DEV Community: vmodal_ai

Rise of the Jev Tools Ecosystem: Architecture, Primitives, and the Shift to System One AI

vmodal_ai — Sat, 26 Sep 2026 12:35:04 +0000

The Rise of the Jev Tools Ecosystem: Architecture, Primitives, and the Shift to System One AI

The landscape of artificial intelligence underwent a fundamental architectural shift in September 2026 with the release of Jev by TypeSafe AI. Founded by former OpenAI researcher Diogo Almeida, a co-inventor of reinforcement learning from human feedback (RLHF), TypeSafe AI introduced Jev not as another generative chatbot, but as the pioneer of a new class of artificial intelligence: "System One" decision models.

Named after Daniel Kahneman’s cognitive framework of fast, intuitive, and non-deliberative thinking, Jev does not output text token-by-token. Instead, it evaluates an arbitrary state alongside a set of predefined natural-language questions and directly returns structured, typed decisions accompanied by calibrated confidence probabilities. By bypassing the heavy token-generation pipelines of traditional large language models (LLMs), Jev operates up to 200 times faster and 400 times cheaper than frontier generative models, maintaining sub-500-millisecond latencies at a fraction of the operational cost.
This architectural departure has ignited an explosive software ecosystem. Developers quickly realized that running heavy generative models for basic classification, routing, and guardrailing was an expensive anti-pattern. In the weeks following Jev’s debut, the public developer ecosystem expanded exponentially, giving rise to hundreds of open-source libraries, database extensions, model routers, and agentic harnesses compiled within the community-driven GitHub awesome-jev list.

The Core Primitives of Jev Software Engineering

To understand the tools ecosystem built around Jev, one must first understand its structural outputs. Unlike generative LLMs that require strict JSON schemas, prompt engineering, and brittle regex parsing to guarantee structured text, Jev is natively constrained at the logit level. It exposes three core primitives:

Noul: A probabilistic truth evaluation. It answers how accurately a state matches a given natural-language condition, returning a boolean judgment paired with a confidence score.
Choice: A discrete selection engine. Given an unstructured state and a predefined dictionary of categorical options, Jev forces a single selection, completely eliminating the possibility of formatting hallucinations.
Score: A rubric-based grading mechanism. It ranks an input against an explicit criteria scale, providing an immediate numerical assessment.

Because these primitives deliver deterministic structures instantly, developers can use them as embedded, intelligent conditional statements within traditional codebases. The Jev tools ecosystem is fundamentally designed to operationalize these three primitives across modern software architectures.

Embedded Data: Database and SQL Extensions

One of the most immediate expansions of the Jev ecosystem occurred at the data layer, where developers integrated non-autoregressive classification directly into database engines. Rather than pulling millions of rows out of a data warehouse to parse them through an external LLM pipeline, new extensions allow engineers to execute natural-language conditions directly inside SQL queries.
The tool duckdb-jev, a native extension for DuckDB, applies Jev decisions straight to localized data rows. Benchmarks indicate that duckdb-jev can process up to 1,943 rows per second for complex Choice classifications under bounded concurrency. Similarly, sqlite-jev introduces a loadable C extension and Python package that exposes Noul, Choice, and Score as standard SQL functions, unlocking virtual-table queries capable of batch-filtering unstructured text data natively. At the enterprise scale, integrations like pg-jev and specialized architectures utilizing the Oracle AI Database have emerged to govern agentic memory, allowing databases to evaluate, prune, and sort transactional records using semantic criteria before heavy reasoning workloads are even triggered.

Intelligent Infrastructure: Model Routing and Cascades

As frontier reasoning models become more powerful and computationally expensive, the cost of routing everyday inquiries grows unsustainable. Jev has become the default orchestration layer for managing multi-model cascades, ensuring that expensive cognitive compute is only deployed when strictly necessary.
A prime example of this infrastructure is the Jev Router documentation on OpenRouter. OpenRouter launched the Jev Router as a zero-cost utility endpoint that dynamically assesses incoming user requests. Using Jev’s Choice primitive, the router evaluates the implicit difficulty of an evolving conversation and routes the query to the lowest-cost model capable of completing the task.
Within custom application stacks, open-source projects like Jev

Codex Router and specialized evaluation codebases use Jev to analyze code complexity, user intent, and required tools. If Jev flags an incoming request as a simple lookup or basic text extraction, the application handles it locally or via a highly optimized small model. If Jev evaluates the state with low confidence or identifies an architectural ambiguity, it escalates the workload to a frontier reasoning engine.

Agentic Harnesses and the Model Context Protocol (MCP)

Autonomous AI agents spend a massive percentage of their execution loops determining which tool to use, verifying if an action was successful, and checking if they have strayed off course. Generative models struggle with the latency demands of these micro-decisions. The Jev ecosystem addresses this bottleneck by providing a high-speed coprocessor for agent frameworks.

Through the Model Context Protocol (MCP), tools like jev-mcp and the Jev MCP server turn Jev into a first-class classifier tool that sits directly inside agent codebases like Claude Code, Cursor, or LangGraph. Instead of an agent writing a long explanation to justify a tool call, the agent hands the state over to Jev.
This architecture allows for real-time loops across diverse workflows:

Browser and Desktop Automation: Tools like jev-ultrafast pass raw DOM states to Jev to instantly select the next clickable element, avoiding the multi-second latency of full multimodal models.
Safety Gates: Before an agent executes a terminal script or modifies a database, Jev classifies the command as read-only, reversible, or destructive. High-confidence safe commands proceed instantly, while risky operations trigger human-in-the-loop interventions.
Context Compaction: Tools like fast-jev-compaction and Winnow act as automated context garbage collectors, using Jev to scan an agent’s ballooning memory, strip out redundant logs, and preserve only critical operational details.

The Open-Source Replication Wave

While TypeSafe AI operates Jev as a highly optimized, closed API, the sheer utility of the System One paradigm triggered an immediate open-source replication wave. Engineers sought to build local, decoupled alternatives that could run entirely on commodity hardware or edge devices.

The most notable open-source equivalent is Laya, an independent, 421-million-parameter non-autoregressive decision model released under the Apache 2.0 license. As outlined in the Jev vs Laya, Laya provides a completely local alternative that can be deployed on a single NVIDIA T4 GPU or an Apple Silicon MacBook, passing thousands of GitHub stars within days of its release. Concurrently, projects like Bespoke Nimble demonstrated how to fine-tune standard open weights (such as Qwen 3.5) using synthetic contrastive data curation and constrained decoding to replicate Jev's exact discriminative behaviors, achieving sub-100ms latencies on local hardware. For ultra-low-power applications, models like Kev-0.5B scale the architecture down further, allowing background applications to run continuous, near-zero-cost judgment layers for notifications and local UI adaptations.

Future Horizons

The rapid evolution of the Jev tools ecosystem proves that the future of artificial intelligence is not monolithically generative.

By splitting the cognitive stack into a fast, probabilistic decision layer (System One) and a slower, expressive reasoning layer (System Two), developers are building software that is drastically cheaper, safer, and faster.

The ecosystem is already moving beyond text. Recent developments highlighted in the JEV-Based Image Models Architecture Report detail the arrival of Visual Jev and PixelJev. These models bypass heavy multimodal decoding to achieve sub-20ms visual choice selections, filtering out defective generations in automated diffusion pipelines before a single pixel is fully rendered. As these toolsets continue to mature, the combination of embedded database classifiers, open-source local decision nodes, and lightning-fast agentic gates will firmly establish System One engines as an indispensable tier of the modern enterprise software stack.

Provide by V-Modal AI Team
Github: https://github.com/v-modal

Building an Offline-First Robot Control App with Kotlin

vmodal_ai — Fri, 25 Sep 2026 20:32:30 +0000

Building an Offline-First Robot Control App with Kotlin

What You Will Build

In this tutorial, you will build a practical Kotlin/Android component for a robotics or Physical AI system. The design emphasizes asynchronous processing, lifecycle-aware state, real-time data handling, observability, and safe separation between the Android interface and physical robot control.

Architecture

Android Kotlin + Jetpack Compose
             ↓
       ViewModel / Flow
             ↓
      Repository / API
             ↓
   ROS 2 / Jetson / AI Backend
             ↓
        Robot System

Step 1 — Define local robot state

data class LocalRobotState(
    val connected: Boolean = false,
    val pendingCommands: List<String> = emptyList()
)

Step 2 — Cache the latest known state

Use a local persistence layer appropriate to your application so the UI can open without a network connection.

Step 3 — Queue commands

data class PendingCommand(
    val id: String,
    val command: String,
    val createdAt: Long
)

Step 4 — Sync when connected

if (connected) {
    pendingCommands.forEach { send(it) }
}

Step 5 — Make commands idempotent

Use unique command IDs so a reconnect does not accidentally execute the same command twice.

Step 6 — Define offline limits

Do not allow dangerous physical actions to queue indefinitely while disconnected. Some commands should be disabled entirely when the robot cannot be verified as connected.

Step 7 — Show connection state

Make it obvious whether the user is operating the live robot, viewing cached information, or waiting for synchronization.

Performance Checklist

Keep CPU-heavy work off the main thread.
Use bounded buffers for high-rate streams.
Prefer StateFlow for observable UI state.
Sample high-frequency telemetry before rendering.
Measure end-to-end latency instead of only model latency.
Handle reconnects and stale data explicitly.
Keep emergency controls independent of high-bandwidth streams.

Testing Checklist

Test with no network connection.
Test reconnect and duplicate messages.
Test high-rate telemetry.
Test lifecycle cancellation.
Test low battery and degraded network conditions.
Test emergency-stop behavior.
Verify that AI-generated instructions cannot bypass the deterministic safety layer.

Conclusion

The resulting Kotlin layer can be extended with real ROS 2 bridges, NVIDIA Jetson services, computer vision models, smart-glasses SDKs, or multimodal AI backends. Keep hardware-specific code behind interfaces so the Android application remains maintainable as the robotics stack evolves.

Useful Links

Website: www.v-modal.com

SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter

SDK Android: https://github.com/v-modal/vmodal_sdk_android

Discord: https://discord.gg/K72z28KUx

Reddit: https://www.reddit.com/r/v_modal/

Building a Robot Diagnostics Dashboard with Kotlin and Compose

vmodal_ai — Fri, 25 Sep 2026 20:31:40 +0000

Building a Robot Diagnostics Dashboard with Kotlin and Compose

What You Will Build

Architecture

Android Kotlin + Jetpack Compose
             ↓
       ViewModel / Flow
             ↓
      Repository / API
             ↓
   ROS 2 / Jetson / AI Backend
             ↓
        Robot System

Step 1 — Define diagnostics

data class Diagnostics(
    val connected: Boolean,
    val battery: Float,
    val temperature: Float,
    val cpu: Float,
    val gpu: Float,
    val lastMessageMs: Long
)

Step 2 — Expose diagnostics through StateFlow

private val _diagnostics =
    MutableStateFlow(Diagnostics(false, 0f, 0f, 0f, 0f, 0))

Step 3 — Create Compose metric cards

@Composable
fun DiagnosticCard(title: String, value: String) {
    Card {
        Column(Modifier.padding(12.dp)) {
            Text(title)
            Text(value)
        }
    }
}

Step 4 — Detect stale telemetry

val stale =
    System.currentTimeMillis() - state.lastMessageMs > 3000

Step 5 — Add logs

Store structured events such as connection changes, model failures, sensor errors, and watchdog activations.

Step 6 — Add export

For production systems, allow diagnostics to be exported for support and debugging, while avoiding sensitive information.

Performance Checklist

Keep CPU-heavy work off the main thread.
Use bounded buffers for high-rate streams.
Prefer StateFlow for observable UI state.
Sample high-frequency telemetry before rendering.
Measure end-to-end latency instead of only model latency.
Handle reconnects and stale data explicitly.
Keep emergency controls independent of high-bandwidth streams.

Testing Checklist

Test with no network connection.
Test reconnect and duplicate messages.
Test high-rate telemetry.
Test lifecycle cancellation.
Test low battery and degraded network conditions.
Test emergency-stop behavior.
Verify that AI-generated instructions cannot bypass the deterministic safety layer.

Conclusion

Useful Links

Website: www.v-modal.com

SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter

SDK Android: https://github.com/v-modal/vmodal_sdk_android

Discord: https://discord.gg/K72z28KUx

Reddit: https://www.reddit.com/r/v_modal/

Kotlin + Edge AI: Reducing Robot Inference Latency

vmodal_ai — Fri, 25 Sep 2026 20:31:14 +0000

Kotlin + Edge AI: Reducing Robot Inference Latency

What You Will Build

Architecture

Android Kotlin + Jetpack Compose
             ↓
       ViewModel / Flow
             ↓
      Repository / API
             ↓
   ROS 2 / Jetson / AI Backend
             ↓
        Robot System

Step 1 — Measure the pipeline

val start = System.nanoTime()
val preprocessed = preprocess(frame)
val preprocessingMs =
    (System.nanoTime() - start) / 1_000_000

Measure separately:

capture
preprocessing
inference
postprocessing
network
UI rendering

Step 2 — Run inference off the main thread

val result = withContext(Dispatchers.Default) {
    model.run(input)
}

Step 3 — Reduce allocations

Reuse buffers where the inference API allows it and avoid converting the same frame through multiple image formats.

Step 4 — Reduce unnecessary inference

frameFlow
    .sample(100)
    .collect { runInference(it) }

Step 5 — Consider model optimization

Depending on the runtime and model, investigate quantization, smaller input sizes, hardware acceleration, and model architecture changes.

Step 6 — Compare end-to-end latency

A model's benchmark inference time is not the same as application latency. Measure the complete pipeline.

Performance Checklist

Keep CPU-heavy work off the main thread.
Use bounded buffers for high-rate streams.
Prefer StateFlow for observable UI state.
Sample high-frequency telemetry before rendering.
Measure end-to-end latency instead of only model latency.
Handle reconnects and stale data explicitly.
Keep emergency controls independent of high-bandwidth streams.

Testing Checklist

Test with no network connection.
Test reconnect and duplicate messages.
Test high-rate telemetry.
Test lifecycle cancellation.
Test low battery and degraded network conditions.
Test emergency-stop behavior.
Verify that AI-generated instructions cannot bypass the deterministic safety layer.

Conclusion

Useful Links

Website: www.v-modal.com

SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter

SDK Android: https://github.com/v-modal/vmodal_sdk_android

Discord: https://discord.gg/K72z28KUx

Reddit: https://www.reddit.com/r/v_modal/

Building a Multimodal AI Robot Interface with Kotlin

vmodal_ai — Fri, 25 Sep 2026 20:30:39 +0000

Building a Multimodal AI Robot Interface with Kotlin

What You Will Build

Architecture

Android Kotlin + Jetpack Compose
             ↓
       ViewModel / Flow
             ↓
      Repository / API
             ↓
   ROS 2 / Jetson / AI Backend
             ↓
        Robot System

Step 1 — Define multimodal input

data class MultimodalInput(
    val text: String?,
    val image: ByteArray?,
    val audio: ByteArray?
)

Step 2 — Build a repository

interface AiRobotRepository {
    suspend fun analyze(input: MultimodalInput): RobotResponse
}

Step 3 — Display AI results

data class RobotResponse(
    val summary: String,
    val suggestedAction: String?
)

Step 4 — Connect to robotics

Image ─┐
Audio ─┼→ AI backend → structured result
Text ──┘                     ↓
                         validator
                             ↓
                         robot plan

Step 5 — Keep actuator control separate

The multimodal model should propose an action. A deterministic robotics layer decides whether the action is valid and safe.

Performance Checklist

Keep CPU-heavy work off the main thread.
Use bounded buffers for high-rate streams.
Prefer StateFlow for observable UI state.
Sample high-frequency telemetry before rendering.
Measure end-to-end latency instead of only model latency.
Handle reconnects and stale data explicitly.
Keep emergency controls independent of high-bandwidth streams.

Testing Checklist

Test with no network connection.
Test reconnect and duplicate messages.
Test high-rate telemetry.
Test lifecycle cancellation.
Test low battery and degraded network conditions.
Test emergency-stop behavior.
Verify that AI-generated instructions cannot bypass the deterministic safety layer.

Conclusion

Useful Links

Website: www.v-modal.com

SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter

SDK Android: https://github.com/v-modal/vmodal_sdk_android

Discord: https://discord.gg/K72z28KUx

Reddit: https://www.reddit.com/r/v_modal/

Kotlin + LLM + ROS 2: Natural Language Robot Control

vmodal_ai — Fri, 25 Sep 2026 20:30:03 +0000

Kotlin + LLM + ROS 2: Natural Language Robot Control

What You Will Build

Architecture

Android Kotlin + Jetpack Compose
             ↓
       ViewModel / Flow
             ↓
      Repository / API
             ↓
   ROS 2 / Jetson / AI Backend
             ↓
        Robot System

Step 1 — Define the natural-language request

@Serializable
data class RobotRequest(
    val instruction: String
)

Step 2 — Send it to the backend

suspend fun submit(instruction: String) {
    api.submit(RobotRequest(instruction))
}

Step 3 — Return a structured plan

Prefer structured data over free-form motor commands.

@Serializable
data class RobotPlan(
    val action: String,
    val parameters: Map<String, String>
)

Step 4 — Validate

fun isAllowed(plan: RobotPlan): Boolean =
    plan.action in setOf("NAVIGATE", "INSPECT", "STOP")

Step 5 — Execute through ROS 2

Only validated plans should be converted into ROS 2 actions/services/topics.

Step 6 — Confirm risky actions

Require explicit human confirmation for actions that could affect people, equipment, or restricted areas.

Performance Checklist

Keep CPU-heavy work off the main thread.
Use bounded buffers for high-rate streams.
Prefer StateFlow for observable UI state.
Sample high-frequency telemetry before rendering.
Measure end-to-end latency instead of only model latency.
Handle reconnects and stale data explicitly.
Keep emergency controls independent of high-bandwidth streams.

Testing Checklist

Test with no network connection.
Test reconnect and duplicate messages.
Test high-rate telemetry.
Test lifecycle cancellation.
Test low battery and degraded network conditions.
Test emergency-stop behavior.
Verify that AI-generated instructions cannot bypass the deterministic safety layer.

Conclusion

Useful Links

Website: www.v-modal.com

SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter

SDK Android: https://github.com/v-modal/vmodal_sdk_android

Discord: https://discord.gg/K72z28KUx

Reddit: https://www.reddit.com/r/v_modal/

Building an AI Robot Voice Assistant with Kotlin

vmodal_ai — Fri, 25 Sep 2026 20:29:24 +0000

Building an AI Robot Voice Assistant with Kotlin

What You Will Build

Architecture

Android Kotlin + Jetpack Compose
             ↓
       ViewModel / Flow
             ↓
      Repository / API
             ↓
   ROS 2 / Jetson / AI Backend
             ↓
        Robot System

Step 1 — Define voice commands

data class VoiceCommand(
    val text: String,
    val confidence: Float
)

Step 2 — Convert speech to text

Use the Android speech-recognition mechanism appropriate for your target devices and permissions.

Step 3 — Parse the intent

fun parseCommand(text: String): String =
    when {
        text.contains("stop", ignoreCase = true) -> "STOP"
        text.contains("forward", ignoreCase = true) -> "FORWARD"
        else -> "UNKNOWN"
    }

Step 4 — Add confirmation

User: "Move forward"
Assistant: "Move forward at 0.3 m/s?"
User: "Confirm"
Robot: execute

Step 5 — Keep safety deterministic

Voice/LLM output should never directly control motors. Convert it into a validated command and apply robot-side safety limits.

Performance Checklist

Keep CPU-heavy work off the main thread.
Use bounded buffers for high-rate streams.
Prefer StateFlow for observable UI state.
Sample high-frequency telemetry before rendering.
Measure end-to-end latency instead of only model latency.
Handle reconnects and stale data explicitly.
Keep emergency controls independent of high-bandwidth streams.

Testing Checklist

Test with no network connection.
Test reconnect and duplicate messages.
Test high-rate telemetry.
Test lifecycle cancellation.
Test low battery and degraded network conditions.
Test emergency-stop behavior.
Verify that AI-generated instructions cannot bypass the deterministic safety layer.

Conclusion

Useful Links

Website: www.v-modal.com

SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter

SDK Android: https://github.com/v-modal/vmodal_sdk_android

Discord: https://discord.gg/K72z28KUx

Reddit: https://www.reddit.com/r/v_modal/

Kotlin + NVIDIA Jetson Camera Streaming for Robotics

vmodal_ai — Fri, 25 Sep 2026 20:29:13 +0000

Kotlin + NVIDIA Jetson Camera Streaming for Robotics

What You Will Build

Architecture

Android Kotlin + Jetpack Compose
             ↓
       ViewModel / Flow
             ↓
      Repository / API
             ↓
   ROS 2 / Jetson / AI Backend
             ↓
        Robot System

Step 1 — Separate control and video channels

Android Kotlin
   ├── control WebSocket
   └── video transport
             ↓
        NVIDIA Jetson

Do not send high-bandwidth video through the same queue as emergency control commands.

Step 2 — Define stream state

data class VideoState(
    val connected: Boolean = false,
    val fps: Int = 0,
    val latencyMs: Long = 0
)

Step 3 — Monitor stream health

_state.update {
    it.copy(
        connected = true,
        fps = measuredFps,
        latencyMs = measuredLatency
    )
}

Step 4 — Optimize

Use an appropriate low-latency video transport, avoid unnecessary transcoding, and adapt resolution/frame rate to network conditions.

Step 5 — Add fallback

When the video stream fails, keep robot telemetry and emergency controls available.

Performance Checklist

Keep CPU-heavy work off the main thread.
Use bounded buffers for high-rate streams.
Prefer StateFlow for observable UI state.
Sample high-frequency telemetry before rendering.
Measure end-to-end latency instead of only model latency.
Handle reconnects and stale data explicitly.
Keep emergency controls independent of high-bandwidth streams.

Testing Checklist

Test with no network connection.
Test reconnect and duplicate messages.
Test high-rate telemetry.
Test lifecycle cancellation.
Test low battery and degraded network conditions.
Test emergency-stop behavior.
Verify that AI-generated instructions cannot bypass the deterministic safety layer.

Conclusion

Useful Links

Website: www.v-modal.com

SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter

SDK Android: https://github.com/v-modal/vmodal_sdk_android

Discord: https://discord.gg/K72z28KUx

Reddit: https://www.reddit.com/r/v_modal/

Kotlin + WebSocket for NVIDIA Jetson Robot Telemetry

vmodal_ai — Fri, 25 Sep 2026 20:28:16 +0000

Kotlin + WebSocket for NVIDIA Jetson Robot Telemetry

What You Will Build

Architecture

Android Kotlin + Jetpack Compose
             ↓
       ViewModel / Flow
             ↓
      Repository / API
             ↓
   ROS 2 / Jetson / AI Backend
             ↓
        Robot System

Step 1 — Define telemetry

@Serializable
data class JetsonTelemetry(
    val cpu: Float,
    val gpu: Float,
    val temperature: Float,
    val battery: Float
)

Step 2 — Create a WebSocket repository

class TelemetryRepository(
    private val socket: RobotSocket
) {
    fun telemetry(): Flow<JetsonTelemetry> =
        socket.messages()
            .map { Json.decodeFromString(it) }
}

Step 3 — Expose StateFlow

val telemetry = repository.telemetry()
    .stateIn(
        viewModelScope,
        SharingStarted.WhileSubscribed(5_000),
        JetsonTelemetry(0f, 0f, 0f, 0f)
    )

Step 4 — Display metrics

Text("GPU: ${state.gpu}%")
Text("CPU: ${state.cpu}%")
Text("Temperature: ${state.temperature}°C")

Step 5 — Reconnect safely

Use bounded retry delays and stop retrying when the ViewModel is destroyed.

Performance Checklist

Keep CPU-heavy work off the main thread.
Use bounded buffers for high-rate streams.
Prefer StateFlow for observable UI state.
Sample high-frequency telemetry before rendering.
Measure end-to-end latency instead of only model latency.
Handle reconnects and stale data explicitly.
Keep emergency controls independent of high-bandwidth streams.

Testing Checklist

Test with no network connection.
Test reconnect and duplicate messages.
Test high-rate telemetry.
Test lifecycle cancellation.
Test low battery and degraded network conditions.
Test emergency-stop behavior.
Verify that AI-generated instructions cannot bypass the deterministic safety layer.

Conclusion

Useful Links

Website: www.v-modal.com

SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter

SDK Android: https://github.com/v-modal/vmodal_sdk_android

Discord: https://discord.gg/K72z28KUx

Reddit: https://www.reddit.com/r/v_modal/

Kotlin + MQTT for Real-Time Robot Fleet Management

vmodal_ai — Fri, 25 Sep 2026 20:28:03 +0000

Kotlin + MQTT for Real-Time Robot Fleet Management

What You Will Build

Architecture

Android Kotlin + Jetpack Compose
             ↓
       ViewModel / Flow
             ↓
      Repository / API
             ↓
   ROS 2 / Jetson / AI Backend
             ↓
        Robot System

Step 1 — Define fleet telemetry

@Serializable
data class RobotTelemetry(
    val robotId: String,
    val battery: Float,
    val status: String
)

Step 2 — Connect with MQTT

Use an MQTT client supported by your Android project and configure TLS authentication for production.

fun onTelemetry(payload: String) {
    val telemetry =
        Json.decodeFromString<RobotTelemetry>(payload)
}

Step 3 — Maintain fleet state

private val _robots =
    MutableStateFlow<Map<String, RobotTelemetry>>(emptyMap())

Step 4 — Update one robot

_robots.update { current ->
    current + (telemetry.robotId to telemetry)
}

Step 5 — Display the fleet

LazyColumn {
    items(robots.values.toList()) {
        Text("${it.robotId}: ${it.status}")
    }
}

Step 6 — Handle offline robots

Record the last-seen timestamp and visually distinguish stale telemetry from live data.

Performance Checklist

Keep CPU-heavy work off the main thread.
Use bounded buffers for high-rate streams.
Prefer StateFlow for observable UI state.
Sample high-frequency telemetry before rendering.
Measure end-to-end latency instead of only model latency.
Handle reconnects and stale data explicitly.
Keep emergency controls independent of high-bandwidth streams.

Testing Checklist

Test with no network connection.
Test reconnect and duplicate messages.
Test high-rate telemetry.
Test lifecycle cancellation.
Test low battery and degraded network conditions.
Test emergency-stop behavior.
Verify that AI-generated instructions cannot bypass the deterministic safety layer.

Conclusion

Useful Links

Website: www.v-modal.com

SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter

SDK Android: https://github.com/v-modal/vmodal_sdk_android

Discord: https://discord.gg/K72z28KUx

Reddit: https://www.reddit.com/r/v_modal/

Building a Kotlin Android Camera Viewer for ROS 2 Robots

vmodal_ai — Fri, 25 Sep 2026 20:28:02 +0000

Building a Kotlin Android Camera Viewer for ROS 2 Robots

What You Will Build

Architecture

Android Kotlin + Jetpack Compose
             ↓
       ViewModel / Flow
             ↓
      Repository / API
             ↓
   ROS 2 / Jetson / AI Backend
             ↓
        Robot System

Step 1 — Define camera frames

data class RobotFrame(
    val data: ByteArray,
    val timestamp: Long
)

Step 2 — Create a bounded stream

val frames = Channel<RobotFrame>(
    capacity = 2,
    onBufferOverflow = BufferOverflow.DROP_OLDEST
)

Step 3 — Receive frames

fun onFrame(data: ByteArray) {
    frames.trySend(
        RobotFrame(data, System.currentTimeMillis())
    )
}

Step 4 — Process asynchronously

viewModelScope.launch(Dispatchers.Default) {
    for (frame in frames) {
        processFrame(frame)
    }
}

Step 5 — Display the stream

Use an Android-compatible video/image rendering component appropriate to the transport. Keep decoding away from the main thread.

Step 6 — Measure latency

Track capture, network receive, decode, and display timestamps independently.

Performance Checklist

Keep CPU-heavy work off the main thread.
Use bounded buffers for high-rate streams.
Prefer StateFlow for observable UI state.
Sample high-frequency telemetry before rendering.
Measure end-to-end latency instead of only model latency.
Handle reconnects and stale data explicitly.
Keep emergency controls independent of high-bandwidth streams.

Testing Checklist

Test with no network connection.
Test reconnect and duplicate messages.
Test high-rate telemetry.
Test lifecycle cancellation.
Test low battery and degraded network conditions.
Test emergency-stop behavior.
Verify that AI-generated instructions cannot bypass the deterministic safety layer.

Conclusion

Useful Links

Website: www.v-modal.com

SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter

SDK Android: https://github.com/v-modal/vmodal_sdk_android

Discord: https://discord.gg/K72z28KUx

Reddit: https://www.reddit.com/r/v_modal/

Building a ROS 2 Robot Map Viewer with Kotlin

vmodal_ai — Fri, 25 Sep 2026 20:26:41 +0000

Building a ROS 2 Robot Map Viewer with Kotlin

What You Will Build

Architecture

Android Kotlin + Jetpack Compose
             ↓
       ViewModel / Flow
             ↓
      Repository / API
             ↓
   ROS 2 / Jetson / AI Backend
             ↓
        Robot System

Step 1 — Define map data

data class MapPoint(
    val x: Float,
    val y: Float
)

Step 2 — Receive map updates

val mapFlow: Flow<List<MapPoint>> = rosBridge.mapFlow()

Step 3 — Collect lifecycle-safely

val points by mapFlow.collectAsStateWithLifecycle(emptyList())

Step 4 — Render the map

Canvas(Modifier.fillMaxSize()) {
    points.forEach {
        drawCircle(
            radius = 2f,
            center = Offset(it.x, it.y)
        )
    }
}

Step 5 — Optimize rendering

Do not redraw the entire map for every unrelated UI state change. Sample high-frequency updates and keep coordinate transformations outside expensive composables where possible.

Performance Checklist

Keep CPU-heavy work off the main thread.
Use bounded buffers for high-rate streams.
Prefer StateFlow for observable UI state.
Sample high-frequency telemetry before rendering.
Measure end-to-end latency instead of only model latency.
Handle reconnects and stale data explicitly.
Keep emergency controls independent of high-bandwidth streams.

Testing Checklist

Test with no network connection.
Test reconnect and duplicate messages.
Test high-rate telemetry.
Test lifecycle cancellation.
Test low battery and degraded network conditions.
Test emergency-stop behavior.
Verify that AI-generated instructions cannot bypass the deterministic safety layer.

Conclusion

Useful Links

Website: www.v-modal.com

SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter

SDK Android: https://github.com/v-modal/vmodal_sdk_android

Discord: https://discord.gg/K72z28KUx

Reddit: https://www.reddit.com/r/v_modal/