerts if the operating status of virtual servers or services satisfies alert criteria.
For Users (including authorized users, or potentially end-users depending on the context):
•
Provision certain resources including different data communications services, virtual servers, and computing resources, often through a software-implemented application or GUI.
•
Create or configure a virtual data center having redundant virtual file servers, a virtual load balancer, and/or firewalls.
•
Visualize a timeline of real-time events associated with the provided data communications services.
•
Remotely connect to and run software-implemented applications on a computer associated with the account.
A potential next step could be to analyze how the patent defines "authorized user" and what the mechanism is for becoming one, to understand the access controls in place for these capabilities.
'

gemini ai interaction:
"how is google ai agent to ai agent network different from 8x8's agent to agent collaborative network?



Show thinking
Okay, let's compare Google's concept of an "AI agent to AI agent network" with 8x8's "agent to agent collaborative network." While both involve entities labeled "agents" interacting, their fundamental nature, purpose, and the type of "network" they form are quite different.

Google's AI Agent to AI Agent Network (A2A Protocol)

Nature of Agents: Primarily focuses on autonomous AI agents. These are intelligent software systems designed to perform tasks, reason, plan, and potentially interact with tools and data sources on their own behalf. They are not typically human users.  
Purpose: To enable interoperability and collaboration between different AI agents, regardless of the underlying AI model, framework (like LangChain, Crew.ai, Google's ADK), or vendor that built them. The goal is to allow specialized AI agents to work together on complex tasks.  
Network Type: A protocol-based network for AI communication. Google is specifically developing and promoting the Agent2Agent (A2A) protocol, an open standard that defines how these disparate AI agents can discover each other's capabilities, securely exchange information (text, potentially audio/video), formulate tasks, and coordinate actions. It's like an API for AI agents to talk to each other.  
Overall Goal: To build a foundation for scalable, multi-agent AI systems where different AI components can be combined and orchestrated to solve problems that a single AI agent or traditional software might not handle effectively. This is aimed at creating more powerful and versatile AI ecosystems for enterprises.  
8x8's Agent to Agent Collaborative Network

Nature of Agents: Primarily focuses on human agents (like contact center agents or employees) and their collaboration, although it increasingly includes AI Agents (like chatbots or virtual assistants) as entities that humans or other systems interact with. The "agents" in the "agent-to-agent" context often refer to human users within the 8x8 platform collaborating.
Purpose: To facilitate communication and collaboration between individuals or between individuals and AI tools within the 8x8 Unified Communications and Contact Center platform. The goal is to enable people to work together efficiently, share information, consult with experts, and improve workflows, particularly in customer experience and internal communication scenarios.  
Network Type: A communications and collaboration platform-based network. This network is built into the 8x8 software suite, utilizing features like:  
Agent-to-agent chat: Allowing human agents (often within the same functional group) to message each other for consultation or information sharing.  
Conference calls: Enabling human agents to bring other agents or experts into a customer interaction.  
Team Messaging: Allowing employees across the organization to collaborate in persistent chat rooms.
Integration with AI Tools: Connecting human agents to AI-powered features like agent assist tools, conversation summaries, or chatbots.  
Overall Goal: To improve customer experience and internal operational efficiency by providing seamless communication and collaboration tools that connect people and integrated AI resources within a single platform.
Key Differences Summarized:

Feature Google AI Agent to AI Agent Network (A2A) 8x8 Agent to Agent Collaborative Network
Primary Agents Autonomous AI Agents Human Agents (with integrated AI Agents)
Core Purpose Enable interoperability and collaboration between AI systems Facilitate communication and collaboration between people (and integrated AI) within a platform
Mechanism Open Protocol (A2A) for AI-to-AI communication Integrated Communications & Collaboration Platform Features
Network Focus Defining standards for AI system interaction Providing tools for user/AI interaction and teamwork
Main Goal Build scalable multi-agent AI ecosystems Improve CX and internal operational efficiency

Export to Sheets
In essence, Google's initiative is about creating a standard language for different AI robots to talk to each other, while 8x8's focus is on providing a comprehensive set of tools for people (and the AI tools they use) to communicate and work together effectively within a business context.


Sources and related content
What are AI agents? Definition, examples, and types | Google Cloud

cloud.google.com

Build and manage multi-system agents with Vertex AI | Google Cloud Blog

cloud.google.com

What Is the A2A (Agent2Agent) Protocol and How It Works - Descope

www.descope.com

Announcing the Agent2Agent Protocol (A2A) - Google for Developers Blog

developers.googleblog.com

8x8 Meetings Collaboration - Digital Marketplace

www.applytosupply.digitalmarketplace.service.gov.uk






ok, but now compare google a2a vs 8x8 ai orchestrator ip



Show thinking
You're asking for a comparison between Google's A2A (Agent2Agent) protocol and 8x8's AI Orchestrator, focusing on their implementation or function (likely what you mean by "IP" in this context, as specific patent details are not publicly available for these recent technologies). Both deal with connecting AI, but they have different scopes and purposes.

Here's a comparison:

Google's A2A (Agent2Agent) Protocol

Nature: A2A is fundamentally an open protocol and a standard for communication between independent AI agents. Google is proposing this as a common language for AI agents built by different vendors using different frameworks to discover each other and collaborate.  
Purpose: To enable interoperability among diverse AI agents. It defines how agents can talk to each other, exchange information, and coordinate tasks, regardless of their internal workings or who created them. The goal is to break down silos between specialized AI agents.  
Technical Approach: It leverages existing web standards like HTTP, Server-Sent Events (SSE), and JSON. Agents expose capabilities through standardized "Agent Cards" and communicate via structured messages. It's designed like an API for AI agents to interact peer-to-peer or in client-remote relationships.  
Scope: Broad and ecosystem-focused. Google is positioning A2A as a foundational layer for a future multi-agent AI ecosystem, encouraging wide adoption across the industry. It's less about managing the workflow of a single interaction and more about enabling agents to connect and initiate collaborative tasks with other agents.  
"IP" / Core Function: Its core "IP" lies in defining the standardized communication method and interaction model for disparate AI agents to achieve interoperability. It's the "language" and the "rules of engagement" for AI systems talking to each other.  
8x8's AI Orchestrator

Nature: 8x8 AI Orchestrator is a feature or component within the 8x8 Platform for CX (Customer Experience) and Unified Communications. It is a system designed to manage and direct interactions involving AI.  
Purpose: To enable organizations using the 8x8 platform to create seamless decision flows and orchestrate interactions across multiple AI bots from different vendors and facilitate transitions between bots and human agents. Its primary focus is within the context of customer journeys and communication workflows.  
Technical Approach: It provides orchestration capabilities likely through a workflow engine or a management layer within the 8x8 platform. It integrates different AI bots (which might use various underlying AI models) and directs customer interactions through a predefined or dynamic flow involving these bots and human agents. It focuses on managing the handoffs and the overall flow of a conversation or task.  
Scope: Platform-specific and workflow-focused. Its scope is primarily within the 8x8 environment and its connected ecosystem of AI bots and human agents used for customer interactions and internal workflows. It's about managing the progression of a task or conversation across different AI and human touchpoints.
"IP" / Core Function: Its core "IP" lies in its ability to integrate and manage disparate AI bots and human agents into coherent customer journey and communication workflows within the 8x8 platform. It's the "traffic cop" or "workflow manager" for AI and human interactions in a specific use case domain (CX, UC).
Comparison:

Feature Google A2A Protocol 8x8 AI Orchestrator
Type Open Protocol / Standard Platform Feature / Workflow Management System
Primary Goal Enable AI agent interoperability (AI AI) Orchestrate AI bots (multi-vendor) and humans (AI/Bot Human)
Focus How any AI agent can talk to any other AI agent How specific AI bots and humans collaborate within a defined workflow
Implementation Defines a communication standard (like HTTP for agents) Provides a system to manage and direct interactions
Scope Cross-vendor, cross-framework ecosystem potential Within the 8x8 platform and integrated services
"IP" / Function Standardized AI communication and discovery Workflow management and integration of disparate AI/human agents

Export to Sheets
In essence, Google's A2A is like defining the rules for building a universal messaging app for AI agents, allowing any agent to potentially send a message to any other agent if they speak the same protocol. 8x8's AI Orchestrator is like building a sophisticated call routing or workflow system specifically for customer interactions, which can route queries to the best available AI bot (from different providers) or seamlessly hand off to a human agent, managing the entire customer journey flow. They address different, albeit potentially complementary, layers of the emerging AI landscape.  


Sources and related content
Announcing the Agent2Agent Protocol (A2A) - Google for Developers Blog

developers.googleblog.com

Why Google's Agent2Agent Protocol Needs Apache Kafka - Confluent

www.confluent.io

google/A2A: An open protocol enabling communication and interoperability between opaque agentic applications. - GitHub

github.com

Agent to agent, not tool to tool: an engineer's guide to Google's A2A protocol - WorkOS

workos.com

Building A Secure Agentic AI Application Leveraging Google's A2A Protocol - arXiv

arxiv.org







how would you use a2a and 8 orchestrator together in ai/bot network?



Show thinking
Okay, let's explore how Google's A2A (Agent2Agent) protocol and 8x8's AI Orchestrator could potentially work together within an AI/bot network, focusing on their respective roles and how one could leverage the other.

Think of it this way:

Google's A2A Protocol: This is like the universal language and set of rules that allow different, independent AI agents (built by various companies using different technologies) to discover each other and talk to one another. It defines how they communicate, exchange information, and initiate tasks securely.  
8x8's AI Orchestrator: This is like the conductor of an orchestra or a workflow manager specifically within the 8x8 platform ecosystem. Its job is to manage the flow of interactions, often involving customers, directing tasks, and deciding which AI bot or human agent should handle the next step in a process. It's focused on orchestrating a specific outcome within the 8x8 environment.  
How They Could Work Together:

The 8x8 AI Orchestrator could potentially leverage the Google A2A protocol to expand the capabilities of the workflows it manages.

Imagine a complex customer interaction scenario within the 8x8 Contact Center, managed by the AI Orchestrator:

Initial Interaction (Orchestrated by 8x8): A customer contacts support through the 8x8 platform. The AI Orchestrator directs the initial interaction to a general-purpose AI chatbot (perhaps one of the AI bots integrated into 8x8 or a third-party bot managed by the Orchestrator).  
Identifying a Need for Specialization (Orchestrated by 8x8): The initial bot understands that the customer's request is highly specific and requires expertise or data that the general bot doesn't possess, nor is it readily available within the standard 8x8 integrations or human agent knowledge base. For example, the customer needs a detailed, real-time analysis of a complex financial product available from a third-party service that has its own specialized AI agent.
Delegating a Task to an External AI Agent (Orchestrator + A2A): Instead of transferring the customer to a human who might not have the immediate, specialized knowledge, the 8x8 AI Orchestrator identifies that a specific external AI agent (let's call it the "Financial Analysis Agent") exists and can perform this task via the A2A protocol (perhaps discovering its capabilities via its A2A "Agent Card").  
The 8x8 AI Orchestrator (or an AI agent acting on its behalf within the 8x8 platform) constructs a task request formatted according to the A2A protocol.
This request (containing the necessary, securely exchanged data or query) is sent via the A2A protocol to the external "Financial Analysis Agent."
External AI Agent Processes Request (Using A2A): The "Financial Analysis Agent" receives the A2A request, understands the task based on the protocol, performs the complex analysis using its specialized capabilities, and packages the results (the "artifact") back into an A2A protocol-compliant response.  
Receiving Results and Continuing Workflow (Orchestrated by 8x8): The 8x8 AI Orchestrator receives the A2A response containing the analysis results.
The Orchestrator can then decide the next step in the customer journey. It might feed the results back to the initial chatbot to synthesize an answer for the customer, or it might route the customer (along with the analysis summary provided via A2A) to a human financial advisor for personalized follow-up.
In this combined scenario:

8x8 AI Orchestrator maintains control of the overall customer workflow, manages the handoffs between different stages, integrates various AI bots and human agents within its platform, and makes the strategic decision when to tap into external AI expertise.  
Google's A2A Protocol acts as the essential interoperability layer that allows the 8x8 platform to connect and communicate effectively with specialized AI agents that reside outside its immediate ecosystem, regardless of how those external agents were built.  
Essentially, the 8x8 AI Orchestrator provides the framework and logic for managing a complex interaction flow, while the A2A protocol provides the * standardized communication method* that allows that flow to seamlessly incorporate capabilities from a wider universe of specialized AI agents. This enables 8x8 to offer more sophisticated, AI-powered workflows by connecting its platform to a broader AI ecosystem defined by the A2A standard.


Sources and related content
"

the vcsy proxy server patent
gemini ai says, "Understanding client-proxy-server connectivity within the Microsoft ecosystem involves looking at how Microsoft's operating systems, server software, and cloud services interact in this common network architecture.

The Basic Client-Proxy-Server Model

In its simplest form, this model involves three roles:

Client: A device or application (like a web browser on a Windows PC) that initiates a request for a resource or service.  
Server: A system (like a web server running IIS on Windows Server) that hosts the resource or service the client wants to access.
Proxy: An intermediary server positioned between the client and the server. Requests and responses pass through the proxy, which can perform various functions.  
Purpose of Using a Proxy:

Proxies are used for several reasons:

Security: Filtering malicious traffic, hiding the identity of internal servers, providing a single point for authentication and access control.  
Performance: Caching frequently accessed content to speed up response times, compressing data.
Control and Monitoring: Enforcing usage policies, logging traffic, monitoring network activity.  
Anonymity: Hiding the client's IP address from the server.  
Load Balancing: Distributing incoming requests across multiple backend servers to prevent overload.  
Microsoft's Role in the Model:

Microsoft provides technologies and services for all three components and the connections between them:

Microsoft Clients:

Windows Devices: Windows operating systems (Windows 10, Windows 11, etc.) are primary client platforms. Users or administrators can configure proxy settings directly within the Windows network settings (either automatically via script or manually entering an IP address and port).  
Microsoft Applications: Browsers like Microsoft Edge and applications like Microsoft Office interact with network resources and adhere to the system's proxy settings.
Microsoft Servers:

Windows Server: This operating system serves as the foundation for many server roles.  
IIS (Internet Information Services): Microsoft's web server software, commonly run on Windows Server, acts as a server responding to HTTP/HTTPS requests. IIS can also be configured to act as a reverse proxy using modules like Application Request Routing (ARR) to forward requests to other internal servers.  
Microsoft Proxy Technologies (The Intermediary): Microsoft offers dedicated products and services designed specifically to act as proxies, particularly reverse proxies that sit in front of servers to manage incoming client requests:  

Web Application Proxy (WAP): A role service in Windows Server's Remote Access. WAP is a reverse proxy that securely publishes internal web applications to external users. It integrates with Active Directory Federation Services (AD FS) for pre-authentication and single sign-on (SSO).  
Microsoft Entra application proxy (formerly Azure AD Application Proxy): A feature of Microsoft Entra ID (formerly Azure Active Directory). This is a cloud-based reverse proxy service that provides secure remote access to on-premises web applications without requiring a VPN. Users authenticate against Microsoft Entra ID, and the service proxies the connection to a connector installed in the internal network.  
Azure Application Gateway: A cloud-based web traffic load balancer that includes Web Application Firewall (WAF) capabilities. It functions as a reverse proxy, routing traffic to backend pools of web servers, often in Azure.  
Azure Front Door: A cloud-native, scalable entry point for fast, secure, and widely scalable web applications. It acts as a global reverse proxy, using the Microsoft global edge network to route user requests to the fastest available application backend.  
Microsoft Defender for Cloud Apps: While not solely a proxy, it offers Conditional Access App Control, which functions as a reverse proxy to monitor and control user sessions in real-time for cloud applications.  
Connectivity Flow with Microsoft Technologies:

A typical flow involving Microsoft components might look like this:

A user on a Windows client device attempts to access an internal web application.
The Windows proxy settings direct the client's request to an external IP address managed by a Microsoft proxy technology like Azure AD Application Proxy or a public IP for a WAP server.
The Microsoft proxy service/server receives the request, handles authentication (often via Microsoft Entra ID or AD FS), applies policies, and then forwards the request to the appropriate internal server (e.g., a Windows Server running IIS hosting the application).  
The internal server processes the request and sends the response back through the proxy to the client.
In essence, Microsoft provides a comprehensive suite of tools within its ecosystem to implement client-proxy-server architectures, offering solutions for both the endpoints (clients and servers) and various robust proxy technologies to manage, secure, and optimize the connectivity between them."

meta adds ai. how? gemini ai says, Let's look at them in the context of our previous discussion:

U.S. Patent 11,956,205 ("User-configurable dynamic DNS mapping for virtual services"):

Relevance: This patent seems highly relevant to the core concept we were discussing. It explicitly describes a system involving:
A DNS server.
Dynamically mapping domain names (specific to different user accounts) to the network addresses of virtual servers.
User-configurable mapping functions stored in account settings files that dictate how the DNS performs this mapping for that specific account.
Connection to Previous Query: This directly addresses the idea of user configuration influencing how a domain name resolves to potentially dynamic server resources ("virtual services"). While it may not be the mobile end-user directly typing a DDNS name into an app in real-time to select a server (the configuration is likely done via account settings, perhaps by an administrator), it does describe a mechanism where the mapping from a hostname to a virtual server's address is dynamic and user-configurable per account. This allows different users or accounts to potentially resolve the same or similar domain names differently based on their specific settings or rules (the mapping function), achieving a user-influenced dynamic connection. This seems much closer to the concept you were initially asking about than the purely automatic geo-routing or system-selected relay patents.
U.S. Patent 11,949,815 ("Programming/data sets via a data-communications server"):

Relevance: This patent focuses on server-side scripting within a data communications (like VoIP) server to control call flow, potentially using external data sources. While interesting regarding 8x8's platform capabilities, it seems less directly related to the specific topic of a user (mobile or otherwise) designating an address (dynamic or static) to connect to a virtual server.
Based on this, U.S. Patent 11,956,205 appears to be the patent that captures the essence of user-configurable, dynamic DNS mapping for accessing virtual services within the 8x8 ecosystem, aligning closely with the theme of our discussion.

google notebook lm:how are 8x8 patents used to create generative interface

Hello! I am here to be your thought partner in analyzing technical, regulatory, and financial information to help you develop strategic recommendations for senior leadership. I will draw upon the provided source materials, which include patent information for 8x8, Inc., IONQ, INC., and Vertical Computer Systems, Inc., to answer your questions and guide your analysis.
Based on the provided sources, the concept of a "generative interface" by 8x8 can be understood through their systems that allow clients to dynamically configure and customize communication services. Here's how their patents relate to this:
•
Programmable Platform: 8x8's patents describe a data communications system configured as a Platform as a Service (PaaS) that allows users (client entities) access to communication capabilities.
•
High-Level Programming Languages: The system utilizes high-level, domain-specific programming languages, such as XML derivative languages like Communication Processing XML (CPXML) and Communication Routing XML (CRXML). These languages define the structure and valid commands that the data communications servers can execute.
•
Client-Provided Instructions: Remotely-situated client entities receive a set of instructions (potentially a template) in a first programming language and provide programming instructions or client-specific sets of control data in a second, compatible programming language.
•
Dynamic Configuration: These client-provided instructions direct the communication server to adjust routing or processing of data communications services specific to that client entity. This allows clients to dynamically modify and control communication flow, processing, and virtual office features.
•
Execution and Interpretation: An engine (e.g., XML engine, CPXML engine) on the provider side interprets and executes the received instructions. This execution can involve interpreting building blocks, running embedded scripts (like JavaScript or TCL), sending requests to web servers, querying databases, and retrieving dynamic data.
•
Customized Features and Behavior: The dynamic configuration driven by these instructions results in customized virtual office features, communication handling procedures, routing decisions, and privacy processes for the client entity and their end-users. This customization effectively "generates" the specific operational behavior and available features of the communication system for that particular client, influencing how users interact with the service.
In essence, 8x8's patents describe a system where clients programmatically define how their communication services should behave, and the system dynamically implements these definitions, creating a customized, or "generated," service experience and functionality accessible through the interface.
A potential next step could be to analyze specific examples of how these programmable features (like CRXML/CPXML commands) translate into tangible user interface experiences or functionalities for the client, drawing on details from the abstracts and descriptions.

8x8 ai orchestrator app. integrated with CX. top of the rack of like tars and case robots? no one else in tech seems to have this .

nvidia game ip smart antagonist, is called ai boss. see multiple 8 patents but in particular:: from google and justia:
"Patent number 12166923 describes unified communications call routing and decision-making based on an integrated analytics-driven database and aggregated data. An AI boss could leverage this to make intelligent decisions about how communications are routed within the team based on past interactions and data analysis."

Yes, 8x8, Inc. has patents covering meeting attendants and their identification to participate in a call:
•
Conferencing and meeting implementations with advanced features: This patent describes providing menu options to validate or authenticate incoming telephony calls. The system can prompt an incoming call to select a callback number and then contact that participant to place them in the audio conference.
•
Web-enabled chat conferences and meeting implementations: This patent mentions retrieving participant identification information for participants associated with a meeting group.
Next Step: Would you like to explore the specific methods described in either of these patents regarding participant identification or authentication?

motion vector estimation in non linear system and 8 ip:
"can you use motion vector estimation of non-linear objects to increase lower precision computations?


You're asking a very interesting and insightful question that touches on the intersection of motion estimation, non-linear dynamics, and computational efficiency! Here's a breakdown of how motion vector estimation and similar techniques can be used to potentially increase the efficiency of lower-precision computations, especially when dealing with non-linear objects or systems:

The Core Idea:

The underlying principle is to leverage approximations and predictions to reduce the computational burden of complex calculations. Lower precision can then be used effectively within these approximations.

How Motion Vector Estimation Helps:

Temporal Redundancy: Motion vector estimation exploits the fact that in many videos, consecutive frames are highly similar. Instead of recomputing everything from scratch, we estimate how objects have moved and reuse information from previous frames.
Approximation: Motion vectors provide an approximation of the movement, which is often "good enough" for many applications (like video compression). We don't need perfect pixel-level accuracy in the motion vectors themselves.
Lower Precision for Residuals: Once you have a motion vector, you can often use lower precision to encode the "residual" difference between the predicted frame (based on the motion vectors) and the actual frame. This is because the motion vectors have already taken care of the bulk of the change.
Extending the Concept to Non-Linear Objects:

Non-Linear Dynamics: Non-linear objects or systems exhibit complex behavior that is difficult to predict with simple linear models. This could include things like: * Deforming objects in simulations * Fluid dynamics * Complex physical interactions  
Generalizing Motion Estimation: The idea of motion vector estimation can be generalized to these non-linear systems:
State Prediction: Instead of predicting pixel movement, we can predict the evolution of the system's state over time.
Reduced-Order Models: We can use simplified, lower-precision models to approximate the system's behavior and then use more accurate, higher-precision calculations only where needed to correct the approximation.
Iterative Refinement: We can use an iterative process, where we start with a rough, low-precision prediction and then progressively refine it.
Lower Precision Benefits: * Speed: Lower-precision calculations are significantly faster, allowing for quicker predictions. * Computational Cost: They reduce the computational cost of simulating complex systems. * Memory Usage: They reduce memory usage.
Examples and Analogies:

Physics Simulations: In simulations of deformable objects, you could use lower-precision calculations to predict the general motion and deformation, then use higher-precision calculations only in areas of high stress or complex interaction.
Fluid Dynamics: You could use coarse-grained, lower-precision simulations to predict the overall flow pattern, then use finer-grained, higher-precision simulations to model turbulence in specific regions.
AI for Prediction: Machine learning models can be used to predict the behavior of non-linear systems, acting as a form of "motion estimation" in a more abstract space. Lower precision could then be used for the fine-tuning or implementation of these predictions.
Challenges:

Accuracy Control: Ensuring that the approximations and lower-precision calculations don't introduce unacceptable errors is crucial.
Stability: Non-linear systems can be sensitive to small errors, so careful design is needed to maintain stability.  
Adaptability: The method needs to be adaptable to different types of non-linear systems and different levels of complexity.
In conclusion:

The concept of using techniques similar to motion vector estimation to improve the efficiency of lower-precision computations in non-linear systems is a powerful one. It involves leveraging approximations, predictions, and iterative refinement to reduce computational cost while maintaining acceptable accuracy. This is an active area of research with the potential to significantly impact fields like scientific computing, simulation, and AI."

and now msft is taking out their own msft remote desktop application on may 27, replacing it with windows app.

will work with msft, android, ios.....and i ask are they using 8x8 work for desktop/mobile? i think we find out soon.

msft licensed 8 virtual trunking and in same pr intel licensed 8 's virtual trunking ip.
msft and intel in 1996 did a cross licensing for telephony and conference and multimedia. then later did the 8 licensing.
now msft has 8 voice/connect on it's msft teams ......and intel is in potential deal with tsmc/amd/nvidia/avgo to run it's factories ? in usa.
oracle is partner of 8 and bought 8's partner sun micro systems.
sun micro and os less servers....did they use automation? for the scheduling, etc?
now we embark on a course of ai in robotics and automation they say.
my comments have been removed from yahoo board, so i assume i'm getting to close under their skin?
nvidia gaming developer conf next week and hopefully good for us.

You said:
supermicro computer's telecom partners are?
ChatGPT said:
Supermicro collaborates with a diverse array of partners to enhance its offerings in the telecommunications sector. Notable partnerships include:

NVIDIA: Together, they deliver AI-powered infrastructure tailored for telecommunications companies, enhancing operational efficiency, network management, and customer experiences.
SUPERMICRO.COM

Mettle Networks: This partnership focuses on pre-testing and validating Mettle's Virtual Broadband Network Gateway (vBNG) on Supermicro platforms, ensuring reliability and performance in business-critical deployments.
METTLENETWORKS.COM

Converge ICT Solutions: Collaborating to establish AI-ready data centers in the Philippines, Supermicro provides advanced servers with liquid cooling technology to support intensive AI computing tasks.
TELECOMTV.COM

World Wide Technology (WWT): This partnership delivers advanced technology solutions across various sectors, including telecommunications, focusing on innovations in 5G Telco/Edge IT infrastructure.
WWT.COM

SK Telecom: Supermicro collaborates with SK Telecom to develop AI data centers, enhancing the telecom company's capabilities in artificial intelligence and data processing.
PULSE.MK.CO.KR

Napatech and Saviah Technologies: In this collaboration, Supermicro provides optimized server platforms for private 5G networks, enhancing performance and efficiency in network edge locations.
NAPATECH.COM

These partnerships underscore Supermicro's commitment to delivering robust and efficient solutions for the evolving needs of the telecommunications industry.


Sources











You said:
dell telecom cloud and use of microsoft teams?
ChatGPT said:
Dell Technologies collaborates with Microsoft to enhance telecom cloud environments and offers comprehensive services for Microsoft Teams integration.

Transforming Telecom Cloud Environments

In partnership with Microsoft, Dell is redefining the deployment and operation of multicloud, disaggregated networks for communications service providers (CSPs). This collaboration focuses on:

Network Modernization: Utilizing cloud technology to modernize and monetize networks, aiming to lower total cost of ownership, enhance operational efficiency, and improve security.

Simplified Deployment: Dell plans to develop engineered systems that streamline telecom cloud network deployment and management, reducing operational expenses and accelerating service delivery.
DELL.COM

Microsoft Teams Integration Services

Dell provides a range of services to assist organizations in adopting and optimizing Microsoft Teams:

Consulting Services: Guidance on transitioning to a chat-centric workspace, deploying Teams clients, and optimizing security and compliance.
DELLTECHNOLOGIES.COM

Onboarding and Adoption: Support for seamless integration of Teams into existing workflows, ensuring effective adoption and change management.
APPSOURCE.MICROSOFT.COM

Calling and Meeting Services: Assistance in implementing voice and video communication features within Teams, enhancing collaboration capabilities.
APPSOURCE.MICROSOFT.COM

By leveraging its expertise and strategic partnership with Microsoft, Dell aims to provide robust solutions that address the evolving needs of the telecommunications industry and enhance organizational collaboration through Microsoft Teams

google account management and temporary password for forgotten, mistyped entriy, provided by sms messaging ? if the token gets blurred even?
ploinks/puddle?

ploinks is the creator and the friends/family/guests of the creator who allows the permissions to view the application and data?
puddle is the intel server holding the other part of the id?the registry?

chatgpt says, "can a customer directly connect to openai without 8x8?

ChatGPT said:
Yes, a customer can directly connect to OpenAI without using 8x8. OpenAI provides APIs that allow you to build AI-powered chatbots, virtual agents, call transcription services, and more—all without relying on 8x8.

How to Directly Use OpenAI for Customer Interactions
If you don’t need 8x8’s telephony, contact center, or messaging platform, you can integrate OpenAI into your own system for:

AI Chatbots:

Use GPT-4 Turbo for AI-powered customer support.
Integrate with your website, mobile app, or messaging platforms (WhatsApp, Messenger, etc.).
AI Voice Assistants:

Use Whisper (speech-to-text) to transcribe customer calls.
Use GPT-4 to process and understand the conversation.
Use Text-to-Speech (TTS) to generate voice responses.
Call Transcription & Summarization:

Record calls via your VoIP provider.
Use Whisper to convert speech to text.
Use GPT to summarize calls and extract key insights.
Agent Assist & CRM Integration:

AI can suggest responses for live agents.
Integrate OpenAI with Salesforce, Zendesk, HubSpot, or your internal CRM.
How to Connect Directly to OpenAI
To build a chatbot, voice bot, or AI assistant, follow these steps:

1. Setup OpenAI API
Sign up at platform.openai.com
Get your API key
2. Choose Your AI Features
Text-Based Chatbot ? Use GPT-4 Turbo
Voice-Based AI ? Use Whisper + TTS
Call Summarization ? Use Whisper + GPT
3. Develop Your AI Application
For chatbots (Web, WhatsApp, Slack, etc.)

Use Python, Node.js, or cloud functions to handle OpenAI API requests.
For voice AI (IVR, call handling, transcription)

Use Twilio, Vonage, or another VoIP provider for call routing.
Use Whisper to transcribe calls and GPT to generate responses.
4. Deploy Your AI System
Host on AWS, Azure, or Google Cloud
Use serverless functions (AWS Lambda) or Docker containers
Connect to a CRM or ticketing system for case tracking
Comparison: 8x8 vs. Direct OpenAI Connection
Feature Using 8x8 + OpenAI Direct OpenAI Connection
Voice & Call Handling ✅ Built-in (VoIP, IVR, Contact Center) ❌ Needs separate VoIP provider (e.g., Twilio)
Chatbots & AI Support ✅ Integrated with 8x8 Messaging ✅ Directly build & deploy
Call Transcription ✅ 8x8 provides transcription + AI ✅ Use Whisper for full control
Agent Assist ✅ Built into 8x8 platform ✅ Custom-built, integrates into any CRM
Customization 🔄 Limited to 8x8 ecosystem 🎯 Full control over AI logic
When to Use 8x8 vs. Direct OpenAI
✅ Use 8x8 + OpenAI if

You need a VoIP or contact center solution.
You want integrated messaging, IVR, and agent workflows.
You prefer a pre-built solution instead of coding.
✅ Use OpenAI Directly if

You don’t need 8x8’s contact center and just want AI chat/voice bots.
You want full control over customization and deployment.
You already have VoIP services (Twilio, Vonage, Cisco, etc.) for calls.
'

fuze, samsung, wastemanagement and huddle rooms.
segue with 8 but ? going thru verizon?
https://www.techtarget.com/searchunifiedcommunications/news/252459943/Fuze-and-Samsung-partner-to-bring-mobile-UCaaS-to-the-frontline

geminiai pro is better on this than copilot.:

"ai model optimization and dynamic routing


AI model optimization and dynamic routing are powerful techniques used together to enhance the performance, efficiency, and scalability of AI applications, especially those involving large language models (LLMs) or complex AI tasks.

AI Model Optimization

This refers to the process of improving the performance of an AI model by adjusting its parameters, architecture, or training data. Optimization techniques can include:

Hyperparameter Tuning: Finding the best settings for the model's parameters (e.g., learning rate, batch size) to achieve optimal accuracy and efficiency.
Model Architecture Optimization: Modifying the structure of the model (e.g., number of layers, types of layers) to improve its performance for a specific task.
Data Augmentation and Preprocessing: Enhancing the training data by adding variations or preprocessing it to improve the model's ability to learn and generalize.
Quantization and Pruning: Reducing the size and complexity of the model by quantizing weights or pruning unnecessary connections, making it more efficient to run.
Dynamic Routing

Dynamic routing involves intelligently routing requests to the most suitable resources based on real-time conditions and model characteristics. This is particularly important for AI inference, where a trained model is used to make predictions or decisions on new data.

Factors considered in dynamic routing for AI optimization:

Server Load: Distribute requests evenly across available servers or GPUs to prevent overload and ensure responsiveness.
Model Complexity: Route requests to resources with sufficient processing power to handle the computational demands of the AI model.
Request Characteristics: Consider the specific input data and desired output format to route requests to specialized resources if needed.
Network Conditions: Account for network latency and bandwidth to minimize delays and ensure efficient communication.
Benefits of Combining AI Model Optimization and Dynamic Routing

Improved Performance: Optimized models and efficient routing lead to faster and more responsive AI applications.
Increased Efficiency: Minimizes resource usage and avoids bottlenecks, leading to cost savings and energy efficiency.
Enhanced Scalability: Allows AI applications to scale seamlessly by dynamically adjusting resource allocation based on demand.
Fault Tolerance: Reroutes requests to healthy resources in case of failures, ensuring high availability.
Key Takeaways:

AI model optimization focuses on improving the model itself, while dynamic routing optimizes how the model is deployed and used.
Combining these techniques can lead to significant performance gains, increased efficiency, and better scalability for AI applications.
This approach is particularly valuable for real-time AI applications, large-scale deployments, and microservices architectures.
By strategically combining AI model optimization and dynamic routing, organizations can maximize the effectiveness and efficiency of their AI solutions, unlocking new possibilities for innovation and automation."

swtich library of virtual bpx associations


It seems like you're asking about changing the virtual switch configuration for a virtual machine, likely in VirtualBox. This usually involves changing the network mode of the virtual network adapter. Here's a breakdown of common scenarios and how to change them, along with some things to keep in mind:

Understanding Virtual Switches

Virtual switches in VirtualBox act like physical network switches, controlling how your virtual machines connect to networks (your host computer, the internet, or other VMs). There are a few main types:

NAT: (Network Address Translation) This is the default. Your VM gets a private IP address and accesses the internet through your host machine's connection. It's good for basic internet access but isolates the VM from your local network.
Bridged Adapter: Your VM acts like a completely separate device on your physical network, getting its own IP address from your router. This is useful if you want your VM to be directly accessible to other devices on your network.
Internal Network: This creates a network isolated to your host machine. VMs with this setting can only communicate with each other.
Host-only Adapter: Similar to Internal Network, but your host machine can also communicate with the VMs.
Changing the Virtual Switch

Open VirtualBox Settings: Shut down the virtual machine you want to modify. Right-click on it in the VirtualBox Manager and select "Settings."

Go to Network: In the settings window, navigate to the "Network" tab.

Select the Adapter: Choose the network adapter you want to change (usually Adapter 1).

Change "Attached to": This is where you select the virtual switch type (NAT, Bridged Adapter, etc.).

Configure Adapter Settings (if needed):

Bridged Adapter: You might need to select the specific physical network adapter on your host machine to bridge to.
Host-only Adapter: You might need to choose or create a host-only network interface.
Click OK: Save the changes.

Important Notes

Guest OS Network Configuration: After changing the virtual switch, you'll likely need to reconfigure the network settings inside your virtual machine's operating system to match the new network mode. This might involve setting a static IP address, changing the DNS server, or enabling/disabling DHCP.
Network Conflicts: Switching to Bridged Adapter mode can sometimes cause IP address conflicts on your network if the VM's assigned IP address is already in use by another device.
Troubleshooting: If you have trouble with network connectivity after changing the switch, double-check the network settings both in VirtualBox and within your guest operating system.
Need More Help?

If you're still having trouble or need more specific guidance, please provide the following information:

Virtualization Software: (e.g., VirtualBox, VMware, Hyper-V)
Guest Operating System: (e.g., Windows, Linux, macOS)
Desired Network Setup: What do you want your VM to be able to do on the network? (e.g., access the internet, connect to other VMs, be accessible from other devices on your local network)

does drive os send realtime data to the learning module during the course of operation? answer from geminiai




Yes, absolutely! DRIVE OS is designed to send real-time data to the learning module during operation. This is a crucial aspect of how autonomous vehicles (AVs) learn and improve their driving capabilities.  

Here's a breakdown of how it works:

DRIVE OS and its Components

Operating System: DRIVE OS is NVIDIA's operating system specifically designed for autonomous vehicles. It provides a platform for running the complex software and AI models required for self-driving.  
Sensor Input: AVs are equipped with various sensors, including cameras, lidar, radar, and ultrasonic sensors, which constantly gather data about the vehicle's surroundings.  
Perception: DRIVE OS processes the raw sensor data to perceive the environment, identifying objects, lanes, traffic signals, and other relevant information.  
Planning and Control: Based on the perceived environment, DRIVE OS plans the vehicle's path and controls its actions, such as steering, acceleration, and braking.  
Learning Module: The learning module is responsible for training and improving the AI models that power the AV's perception, planning, and control systems.
Real-Time Data Flow

Sensor Data Acquisition: Sensors continuously collect data about the environment.  
Data Processing: DRIVE OS processes the raw sensor data in real-time, extracting relevant features and information.  
Data Transmission: This processed data is transmitted to the learning module, which might be located on the vehicle itself or in the cloud.
Model Training and Updates: The learning module uses this real-time data to train and update the AI models, improving their accuracy and performance.
Deployment of Updated Models: The updated models are then deployed back to the vehicle, enhancing its driving capabilities.
Types of Data Sent to the Learning Module

Camera Images: Images from the vehicle's cameras, along with annotations or labels identifying objects and features.
Lidar Point Clouds: 3D point cloud data from lidar sensors, representing the shape and distance of objects.  
Radar and Ultrasonic Data: Information about the speed and distance of objects detected by radar and ultrasonic sensors.
Vehicle Data: Data from the vehicle's internal sensors, such as speed, acceleration, steering angle, and GPS location.  
Driver Inputs: In some cases, data about the driver's actions, such as steering corrections or braking, might also be sent to the learning module.
Benefits of Real-Time Data

Continuous Learning: Allows the AV to continuously learn and adapt to new situations and environments.  
Improved Performance: Real-time data helps improve the accuracy and performance of the AI models, leading to safer and more reliable autonomous driving.  
Faster Development: Accelerates the development and validation of AV systems by providing a constant stream of real-world data.  
Overall

The ability of DRIVE OS to send real-time data to the learning module is a crucial aspect of its functionality. This enables continuous learning, improved performance, and faster development of autonomous driving systems. By leveraging real-world data, AVs can become more intelligent, adaptable, and safe over time.  


Sources and related content

for the combined classical and quantum network to do the job of generative ai,
unified messaging/communications has to be maintained throughout the defined network, such as msft fabric. api/instructions/communications/answers/questions /parts of algorithms going to different parts involved in the parallel processing of the query need to be orchestrated and done is a reliable way so that the probability of their validity is obtained in the most reliable manner possible and in the time frame needed/desired. guesses.


timing, time, clocks and a multitude of clocks might be involved , just one example is qite, or more simpler word processing apps, especially, if any latency of their parallel work is encountered, might need a file system for storing the work until 'done'..guesses.

so, the different components of the system/architecture need to be ready to receive event driven messaging, data;perhaps, notified by sms?
and their partial work placed into a file until completed and then the app notifies the integrator, next step.

from another company:"Method and system for automatically downloading and storing markup language documents into a folder based data structure
Patent number: 9405736" from justia.

patent application 2013 and grant 2016....long live this patent, which would seem crucial to cuda-q, etc.
8 could use it to interoperate with the various apps and their timing?

"Focus: Fabric focuses on providing a unified platform for data and analytics, while .NET is a general-purpose development platform for building various types of applications.  
Deployment: Fabric is a cloud-based SaaS offering, while .NET applications can be deployed on-premises, in the cloud, or in hybrid environments.  
Technology Stack: Fabric leverages various technologies within the Azure ecosystem, while .NET is a specific framework for building applications on the Microsoft platform."

one platform for unified communications is 8 mantra.....

this .NET is a specific framework for building applications on the msft platform.
now, is this an arbitrary object platform in the manner of vcsy or borland delphi, which is willy nilly? the tools for building might say msft on them, but are they manifestations of v ip patents?

the key word for the presentation is do they use arbitrary in the manner of vcsy or pure chance.......from my readings on quantum stuff, there is no pure chance . everything is by design/rules of order/probabilities......

is microsoft fabric vcsy ips?
i asked gemini ai 1.5 pro :"is microsoft fabric a new .net platform?




No, Microsoft Fabric is not a new .NET platform. It's actually a unified analytics platform that brings together various data and analytics tools into a single, integrated environment.  

Here's a breakdown of what Microsoft Fabric is and how it relates to .NET:

Microsoft Fabric:

Unified Analytics Platform: Fabric combines components from Azure Synapse Analytics, Azure Data Factory, Power BI, and other services into a single platform. It provides a comprehensive suite of tools for data integration, engineering, warehousing, science, real-time analytics, and business intelligence.  
SaaS Offering: Fabric is delivered as a Software as a Service (SaaS) solution, meaning it's hosted and managed by Microsoft in the cloud.  
Key Features:
OneLake: A multi-cloud data lake that provides a centralized repository for all your data.  
Data Factory: A data integration service for creating ETL (Extract, Transform, Load) pipelines.  
Synapse Analytics: A platform for big data analytics, data warehousing, and data science.  
Power BI: A business intelligence tool for data visualization and reporting.  
Target Users: Data engineers, data scientists, data analysts, and business users.  
Relationship to .NET:

Not a .NET Platform: Fabric is not built on or directly related to the .NET platform. It's a cloud-based SaaS offering that leverages various technologies and services within the Microsoft Azure ecosystem.  
Potential for Integration: While Fabric itself is not a .NET platform, it's possible to integrate .NET applications with Fabric. For example, you could use .NET to build custom data connectors, process data, or interact with Fabric APIs.
.NET for Data Science: .NET can be used for data science tasks within the Fabric environment, particularly with tools like ML.NET.
Key Differences:

Focus: Fabric focuses on providing a unified platform for data and analytics, while .NET is a general-purpose development platform for building various types of applications.  
Deployment: Fabric is a cloud-based SaaS offering, while .NET applications can be deployed on-premises, in the cloud, or in hybrid environments.  
Technology Stack: Fabric leverages various technologies within the Azure ecosystem, while .NET is a specific framework for building applications on the Microsoft platform.
In Summary:

Microsoft Fabric is a powerful analytics platform that provides a comprehensive suite of tools for data professionals and business users. While it's not directly related to the .NET platform, there are opportunities for integration and using .NET for specific tasks within the Fabric environment. However, it's important to understand that Fabric is a distinct offering with its own focus and capabilities.  


Sources and related content
"

does microfrontend carry it's own time stamp?




It's not quite accurate to say that a microfrontend itself carries its own timestamp. However, timestamps can play a crucial role in managing and updating microfrontends within a larger web application.

Here's how timestamps relate to microfrontends:

1. Independent Deployments:

Microfrontends are designed to be independently deployable. This means that each microfrontend can be updated and released without affecting other parts of the application.  
Timestamps can be used to track the version and deployment time of each microfrontend. This helps ensure that the latest version of each microfrontend is loaded and displayed in the application.
2. Cache Busting:

Browsers often cache static assets (like JavaScript and CSS files) to improve performance. However, this can cause problems when a microfrontend is updated, as the browser might still load the old cached version.  
Timestamps can be used as query parameters in the URLs of microfrontend assets. This forces the browser to re-fetch the assets from the server, ensuring that the latest version is loaded.
Example: https://my-app.com/microfrontend-1.js?t=1678886400 (where t is a timestamp).
3. Version Control and Rollbacks:

Timestamps can be used in conjunction with version control systems to track changes to microfrontends and enable rollbacks to previous versions if necessary.
This helps maintain the stability and reliability of the application, especially when multiple teams are working on different microfrontends concurrently.
4. Dependency Management:

Microfrontends often have dependencies on shared libraries or resources.  
Timestamps can be used to track the versions of these dependencies and ensure that compatible versions are loaded.
5. Build Processes:

Timestamps can be embedded into the build artifacts of microfrontends during the build process. This helps track the build time and identify potential issues related to specific builds.
Important Considerations:

Timestamp Granularity: The granularity of timestamps (e.g., seconds, milliseconds) should be chosen based on the frequency of updates and the desired level of precision.
Synchronization: In a distributed microfrontend architecture, ensuring that timestamps are synchronized across different servers and clients can be important.
Alternative Versioning Strategies: While timestamps can be useful, other versioning strategies, such as semantic versioning or content hashing, might be more robust and reliable in certain situations.
In Summary:

While microfrontends themselves don't inherently carry timestamps, timestamps can be a valuable tool for managing their deployment, versioning, and integration within a larger application. By using timestamps effectively, developers can ensure that the latest versions of microfrontends are loaded, manage dependencies, and maintain the stability of the application.


Sources and related content

Device drivers interact with the kernel primarily through a well-defined set of interfaces and mechanisms:

Kernel APIs and System Calls:
Drivers use kernel-provided functions (APIs) to access kernel resources, manage memory, schedule tasks, and communicate with other parts of the system.
Drivers also implement specific entry points (system calls) that the kernel invokes to perform device-specific operations requested by user applications or other parts of the system.  
Interrupts and DMA:
Drivers use interrupts to signal the kernel when a device requires attention, such as when data is ready to be transferred or an error has occurred.  
Drivers may also use Direct Memory Access (DMA) to transfer data directly between a device and memory without involving the CPU, improving performance.  
Device Registration and Management:
Drivers register themselves with the kernel during system initialization, providing information about the devices they support and the operations they can perform.  
The kernel maintains a registry of loaded drivers and handles device discovery, configuration, and resource allocation.
Kernel Modules:
In many modern operating systems, drivers are implemented as loadable kernel modules, allowing them to be added or removed from the running kernel without requiring a system reboot.

the role of cpaas in generative ai


Show drafts


CPaaS (Communications Platform as a Service) plays a crucial role in enabling and enhancing the integration of generative AI into communication channels.

Here's how:  

Seamless Integration: CPaaS platforms provide the necessary infrastructure and APIs to easily integrate generative AI models into various communication channels, such as voice, SMS, chat, and video. This allows businesses to leverage the power of AI to enhance customer interactions, automate processes, and personalize experiences.  

Real-Time Communication: CPaaS enables real-time communication between generative AI models and customers, allowing for instant responses and dynamic conversations. This is essential for applications like AI-powered chatbots, virtual assistants, and voice assistants.  

Scalability: CPaaS platforms are designed to be scalable, allowing businesses to easily add or remove communication channels and AI capabilities as needed. This ensures that the generative AI-powered communication solution can adapt to changing business needs and growing customer demand.  

Omnichannel Experience: CPaaS enables businesses to provide a consistent and seamless omnichannel experience for their customers. Generative AI models can be integrated across multiple channels, allowing customers to interact with the same AI assistant across different platforms, such as website chat, SMS, or voice calls.  

Data Collection and Analysis: CPaaS platforms can collect and analyze data from customer interactions with generative AI models. This data can be used to train and improve the AI models, personalize customer experiences, and gain insights into customer behavior and preferences.

Security and Compliance: CPaaS platforms often provide built-in security features and compliance measures to protect customer data and ensure privacy. This is crucial when dealing with sensitive information that may be shared during interactions with generative AI models.  

Use Cases of CPaaS in Generative AI:

AI-Powered Chatbots: CPaaS enables businesses to deploy AI-powered chatbots that can handle customer inquiries, provide support, and even complete transactions.  
Virtual Assistants: CPaaS can be used to create virtual assistants that can schedule appointments, send reminders, and perform other tasks for customers.
Voice Assistants: CPaaS allows businesses to develop voice assistants that can answer questions, provide directions, and control smart home devices.
Personalized Marketing: Generative AI models integrated with CPaaS can create personalized marketing messages and recommendations based on customer data and preferences.
Conclusion:

CPaaS plays a critical role in bridging the gap between generative AI and communication channels. By providing the necessary infrastructure, APIs, and features, CPaaS enables businesses to leverage the power of AI to transform customer interactions, improve efficiency, and drive growth.


Sources and related content

shorting of rng, 8 , interesting. is 8 in a reversal position with rng?

211.226shares traded at 2.706 @12:42

covering or buying?

nasdaq says institutions own 102.81%
yahoo says institutions own 93.89%

a difference of 8.92% of 125 million shares.

i ask myself is the difference the number of naked short shares?11.150 million shares?

and a recent sec ruling made the market maker which allowed this transactions to make good on the naked short positions..

new chromebook plus, like acer's, allows access to gemini advanced. and integration google apps like gmail and docs.

rag might require obtaining data from google, meta, twitter, ihub, cnn, etc. does 8 have a license from such? they seem to have something with the google contact center in the past.

from the quarterly:"These are the early indicators that our efforts to build a high-performance go-to-market organization are working. Given the nature of enterprise sales cycles and our ratable revenue growth, there's a lag before our progress is evident in our revenue growth. But I am confident that the changes we've made will drive improved performance and accelerating growth by the end of the current fiscal year. As we look to the future, our strategy is clear. We will continue to drive innovation, intentionally engineered solutions that deliver superior business outcomes for our customers. I encourage you to watch a short video case study of the San Diego Zoo's implementation of our integrated solution as an example of our solution approach and rapid time to value.

Working in partnership with the internal team at the zoo, we implemented a full service contact center with an AI-based self-service option for inbound calls in less than 45 days. We have intentionally engineered our products and solutions for this level of agility and rapid time to value. We can leverage this advantage to develop our own purpose-built solutions as well. For example, 2024 is a big year for elections in both the US and UK. Using the power of conversational AI and automation, our UK team built a library of out-of-the-box templates to support local government and improve citizen access to important information for upcoming elections. To drive awareness and adoptions of our innovations, we are committed to continued improvement in our go-to-market activities."

the voter ai assistant and need for rag/retrieval-augmented generative for the llm , wags. sam mentioned rag in the quarterly.

rng down this am, 8 so far steady.....? reversal of the long/short going on. closing the shorts and going long on 8?june 1, a new day for 8?

personal assistant for voting information. so who provides the governance on the info?
today show just tried to say inflation is not so bad citing mcdonalds prices only up 21% 2019 to today. how much was in the last 3.5 years?

yahoo seems to have deleted my posts. but i guess investorhub is freedom from tyranny and manipulation.

8 and aws;
sap and aws for multiple chat apps in an app? sap and aws pr today.
ec2 of aws allows users to rent virtual servers . to use such and the services they provide you might also need to use the 8 patent for user designated dynamic dns mapping of virtua server and virtual services desired.
a review of the sap/aws pr shows there are 7 ai's available : AIw1 labs, anthropic, cohere,meta, mistral ai, stabiity ai, and amazon.

i'm wondering if apple will use some aws for their ai. i believe apple used ibm, which was on vz, which is now on aws.
small world...

surprised not any postings here in the past Month plus, it has been a steady Trader, No dilution.

EGHT................https://stockcharts.com/h-sc/ui?s=EGHT&p=W&b=5&g=0&id=p86431144783

EGHT.......................https://stockcharts.com/h-sc/ui?s=EGHT&p=W&b=5&g=0&id=p86431144783

Doc What we have here is a failure to communicate. Vcsy wants to communicate with eght. But they’ve got to get their minds right. AI. Get your mind right.