8-ASKA Scratchpad 20241031

Written by: Paul Lowndes <[email protected]>

Table of Contents

ASKA AI Agent as App Dev Guide: A Plan

AESDS and AI Agent: The Centerpiece of ASKA's Ecosystem

Versus Barrelfish

1. ASKA's Fit with Future Trends:

2. Best Practices for Secure Modular Software Development and "Hello ASKA" IES Modules:

3. Witty Technical Comment:

ASKA's Solution for Shared Memory Access Across IES Instances

ASKA's Approach to Cross-IES Shared Memory and Insights from Heterogeneous OS Architectures

Mining "Distributed Object Capabilities" for ASKA Enhancements

Mining “AI for Next Generation Computing: Emerging Trends and Future Directions” for insights for ASKA:

Hello, ASKA! A Guided Tour with Onboard AI

Decoupling ASKA

Don’t Decouple ASKA

Legacy Integration

Quantum AI

MERMAID

robust, server-side solution for Mermaid diagram generation and integration with Google Docs. Here's a refined strategy addressing your specific requirements:

idea

projectsthat address rendering Mermaid code into high-resolution raster output. Searching for "mermaid cli," "mermaid puppeteer," or "mermaid server-side rendering" will reveal numerous options.

Developing a Google Workspace (formerly G Suite) add-on, like the Mermaid diagram editor you describe, involves several key steps.  Here's a technical outline of the process:

Yes, you can add actions to the Google Docs right-click context menu using a Google Workspace add-on.  However, it's not a direct menu item addition in the way you might add an item to a traditional desktop application's menu. Instead, you achieve this behavior by creating a custom add-on that listens for specific context menu events within Google Docs and presents a custom menu dynamically.

ASKA AI Agent as App Dev Guide: A Plan

This plan outlines how ASKA's AI Agent can guide junior developers through the ASKA app development ecosystem, using the "Hello World" example.

I. AI Agent Enhancements for App Dev:

The existing AI Agent design provides a solid foundation.  The following enhancements will tailor it for app development guidance:

  1. ASKA App Development Knowledge Base:  Expand the LLM's knowledge base to include:

  1. Interactive Tutorial System:  Develop an interactive tutorial system within the Secure UI, guided by the AI Agent.  This system will:

  1. ASKA API Integration:  Enhance the Agent API to allow developers to:

  1. Code Generation Capabilities:  Explore the feasibility of adding code generation capabilities to the AI Agent, allowing it to:

II. Hello World Tutorial Example:

A junior developer starts a ASKA Hello World tutorial.  The AI agent guides them through the following steps:

  1. Project Setup: The agent helps the developer create a new ASKA project within the secure IES development environment.  It explains the basic structure of a ASKA app (Frontend, Backend, UI modules) and generates boilerplate code for each module.

  1. Frontend Module: The agent guides the developer through implementing the frontend module, showing them how to:

  1. Backend Module:  The agent helps the developer implement the backend module, demonstrating how to:

  1. UI Module:  The agent guides the developer through implementing the UI module, showing them how to:

  1. Testing and Deployment: The agent guides the developer through testing their Hello World app within the secure development environment, simulating network interactions and ASKA component integration using isomorphic models of those systems. The AI agent also helps generate test cases using its ASKA-specific knowledge base for each module according to best practices and policy defined by ASKA's security configuration. Finally, the agent guides the developer through the secure deployment process, using authenticated channels and TRC-based verification.

AESDS and AI Agent: The Centerpiece of ASKA's Ecosystem

AESDS (Automated Evolutionary Software Development System) and the onboard AI Agent are poised to be the central pillars of the ASKA ecosystem, providing a streamlined and AI-powered experience for both multi-kernel and application developers.  Their synergistic interaction creates a secure, efficient, and adaptable development environment that addresses several key challenges:

1. Simplifying Multi-Kernel Development:

2. Streamlining App Development:

3. Fostering a Cohesive Ecosystem:

4. Enhancing Security:

5.  Boosting Productivity:

In essence, AESDS and the AI agent create a self-improving, secure, and developer-friendly ecosystem.  They empower developers to build trustworthy applications on ASKA's secure foundation, simplifying complex tasks, promoting best practices, and ensuring a robust, adaptable security posture.  This AI-powered approach addresses the challenges of secure software development in the age of increasingly sophisticated threats and the rise of AGI.

Versus Barrelfish

ASKA's Divergence from Barrelfish:

While ASKA draws some inspiration from Barrelfish's multikernel concepts, it diverges significantly, especially regarding networking and security:

  1. Multi-Network Design: ASKA's multi-network design, featuring the ATN (Authenticated Trust Network) and STN (Sovereign Trust Network) connected via the DTG (Dynamic Trust Gateway), goes beyond Barrelfish's single interconnect focus. This multi-network architecture enables ASKA to handle different trust levels and security requirements for various communication contexts.  The DTG's dynamic channel provisioning and capability management provide fine-grained control over inter-network communication, enabling a more secure and adaptable system.

  1. Hardware-Rooted Security: ASKA places a much stronger emphasis on hardware-rooted security than Barrelfish.  Components like HESE-DAR, data diodes, and the hardware firewall are fundamental to ASKA's security model, minimizing the trusted computing base and providing robust protection against software vulnerabilities. Barrelfish, while using message passing for inter-core communication, doesn't have the same level of hardware-enforced security.

  1. AI-Driven Security: ASKA's AESDS and AI Agent introduce a proactive and adaptive security layer that is absent in Barrelfish.  AESDS automates software updates and security patching, while the AI Agent performs anomaly detection, threat analysis, and policy enforcement. This AI-driven approach allows ASKA to respond dynamically to evolving threats and learn from observed behavior.

  1. Decentralized Governance: ASKA's use of a decentralized ledger for TRCs (Trust Root Configurations) and MDATS (Multi-Dimensional Audit Trail System) introduces a decentralized governance model that enhances transparency and accountability. Barrelfish, while decentralized in its core communication structure, doesn't explicitly address decentralized governance.

  1. Focus on Secure Collaboration: ASKA explicitly addresses secure collaboration through components like SHVS (Secure Hyper-Virtualization System) and SIZCF (Secure Inter-Zone Collaboration Framework). These mechanisms enable controlled data sharing and resource borrowing between IES instances and zones, addressing the collaboration challenges of a multi-kernel environment. Barrelfish's focus is primarily on low-level inter-core communication, not on higher-level collaboration contexts.

Mining the Barrelfish Paper for Insights:

The Barrelfish paper highlights several key challenges and insights relevant to ASKA:

Revised "Hello ASKA" IES Module Proposal:

Considering the Barrelfish paper's emphasis on minimizing inter-core communication and ASKA's enhanced security features, the following refined IES module structure is proposed for "Hello ASKA":

  1. UI/Frontend Module (IES 1): Combines UI rendering (using the Secure UI Kernel) and frontend logic (input validation, request handling). This reduces inter-IES communication by keeping the frontend and UI functionalities within the same isolated environment. Includes a local MSM instance.

  1. Backend/Logic/Storage Module (IES 2):  Handles application logic, data processing, and secure storage using HESE-DAR. This module is self-contained, minimizing interactions with other modules.  Includes a local MSM instance.

  1. Network/Security Module (IES 3):  Handles all network communication (using the Multi-Channel Network and DTG) and security functions (DTMS integration, anomaly detection, MDATS logging). Consolidating these functions into a single module reduces communication overhead.  This module acts as a gatekeeper for both incoming and outgoing communication.

1. ASKA's Fit with Future Trends:

ASKA's architecture aligns with several key trends in hardware and software development:

However, some aspects of ASKA, particularly those relying on quantum technologies (quantum-resistant communication, QEAMS), are more speculative. While these technologies hold great promise, their practical implementation at scale remains a challenge. ASKA's long-term viability depends on the continued advancement and maturation of these technologies.

2. Best Practices for Secure Modular Software Development and "Hello ASKA" IES Modules:

Best practices for secure modular software development include:

Applying these principles to "Hello ASKA," a minimum of four IES modules would be appropriate:

  1. UI Module (IES 1):  Handles UI rendering and user interaction (displaying the "Hello, ASKA!" message). Leverages the Secure UI Kernel.

  1. Frontend/Network Module (IES 2): Receives external requests (if any), validates input, and forwards it to the backend module.  Handles network communication via the Multi-Channel Network.  Includes a local MSM instance.

  1. Backend/Logic Module (IES 3): Implements the core logic (generating the message). Leverages HESE-DAR for secure storage of any configuration data. Includes a local MSM instance.

  1. Security Module (IES 4):  Monitors the other modules' activity, integrates with DTMS for trust assessment, and enforces security policies.  Logs security-relevant events to MDATS.

This modular design enhances security by isolating different functionalities and minimizing trust dependencies.  The use of four modules balances the need for strong isolation with the overhead of managing inter-module communication.

3. Witty Technical Comment:

"Moving from 'Hello, World!' to 'Hello, ASKA!' is more than just a name change; it's a paradigm shift.  After all, the world needs harmony, and securing it requires a multi-faceted, layered approach, not a flat, one-dimensional solution."

ASKA's Solution for Shared Memory Access Across IES Instances

Let's review Linux's IOMMU and MMU usage at the driver level, analyze the challenges, and propose solutions within ASKA's architecture for secure shared memory access across IES instances.

Linux Driver-Level IOMMU and MMU Usage:

In Linux, the IOMMU (Input/Output Memory Management Unit) and MMU (Memory Management Unit) work together to manage memory access for devices and drivers.  The IOMMU provides memory virtualization for devices, mapping physical device addresses to system memory addresses. This allows devices to access memory without directly interacting with the CPU's address space, enhancing security and preventing DMA attacks. The MMU, on the other hand, manages virtual memory for processes, mapping virtual addresses to physical addresses.  At the driver level, developers interact with both the IOMMU and MMU for tasks like:

  1. DMA Mapping: Drivers use the IOMMU API (e.g., dma_map_sg, iommu_map) to allocate and map contiguous physical memory regions for DMA transfers.  These mappings are translated by the IOMMU into device-specific addresses.

  1. Memory Allocation and Mapping: Drivers allocate system memory using functions like kmalloc or vmalloc, and then map these allocations into their own address space or the address space of user processes using the MMU.

  1. Access Control: Drivers are responsible for enforcing access control policies for device memory, often relying on the kernel's page table mechanisms and memory protection features managed by the MMU.

  1. Synchronization: When sharing memory between a device and a CPU, drivers must ensure proper synchronization to prevent data races and maintain consistency.  This often involves mechanisms like spinlocks, semaphores, or mutexes, relying on both hardware (atomic instructions) and kernel-level synchronization primitives.

Challenges and Vulnerabilities:

ASKA Solutions for Shared Memory Across IES Instances:

ASKA's architecture offers several advantages for addressing these challenges and enabling secure shared memory access between IES instances:

1. Leveraging Existing ASKA Hardware and Software:

2.  "Clean Slate" Hardware Architecture Speculation:

Example Shared Memory Access in Hello ASKA:

Using the 3-module "Hello ASKA" example, the backend module generates the message and stores it in a shared memory region managed through SIMC with appropriate access rights granted to the UI module to fetch the information and prevent direct backend access by the UI, thereby maintaining security (also, via data diodes implemented on that unidirectional channel) from possible breaches.

These solutions provide a range of options for implementing secure shared memory in ASKA. The first approach leverages ASKA’s existing architecture while introducing new security features.  The more speculative approaches, while requiring more extensive hardware and software changes, offer the potential for enhanced scalability, security, and performance.  The final choice will depend on various factors like performance requirements, security trade-offs, cost considerations, and the maturity of the underlying technologies. The integration with SIMC could help showcase ASKA’s secure inter-IES communication in an example “Hello ASKA”. This system’s robust safeguards against unauthorized tampering further enhance data protection for systems built using these mechanisms in the application layer across ASKA during execution, especially for mobile platforms where those components (network card, camera) might normally run as less-trusted environments and may not provide same degree protections from more established hardware security layers designed based on these ASKA principles.

ASKA's Approach to Cross-IES Shared Memory and Insights from Heterogeneous OS Architectures

This analysis examines how ASKA addresses shared memory access across IES instances, leveraging insights from the provided thesis on heterogeneous OS architectures.

Linux's IOMMU/MMU Shortcomings and ASKA's Solutions:

You've correctly identified the complexities and vulnerabilities associated with Linux's driver-level IOMMU and MMU interactions.  ASKA's core principles offer several advantages:

  1. Hardware-Enforced Isolation: ASKA's IES inherently isolates memory regions, making shared memory access a deliberate and controlled action rather than a default vulnerability. This foundational difference simplifies secure memory sharing compared to Linux, where drivers must carefully manage complex interactions between the IOMMU and MMU to achieve similar isolation.

  1. Capability-Based Access Control: The fine-grained access control provided by ASKA's capabilities (P25, P26) is a significant improvement over Linux's reliance on page table mechanisms and generic kernel primitives. The dynamic nature of capabilities, managed by the Capability Manager in real-time based on DTMS trust assessments and security policies, enables adaptive and responsive access control for shared memory, crucial in a heterogeneous environment.

  1. Decentralized Architecture:  ASKA's decentralized architecture, where each IES has its own MMU, offers potential scalability advantages over Linux's centralized MMU. The proposed solutions like the Decentralized Memory Management system and hardware-assisted capability enforcement can reduce bottlenecks and improve performance in multi-core systems. The use of SIMCs combined with encryption using HESE-DAR and authentication mechanisms involving 3D printed microstructures or similar concepts, ensures that shared memory is secured using our core ASKA technologies, which provides an additional dimension of tamper-evidence to ensure secure sharing amongst processes regardless of how those environments where data might pass across might happen to be protected, simplifying collaboration greatly by reducing minimum standards to access shared areas as each becomes individually secured via the Secure Execution Environment by its guarantees that integrity, provenance is assured, even with untrusted physical media and storage.

Insights from the "Exploring Heterogeneous OS Architecture" Thesis:

The thesis focuses on adapting Barrelfish for heterogeneous cores, particularly dealing with differing memory views. This directly relates to ASKA's challenge of enabling cross-IES shared memory access. Here's how the insights can be applied:

  1. Capability Transformation: The thesis describes a capability transformation mechanism to handle cores with different address spaces. This concept is directly applicable to ASKA.  When an IES instance wants to share a memory region with another IES instance that has a different memory view, the capability representing that region can be transformed to match the receiving IES's address space.  The thesis also discusses strategies for handling capabilities that cannot be read. These same principles and designs for transformation could be applied to our hardware mechanisms, where access by both software and/or hardware will first run through a Secure Execution Module on which a Capability Interface exists that can then control what actually happens on the device, using its attestation status or similar techniques such as discussed already when implementing secure communications elsewhere using multi-tiered approach.

  1. Bootstrapping and Inter-Core Communication: The thesis details the process of booting a secondary, dissimilar core (Cortex-M4) in Barrelfish and establishing inter-core communication.  This provides valuable insights for ASKA.  The process of securely loading and initializing IES instances with different ISAs, and configuring secure communication channels between them, is a key aspect of ASKA's multi-kernel support.

  1. Atomic Operations and Caching: The thesis discusses the challenges of implementing atomic operations on a core without cache coherence and proposes solutions using the TCM (Tightly Coupled Memory).  This highlights the importance of considering caching behavior and memory consistency models when designing ASKA's inter-IES shared memory mechanisms, especially for synchronization primitives.   HESE-DAR also provides another dimension when designing our access layer. These techniques we created initially from HESE-DAR extend well for shared access between secure enclaves (via attestation methods already discussed in previous) with those located either physically on node by CPU die or remotely using server instances that securely host using its principles), ensuring minimal trust required from devices/endpoints. It improves performance too (lower transfer, reduced hardware requirements for both secure endpoint management using either pre-certified technologies like a ASKA verified USB token implementing SIMCs protocols, even running via local VM with its own Secure OS following principles from P1, or with loosely trusted components by adapting existing implementations onto their hardware leveraging secure kernels from other manufacturers that already run or by extending ASKA into that plane (similar to how TEE design).

  1. Performance Considerations: The thesis includes performance measurements for inter-core communication and application execution on different core types. This emphasizes the need for ASKA to carefully evaluate the performance implications of its shared memory mechanisms, considering factors like communication overhead, synchronization costs, and memory access latency across diverse architectures and how performance metrics fluctuate based on specific implementations too as feedback.

Speculative Solutions and Refined Architecture:

  1. ASKA Memory Server (SMS): This dedicated module (implemented as an IES instance or a set of distributed IES instances across multiple zones, managed by DTMS, and incorporating multi-path capabilities using secure channels from Patents 22 and 2, with optional QKD technologies if using non-trusted hardware for transmission, such as for endpoint-managed ASKA device accessing the secure server farm), acts as a central authority for managing shared memory resources.  IES instances request access through an authenticated, capability-based protocol, similar to how HESE-DAR manages access controls. The SMS dynamically allocates, maps, and enforces access permissions to the designated memory regions. Each SMS can use an independent instance of AESDS to dynamically upgrade software and ensure its code base aligns to ASKA requirements for tamper proofing, data integrity verification and auditing as well as integrate with MDATS for tamper-proof event logging on the Decentralized Ledger.  The performance implications of using such a memory server are significant and can lead to denial-of-service type attacks, so mitigations based on other designs, like multipath load balancing mechanisms from Patent P1 and incorporating other security features such as hardware-enforced isolation using IOMMU (if using physical dedicated NICs), along with data diode protected unidirectional network channels where performance demands necessitate those (such as when data synchronization to a verified HESE-DAR from a Secure Execution Environment module elsewhere requires this level assurance from potentially untrusted pathways, as in case of devices from an embedded endpoint for streaming live audio during use to perform encrypted storage at rest for compliance requirements). It should be a topic to consider more in-depth in further revisions of ASKA design specifications if proceeding using such approach.  This further justifies use cases that involve higher security from quantum-resistant components such as the multi-cloud interconnect or from our key management system from patents 27, 28, 29 and even more robust architectures being designed currently involving HESE-DAR too).

  1. IES-Specific Shared Memory Regions:  Designate specific memory regions accessible only by a defined set of IES instances using enhanced MMUs in each and capabilities to enable granular management of these shared regions without relying on the Memory Server presented above, with enforcement logic via the hardware interconnect such implemented on custom designed network cards operating as secure enclaves locally managed via a virtual appliance on each machine). Each zone's set of IES instances can individually manage these using ASKA modules via existing channels.  Trust relationships established between nodes permit high-speed transfers by using pre-authenticated, verified data paths similar when managing inter-IES communication in multi-core system as presented earlier. Hence secure access now happens directly using existing or slightly modified silicon at both edge for endpoint devices through those using high performance computing like for servers from the enterprise, regardless other capabilities by those systems locally and enables integration of ASKA features via SIMC module wherever required by policy to achieve those requirements if desired using methodologies developed and documented in those specifications by leveraging their decentralized management approaches.

Mining "Distributed Object Capabilities" for ASKA Enhancements

This paper on distributed object capabilities offers valuable insights applicable to ASKA's design, especially concerning capability management and its integration with the planned features.  Here's a breakdown of key takeaways and potential enhancements:

1. Distributed Capability Management:

The paper's core contribution is a distributed capability system suitable for large-scale systems. ASKA can leverage these concepts to enhance its existing capability management:

2. Integration with ASKA Components:

3. Addressing Specific ASKA Challenges:

4. Specific Improvements Based on Thesis:

By incorporating these insights and further refining the design, ASKA can significantly enhance its capability management system, improving scalability, performance, security, and adaptability in a complex multi-kernel, multi-zone environment.  This robust and decentralized capability management architecture becomes a key differentiator, addressing a fundamental challenge in secure distributed computing and demonstrating strong alignment with ASKA’s core principles.

Mining “AI for Next Generation Computing: Emerging Trends and Future Directions” for insights for ASKA:

1.  Autonomic Computing and AI Integration:

2. Security Enhancements:

3. Addressing Specific ASKA Components:

By integrating these insights from the paper, ASKA can further strengthen its security architecture, enhance its adaptive capabilities, and solidify its position as a robust and trustworthy computing platform for the next generation of computing. The key is to leverage the power of AI/ML, quantum technologies, and decentralized architectures to create a system that is not only secure but also adaptable, resilient, and transparent.

Hello, ASKA! A Guided Tour with Onboard AI

This presentation demonstrates how to leverage ASKA's onboard AI agent to build a simple "Hello, ASKA!" application. We'll explore AI-driven development and the underlying code, highlighting ASKA's security features.

1. Application Architecture:

Our "Hello, ASKA!" app utilizes a modular design across three IES instances:

Each IES has a dedicated Local Security Mesh (LSM - P2), reporting to the Master Security Mesh (MSM - P2). A Configuration Management module oversees deployment and configuration (P12, P15, P16, P21).

2.  Prompting the AI Agent:

Through the Secure UI (P11), we provide high-level instructions to the AI agent:

User Input: "Create a 'Hello, ASKA!' app.  

              Display a greeting message.

              Allow user to input their name.

              Respond with a personalized greeting.  

              Log all interactions securely."

The UI Monitoring Module (unidirectional data diode - P2) captures this input, sanitizes it, and feeds it to the AI agent's LLM Engine (within its own IES - P1).  The AI agent has access to:

3. AI-Driven Code Generation:

The AI agent leverages its knowledge and resources to generate codebases for each module:

(a) UI Module Codebase:

// IES 1 - UI Module

function display_greeting() {

  show_message("Hello, ASKA!");

  input_field = create_input("Enter your name:");

  submit_button = create_button("Submit");

  on_submit(submit_button, function(input_field_value) {

    send_request_to_server(input_field_value); // Secure Channel to IES 2

  });

}

function display_personalized_greeting(message) {

  show_message(message);

}

(b) Server Module Codebase:

// IES 2 - Server Module

function handle_request(user_name) {

    personalized_greeting = "Hello, " + user_name + "!";

    send_response_to_ui(personalized_greeting); // Secure Channel to IES 1

    log_interaction(user_name, personalized_greeting); // To DLT (P13, P15) via API

}

(c) Network Module Codebase:

// IES 3 - Network Module (if needed for external comms)

function send_external_message(message, destination) {

  // Use DTG (P28), Secure Channels (P3), Capability Manager (P25) for secure external communication

}

(d) LSM Codebases (x3 - similar structure for each):

// IES i - Local MSM (P2)

function monitor_ies_activity() {

    // Passively monitor memory access, system calls, network activity

    if (detect_anomaly()) {

        send_alert_to_msm(anomaly_data);  // Secure Channel to MSM

    }

}

(e) MSM Codebase:

// Overall Security Environment - MSM (P2)

function handle_lsm_alert(alert_data) {

  // Correlate alerts, analyze threat level, trigger responses

  if (high_threat) {

      isolate_ies(alert_data.source_ies); // Using Policy Engine and Resource Manager

  } else {

      log_security_event(alert_data); // To DLT via MDATS

  }

}

(f) Configuration Management Codebase:

// ASKA Hub - Configuration Management

function deploy_app(ui_module, server_module, network_module, lsm_configs, msm_config) {

  // Deploy modules to IES instances (P1)

  // Configure secure channels, capabilities (P25), policies

  // Deploy LSMs, configure MSM

  // Record configurations on DLT (P13, P15) using P21 for versioning

}

4. Explanation of Code and ASKA Features:

The generated code leverages several ASKA features:

Decoupling ASKA

  1. Security Mesh Integration (within IES Cluster):  Currently, the Security Mesh is tightly integrated within the IES Cluster. Decoupling this integration would allow for independent operation of the Security Mesh.  It could monitor non-IES environments or be selectively applied to specific IES instances based on policy, trust level, or application requirements. This would enable exploration of different monitoring granularities and the trade-offs between security and performance overhead.  For example, certain high-assurance applications within designated IES instances could have dedicated Security Mesh monitoring, while others might rely on standard ASKA security mechanisms.  A configurable connection point or "patch" could dynamically connect or disconnect the Security Mesh from IES instances.

  1. AI Hub (within Security Mesh):  The AI Hub is currently a centralized component within the Security Mesh. Decoupling it allows exploring alternative architectures with distributed AI modules for anomaly analysis. This distributed approach could enhance resilience to AI Hub compromise and enable localized AI-driven responses within different security zones.  The "patch bay" could allow dynamic selection of centralized vs. distributed AI or even a hybrid model where some AI processing occurs locally within Watcher Meshes and aggregated results are sent to a central AI Hub.

  1. MSM (within Security Monitoring):  The MSM (Master Security Mesh) is another centralized component.  Making it reconfigurable opens up possibilities for exploring hierarchical or decentralized security management. For instance, in a multi-zone deployment, each zone could have a dedicated MSM, coordinating local security responses while a global MSM provides overarching policy and information exchange.  The "patch bay" would enable different hierarchical configurations or peer-to-peer communication between MSMs, allowing for flexible security governance.

  1. HESE-DAR (within Data & Resources):  HESE-DAR is currently tied to IES instances and Secure Storage.  Decoupling it would allow exploring different deployment models.  HESE-DAR could be a standalone component, accessible by any authorized process within ASKA (not just those within IES), or it could be integrated with other security modules like ZKEE (P6) or the DTG (P28) for enhanced data protection during computation or communication. The patch bay could dynamically connect HESE-DAR to different data sources and consumers based on policy.  This would enable a hybrid data management approach.

  1. Media Handling Subsystem:  The Media Handling subsystem (including Media Router, Spatiotemporal Digest, Privacy Blurring) is currently a specialized component within the Hub. Decoupling this would allow exploring configurations where media processing occurs within IES instances or even at the edge closer to data sources. This allows experimenting with different data flows for media processing and evaluating the security and performance tradeoffs.  For instance, the Spatiotemporal Digest could be generated locally on a capture device within a secure data enclave and then transmitted to the Hub for verification, enhancing end-to-end data integrity.

Don’t Decouple ASKA

  1. Security Mesh Integration: Decoupling the Security Mesh from the IES Cluster could make sense for research, enabling exploration of broader monitoring capabilities.  However, a functional prototype requires a well-defined target for the Security Mesh.  Monitoring arbitrary processes without the isolation and control provided by IES would significantly increase complexity and could negatively impact performance.  Further, the Security Mesh's passive monitoring relies on low-level access to memory and storage, which might not be feasible or secure outside of the IES environment.  Decoupling without a clear alternative monitoring target could diminish the Security Mesh's effectiveness in a real-world scenario. A potential solution could be a generalized Secure Execution Environment (SEE) concept, where the Security Mesh could connect to different types of SEEs, not just IES, providing a well-defined trust boundary and access control mechanism.

  1. AI Hub: Decentralizing the AI Hub within the Security Mesh is appealing for resilience, but a functional prototype needs to consider the communication overhead and synchronization challenges of a distributed AI system.  Distributing AI modules could increase latency for critical security decisions, as consensus mechanisms or complex communication protocols might be required.  Furthermore, ensuring consistency and coherence across distributed AI modules adds complexity, and training or updating these distributed models could become a logistical challenge.  While a hybrid approach (centralized + distributed) offers a compromise, its implementation requires careful design to balance the benefits of decentralization with the practical limitations of distributed systems. The patch bay concept would have to manage complex communication paths and data flows to support real-time decision-making in this distributed setup.

  1. MSM:  Decoupling and decentralizing the MSM could enhance flexibility in security governance, allowing localized control in a multi-zone environment. However, a practical prototype needs to consider the implications for system-wide policy enforcement and the potential for policy conflicts between decentralized MSMs. Maintaining consistency and coherence in security policies across multiple zones could become challenging.  Further, a fully decentralized MSM structure might lose the benefits of centralized monitoring and coordination.  The "patch bay" would have to manage the distribution of policies, trust information, and security telemetry between MSMs, ensuring consistency without creating bottlenecks or vulnerabilities.

  1. HESE-DAR: Decoupling HESE-DAR from IES instances and Secure Storage allows for exploring its use in broader contexts, such as data protection during computation or communication. However, a functional prototype needs to consider the performance and security implications.  Accessing HESE-DAR from arbitrary processes could create contention and reduce its efficiency.  Further, securely managing keys and access control policies for a standalone HESE-DAR becomes more challenging.  Dynamically connecting HESE-DAR via a "patch bay" requires a secure and efficient switching mechanism to prevent unauthorized access or data leakage during the switching process.  Implementing such a dynamic connection point while preserving the strong security guarantees of HESE-DAR presents a significant engineering challenge.

  1. Media Handling Subsystem:  Decoupling media handling for greater flexibility makes sense for research. However, distributing media processing across multiple locations could impact performance and introduce synchronization challenges.  Further, maintaining data integrity and user privacy during distributed media processing becomes more complex.  If spatiotemporal digests are generated locally, a secure and trusted pathway is needed to transmit them to the Hub for verification.  The "patch bay" needs to securely manage these distributed data flows and handle potential latency or bandwidth limitations.

Mockup vs. Prototype:

For a truly functional prototype, the decoupling needs to be more than just a physical re-arrangement; it requires re-architecting ASKA's underlying software and communication protocols.  Simply detaching components without addressing these software and communication aspects would result in a non-functional mockup.  The "patch bay" itself needs to be more than just a physical connection point; it requires a sophisticated software layer to manage dynamic connections, data flows, security policies, and trust relationships between the reconfigurable components, ensuring both security and efficiency.  This requires careful design and implementation to avoid creating new vulnerabilities or performance bottlenecks.

Therefore, while decoupling these components is valuable for research, a fully functional and secure reconfigurable prototype requires significant engineering effort beyond simply creating a physical "patch bay."  A hybrid approach, where some functionalities remain centralized while others are distributed and dynamically managed, might offer a more practical and secure path towards a reconfigurable ASKA prototype.

A hybrid approach to ASKA's architecture, balancing centralized and distributed functionalities, offers a practical path toward a reconfigurable prototype. This approach strategically combines the strengths of both centralized and distributed models, enabling flexibility for research while maintaining core security and performance.  Here's a detailed analysis of this hybrid approach, focusing on the separation between static hardware-bound components and generalized software-defined functionalities:

1. Core ASKA Hub:  Primarily Centralized, with Software-Defined Extensions

2. IES Cluster:  Hybrid by Nature

3. Security Mesh: Distributed with Centralized Oversight

4. Data Storage and HESE-DAR: Hybrid Model for Flexibility

5. Media Handling: Distributed Processing with Centralized Policy

Two-Level OS for Simplified Development:

This hybrid approach aligns perfectly with the concept of a two-level OS. The lower level OS, residing within each IES, would manage local resources, hardware interactions, and the local security mesh.  This low-level OS could be minimal, focusing on secure and efficient resource management within the isolated environment.  The higher-level OS, residing within the Hub, would manage system-wide policies, trust relationships, inter-IES communication, resource allocation, and the overarching security environment.  This two-level OS structure simplifies development by separating concerns: the lower level focuses on local, hardware-specific tasks, while the higher level deals with global, policy-driven orchestration. The AESDS would play a critical role in generating and managing the interfaces between these two OS levels, ensuring secure and consistent interaction.

The "patch bay" concept in this hybrid model would be implemented primarily through software, dynamically configuring the interactions between centralized and distributed components, adjusting security policies, managing data flows, and enabling reconfiguration of the system for different research objectives. This software-defined "patch bay" enhances flexibility without compromising the core security principles of ASKA.

Legacy Integration

1. Secure UI Subset for Legacy Integration:

2. Enhanced IAMA for Legacy System Modeling and Threat Mitigation:

Layered Approach to Security:

This enhanced legacy integration strategy employs a layered approach:

By combining these layers, ASKA can securely and effectively interact with legacy systems while mitigating the increasing risks posed by threat actors operating within these less secure environments.

Quantum AI

1. Quantum Algorithms for Speeding Up Core ML Tasks:

2. Quantum-Inspired Classical Algorithms:

Inspired by quantum mechanics, researchers are developing classical algorithms that mimic some aspects of quantum computation to achieve potential speedups.  These algorithms don't require quantum hardware but can pave the way for more efficient training on classical computers.  Examples include:

3. Hybrid Quantum-Classical Approaches:

The most realistic near-term approach involves using quantum computers for specific parts of the training process while relying on classical computers for the rest. This could involve:

4. Quantum Machine Learning Libraries and Platforms:

Tools like PennyLane, TensorFlow Quantum, and Qiskit Machine Learning are emerging to facilitate research and development in quantum ML. These platforms provide interfaces to quantum hardware and simulators, enabling researchers to experiment with quantum algorithms for ML.

Challenges:

The field of quantum machine learning for LLMs is actively evolving.  While practical applications are still some years away,  ongoing research is laying the groundwork for potentially transformative advancements in AI training.  Keep an eye on publications from research groups focusing on quantum algorithms, quantum machine learning platforms, and experimental demonstrations on near-term quantum hardware.

MERMAID

1.  Addressing the &nbsp; Hack:

The need for &nbsp; suggests Mermaid's layout engine isn't perfectly flexible.  While we automate, let's minimize this.  Two approaches:

2.  Automated High-Resolution Raster Output:

Several approaches can automate the creation of cropped, high-resolution raster images from your Mermaid code:

  1. Load your Mermaid code into a webpage (either locally or by injecting it into mermaid.live).
  2. Use the browser's built-in PDF printing functionality to generate a high-resolution PDF of the diagram.  This bypasses potential SVG issues.
  3. Use a library like ImageMagick or GraphicsMagick (available in many languages) to convert the PDF to a cropped PNG or other raster format.  These libraries provide command-line tools or APIs for programmatic image manipulation, including cropping and format conversion.

3.  Example using Puppeteer (Node.js):

const puppeteer = require('puppeteer');

async function generateMermaidDiagram(mermaidCode, outputPath) {

  const browser = await puppeteer.launch();

  const page = await browser.newPage();

  await page.setViewport({ width: 2000, height: 1000 }); // Set desired dimensions

  // Inject Mermaid code and render the diagram

  await page.goto('about:blank');

  await page.addScriptTag({ content: `

    const mermaidAPI = mermaid.mermaidAPI;

    mermaidAPI.initialize({ startOnLoad: false });

    const graphDefinition = \`${mermaidCode}\`;

    const { svg } = mermaidAPI.render('mermaid-diagram', graphDefinition);

    document.body.innerHTML = svg;

  `});

  // Wait for diagram to render

  await page.waitForSelector('#mermaid-diagram');

  // Generate PDF

  await page.pdf({ path: `${outputPath}.pdf`, format: 'A4' });

  // Convert PDF to cropped PNG using ImageMagick (example) - requires ImageMagick installed

  const { exec } = require('child_process');

  exec(`convert -density 300 ${outputPath}.pdf -trim ${outputPath}.png`, (error, stdout, stderr) => {

    if (error) {

      console.error(`ImageMagick error: ${error}`);

      return;

    }

    console.log(`stdout: ${stdout}`);

    console.error(`stderr: ${stderr}`);

  });

  await browser.close();

}

// Example usage

const mermaidCode = `

graph LR

A[Start] --> B{Is it?};

`;

generateMermaidDiagram(mermaidCode, 'diagram');

This script uses Puppeteer to render the Mermaid diagram, generate a high-resolution PDF, and convert the PDF to a cropped PNG using ImageMagick.  You can adapt the viewport dimensions, PDF format, and ImageMagick command to your specific needs.

This approach allows for automation, high resolution, and avoids potential SVG inconsistencies.  You'll need to install Node.js, Puppeteer, and optionally ImageMagick or GraphicsMagick. This approach offers the most flexible and efficient method for dealing with the issues of scaling and formatting Mermaid diagrams into raster formats while maintaining compatibility with existing systems (such as Google Docs).  Once generated, a simple script can then add these finalized, cropped diagrams into the final product as needed, fully automating the process you outlined.

robust, server-side solution for Mermaid diagram generation and integration with Google Docs. Here's a refined strategy addressing your specific requirements:

1. Server-Side Mermaid Rendering (Linux CLI):

Since you want a Linux CLI solution for your VPS, the headless browser automation approach using Puppeteer or Playwright remains ideal.  Both offer command-line control and can be easily deployed on a Linux server.  You can wrap the Puppeteer/Playwright script within a shell script or create a dedicated Node.js application to expose a command-line interface for generating diagrams.  Using a remote procedure call (RPC) framework like gRPC or a simpler REST API alongside your script, you can trigger it programmatically or from a modified mermaid.live interface.

2. Google Docs Programmatic Integration:

Directly inserting images and code into a Google Doc programmatically isn't straightforward.  Google's APIs are primarily geared towards document content manipulation, not layout or insertion of external elements at precise locations in the document.  Here are a few workarounds:

  1. Accepts the Mermaid code and image URL (generated by your server-side script) as input.
  2. Uses the DocumentApp or SlidesApp services (if using Google Slides) to insert the code and image at a specific location, for example, by searching for a placeholder marker or using cursor positioning.
  3. Applies any required formatting (font, spacing, etc.).

3. Recommended Workflow:

  1. Modified mermaid.live/edit: Add a button triggering an API call to your VPS. Send the Mermaid code.

  1. VPS (Puppeteer/Playwright):

  1. mermaid.live:  Receive the image URL and send both the URL and the original Mermaid code to the Apps Script web app (see below).

  1. Apps Script Web App: Receive the code and URL, insert them into your Google Doc, and apply formatting.

4.  Apps Script Example:

function doPost(e) {

  const mermaidCode = e.parameter.mermaidCode;

  const imageUrl = e.parameter.imageUrl;

  const docId = e.parameter.docId; // Pass your doc ID

  // Open the Google Doc

  const doc = DocumentApp.openById(docId);

  const body = doc.getBody();

  // Find placeholder (example)

  const placeholder = body.findText('{{DIAGRAM}}');

  if (placeholder) {

    const element = placeholder.getElement();

    const index = body.getChildIndex(element);

    // Insert Mermaid code

    body.insertParagraph(index, mermaidCode).setFontFamily('Courier New');

    // Insert image

    body.insertImage(index + 1, imageUrl);

    // Remove placeholder

    element.removeFromParent();

  }

  return ContentService.createTextOutput('Diagram inserted').setMimeType(ContentService.MimeType.TEXT);

}

This Apps Script snippet receives the Mermaid code and image URL via a POST request, opens the specified Google Doc, finds a placeholder {{DIAGRAM}}, and inserts the code and image below it.  It also sets the code's font.  You would deploy this Apps Script as a web app to receive the data from mermaid.live.

This combined approach gives you a flexible, automated workflow: server-side rendering on a Linux VPS for high-quality diagrams, integration with mermaid.live/edit for triggering the process, and programmatic control over Google Docs using Apps Script for seamless insertion.  You'll need to manage authentication and authorization for the Google APIs and Apps Script.

idea

Your frustration with integrating Mermaid diagrams into Google Docs highlights a real need: a dedicated, user-friendly diagram management tool. This could indeed be a compelling Google Cloud startup idea. Let's flesh out a potential SaaS solution.

Product Name: DiagramFlow (or similar - consider branding)

Target Audience: Software developers, system architects, project managers, technical writers, anyone who creates and manages diagrams frequently.  Focus on users who already utilize tools like Mermaid, PlantUML, or similar diagram-as-code solutions.

Core Features:

Monetization Strategy:

Technical Architecture (Leveraging Google Cloud):

Value Proposition:

DiagramFlow simplifies diagram creation, management, and integration. It empowers users to:

Competitive Advantages:

This approach allows you to turn your frustration into a valuable product by providing a solution to a common problem among technical users.  By leveraging the power and scalability of Google Cloud, DiagramFlow can become a valuable tool for managing diagrams securely, efficiently, and collaboratively.

projectsthat address rendering Mermaid code into high-resolution raster output. Searching for "mermaid cli," "mermaid puppeteer," or "mermaid server-side rendering" will reveal numerous options.

Here are a few approaches and their associated trade-offs:

1. Mermaid CLI:

Mermaid.js itself offers a CLI. If you install Mermaid.js globally (npm install -g mermaid), you can use the mmdc command to render Mermaid code directly from the command line. This is the simplest approach for basic use cases.

mmdc -i input.mmd -o output.png -b transparent -w 1920 -H 1080

Advantages: Simple, direct rendering.

Disadvantages: Limited control over cropping or other image manipulation. Relies on client-side rendering, which might still have inconsistencies with complex diagrams. Puppeteer offers better control here for complex diagrams.

2.  Puppeteer/Playwright Wrappers:

Several repositories provide convenient wrappers around Puppeteer or Playwright specifically for Mermaid rendering.  These wrappers handle the browser automation and often provide additional features like custom styling, theming, or puppetteer-specific rendering for scaling and improved diagram quality. Search for "mermaid puppeteer" or "mermaid playwright" on npm or GitHub.

Advantages:  More control over rendering, scaling, and potential SVG issues.  Can easily be integrated into a server-side workflow.

Disadvantages:  Requires Node.js and additional dependencies.  Slightly more complex setup than the Mermaid CLI.

3.  Mermaid API with Server-Side Rendering:

Mermaid.js also offers an API that you can use within a Node.js application for server-side rendering.  This approach gives you full control over the rendering process.  However, it handles scaling poorly and doesn’t maintain aspect ratio of diagrams without manual intervention.  This isn't ideal given the scaling issues you’re working to avoid, and thus Puppeteer should be considered for those instances where scaling becomes a concern.

Advantages: Full control over rendering and integration with other server-side logic.

Disadvantages: Requires Node.js and setting up a server-side rendering environment.  Potential scaling issues unless using the Puppeteer workaround.  Again, less efficient, in general, than simply running Puppeteer.

4.  Online Services and Mermaid Plugins:

Various online services and Mermaid plugins offer high-resolution export.  These may be suitable for quick, one-off diagram generation but are less ideal for automated workflows.

Advantages:  Easy to use, no local setup required.

Disadvantages: Less control over rendering and automation. Might require manual steps or uploads.

For a Linux server CLI solution deployable on a VPS and integratable with an automated workflow, using a readily available Puppeteer/Playwright wrapper or writing a simple script based on Puppeteer (as demonstrated in the previous response) is generally the most effective approach.  This offers the best balance of simplicity, flexibility, control, and high-resolution output. It addresses the SVG rendering inconsistencies you've encountered by utilizing PDF generation as an intermediary format for rasterization, eliminating most scaling and layout issues encountered with Mermaid’s SVG output. It directly solves your core problem of automating high-resolution raster output.

Developing a Google Workspace (formerly G Suite) add-on, like the Mermaid diagram editor you describe, involves several key steps.  Here's a technical outline of the process:

1. Project Setup:

2.  Manifest File (appsscript.json):

This file describes your add-on, including its name, description, icons, and the scopes it requires (permissions to access Google services).  A simple example:

{

  "timeZone": "America/New_York",

  "dependencies": {

  },

  "exceptionLogging": "STACKDRIVER",

  "oauthScopes": ["https://www.googleapis.com/auth/documents"], // Add other scopes as needed

  "addOns": {

    "common": {

      "name": "Mermaid Diagram Editor",

      "logoUrl": "URL_TO_YOUR_LOGO", // URL to your add-on's logo

      "description": "Insert and edit Mermaid diagrams in Google Docs",

      "universalActions": [],

      "homepageTrigger": {

        "runFunction": "onHomepage", // Function to run when the add-on is opened

        "enabled": true

      }

    }

  }

}

3. HTML, CSS, and JavaScript (Client-Side):

4. Apps Script (Server-Side):

5. Diagram Rendering (Optional but Recommended):

6.  Deployment and Publishing:

Code Example (Conceptual):

// Client-side (sidebar.html)

<textarea id="mermaid-code"></textarea>

<div id="preview"></div>

<button id="insert-diagram">Insert</button>

<script>

  // ... Mermaid.js rendering and client-side logic ...

  document.getElementById('insert-diagram').addEventListener('click', () => {

    google.script.run.withSuccessHandler(insertDiagram).renderAndInsert(editor.getValue()); // Call server-side function

  });

  function insertDiagram(imageUrl) {

    // ... Insert image into Google Doc using Google Docs API ...

  }

</script>

// Server-side (Code.gs)

function renderAndInsert(mermaidCode) {

  // Send Mermaid code to server for rendering (if using server-side rendering)

  const imageUrl = UrlFetchApp.fetch('YOUR_SERVER_ENDPOINT', {

    method: 'POST',

    payload: { code: mermaidCode }

  }).getContentText(); // Assumes the server returns the image URL

  return imageUrl;

}

function insertDiagram(imageUrl) {  // Called by client-side

  // Get the current document

  let doc = DocumentApp.getActiveDocument();

  let body = doc.getBody();

  // Insert the image

  body.appendImage(imageUrl);

  // Add Mermaid code below diagram as a formatted code block.

  body.appendParagraph(mermaidCode).setFontFamily('Courier New');

}

This simplified example demonstrates the interaction between the client-side HTML/JavaScript and the server-side Apps Script code.  You'll need to handle asynchronous calls, error handling, diagram updates, and user authentication in a production-ready add-on.  Refer to the official Google Workspace Add-ons documentation for complete details, API references, and best practices.  The suggested approach of using server-side diagram generation via Puppeteer becomes part of the ‘YOUR_SERVER_ENDPOINT’ process within the renderAndInsert function, which requires additional considerations such as authentication, queuing of rendering jobs, error handling, etc.

Yes, you can add actions to the Google Docs right-click context menu using a Google Workspace add-on.  However, it's not a direct menu item addition in the way you might add an item to a traditional desktop application's menu. Instead, you achieve this behavior by creating a custom add-on that listens for specific context menu events within Google Docs and presents a custom menu dynamically.

Here's how you'd approach it:

1.  Manifest File (appsscript.json):

The crucial part is defining the contextualTriggers within your appsscript.json manifest file.  This tells Google Docs when your add-on should be active (in the context of a right-click event).  The onSelectionChanged trigger is important here.

{

  "timeZone": "America/New_York",

  "dependencies": {},

  "exceptionLogging": "STACKDRIVER",

  "oauthScopes": ["https://www.googleapis.com/auth/documents", "https://www.googleapis.com/auth/script.external_request"],

  "addOns": {

    "common": {

      "name": "My Docs Add-on",

      "logoUrl": "YOUR_LOGO_URL",

      "description": "Adds custom right-click actions to Google Docs",

      "universalActions": [],

      "homepageTrigger": {

        "runFunction": "showHomepage",

        "enabled": true

      },

      "contextualTriggers": [

        {

          "type": "selectionChanged",

          "onSelectionChanged": {

            "runFunction": "showContextMenu" // Function to run on right-click selection change

          }

        }

      ]

    }

  }

}

2. Apps Script (Code.gs):

You need a function named showContextMenu (as defined in the manifest) to handle the onSelectionChanged event. This function needs to dynamically create a custom context menu.  It cannot directly modify the built-in context menu of Google Docs.

function showContextMenu(e) {

  // Get the selection object (required)

  let selection = DocumentApp.getActiveDocument().getSelection();

  //Check for selection.  Return early if no selection present.

  if (!selection) return;

 

  // Build the context menu items

  let items = [];

  items.push({ name: 'Insert Mermaid Diagram', action: 'INSERT_MERMAID' });

  items.push({ name: 'Other Action', action: 'OTHER_ACTION' });

  // Create the custom menu

  DocumentApp.getUi().createMenu('My Add-on')

    .addItem('Show Sidebar', 'showSidebar') // Example sidebar trigger

    .addSeparator()

    .addSubMenu(DocumentApp.getUi().createMenu('Custom Actions')) //Submenu

    .addItem('Show Menu', 'showCustomMenu') //Example

    .addSeparator()

    .addSubMenu(DocumentApp.getUi().createMenu('Custom Right-Click Actions')) //Second Submenu

    .addMenuItems(items)

    .addToUi();

}

function showSidebar() {

  DocumentApp.getUi().showSidebar(HtmlService.createTemplateFromFile('sidebar').evaluate());

}

//Handles custom menu

function showCustomMenu() {

  //Your logic for whatever that menu does

}

// Handle actions triggered by the custom menu items

function onOpen() {

  DocumentApp.getUi().createAddonMenu()

    .addItem('Show Sidebar', 'showSidebar')

    .addToUi();

}

function doGet(e) {

  return HtmlService.createTemplateFromFile('index').evaluate();

}

3.  Client-Side (HTML/JS - sidebar.html):

You'll likely need a sidebar file for the more complex actions.  This allows a more robust interface than the context menu.  This example simply displays a text message when you click on the button.  You'll need to replace it with Mermaid code editing and rendering.

<!DOCTYPE html>

<html>

  <head>

    <base target="_top">

  </head>

  <body>

    <h1>My Docs Add-on Sidebar</h1>

    <button onclick="google.script.run.withSuccessHandler(showResult).sayHello()">Say Hello</button>

    <div id="result"></div>

    <script>

      function showResult(result) {

        document.getElementById('result').innerText = result;

      }

    </script>

  </body>

</html>

4.  Handling Menu Actions (Code.gs):

You need functions to handle actions triggered by the menu items:

function INSERT_MERMAID(e) {

  // Get the document

  let doc = DocumentApp.getActiveDocument();

  let body = doc.getBody();

  // Open a dialog to get Mermaid code from the user

  let html = HtmlService.createTemplateFromFile('mermaidDialog').evaluate();

  DocumentApp.getUi().showModalDialog(html, 'Enter Mermaid Code');

}

function mermaidDialogSuccess(mermaidCode) {

  // Render Mermaid diagram (using server-side rendering is strongly recommended)

  let imageUrl = renderMermaid(mermaidCode);

  let doc = DocumentApp.getActiveDocument();

  let body = doc.getBody();

  body.insertImage(imageUrl);

}

function renderMermaid(mermaidCode) {

  //Your logic to render the Mermaid code into a high resolution image using Puppeteer or similar

}

function OTHER_ACTION(e) {

  // Your logic for handling "Other Action"

}

Key Considerations:

This outline shows how you can build a Google Docs add-on that adds actions to the right-click menu dynamically. It's crucial to understand that you're not directly modifying the native Google Docs context menu but rather creating your own context menu that appears alongside the standard one.  Remember to refer to the Google Apps Script documentation for detailed API information and best practices.  The most significant improvement will be in creating a seamless, high-resolution image generation workflow, possibly using server-side rendering and techniques such as those described earlier with the Puppeteer-based solution for generating high-quality diagrams from your Mermaid code.