Consider the following scenarios:
Scenario 1: The "Command & Control" Loop (Low Latency)Focus: Small payloads, Core NATS, bi-directional JSON.The Action: A user on a JavaScript dashboard clicks a "Start Simulation" button. This sends a JSON configuration (parameters like step_size and iterations) to Julia.The Flow: * JS (Sender): Recognizes the message is small ($< 10KB$). Packages it as a direct transport JSON envelope.Julia (Receiver): Listens on the NATS subject, decodes the JSON, and immediately acknowledges receipt with a "Running" status.Project Requirement Met: Fast, low-overhead communication for control signals without involving the fileserver.
Scenario 2: The "Deep Dive" Analysis (High Bandwidth)Focus: Large Arrow tables, Claim-Check pattern, Julia to JS.The Action: Julia finishes a heavy computation and produces a 500MB DataFrame with 10 million rows. It needs to send this to the JS frontend for visualization (e.g., using Perspective.js or D3).The Flow:Julia (Sender): Converts the DataFrame to an Arrow IPC stream. It sees the size is $> 1MB$, so it uploads the bytes to the HTTP fileserver. It then publishes a NATS message with transport: "link" and the URL.JS (Receiver): Receives the URL, fetches the data via fetch(), and uses tableFromIPC() to load the data into memory with zero-copy.Project Requirement Met: Handling massive datasets that exceed NATS message limits while maintaining data integrity across languages.
Scenario 3: Live Audio/Signal Processing (Multimedia & Metadata)Focus: Raw binary, bi-directional streaming, NATS Headers.The Action: The JS client captures a 2-second "chunk" of microphone audio. It needs Julia to perform a Fast Fourier Transform (FFT) or AI transcription.The Flow:JS (Sender): Sends the raw binary WAV/PCM data. It uses NATS Headers to store the metadata ($fs = 44.1kHz$, $channels = 1$) to keep the payload purely binary.Julia (Receiver): Processes the audio and sends back a JSON result (the transcription) and an Arrow Table (the frequency spectrum data).Project Requirement Met: Bi-directional flow involving mixed media (Audio) and technical results (Arrow).
Scenario 4: The "Catch-Up" (Persistence & JetStream)Focus: NATS JetStream, late-joining consumers, state sync.The Action: Julia is constantly publishing "System Health" updates. The JS dashboard is closed for 10 minutes. When the user re-opens the dashboard, they need to see the last 10 minutes of history.The Flow:NATS (Server): Uses a JetStream with a Limits retention policy.JS (Consumer): Connects and requests a "Replay" from the last 10 minutes. It receives a mix of direct (small updates) and link (historical snapshots) messages.Project Requirement Met: Temporal decoupling—consumers can receive data that was sent while they were offline.

Role: Principal Systems Architect & Lead Software Engineer.Objective: Implement a high-performance, bi-directional data bridge between a Julia service and a JavaScript (Node.js) service using NATS (Core & JetStream).⚠️ STRICT ARCHITECTURAL CONSTRAINTS (Non-Negotiable)Transport Strategy (Claim-Check Pattern):Direct Path: If payload is < 1MB, send data directly via NATS inside the message envelope (Base64 encoded).Link Path: If payload is > 1MB, upload to a shared HTTP fileserver/store. The NATS message must only contain the metadata and the download URL.Tabular Data Format: * MUST use Apache Arrow IPC Stream for all tables/DataFrames. No CSV or standard JSON-serialization of tables allowed.System Symmetry: * Both services must function as Producers AND Consumers.Modular Elegance: * Implementation must be abstracted into a SmartSend function and a SmartReceive handler. The developer calling these functions should not need to care if the data is going via NATS direct or HTTP link.Technical Stack & Use CasesJulia: NATS.jl, Arrow.jl, JSON3.jl, HTTP.jl.Node.js: nats.js, apache-arrow.Scenarios to Support: * Large Data: Sending a 500MB Arrow table from Julia $\rightarrow$ JS.Media: Sending a 5MB WAV file from JS $\rightarrow$ Julia.Signals: Sending small JSON control commands ($< 10KB$) directly via NATS.Implementation Requirements1. Unified JSON Envelope:Define a schema containing: correlation_id (UUID), type (table/binary/json), transport (direct/link), payload (if direct), and url (if link).2. The Julia Module:Implement SmartSend(subject, data, type): Handles Arrow serialization to an IOBuffer, checks size, and manages HTTP uploads for large blobs.Implement SmartReceive(msg): Parses envelope, handles the HTTP fetch with Exponential Backoff (to avoid race conditions), and restores the DataFrame.Include a basic HTTP.listen server to serve as the temporary storage.3. The JavaScript Module:Implement a symmetric SmartSend using nats.js and apache-arrow.Implement a JetStream Pull Consumer for SmartReceive to ensure backpressure and memory safety.4. Performance & Reliability:Demonstrate "Zero-Copy" reading of the Arrow IPC stream on the JS side.Log the correlation_id at every stage for distributed tracing.







Create a walkthrough for Julia service-A service sending a mix-content chat message to Julia service-B. the chat message must includes 





I updated the following:
- NATSBridge.jl. Essentially I add NATS_connection keyword and new publish_message function to support the keyword. 
Use them and ONLY them as ground truth.
Then update the following files accordingly:
- architecture.md
- implementation.md

All API should be semantically consistent and naming should be consistent across the board.









Task: Update NATSBridge.js to reflect recent changes in NATSBridge.jl and docs
Context: NATSBridge.jl and docs has been updated.
Requirements:
Source of Truth: Treat the updated NATSBridge.jl and docs as the definitive source.
API Consistency: Ensure the Main Package API (e.g., smartsend(), publish_message()) uses consistent naming across all three supported languages.
Ecosystem Variance: Low-level native functions (e.g., NATS.connect(), JSON.read()) should follow the conventions of the specific language ecosystem and do not require cross-language consistency.







I'm expanding this Julia package (NATSBridge) into a cross-platform project by adding a JavaScript and Python/MicroPython implementation. To ensure accuracy, the Julia src directory will serve as the ground truth, as the documentation may be outdated.

My goal is to maintain interface parity at the high-level API for a consistent user experience, while ensuring the low-level implementation adheres strictly to the idiomatic conventions of each respective language (e.g., multiple dispatch in Julia vs. asynchronous, prototype, or class-based patterns in JS and Python/MicroPython)

Now, help me do the following:
1) check architecture.md for any mistake.




Help me expands this Julia package (NATSBridge) into a cross-platform project by adding a JavaScript and Python/MicroPython implementation. To ensure accuracy,  NATSBridge.jl will serve as the ground truth, as the documentation may be outdated.

My goal is to maintain interface parity at the high-level API for a consistent user experience, while ensuring the low-level implementation adheres strictly to the idiomatic conventions of each respective language (e.g., multiple dispatch in Julia vs. asynchronous, prototype, or class-based patterns in JS and Python/MicroPython)

Now do the following:
1) check docs to see if there is any mistake.





I'm expanding this Julia package (NATSBridge) into a cross-platform project by adding 
a JavaScript, Python and MicroPython implementation. 
The following will serve as the ground truth:
- test_julia_mix_payloads_sender.jl
- NATSBridge.jl
- test_julia_mix_payloads_receiver.jl
- architecture.md

My goal is to maintain interface parity at the high-level API for a consistent user experience, 
while ensuring the low-level implementation adheres strictly to the idiomatic conventions of each 
respective language (e.g., multiple dispatch in Julia vs. asynchronous, prototype, or class-based 
patterns in JS, Python and MicroPython)

Now, help me do the following:
1) Check whether natsbridge.js needs update or it already up to date.







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Got it — let’s rebuild your table in my own teaching style, keeping it crisp, intuitive, and easy for students to grasp. I’ll emphasize **purpose, audience, format, example, and KPI** in a way that flows like a story of how projects move from idea → contract → design → code → review → operations.

---

### SDD + GitOps Documentation Framework

| Document        | Purpose (Rationale) | Primary Audience | Format / Content | Example (SaaS Context) | Measurement (KPI) |
|-----------------|---------------------|-----------------|------------------|------------------------|-------------------|
| **Requirements** | Capture the **business intent** — why we’re building this and what success looks like. Defines boundaries and user‑visible outcomes. | Stakeholders, Product Owners, Lead Developers | User stories, PRDs, acceptance criteria, non‑functional constraints. | “System must process tabular data from Julia to SvelteKit UI with <200ms latency for 5‑member teams.” | 95% of requests complete <200ms (synthetic monitoring). |
| **Specification** | The **technical contract** — precise rules for inputs, outputs, and data shape. Ensures consistency across dev and test. | Developers, QA Engineers, CI/CD pipelines | OpenAPI, Protobuf, AsyncAPI. Endpoint definitions, schemas, error codes. | `contract.yaml` defining a NATS subject that accepts Arrow streams with snake_case headers. | 100% of messages validated against spec (CI block rate). |
| **Architecture** | The **blueprint** — how components fit together, interact, and scale. Guides system structure and trade‑offs. | Architects, Senior Developers, DevOps | C4 diagrams, Mermaid.js, component/network/storage models. | Diagram showing 6‑node cluster routing traffic via Caddy → Node.js API → Julia pods. | 100% of major decisions logged with trade‑off analysis. |
| **Walkthrough** | The **story of flow** — shows how pieces connect end‑to‑end and why steps are sequenced. Builds intuition for new devs. | New Developers, Team Members | TOUR.md, Loom videos, sequence diagrams. Step‑by‑step traces with rationale. | “UI sends JSON → Node.js wraps Claim‑Check → Julia pulls Arrow data (prevents NATS overflow).” | New developers ship feature in <2 days (PR timeline). |
| **Implementation** | The **real code** — business logic, helpers, tests, configs. Where design becomes executable. | Developers, Code Reviewers | Source code, README.md, unit tests, setup scripts. | Julia function for matrix calculation + SvelteKit component rendering table. | >80% unit test coverage, <5% drift from spec. |
| **Validation** | The **enforcer** — ensures implementation matches the spec. Blocks drift and human error. | Automation servers, QA, Lead Developers | CI jobs, contract tests, linting, integration checks. | CI job rejects PR with camelCase field not allowed by YAML spec. | <1% of PRs bypass validation gates. |
| **Runbook** | The **operational manual** — how the system lives in production, scales, and recovers. Guides on‑call engineers. | DevOps, SREs, On‑call Developers | K8s manifests, Helm charts, Markdown guides. Deployment, scaling, backup/restore, troubleshooting. | GitOps manifest ensuring 6 Julia replicas restart if memory >80%. | MTTR <15 minutes for P1 incidents. |








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SDD + GitOps Documentation Stack
Document,"Purpose (The ""Rationale"")",Primary Audience,Format / Content,Example (SaaS Context),"Measurement (KPI)"
Requirements,"Defines the ""Why"" and the Business Boundary. It sets the constraints and success criteria so the team knows when a feature is ""done"" from a user's perspective.","Stakeholders, Product Owners, Lead Developers","Format: User Stories, PRDs. Content: Functional goals, non-functional requirements (latency, scale), and explicit ""out-of-scope"" items.","""The system must process high-volume tabular data from Julia to the SvelteKit UI with <200ms latency for 5-member teams."",""Pass/Fail: 95% of requests complete <200ms (measured via synthetic monitoring)""
The Spec,"The Technical Contract. It serves as the single source of truth that defines the shape of data. In SDD, this file drives code generation and automated testing.","Developers, QA Engineers, CI/CD Pipelines","Format: OpenAPI (YAML), Protobuf, AsyncAPI. Content: Endpoint definitions, strict data types, error codes, and request/response schemas.",A contract.yaml defining a NATS subject that accepts an Apache Arrow stream with specific snake_case headers.",""Schema Validation Rate: 100% of messages validated against spec (CI block rate)""
Architecture,"The Structural Blueprint. It explains how the ""pieces"" are arranged in the cluster. It defines the relationships between services, databases, and external providers.","System Architects, Senior Developers, DevOps","Format: C4 Model Diagrams, Mermaid.js. Content: Component diagrams, network flow, storage strategy, and technology stack definitions.",A diagram showing how the 6-node cluster routes traffic through Caddy to the Node.js API and offloads heavy math to Julia pods.",""Architecture Decision Log: 100% of major decisions documented with trade-off analysis""
Walkthrough,"The Intuition & Flow. It connects multiple APIs/services into a cohesive end-to-end story. It explains the ""steps"" and the ""rationale"" behind the sequence of operations.","New Developers, Current Team Members","Format: TOUR.md, Loom videos, Sequence Diagrams. Content: Step-by-step trace of a feature, explanation of state changes, and the ""why"" behind complex logic.","""End-to-End Trace:"" 1. UI sends JSON to Node.js. 2. Node.js wraps it in a Claim-Check. 3. Julia pulls the Arrow data. Rationale: This prevents NATS memory overflow.",""Onboarding Velocity: New developers deploy feature in <2 days (tracked via PR timeline)""
Implementation,"The Functional Reality. This is the actual execution of the logic. In SDD, parts of this are auto-generated to ensure it never drifts from the Spec.","Developers, Code Reviewers","Format: Source Code (Git), README.md. Content: Business logic, internal helper functions, unit tests, and local setup instructions.",The Julia function that performs the matrix calculation and the SvelteKit component that renders the resulting table.",""Code Coverage: >80% unit test coverage, <5% test drift from spec""
Validation,"The Enforcement Layer. It ensures that the ""Reality"" (Code) actually matches the ""Contract"" (Spec). It prevents human error from breaking the system.","Automation Servers, QA, Lead Developers","Format: GitHub Actions, Dredd, Prism. Content: Contract tests, linting rules, and integration tests that check API compliance.",A CI job that blocks a Pull Request because a developer added a camelCase field that isn't allowed in the shared YAML spec.",""Block Rate: <1% of PRs reach production without validation (CI gate pass rate)""
Runbook,"The Operational Life-Support. It defines how the system lives in production and how to fix it. In GitOps, the ""State"" is declared here.","DevOps, SREs, On-call Developers","Format: K8s Manifests, Helm Charts, Markdown. Content: Deployment steps, scaling triggers, backup/restore commands, and troubleshooting guides.",A GitOps manifest in Flux that ensures 6 replicas of the Julia service are always running and restarts them if memory hits 80%.",""MTTR: <15 minutes for P1 incidents (tracked via incident management system)""

Do you understand the provided text? Don't fucking change the table content. I want you to add "Measurement (KPI)" column. it is only example of course. This table will be used for consult and teaching.


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Can you write the table and explain this approach and each doc in details then save to docs/SDD_FRAMEWORK.md so I can consult it later.
Don't forget to add How to use this approach effectively.


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Since I develop src folder before I adopt SDD_FRAMEWORK.md approach, can you check src folder and my current doc files then write docs/requirements.md  according to SDD framework? Treat src as ground truth.

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